From 0d88f483e0520e23811ecf19dd06395ef22b730a Mon Sep 17 00:00:00 2001 From: StevenPuttemans Date: Wed, 26 Nov 2014 10:45:56 +0100 Subject: [PATCH 01/32] updating github documentation structure --- modules/README.md | 34 +++++++++++++++++++--------------- modules/datasets/README.md | 2 ++ modules/latentsvm/README.md | 2 ++ 3 files changed, 23 insertions(+), 15 deletions(-) create mode 100644 modules/datasets/README.md create mode 100644 modules/latentsvm/README.md diff --git a/modules/README.md b/modules/README.md index 667997fc0..9de3e91ae 100644 --- a/modules/README.md +++ b/modules/README.md @@ -18,32 +18,36 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=/modules -D BUILD_opencv_re 5. **opencv_cvv**: GUI for Interactive Visual Debugging of Computer Vision Programs. -6. **opencv_datasettools**: Tools for working with different datasets. +6. **opencv_datasets**: Interface for interfacing with existing computer vision databases. -7. **opencv_face**: Recently added face recognition software which is not yet stabalized. +7. **opencv_datasettools**: Tools for working with different datasets. -8. **opencv_line_descriptor**: Binary descriptors for lines extracted from an image. +8. **opencv_face**: Recently added face recognition software which is not yet stabalized. -9. **opencv_matlab**: OpenCV Matlab Code Generator. +9. **opencv_latentsvm**: Implementation of the LatentSVM detector algorithm. -10. **opencv_optflow**: Optical Flow Algorithms for tracking points. +10. **opencv_line_descriptor**: Binary descriptors for lines extracted from an image. -11. **opencv_reg**: Image Registration module. +11. **opencv_matlab**: OpenCV Matlab Code Generator. -12. **opencv_rgbd**: RGB-Depth Processing module. +12. **opencv_optflow**: Optical Flow Algorithms for tracking points. -13. **opencv_saliency**: Saliency API, understanding where humans focus given a scene. +13. **opencv_reg**: Image Registration module. -14. **opencv_surface_matching**: Surface Matching Algorithm Through 3D Features. +14. **opencv_rgbd**: RGB-Depth Processing module. -15. **opencv_text**: Scene Text Detection and Recognition in Natural Scene Images. +15. **opencv_saliency**: Saliency API, understanding where humans focus given a scene. -16. **opencv_tracking**: Long-term optical tracking API. +16. **opencv_surface_matching**: Surface Matching Algorithm Through 3D Features. -17. **opencv_xfeatures2d**: Extra 2D Features Framework containing experimental and non-free 2D feature algorithms. +17. **opencv_text**: Scene Text Detection and Recognition in Natural Scene Images. -18. **opencv_ximgproc**: Extended Image Processing: Structured Forests / Domain Transform Filter / Guided Filter / Adaptive Manifold Filter / Joint Bilateral Filter / Superpixels. +18. **opencv_tracking**: Long-term optical tracking API. + +19. **opencv_xfeatures2d**: Extra 2D Features Framework containing experimental and non-free 2D feature algorithms. + +20. **opencv_ximgproc**: Extended Image Processing: Structured Forests / Domain Transform Filter / Guided Filter / Adaptive Manifold Filter / Joint Bilateral Filter / Superpixels. -19. **opencv_xobjdetect**: Integral Channel Features Detector Framework. +21. **opencv_xobjdetect**: Integral Channel Features Detector Framework. -20. **opencv_xphoto**: Additional photo processing algorithms: Color balance / Denoising / Inpainting. \ No newline at end of file +22. **opencv_xphoto**: Additional photo processing algorithms: Color balance / Denoising / Inpainting. diff --git a/modules/datasets/README.md b/modules/datasets/README.md new file mode 100644 index 000000000..54a3a5423 --- /dev/null +++ b/modules/datasets/README.md @@ -0,0 +1,2 @@ +Interface for interfacing with existing computer vision databases +================================================================= diff --git a/modules/latentsvm/README.md b/modules/latentsvm/README.md new file mode 100644 index 000000000..2cfec51a4 --- /dev/null +++ b/modules/latentsvm/README.md @@ -0,0 +1,2 @@ +Implementation of the LatentSVM detector algorithm +================================================== From f52ba671dce50ce062de52416f1b4cb7bfc8e7b5 Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Mon, 8 Dec 2014 14:37:06 +0300 Subject: [PATCH 02/32] Disable some modules for iOS framework world build --- modules/adas/CMakeLists.txt | 4 ++++ modules/saliency/CMakeLists.txt | 1 + modules/ximgproc/CMakeLists.txt | 1 + 3 files changed, 6 insertions(+) diff --git a/modules/adas/CMakeLists.txt b/modules/adas/CMakeLists.txt index 8284dfc57..fb122143a 100644 --- a/modules/adas/CMakeLists.txt +++ b/modules/adas/CMakeLists.txt @@ -1,3 +1,7 @@ +if(IOS) + ocv_module_disable(adas) +endif() + set(the_description "Automatic driver assistance algorithms") ocv_define_module(adas opencv_xobjdetect) diff --git a/modules/saliency/CMakeLists.txt b/modules/saliency/CMakeLists.txt index fb53b45c6..e2eda6568 100644 --- a/modules/saliency/CMakeLists.txt +++ b/modules/saliency/CMakeLists.txt @@ -1,2 +1,3 @@ set(the_description "Saliency API") +set(OPENCV_MODULE_IS_PART_OF_WORLD OFF) ocv_define_module(saliency opencv_imgproc opencv_highgui opencv_features2d) diff --git a/modules/ximgproc/CMakeLists.txt b/modules/ximgproc/CMakeLists.txt index 6b68a3243..8aa4b6444 100644 --- a/modules/ximgproc/CMakeLists.txt +++ b/modules/ximgproc/CMakeLists.txt @@ -1,4 +1,5 @@ set(the_description "Extended image processing module. It includes edge-aware filters and etc.") +set(OPENCV_MODULE_IS_PART_OF_WORLD OFF) ocv_define_module(ximgproc opencv_imgproc opencv_core opencv_highgui) target_link_libraries(opencv_ximgproc) From 3957fd7d6fca1f0d5a97a314962005d017cdb37e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Beat=20K=C3=BCng?= Date: Sun, 28 Dec 2014 12:39:46 +0100 Subject: [PATCH 03/32] surface matching: fix memory leak --- modules/surface_matching/src/t_hash_int.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modules/surface_matching/src/t_hash_int.cpp b/modules/surface_matching/src/t_hash_int.cpp index 52742f376..8e86e8159 100644 --- a/modules/surface_matching/src/t_hash_int.cpp +++ b/modules/surface_matching/src/t_hash_int.cpp @@ -347,7 +347,10 @@ hashtable_int *hashtableRead(FILE* f) { data=malloc(dataSize); if (!data) + { + hashtableDestroy(hashtbl); return NULL; + } status = fread(data, dataSize, 1, f); } else From 6ca0aa4784e51c668b2212d8fbf11b92be3ec46c Mon Sep 17 00:00:00 2001 From: Rok Mandeljc Date: Fri, 26 Dec 2014 15:24:18 +0100 Subject: [PATCH 04/32] matlab/CMakeLists.txt: add CMAKE_BINARY_DIR to MEX_INCLUDE_DIRS Otherwise we cannot find opencv2/opencv_modules.hpp, which is located directly inside the CMAKE_BINARY_DIR. --- modules/matlab/CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/matlab/CMakeLists.txt b/modules/matlab/CMakeLists.txt index 36435c6fd..8c1382b53 100644 --- a/modules/matlab/CMakeLists.txt +++ b/modules/matlab/CMakeLists.txt @@ -104,6 +104,7 @@ set(HDR_PARSER_PATH ${CMAKE_SOURCE_DIR}/modules/python/src2) set(RST_PARSER_PATH ${CMAKE_SOURCE_DIR}/modules/java/generator) # set mex compiler options +prepend("-I" MEX_INCLUDE_DIRS ${CMAKE_BINARY_DIR}) prepend("-I" MEX_INCLUDE_DIRS ${CMAKE_CURRENT_SOURCE_DIR}/include) if (MSVC) prepend("-L" MEX_LIB_DIR ${LIBRARY_OUTPUT_PATH}/${CMAKE_CFG_INTDIR}) From 63008ba79ddeac8365f17bb39e9251eaa067df8f Mon Sep 17 00:00:00 2001 From: Rok Mandeljc Date: Fri, 26 Dec 2014 15:56:35 +0100 Subject: [PATCH 05/32] matlab/bridge.hpp: added missing definitions for Ptr_Stitcher --- modules/matlab/include/opencv2/matlab/bridge.hpp | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/modules/matlab/include/opencv2/matlab/bridge.hpp b/modules/matlab/include/opencv2/matlab/bridge.hpp index 4c75ac6cb..64592ca4e 100644 --- a/modules/matlab/include/opencv2/matlab/bridge.hpp +++ b/modules/matlab/include/opencv2/matlab/bridge.hpp @@ -53,6 +53,7 @@ #include #include #include +#include #include namespace cv { @@ -87,6 +88,7 @@ typedef cv::Ptr Ptr_DenseOpticalFlow; typedef cv::Ptr Ptr_MergeDebevec; typedef cv::Ptr Ptr_MergeMertens; typedef cv::Ptr Ptr_MergeRobertson; +typedef cv::Ptr Ptr_Stitcher; typedef cv::Ptr Ptr_Tonemap; typedef cv::Ptr Ptr_TonemapDrago; typedef cv::Ptr Ptr_TonemapDurand; @@ -481,6 +483,11 @@ public: Ptr_MergeRobertson toPtrMergeRobertson() { return Ptr_MergeRobertson(); } operator Ptr_MergeRobertson() { return toPtrMergeRobertson(); } + // --------------------------- Ptr_Stitcher ------------------------------ + Bridge& operator=(const Ptr_Stitcher& ) { return *this; } + Ptr_Stitcher toPtrStitcher() { return Ptr_Stitcher(); } + operator Ptr_Stitcher() { return toPtrStitcher(); } + // --------------------------- Ptr_Tonemap ------------------------------ Bridge& operator=(const Ptr_Tonemap& ) { return *this; } Ptr_Tonemap toPtrTonemap() { return Ptr_Tonemap(); } From 634c3a37c766aaa38aa21d80fd78779bd859cfb3 Mon Sep 17 00:00:00 2001 From: Str3iber Date: Mon, 5 Jan 2015 00:38:03 +0100 Subject: [PATCH 06/32] initial LUCID inclusion --- modules/xfeatures2d/doc/extra_features.rst | 25 ++ modules/xfeatures2d/doc/xfeatures2d.bib | 9 + .../include/opencv2/xfeatures2d.hpp | 21 +- modules/xfeatures2d/src/lucid.cpp | 226 ++++++++++++++++++ modules/xfeatures2d/test/test_features2d.cpp | 8 + 5 files changed, 288 insertions(+), 1 deletion(-) create mode 100644 modules/xfeatures2d/src/lucid.cpp diff --git a/modules/xfeatures2d/doc/extra_features.rst b/modules/xfeatures2d/doc/extra_features.rst index ca3296f7b..cb5693c1f 100644 --- a/modules/xfeatures2d/doc/extra_features.rst +++ b/modules/xfeatures2d/doc/extra_features.rst @@ -91,3 +91,28 @@ We notice that for keypoint matching applications, image content has little effe :param keypoints: Set of detected keypoints :param corrThresh: Correlation threshold. :param verbose: Prints pair selection informations. + + + +LUCID +------------------------ +.. ocv:class:: LUCID : public DescriptorExtractor + +Class for computing LUCID descriptors described in a paper by Ziegler, Andrew, +Eric Christiansen, David Kriegman, and Serge J. Belongie. +*Locally uniform comparison image descriptor.* In Advances in Neural Information Processing Systems, pp. 1-9. 2012. :: + + class LUCID : public DescriptorExtractor + { + public: + LUCID(const int lucid_kernel = 1, const int blur_kernel = 2); + + virtual int descriptorSize() const; + virtual int descriptorType() const; + virtual int defaultNorm() const; + + virtual void compute(InputArray _src, std::vector &keypoints, OutputArray _desc); + + protected: + int l_kernel, b_kernel; + }; diff --git a/modules/xfeatures2d/doc/xfeatures2d.bib b/modules/xfeatures2d/doc/xfeatures2d.bib index 0fa73c8cd..f23c529ab 100644 --- a/modules/xfeatures2d/doc/xfeatures2d.bib +++ b/modules/xfeatures2d/doc/xfeatures2d.bib @@ -44,3 +44,12 @@ year={2012}, organization={Ieee} } + +@incollection{LUCID, + title={Locally uniform comparison image descriptor}, + author={Ziegler, Andrew, Eric Christiansen, David Kriegman, and Serge J. Belongie} + booktitle={Advances in Neural Information Processing Systems} + pages={1--9} + year={2012} + publisher={NIPS} +} diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp index b6f2909fc..4c5584257 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp @@ -128,7 +128,26 @@ class CV_EXPORTS BriefDescriptorExtractor : public DescriptorExtractor public: static Ptr create( int bytes = 32 ); }; - + + +// Locally Uniform Comparison Image Descriptor + +// @brief This class implements the Locally Uniform Comparison Image Descriptor @cite LUCID +class CV_EXPORTS LUCID : public DescriptorExtractor +{ +public: + static Ptr create(const int lucid_kernel, const int blur_kernel); +}; + +/** @brief Separable box filter blur, needed by LUCID, also exposed for the user + +@param _src Image on which blur should be applied +@param _dst Image resulting from _src having blur applied, the output image +@param kernel Blur kernel size where 1 equates a 3x3 matrix, 2 = 5x5, 3 = 7x7, and so on +*/ +CV_EXPORTS void separable_blur(const InputArray _src, CV_OUT OutputArray _dst, const int kernel); + + //! @} } diff --git a/modules/xfeatures2d/src/lucid.cpp b/modules/xfeatures2d/src/lucid.cpp new file mode 100644 index 000000000..1a1d4fafa --- /dev/null +++ b/modules/xfeatures2d/src/lucid.cpp @@ -0,0 +1,226 @@ +// This implementation of, and any deviation from, the original algorithm as +// proposed by Ziegler et al. is not endorsed by Ziegler et al. nor does it +// claim to represent their definition of locally uniform comparison image +// descriptor. The original LUCID algorithm as proposed by Ziegler et al. remains +// the property of its respective authors. This implementation is an adaptation of +// said algorithm and contributed to OpenCV by Str3iber. + +// References: +// Ziegler, Andrew, Eric Christiansen, David Kriegman, and Serge J. Belongie. +// "Locally uniform comparison image descriptor." In Advances in Neural Information +// Processing Systems, pp. 1-9. 2012. + +/* +By downloading, copying, installing or using the software you agree to this license. +If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are disclaimed. +In no event shall copyright holders or contributors be liable for any direct, +indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#include "precomp.hpp" + +namespace cv { + namespace xfeatures2d { + void separable_blur(const InputArray _src, OutputArray _dst, const int kernel) { + int z, p, r = 0, g = 0, b = 0, m = kernel*2+1, width, height; + + Point3_ *pnt; + + Mat_ src = _src.getMat(); + if (src.empty()) { + CV_Error(Error::StsBadArg, "empty source image supplied"); + + return; + } + + _dst.create(src.size(), src.type()); + Mat_ dst = _dst.getMat(); + + width = dst.cols, height = dst.rows; + + for (int y = 0; y < height; ++y) { + for (int x = 0; x < width; ++x) { + z = kernel*-1; + + if (!x) { + r = 0, g = 0, b = 0; + + for (p = x+z; z <= kernel; ++z, p=x+z) { + pnt = src.ptr >(y, (p < 0 ? width+p : p >= width ? p-width : p)); + r += pnt->z; + g += pnt->y; + b += pnt->x; + } + } + else { + p = x+z-1; + + pnt = src.ptr >(y, (p < 0 ? width+p : p >= width ? p-width : p)); + r -= pnt->z; + g -= pnt->y; + b -= pnt->x; + + p = x+kernel; + + pnt = src.ptr >(y, (p < 0 ? width+p : p >= width ? p-width : p)); + r += pnt->z; + g += pnt->y; + b += pnt->x; + } + + pnt = dst.ptr >(y, x); + pnt->z = static_cast(r/m); + pnt->y = static_cast(g/m); + pnt->x = static_cast(b/m); + } + } + + for (int x = 0, rl = 0, gl = 0, bl = 0, rn = 0, gn = 0, bn = 0; x < width; ++x) { + for (int y = 0; y < height; ++y) { + z = kernel*-1; + + if (!y) { + r = 0, g = 0, b = 0; + + for (p = y+z; z <= kernel; ++z, p=y+z) { + pnt = dst.ptr >((p < 0 ? height+p : p >= height ? p-height : p), x); + r += pnt->z; + g += pnt->y; + b += pnt->x; + } + } + else { + p = y+z-1; + + pnt = dst.ptr >((p < 0 ? height+p : p >= height ? p-height : p), x); + r -= pnt->z, r -= rl; + g -= pnt->y, g -= gl; + b -= pnt->x, b -= bl; + + p = y+kernel; + + pnt = dst.ptr >((p < 0 ? height+p : p >= height ? p-height : p), x); + r += pnt->z, r += rn; + g += pnt->y, g += gn; + b += pnt->x, b += bn; + } + + pnt = dst.ptr >(y, x); + rl = pnt->z; + gl = pnt->y; + bl = pnt->x; + rn = r/m; + gn = g/m; + bn = b/m; + pnt->z = static_cast(rn); + pnt->y = static_cast(gn); + pnt->x = static_cast(bn); + } + } + } + + class LUCIDImpl : public LUCID { + public: + LUCIDImpl(const int lucid_kernel = 1, const int blur_kernel = 2); + + virtual int descriptorSize() const; + virtual int descriptorType() const; + virtual int defaultNorm() const; + + virtual void compute(InputArray _src, std::vector &keypoints, OutputArray _desc); + + protected: + int l_kernel, b_kernel; + }; + + Ptr LUCID::create(const int lucid_kernel, const int blur_kernel) { + return makePtr(lucid_kernel, blur_kernel); + } + + LUCIDImpl::LUCIDImpl(const int lucid_kernel, const int blur_kernel) { + l_kernel = lucid_kernel; + b_kernel = blur_kernel; + } + + int LUCIDImpl::descriptorSize() const { + return (l_kernel*2+1)*(l_kernel*2+1)*3; + } + + int LUCIDImpl::descriptorType() const { + return CV_8UC1; + } + + int LUCIDImpl::defaultNorm() const { + return NORM_HAMMING; + } + + // gliese581h suggested filling a cv::Mat with descriptors to enable BFmatcher compatibility + // speed-ups and enhancements by gliese581h + void LUCIDImpl::compute(InputArray _src, std::vector &keypoints, OutputArray _desc) { + if (_src.getMat().empty()) + return; + + Mat_ src; + + separable_blur(_src.getMat(), src, b_kernel); + + int x, y, j, d, p, m = (l_kernel*2+1)*(l_kernel*2+1)*3, width = src.cols, height = src.rows, r, c; + + Mat_ desc(keypoints.size(), m); + + for (std::size_t i = 0; i < keypoints.size(); ++i) { + x = static_cast(keypoints[i].pt.x)-l_kernel, y = static_cast(keypoints[i].pt.y)-l_kernel, d = x+2*l_kernel, p = y+2*l_kernel, j = x, r = static_cast(i), c = 0; + + while (x <= d) { + Vec3b &pix = src((y < 0 ? height+y : y >= height ? y-height : y), (x < 0 ? width+x : x >= width ? x-width : x)); + + desc(r, c++) = pix[0]; + desc(r, c++) = pix[1]; + desc(r, c++) = pix[2]; + + ++x; + if (x > d) { + if (y < p) { + ++y; + x = j; + } + else + break; + } + } + } + + if (_desc.needed()) + sort(desc, _desc, SORT_EVERY_ROW | SORT_ASCENDING); + } + } +} diff --git a/modules/xfeatures2d/test/test_features2d.cpp b/modules/xfeatures2d/test/test_features2d.cpp index d5f001ce1..303274efa 100644 --- a/modules/xfeatures2d/test/test_features2d.cpp +++ b/modules/xfeatures2d/test/test_features2d.cpp @@ -1025,6 +1025,14 @@ TEST( Features2d_DescriptorExtractor_BRIEF, regression ) test.safe_run(); } +TEST( Features2d_DescriptorExtractor_LUCID, regression ) +{ + CV_DescriptorExtractorTest test( "descriptor-lucid", 1, + LUCID::create(1, 2) ); + test.safe_run(); +} + + /*#if CV_SSE2 TEST( Features2d_DescriptorExtractor_Calonder_uchar, regression ) From 84c598f39e1b5cd090cc3ed150c7aeab84dbb66f Mon Sep 17 00:00:00 2001 From: Str3iber Date: Fri, 16 Jan 2015 15:28:11 +0100 Subject: [PATCH 07/32] avoid msvc warning --- modules/xfeatures2d/src/lucid.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/xfeatures2d/src/lucid.cpp b/modules/xfeatures2d/src/lucid.cpp index 1a1d4fafa..dfaf14125 100644 --- a/modules/xfeatures2d/src/lucid.cpp +++ b/modules/xfeatures2d/src/lucid.cpp @@ -195,7 +195,7 @@ namespace cv { int x, y, j, d, p, m = (l_kernel*2+1)*(l_kernel*2+1)*3, width = src.cols, height = src.rows, r, c; - Mat_ desc(keypoints.size(), m); + Mat_ desc(static_cast(keypoints.size()), m); for (std::size_t i = 0; i < keypoints.size(); ++i) { x = static_cast(keypoints[i].pt.x)-l_kernel, y = static_cast(keypoints[i].pt.y)-l_kernel, d = x+2*l_kernel, p = y+2*l_kernel, j = x, r = static_cast(i), c = 0; From 499c9436013991b0a7e068c4de45b8f0da7d5bd0 Mon Sep 17 00:00:00 2001 From: Vadim Pisarevsky Date: Tue, 20 Jan 2015 17:08:08 +0300 Subject: [PATCH 08/32] added information about the patents related to the surface matching algorithm --- modules/surface_matching/PATENTS.txt | 29 ++++++++++++++++ .../include/opencv2/surface_matching.hpp | 34 +++++++++++++++++++ 2 files changed, 63 insertions(+) create mode 100644 modules/surface_matching/PATENTS.txt diff --git a/modules/surface_matching/PATENTS.txt b/modules/surface_matching/PATENTS.txt new file mode 100644 index 000000000..6b787f8bb --- /dev/null +++ b/modules/surface_matching/PATENTS.txt @@ -0,0 +1,29 @@ +The following patents have been issued for methods embodied in this +software: "Recognition and pose determination of 3D objects in 3D scenes +using geometric point pair descriptors and the generalized Hough +Transform", Bertram Heinrich Drost, Markus Ulrich, EP Patent 2385483 +(Nov. 21, 2012), assignee: MVTec Software GmbH, 81675 Muenchen +(Germany); "Recognition and pose determination of 3D objects in 3D +scenes", Bertram Heinrich Drost, Markus Ulrich, US Patent 8830229 (Sept. +9, 2014), assignee: MVTec Software GmbH, 81675 Muenchen (Germany). +Further patents are pending. For further details, contact MVTec Software +GmbH (info@mvtec.com). + +Note that restrictions imposed by these patents (and possibly others) +exist independently of and may be in conflict with the freedoms granted +in OpenCV license, which refers to copyright of the program, not patents +for any methods that it implements. Both copyright and patent law must +be obeyed to legally use and redistribute this program and it is not the +purpose of this license to induce you to infringe any patents or other +property right claims or to contest validity of any such claims. If you +redistribute or use the program, then this license merely protects you +from committing copyright infringement. It does not protect you from +committing patent infringement. So, before you do anything with this +program, make sure that you have permission to do so not merely in terms +of copyright, but also in terms of patent law. + +Please note that this license is not to be understood as a guarantee +either. If you use the program according to this license, but in +conflict with patent law, it does not mean that the licensor will refund +you for any losses that you incur if you are sued for your patent +infringement. diff --git a/modules/surface_matching/include/opencv2/surface_matching.hpp b/modules/surface_matching/include/opencv2/surface_matching.hpp index 160afe112..eee5d2e28 100644 --- a/modules/surface_matching/include/opencv2/surface_matching.hpp +++ b/modules/surface_matching/include/opencv2/surface_matching.hpp @@ -43,6 +43,40 @@ /** @defgroup surface_matching Surface Matching +Note about the License and Patents +----------------------------------- + +The following patents have been issued for methods embodied in this +software: "Recognition and pose determination of 3D objects in 3D scenes +using geometric point pair descriptors and the generalized Hough +Transform", Bertram Heinrich Drost, Markus Ulrich, EP Patent 2385483 +(Nov. 21, 2012), assignee: MVTec Software GmbH, 81675 Muenchen +(Germany); "Recognition and pose determination of 3D objects in 3D +scenes", Bertram Heinrich Drost, Markus Ulrich, US Patent 8830229 (Sept. +9, 2014), assignee: MVTec Software GmbH, 81675 Muenchen (Germany). +Further patents are pending. For further details, contact MVTec Software +GmbH (info@mvtec.com). + +Note that restrictions imposed by these patents (and possibly others) +exist independently of and may be in conflict with the freedoms granted +in this license, which refers to copyright of the program, not patents +for any methods that it implements. Both copyright and patent law must +be obeyed to legally use and redistribute this program and it is not the +purpose of this license to induce you to infringe any patents or other +property right claims or to contest validity of any such claims. If you +redistribute or use the program, then this license merely protects you +from committing copyright infringement. It does not protect you from +committing patent infringement. So, before you do anything with this +program, make sure that you have permission to do so not merely in terms +of copyright, but also in terms of patent law. + +Please note that this license is not to be understood as a guarantee +either. If you use the program according to this license, but in +conflict with patent law, it does not mean that the licensor will refund +you for any losses that you incur if you are sued for your patent +infringement. + + Introduction to Surface Matching -------------------------------- From 25aad108acf6f9c7567cffb20a44d39c0f96ba22 Mon Sep 17 00:00:00 2001 From: Str3iber Date: Tue, 20 Jan 2015 16:37:56 +0100 Subject: [PATCH 09/32] make suggested changes --- modules/xfeatures2d/doc/extra_features.rst | 25 ---- .../include/opencv2/xfeatures2d.hpp | 17 ++- modules/xfeatures2d/src/lucid.cpp | 116 +++--------------- 3 files changed, 23 insertions(+), 135 deletions(-) diff --git a/modules/xfeatures2d/doc/extra_features.rst b/modules/xfeatures2d/doc/extra_features.rst index cb5693c1f..ca3296f7b 100644 --- a/modules/xfeatures2d/doc/extra_features.rst +++ b/modules/xfeatures2d/doc/extra_features.rst @@ -91,28 +91,3 @@ We notice that for keypoint matching applications, image content has little effe :param keypoints: Set of detected keypoints :param corrThresh: Correlation threshold. :param verbose: Prints pair selection informations. - - - -LUCID ------------------------- -.. ocv:class:: LUCID : public DescriptorExtractor - -Class for computing LUCID descriptors described in a paper by Ziegler, Andrew, -Eric Christiansen, David Kriegman, and Serge J. Belongie. -*Locally uniform comparison image descriptor.* In Advances in Neural Information Processing Systems, pp. 1-9. 2012. :: - - class LUCID : public DescriptorExtractor - { - public: - LUCID(const int lucid_kernel = 1, const int blur_kernel = 2); - - virtual int descriptorSize() const; - virtual int descriptorType() const; - virtual int defaultNorm() const; - - virtual void compute(InputArray _src, std::vector &keypoints, OutputArray _desc); - - protected: - int l_kernel, b_kernel; - }; diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp index 4c5584257..6df6f526d 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp @@ -130,22 +130,21 @@ public: }; -// Locally Uniform Comparison Image Descriptor +/** @brief Class implementing the locally uniform comparison image descriptor, described in @cite LUCID -// @brief This class implements the Locally Uniform Comparison Image Descriptor @cite LUCID +An image descriptor that can be computed very fast, while being +about as robust as, for example, SURF or BRIEF. + */ class CV_EXPORTS LUCID : public DescriptorExtractor { public: + /** + * @param lucid_kernel kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth + * @param blur_kernel kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth + */ static Ptr create(const int lucid_kernel, const int blur_kernel); }; -/** @brief Separable box filter blur, needed by LUCID, also exposed for the user - -@param _src Image on which blur should be applied -@param _dst Image resulting from _src having blur applied, the output image -@param kernel Blur kernel size where 1 equates a 3x3 matrix, 2 = 5x5, 3 = 7x7, and so on -*/ -CV_EXPORTS void separable_blur(const InputArray _src, CV_OUT OutputArray _dst, const int kernel); //! @} diff --git a/modules/xfeatures2d/src/lucid.cpp b/modules/xfeatures2d/src/lucid.cpp index dfaf14125..22c5fcd4c 100644 --- a/modules/xfeatures2d/src/lucid.cpp +++ b/modules/xfeatures2d/src/lucid.cpp @@ -50,110 +50,24 @@ the use of this software, even if advised of the possibility of such damage. namespace cv { namespace xfeatures2d { - void separable_blur(const InputArray _src, OutputArray _dst, const int kernel) { - int z, p, r = 0, g = 0, b = 0, m = kernel*2+1, width, height; - - Point3_ *pnt; - - Mat_ src = _src.getMat(); - if (src.empty()) { - CV_Error(Error::StsBadArg, "empty source image supplied"); - - return; - } - - _dst.create(src.size(), src.type()); - Mat_ dst = _dst.getMat(); - - width = dst.cols, height = dst.rows; - - for (int y = 0; y < height; ++y) { - for (int x = 0; x < width; ++x) { - z = kernel*-1; - - if (!x) { - r = 0, g = 0, b = 0; - - for (p = x+z; z <= kernel; ++z, p=x+z) { - pnt = src.ptr >(y, (p < 0 ? width+p : p >= width ? p-width : p)); - r += pnt->z; - g += pnt->y; - b += pnt->x; - } - } - else { - p = x+z-1; - - pnt = src.ptr >(y, (p < 0 ? width+p : p >= width ? p-width : p)); - r -= pnt->z; - g -= pnt->y; - b -= pnt->x; - - p = x+kernel; - - pnt = src.ptr >(y, (p < 0 ? width+p : p >= width ? p-width : p)); - r += pnt->z; - g += pnt->y; - b += pnt->x; - } - - pnt = dst.ptr >(y, x); - pnt->z = static_cast(r/m); - pnt->y = static_cast(g/m); - pnt->x = static_cast(b/m); - } - } - - for (int x = 0, rl = 0, gl = 0, bl = 0, rn = 0, gn = 0, bn = 0; x < width; ++x) { - for (int y = 0; y < height; ++y) { - z = kernel*-1; - - if (!y) { - r = 0, g = 0, b = 0; - - for (p = y+z; z <= kernel; ++z, p=y+z) { - pnt = dst.ptr >((p < 0 ? height+p : p >= height ? p-height : p), x); - r += pnt->z; - g += pnt->y; - b += pnt->x; - } - } - else { - p = y+z-1; - - pnt = dst.ptr >((p < 0 ? height+p : p >= height ? p-height : p), x); - r -= pnt->z, r -= rl; - g -= pnt->y, g -= gl; - b -= pnt->x, b -= bl; - - p = y+kernel; - - pnt = dst.ptr >((p < 0 ? height+p : p >= height ? p-height : p), x); - r += pnt->z, r += rn; - g += pnt->y, g += gn; - b += pnt->x, b += bn; - } - - pnt = dst.ptr >(y, x); - rl = pnt->z; - gl = pnt->y; - bl = pnt->x; - rn = r/m; - gn = g/m; - bn = b/m; - pnt->z = static_cast(rn); - pnt->y = static_cast(gn); - pnt->x = static_cast(bn); - } - } - } - + /*! + LUCID implementation + */ class LUCIDImpl : public LUCID { public: + /** Constructor + * @param lucid_kernel kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth + * @param blur_kernel kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth + */ LUCIDImpl(const int lucid_kernel = 1, const int blur_kernel = 2); + /** returns the descriptor length */ virtual int descriptorSize() const; + + /** returns the descriptor type */ virtual int descriptorType() const; + + /** returns the default norm type */ virtual int defaultNorm() const; virtual void compute(InputArray _src, std::vector &keypoints, OutputArray _desc); @@ -168,7 +82,7 @@ namespace cv { LUCIDImpl::LUCIDImpl(const int lucid_kernel, const int blur_kernel) { l_kernel = lucid_kernel; - b_kernel = blur_kernel; + b_kernel = blur_kernel*2+1; } int LUCIDImpl::descriptorSize() const { @@ -191,7 +105,7 @@ namespace cv { Mat_ src; - separable_blur(_src.getMat(), src, b_kernel); + blur(_src.getMat(), src, cv::Size(b_kernel, b_kernel)); int x, y, j, d, p, m = (l_kernel*2+1)*(l_kernel*2+1)*3, width = src.cols, height = src.rows, r, c; @@ -223,4 +137,4 @@ namespace cv { sort(desc, _desc, SORT_EVERY_ROW | SORT_ASCENDING); } } -} +} // END NAMESPACE CV From 63a8a60bdbcb8044c33a370e462cfc3e1e72cd06 Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Wed, 3 Dec 2014 16:08:53 +0300 Subject: [PATCH 10/32] Fixed cvv module build - updated header inclusions - added export instruction to public methods - fixed some small problems --- modules/cvv/CMakeLists.txt | 2 +- modules/cvv/include/opencv2/cvv.hpp | 26 +++++++++++++++++++ modules/cvv/include/opencv2/cvv/cvv.hpp | 25 +++--------------- modules/cvv/include/opencv2/cvv/dmatch.hpp | 6 ++--- modules/cvv/include/opencv2/cvv/filter.hpp | 4 +-- .../cvv/include/opencv2/cvv/final_show.hpp | 3 ++- .../cvv/include/opencv2/cvv/show_image.hpp | 4 +-- modules/cvv/samples/cvv_demo.cpp | 13 ++++++---- modules/cvv/src/extension_api/api.hpp | 2 +- .../cvv/src/gui/rawview_group_subtable.cpp | 6 ++--- modules/cvv/src/gui/rawview_table_row.cpp | 2 ++ modules/cvv/src/gui/rawview_table_row.hpp | 4 +-- modules/cvv/src/impl/call.hpp | 4 +-- modules/cvv/src/impl/data_controller.hpp | 2 +- modules/cvv/src/impl/filter_call.hpp | 2 +- modules/cvv/src/impl/match_call.hpp | 4 +-- modules/cvv/src/impl/single_image_call.hpp | 2 +- modules/cvv/src/qtutil/autofilterwidget.hpp | 2 +- .../src/qtutil/filter/diffFilterWidget.cpp | 6 ++--- .../src/qtutil/filter/grayfilterwidget.hpp | 2 +- .../src/qtutil/filter/overlayfilterwidget.cpp | 2 +- .../src/qtutil/filter/sobelfilterwidget.cpp | 3 ++- .../cvv/src/qtutil/filterfunctionwidget.hpp | 2 +- .../cvv/src/qtutil/filterselectorwidget.hpp | 2 +- modules/cvv/src/qtutil/histogram.cpp | 6 ++--- modules/cvv/src/qtutil/histogram.hpp | 5 ++-- .../cvv/src/qtutil/matchview/colorutil.hpp | 2 +- .../cvv/src/qtutil/matchview/cvvkeypoint.hpp | 4 +-- modules/cvv/src/qtutil/matchview/cvvmatch.hpp | 4 +-- .../matchview/falsecolorkeypointpen.hpp | 2 +- .../qtutil/matchview/falsecolormatchpen.hpp | 2 +- .../matchview/keypointintervallselection.hpp | 2 +- .../qtutil/matchview/keypointmanagement.hpp | 2 +- .../matchview/keypointportionselector.hpp | 2 +- .../qtutil/matchview/keypointselection.hpp | 2 +- .../matchview/keypointselectionselector.hpp | 2 +- .../matchview/keypointsettingsselector.hpp | 2 +- .../qtutil/matchview/keypointshowsetting.hpp | 2 +- .../qtutil/matchview/keypointvaluechooser.hpp | 2 +- .../matchview/matchintervallselection.hpp | 2 +- .../src/qtutil/matchview/matchmanagement.hpp | 2 +- .../qtutil/matchview/matchportionselector.hpp | 2 +- .../cvv/src/qtutil/matchview/matchscene.hpp | 4 +-- .../src/qtutil/matchview/matchselection.hpp | 2 +- .../matchview/matchselectionselector.hpp | 2 +- .../matchview/matchsettingsselector.hpp | 2 +- .../src/qtutil/matchview/matchshowsetting.hpp | 2 +- .../src/qtutil/matchview/rawview_window.hpp | 4 +-- .../qtutil/matchview/showinrawviewwidget.hpp | 2 +- modules/cvv/src/qtutil/signalslot.hpp | 2 +- modules/cvv/src/qtutil/types.hpp | 2 +- modules/cvv/src/qtutil/util.cpp | 2 -- modules/cvv/src/qtutil/util.hpp | 5 ++-- modules/cvv/src/qtutil/zoomableimage.cpp | 1 + modules/cvv/src/qtutil/zoomableimage.hpp | 2 +- .../cvv/src/qtutil/zoomableimageoptpanel.hpp | 2 +- modules/cvv/src/view/defaultfilterview.hpp | 2 +- modules/cvv/src/view/dual_filter_view.cpp | 2 +- modules/cvv/src/view/filter_view.hpp | 2 +- modules/cvv/src/view/image_view.cpp | 2 +- modules/cvv/src/view/image_view.hpp | 7 +++-- modules/cvv/src/view/linematchview.hpp | 4 +-- modules/cvv/src/view/match_view.hpp | 4 +-- modules/cvv/src/view/pointmatchview.hpp | 4 +-- modules/cvv/src/view/rawview.hpp | 20 +++++++------- modules/cvv/src/view/singlefilterview.hpp | 2 +- .../cvv/src/view/translationsmatchview.hpp | 4 +-- modules/cvv/test/test_precomp.hpp | 4 +-- 68 files changed, 141 insertions(+), 127 deletions(-) create mode 100644 modules/cvv/include/opencv2/cvv.hpp diff --git a/modules/cvv/CMakeLists.txt b/modules/cvv/CMakeLists.txt index 609e5cc09..2bcbb1b7a 100644 --- a/modules/cvv/CMakeLists.txt +++ b/modules/cvv/CMakeLists.txt @@ -5,7 +5,7 @@ endif() # we need C++11 and want warnings: set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Wextra -pedantic") -ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshadow) +ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshadow -Wmissing-declarations) # Qt5 set(CMAKE_AUTOMOC ON) diff --git a/modules/cvv/include/opencv2/cvv.hpp b/modules/cvv/include/opencv2/cvv.hpp new file mode 100644 index 000000000..38dfadbb1 --- /dev/null +++ b/modules/cvv/include/opencv2/cvv.hpp @@ -0,0 +1,26 @@ +#ifndef __OPENCV_CVV_HPP__ +#define __OPENCV_CVV_HPP__ + +/** +@defgroup cvv GUI for Interactive Visual Debugging of Computer Vision Programs + +Namespace for all functions is **cvv**, i.e. *cvv::showImage()*. + +Compilation: + +- For development, i.e. for cvv GUI to show up, compile your code using cvv with + *g++ -DCVVISUAL_DEBUGMODE*. +- For release, i.e. cvv calls doing nothing, compile your code without above flag. + +See cvv tutorial for a commented example application using cvv. + +*/ + +#include +#include +#include +#include +#include +#include + +#endif //__OPENCV_CVV_HPP__ diff --git a/modules/cvv/include/opencv2/cvv/cvv.hpp b/modules/cvv/include/opencv2/cvv/cvv.hpp index ef9e29b0f..592096db7 100644 --- a/modules/cvv/include/opencv2/cvv/cvv.hpp +++ b/modules/cvv/include/opencv2/cvv/cvv.hpp @@ -1,22 +1,5 @@ -/** -@defgroup cvv GUI for Interactive Visual Debugging of Computer Vision Programs - -Namespace for all functions is **cvv**, i.e. *cvv::showImage()*. - -Compilation: - -- For development, i.e. for cvv GUI to show up, compile your code using cvv with - *g++ -DCVVISUAL_DEBUGMODE*. -- For release, i.e. cvv calls doing nothing, compile your code without above flag. - -See cvv tutorial for a commented example application using cvv. - -*/ - -#include -#include -#include -#include -#include -#include +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif +#include "opencv2/cvv.hpp" diff --git a/modules/cvv/include/opencv2/cvv/dmatch.hpp b/modules/cvv/include/opencv2/cvv/dmatch.hpp index 597bced31..d152cb43a 100644 --- a/modules/cvv/include/opencv2/cvv/dmatch.hpp +++ b/modules/cvv/include/opencv2/cvv/dmatch.hpp @@ -3,8 +3,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "call_meta_data.hpp" #include "debug_mode.hpp" @@ -21,7 +21,7 @@ namespace cvv namespace impl { -void debugDMatch(cv::InputArray img1, std::vector keypoints1, +CV_EXPORTS void debugDMatch(cv::InputArray img1, std::vector keypoints1, cv::InputArray img2, std::vector keypoints2, std::vector matches, const CallMetaData &data, const char *description, const char *view, diff --git a/modules/cvv/include/opencv2/cvv/filter.hpp b/modules/cvv/include/opencv2/cvv/filter.hpp index 07cc3b1db..824df2437 100644 --- a/modules/cvv/include/opencv2/cvv/filter.hpp +++ b/modules/cvv/include/opencv2/cvv/filter.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "call_meta_data.hpp" #include "debug_mode.hpp" @@ -21,7 +21,7 @@ namespace cvv namespace impl { // implementation outside API -void debugFilter(cv::InputArray original, cv::InputArray result, +CV_EXPORTS void debugFilter(cv::InputArray original, cv::InputArray result, const CallMetaData &data, const char *description, const char *view); } // namespace impl diff --git a/modules/cvv/include/opencv2/cvv/final_show.hpp b/modules/cvv/include/opencv2/cvv/final_show.hpp index 29df5eec2..60a096c90 100644 --- a/modules/cvv/include/opencv2/cvv/final_show.hpp +++ b/modules/cvv/include/opencv2/cvv/final_show.hpp @@ -1,6 +1,7 @@ #ifndef CVVISUAL_FINAL_SHOW_HPP #define CVVISUAL_FINAL_SHOW_HPP +#include "opencv2/core.hpp" #include "debug_mode.hpp" namespace cvv @@ -11,7 +12,7 @@ namespace cvv namespace impl { -void finalShow(); +CV_EXPORTS void finalShow(); } /** @brief Passes the control to the debug-window for a last time. diff --git a/modules/cvv/include/opencv2/cvv/show_image.hpp b/modules/cvv/include/opencv2/cvv/show_image.hpp index bf56e7e49..417dfb2ac 100644 --- a/modules/cvv/include/opencv2/cvv/show_image.hpp +++ b/modules/cvv/include/opencv2/cvv/show_image.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "call_meta_data.hpp" #include "debug_mode.hpp" @@ -21,7 +21,7 @@ namespace cvv namespace impl { // implementation outside API -void showImage(cv::InputArray img, const CallMetaData &data, +CV_EXPORTS void showImage(cv::InputArray img, const CallMetaData &data, const char *description, const char *view); } // namespace impl diff --git a/modules/cvv/samples/cvv_demo.cpp b/modules/cvv/samples/cvv_demo.cpp index b8f5bd4d2..096d7f608 100644 --- a/modules/cvv/samples/cvv_demo.cpp +++ b/modules/cvv/samples/cvv_demo.cpp @@ -3,9 +3,11 @@ #include // library includes -#include -#include -#include +#include +#include +#include +#include +#include #define CVVISUAL_DEBUGMODE #include @@ -14,6 +16,7 @@ #include #include +using namespace cv; template std::string toString(const T& p_arg) { @@ -83,7 +86,7 @@ main(int argc, char** argv) cv::Mat prevDescriptors; int maxFeatureCount = 500; - cv::ORB detector(maxFeatureCount); + Ptr detector = ORB::create(maxFeatureCount); cv::BFMatcher matcher(cv::NORM_HAMMING); @@ -105,7 +108,7 @@ main(int argc, char** argv) // detect ORB features std::vector keypoints; cv::Mat descriptors; - detector(imgGray, cv::noArray(), keypoints, descriptors); + detector->detectAndCompute(imgGray, cv::noArray(), keypoints, descriptors); printf("%d: detected %zd keypoints\n", imgId, keypoints.size()); // match them to previous image (if available) diff --git a/modules/cvv/src/extension_api/api.hpp b/modules/cvv/src/extension_api/api.hpp index cab7b6c16..746fb3d39 100644 --- a/modules/cvv/src/extension_api/api.hpp +++ b/modules/cvv/src/extension_api/api.hpp @@ -1,7 +1,7 @@ #ifndef CVVISUAL_EXTENSION_API_HPP #define CVVISUAL_EXTENSION_API_HPP -#include +#include #include #include diff --git a/modules/cvv/src/gui/rawview_group_subtable.cpp b/modules/cvv/src/gui/rawview_group_subtable.cpp index cd2daac43..c6f6f6e36 100644 --- a/modules/cvv/src/gui/rawview_group_subtable.cpp +++ b/modules/cvv/src/gui/rawview_group_subtable.cpp @@ -3,8 +3,8 @@ #include #include -#include -#include +#include +#include #include #include @@ -128,7 +128,7 @@ void RawviewGroupSubtable::customMenuRequested(QPoint location) { menu->addAction(new QAction("Show selected rows in view", this)); } - + auto formats = RawviewTableRow::getAvailableTextFormats(); for (auto format : formats) { diff --git a/modules/cvv/src/gui/rawview_table_row.cpp b/modules/cvv/src/gui/rawview_table_row.cpp index 6d7b45b11..c69cb47d1 100644 --- a/modules/cvv/src/gui/rawview_table_row.cpp +++ b/modules/cvv/src/gui/rawview_table_row.cpp @@ -10,6 +10,8 @@ #include "../qtutil/util.hpp" #include "../stfl/stringutils.hpp" +#include + namespace cvv { namespace gui diff --git a/modules/cvv/src/gui/rawview_table_row.hpp b/modules/cvv/src/gui/rawview_table_row.hpp index e42f81be7..8a8ae1712 100644 --- a/modules/cvv/src/gui/rawview_table_row.hpp +++ b/modules/cvv/src/gui/rawview_table_row.hpp @@ -4,8 +4,8 @@ #include #include -#include -#include +#include +#include #include #include diff --git a/modules/cvv/src/impl/call.hpp b/modules/cvv/src/impl/call.hpp index 042510947..e7e924aa5 100644 --- a/modules/cvv/src/impl/call.hpp +++ b/modules/cvv/src/impl/call.hpp @@ -5,8 +5,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "opencv2/cvv/call_meta_data.hpp" diff --git a/modules/cvv/src/impl/data_controller.hpp b/modules/cvv/src/impl/data_controller.hpp index 18956d1c2..015a84c85 100644 --- a/modules/cvv/src/impl/data_controller.hpp +++ b/modules/cvv/src/impl/data_controller.hpp @@ -4,7 +4,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "call.hpp" #include "../controller/view_controller.hpp" diff --git a/modules/cvv/src/impl/filter_call.hpp b/modules/cvv/src/impl/filter_call.hpp index 885873c8d..445f108bb 100644 --- a/modules/cvv/src/impl/filter_call.hpp +++ b/modules/cvv/src/impl/filter_call.hpp @@ -5,7 +5,7 @@ #include "call.hpp" -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" namespace cvv { diff --git a/modules/cvv/src/impl/match_call.hpp b/modules/cvv/src/impl/match_call.hpp index e4cd14ddd..ee548510b 100644 --- a/modules/cvv/src/impl/match_call.hpp +++ b/modules/cvv/src/impl/match_call.hpp @@ -5,8 +5,8 @@ #include #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "call.hpp" diff --git a/modules/cvv/src/impl/single_image_call.hpp b/modules/cvv/src/impl/single_image_call.hpp index 5d04b1f68..81d748d3d 100644 --- a/modules/cvv/src/impl/single_image_call.hpp +++ b/modules/cvv/src/impl/single_image_call.hpp @@ -5,7 +5,7 @@ #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" namespace cvv { diff --git a/modules/cvv/src/qtutil/autofilterwidget.hpp b/modules/cvv/src/qtutil/autofilterwidget.hpp index d36ecfdc7..3024f139b 100644 --- a/modules/cvv/src/qtutil/autofilterwidget.hpp +++ b/modules/cvv/src/qtutil/autofilterwidget.hpp @@ -5,7 +5,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include #include diff --git a/modules/cvv/src/qtutil/filter/diffFilterWidget.cpp b/modules/cvv/src/qtutil/filter/diffFilterWidget.cpp index 7a819d106..05e296389 100644 --- a/modules/cvv/src/qtutil/filter/diffFilterWidget.cpp +++ b/modules/cvv/src/qtutil/filter/diffFilterWidget.cpp @@ -1,6 +1,6 @@ -#include -#include -#include +#include +#include +#include #include diff --git a/modules/cvv/src/qtutil/filter/grayfilterwidget.hpp b/modules/cvv/src/qtutil/filter/grayfilterwidget.hpp index 4aec4eced..5701192c9 100644 --- a/modules/cvv/src/qtutil/filter/grayfilterwidget.hpp +++ b/modules/cvv/src/qtutil/filter/grayfilterwidget.hpp @@ -10,7 +10,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "../filterfunctionwidget.hpp" #include "../../util/observer_ptr.hpp" diff --git a/modules/cvv/src/qtutil/filter/overlayfilterwidget.cpp b/modules/cvv/src/qtutil/filter/overlayfilterwidget.cpp index f153253a2..a3e5d2fd9 100644 --- a/modules/cvv/src/qtutil/filter/overlayfilterwidget.cpp +++ b/modules/cvv/src/qtutil/filter/overlayfilterwidget.cpp @@ -1,5 +1,5 @@ -#include +#include #include #include "QLabel" diff --git a/modules/cvv/src/qtutil/filter/sobelfilterwidget.cpp b/modules/cvv/src/qtutil/filter/sobelfilterwidget.cpp index c3311c685..acb86229a 100644 --- a/modules/cvv/src/qtutil/filter/sobelfilterwidget.cpp +++ b/modules/cvv/src/qtutil/filter/sobelfilterwidget.cpp @@ -1,6 +1,7 @@ #include "sobelfilterwidget.hpp" -#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/imgproc/types_c.h" #include #include diff --git a/modules/cvv/src/qtutil/filterfunctionwidget.hpp b/modules/cvv/src/qtutil/filterfunctionwidget.hpp index da6c16347..a55f7fc53 100644 --- a/modules/cvv/src/qtutil/filterfunctionwidget.hpp +++ b/modules/cvv/src/qtutil/filterfunctionwidget.hpp @@ -9,7 +9,7 @@ #include // OCV -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" // cvv #include "signalslot.hpp" diff --git a/modules/cvv/src/qtutil/filterselectorwidget.hpp b/modules/cvv/src/qtutil/filterselectorwidget.hpp index d4a2887e7..bbadc20fc 100644 --- a/modules/cvv/src/qtutil/filterselectorwidget.hpp +++ b/modules/cvv/src/qtutil/filterselectorwidget.hpp @@ -12,7 +12,7 @@ #include // OCV -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" // CVV #include "signalslot.hpp" diff --git a/modules/cvv/src/qtutil/histogram.cpp b/modules/cvv/src/qtutil/histogram.cpp index ddfbe2528..e5227009f 100644 --- a/modules/cvv/src/qtutil/histogram.cpp +++ b/modules/cvv/src/qtutil/histogram.cpp @@ -123,10 +123,10 @@ cv::Mat Histogram::drawHist(const std::vector& channelHists, cv::Size h int thickness = 1; for (int binTextId = 0; binTextId < binCount; binTextId += binTextStep) { - auto text = QString::number(binTextId).toStdString(); - auto textSize = cv::getTextSize(text, fontFace, fontScale, thickness, NULL); + auto text = QString::number(binTextId).toLatin1(); + auto textSize = cv::getTextSize(text.data(), fontFace, fontScale, thickness, NULL); auto textPt = cv::Point(std::max(0, binWidth * binTextId - textSize.width/2), histSize.height); - cv::putText(histMat, text, textPt, fontFace, fontScale, textColor, thickness); + cv::putText(histMat, text.data(), textPt, fontFace, fontScale, textColor, thickness); auto linePt1 = cv::Point(binWidth * binTextId, 0); auto linePt2 = cv::Point(binWidth * binTextId, histSize.height - textSize.height); cv::line(histMat, linePt1, linePt2, textColor); diff --git a/modules/cvv/src/qtutil/histogram.hpp b/modules/cvv/src/qtutil/histogram.hpp index 33b44ba3a..1aabcda36 100644 --- a/modules/cvv/src/qtutil/histogram.hpp +++ b/modules/cvv/src/qtutil/histogram.hpp @@ -3,9 +3,8 @@ #include -#include -#include -#include +#include +#include #include "zoomableimage.hpp" diff --git a/modules/cvv/src/qtutil/matchview/colorutil.hpp b/modules/cvv/src/qtutil/matchview/colorutil.hpp index 21064919c..bdc3a6624 100644 --- a/modules/cvv/src/qtutil/matchview/colorutil.hpp +++ b/modules/cvv/src/qtutil/matchview/colorutil.hpp @@ -5,7 +5,7 @@ #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" namespace cvv { diff --git a/modules/cvv/src/qtutil/matchview/cvvkeypoint.hpp b/modules/cvv/src/qtutil/matchview/cvvkeypoint.hpp index 7b1911835..4ab57ba2d 100644 --- a/modules/cvv/src/qtutil/matchview/cvvkeypoint.hpp +++ b/modules/cvv/src/qtutil/matchview/cvvkeypoint.hpp @@ -9,8 +9,8 @@ #include #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "keypointsettings.hpp" #include "../zoomableimage.hpp" diff --git a/modules/cvv/src/qtutil/matchview/cvvmatch.hpp b/modules/cvv/src/qtutil/matchview/cvvmatch.hpp index dd57d952a..adedc212e 100644 --- a/modules/cvv/src/qtutil/matchview/cvvmatch.hpp +++ b/modules/cvv/src/qtutil/matchview/cvvmatch.hpp @@ -9,8 +9,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "matchsettings.hpp" #include "cvvkeypoint.hpp" diff --git a/modules/cvv/src/qtutil/matchview/falsecolorkeypointpen.hpp b/modules/cvv/src/qtutil/matchview/falsecolorkeypointpen.hpp index 137e7b04d..555730d2b 100644 --- a/modules/cvv/src/qtutil/matchview/falsecolorkeypointpen.hpp +++ b/modules/cvv/src/qtutil/matchview/falsecolorkeypointpen.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointvaluechooser.hpp" #include "keypointsettings.hpp" diff --git a/modules/cvv/src/qtutil/matchview/falsecolormatchpen.hpp b/modules/cvv/src/qtutil/matchview/falsecolormatchpen.hpp index ca3ba0fc8..259c5f09a 100644 --- a/modules/cvv/src/qtutil/matchview/falsecolormatchpen.hpp +++ b/modules/cvv/src/qtutil/matchview/falsecolormatchpen.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchsettings.hpp" diff --git a/modules/cvv/src/qtutil/matchview/keypointintervallselection.hpp b/modules/cvv/src/qtutil/matchview/keypointintervallselection.hpp index c6dcb8040..7f88cc532 100644 --- a/modules/cvv/src/qtutil/matchview/keypointintervallselection.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointintervallselection.hpp @@ -1,7 +1,7 @@ #ifndef CVVISUAL_KEY_POINT_INTERVALL_SELECTOR #define CVVISUAL_KEY_POINT_INTERVALL_SELECTOR -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointselection.hpp" #include "keypointvaluechooser.hpp" diff --git a/modules/cvv/src/qtutil/matchview/keypointmanagement.hpp b/modules/cvv/src/qtutil/matchview/keypointmanagement.hpp index a81286015..1c85a442b 100644 --- a/modules/cvv/src/qtutil/matchview/keypointmanagement.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointmanagement.hpp @@ -5,7 +5,7 @@ #include "../../util/util.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointselectionselector.hpp" #include "keypointsettingsselector.hpp" diff --git a/modules/cvv/src/qtutil/matchview/keypointportionselector.hpp b/modules/cvv/src/qtutil/matchview/keypointportionselector.hpp index 77c382c95..ac03b648c 100644 --- a/modules/cvv/src/qtutil/matchview/keypointportionselector.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointportionselector.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointselection.hpp" #include "keypointvaluechooser.hpp" diff --git a/modules/cvv/src/qtutil/matchview/keypointselection.hpp b/modules/cvv/src/qtutil/matchview/keypointselection.hpp index 02cafcafd..0d567dfd2 100644 --- a/modules/cvv/src/qtutil/matchview/keypointselection.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointselection.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" namespace cvv{ namespace qtutil{ diff --git a/modules/cvv/src/qtutil/matchview/keypointselectionselector.hpp b/modules/cvv/src/qtutil/matchview/keypointselectionselector.hpp index e272bb013..d4ccfc7b1 100644 --- a/modules/cvv/src/qtutil/matchview/keypointselectionselector.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointselectionselector.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointselection.hpp" #include "../registerhelper.hpp" diff --git a/modules/cvv/src/qtutil/matchview/keypointsettingsselector.hpp b/modules/cvv/src/qtutil/matchview/keypointsettingsselector.hpp index 9049217b4..6c99099f4 100644 --- a/modules/cvv/src/qtutil/matchview/keypointsettingsselector.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointsettingsselector.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointsettings.hpp" #include "../registerhelper.hpp" diff --git a/modules/cvv/src/qtutil/matchview/keypointshowsetting.hpp b/modules/cvv/src/qtutil/matchview/keypointshowsetting.hpp index 8b9eac5c6..46c984a07 100644 --- a/modules/cvv/src/qtutil/matchview/keypointshowsetting.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointshowsetting.hpp @@ -5,7 +5,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "keypointsettings.hpp" namespace cvv{ namespace qtutil{ diff --git a/modules/cvv/src/qtutil/matchview/keypointvaluechooser.hpp b/modules/cvv/src/qtutil/matchview/keypointvaluechooser.hpp index 845b4f273..4a050729a 100644 --- a/modules/cvv/src/qtutil/matchview/keypointvaluechooser.hpp +++ b/modules/cvv/src/qtutil/matchview/keypointvaluechooser.hpp @@ -4,7 +4,7 @@ #include #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" namespace cvv{ namespace qtutil{ diff --git a/modules/cvv/src/qtutil/matchview/matchintervallselection.hpp b/modules/cvv/src/qtutil/matchview/matchintervallselection.hpp index cfbff6cdb..53985b793 100644 --- a/modules/cvv/src/qtutil/matchview/matchintervallselection.hpp +++ b/modules/cvv/src/qtutil/matchview/matchintervallselection.hpp @@ -1,7 +1,7 @@ #ifndef CVVISUAL_MATCH_INTERVALL_SELECTOR #define CVVISUAL_MATCH_INTERVALL_SELECTOR -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchselection.hpp" #include "../intervallselector.hpp" diff --git a/modules/cvv/src/qtutil/matchview/matchmanagement.hpp b/modules/cvv/src/qtutil/matchview/matchmanagement.hpp index 63142450e..ab617892d 100644 --- a/modules/cvv/src/qtutil/matchview/matchmanagement.hpp +++ b/modules/cvv/src/qtutil/matchview/matchmanagement.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchselectionselector.hpp" #include "matchsettingsselector.hpp" diff --git a/modules/cvv/src/qtutil/matchview/matchportionselector.hpp b/modules/cvv/src/qtutil/matchview/matchportionselector.hpp index 783cb8180..e860914f9 100644 --- a/modules/cvv/src/qtutil/matchview/matchportionselector.hpp +++ b/modules/cvv/src/qtutil/matchview/matchportionselector.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchselection.hpp" #include "../portionselector.hpp" diff --git a/modules/cvv/src/qtutil/matchview/matchscene.hpp b/modules/cvv/src/qtutil/matchview/matchscene.hpp index 001e358aa..e9b739737 100644 --- a/modules/cvv/src/qtutil/matchview/matchscene.hpp +++ b/modules/cvv/src/qtutil/matchview/matchscene.hpp @@ -8,8 +8,8 @@ #include #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "cvvmatch.hpp" #include "cvvkeypoint.hpp" #include "zoomableproxyobject.hpp" diff --git a/modules/cvv/src/qtutil/matchview/matchselection.hpp b/modules/cvv/src/qtutil/matchview/matchselection.hpp index 4499d775f..f5a85e550 100644 --- a/modules/cvv/src/qtutil/matchview/matchselection.hpp +++ b/modules/cvv/src/qtutil/matchview/matchselection.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" namespace cvv{ namespace qtutil{ diff --git a/modules/cvv/src/qtutil/matchview/matchselectionselector.hpp b/modules/cvv/src/qtutil/matchview/matchselectionselector.hpp index 09265fef4..e245b8308 100644 --- a/modules/cvv/src/qtutil/matchview/matchselectionselector.hpp +++ b/modules/cvv/src/qtutil/matchview/matchselectionselector.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchselection.hpp" #include "../registerhelper.hpp" diff --git a/modules/cvv/src/qtutil/matchview/matchsettingsselector.hpp b/modules/cvv/src/qtutil/matchview/matchsettingsselector.hpp index e1ecc9ba1..5c73e9e8f 100644 --- a/modules/cvv/src/qtutil/matchview/matchsettingsselector.hpp +++ b/modules/cvv/src/qtutil/matchview/matchsettingsselector.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchsettings.hpp" #include "../registerhelper.hpp" diff --git a/modules/cvv/src/qtutil/matchview/matchshowsetting.hpp b/modules/cvv/src/qtutil/matchview/matchshowsetting.hpp index 544ad7ffd..4fd4c814a 100644 --- a/modules/cvv/src/qtutil/matchview/matchshowsetting.hpp +++ b/modules/cvv/src/qtutil/matchview/matchshowsetting.hpp @@ -5,7 +5,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchsettings.hpp" #include "cvvmatch.hpp" diff --git a/modules/cvv/src/qtutil/matchview/rawview_window.hpp b/modules/cvv/src/qtutil/matchview/rawview_window.hpp index 56e82ba11..7198c2cdc 100644 --- a/modules/cvv/src/qtutil/matchview/rawview_window.hpp +++ b/modules/cvv/src/qtutil/matchview/rawview_window.hpp @@ -3,8 +3,8 @@ #include -#include -#include +#include +#include #include #include diff --git a/modules/cvv/src/qtutil/matchview/showinrawviewwidget.hpp b/modules/cvv/src/qtutil/matchview/showinrawviewwidget.hpp index d496197f9..2961b7232 100644 --- a/modules/cvv/src/qtutil/matchview/showinrawviewwidget.hpp +++ b/modules/cvv/src/qtutil/matchview/showinrawviewwidget.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/features2d.hpp" #include "matchmanagement.hpp" #include "keypointmanagement.hpp" diff --git a/modules/cvv/src/qtutil/signalslot.hpp b/modules/cvv/src/qtutil/signalslot.hpp index 634e48f12..d00140b5e 100644 --- a/modules/cvv/src/qtutil/signalslot.hpp +++ b/modules/cvv/src/qtutil/signalslot.hpp @@ -5,7 +5,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include // QT #include diff --git a/modules/cvv/src/qtutil/types.hpp b/modules/cvv/src/qtutil/types.hpp index 78330bb54..872ffd10d 100644 --- a/modules/cvv/src/qtutil/types.hpp +++ b/modules/cvv/src/qtutil/types.hpp @@ -1,7 +1,7 @@ #ifndef CVVISUAL_TYPES_HPP #define CVVISUAL_TYPES_HPP -#include "opencv2/core/core.hpp" +#include namespace cvv diff --git a/modules/cvv/src/qtutil/util.cpp b/modules/cvv/src/qtutil/util.cpp index 945132883..b1c59f3bc 100644 --- a/modules/cvv/src/qtutil/util.cpp +++ b/modules/cvv/src/qtutil/util.cpp @@ -5,8 +5,6 @@ #include #include -#include - #include #include #include diff --git a/modules/cvv/src/qtutil/util.hpp b/modules/cvv/src/qtutil/util.hpp index f47726cba..bd13dee27 100644 --- a/modules/cvv/src/qtutil/util.hpp +++ b/modules/cvv/src/qtutil/util.hpp @@ -9,8 +9,9 @@ #include #include -#include "opencv2/core/core.hpp" -#include "opencv2/opencv.hpp" +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/features2d.hpp" namespace cvv diff --git a/modules/cvv/src/qtutil/zoomableimage.cpp b/modules/cvv/src/qtutil/zoomableimage.cpp index be76c2dc4..6f9220c12 100644 --- a/modules/cvv/src/qtutil/zoomableimage.cpp +++ b/modules/cvv/src/qtutil/zoomableimage.cpp @@ -12,6 +12,7 @@ #include "types.hpp" #include +#include /** * @brief Puts a value into a stringstream. (used to print char and uchar as a diff --git a/modules/cvv/src/qtutil/zoomableimage.hpp b/modules/cvv/src/qtutil/zoomableimage.hpp index 17a015448..785a238ab 100644 --- a/modules/cvv/src/qtutil/zoomableimage.hpp +++ b/modules/cvv/src/qtutil/zoomableimage.hpp @@ -12,7 +12,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "util.hpp" #include "../util/util.hpp" diff --git a/modules/cvv/src/qtutil/zoomableimageoptpanel.hpp b/modules/cvv/src/qtutil/zoomableimageoptpanel.hpp index 64b6723e0..96cbbd7e3 100644 --- a/modules/cvv/src/qtutil/zoomableimageoptpanel.hpp +++ b/modules/cvv/src/qtutil/zoomableimageoptpanel.hpp @@ -6,7 +6,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "zoomableimage.hpp" #include "util.hpp" diff --git a/modules/cvv/src/view/defaultfilterview.hpp b/modules/cvv/src/view/defaultfilterview.hpp index 8925fea59..e16722e34 100644 --- a/modules/cvv/src/view/defaultfilterview.hpp +++ b/modules/cvv/src/view/defaultfilterview.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "filter_view.hpp" diff --git a/modules/cvv/src/view/dual_filter_view.cpp b/modules/cvv/src/view/dual_filter_view.cpp index 3bf2abe83..37e0fa32a 100644 --- a/modules/cvv/src/view/dual_filter_view.cpp +++ b/modules/cvv/src/view/dual_filter_view.cpp @@ -5,7 +5,7 @@ #include #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include #include diff --git a/modules/cvv/src/view/filter_view.hpp b/modules/cvv/src/view/filter_view.hpp index 8cf2b374b..a2837514b 100644 --- a/modules/cvv/src/view/filter_view.hpp +++ b/modules/cvv/src/view/filter_view.hpp @@ -7,7 +7,7 @@ #include #include -#include +#include namespace cvv { diff --git a/modules/cvv/src/view/image_view.cpp b/modules/cvv/src/view/image_view.cpp index c7d08bd12..3f5077bc2 100644 --- a/modules/cvv/src/view/image_view.cpp +++ b/modules/cvv/src/view/image_view.cpp @@ -6,7 +6,7 @@ #include #include -#include +#include #include "../qtutil/accordion.hpp" #include "../qtutil/zoomableimageoptpanel.hpp" diff --git a/modules/cvv/src/view/image_view.hpp b/modules/cvv/src/view/image_view.hpp index 829a31fa2..961e49750 100644 --- a/modules/cvv/src/view/image_view.hpp +++ b/modules/cvv/src/view/image_view.hpp @@ -4,7 +4,7 @@ #include #include -#include +#include #include "../qtutil/zoomableimage.hpp" #include "../util/observer_ptr.hpp" @@ -44,12 +44,12 @@ signals: * @param parent of this QWidget. **/ ImageView(const cv::Mat &image, QWidget *parent = nullptr); - + /** * @brief Shows the full image. */ void showFullImage(); - + private: util::ObserverPtr image; }; @@ -57,4 +57,3 @@ signals: } // namespaces #endif - diff --git a/modules/cvv/src/view/linematchview.hpp b/modules/cvv/src/view/linematchview.hpp index f08f83827..049c02fd6 100644 --- a/modules/cvv/src/view/linematchview.hpp +++ b/modules/cvv/src/view/linematchview.hpp @@ -3,8 +3,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "../qtutil/matchview/matchmanagement.hpp" #include "../qtutil/matchview/keypointmanagement.hpp" diff --git a/modules/cvv/src/view/match_view.hpp b/modules/cvv/src/view/match_view.hpp index 32baa0899..074951d44 100644 --- a/modules/cvv/src/view/match_view.hpp +++ b/modules/cvv/src/view/match_view.hpp @@ -5,8 +5,8 @@ #include #include -#include -#include +#include +#include #include "../impl/match_call.hpp" diff --git a/modules/cvv/src/view/pointmatchview.hpp b/modules/cvv/src/view/pointmatchview.hpp index 1eb18a2d0..e9919ac21 100644 --- a/modules/cvv/src/view/pointmatchview.hpp +++ b/modules/cvv/src/view/pointmatchview.hpp @@ -3,8 +3,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "../qtutil/matchview/matchmanagement.hpp" #include "match_view.hpp" diff --git a/modules/cvv/src/view/rawview.hpp b/modules/cvv/src/view/rawview.hpp index 665d88d9c..94678b0df 100644 --- a/modules/cvv/src/view/rawview.hpp +++ b/modules/cvv/src/view/rawview.hpp @@ -3,8 +3,8 @@ #include -#include -#include +#include +#include #include #include @@ -74,7 +74,7 @@ public: * @param parent of this QWidget. */ Rawview(const impl::MatchCall &call, QWidget *parent = nullptr) - : Rawview(call.keyPoints1(), call.keyPoints2(), + : Rawview(call.keyPoints1(), call.keyPoints2(), call.matches(), call.usesTrainDescriptor()) { setParent(parent); @@ -87,11 +87,11 @@ public: * menus? */ bool doesShowShowInViewMenu(); - + virtual std::vector getMatchSelection(); virtual std::vector getKeyPointSelection(); - + signals: /** * @brief Requests to update the left footer of the window that displays @@ -119,7 +119,7 @@ signals: * @param keyPoints seleted single key points */ void keyPointsSelected(const std::vector &keyPoints); - + public slots: /** @@ -138,11 +138,11 @@ public slots: */ void selectKeyPoints(const std::vector &keyPoints); - + virtual void setMatchSelection(std::vector matches); virtual void setKeyPointSelection(std::vector keyPoints); - + /** * @brief Issues the matchesSelected and the keyPointsSelected signal. * It uses the referenced key points (via the given matches) to find the @@ -150,7 +150,7 @@ public slots: * @param matches the user selected matches. */ void matchesKeyPointsSelected(const std::vector &matches); - + private slots: void filterQuery(QString query); @@ -170,7 +170,7 @@ private: gui::RawviewTable *table; bool showShowInViewMenu = false; bool usesTrainDescriptor = true; - + void initEngine(); }; } diff --git a/modules/cvv/src/view/singlefilterview.hpp b/modules/cvv/src/view/singlefilterview.hpp index 6368241c1..7bc1a8826 100644 --- a/modules/cvv/src/view/singlefilterview.hpp +++ b/modules/cvv/src/view/singlefilterview.hpp @@ -3,7 +3,7 @@ #include -#include "opencv2/core/core.hpp" +#include "opencv2/core.hpp" #include "filter_view.hpp" #include "../impl/filter_call.hpp" diff --git a/modules/cvv/src/view/translationsmatchview.hpp b/modules/cvv/src/view/translationsmatchview.hpp index b2f48cfab..3cbdcd47e 100644 --- a/modules/cvv/src/view/translationsmatchview.hpp +++ b/modules/cvv/src/view/translationsmatchview.hpp @@ -3,8 +3,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/features2d/features2d.hpp" +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" #include "../qtutil/matchview/matchmanagement.hpp" #include "../qtutil/matchview/keypointmanagement.hpp" diff --git a/modules/cvv/test/test_precomp.hpp b/modules/cvv/test/test_precomp.hpp index a2cf12fd4..ec8333687 100644 --- a/modules/cvv/test/test_precomp.hpp +++ b/modules/cvv/test/test_precomp.hpp @@ -11,7 +11,7 @@ #include #include -#include -#include +#include +#include #endif From 2b74ca6f120cd08c8b0f5688897b8c048a65c493 Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Wed, 4 Feb 2015 17:32:50 +0300 Subject: [PATCH 11/32] Added opencv_contrib_world module and fixed some compilation warnings on iOS --- modules/adas/tools/fcw_detect/CMakeLists.txt | 2 +- modules/adas/tools/fcw_train/CMakeLists.txt | 2 +- modules/bioinspired/src/retinacolor.hpp | 2 +- modules/bioinspired/src/retinafilter.hpp | 2 +- modules/contrib_world/CMakeLists.txt | 43 +++++++++++++++++++ .../include/opencv2/contrib_world.hpp | 5 +++ modules/contrib_world/src/dummy.cpp | 1 + modules/datasets/src/ar_hmdb.cpp | 2 +- modules/datasets/src/or_imagenet.cpp | 4 +- modules/datasets/src/or_sun.cpp | 2 +- .../line_descriptor/src/binary_descriptor.cpp | 6 +-- modules/surface_matching/src/icp.cpp | 4 +- modules/text/src/erfilter.cpp | 2 +- modules/xphoto/src/dct_image_denoising.cpp | 4 +- 14 files changed, 65 insertions(+), 16 deletions(-) create mode 100644 modules/contrib_world/CMakeLists.txt create mode 100644 modules/contrib_world/include/opencv2/contrib_world.hpp create mode 100644 modules/contrib_world/src/dummy.cpp diff --git a/modules/adas/tools/fcw_detect/CMakeLists.txt b/modules/adas/tools/fcw_detect/CMakeLists.txt index b23e35fd6..604aec718 100644 --- a/modules/adas/tools/fcw_detect/CMakeLists.txt +++ b/modules/adas/tools/fcw_detect/CMakeLists.txt @@ -32,4 +32,4 @@ if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${the_target} PROPERTIES FOLDER "applications") endif() -install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main) +install(TARGETS ${the_target} OPTIONAL RUNTIME DESTINATION bin COMPONENT main) diff --git a/modules/adas/tools/fcw_train/CMakeLists.txt b/modules/adas/tools/fcw_train/CMakeLists.txt index bdbc28939..f1cbecf7b 100644 --- a/modules/adas/tools/fcw_train/CMakeLists.txt +++ b/modules/adas/tools/fcw_train/CMakeLists.txt @@ -32,4 +32,4 @@ if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${the_target} PROPERTIES FOLDER "applications") endif() -install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main) +install(TARGETS ${the_target} OPTIONAL RUNTIME DESTINATION bin COMPONENT main) diff --git a/modules/bioinspired/src/retinacolor.hpp b/modules/bioinspired/src/retinacolor.hpp index ff7990638..2b50e6618 100644 --- a/modules/bioinspired/src/retinacolor.hpp +++ b/modules/bioinspired/src/retinacolor.hpp @@ -338,7 +338,7 @@ namespace bioinspired class Parallel_computeGradient: public cv::ParallelLoopBody { - private: + protected: float *imageGradient; const float *luminance; unsigned int nbColumns, doubleNbColumns, nbRows, nbPixels; diff --git a/modules/bioinspired/src/retinafilter.hpp b/modules/bioinspired/src/retinafilter.hpp index 5b254c8ac..057306bbe 100644 --- a/modules/bioinspired/src/retinafilter.hpp +++ b/modules/bioinspired/src/retinafilter.hpp @@ -503,7 +503,7 @@ public: inline unsigned int getOutputNBpixels() { return _photoreceptorsPrefilter.getNBpixels(); } -private: +protected: // processing activation flags bool _useParvoOutput; diff --git a/modules/contrib_world/CMakeLists.txt b/modules/contrib_world/CMakeLists.txt new file mode 100644 index 000000000..dafe6d5d6 --- /dev/null +++ b/modules/contrib_world/CMakeLists.txt @@ -0,0 +1,43 @@ +set(the_description "Separate world module containing all contrib modules") + +set(OPENCV_MODULE_IS_PART_OF_WORLD FALSE) +set(BUILD_opencv_contrib_world_INIT OFF) # disabled by default + +# add new submodules to this list +set(OPENCV_MODULE_CHILDREN + bgsegm + bioinspired + ccalib + cvv + datasets + face + latentsvm + line_descriptor + optflow + reg + rgbd + saliency + surface_matching + text + tracking + xfeatures2d + ximgproc + xobjdetect + xphoto +) +ocv_list_add_prefix(OPENCV_MODULE_CHILDREN "opencv_") + +ocv_define_module(contrib_world) + +# ocv_add_module(contrib_world) + +# set(link_deps "") +# foreach(m ${OPENCV_MODULE_opencv_contrib_world_CHILDREN}) +# list(APPEND link_deps ${OPENCV_MODULE_${m}_LINK_DEPS}) +# endforeach() + +# +# ocv_glob_module_sources() +# ocv_module_include_directories() +# +# ocv_create_module(${link_deps}) diff --git a/modules/contrib_world/include/opencv2/contrib_world.hpp b/modules/contrib_world/include/opencv2/contrib_world.hpp new file mode 100644 index 000000000..2c1c4e25b --- /dev/null +++ b/modules/contrib_world/include/opencv2/contrib_world.hpp @@ -0,0 +1,5 @@ +#ifndef __OPENCV_CONTRIB_WORLD_HPP__ +#define __OPENCV_CONTRIB_WORLD_HPP__ + + +#endif diff --git a/modules/contrib_world/src/dummy.cpp b/modules/contrib_world/src/dummy.cpp new file mode 100644 index 000000000..a8f2cce0f --- /dev/null +++ b/modules/contrib_world/src/dummy.cpp @@ -0,0 +1 @@ +#include "opencv2/contrib_world.hpp" diff --git a/modules/datasets/src/ar_hmdb.cpp b/modules/datasets/src/ar_hmdb.cpp index 43a0aadb3..f4b42e33a 100644 --- a/modules/datasets/src/ar_hmdb.cpp +++ b/modules/datasets/src/ar_hmdb.cpp @@ -111,7 +111,7 @@ void AR_hmdbImp::loadDatasetSplit(const string &path, int number) if (itId == actionsId.end()) { actionsId.insert(make_pair(action, actionsId.size())); - id = actionsId.size(); + id = (int)actionsId.size(); } else { id = (*itId).second; diff --git a/modules/datasets/src/or_imagenet.cpp b/modules/datasets/src/or_imagenet.cpp index 05687b851..371ade1c5 100644 --- a/modules/datasets/src/or_imagenet.cpp +++ b/modules/datasets/src/or_imagenet.cpp @@ -129,7 +129,7 @@ void OR_imagenetImp::loadDataset(const string &path) { Ptr curr(new OR_imagenetObj); curr->id = atoi(line.c_str()); - numberToString(validation.back().size()+1, curr->image); + numberToString((int)validation.back().size()+1, curr->image); curr->image = "val/ILSVRC2010_val_" + curr->image + ".JPEG"; validation.back().push_back(curr); @@ -153,7 +153,7 @@ void OR_imagenetImp::loadDataset(const string &path) { Ptr curr(new OR_imagenetObj); curr->id = *it; - numberToString(test.back().size()+1, curr->image); + numberToString((int)test.back().size()+1, curr->image); curr->image = "test/ILSVRC2010_test_" + curr->image + ".JPEG"; test.back().push_back(curr); diff --git a/modules/datasets/src/or_sun.cpp b/modules/datasets/src/or_sun.cpp index 305eb2177..2413fc05b 100644 --- a/modules/datasets/src/or_sun.cpp +++ b/modules/datasets/src/or_sun.cpp @@ -98,7 +98,7 @@ void OR_sunImp::loadDatasetPart(const string &path, vector< Ptr > &datas curr->label = (*it).second; } else { - curr->label = pathLabel.size(); + curr->label = (int)pathLabel.size(); pathLabel.insert(make_pair(labelStr, curr->label)); paths.push_back(labelStr); } diff --git a/modules/line_descriptor/src/binary_descriptor.cpp b/modules/line_descriptor/src/binary_descriptor.cpp index 5ea1313f2..2d2929f9c 100644 --- a/modules/line_descriptor/src/binary_descriptor.cpp +++ b/modules/line_descriptor/src/binary_descriptor.cpp @@ -570,7 +570,7 @@ void BinaryDescriptor::computeImpl( const Mat& imageSrc, std::vector& k /* create a map to record association between KeyLines and their position in ScaleLines vector */ - std::map, int> correspondences; + std::map, size_t> correspondences; /* fill ScaleLines object */ for ( size_t slCounter = 0; slCounter < keylines.size(); slCounter++ ) @@ -601,7 +601,7 @@ void BinaryDescriptor::computeImpl( const Mat& imageSrc, std::vector& k /* update map */ int id = kl.class_id; int oct = kl.octave; - correspondences.insert( std::pair, int>( std::pair( id, oct ), slCounter ) ); + correspondences.insert( std::pair, size_t>( std::pair( id, oct ), slCounter ) ); } /* delete useless OctaveSingleLines */ @@ -632,7 +632,7 @@ void BinaryDescriptor::computeImpl( const Mat& imageSrc, std::vector& k { /* get original index of keypoint */ int lineOctave = ( sl[k][lineC] ).octaveCount; - int originalIndex = correspondences.find( std::pair( k, lineOctave ) )->second; + int originalIndex = (int)correspondences.find( std::pair( k, lineOctave ) )->second; if( !returnFloatDescr ) { diff --git a/modules/surface_matching/src/icp.cpp b/modules/surface_matching/src/icp.cpp index 8614ed521..a5dd5708a 100644 --- a/modules/surface_matching/src/icp.cpp +++ b/modules/surface_matching/src/icp.cpp @@ -425,9 +425,9 @@ int ICP::registerModelToScene(const Mat& srcPC, const Mat& dstPC, double& residu if (node) { // select the first node - int idx = reinterpret_cast(node->data)-1, dn=0; + long idx = reinterpret_cast(node->data)-1, dn=0; int dup = (int)node->key-1; - int minIdxD = idx; + long minIdxD = idx; float minDist = distances[idx]; while ( node ) diff --git a/modules/text/src/erfilter.cpp b/modules/text/src/erfilter.cpp index bc269f8cf..8237c9c77 100644 --- a/modules/text/src/erfilter.cpp +++ b/modules/text/src/erfilter.cpp @@ -1998,7 +1998,7 @@ enum { computation. */ class dissimilarity { -private: +protected: double * Xa; auto_array_ptr Xnew; ptrdiff_t dim; // size_t saves many statis_cast<> in products diff --git a/modules/xphoto/src/dct_image_denoising.cpp b/modules/xphoto/src/dct_image_denoising.cpp index 6afa1ccb9..89e8af017 100644 --- a/modules/xphoto/src/dct_image_denoising.cpp +++ b/modules/xphoto/src/dct_image_denoising.cpp @@ -70,7 +70,7 @@ namespace xphoto void operator() (const Range &range) const; - private: + protected: const Mat &src; std::vector &patches; // image decomposition into sliding patches @@ -182,4 +182,4 @@ namespace xphoto } } -} \ No newline at end of file +} From c4bec8a5c5eb12a6112857b3c875ec8d0cbb3f9f Mon Sep 17 00:00:00 2001 From: Sh0 Date: Fri, 13 Feb 2015 16:01:48 +0900 Subject: [PATCH 12/32] Function cuda::integral() does not require an extra buffer any more. --- modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp | 2 +- modules/xfeatures2d/src/surf.cuda.cpp | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp index 3dc57c7ff..16039a5a7 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp @@ -152,7 +152,7 @@ public: //! max keypoints = min(keypointsRatio * img.size().area(), 65535) float keypointsRatio; - GpuMat sum, mask1, maskSum, intBuffer; + GpuMat sum, mask1, maskSum; GpuMat det, trace; diff --git a/modules/xfeatures2d/src/surf.cuda.cpp b/modules/xfeatures2d/src/surf.cuda.cpp index 208156c98..6f7a0f45f 100644 --- a/modules/xfeatures2d/src/surf.cuda.cpp +++ b/modules/xfeatures2d/src/surf.cuda.cpp @@ -146,13 +146,13 @@ namespace bindImgTex(img); - cuda::integral(img, surf_.sum, surf_.intBuffer); + cuda::integral(img, surf_.sum); sumOffset = bindSumTex(surf_.sum); if (use_mask) { cuda::min(mask, 1.0, surf_.mask1); - cuda::integral(surf_.mask1, surf_.maskSum, surf_.intBuffer); + cuda::integral(surf_.mask1, surf_.maskSum); maskOffset = bindMaskSumTex(surf_.maskSum); } } @@ -425,7 +425,6 @@ void cv::cuda::SURF_CUDA::releaseMemory() sum.release(); mask1.release(); maskSum.release(); - intBuffer.release(); det.release(); trace.release(); maxPosBuffer.release(); From 34131405df8ff3e1a2ebc98d1633cc36ab9a1c13 Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Thu, 5 Feb 2015 17:40:21 +0300 Subject: [PATCH 13/32] Removed some methods from cv::Algorithm and changed ml module interfaces --- modules/bgsegm/src/bgfg_gaussmix.cpp | 2 - modules/bgsegm/src/bgfg_gmg.cpp | 2 - .../datasets/samples/ar_hmdb_benchmark.cpp | 21 +- modules/face/include/opencv2/face.hpp | 312 ++++- modules/face/include/opencv2/face/facerec.hpp | 323 +----- modules/face/samples/facerec_demo.cpp | 8 +- modules/face/src/eigen_faces.cpp | 144 +++ modules/face/src/face_basic.hpp | 175 +++ modules/face/src/facerec.cpp | 1013 +---------------- modules/face/src/fisher_faces.cpp | 163 +++ modules/face/src/lbph_faces.cpp | 430 +++++++ modules/face/src/precomp.hpp | 8 +- .../include/opencv2/line_descriptor.hpp | 6 - .../opencv2/line_descriptor/descriptor.hpp | 13 - .../src/line_descriptor_init.cpp | 64 -- modules/optflow/src/deepflow.cpp | 2 - modules/optflow/src/interfaces.cpp | 2 - modules/rgbd/include/opencv2/rgbd.hpp | 94 +- modules/rgbd/samples/odometry_evaluation.cpp | 4 +- modules/rgbd/src/odometry.cpp | 37 +- modules/rgbd/src/rgbd_init.cpp | 119 -- modules/rgbd/test/test_normal.cpp | 2 +- modules/rgbd/test/test_odometry.cpp | 8 +- modules/saliency/include/opencv2/saliency.hpp | 8 - .../saliency/saliencySpecializedClasses.hpp | 14 +- modules/saliency/src/saliency.cpp | 10 +- modules/saliency/src/saliency_init.cpp | 73 -- modules/tracking/include/opencv2/tracking.hpp | 6 +- .../include/opencv2/tracking/tracker.hpp | 1 - modules/tracking/src/tracker.cpp | 4 - modules/tracking/src/tracking_init.cpp | 53 - .../include/opencv2/xfeatures2d/cuda.hpp | 2 +- .../samples/bagofwords_classification.cpp | 15 +- modules/xfeatures2d/src/surf.cuda.cpp | 5 +- .../include/opencv2/ximgproc/edge_filter.hpp | 7 + .../src/adaptive_manifold_filter_n.cpp | 27 +- .../ximgproc/test/test_adaptive_manifold.cpp | 6 +- .../test/test_adaptive_manifold_ref_impl.cpp | 25 +- .../xobjdetect/include/opencv2/xobjdetect.hpp | 11 - 39 files changed, 1461 insertions(+), 1758 deletions(-) create mode 100644 modules/face/src/eigen_faces.cpp create mode 100644 modules/face/src/face_basic.hpp create mode 100644 modules/face/src/fisher_faces.cpp create mode 100644 modules/face/src/lbph_faces.cpp delete mode 100644 modules/line_descriptor/src/line_descriptor_init.cpp delete mode 100644 modules/rgbd/src/rgbd_init.cpp delete mode 100644 modules/saliency/src/saliency_init.cpp delete mode 100644 modules/tracking/src/tracking_init.cpp diff --git a/modules/bgsegm/src/bgfg_gaussmix.cpp b/modules/bgsegm/src/bgfg_gaussmix.cpp index 62248fbf5..9e9d448da 100644 --- a/modules/bgsegm/src/bgfg_gaussmix.cpp +++ b/modules/bgsegm/src/bgfg_gaussmix.cpp @@ -122,8 +122,6 @@ public: bgmodel = Scalar::all(0); } - virtual AlgorithmInfo* info() const { return 0; } - virtual void getBackgroundImage(OutputArray) const { CV_Error( Error::StsNotImplemented, "" ); diff --git a/modules/bgsegm/src/bgfg_gmg.cpp b/modules/bgsegm/src/bgfg_gmg.cpp index cb6900844..5f5f60f7b 100644 --- a/modules/bgsegm/src/bgfg_gmg.cpp +++ b/modules/bgsegm/src/bgfg_gmg.cpp @@ -81,8 +81,6 @@ public: { } - virtual AlgorithmInfo* info() const { return 0; } - /** * Validate parameters and set up data structures for appropriate image size. * Must call before running on data. diff --git a/modules/datasets/samples/ar_hmdb_benchmark.cpp b/modules/datasets/samples/ar_hmdb_benchmark.cpp index f9412ee74..a276ee898 100644 --- a/modules/datasets/samples/ar_hmdb_benchmark.cpp +++ b/modules/datasets/samples/ar_hmdb_benchmark.cpp @@ -188,17 +188,16 @@ int main(int argc, char *argv[]) fillData(path, curr, flann_index, trainData, trainLabels); printf("train svm\n"); - SVM::Params params; - params.svmType = SVM::C_SVC; - params.kernelType = SVM::POLY; //SVM::RBF; - params.degree = 0.5; - params.gamma = 1; - params.coef0 = 1; - params.C = 1; - params.nu = 0.5; - params.p = 0; - params.termCrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01); - Ptr svm = SVM::create(params); + Ptr svm = SVM::create(); + svm->setType(SVM::C_SVC); + svm->setKernel(SVM::POLY); //SVM::RBF; + svm->setDegree(0.5); + svm->setGamma(1); + svm->setCoef0(1); + svm->setC(1); + svm->setNu(0.5); + svm->setP(0); + svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01)); svm->train(trainData, ROW_SAMPLE, trainLabels); // prepare to predict diff --git a/modules/face/include/opencv2/face.hpp b/modules/face/include/opencv2/face.hpp index be6decd5f..a659fddc7 100644 --- a/modules/face/include/opencv2/face.hpp +++ b/modules/face/include/opencv2/face.hpp @@ -39,8 +39,6 @@ the use of this software, even if advised of the possibility of such damage. #ifndef __OPENCV_FACE_HPP__ #define __OPENCV_FACE_HPP__ -#include "opencv2/face/facerec.hpp" - /** @defgroup face Face Recognition @@ -49,4 +47,314 @@ the use of this software, even if advised of the possibility of such damage. */ +#include "opencv2/core.hpp" +#include + +namespace cv { namespace face { + +//! @addtogroup face +//! @{ + +/** @brief Abstract base class for all face recognition models + +All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which +provides a unified access to all face recongition algorithms in OpenCV. + +### Description + +I'll go a bit more into detail explaining FaceRecognizer, because it doesn't look like a powerful +interface at first sight. But: Every FaceRecognizer is an Algorithm, so you can easily get/set all +model internals (if allowed by the implementation). Algorithm is a relatively new OpenCV concept, +which is available since the 2.4 release. I suggest you take a look at its description. + +Algorithm provides the following features for all derived classes: + +- So called “virtual constructor”. That is, each Algorithm derivative is registered at program + start and you can get the list of registered algorithms and create instance of a particular + algorithm by its name (see Algorithm::create). If you plan to add your own algorithms, it is + good practice to add a unique prefix to your algorithms to distinguish them from other + algorithms. +- Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from + OpenCV highgui module, you are probably familar with cv::cvSetCaptureProperty, +ocvcvGetCaptureProperty, VideoCapture::set and VideoCapture::get. Algorithm provides similar + method where instead of integer id's you specify the parameter names as text Strings. See + Algorithm::set and Algorithm::get for details. +- Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store + all its parameters and then read them back. There is no need to re-implement it each time. + +Moreover every FaceRecognizer supports the: + +- **Training** of a FaceRecognizer with FaceRecognizer::train on a given set of images (your face + database!). +- **Prediction** of a given sample image, that means a face. The image is given as a Mat. +- **Loading/Saving** the model state from/to a given XML or YAML. +- **Setting/Getting labels info**, that is stored as a string. String labels info is useful for + keeping names of the recognized people. + +@note When using the FaceRecognizer interface in combination with Python, please stick to Python 2. +Some underlying scripts like create_csv will not work in other versions, like Python 3. Setting the +Thresholds +++++++++++++++++++++++ + +Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common +scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is +unknown. You might wonder, why there's no public API in FaceRecognizer to set the threshold for the +prediction, but rest assured: It's supported. It just means there's no generic way in an abstract +class to provide an interface for setting/getting the thresholds of *every possible* FaceRecognizer +algorithm. The appropriate place to set the thresholds is in the constructor of the specific +FaceRecognizer and since every FaceRecognizer is a Algorithm (see above), you can get/set the +thresholds at runtime! + +Here is an example of setting a threshold for the Eigenfaces method, when creating the model: + +@code +// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0 +int num_components = 10; +double threshold = 10.0; +// Then if you want to have a cv::FaceRecognizer with a confidence threshold, +// create the concrete implementation with the appropiate parameters: +Ptr model = createEigenFaceRecognizer(num_components, threshold); +@endcode + +Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to +Algorithm it's possible to set internal model thresholds during runtime. Let's see how we would +set/get the prediction for the Eigenface model, we've created above: + +@code +// The following line reads the threshold from the Eigenfaces model: +double current_threshold = model->getDouble("threshold"); +// And this line sets the threshold to 0.0: +model->set("threshold", 0.0); +@endcode + +If you've set the threshold to 0.0 as we did above, then: + +@code +// +Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); +// Get a prediction from the model. Note: We've set a threshold of 0.0 above, +// since the distance is almost always larger than 0.0, you'll get -1 as +// label, which indicates, this face is unknown +int predicted_label = model->predict(img); +// ... +@endcode + +is going to yield -1 as predicted label, which states this face is unknown. + +### Getting the name of a FaceRecognizer + +Since every FaceRecognizer is a Algorithm, you can use Algorithm::name to get the name of a +FaceRecognizer: + +@code +// Create a FaceRecognizer: +Ptr model = createEigenFaceRecognizer(); +// And here's how to get its name: +String name = model->name(); +@endcode + + */ +class CV_EXPORTS_W FaceRecognizer : public Algorithm +{ +public: + /** @brief Trains a FaceRecognizer with given data and associated labels. + + @param src The training images, that means the faces you want to learn. The data has to be + given as a vector\. + @param labels The labels corresponding to the images have to be given either as a vector\ + or a + + The following source code snippet shows you how to learn a Fisherfaces model on a given set of + images. The images are read with imread and pushed into a std::vector\. The labels of each + image are stored within a std::vector\ (you could also use a Mat of type CV_32SC1). Think of + the label as the subject (the person) this image belongs to, so same subjects (persons) should have + the same label. For the available FaceRecognizer you don't have to pay any attention to the order of + the labels, just make sure same persons have the same label: + + @code + // holds images and labels + vector images; + vector labels; + // images for first person + images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); + images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); + images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); + // images for second person + images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); + images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); + images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); + @endcode + + Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create + a Fisherfaces model and decide to keep all of the possible Fisherfaces: + + @code + // Create a new Fisherfaces model and retain all available Fisherfaces, + // this is the most common usage of this specific FaceRecognizer: + // + Ptr model = createFisherFaceRecognizer(); + @endcode + + And finally train it on the given dataset (the face images and labels): + + @code + // This is the common interface to train all of the available cv::FaceRecognizer + // implementations: + // + model->train(images, labels); + @endcode + */ + CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0; + + /** @brief Updates a FaceRecognizer with given data and associated labels. + + @param src The training images, that means the faces you want to learn. The data has to be given + as a vector\. + @param labels The labels corresponding to the images have to be given either as a vector\ or + a + + This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The + Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated. + For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to + re-estimate the model with FaceRecognizer::train. In any case, a call to train empties the existing + model and learns a new model, while update does not delete any model data. + + @code + // Create a new LBPH model (it can be updated) and use the default parameters, + // this is the most common usage of this specific FaceRecognizer: + // + Ptr model = createLBPHFaceRecognizer(); + // This is the common interface to train all of the available cv::FaceRecognizer + // implementations: + // + model->train(images, labels); + // Some containers to hold new image: + vector newImages; + vector newLabels; + // You should add some images to the containers: + // + // ... + // + // Now updating the model is as easy as calling: + model->update(newImages,newLabels); + // This will preserve the old model data and extend the existing model + // with the new features extracted from newImages! + @endcode + + Calling update on an Eigenfaces model (see createEigenFaceRecognizer), which doesn't support + updating, will throw an error similar to: + + @code + OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305 + terminate called after throwing an instance of 'cv::Exception' + @endcode + + @note The FaceRecognizer does not store your training images, because this would be very + memory intense and it's not the responsibility of te FaceRecognizer to do so. The caller is + responsible for maintaining the dataset, he want to work with. + */ + CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels); + + /** @overload */ + virtual int predict(InputArray src) const = 0; + + /** @brief Predicts a label and associated confidence (e.g. distance) for a given input image. + + @param src Sample image to get a prediction from. + @param label The predicted label for the given image. + @param confidence Associated confidence (e.g. distance) for the predicted label. + + The suffix const means that prediction does not affect the internal model state, so the method can + be safely called from within different threads. + + The following example shows how to get a prediction from a trained model: + + @code + using namespace cv; + // Do your initialization here (create the cv::FaceRecognizer model) ... + // ... + // Read in a sample image: + Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); + // And get a prediction from the cv::FaceRecognizer: + int predicted = model->predict(img); + @endcode + + Or to get a prediction and the associated confidence (e.g. distance): + + @code + using namespace cv; + // Do your initialization here (create the cv::FaceRecognizer model) ... + // ... + Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); + // Some variables for the predicted label and associated confidence (e.g. distance): + int predicted_label = -1; + double predicted_confidence = 0.0; + // Get the prediction and associated confidence from the model + model->predict(img, predicted_label, predicted_confidence); + @endcode + */ + CV_WRAP virtual void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const = 0; + + /** @brief Saves a FaceRecognizer and its model state. + + Saves this model to a given filename, either as XML or YAML. + @param filename The filename to store this FaceRecognizer to (either XML/YAML). + + Every FaceRecognizer overwrites FaceRecognizer::save(FileStorage& fs) to save the internal model + state. FaceRecognizer::save(const String& filename) saves the state of a model to the given + filename. + + The suffix const means that prediction does not affect the internal model state, so the method can + be safely called from within different threads. + */ + CV_WRAP virtual void save(const String& filename) const; + + /** @brief Loads a FaceRecognizer and its model state. + + Loads a persisted model and state from a given XML or YAML file . Every FaceRecognizer has to + overwrite FaceRecognizer::load(FileStorage& fs) to enable loading the model state. + FaceRecognizer::load(FileStorage& fs) in turn gets called by + FaceRecognizer::load(const String& filename), to ease saving a model. + */ + CV_WRAP virtual void load(const String& filename); + + /** @overload + Saves this model to a given FileStorage. + @param fs The FileStorage to store this FaceRecognizer to. + */ + virtual void save(FileStorage& fs) const = 0; + + /** @overload */ + virtual void load(const FileStorage& fs) = 0; + + /** @brief Sets string info for the specified model's label. + + The string info is replaced by the provided value if it was set before for the specified label. + */ + CV_WRAP virtual void setLabelInfo(int label, const String& strInfo); + + /** @brief Gets string information by label. + + If an unknown label id is provided or there is no label information associated with the specified + label id the method returns an empty string. + */ + CV_WRAP virtual String getLabelInfo(int label) const; + + /** @brief Gets vector of labels by string. + + The function searches for the labels containing the specified sub-string in the associated string + info. + */ + CV_WRAP virtual std::vector getLabelsByString(const String& str) const; + +protected: + // Stored pairs "label id - string info" + std::map _labelsInfo; +}; + +//! @} + +}} + +#include "opencv2/face/facerec.hpp" + #endif diff --git a/modules/face/include/opencv2/face/facerec.hpp b/modules/face/include/opencv2/face/facerec.hpp index 49291b46c..ddb933997 100644 --- a/modules/face/include/opencv2/face/facerec.hpp +++ b/modules/face/include/opencv2/face/facerec.hpp @@ -8,6 +8,7 @@ #ifndef __OPENCV_FACEREC_HPP__ #define __OPENCV_FACEREC_HPP__ +#include "opencv2/face.hpp" #include "opencv2/core.hpp" namespace cv { namespace face { @@ -15,299 +16,17 @@ namespace cv { namespace face { //! @addtogroup face //! @{ -/** @brief Abstract base class for all face recognition models - -All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which -provides a unified access to all face recongition algorithms in OpenCV. - -### Description - -I'll go a bit more into detail explaining FaceRecognizer, because it doesn't look like a powerful -interface at first sight. But: Every FaceRecognizer is an Algorithm, so you can easily get/set all -model internals (if allowed by the implementation). Algorithm is a relatively new OpenCV concept, -which is available since the 2.4 release. I suggest you take a look at its description. - -Algorithm provides the following features for all derived classes: - -- So called “virtual constructor”. That is, each Algorithm derivative is registered at program - start and you can get the list of registered algorithms and create instance of a particular - algorithm by its name (see Algorithm::create). If you plan to add your own algorithms, it is - good practice to add a unique prefix to your algorithms to distinguish them from other - algorithms. -- Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from - OpenCV highgui module, you are probably familar with cv::cvSetCaptureProperty, -ocvcvGetCaptureProperty, VideoCapture::set and VideoCapture::get. Algorithm provides similar - method where instead of integer id's you specify the parameter names as text Strings. See - Algorithm::set and Algorithm::get for details. -- Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store - all its parameters and then read them back. There is no need to re-implement it each time. - -Moreover every FaceRecognizer supports the: - -- **Training** of a FaceRecognizer with FaceRecognizer::train on a given set of images (your face - database!). -- **Prediction** of a given sample image, that means a face. The image is given as a Mat. -- **Loading/Saving** the model state from/to a given XML or YAML. -- **Setting/Getting labels info**, that is stored as a string. String labels info is useful for - keeping names of the recognized people. - -@note When using the FaceRecognizer interface in combination with Python, please stick to Python 2. -Some underlying scripts like create_csv will not work in other versions, like Python 3. Setting the -Thresholds +++++++++++++++++++++++ - -Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common -scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is -unknown. You might wonder, why there's no public API in FaceRecognizer to set the threshold for the -prediction, but rest assured: It's supported. It just means there's no generic way in an abstract -class to provide an interface for setting/getting the thresholds of *every possible* FaceRecognizer -algorithm. The appropriate place to set the thresholds is in the constructor of the specific -FaceRecognizer and since every FaceRecognizer is a Algorithm (see above), you can get/set the -thresholds at runtime! - -Here is an example of setting a threshold for the Eigenfaces method, when creating the model: - -@code -// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0 -int num_components = 10; -double threshold = 10.0; -// Then if you want to have a cv::FaceRecognizer with a confidence threshold, -// create the concrete implementation with the appropiate parameters: -Ptr model = createEigenFaceRecognizer(num_components, threshold); -@endcode - -Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to -Algorithm it's possible to set internal model thresholds during runtime. Let's see how we would -set/get the prediction for the Eigenface model, we've created above: - -@code -// The following line reads the threshold from the Eigenfaces model: -double current_threshold = model->getDouble("threshold"); -// And this line sets the threshold to 0.0: -model->set("threshold", 0.0); -@endcode - -If you've set the threshold to 0.0 as we did above, then: - -@code -// -Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); -// Get a prediction from the model. Note: We've set a threshold of 0.0 above, -// since the distance is almost always larger than 0.0, you'll get -1 as -// label, which indicates, this face is unknown -int predicted_label = model->predict(img); -// ... -@endcode - -is going to yield -1 as predicted label, which states this face is unknown. - -### Getting the name of a FaceRecognizer - -Since every FaceRecognizer is a Algorithm, you can use Algorithm::name to get the name of a -FaceRecognizer: - -@code -// Create a FaceRecognizer: -Ptr model = createEigenFaceRecognizer(); -// And here's how to get its name: -String name = model->name(); -@endcode - - */ -class CV_EXPORTS_W FaceRecognizer : public Algorithm +// base for two classes +class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer { public: - //! virtual destructor - virtual ~FaceRecognizer() {} - - /** @brief Trains a FaceRecognizer with given data and associated labels. - - @param src The training images, that means the faces you want to learn. The data has to be - given as a vector\. - @param labels The labels corresponding to the images have to be given either as a vector\ - or a - - The following source code snippet shows you how to learn a Fisherfaces model on a given set of - images. The images are read with imread and pushed into a std::vector\. The labels of each - image are stored within a std::vector\ (you could also use a Mat of type CV_32SC1). Think of - the label as the subject (the person) this image belongs to, so same subjects (persons) should have - the same label. For the available FaceRecognizer you don't have to pay any attention to the order of - the labels, just make sure same persons have the same label: - - @code - // holds images and labels - vector images; - vector labels; - // images for first person - images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - // images for second person - images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - @endcode - - Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create - a Fisherfaces model and decide to keep all of the possible Fisherfaces: - - @code - // Create a new Fisherfaces model and retain all available Fisherfaces, - // this is the most common usage of this specific FaceRecognizer: - // - Ptr model = createFisherFaceRecognizer(); - @endcode - - And finally train it on the given dataset (the face images and labels): - - @code - // This is the common interface to train all of the available cv::FaceRecognizer - // implementations: - // - model->train(images, labels); - @endcode - */ - CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0; - - /** @brief Updates a FaceRecognizer with given data and associated labels. - - @param src The training images, that means the faces you want to learn. The data has to be given - as a vector\. - @param labels The labels corresponding to the images have to be given either as a vector\ or - a - - This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The - Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated. - For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to - re-estimate the model with FaceRecognizer::train. In any case, a call to train empties the existing - model and learns a new model, while update does not delete any model data. - - @code - // Create a new LBPH model (it can be updated) and use the default parameters, - // this is the most common usage of this specific FaceRecognizer: - // - Ptr model = createLBPHFaceRecognizer(); - // This is the common interface to train all of the available cv::FaceRecognizer - // implementations: - // - model->train(images, labels); - // Some containers to hold new image: - vector newImages; - vector newLabels; - // You should add some images to the containers: - // - // ... - // - // Now updating the model is as easy as calling: - model->update(newImages,newLabels); - // This will preserve the old model data and extend the existing model - // with the new features extracted from newImages! - @endcode - - Calling update on an Eigenfaces model (see createEigenFaceRecognizer), which doesn't support - updating, will throw an error similar to: - - @code - OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305 - terminate called after throwing an instance of 'cv::Exception' - @endcode - - @note The FaceRecognizer does not store your training images, because this would be very - memory intense and it's not the responsibility of te FaceRecognizer to do so. The caller is - responsible for maintaining the dataset, he want to work with. - */ - CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels) = 0; - - /** @overload */ - virtual int predict(InputArray src) const = 0; - - /** @brief Predicts a label and associated confidence (e.g. distance) for a given input image. - - @param src Sample image to get a prediction from. - @param label The predicted label for the given image. - @param confidence Associated confidence (e.g. distance) for the predicted label. - - The suffix const means that prediction does not affect the internal model state, so the method can - be safely called from within different threads. - - The following example shows how to get a prediction from a trained model: - - @code - using namespace cv; - // Do your initialization here (create the cv::FaceRecognizer model) ... - // ... - // Read in a sample image: - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // And get a prediction from the cv::FaceRecognizer: - int predicted = model->predict(img); - @endcode - - Or to get a prediction and the associated confidence (e.g. distance): - - @code - using namespace cv; - // Do your initialization here (create the cv::FaceRecognizer model) ... - // ... - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // Some variables for the predicted label and associated confidence (e.g. distance): - int predicted_label = -1; - double predicted_confidence = 0.0; - // Get the prediction and associated confidence from the model - model->predict(img, predicted_label, predicted_confidence); - @endcode - */ - CV_WRAP virtual void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const = 0; - - /** @brief Saves a FaceRecognizer and its model state. - - Saves this model to a given filename, either as XML or YAML. - @param filename The filename to store this FaceRecognizer to (either XML/YAML). - - Every FaceRecognizer overwrites FaceRecognizer::save(FileStorage& fs) to save the internal model - state. FaceRecognizer::save(const String& filename) saves the state of a model to the given - filename. - - The suffix const means that prediction does not affect the internal model state, so the method can - be safely called from within different threads. - */ - CV_WRAP virtual void save(const String& filename) const = 0; - - /** @brief Loads a FaceRecognizer and its model state. - - Loads a persisted model and state from a given XML or YAML file . Every FaceRecognizer has to - overwrite FaceRecognizer::load(FileStorage& fs) to enable loading the model state. - FaceRecognizer::load(FileStorage& fs) in turn gets called by - FaceRecognizer::load(const String& filename), to ease saving a model. - */ - CV_WRAP virtual void load(const String& filename) = 0; - - /** @overload - Saves this model to a given FileStorage. - @param fs The FileStorage to store this FaceRecognizer to. - */ - virtual void save(FileStorage& fs) const = 0; - - /** @overload */ - virtual void load(const FileStorage& fs) = 0; - - /** @brief Sets string info for the specified model's label. - - The string info is replaced by the provided value if it was set before for the specified label. - */ - virtual void setLabelInfo(int label, const String& strInfo) = 0; - - /** @brief Gets string information by label. - - If an unknown label id is provided or there is no label information associated with the specified - label id the method returns an empty string. - */ - virtual String getLabelInfo(int label) const = 0; - - /** @brief Gets vector of labels by string. - - The function searches for the labels containing the specified sub-string in the associated string - info. - */ - virtual std::vector getLabelsByString(const String& str) const = 0; + CV_PURE_PROPERTY(int, NumComponents) + CV_PURE_PROPERTY(double, Threshold) + CV_PURE_PROPERTY_RO(std::vector, Projections) + CV_PURE_PROPERTY_RO(cv::Mat, Labels) + CV_PURE_PROPERTY_RO(cv::Mat, EigenValues) + CV_PURE_PROPERTY_RO(cv::Mat, EigenVectors) + CV_PURE_PROPERTY_RO(cv::Mat, Mean) }; /** @@ -339,7 +58,8 @@ number. Keeping 80 components should almost always be sufficient. - labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. */ -CV_EXPORTS_W Ptr createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); +CV_EXPORTS_W Ptr createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); + /** @param num_components The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that @@ -370,7 +90,20 @@ is larger than the threshold, this method returns -1. - projections The projections of the training data. - labels The labels corresponding to the projections. */ -CV_EXPORTS_W Ptr createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); +CV_EXPORTS_W Ptr createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); + +class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer +{ +public: + CV_PURE_PROPERTY(int, GridX) + CV_PURE_PROPERTY(int, GridY) + CV_PURE_PROPERTY(int, Radius) + CV_PURE_PROPERTY(int, Neighbors) + CV_PURE_PROPERTY(double, Threshold) + CV_PURE_PROPERTY_RO(std::vector, Histograms) + CV_PURE_PROPERTY_RO(cv::Mat, Labels) +}; + /** @param radius The radius used for building the Circular Local Binary Pattern. The greater the radius, the @@ -403,9 +136,7 @@ is larger than the threshold, this method returns -1. none was given). - labels Labels corresponding to the calculated Local Binary Patterns Histograms. */ -CV_EXPORTS_W Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX); - -bool initModule_facerec(); +CV_EXPORTS_W Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX); //! @} diff --git a/modules/face/samples/facerec_demo.cpp b/modules/face/samples/facerec_demo.cpp index ab9e7e568..26ad17757 100644 --- a/modules/face/samples/facerec_demo.cpp +++ b/modules/face/samples/facerec_demo.cpp @@ -128,7 +128,7 @@ int main(int argc, const char *argv[]) { // // cv::createEigenFaceRecognizer(10, 123.0); // - Ptr model = createEigenFaceRecognizer(); + Ptr model = createEigenFaceRecognizer(); for( int i = 0; i < nlabels; i++ ) model->setLabelInfo(i, labelsInfo[i]); model->train(images, labels); @@ -162,16 +162,16 @@ int main(int argc, const char *argv[]) { // to 0.0 without retraining the model. This can be useful if // you are evaluating the model: // - model->set("threshold", 0.0); + model->setThreshold(0.0); // Now the threshold of this model is set to 0.0. A prediction // now returns -1, as it's impossible to have a distance below // it predictedLabel = model->predict(testSample); cout << "Predicted class = " << predictedLabel << endl; // Here is how to get the eigenvalues of this Eigenfaces model: - Mat eigenvalues = model->getMat("eigenvalues"); + Mat eigenvalues = model->getEigenValues(); // And we can do the same to display the Eigenvectors (read Eigenfaces): - Mat W = model->getMat("eigenvectors"); + Mat W = model->getEigenVectors(); // From this we will display the (at most) first 10 Eigenfaces: for (int i = 0; i < min(10, W.cols); i++) { string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at(i)); diff --git a/modules/face/src/eigen_faces.cpp b/modules/face/src/eigen_faces.cpp new file mode 100644 index 000000000..8247ad79f --- /dev/null +++ b/modules/face/src/eigen_faces.cpp @@ -0,0 +1,144 @@ +/* + * Copyright (c) 2011,2012. Philipp Wagner . + * Released to public domain under terms of the BSD Simplified license. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of the organization nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * See + */ +#include "precomp.hpp" +#include "face_basic.hpp" +#include +#include +#include + +namespace cv +{ +namespace face +{ + +// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of +// Cognitive Neuroscience 3 (1991), 71–86. +class Eigenfaces : public BasicFaceRecognizerImpl +{ + +public: + // Initializes an empty Eigenfaces model. + Eigenfaces(int num_components = 0, double threshold = DBL_MAX) + : BasicFaceRecognizerImpl(num_components, threshold) + {} + + // Computes an Eigenfaces model with images in src and corresponding labels + // in labels. + void train(InputArrayOfArrays src, InputArray labels); + + // Predicts the label of a query image in src. + int predict(InputArray src) const; + + // Predicts the label and confidence for a given sample. + void predict(InputArray _src, int &label, double &dist) const; +}; + +//------------------------------------------------------------------------------ +// Eigenfaces +//------------------------------------------------------------------------------ +void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) { + if(_src.total() == 0) { + String error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); + CV_Error(Error::StsBadArg, error_message); + } else if(_local_labels.getMat().type() != CV_32SC1) { + String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _local_labels.type()); + CV_Error(Error::StsBadArg, error_message); + } + // make sure data has correct size + if(_src.total() > 1) { + for(int i = 1; i < static_cast(_src.total()); i++) { + if(_src.getMat(i-1).total() != _src.getMat(i).total()) { + String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", _src.getMat(i-1).total(), _src.getMat(i).total()); + CV_Error(Error::StsUnsupportedFormat, error_message); + } + } + } + // get labels + Mat labels = _local_labels.getMat(); + // observations in row + Mat data = asRowMatrix(_src, CV_64FC1); + + // number of samples + int n = data.rows; + // assert there are as much samples as labels + if(static_cast(labels.total()) != n) { + String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total()); + CV_Error(Error::StsBadArg, error_message); + } + // clear existing model data + _labels.release(); + _projections.clear(); + // clip number of components to be valid + if((_num_components <= 0) || (_num_components > n)) + _num_components = n; + + // perform the PCA + PCA pca(data, Mat(), PCA::DATA_AS_ROW, _num_components); + // copy the PCA results + _mean = pca.mean.reshape(1,1); // store the mean vector + _eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row + transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column + // store labels for prediction + _labels = labels.clone(); + // save projections + for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) { + Mat p = LDA::subspaceProject(_eigenvectors, _mean, data.row(sampleIdx)); + _projections.push_back(p); + } +} + +void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const { + // get data + Mat src = _src.getMat(); + // make sure the user is passing correct data + if(_projections.empty()) { + // throw error if no data (or simply return -1?) + String error_message = "This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?"; + CV_Error(Error::StsError, error_message); + } else if(_eigenvectors.rows != static_cast(src.total())) { + // check data alignment just for clearer exception messages + String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total()); + CV_Error(Error::StsBadArg, error_message); + } + // project into PCA subspace + Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1,1)); + minDist = DBL_MAX; + minClass = -1; + for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { + double dist = norm(_projections[sampleIdx], q, NORM_L2); + if((dist < minDist) && (dist < _threshold)) { + minDist = dist; + minClass = _labels.at((int)sampleIdx); + } + } +} + +int Eigenfaces::predict(InputArray _src) const { + int label; + double dummy; + predict(_src, label, dummy); + return label; +} + +Ptr createEigenFaceRecognizer(int num_components, double threshold) +{ + return makePtr(num_components, threshold); +} + +} +} diff --git a/modules/face/src/face_basic.hpp b/modules/face/src/face_basic.hpp new file mode 100644 index 000000000..ce9be823c --- /dev/null +++ b/modules/face/src/face_basic.hpp @@ -0,0 +1,175 @@ +#ifndef __OPENCV_FACE_BASIC_HPP +#define __OPENCV_FACE_BASIC_HPP + +#include "opencv2/face.hpp" +#include "precomp.hpp" + +#include +#include +#include + +using namespace cv; + +// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector. +template +inline void readFileNodeList(const FileNode& fn, std::vector<_Tp>& result) { + if (fn.type() == FileNode::SEQ) { + for (FileNodeIterator it = fn.begin(); it != fn.end();) { + _Tp item; + it >> item; + result.push_back(item); + } + } +} + +// Writes the a list of given items to a cv::FileStorage. +template +inline void writeFileNodeList(FileStorage& fs, const String& name, + const std::vector<_Tp>& items) { + // typedefs + typedef typename std::vector<_Tp>::const_iterator constVecIterator; + // write the elements in item to fs + fs << name << "["; + for (constVecIterator it = items.begin(); it != items.end(); ++it) { + fs << *it; + } + fs << "]"; +} + +inline Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) { + // make sure the input data is a vector of matrices or vector of vector + if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) { + String error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >)."; + CV_Error(Error::StsBadArg, error_message); + } + // number of samples + size_t n = src.total(); + // return empty matrix if no matrices given + if(n == 0) + return Mat(); + // dimensionality of (reshaped) samples + size_t d = src.getMat(0).total(); + // create data matrix + Mat data((int)n, (int)d, rtype); + // now copy data + for(unsigned int i = 0; i < n; i++) { + // make sure data can be reshaped, throw exception if not! + if(src.getMat(i).total() != d) { + String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total()); + CV_Error(Error::StsBadArg, error_message); + } + // get a hold of the current row + Mat xi = data.row(i); + // make reshape happy by cloning for non-continuous matrices + if(src.getMat(i).isContinuous()) { + src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta); + } else { + src.getMat(i).clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta); + } + } + return data; +} + +// Utility structure to load/save face label info (a pair of int and string) via FileStorage +struct LabelInfo +{ + LabelInfo():label(-1), value("") {} + LabelInfo(int _label, const String &_value): label(_label), value(_value) {} + int label; + String value; + void write(cv::FileStorage& fs) const + { + fs << "{" << "label" << label << "value" << value << "}"; + } + void read(const cv::FileNode& node) + { + label = (int)node["label"]; + value = (String)node["value"]; + } + std::ostream& operator<<(std::ostream& out) + { + out << "{ label = " << label << ", " << "value = " << value.c_str() << "}"; + return out; + } +}; + +inline void write(cv::FileStorage& fs, const String&, const LabelInfo& x) +{ + x.write(fs); +} + +inline void read(const cv::FileNode& node, LabelInfo& x, const LabelInfo& default_value = LabelInfo()) +{ + if(node.empty()) + x = default_value; + else + x.read(node); +} + + +class BasicFaceRecognizerImpl : public cv::face::BasicFaceRecognizer +{ +public: + BasicFaceRecognizerImpl(int num_components = 0, double threshold = DBL_MAX) + : _num_components(num_components), _threshold(threshold) + {} + + void load(const FileStorage& fs) + { + //read matrices + fs["num_components"] >> _num_components; + fs["mean"] >> _mean; + fs["eigenvalues"] >> _eigenvalues; + fs["eigenvectors"] >> _eigenvectors; + // read sequences + readFileNodeList(fs["projections"], _projections); + fs["labels"] >> _labels; + const FileNode& fn = fs["labelsInfo"]; + if (fn.type() == FileNode::SEQ) + { + _labelsInfo.clear(); + for (FileNodeIterator it = fn.begin(); it != fn.end();) + { + LabelInfo item; + it >> item; + _labelsInfo.insert(std::make_pair(item.label, item.value)); + } + } + } + + void save(FileStorage& fs) const + { + // write matrices + fs << "num_components" << _num_components; + fs << "mean" << _mean; + fs << "eigenvalues" << _eigenvalues; + fs << "eigenvectors" << _eigenvectors; + // write sequences + writeFileNodeList(fs, "projections", _projections); + fs << "labels" << _labels; + fs << "labelsInfo" << "["; + for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) + fs << LabelInfo(it->first, it->second); + fs << "]"; + } + + CV_IMPL_PROPERTY(int, NumComponents, _num_components) + CV_IMPL_PROPERTY(double, Threshold, _threshold) + CV_IMPL_PROPERTY_RO(std::vector, Projections, _projections) + CV_IMPL_PROPERTY_RO(cv::Mat, Labels, _labels) + CV_IMPL_PROPERTY_RO(cv::Mat, EigenValues, _eigenvalues) + CV_IMPL_PROPERTY_RO(cv::Mat, EigenVectors, _eigenvectors) + CV_IMPL_PROPERTY_RO(cv::Mat, Mean, _mean) + +protected: + int _num_components; + double _threshold; + std::vector _projections; + Mat _labels; + Mat _eigenvectors; + Mat _eigenvalues; + Mat _mean; +}; + +#endif // __OPENCV_FACE_BASIC_HPP + diff --git a/modules/face/src/facerec.cpp b/modules/face/src/facerec.cpp index 497e8351d..dac7d082b 100644 --- a/modules/face/src/facerec.cpp +++ b/modules/face/src/facerec.cpp @@ -16,1009 +16,60 @@ * See */ #include "precomp.hpp" -#include -#include -#include +#include "opencv2/face.hpp" namespace cv { namespace face { -// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector. -template -inline void readFileNodeList(const FileNode& fn, std::vector<_Tp>& result) { - if (fn.type() == FileNode::SEQ) { - for (FileNodeIterator it = fn.begin(); it != fn.end();) { - _Tp item; - it >> item; - result.push_back(item); - } - } -} - -// Writes the a list of given items to a cv::FileStorage. -template -inline void writeFileNodeList(FileStorage& fs, const String& name, - const std::vector<_Tp>& items) { - // typedefs - typedef typename std::vector<_Tp>::const_iterator constVecIterator; - // write the elements in item to fs - fs << name << "["; - for (constVecIterator it = items.begin(); it != items.end(); ++it) { - fs << *it; - } - fs << "]"; -} - -static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) { - // make sure the input data is a vector of matrices or vector of vector - if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) { - String error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >)."; - CV_Error(Error::StsBadArg, error_message); - } - // number of samples - size_t n = src.total(); - // return empty matrix if no matrices given - if(n == 0) - return Mat(); - // dimensionality of (reshaped) samples - size_t d = src.getMat(0).total(); - // create data matrix - Mat data((int)n, (int)d, rtype); - // now copy data - for(unsigned int i = 0; i < n; i++) { - // make sure data can be reshaped, throw exception if not! - if(src.getMat(i).total() != d) { - String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total()); - CV_Error(Error::StsBadArg, error_message); - } - // get a hold of the current row - Mat xi = data.row(i); - // make reshape happy by cloning for non-continuous matrices - if(src.getMat(i).isContinuous()) { - src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta); - } else { - src.getMat(i).clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta); - } - } - return data; -} - - -// Removes duplicate elements in a given vector. -template -inline std::vector<_Tp> remove_dups(const std::vector<_Tp>& src) { - typedef typename std::set<_Tp>::const_iterator constSetIterator; - typedef typename std::vector<_Tp>::const_iterator constVecIterator; - std::set<_Tp> set_elems; - for (constVecIterator it = src.begin(); it != src.end(); ++it) - set_elems.insert(*it); - std::vector<_Tp> elems; - for (constSetIterator it = set_elems.begin(); it != set_elems.end(); ++it) - elems.push_back(*it); - return elems; -} - -class FaceRecognizerImpl : public FaceRecognizer -{ -public: - using FaceRecognizer::save; - using FaceRecognizer::load; - - // Serializes this object to a given filename. - virtual void save(const String& filename) const - { - FileStorage fs(filename, FileStorage::WRITE); - if (!fs.isOpened()) - CV_Error(Error::StsError, "File can't be opened for writing!"); - this->save(fs); - fs.release(); - } - - // Deserializes this object from a given filename. - virtual void load(const String& filename) - { - FileStorage fs(filename, FileStorage::READ); - if (!fs.isOpened()) - CV_Error(Error::StsError, "File can't be opened for writing!"); - this->load(fs); - fs.release(); - } - - // Updates a FaceRecognizer. - virtual void update(InputArrayOfArrays src, InputArray labels) - { - (void)src; - (void)labels; - String error_msg = format("This FaceRecognizer (%s) does not support updating, you have to use FaceRecognizer::train to update it.", this->name().c_str()); - CV_Error(Error::StsNotImplemented, error_msg); - } - - - // Sets additional string info for the label - virtual void setLabelInfo(int label, const String& strInfo) - { - _labelsInfo[label] = strInfo; - } - - // Gets string info by label - virtual String getLabelInfo(int label) const - { - std::map::const_iterator iter(_labelsInfo.find(label)); - return iter != _labelsInfo.end() ? iter->second : ""; - } - - // Gets labels by string - virtual std::vector getLabelsByString(const String& str) const - { - std::vector labels; - for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) - { - size_t found = (it->second).find(str); - if (found != String::npos) - labels.push_back(it->first); - } - return labels; - } - -protected: - // Stored pairs "label id - string info" - std::map _labelsInfo; -}; - - - -// Utility structure to load/save face label info (a pair of int and string) via FileStorage -struct LabelInfo -{ - LabelInfo():label(-1), value("") {} - LabelInfo(int _label, const String &_value): label(_label), value(_value) {} - int label; - String value; - void write(cv::FileStorage& fs) const - { - fs << "{" << "label" << label << "value" << value << "}"; - } - void read(const cv::FileNode& node) - { - label = (int)node["label"]; - value = (String)node["value"]; - } - std::ostream& operator<<(std::ostream& out) - { - out << "{ label = " << label << ", " << "value = " << value.c_str() << "}"; - return out; - } -}; - -static void write(cv::FileStorage& fs, const String&, const LabelInfo& x) -{ - x.write(fs); -} - -static void read(const cv::FileNode& node, LabelInfo& x, const LabelInfo& default_value = LabelInfo()) -{ - if(node.empty()) - x = default_value; - else - x.read(node); -} - - - -// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of -// Cognitive Neuroscience 3 (1991), 71–86. -class Eigenfaces : public FaceRecognizerImpl -{ -private: - int _num_components; - double _threshold; - std::vector _projections; - Mat _labels; - Mat _eigenvectors; - Mat _eigenvalues; - Mat _mean; - -public: - using FaceRecognizer::save; - using FaceRecognizer::load; - - // Initializes an empty Eigenfaces model. - Eigenfaces(int num_components = 0, double threshold = DBL_MAX) : - _num_components(num_components), - _threshold(threshold) {} - - // Initializes and computes an Eigenfaces model with images in src and - // corresponding labels in labels. num_components will be kept for - // classification. - Eigenfaces(InputArrayOfArrays src, InputArray labels, - int num_components = 0, double threshold = DBL_MAX) : - _num_components(num_components), - _threshold(threshold) { - train(src, labels); - } - - // Computes an Eigenfaces model with images in src and corresponding labels - // in labels. - void train(InputArrayOfArrays src, InputArray labels); - - // Predicts the label of a query image in src. - int predict(InputArray src) const; - - // Predicts the label and confidence for a given sample. - void predict(InputArray _src, int &label, double &dist) const; - - // See FaceRecognizer::load. - void load(const FileStorage& fs); - - // See FaceRecognizer::save. - void save(FileStorage& fs) const; - - AlgorithmInfo* info() const; -}; - -// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher- -// faces: Recognition using class specific linear projection.". IEEE -// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), -// 711–720. -class Fisherfaces: public FaceRecognizerImpl -{ -private: - int _num_components; - double _threshold; - Mat _eigenvectors; - Mat _eigenvalues; - Mat _mean; - std::vector _projections; - Mat _labels; - -public: - using FaceRecognizer::save; - using FaceRecognizer::load; - - // Initializes an empty Fisherfaces model. - Fisherfaces(int num_components = 0, double threshold = DBL_MAX) : - _num_components(num_components), - _threshold(threshold) {} - - // Initializes and computes a Fisherfaces model with images in src and - // corresponding labels in labels. num_components will be kept for - // classification. - Fisherfaces(InputArrayOfArrays src, InputArray labels, - int num_components = 0, double threshold = DBL_MAX) : - _num_components(num_components), - _threshold(threshold) { - train(src, labels); - } - - ~Fisherfaces() {} - - // Computes a Fisherfaces model with images in src and corresponding labels - // in labels. - void train(InputArrayOfArrays src, InputArray labels); - - // Predicts the label of a query image in src. - int predict(InputArray src) const; - - // Predicts the label and confidence for a given sample. - void predict(InputArray _src, int &label, double &dist) const; - - // See FaceRecognizer::load. - void load(const FileStorage& fs); - - // See FaceRecognizer::save. - void save(FileStorage& fs) const; - - AlgorithmInfo* info() const; -}; - -// Face Recognition based on Local Binary Patterns. -// -// Ahonen T, Hadid A. and Pietikäinen M. "Face description with local binary -// patterns: Application to face recognition." IEEE Transactions on Pattern -// Analysis and Machine Intelligence, 28(12):2037-2041. -// -class LBPH : public FaceRecognizerImpl -{ -private: - int _grid_x; - int _grid_y; - int _radius; - int _neighbors; - double _threshold; - - std::vector _histograms; - Mat _labels; - - // Computes a LBPH model with images in src and - // corresponding labels in labels, possibly preserving - // old model data. - void train(InputArrayOfArrays src, InputArray labels, bool preserveData); - - -public: - using FaceRecognizer::save; - using FaceRecognizer::load; - - // Initializes this LBPH Model. The current implementation is rather fixed - // as it uses the Extended Local Binary Patterns per default. - // - // radius, neighbors are used in the local binary patterns creation. - // grid_x, grid_y control the grid size of the spatial histograms. - LBPH(int radius_=1, int neighbors_=8, - int gridx=8, int gridy=8, - double threshold = DBL_MAX) : - _grid_x(gridx), - _grid_y(gridy), - _radius(radius_), - _neighbors(neighbors_), - _threshold(threshold) {} - - // Initializes and computes this LBPH Model. The current implementation is - // rather fixed as it uses the Extended Local Binary Patterns per default. - // - // (radius=1), (neighbors=8) are used in the local binary patterns creation. - // (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms. - LBPH(InputArrayOfArrays src, - InputArray labels, - int radius_=1, int neighbors_=8, - int gridx=8, int gridy=8, - double threshold = DBL_MAX) : - _grid_x(gridx), - _grid_y(gridy), - _radius(radius_), - _neighbors(neighbors_), - _threshold(threshold) { - train(src, labels); - } - - ~LBPH() { } - - // Computes a LBPH model with images in src and - // corresponding labels in labels. - void train(InputArrayOfArrays src, InputArray labels); - - // Updates this LBPH model with images in src and - // corresponding labels in labels. - void update(InputArrayOfArrays src, InputArray labels); - - // Predicts the label of a query image in src. - int predict(InputArray src) const; - - // Predicts the label and confidence for a given sample. - void predict(InputArray _src, int &label, double &dist) const; - - // See FaceRecognizer::load. - void load(const FileStorage& fs); - - // See FaceRecognizer::save. - void save(FileStorage& fs) const; - - // Getter functions. - int neighbors() const { return _neighbors; } - int radius() const { return _radius; } - int grid_x() const { return _grid_x; } - int grid_y() const { return _grid_y; } - - AlgorithmInfo* info() const; -}; - - -//------------------------------------------------------------------------------ -// Eigenfaces -//------------------------------------------------------------------------------ -void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) { - if(_src.total() == 0) { - String error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); - CV_Error(Error::StsBadArg, error_message); - } else if(_local_labels.getMat().type() != CV_32SC1) { - String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _local_labels.type()); - CV_Error(Error::StsBadArg, error_message); - } - // make sure data has correct size - if(_src.total() > 1) { - for(int i = 1; i < static_cast(_src.total()); i++) { - if(_src.getMat(i-1).total() != _src.getMat(i).total()) { - String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", _src.getMat(i-1).total(), _src.getMat(i).total()); - CV_Error(Error::StsUnsupportedFormat, error_message); - } - } - } - // get labels - Mat labels = _local_labels.getMat(); - // observations in row - Mat data = asRowMatrix(_src, CV_64FC1); - - // number of samples - int n = data.rows; - // assert there are as much samples as labels - if(static_cast(labels.total()) != n) { - String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total()); - CV_Error(Error::StsBadArg, error_message); - } - // clear existing model data - _labels.release(); - _projections.clear(); - // clip number of components to be valid - if((_num_components <= 0) || (_num_components > n)) - _num_components = n; - - // perform the PCA - PCA pca(data, Mat(), PCA::DATA_AS_ROW, _num_components); - // copy the PCA results - _mean = pca.mean.reshape(1,1); // store the mean vector - _eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row - transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column - // store labels for prediction - _labels = labels.clone(); - // save projections - for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) { - Mat p = LDA::subspaceProject(_eigenvectors, _mean, data.row(sampleIdx)); - _projections.push_back(p); - } -} - -void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const { - // get data - Mat src = _src.getMat(); - // make sure the user is passing correct data - if(_projections.empty()) { - // throw error if no data (or simply return -1?) - String error_message = "This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?"; - CV_Error(Error::StsError, error_message); - } else if(_eigenvectors.rows != static_cast(src.total())) { - // check data alignment just for clearer exception messages - String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total()); - CV_Error(Error::StsBadArg, error_message); - } - // project into PCA subspace - Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1,1)); - minDist = DBL_MAX; - minClass = -1; - for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { - double dist = norm(_projections[sampleIdx], q, NORM_L2); - if((dist < minDist) && (dist < _threshold)) { - minDist = dist; - minClass = _labels.at((int)sampleIdx); - } - } -} - -int Eigenfaces::predict(InputArray _src) const { - int label; - double dummy; - predict(_src, label, dummy); - return label; -} - -void Eigenfaces::load(const FileStorage& fs) { - //read matrices - fs["num_components"] >> _num_components; - fs["mean"] >> _mean; - fs["eigenvalues"] >> _eigenvalues; - fs["eigenvectors"] >> _eigenvectors; - // read sequences - readFileNodeList(fs["projections"], _projections); - fs["labels"] >> _labels; - const FileNode& fn = fs["labelsInfo"]; - if (fn.type() == FileNode::SEQ) - { - _labelsInfo.clear(); - for (FileNodeIterator it = fn.begin(); it != fn.end();) - { - LabelInfo item; - it >> item; - _labelsInfo.insert(std::make_pair(item.label, item.value)); - } - } -} - -void Eigenfaces::save(FileStorage& fs) const { - // write matrices - fs << "num_components" << _num_components; - fs << "mean" << _mean; - fs << "eigenvalues" << _eigenvalues; - fs << "eigenvectors" << _eigenvectors; - // write sequences - writeFileNodeList(fs, "projections", _projections); - fs << "labels" << _labels; - fs << "labelsInfo" << "["; - for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) - fs << LabelInfo(it->first, it->second); - fs << "]"; -} - -//------------------------------------------------------------------------------ -// Fisherfaces -//------------------------------------------------------------------------------ -void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) { - if(src.total() == 0) { - String error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); - CV_Error(Error::StsBadArg, error_message); - } else if(_lbls.getMat().type() != CV_32SC1) { - String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type()); - CV_Error(Error::StsBadArg, error_message); - } - // make sure data has correct size - if(src.total() > 1) { - for(int i = 1; i < static_cast(src.total()); i++) { - if(src.getMat(i-1).total() != src.getMat(i).total()) { - String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", src.getMat(i-1).total(), src.getMat(i).total()); - CV_Error(Error::StsUnsupportedFormat, error_message); - } - } - } - // get data - Mat labels = _lbls.getMat(); - Mat data = asRowMatrix(src, CV_64FC1); - // number of samples - int N = data.rows; - // make sure labels are passed in correct shape - if(labels.total() != (size_t) N) { - String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total()); - CV_Error(Error::StsBadArg, error_message); - } else if(labels.rows != 1 && labels.cols != 1) { - String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols); - CV_Error(Error::StsBadArg, error_message); - } - // clear existing model data - _labels.release(); - _projections.clear(); - // safely copy from cv::Mat to std::vector - std::vector ll; - for(unsigned int i = 0; i < labels.total(); i++) { - ll.push_back(labels.at(i)); - } - // get the number of unique classes - int C = (int) remove_dups(ll).size(); - // clip number of components to be a valid number - if((_num_components <= 0) || (_num_components > (C-1))) - _num_components = (C-1); - // perform a PCA and keep (N-C) components - PCA pca(data, Mat(), PCA::DATA_AS_ROW, (N-C)); - // project the data and perform a LDA on it - LDA lda(pca.project(data),labels, _num_components); - // store the total mean vector - _mean = pca.mean.reshape(1,1); - // store labels - _labels = labels.clone(); - // store the eigenvalues of the discriminants - lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1); - // Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors. - // Note: OpenCV stores the eigenvectors by row, so we need to transpose it! - gemm(pca.eigenvectors, lda.eigenvectors(), 1.0, Mat(), 0.0, _eigenvectors, GEMM_1_T); - // store the projections of the original data - for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) { - Mat p = LDA::subspaceProject(_eigenvectors, _mean, data.row(sampleIdx)); - _projections.push_back(p); - } -} - -void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const { - Mat src = _src.getMat(); - // check data alignment just for clearer exception messages - if(_projections.empty()) { - // throw error if no data (or simply return -1?) - String error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?"; - CV_Error(Error::StsBadArg, error_message); - } else if(src.total() != (size_t) _eigenvectors.rows) { - String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total()); - CV_Error(Error::StsBadArg, error_message); - } - // project into LDA subspace - Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1,1)); - // find 1-nearest neighbor - minDist = DBL_MAX; - minClass = -1; - for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { - double dist = norm(_projections[sampleIdx], q, NORM_L2); - if((dist < minDist) && (dist < _threshold)) { - minDist = dist; - minClass = _labels.at((int)sampleIdx); - } - } -} - -int Fisherfaces::predict(InputArray _src) const { - int label; - double dummy; - predict(_src, label, dummy); - return label; -} - -// See FaceRecognizer::load. -void Fisherfaces::load(const FileStorage& fs) { - //read matrices - fs["num_components"] >> _num_components; - fs["mean"] >> _mean; - fs["eigenvalues"] >> _eigenvalues; - fs["eigenvectors"] >> _eigenvectors; - // read sequences - readFileNodeList(fs["projections"], _projections); - fs["labels"] >> _labels; - const FileNode& fn = fs["labelsInfo"]; - if (fn.type() == FileNode::SEQ) - { - _labelsInfo.clear(); - for (FileNodeIterator it = fn.begin(); it != fn.end();) - { - LabelInfo item; - it >> item; - _labelsInfo.insert(std::make_pair(item.label, item.value)); - } - } -} - -// See FaceRecognizer::save. -void Fisherfaces::save(FileStorage& fs) const { - // write matrices - fs << "num_components" << _num_components; - fs << "mean" << _mean; - fs << "eigenvalues" << _eigenvalues; - fs << "eigenvectors" << _eigenvectors; - // write sequences - writeFileNodeList(fs, "projections", _projections); - fs << "labels" << _labels; - fs << "labelsInfo" << "["; - for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) - fs << LabelInfo(it->first, it->second); - fs << "]"; -} - -//------------------------------------------------------------------------------ -// LBPH -//------------------------------------------------------------------------------ - -template static -void olbp_(InputArray _src, OutputArray _dst) { - // get matrices - Mat src = _src.getMat(); - // allocate memory for result - _dst.create(src.rows-2, src.cols-2, CV_8UC1); - Mat dst = _dst.getMat(); - // zero the result matrix - dst.setTo(0); - // calculate patterns - for(int i=1;i(i,j); - unsigned char code = 0; - code |= (src.at<_Tp>(i-1,j-1) >= center) << 7; - code |= (src.at<_Tp>(i-1,j) >= center) << 6; - code |= (src.at<_Tp>(i-1,j+1) >= center) << 5; - code |= (src.at<_Tp>(i,j+1) >= center) << 4; - code |= (src.at<_Tp>(i+1,j+1) >= center) << 3; - code |= (src.at<_Tp>(i+1,j) >= center) << 2; - code |= (src.at<_Tp>(i+1,j-1) >= center) << 1; - code |= (src.at<_Tp>(i,j-1) >= center) << 0; - dst.at(i-1,j-1) = code; - } - } -} - -//------------------------------------------------------------------------------ -// cv::elbp -//------------------------------------------------------------------------------ -template static -inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors) { - //get matrices - Mat src = _src.getMat(); - // allocate memory for result - _dst.create(src.rows-2*radius, src.cols-2*radius, CV_32SC1); - Mat dst = _dst.getMat(); - // zero - dst.setTo(0); - for(int n=0; n(radius * cos(2.0*CV_PI*n/static_cast(neighbors))); - float y = static_cast(-radius * sin(2.0*CV_PI*n/static_cast(neighbors))); - // relative indices - int fx = static_cast(floor(x)); - int fy = static_cast(floor(y)); - int cx = static_cast(ceil(x)); - int cy = static_cast(ceil(y)); - // fractional part - float ty = y - fy; - float tx = x - fx; - // set interpolation weights - float w1 = (1 - tx) * (1 - ty); - float w2 = tx * (1 - ty); - float w3 = (1 - tx) * ty; - float w4 = tx * ty; - // iterate through your data - for(int i=radius; i < src.rows-radius;i++) { - for(int j=radius;j < src.cols-radius;j++) { - // calculate interpolated value - float t = static_cast(w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx)); - // floating point precision, so check some machine-dependent epsilon - dst.at(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) || (std::abs(t-src.at<_Tp>(i,j)) < std::numeric_limits::epsilon())) << n; - } - } - } -} - -static void elbp(InputArray src, OutputArray dst, int radius, int neighbors) -{ - int type = src.type(); - switch (type) { - case CV_8SC1: elbp_(src,dst, radius, neighbors); break; - case CV_8UC1: elbp_(src, dst, radius, neighbors); break; - case CV_16SC1: elbp_(src,dst, radius, neighbors); break; - case CV_16UC1: elbp_(src,dst, radius, neighbors); break; - case CV_32SC1: elbp_(src,dst, radius, neighbors); break; - case CV_32FC1: elbp_(src,dst, radius, neighbors); break; - case CV_64FC1: elbp_(src,dst, radius, neighbors); break; - default: - String error_msg = format("Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!", type); - CV_Error(Error::StsNotImplemented, error_msg); - break; - } -} - -static Mat -histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false) -{ - Mat result; - // Establish the number of bins. - int histSize = maxVal-minVal+1; - // Set the ranges. - float range[] = { static_cast(minVal), static_cast(maxVal+1) }; - const float* histRange = { range }; - // calc histogram - calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false); - // normalize - if(normed) { - result /= (int)src.total(); - } - return result.reshape(1,1); -} - -static Mat histc(InputArray _src, int minVal, int maxVal, bool normed) +std::vector FaceRecognizer::getLabelsByString(const String &str) const { - Mat src = _src.getMat(); - switch (src.type()) { - case CV_8SC1: - return histc_(Mat_(src), minVal, maxVal, normed); - break; - case CV_8UC1: - return histc_(src, minVal, maxVal, normed); - break; - case CV_16SC1: - return histc_(Mat_(src), minVal, maxVal, normed); - break; - case CV_16UC1: - return histc_(src, minVal, maxVal, normed); - break; - case CV_32SC1: - return histc_(Mat_(src), minVal, maxVal, normed); - break; - case CV_32FC1: - return histc_(src, minVal, maxVal, normed); - break; - default: - CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet."); break; - } - return Mat(); + std::vector labels; + for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) + { + size_t found = (it->second).find(str); + if (found != String::npos) + labels.push_back(it->first); + } + return labels; } - -static Mat spatial_histogram(InputArray _src, int numPatterns, - int grid_x, int grid_y, bool /*normed*/) +String FaceRecognizer::getLabelInfo(int label) const { - Mat src = _src.getMat(); - // calculate LBP patch size - int width = src.cols/grid_x; - int height = src.rows/grid_y; - // allocate memory for the spatial histogram - Mat result = Mat::zeros(grid_x * grid_y, numPatterns, CV_32FC1); - // return matrix with zeros if no data was given - if(src.empty()) - return result.reshape(1,1); - // initial result_row - int resultRowIdx = 0; - // iterate through grid - for(int i = 0; i < grid_y; i++) { - for(int j = 0; j < grid_x; j++) { - Mat src_cell = Mat(src, Range(i*height,(i+1)*height), Range(j*width,(j+1)*width)); - Mat cell_hist = histc(src_cell, 0, (numPatterns-1), true); - // copy to the result matrix - Mat result_row = result.row(resultRowIdx); - cell_hist.reshape(1,1).convertTo(result_row, CV_32FC1); - // increase row count in result matrix - resultRowIdx++; - } - } - // return result as reshaped feature vector - return result.reshape(1,1); -} - -//------------------------------------------------------------------------------ -// wrapper to cv::elbp (extended local binary patterns) -//------------------------------------------------------------------------------ - -static Mat elbp(InputArray src, int radius, int neighbors) { - Mat dst; - elbp(src, dst, radius, neighbors); - return dst; -} - -void LBPH::load(const FileStorage& fs) { - fs["radius"] >> _radius; - fs["neighbors"] >> _neighbors; - fs["grid_x"] >> _grid_x; - fs["grid_y"] >> _grid_y; - //read matrices - readFileNodeList(fs["histograms"], _histograms); - fs["labels"] >> _labels; - const FileNode& fn = fs["labelsInfo"]; - if (fn.type() == FileNode::SEQ) - { - _labelsInfo.clear(); - for (FileNodeIterator it = fn.begin(); it != fn.end();) - { - LabelInfo item; - it >> item; - _labelsInfo.insert(std::make_pair(item.label, item.value)); - } - } -} - -// See FaceRecognizer::save. -void LBPH::save(FileStorage& fs) const { - fs << "radius" << _radius; - fs << "neighbors" << _neighbors; - fs << "grid_x" << _grid_x; - fs << "grid_y" << _grid_y; - // write matrices - writeFileNodeList(fs, "histograms", _histograms); - fs << "labels" << _labels; - fs << "labelsInfo" << "["; - for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) - fs << LabelInfo(it->first, it->second); - fs << "]"; -} - -void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels) { - this->train(_in_src, _in_labels, false); -} - -void LBPH::update(InputArrayOfArrays _in_src, InputArray _in_labels) { - // got no data, just return - if(_in_src.total() == 0) - return; - - this->train(_in_src, _in_labels, true); -} - -void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserveData) { - if(_in_src.kind() != _InputArray::STD_VECTOR_MAT && _in_src.kind() != _InputArray::STD_VECTOR_VECTOR) { - String error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >)."; - CV_Error(Error::StsBadArg, error_message); - } - if(_in_src.total() == 0) { - String error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); - CV_Error(Error::StsUnsupportedFormat, error_message); - } else if(_in_labels.getMat().type() != CV_32SC1) { - String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _in_labels.type()); - CV_Error(Error::StsUnsupportedFormat, error_message); - } - // get the vector of matrices - std::vector src; - _in_src.getMatVector(src); - // get the label matrix - Mat labels = _in_labels.getMat(); - // check if data is well- aligned - if(labels.total() != src.size()) { - String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total()); - CV_Error(Error::StsBadArg, error_message); - } - // if this model should be trained without preserving old data, delete old model data - if(!preserveData) { - _labels.release(); - _histograms.clear(); - } - // append labels to _labels matrix - for(size_t labelIdx = 0; labelIdx < labels.total(); labelIdx++) { - _labels.push_back(labels.at((int)labelIdx)); - } - // store the spatial histograms of the original data - for(size_t sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) { - // calculate lbp image - Mat lbp_image = elbp(src[sampleIdx], _radius, _neighbors); - // get spatial histogram from this lbp image - Mat p = spatial_histogram( - lbp_image, /* lbp_image */ - static_cast(std::pow(2.0, static_cast(_neighbors))), /* number of possible patterns */ - _grid_x, /* grid size x */ - _grid_y, /* grid size y */ - true); - // add to templates - _histograms.push_back(p); - } + std::map::const_iterator iter(_labelsInfo.find(label)); + return iter != _labelsInfo.end() ? iter->second : ""; } -void LBPH::predict(InputArray _src, int &minClass, double &minDist) const { - if(_histograms.empty()) { - // throw error if no data (or simply return -1?) - String error_message = "This LBPH model is not computed yet. Did you call the train method?"; - CV_Error(Error::StsBadArg, error_message); - } - Mat src = _src.getMat(); - // get the spatial histogram from input image - Mat lbp_image = elbp(src, _radius, _neighbors); - Mat query = spatial_histogram( - lbp_image, /* lbp_image */ - static_cast(std::pow(2.0, static_cast(_neighbors))), /* number of possible patterns */ - _grid_x, /* grid size x */ - _grid_y, /* grid size y */ - true /* normed histograms */); - // find 1-nearest neighbor - minDist = DBL_MAX; - minClass = -1; - for(size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) { - double dist = compareHist(_histograms[sampleIdx], query, HISTCMP_CHISQR_ALT); - if((dist < minDist) && (dist < _threshold)) { - minDist = dist; - minClass = _labels.at((int) sampleIdx); - } - } -} - -int LBPH::predict(InputArray _src) const { - int label; - double dummy; - predict(_src, label, dummy); - return label; -} - - -Ptr createEigenFaceRecognizer(int num_components, double threshold) +void FaceRecognizer::setLabelInfo(int label, const String &strInfo) { - return makePtr(num_components, threshold); + _labelsInfo[label] = strInfo; } -Ptr createFisherFaceRecognizer(int num_components, double threshold) +void FaceRecognizer::update(InputArrayOfArrays src, InputArray labels) { - return makePtr(num_components, threshold); + (void)src; + (void)labels; + String error_msg = format("This FaceRecognizer does not support updating, you have to use FaceRecognizer::train to update it."); + CV_Error(Error::StsNotImplemented, error_msg); } -Ptr createLBPHFaceRecognizer(int radius, int neighbors, - int grid_x, int grid_y, double threshold) +void FaceRecognizer::load(const String &filename) { - return makePtr(radius, neighbors, grid_x, grid_y, threshold); + FileStorage fs(filename, FileStorage::READ); + if (!fs.isOpened()) + CV_Error(Error::StsError, "File can't be opened for writing!"); + this->load(fs); + fs.release(); } -CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces", - obj.info()->addParam(obj, "ncomponents", obj._num_components); - obj.info()->addParam(obj, "threshold", obj._threshold); - obj.info()->addParam(obj, "projections", obj._projections, true); - obj.info()->addParam(obj, "labels", obj._labels, true); - obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true); - obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true); - obj.info()->addParam(obj, "mean", obj._mean, true)) - -CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces", - obj.info()->addParam(obj, "ncomponents", obj._num_components); - obj.info()->addParam(obj, "threshold", obj._threshold); - obj.info()->addParam(obj, "projections", obj._projections, true); - obj.info()->addParam(obj, "labels", obj._labels, true); - obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true); - obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true); - obj.info()->addParam(obj, "mean", obj._mean, true)) - -CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH", - obj.info()->addParam(obj, "radius", obj._radius); - obj.info()->addParam(obj, "neighbors", obj._neighbors); - obj.info()->addParam(obj, "grid_x", obj._grid_x); - obj.info()->addParam(obj, "grid_y", obj._grid_y); - obj.info()->addParam(obj, "threshold", obj._threshold); - obj.info()->addParam(obj, "histograms", obj._histograms, true); - obj.info()->addParam(obj, "labels", obj._labels, true)) - -bool initModule_facerec() +void FaceRecognizer::save(const String &filename) const { - return !Eigenfaces_info_auto.name().empty() && !Fisherfaces_info_auto.name().empty() && !LBPH_info_auto.name().empty(); + FileStorage fs(filename, FileStorage::WRITE); + if (!fs.isOpened()) + CV_Error(Error::StsError, "File can't be opened for writing!"); + this->save(fs); + fs.release(); } } diff --git a/modules/face/src/fisher_faces.cpp b/modules/face/src/fisher_faces.cpp new file mode 100644 index 000000000..d8466f996 --- /dev/null +++ b/modules/face/src/fisher_faces.cpp @@ -0,0 +1,163 @@ +/* + * Copyright (c) 2011,2012. Philipp Wagner . + * Released to public domain under terms of the BSD Simplified license. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of the organization nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * See + */ +#include "precomp.hpp" +#include "face_basic.hpp" + +namespace cv { namespace face { + +// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher- +// faces: Recognition using class specific linear projection.". IEEE +// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), +// 711–720. +class Fisherfaces: public BasicFaceRecognizerImpl +{ +public: + // Initializes an empty Fisherfaces model. + Fisherfaces(int num_components = 0, double threshold = DBL_MAX) + : BasicFaceRecognizerImpl(num_components, threshold) + { } + + // Computes a Fisherfaces model with images in src and corresponding labels + // in labels. + void train(InputArrayOfArrays src, InputArray labels); + + // Predicts the label of a query image in src. + int predict(InputArray src) const; + + // Predicts the label and confidence for a given sample. + void predict(InputArray _src, int &label, double &dist) const; +}; + +// Removes duplicate elements in a given vector. +template +inline std::vector<_Tp> remove_dups(const std::vector<_Tp>& src) { + typedef typename std::set<_Tp>::const_iterator constSetIterator; + typedef typename std::vector<_Tp>::const_iterator constVecIterator; + std::set<_Tp> set_elems; + for (constVecIterator it = src.begin(); it != src.end(); ++it) + set_elems.insert(*it); + std::vector<_Tp> elems; + for (constSetIterator it = set_elems.begin(); it != set_elems.end(); ++it) + elems.push_back(*it); + return elems; +} + +//------------------------------------------------------------------------------ +// Fisherfaces +//------------------------------------------------------------------------------ +void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) { + if(src.total() == 0) { + String error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); + CV_Error(Error::StsBadArg, error_message); + } else if(_lbls.getMat().type() != CV_32SC1) { + String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type()); + CV_Error(Error::StsBadArg, error_message); + } + // make sure data has correct size + if(src.total() > 1) { + for(int i = 1; i < static_cast(src.total()); i++) { + if(src.getMat(i-1).total() != src.getMat(i).total()) { + String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", src.getMat(i-1).total(), src.getMat(i).total()); + CV_Error(Error::StsUnsupportedFormat, error_message); + } + } + } + // get data + Mat labels = _lbls.getMat(); + Mat data = asRowMatrix(src, CV_64FC1); + // number of samples + int N = data.rows; + // make sure labels are passed in correct shape + if(labels.total() != (size_t) N) { + String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total()); + CV_Error(Error::StsBadArg, error_message); + } else if(labels.rows != 1 && labels.cols != 1) { + String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols); + CV_Error(Error::StsBadArg, error_message); + } + // clear existing model data + _labels.release(); + _projections.clear(); + // safely copy from cv::Mat to std::vector + std::vector ll; + for(unsigned int i = 0; i < labels.total(); i++) { + ll.push_back(labels.at(i)); + } + // get the number of unique classes + int C = (int) remove_dups(ll).size(); + // clip number of components to be a valid number + if((_num_components <= 0) || (_num_components > (C-1))) + _num_components = (C-1); + // perform a PCA and keep (N-C) components + PCA pca(data, Mat(), PCA::DATA_AS_ROW, (N-C)); + // project the data and perform a LDA on it + LDA lda(pca.project(data),labels, _num_components); + // store the total mean vector + _mean = pca.mean.reshape(1,1); + // store labels + _labels = labels.clone(); + // store the eigenvalues of the discriminants + lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1); + // Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors. + // Note: OpenCV stores the eigenvectors by row, so we need to transpose it! + gemm(pca.eigenvectors, lda.eigenvectors(), 1.0, Mat(), 0.0, _eigenvectors, GEMM_1_T); + // store the projections of the original data + for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) { + Mat p = LDA::subspaceProject(_eigenvectors, _mean, data.row(sampleIdx)); + _projections.push_back(p); + } +} + +void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const { + Mat src = _src.getMat(); + // check data alignment just for clearer exception messages + if(_projections.empty()) { + // throw error if no data (or simply return -1?) + String error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?"; + CV_Error(Error::StsBadArg, error_message); + } else if(src.total() != (size_t) _eigenvectors.rows) { + String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total()); + CV_Error(Error::StsBadArg, error_message); + } + // project into LDA subspace + Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1,1)); + // find 1-nearest neighbor + minDist = DBL_MAX; + minClass = -1; + for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { + double dist = norm(_projections[sampleIdx], q, NORM_L2); + if((dist < minDist) && (dist < _threshold)) { + minDist = dist; + minClass = _labels.at((int)sampleIdx); + } + } +} + +int Fisherfaces::predict(InputArray _src) const { + int label; + double dummy; + predict(_src, label, dummy); + return label; +} + +Ptr createFisherFaceRecognizer(int num_components, double threshold) +{ + return makePtr(num_components, threshold); +} + +} } diff --git a/modules/face/src/lbph_faces.cpp b/modules/face/src/lbph_faces.cpp new file mode 100644 index 000000000..bc01acefb --- /dev/null +++ b/modules/face/src/lbph_faces.cpp @@ -0,0 +1,430 @@ +/* + * Copyright (c) 2011,2012. Philipp Wagner . + * Released to public domain under terms of the BSD Simplified license. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of the organization nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * See + */ +#include "precomp.hpp" +#include "opencv2/face.hpp" +#include "face_basic.hpp" + +namespace cv { namespace face { + +// Face Recognition based on Local Binary Patterns. +// +// Ahonen T, Hadid A. and Pietikäinen M. "Face description with local binary +// patterns: Application to face recognition." IEEE Transactions on Pattern +// Analysis and Machine Intelligence, 28(12):2037-2041. +// +class LBPH : public LBPHFaceRecognizer +{ +private: + int _grid_x; + int _grid_y; + int _radius; + int _neighbors; + double _threshold; + + std::vector _histograms; + Mat _labels; + + // Computes a LBPH model with images in src and + // corresponding labels in labels, possibly preserving + // old model data. + void train(InputArrayOfArrays src, InputArray labels, bool preserveData); + + +public: + using FaceRecognizer::save; + using FaceRecognizer::load; + + // Initializes this LBPH Model. The current implementation is rather fixed + // as it uses the Extended Local Binary Patterns per default. + // + // radius, neighbors are used in the local binary patterns creation. + // grid_x, grid_y control the grid size of the spatial histograms. + LBPH(int radius_=1, int neighbors_=8, + int gridx=8, int gridy=8, + double threshold = DBL_MAX) : + _grid_x(gridx), + _grid_y(gridy), + _radius(radius_), + _neighbors(neighbors_), + _threshold(threshold) {} + + // Initializes and computes this LBPH Model. The current implementation is + // rather fixed as it uses the Extended Local Binary Patterns per default. + // + // (radius=1), (neighbors=8) are used in the local binary patterns creation. + // (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms. + LBPH(InputArrayOfArrays src, + InputArray labels, + int radius_=1, int neighbors_=8, + int gridx=8, int gridy=8, + double threshold = DBL_MAX) : + _grid_x(gridx), + _grid_y(gridy), + _radius(radius_), + _neighbors(neighbors_), + _threshold(threshold) { + train(src, labels); + } + + ~LBPH() { } + + // Computes a LBPH model with images in src and + // corresponding labels in labels. + void train(InputArrayOfArrays src, InputArray labels); + + // Updates this LBPH model with images in src and + // corresponding labels in labels. + void update(InputArrayOfArrays src, InputArray labels); + + // Predicts the label of a query image in src. + int predict(InputArray src) const; + + // Predicts the label and confidence for a given sample. + void predict(InputArray _src, int &label, double &dist) const; + + // See FaceRecognizer::load. + void load(const FileStorage& fs); + + // See FaceRecognizer::save. + void save(FileStorage& fs) const; + + CV_IMPL_PROPERTY(int, GridX, _grid_x) + CV_IMPL_PROPERTY(int, GridY, _grid_y) + CV_IMPL_PROPERTY(int, Radius, _radius) + CV_IMPL_PROPERTY(int, Neighbors, _neighbors) + CV_IMPL_PROPERTY(double, Threshold, _threshold) + CV_IMPL_PROPERTY_RO(std::vector, Histograms, _histograms) + CV_IMPL_PROPERTY_RO(cv::Mat, Labels, _labels) +}; + + +void LBPH::load(const FileStorage& fs) { + fs["radius"] >> _radius; + fs["neighbors"] >> _neighbors; + fs["grid_x"] >> _grid_x; + fs["grid_y"] >> _grid_y; + //read matrices + readFileNodeList(fs["histograms"], _histograms); + fs["labels"] >> _labels; + const FileNode& fn = fs["labelsInfo"]; + if (fn.type() == FileNode::SEQ) + { + _labelsInfo.clear(); + for (FileNodeIterator it = fn.begin(); it != fn.end();) + { + LabelInfo item; + it >> item; + _labelsInfo.insert(std::make_pair(item.label, item.value)); + } + } +} + +// See FaceRecognizer::save. +void LBPH::save(FileStorage& fs) const { + fs << "radius" << _radius; + fs << "neighbors" << _neighbors; + fs << "grid_x" << _grid_x; + fs << "grid_y" << _grid_y; + // write matrices + writeFileNodeList(fs, "histograms", _histograms); + fs << "labels" << _labels; + fs << "labelsInfo" << "["; + for (std::map::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++) + fs << LabelInfo(it->first, it->second); + fs << "]"; +} + +void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels) { + this->train(_in_src, _in_labels, false); +} + +void LBPH::update(InputArrayOfArrays _in_src, InputArray _in_labels) { + // got no data, just return + if(_in_src.total() == 0) + return; + + this->train(_in_src, _in_labels, true); +} + + +//------------------------------------------------------------------------------ +// LBPH +//------------------------------------------------------------------------------ + +template static +void olbp_(InputArray _src, OutputArray _dst) { + // get matrices + Mat src = _src.getMat(); + // allocate memory for result + _dst.create(src.rows-2, src.cols-2, CV_8UC1); + Mat dst = _dst.getMat(); + // zero the result matrix + dst.setTo(0); + // calculate patterns + for(int i=1;i(i,j); + unsigned char code = 0; + code |= (src.at<_Tp>(i-1,j-1) >= center) << 7; + code |= (src.at<_Tp>(i-1,j) >= center) << 6; + code |= (src.at<_Tp>(i-1,j+1) >= center) << 5; + code |= (src.at<_Tp>(i,j+1) >= center) << 4; + code |= (src.at<_Tp>(i+1,j+1) >= center) << 3; + code |= (src.at<_Tp>(i+1,j) >= center) << 2; + code |= (src.at<_Tp>(i+1,j-1) >= center) << 1; + code |= (src.at<_Tp>(i,j-1) >= center) << 0; + dst.at(i-1,j-1) = code; + } + } +} + +//------------------------------------------------------------------------------ +// cv::elbp +//------------------------------------------------------------------------------ +template static +inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors) { + //get matrices + Mat src = _src.getMat(); + // allocate memory for result + _dst.create(src.rows-2*radius, src.cols-2*radius, CV_32SC1); + Mat dst = _dst.getMat(); + // zero + dst.setTo(0); + for(int n=0; n(radius * cos(2.0*CV_PI*n/static_cast(neighbors))); + float y = static_cast(-radius * sin(2.0*CV_PI*n/static_cast(neighbors))); + // relative indices + int fx = static_cast(floor(x)); + int fy = static_cast(floor(y)); + int cx = static_cast(ceil(x)); + int cy = static_cast(ceil(y)); + // fractional part + float ty = y - fy; + float tx = x - fx; + // set interpolation weights + float w1 = (1 - tx) * (1 - ty); + float w2 = tx * (1 - ty); + float w3 = (1 - tx) * ty; + float w4 = tx * ty; + // iterate through your data + for(int i=radius; i < src.rows-radius;i++) { + for(int j=radius;j < src.cols-radius;j++) { + // calculate interpolated value + float t = static_cast(w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx)); + // floating point precision, so check some machine-dependent epsilon + dst.at(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) || (std::abs(t-src.at<_Tp>(i,j)) < std::numeric_limits::epsilon())) << n; + } + } + } +} + +static void elbp(InputArray src, OutputArray dst, int radius, int neighbors) +{ + int type = src.type(); + switch (type) { + case CV_8SC1: elbp_(src,dst, radius, neighbors); break; + case CV_8UC1: elbp_(src, dst, radius, neighbors); break; + case CV_16SC1: elbp_(src,dst, radius, neighbors); break; + case CV_16UC1: elbp_(src,dst, radius, neighbors); break; + case CV_32SC1: elbp_(src,dst, radius, neighbors); break; + case CV_32FC1: elbp_(src,dst, radius, neighbors); break; + case CV_64FC1: elbp_(src,dst, radius, neighbors); break; + default: + String error_msg = format("Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!", type); + CV_Error(Error::StsNotImplemented, error_msg); + break; + } +} + +static Mat +histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false) +{ + Mat result; + // Establish the number of bins. + int histSize = maxVal-minVal+1; + // Set the ranges. + float range[] = { static_cast(minVal), static_cast(maxVal+1) }; + const float* histRange = { range }; + // calc histogram + calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false); + // normalize + if(normed) { + result /= (int)src.total(); + } + return result.reshape(1,1); +} + +static Mat histc(InputArray _src, int minVal, int maxVal, bool normed) +{ + Mat src = _src.getMat(); + switch (src.type()) { + case CV_8SC1: + return histc_(Mat_(src), minVal, maxVal, normed); + break; + case CV_8UC1: + return histc_(src, minVal, maxVal, normed); + break; + case CV_16SC1: + return histc_(Mat_(src), minVal, maxVal, normed); + break; + case CV_16UC1: + return histc_(src, minVal, maxVal, normed); + break; + case CV_32SC1: + return histc_(Mat_(src), minVal, maxVal, normed); + break; + case CV_32FC1: + return histc_(src, minVal, maxVal, normed); + break; + default: + CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet."); break; + } + return Mat(); +} + + +static Mat spatial_histogram(InputArray _src, int numPatterns, + int grid_x, int grid_y, bool /*normed*/) +{ + Mat src = _src.getMat(); + // calculate LBP patch size + int width = src.cols/grid_x; + int height = src.rows/grid_y; + // allocate memory for the spatial histogram + Mat result = Mat::zeros(grid_x * grid_y, numPatterns, CV_32FC1); + // return matrix with zeros if no data was given + if(src.empty()) + return result.reshape(1,1); + // initial result_row + int resultRowIdx = 0; + // iterate through grid + for(int i = 0; i < grid_y; i++) { + for(int j = 0; j < grid_x; j++) { + Mat src_cell = Mat(src, Range(i*height,(i+1)*height), Range(j*width,(j+1)*width)); + Mat cell_hist = histc(src_cell, 0, (numPatterns-1), true); + // copy to the result matrix + Mat result_row = result.row(resultRowIdx); + cell_hist.reshape(1,1).convertTo(result_row, CV_32FC1); + // increase row count in result matrix + resultRowIdx++; + } + } + // return result as reshaped feature vector + return result.reshape(1,1); +} + +//------------------------------------------------------------------------------ +// wrapper to cv::elbp (extended local binary patterns) +//------------------------------------------------------------------------------ + +static Mat elbp(InputArray src, int radius, int neighbors) { + Mat dst; + elbp(src, dst, radius, neighbors); + return dst; +} + +void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserveData) { + if(_in_src.kind() != _InputArray::STD_VECTOR_MAT && _in_src.kind() != _InputArray::STD_VECTOR_VECTOR) { + String error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >)."; + CV_Error(Error::StsBadArg, error_message); + } + if(_in_src.total() == 0) { + String error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); + CV_Error(Error::StsUnsupportedFormat, error_message); + } else if(_in_labels.getMat().type() != CV_32SC1) { + String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _in_labels.type()); + CV_Error(Error::StsUnsupportedFormat, error_message); + } + // get the vector of matrices + std::vector src; + _in_src.getMatVector(src); + // get the label matrix + Mat labels = _in_labels.getMat(); + // check if data is well- aligned + if(labels.total() != src.size()) { + String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total()); + CV_Error(Error::StsBadArg, error_message); + } + // if this model should be trained without preserving old data, delete old model data + if(!preserveData) { + _labels.release(); + _histograms.clear(); + } + // append labels to _labels matrix + for(size_t labelIdx = 0; labelIdx < labels.total(); labelIdx++) { + _labels.push_back(labels.at((int)labelIdx)); + } + // store the spatial histograms of the original data + for(size_t sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) { + // calculate lbp image + Mat lbp_image = elbp(src[sampleIdx], _radius, _neighbors); + // get spatial histogram from this lbp image + Mat p = spatial_histogram( + lbp_image, /* lbp_image */ + static_cast(std::pow(2.0, static_cast(_neighbors))), /* number of possible patterns */ + _grid_x, /* grid size x */ + _grid_y, /* grid size y */ + true); + // add to templates + _histograms.push_back(p); + } +} + +void LBPH::predict(InputArray _src, int &minClass, double &minDist) const { + if(_histograms.empty()) { + // throw error if no data (or simply return -1?) + String error_message = "This LBPH model is not computed yet. Did you call the train method?"; + CV_Error(Error::StsBadArg, error_message); + } + Mat src = _src.getMat(); + // get the spatial histogram from input image + Mat lbp_image = elbp(src, _radius, _neighbors); + Mat query = spatial_histogram( + lbp_image, /* lbp_image */ + static_cast(std::pow(2.0, static_cast(_neighbors))), /* number of possible patterns */ + _grid_x, /* grid size x */ + _grid_y, /* grid size y */ + true /* normed histograms */); + // find 1-nearest neighbor + minDist = DBL_MAX; + minClass = -1; + for(size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) { + double dist = compareHist(_histograms[sampleIdx], query, HISTCMP_CHISQR_ALT); + if((dist < minDist) && (dist < _threshold)) { + minDist = dist; + minClass = _labels.at((int) sampleIdx); + } + } +} + +int LBPH::predict(InputArray _src) const { + int label; + double dummy; + predict(_src, label, dummy); + return label; +} + +Ptr createLBPHFaceRecognizer(int radius, int neighbors, + int grid_x, int grid_y, double threshold) +{ + return makePtr(radius, neighbors, grid_x, grid_y, threshold); +} + + +}} diff --git a/modules/face/src/precomp.hpp b/modules/face/src/precomp.hpp index 883088da5..cde365d85 100644 --- a/modules/face/src/precomp.hpp +++ b/modules/face/src/precomp.hpp @@ -43,12 +43,16 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#include "opencv2/face.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/core.hpp" #include "opencv2/core/utility.hpp" #include "opencv2/core/private.hpp" - +#include "opencv2/core/persistence.hpp" #include +#include +#include +#include +#include #endif + diff --git a/modules/line_descriptor/include/opencv2/line_descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor.hpp index d01d1d577..171d29f0c 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor.hpp @@ -116,10 +116,4 @@ generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-b representation of a single LBD. */ -namespace cv -{ -CV_EXPORTS bool initModule_line_descriptor( void ); - -} - #endif diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp index 263af06ea..426b04d16 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp @@ -74,8 +74,6 @@ namespace line_descriptor //! @addtogroup line_descriptor //! @{ -CV_EXPORTS bool initModule_line_descriptor(); - /** @brief A class to represent a line As aformentioned, it is been necessary to design a class that fully stores the information needed to @@ -307,9 +305,6 @@ class CV_EXPORTS BinaryDescriptor : public Algorithm virtual void computeImpl( const Mat& imageSrc, std::vector& keylines, Mat& descriptors, bool returnFloatDescr, bool useDetectionData ) const; - /** function inherited from Algorithm */ - AlgorithmInfo* info() const; - private: /** struct to represent lines extracted from an octave */ struct OctaveLine @@ -911,10 +906,6 @@ void detectImpl( const Mat& imageSrc, std::vector& keylines, int numOct /* matrices for Gaussian pyramids */ std::vector gaussianPyrs; - -protected: -/* function inherited from Algorithm */ -AlgorithmInfo* info() const; }; /** @brief furnishes all functionalities for querying a dataset provided by user or internal to @@ -1068,10 +1059,6 @@ BinaryDescriptorMatcher(); { } -protected: -/** function inherited from Algorithm */ -AlgorithmInfo* info() const; - private: class BucketGroup { diff --git a/modules/line_descriptor/src/line_descriptor_init.cpp b/modules/line_descriptor/src/line_descriptor_init.cpp deleted file mode 100644 index afa6a3cfc..000000000 --- a/modules/line_descriptor/src/line_descriptor_init.cpp +++ /dev/null @@ -1,64 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2014, Biagio Montesano, all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#include "precomp.hpp" - -namespace cv -{ -namespace line_descriptor -{ - -CV_INIT_ALGORITHM( BinaryDescriptor, "BINARY.DESCRIPTOR", ); -CV_INIT_ALGORITHM( BinaryDescriptorMatcher, "BINARY.DESCRIPTOR.MATCHER", ); -CV_INIT_ALGORITHM( LSDDetector, "LSDDETECTOR", ); - -bool initModule_line_descriptor( void ) -{ - bool all = true; - all &= !BinaryDescriptor_info_auto.name().empty(); - all &= !BinaryDescriptorMatcher_info_auto.name().empty(); - all &= !LSDDetector_info_auto.name().empty(); - - return all; -} - -} -} diff --git a/modules/optflow/src/deepflow.cpp b/modules/optflow/src/deepflow.cpp index 334b1d2b9..9e2fa3a63 100644 --- a/modules/optflow/src/deepflow.cpp +++ b/modules/optflow/src/deepflow.cpp @@ -56,8 +56,6 @@ public: void calc( InputArray I0, InputArray I1, InputOutputArray flow ); void collectGarbage(); -// AlgorithmInfo* info() const; - protected: float sigma; // Gaussian smoothing parameter int minSize; // minimal dimension of an image in the pyramid diff --git a/modules/optflow/src/interfaces.cpp b/modules/optflow/src/interfaces.cpp index f11f54ed0..df510ebc3 100644 --- a/modules/optflow/src/interfaces.cpp +++ b/modules/optflow/src/interfaces.cpp @@ -56,7 +56,6 @@ public: OpticalFlowSimpleFlow(); void calc(InputArray I0, InputArray I1, InputOutputArray flow); void collectGarbage(); -// AlgorithmInfo* info() const; protected: int layers; @@ -129,7 +128,6 @@ public: OpticalFlowFarneback(); void calc(InputArray I0, InputArray I1, InputOutputArray flow); void collectGarbage(); -// AlgorithmInfo* info() const; protected: int numLevels; double pyrScale; diff --git a/modules/rgbd/include/opencv2/rgbd.hpp b/modules/rgbd/include/opencv2/rgbd.hpp index 9edd23953..9684bb1c1 100644 --- a/modules/rgbd/include/opencv2/rgbd.hpp +++ b/modules/rgbd/include/opencv2/rgbd.hpp @@ -137,9 +137,6 @@ namespace rgbd ~RgbdNormals(); - AlgorithmInfo* - info() const; - /** Given a set of 3d points in a depth image, compute the normals at each point. * @param points a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S * @param normals a rows x cols x 3 matrix @@ -153,14 +150,13 @@ namespace rgbd void initialize() const; - /** Return the current method in that normal computer - * @return - */ - int - method() const - { - return method_; - } + CV_IMPL_PROPERTY(int, Rows, rows_) + CV_IMPL_PROPERTY(int, Cols, cols_) + CV_IMPL_PROPERTY(int, WindowSize, window_size_) + CV_IMPL_PROPERTY(int, Depth, depth_) + CV_IMPL_PROPERTY_S(cv::Mat, K, K_) + CV_IMPL_PROPERTY(int, Method, method_) + protected: void initialize_normals_impl(int rows, int cols, int depth, const Mat & K, int window_size, int method) const; @@ -204,9 +200,6 @@ namespace rgbd ~DepthCleaner(); - AlgorithmInfo* - info() const; - /** Given a set of 3d points in a depth image, compute the normals at each point. * @param points a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S * @param depth a rows x cols matrix of the cleaned up depth @@ -220,14 +213,10 @@ namespace rgbd void initialize() const; - /** Return the current method in that normal computer - * @return - */ - int - method() const - { - return method_; - } + CV_IMPL_PROPERTY(int, WindowSize, window_size_) + CV_IMPL_PROPERTY(int, Depth, depth_) + CV_IMPL_PROPERTY(int, Method, method_) + protected: void initialize_cleaner_impl() const; @@ -316,8 +305,14 @@ namespace rgbd void operator()(InputArray points3d, OutputArray mask, OutputArray plane_coefficients); - AlgorithmInfo* - info() const; + CV_IMPL_PROPERTY(int, BlockSize, block_size_) + CV_IMPL_PROPERTY(int, MinSize, min_size_) + CV_IMPL_PROPERTY(int, Method, method_) + CV_IMPL_PROPERTY(double, Threshold, threshold_) + CV_IMPL_PROPERTY(double, SensorErrorA, sensor_error_a_) + CV_IMPL_PROPERTY(double, SensorErrorB, sensor_error_b_) + CV_IMPL_PROPERTY(double, SensorErrorC, sensor_error_c_) + private: /** The method to use to compute the planes */ int method_; @@ -469,6 +464,19 @@ namespace rgbd */ virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; + static Ptr create(const String & odometryType); + + //TODO: which properties are common for all Odometry successors? + CV_PURE_PROPERTY_S(cv::Mat, CameraMatrix) +// CV_PURE_PROPERTY(double, MinDepth) +// CV_PURE_PROPERTY(double, MaxDepth) +// CV_PURE_PROPERTY(double, MaxDepthDiff) +// CV_PURE_PROPERTY_S(cv::Mat, IterationCounts) +// CV_PURE_PROPERTY(double, MaxPointsPart) + CV_PURE_PROPERTY(int, TransformType) +// CV_PURE_PROPERTY(double, MaxTranslation) +// CV_PURE_PROPERTY(double, MaxRotation) + protected: virtual void checkParams() const = 0; @@ -504,8 +512,16 @@ namespace rgbd virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - AlgorithmInfo* - info() const; + CV_IMPL_PROPERTY_S(cv::Mat, CameraMatrix, cameraMatrix) + CV_IMPL_PROPERTY(double, MinDepth, minDepth) + CV_IMPL_PROPERTY(double, MaxDepth, maxDepth) + CV_IMPL_PROPERTY(double, MaxDepthDiff, maxDepthDiff) + CV_IMPL_PROPERTY_S(cv::Mat, IterationCounts, iterCounts) + CV_IMPL_PROPERTY_S(cv::Mat, MinGradientMagnitudes, minGradientMagnitudes) + CV_IMPL_PROPERTY(double, MaxPointsPart, maxPointsPart) + CV_IMPL_PROPERTY(int, TransformType, transformType) + CV_IMPL_PROPERTY(double, MaxTranslation, maxTranslation) + CV_IMPL_PROPERTY(double, MaxRotation, maxRotation) protected: virtual void @@ -553,8 +569,16 @@ namespace rgbd virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - AlgorithmInfo* - info() const; + CV_IMPL_PROPERTY_S(cv::Mat, CameraMatrix, cameraMatrix) + CV_IMPL_PROPERTY(double, MinDepth, minDepth) + CV_IMPL_PROPERTY(double, MaxDepth, maxDepth) + CV_IMPL_PROPERTY(double, MaxDepthDiff, maxDepthDiff) + CV_IMPL_PROPERTY_S(cv::Mat, IterationCounts, iterCounts) + CV_IMPL_PROPERTY(double, MaxPointsPart, maxPointsPart) + CV_IMPL_PROPERTY(int, TransformType, transformType) + CV_IMPL_PROPERTY(double, MaxTranslation, maxTranslation) + CV_IMPL_PROPERTY(double, MaxRotation, maxRotation) + CV_IMPL_PROPERTY_RO(Ptr, NormalsComputer, normalsComputer) protected: virtual void @@ -607,8 +631,17 @@ namespace rgbd virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - AlgorithmInfo* - info() const; + CV_IMPL_PROPERTY_S(cv::Mat, CameraMatrix, cameraMatrix) + CV_IMPL_PROPERTY(double, MinDepth, minDepth) + CV_IMPL_PROPERTY(double, MaxDepth, maxDepth) + CV_IMPL_PROPERTY(double, MaxDepthDiff, maxDepthDiff) + CV_IMPL_PROPERTY(double, MaxPointsPart, maxPointsPart) + CV_IMPL_PROPERTY_S(cv::Mat, IterationCounts, iterCounts) + CV_IMPL_PROPERTY_S(cv::Mat, MinGradientMagnitudes, minGradientMagnitudes) + CV_IMPL_PROPERTY(int, TransformType, transformType) + CV_IMPL_PROPERTY(double, MaxTranslation, maxTranslation) + CV_IMPL_PROPERTY(double, MaxRotation, maxRotation) + CV_IMPL_PROPERTY_RO(Ptr, NormalsComputer, normalsComputer) protected: virtual void @@ -669,3 +702,4 @@ namespace rgbd #endif /* End of file. */ + diff --git a/modules/rgbd/samples/odometry_evaluation.cpp b/modules/rgbd/samples/odometry_evaluation.cpp index 07dbb2acd..b52c9287a 100644 --- a/modules/rgbd/samples/odometry_evaluation.cpp +++ b/modules/rgbd/samples/odometry_evaluation.cpp @@ -173,13 +173,13 @@ int main(int argc, char** argv) Ptr frame_prev = Ptr(new OdometryFrame()), frame_curr = Ptr(new OdometryFrame()); - Ptr odometry = Algorithm::create("RGBD." + string(argv[3]) + "Odometry"); + Ptr odometry = Odometry::create("RGBD." + string(argv[3]) + "Odometry"); if(odometry.empty()) { cout << "Can not create Odometry algorithm. Check the passed odometry name." << endl; return -1; } - odometry->set("cameraMatrix", cameraMatrix); + odometry->setCameraMatrix(cameraMatrix); MyTickMeter gtm; int count = 0; diff --git a/modules/rgbd/src/odometry.cpp b/modules/rgbd/src/odometry.cpp index 4d763d152..17b3dd3d4 100644 --- a/modules/rgbd/src/odometry.cpp +++ b/modules/rgbd/src/odometry.cpp @@ -1069,6 +1069,17 @@ Size Odometry::prepareFrameCache(Ptr &frame, int /*cacheType*/) c return Size(); } +Ptr Odometry::create(const String & odometryType) +{ + if (odometryType == "RgbdOdometry") + return makePtr(); + else if (odometryType == "ICPOdometry") + return makePtr(); + else if (odometryType == "RgbdICPOdometry") + return makePtr(); + return Ptr(); +} + // RgbdOdometry::RgbdOdometry() : minDepth(DEFAULT_MIN_DEPTH()), @@ -1229,10 +1240,15 @@ Size ICPOdometry::prepareFrameCache(Ptr& frame, int cacheType) co else { if(normalsComputer.empty() || - normalsComputer->get("rows") != frame->depth.rows || - normalsComputer->get("cols") != frame->depth.cols || - norm(normalsComputer->get("K"), cameraMatrix) > FLT_EPSILON) - normalsComputer = Ptr(new RgbdNormals(frame->depth.rows, frame->depth.cols, frame->depth.depth(), cameraMatrix, normalWinSize, normalMethod)); + normalsComputer->getRows() != frame->depth.rows || + normalsComputer->getCols() != frame->depth.cols || + norm(normalsComputer->getK(), cameraMatrix) > FLT_EPSILON) + normalsComputer = makePtr(frame->depth.rows, + frame->depth.cols, + frame->depth.depth(), + cameraMatrix, + normalWinSize, + normalMethod); (*normalsComputer)(frame->pyramidCloud[0], frame->normals); } @@ -1338,10 +1354,15 @@ Size RgbdICPOdometry::prepareFrameCache(Ptr& frame, int cacheType else { if(normalsComputer.empty() || - normalsComputer->get("rows") != frame->depth.rows || - normalsComputer->get("cols") != frame->depth.cols || - norm(normalsComputer->get("K"), cameraMatrix) > FLT_EPSILON) - normalsComputer = Ptr(new RgbdNormals(frame->depth.rows, frame->depth.cols, frame->depth.depth(), cameraMatrix, normalWinSize, normalMethod)); + normalsComputer->getRows() != frame->depth.rows || + normalsComputer->getCols() != frame->depth.cols || + norm(normalsComputer->getK(), cameraMatrix) > FLT_EPSILON) + normalsComputer = makePtr(frame->depth.rows, + frame->depth.cols, + frame->depth.depth(), + cameraMatrix, + normalWinSize, + normalMethod); (*normalsComputer)(frame->pyramidCloud[0], frame->normals); } diff --git a/modules/rgbd/src/rgbd_init.cpp b/modules/rgbd/src/rgbd_init.cpp deleted file mode 100644 index e1f14c33b..000000000 --- a/modules/rgbd/src/rgbd_init.cpp +++ /dev/null @@ -1,119 +0,0 @@ -/* - * Software License Agreement (BSD License) - * - * Copyright (c) 2012, Willow Garage, Inc. - * All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above - * copyright notice, this list of conditions and the following - * disclaimer in the documentation and/or other materials provided - * with the distribution. - * * Neither the name of Willow Garage, Inc. nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS - * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE - * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, - * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN - * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - * POSSIBILITY OF SUCH DAMAGE. - * - */ - -#include -#include -#include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" - -namespace cv -{ -namespace rgbd -{ - CV_INIT_ALGORITHM(DepthCleaner, "RGBD.DepthCleaner", - obj.info()->addParam(obj, "window_size", obj.window_size_); - obj.info()->addParam(obj, "depth", obj.depth_); - obj.info()->addParam(obj, "method", obj.method_)) - - CV_INIT_ALGORITHM(RgbdNormals, "RGBD.RgbdNormals", - obj.info()->addParam(obj, "rows", obj.rows_); - obj.info()->addParam(obj, "cols", obj.cols_); - obj.info()->addParam(obj, "window_size", obj.window_size_); - obj.info()->addParam(obj, "depth", obj.depth_); - obj.info()->addParam(obj, "K", obj.K_); - obj.info()->addParam(obj, "method", obj.method_)) - - CV_INIT_ALGORITHM(RgbdPlane, "RGBD.RgbdPlane", - obj.info()->addParam(obj, "block_size", obj.block_size_); - obj.info()->addParam(obj, "min_size", obj.min_size_); - obj.info()->addParam(obj, "method", obj.method_); - obj.info()->addParam(obj, "threshold", obj.threshold_); - obj.info()->addParam(obj, "sensor_error_a", obj.sensor_error_a_); - obj.info()->addParam(obj, "sensor_error_b", obj.sensor_error_b_); - obj.info()->addParam(obj, "sensor_error_c", obj.sensor_error_c_)) - - CV_INIT_ALGORITHM(RgbdOdometry, "RGBD.RgbdOdometry", - obj.info()->addParam(obj, "cameraMatrix", obj.cameraMatrix); - obj.info()->addParam(obj, "minDepth", obj.minDepth); - obj.info()->addParam(obj, "maxDepth", obj.maxDepth); - obj.info()->addParam(obj, "maxDepthDiff", obj.maxDepthDiff); - obj.info()->addParam(obj, "iterCounts", obj.iterCounts); - obj.info()->addParam(obj, "minGradientMagnitudes", obj.minGradientMagnitudes); - obj.info()->addParam(obj, "maxPointsPart", obj.maxPointsPart); - obj.info()->addParam(obj, "transformType", obj.transformType); - obj.info()->addParam(obj, "maxTranslation", obj.maxTranslation); - obj.info()->addParam(obj, "maxRotation", obj.maxRotation);) - - CV_INIT_ALGORITHM(ICPOdometry, "RGBD.ICPOdometry", - obj.info()->addParam(obj, "cameraMatrix", obj.cameraMatrix); - obj.info()->addParam(obj, "minDepth", obj.minDepth); - obj.info()->addParam(obj, "maxDepth", obj.maxDepth); - obj.info()->addParam(obj, "maxDepthDiff", obj.maxDepthDiff); - obj.info()->addParam(obj, "maxPointsPart", obj.maxPointsPart); - obj.info()->addParam(obj, "iterCounts", obj.iterCounts); - obj.info()->addParam(obj, "transformType", obj.transformType); - obj.info()->addParam(obj, "maxTranslation", obj.maxTranslation); - obj.info()->addParam(obj, "maxRotation", obj.maxRotation); - obj.info()->addParam(obj, "normalsComputer", obj.normalsComputer, true, NULL, NULL);) - - CV_INIT_ALGORITHM(RgbdICPOdometry, "RGBD.RgbdICPOdometry", - obj.info()->addParam(obj, "cameraMatrix", obj.cameraMatrix); - obj.info()->addParam(obj, "minDepth", obj.minDepth); - obj.info()->addParam(obj, "maxDepth", obj.maxDepth); - obj.info()->addParam(obj, "maxDepthDiff", obj.maxDepthDiff); - obj.info()->addParam(obj, "maxPointsPart", obj.maxPointsPart); - obj.info()->addParam(obj, "iterCounts", obj.iterCounts); - obj.info()->addParam(obj, "minGradientMagnitudes", obj.minGradientMagnitudes); - obj.info()->addParam(obj, "transformType", obj.transformType); - obj.info()->addParam(obj, "maxTranslation", obj.maxTranslation); - obj.info()->addParam(obj, "maxRotation", obj.maxRotation); - obj.info()->addParam(obj, "normalsComputer", obj.normalsComputer, true, NULL, NULL);) - - bool - initModule_rgbd(void); - bool - initModule_rgbd(void) - { - bool all = true; - all &= !RgbdNormals_info_auto.name().empty(); - all &= !RgbdPlane_info_auto.name().empty(); - all &= !RgbdOdometry_info_auto.name().empty(); - all &= !ICPOdometry_info_auto.name().empty(); - all &= !RgbdICPOdometry_info_auto.name().empty(); - return all; - } -} -} - diff --git a/modules/rgbd/test/test_normal.cpp b/modules/rgbd/test/test_normal.cpp index 278f24415..95e3251e2 100644 --- a/modules/rgbd/test/test_normal.cpp +++ b/modules/rgbd/test/test_normal.cpp @@ -309,7 +309,7 @@ protected: TickMeter tm; tm.start(); Mat in_normals; - if (normals_computer.method() == RgbdNormals::RGBD_NORMALS_METHOD_LINEMOD) + if (normals_computer.getMethod() == RgbdNormals::RGBD_NORMALS_METHOD_LINEMOD) { std::vector channels; split(points3d, channels); diff --git a/modules/rgbd/test/test_odometry.cpp b/modules/rgbd/test/test_odometry.cpp index b73c74ded..c95720e1e 100644 --- a/modules/rgbd/test/test_odometry.cpp +++ b/modules/rgbd/test/test_odometry.cpp @@ -241,7 +241,7 @@ void CV_OdometryTest::run(int) if(!readData(image, depth)) return; - odometry->set("cameraMatrix", K); + odometry->setCameraMatrix(K); Mat calcRt; @@ -338,18 +338,18 @@ void CV_OdometryTest::run(int) TEST(RGBD_Odometry_Rgbd, algorithmic) { - cv::rgbd::CV_OdometryTest test(cv::Algorithm::create("RGBD.RgbdOdometry"), 0.99, 0.94); + cv::rgbd::CV_OdometryTest test(cv::rgbd::Odometry::create("RGBD.RgbdOdometry"), 0.99, 0.94); test.safe_run(); } TEST(RGBD_Odometry_ICP, algorithmic) { - cv::rgbd::CV_OdometryTest test(cv::Algorithm::create("RGBD.ICPOdometry"), 0.99, 0.99); + cv::rgbd::CV_OdometryTest test(cv::rgbd::Odometry::create("RGBD.ICPOdometry"), 0.99, 0.99); test.safe_run(); } TEST(RGBD_Odometry_RgbdICP, algorithmic) { - cv::rgbd::CV_OdometryTest test(cv::Algorithm::create("RGBD.RgbdICPOdometry"), 0.99, 0.99); + cv::rgbd::CV_OdometryTest test(cv::rgbd::Odometry::create("RGBD.RgbdICPOdometry"), 0.99, 0.99); test.safe_run(); } diff --git a/modules/saliency/include/opencv2/saliency.hpp b/modules/saliency/include/opencv2/saliency.hpp index c86e11dd3..56bfe8a04 100644 --- a/modules/saliency/include/opencv2/saliency.hpp +++ b/modules/saliency/include/opencv2/saliency.hpp @@ -80,12 +80,4 @@ To see how API works, try tracker demo: */ -namespace cv -{ -namespace saliency -{ -CV_EXPORTS bool initModule_saliency( void ); -} -} - #endif //__OPENCV_SALIENCY_HPP__ diff --git a/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp b/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp index a826ba1b6..326d91e20 100644 --- a/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp +++ b/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp @@ -47,6 +47,7 @@ #include #include #include +#include "opencv2/core.hpp" namespace cv { @@ -75,9 +76,11 @@ public: void read( const FileNode& fn ); void write( FileStorage& fs ) const; + CV_IMPL_PROPERTY(int, ImageWidth, resImWidth) + CV_IMPL_PROPERTY(int, ImageHeight, resImHeight) + protected: bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ); - AlgorithmInfo* info() const; int resImWidth; int resImHeight; @@ -111,6 +114,9 @@ public: */ bool init(); + CV_IMPL_PROPERTY(int, ImageWidth, imageWidth) + CV_IMPL_PROPERTY(int, ImageHeight, imageHeight) + protected: /** @brief Performs all the operations and calls all internal functions necessary for the accomplishment of the Fast Self-tuning Background Subtraction Algorithm algorithm. @@ -121,7 +127,6 @@ protected: stream). */ bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ); - AlgorithmInfo* info() const; private: @@ -200,6 +205,10 @@ public: */ void setBBResDir( std::string resultsDir ); + CV_IMPL_PROPERTY(double, Base, _base) + CV_IMPL_PROPERTY(int, NSS, _NSS) + CV_IMPL_PROPERTY(int, W, _W) + protected: /** @brief Performs all the operations and calls all internal functions necessary for the accomplishment of the Binarized normed gradients algorithm. @@ -211,7 +220,6 @@ protected: represented by a *Vec4i* for (minX, minY, maxX, maxY). */ bool computeSaliencyImpl( const InputArray image, OutputArray objectnessBoundingBox ); - AlgorithmInfo* info() const; private: diff --git a/modules/saliency/src/saliency.cpp b/modules/saliency/src/saliency.cpp index f05691568..5e86f46df 100644 --- a/modules/saliency/src/saliency.cpp +++ b/modules/saliency/src/saliency.cpp @@ -53,9 +53,13 @@ Saliency::~Saliency() Ptr Saliency::create( const String& saliencyType ) { - - return Algorithm::create( "SALIENCY." + saliencyType ); - + if (saliencyType == "SPECTRAL_RESIDUAL") + return makePtr(); + else if (saliencyType == "BING") + return makePtr(); + else if (saliencyType == "BinWangApr2014") + return makePtr(); + return Ptr(); } bool Saliency::computeSaliency( const InputArray image, OutputArray saliencyMap ) diff --git a/modules/saliency/src/saliency_init.cpp b/modules/saliency/src/saliency_init.cpp deleted file mode 100644 index 81f1cc56e..000000000 --- a/modules/saliency/src/saliency_init.cpp +++ /dev/null @@ -1,73 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2014, OpenCV Foundation, all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#include "precomp.hpp" -#include "opencv2/saliency.hpp" - -namespace cv -{ -namespace saliency -{ - -CV_INIT_ALGORITHM( StaticSaliencySpectralResidual, "SALIENCY.SPECTRAL_RESIDUAL", - obj.info()->addParam( obj, "resImWidth", obj.resImWidth); obj.info()->addParam( obj, "resImHeight", obj.resImHeight) ); - - -CV_INIT_ALGORITHM( - ObjectnessBING, "SALIENCY.BING", - obj.info()->addParam(obj, "_base", obj._base); obj.info()->addParam(obj, "_NSS", obj._NSS); obj.info()->addParam(obj, "_W", obj._W) ); - -CV_INIT_ALGORITHM( MotionSaliencyBinWangApr2014, "SALIENCY.BinWangApr2014", - obj.info()->addParam( obj, "imageWidth", obj.imageWidth); obj.info()->addParam( obj, "imageHeight", obj.imageHeight) ); - -bool initModule_saliency( void ) -{ - bool all = true; - all &= !StaticSaliencySpectralResidual_info_auto.name().empty(); - //all &= !MotionSaliencySuBSENSE_info_auto.name().empty(); - all &= !MotionSaliencyBinWangApr2014_info_auto.name().empty(); - all &= !ObjectnessBING_info_auto.name().empty(); - - return all; -} - -} // namespace saliency -} // namespace cv diff --git a/modules/tracking/include/opencv2/tracking.hpp b/modules/tracking/include/opencv2/tracking.hpp index c4ab41fdd..f3e0ed001 100644 --- a/modules/tracking/include/opencv2/tracking.hpp +++ b/modules/tracking/include/opencv2/tracking.hpp @@ -327,10 +327,6 @@ The first argument is the name of the tracker and the second is a video source. */ -namespace cv -{ -CV_EXPORTS bool initModule_tracking(void); -} - #include "opencv2/tracking/tracker.hpp" + #endif //__OPENCV_TRACKING_LENLEN diff --git a/modules/tracking/include/opencv2/tracking/tracker.hpp b/modules/tracking/include/opencv2/tracking/tracker.hpp index de1a94fff..324112711 100644 --- a/modules/tracking/include/opencv2/tracking/tracker.hpp +++ b/modules/tracking/include/opencv2/tracking/tracker.hpp @@ -574,7 +574,6 @@ class CV_EXPORTS_W Tracker : public virtual Algorithm Ptr featureSet; Ptr sampler; Ptr model; - virtual AlgorithmInfo* info() const; }; /************************************ Specific TrackerStateEstimator Classes ************************************/ diff --git a/modules/tracking/src/tracker.cpp b/modules/tracking/src/tracker.cpp index ca6da2357..58a46b150 100644 --- a/modules/tracking/src/tracker.cpp +++ b/modules/tracking/src/tracker.cpp @@ -104,10 +104,6 @@ bool Tracker::update( const Mat& image, Rect2d& boundingBox ) return updateImpl( image, boundingBox ); } -AlgorithmInfo* Tracker::info() const{ - return 0; -} - Ptr Tracker::create( const String& trackerType ) { BOILERPLATE_CODE("MIL",TrackerMIL); diff --git a/modules/tracking/src/tracking_init.cpp b/modules/tracking/src/tracking_init.cpp deleted file mode 100644 index ffe31dfc4..000000000 --- a/modules/tracking/src/tracking_init.cpp +++ /dev/null @@ -1,53 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2013, OpenCV Foundation, all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#include "precomp.hpp" -#include "opencv2/tracking.hpp" - -namespace cv -{ - -bool initModule_tracking(void) -{ - return true; -} - -} diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp index 3dc57c7ff..16039a5a7 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d/cuda.hpp @@ -152,7 +152,7 @@ public: //! max keypoints = min(keypointsRatio * img.size().area(), 65535) float keypointsRatio; - GpuMat sum, mask1, maskSum, intBuffer; + GpuMat sum, mask1, maskSum; GpuMat det, trace; diff --git a/modules/xfeatures2d/samples/bagofwords_classification.cpp b/modules/xfeatures2d/samples/bagofwords_classification.cpp index 3ad77ba66..b281531ea 100644 --- a/modules/xfeatures2d/samples/bagofwords_classification.cpp +++ b/modules/xfeatures2d/samples/bagofwords_classification.cpp @@ -2327,14 +2327,14 @@ static void removeBowImageDescriptorsByCount( vector& images, vector & svm, const Mat& responses, bool balanceClasses ) { int pos_ex = countNonZero(responses == 1); int neg_ex = countNonZero(responses == -1); cout << pos_ex << " positive training samples; " << neg_ex << " negative training samples" << endl; - svmParams.svmType = SVM::C_SVC; - svmParams.kernelType = SVM::RBF; + svm->setType(SVM::C_SVC); + svm->setKernel(SVM::RBF); if( balanceClasses ) { Mat class_wts( 2, 1, CV_32FC1 ); @@ -2351,8 +2351,7 @@ static void setSVMParams( SVM::Params& svmParams, Mat& class_wts_cv, const Mat& class_wts.at(0) = static_cast(neg_ex)/static_cast(pos_ex+neg_ex); class_wts.at(1) = static_cast(pos_ex)/static_cast(pos_ex+neg_ex); } - class_wts_cv = class_wts; - svmParams.classWeights = class_wts_cv; + svm->setClassWeights(class_wts); } } @@ -2440,10 +2439,8 @@ static Ptr trainSVMClassifier( const SVMTrainParamsExt& svmParamsExt, const } cout << "TRAINING SVM FOR CLASS ..." << objClassName << "..." << endl; - SVM::Params svmParams; - Mat class_wts_cv; - setSVMParams( svmParams, class_wts_cv, responses, svmParamsExt.balanceClasses ); - svm = SVM::create(svmParams); + svm = SVM::create(); + setSVMParams( svm, responses, svmParamsExt.balanceClasses ); ParamGrid c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid; setSVMTrainAutoParams( c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid ); diff --git a/modules/xfeatures2d/src/surf.cuda.cpp b/modules/xfeatures2d/src/surf.cuda.cpp index 208156c98..6f7a0f45f 100644 --- a/modules/xfeatures2d/src/surf.cuda.cpp +++ b/modules/xfeatures2d/src/surf.cuda.cpp @@ -146,13 +146,13 @@ namespace bindImgTex(img); - cuda::integral(img, surf_.sum, surf_.intBuffer); + cuda::integral(img, surf_.sum); sumOffset = bindSumTex(surf_.sum); if (use_mask) { cuda::min(mask, 1.0, surf_.mask1); - cuda::integral(surf_.mask1, surf_.maskSum, surf_.intBuffer); + cuda::integral(surf_.mask1, surf_.maskSum); maskOffset = bindMaskSumTex(surf_.maskSum); } } @@ -425,7 +425,6 @@ void cv::cuda::SURF_CUDA::releaseMemory() sum.release(); mask1.release(); maskSum.release(); - intBuffer.release(); det.release(); trace.release(); maxPosBuffer.release(); diff --git a/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp index 60132e0f2..f9611a16a 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp @@ -216,6 +216,13 @@ public: CV_WRAP virtual void collectGarbage() = 0; CV_WRAP static Ptr create(); + + CV_PURE_PROPERTY(double, SigmaS) + CV_PURE_PROPERTY(double, SigmaR) + CV_PURE_PROPERTY(int, TreeHeight) + CV_PURE_PROPERTY(int, PCAIterations) + CV_PURE_PROPERTY(bool, AdjustOutliers) + CV_PURE_PROPERTY(bool, UseRNG) }; /** @brief Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines. diff --git a/modules/ximgproc/src/adaptive_manifold_filter_n.cpp b/modules/ximgproc/src/adaptive_manifold_filter_n.cpp index 98ee98f3a..3d400a4bc 100644 --- a/modules/ximgproc/src/adaptive_manifold_filter_n.cpp +++ b/modules/ximgproc/src/adaptive_manifold_filter_n.cpp @@ -107,14 +107,19 @@ static void splitChannels(InputArrayOfArrays src, vector& dst) class AdaptiveManifoldFilterN : public AdaptiveManifoldFilter { public: - AlgorithmInfo* info() const; - AdaptiveManifoldFilterN(); void filter(InputArray src, OutputArray dst, InputArray joint); void collectGarbage(); + CV_IMPL_PROPERTY(double, SigmaS, sigma_s_) + CV_IMPL_PROPERTY(double, SigmaR, sigma_r_) + CV_IMPL_PROPERTY(int, TreeHeight, tree_height_) + CV_IMPL_PROPERTY(int, PCAIterations, num_pca_iterations_) + CV_IMPL_PROPERTY(bool, AdjustOutliers, adjust_outliers_) + CV_IMPL_PROPERTY(bool, UseRNG, useRNG) + protected: bool adjust_outliers_; @@ -261,14 +266,6 @@ private: /*Parallelization routines*/ }; -CV_INIT_ALGORITHM(AdaptiveManifoldFilterN, "AdaptiveManifoldFilter", - obj.info()->addParam(obj, "sigma_s", obj.sigma_s_, false, 0, 0, "Filter spatial standard deviation"); - obj.info()->addParam(obj, "sigma_r", obj.sigma_r_, false, 0, 0, "Filter range standard deviation"); - obj.info()->addParam(obj, "tree_height", obj.tree_height_, false, 0, 0, "Height of the manifold tree (default = -1 : automatically computed)"); - obj.info()->addParam(obj, "num_pca_iterations", obj.num_pca_iterations_, false, 0, 0, "Number of iterations to computed the eigenvector v1"); - obj.info()->addParam(obj, "adjust_outliers", obj.adjust_outliers_, false, 0, 0, "Specify adjust outliers using Eq. 9 or not"); - obj.info()->addParam(obj, "use_RNG", obj.useRNG, false, 0, 0, "Specify use random to compute eigenvector or not");) - AdaptiveManifoldFilterN::AdaptiveManifoldFilterN() { sigma_s_ = 16.0; @@ -852,19 +849,17 @@ Ptr AdaptiveManifoldFilter::create() return Ptr(new AdaptiveManifoldFilterN()); } -CV_EXPORTS_W Ptr createAMFilter(double sigma_s, double sigma_r, bool adjust_outliers) { Ptr amf(new AdaptiveManifoldFilterN()); - amf->set("sigma_s", sigma_s); - amf->set("sigma_r", sigma_r); - amf->set("adjust_outliers", adjust_outliers); + amf->setSigmaS(sigma_s); + amf->setSigmaR(sigma_r); + amf->setAdjustOutliers(adjust_outliers); return amf; } -CV_EXPORTS_W void amFilter(InputArray joint, InputArray src, OutputArray dst, double sigma_s, double sigma_r, bool adjust_outliers) { Ptr amf = createAMFilter(sigma_s, sigma_r, adjust_outliers); @@ -872,4 +867,4 @@ void amFilter(InputArray joint, InputArray src, OutputArray dst, double sigma_s, } } -} \ No newline at end of file +} diff --git a/modules/ximgproc/test/test_adaptive_manifold.cpp b/modules/ximgproc/test/test_adaptive_manifold.cpp index a8970041c..12f9686b4 100644 --- a/modules/ximgproc/test/test_adaptive_manifold.cpp +++ b/modules/ximgproc/test/test_adaptive_manifold.cpp @@ -139,7 +139,7 @@ TEST(AdaptiveManifoldTest, AuthorsReferenceAccuracy) Mat res; Ptr amf = createAMFilter(sigma_s, sigma_r, false); - amf->setBool("use_RNG", false); + amf->setUseRNG(false); amf->filter(srcImg, res, srcImg); amf->collectGarbage(); @@ -155,7 +155,7 @@ TEST(AdaptiveManifoldTest, AuthorsReferenceAccuracy) Mat res; Ptr amf = createAMFilter(sigma_s, sigma_r, true); - amf->setBool("use_RNG", false); + amf->setUseRNG(false); amf->filter(srcImg, res, srcImg); amf->collectGarbage(); @@ -216,4 +216,4 @@ INSTANTIATE_TEST_CASE_P(TypicalSet, AdaptiveManifoldRefImplTest, Values("cv/shared/lena.png", "cv/edgefilter/kodim23.png", "cv/npr/test4.png") )); -} \ No newline at end of file +} diff --git a/modules/ximgproc/test/test_adaptive_manifold_ref_impl.cpp b/modules/ximgproc/test/test_adaptive_manifold_ref_impl.cpp index b71a33e4c..70d329e8e 100644 --- a/modules/ximgproc/test/test_adaptive_manifold_ref_impl.cpp +++ b/modules/ximgproc/test/test_adaptive_manifold_ref_impl.cpp @@ -179,14 +179,19 @@ namespace class AdaptiveManifoldFilterRefImpl : public AdaptiveManifoldFilter { public: - AlgorithmInfo* info() const; - AdaptiveManifoldFilterRefImpl(); void filter(InputArray src, OutputArray dst, InputArray joint); void collectGarbage(); + CV_IMPL_PROPERTY(double, SigmaS, sigma_s_) + CV_IMPL_PROPERTY(double, SigmaR, sigma_r_) + CV_IMPL_PROPERTY(int, TreeHeight, tree_height_) + CV_IMPL_PROPERTY(int, PCAIterations, num_pca_iterations_) + CV_IMPL_PROPERTY(bool, AdjustOutliers, adjust_outliers_) + CV_IMPL_PROPERTY(bool, UseRNG, useRNG) + protected: bool adjust_outliers_; double sigma_s_; @@ -237,14 +242,6 @@ namespace min_pixel_dist_to_manifold_squared_.release(); } - CV_INIT_ALGORITHM(AdaptiveManifoldFilterRefImpl, "AdaptiveManifoldFilterRefImpl", - obj.info()->addParam(obj, "sigma_s", obj.sigma_s_, false, 0, 0, "Filter spatial standard deviation"); - obj.info()->addParam(obj, "sigma_r", obj.sigma_r_, false, 0, 0, "Filter range standard deviation"); - obj.info()->addParam(obj, "tree_height", obj.tree_height_, false, 0, 0, "Height of the manifold tree (default = -1 : automatically computed)"); - obj.info()->addParam(obj, "num_pca_iterations", obj.num_pca_iterations_, false, 0, 0, "Number of iterations to computed the eigenvector v1"); - obj.info()->addParam(obj, "adjust_outliers", obj.adjust_outliers_, false, 0, 0, "Specify supress outliers using Eq. 9 or not"); - obj.info()->addParam(obj, "use_RNG", obj.useRNG, false, 0, 0, "Specify use random to compute eigenvector or not");) - inline double Log2(double n) { return log(n) / log(2.0); @@ -974,11 +971,11 @@ Ptr createAMFilterRefImpl(double sigma_s, double sigma_r { Ptr amf(new AdaptiveManifoldFilterRefImpl()); - amf->set("sigma_s", sigma_s); - amf->set("sigma_r", sigma_r); - amf->set("adjust_outliers", adjust_outliers); + amf->setSigmaS(sigma_s); + amf->setSigmaR(sigma_r); + amf->setAdjustOutliers(adjust_outliers); return amf; } -} \ No newline at end of file +} diff --git a/modules/xobjdetect/include/opencv2/xobjdetect.hpp b/modules/xobjdetect/include/opencv2/xobjdetect.hpp index 3086d52eb..4d05ea97c 100644 --- a/modules/xobjdetect/include/opencv2/xobjdetect.hpp +++ b/modules/xobjdetect/include/opencv2/xobjdetect.hpp @@ -153,17 +153,6 @@ public: */ virtual float predict( const Ptr& feature_evaluator) const = 0; - - /** @brief Write WaldBoost to FileStorage - @param fs FileStorage for output - */ - virtual void write(FileStorage& fs) const = 0; - - /** @brief Write WaldBoost to FileNode - - @param node FileNode for reading - */ - virtual void read(const FileNode& node) = 0; }; /** @brief Construct WaldBoost object. From 61f36de542d232e4d80757ac6142296dac0850bf Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Mon, 1 Dec 2014 19:20:12 +0300 Subject: [PATCH 14/32] Doxygen tutorials support --- modules/bgsegm/include/opencv2/bgsegm.hpp | 6 +- modules/bioinspired/doc/retina.markdown | 57 +-- .../images/retina_TreeHdr_retina.jpg | Bin 0 -> 150688 bytes .../tutorials/images/retina_TreeHdr_small.jpg | Bin 0 -> 50051 bytes .../tutorials/images/studentsSample_input.jpg | Bin 0 -> 80282 bytes .../tutorials/images/studentsSample_magno.jpg | Bin 0 -> 28228 bytes .../tutorials/images/studentsSample_parvo.jpg | Bin 0 -> 69443 bytes .../tutorials/retina_model.markdown | 476 ++++++++++++++++++ .../tutorials/images/01_overview_single.jpg | Bin 0 -> 46273 bytes .../tutorials/images/02_single_image_view.jpg | Bin 0 -> 86790 bytes .../cvv/tutorials/images/03_overview_two.jpg | Bin 0 -> 54117 bytes .../images/04_default_filter_view.jpg | Bin 0 -> 101265 bytes .../05_default_filter_view_high_zoom.jpg | Bin 0 -> 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modules/bioinspired/tutorials/images/retina_TreeHdr_small.jpg create mode 100644 modules/bioinspired/tutorials/images/studentsSample_input.jpg create mode 100644 modules/bioinspired/tutorials/images/studentsSample_magno.jpg create mode 100644 modules/bioinspired/tutorials/images/studentsSample_parvo.jpg create mode 100644 modules/bioinspired/tutorials/retina_model.markdown create mode 100644 modules/cvv/tutorials/images/01_overview_single.jpg create mode 100644 modules/cvv/tutorials/images/02_single_image_view.jpg create mode 100644 modules/cvv/tutorials/images/03_overview_two.jpg create mode 100644 modules/cvv/tutorials/images/04_default_filter_view.jpg create mode 100644 modules/cvv/tutorials/images/05_default_filter_view_high_zoom.jpg create mode 100644 modules/cvv/tutorials/images/06_default_filter_view_edges.jpg create mode 100644 modules/cvv/tutorials/images/07_dual_filter_view_edges.jpg create mode 100644 modules/cvv/tutorials/images/08_overview_all.jpg create mode 100644 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modules/ximgproc/tutorials/training.markdown diff --git a/modules/bgsegm/include/opencv2/bgsegm.hpp b/modules/bgsegm/include/opencv2/bgsegm.hpp index ff1e15357..5a4ae3ff8 100644 --- a/modules/bgsegm/include/opencv2/bgsegm.hpp +++ b/modules/bgsegm/include/opencv2/bgsegm.hpp @@ -57,7 +57,7 @@ namespace bgsegm /** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. -The class implements the algorithm described in @cite KB2001. +The class implements the algorithm described in @cite KB2001 . */ class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor { @@ -86,9 +86,9 @@ means some automatic value. CV_EXPORTS_W Ptr createBackgroundSubtractorMOG(int history=200, int nmixtures=5, double backgroundRatio=0.7, double noiseSigma=0); - -/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012. + +/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012 . Takes a series of images and returns a sequence of mask (8UC1) images of the same size, where 255 indicates Foreground and 0 represents Background. diff --git a/modules/bioinspired/doc/retina.markdown b/modules/bioinspired/doc/retina.markdown index 4115f88f5..fb859866c 100644 --- a/modules/bioinspired/doc/retina.markdown +++ b/modules/bioinspired/doc/retina.markdown @@ -1,11 +1,10 @@ -Retina : a Bio mimetic human retina model {#bioinspired_retina} -========================================= +Bioinspired Module Retina Introduction {#bioinspired_retina} +====================================== Retina ------ -**Note** : do not forget that the retina model is included in the following namespace : -*cv::bioinspired*. +@note do not forget that the retina model is included in the following namespace : cv::bioinspired ### Introduction @@ -18,14 +17,13 @@ separable spatio-temporal filter modelling the two main retina information chann From a general point of view, this filter whitens the image spectrum and corrects luminance thanks to local adaptation. An other important property is its hability to filter out spatio-temporal noise -while enhancing details. This model originates from Jeanny Herault work @cite Herault2010. It has been +while enhancing details. This model originates from Jeanny Herault work @cite Herault2010 . It has been involved in Alexandre Benoit phd and his current research @cite Benoit2010, @cite Strat2013 (he currently maintains this module within OpenCV). It includes the work of other Jeanny's phd student such as @cite Chaix2007 and the log polar transformations of Barthelemy Durette described in Jeanny's book. -**NOTES :** - +@note - For ease of use in computer vision applications, the two retina channels are applied homogeneously on all the input images. This does not follow the real retina topology but this can still be done using the log sampling capabilities proposed within the class. @@ -71,7 +69,7 @@ described hereafter. XML parameters file samples are shown at the end of the pag Here is an overview of the abstract Retina interface, allocate one instance with the *createRetina* functions.: - +@code{.cpp} namespace cv{namespace bioinspired{ class Retina : public Algorithm @@ -122,6 +120,7 @@ functions.: cv::Ptr createRetina (Size inputSize); cv::Ptr createRetina (Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0); }} // cv and bioinspired namespaces end +@endcode ### Description @@ -146,59 +145,47 @@ Use : this model can be used basically for spatio-temporal video effects but als - performing motion analysis also taking benefit of the previously cited properties (check out the magnocellular retina channel output, by using the provided **getMagno** methods) - general image/video sequence description using either one or both channels. An example of the - use of Retina in a Bag of Words approach is given in @cite Strat2013. + use of Retina in a Bag of Words approach is given in @cite Strat2013 . Literature ---------- For more information, refer to the following papers : -- Model description : - -[Benoit2010] Benoit A., Caplier A., Durette B., Herault, J., "Using Human Visual System Modeling For Bio-Inspired Low Level Image Processing", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773. DOI - -- Model use in a Bag of Words approach : +- Model description : @cite Benoit2010 -[Strat2013] Strat S., Benoit A., Lambert P., "Retina enhanced SIFT descriptors for video indexing", CBMI2013, Veszprém, Hungary, 2013. +- Model use in a Bag of Words approach : @cite Strat2013 -- Please have a look at the reference work of Jeanny Herault that you can read in his book : - -[Herault2010] Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. +- Please have a look at the reference work of Jeanny Herault that you can read in his book : @cite Herault2010 This retina filter code includes the research contributions of phd/research collegues from which code has been redrawn by the author : - take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color - mosaicing/demosaicing and his reference paper: - -[Chaix2007] B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 + mosaicing/demosaicing and his reference paper: @cite Chaix2007 - take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. More informations in the above cited Jeanny Heraults's book. -- Meylan&al work on HDR tone mapping that is implemented as a specific method within the model : - -[Meylan2007] L. Meylan , D. Alleysson, S. Susstrunk, "A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images", Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816 +- Meylan&al work on HDR tone mapping that is implemented as a specific method within the model : @cite Meylan2007 Demos and experiments ! ----------------------- -**NOTE : Complementary to the following examples, have a look at the Retina tutorial in the +@note Complementary to the following examples, have a look at the Retina tutorial in the tutorial/contrib section for complementary explanations.** Take a look at the provided C++ examples provided with OpenCV : -- **samples/cpp/retinademo.cpp** shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video. - Typical uses are (provided your OpenCV installation is situated in folder - *OpenCVReleaseFolder*) - - - image processing : **OpenCVReleaseFolder/bin/retinademo -image myPicture.jpg** - - video processing : **OpenCVReleaseFolder/bin/retinademo -video myMovie.avi** - - webcam processing: **OpenCVReleaseFolder/bin/retinademo -video** +- **samples/cpp/retinademo.cpp** shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video. + Typical uses are (provided your OpenCV installation is situated in folder *OpenCVReleaseFolder*) + - image processing : **OpenCVReleaseFolder/bin/retinademo -image myPicture.jpg** + - video processing : **OpenCVReleaseFolder/bin/retinademo -video myMovie.avi** + - webcam processing: **OpenCVReleaseFolder/bin/retinademo -video** - **Note :** This demo generates the file *RetinaDefaultParameters.xml* which contains the + @note This demo generates the file *RetinaDefaultParameters.xml* which contains the default parameters of the retina. Then, rename this as *RetinaSpecificParameters.xml*, adjust the parameters the way you want and reload the program to check the effect. @@ -217,7 +204,7 @@ Take a look at the provided C++ examples provided with OpenCV : Note that some sliders are made available to allow you to play with luminance compression. If not using the 'fast' option, then, tone mapping is performed using the full retina model - @cite Benoit2010. It includes spectral whitening that allows luminance energy to be reduced. + @cite Benoit2010 . It includes spectral whitening that allows luminance energy to be reduced. When using the 'fast' option, then, a simpler method is used, it is an adaptation of the - algorithm presented in @cite Meylan2007. This method gives also good results and is faster to + algorithm presented in @cite Meylan2007 . 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/dev/null +++ b/modules/bioinspired/tutorials/retina_model.markdown @@ -0,0 +1,476 @@ +Discovering the human retina and its use for image processing {#tutorial_bioinspired_retina_model} +============================================================= + +Goal +---- + +I present here a model of human retina that shows some interesting properties for image +preprocessing and enhancement. In this tutorial you will learn how to: + +- discover the main two channels outing from your retina +- see the basics to use the retina model +- discover some parameters tweaks + +General overview +---------------- + +The proposed model originates from Jeanny Herault's research @cite Herault2010 at +[Gipsa](http://www.gipsa-lab.inpg.fr). It is involved in image processing applications with +[Listic](http://www.listic.univ-savoie.fr) (code maintainer and user) lab. This is not a complete +model but it already present interesting properties that can be involved for enhanced image +processing experience. The model allows the following human retina properties to be used : + +- spectral whitening that has 3 important effects: high spatio-temporal frequency signals + canceling (noise), mid-frequencies details enhancement and low frequencies luminance energy + reduction. This *all in one* property directly allows visual signals cleaning of classical + undesired distortions introduced by image sensors and input luminance range. +- local logarithmic luminance compression allows details to be enhanced even in low light + conditions. +- decorrelation of the details information (Parvocellular output channel) and transient + information (events, motion made available at the Magnocellular output channel). + +The first two points are illustrated below : + +In the figure below, the OpenEXR image sample *CrissyField.exr*, a High Dynamic Range image is +shown. In order to make it visible on this web-page, the original input image is linearly rescaled +to the classical image luminance range [0-255] and is converted to 8bit/channel format. Such strong +conversion hides many details because of too strong local contrasts. Furthermore, noise energy is +also strong and pollutes visual information. + +![image](images/retina_TreeHdr_small.jpg) + +In the following image, applying the ideas proposed in @cite Benoit2010, as your retina does, local +luminance adaptation, spatial noise removal and spectral whitening work together and transmit +accurate information on lower range 8bit data channels. On this picture, noise in significantly +removed, local details hidden by strong luminance contrasts are enhanced. Output image keeps its +naturalness and visual content is enhanced. Color processing is based on the color +multiplexing/demultiplexing method proposed in @cite Chaix2007 . + +![image](images/retina_TreeHdr_retina.jpg) + +*Note :* image sample can be downloaded from the [OpenEXR website](http://www.openexr.com). +Regarding this demonstration, before retina processing, input image has been linearly rescaled +within 0-255 keeping its channels float format. 5% of its histogram ends has been cut (mostly +removes wrong HDR pixels). Check out the sample +*opencv/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp* for similar +processing. The following demonstration will only consider classical 8bit/channel images. + +The retina model output channels +-------------------------------- + +The retina model presents two outputs that benefit from the above cited behaviors. + +- The first one is called the Parvocellular channel. It is mainly active in the foveal retina area + (high resolution central vision with color sensitive photo-receptors), its aim is to provide + accurate color vision for visual details remaining static on the retina. On the other hand + objects moving on the retina projection are blurred. +- The second well known channel is the Magnocellular channel. It is mainly active in the retina + peripheral vision and send signals related to change events (motion, transient events, etc.). + These outing signals also help visual system to focus/center retina on 'transient'/moving areas + for more detailed analysis thus improving visual scene context and object classification. + +**NOTE :** regarding the proposed model, contrary to the real retina, we apply these two channels on +the entire input images using the same resolution. This allows enhanced visual details and motion +information to be extracted on all the considered images... but remember, that these two channels +are complementary. For example, if Magnocellular channel gives strong energy in an area, then, the +Parvocellular channel is certainly blurred there since there is a transient event. + +As an illustration, we apply in the following the retina model on a webcam video stream of a dark +visual scene. In this visual scene, captured in an amphitheater of the university, some students are +moving while talking to the teacher. + +In this video sequence, because of the dark ambiance, signal to noise ratio is low and color +artifacts are present on visual features edges because of the low quality image capture tool-chain. + +![image](images/studentsSample_input.jpg) + +Below is shown the retina foveal vision applied on the entire image. In the used retina +configuration, global luminance is preserved and local contrasts are enhanced. Also, signal to noise +ratio is improved : since high frequency spatio-temporal noise is reduced, enhanced details are not +corrupted by any enhanced noise. + +![image](images/studentsSample_parvo.jpg) + +Below is the output of the Magnocellular output of the retina model. Its signals are strong where +transient events occur. Here, a student is moving at the bottom of the image thus generating high +energy. The remaining of the image is static however, it is corrupted by a strong noise. Here, the +retina filters out most of the noise thus generating low false motion area 'alarms'. This channel +can be used as a transient/moving areas detector : it would provide relevant information for a low +cost segmentation tool that would highlight areas in which an event is occurring. + +![image](images/studentsSample_magno.jpg) + +Retina use case +--------------- + +This model can be used basically for spatio-temporal video effects but also in the aim of : + +- performing texture analysis with enhanced signal to noise ratio and enhanced details robust + against input images luminance ranges (check out the Parvocellular retina channel output) +- performing motion analysis also taking benefit of the previously cited properties. + +Literature +---------- + +For more information, refer to the following papers : @cite Benoit2010 + +- Please have a look at the reference work of Jeanny Herault that you can read in his book @cite Herault2010 + +This retina filter code includes the research contributions of phd/research collegues from which +code has been redrawn by the author : + +- take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color + mosaicing/demosaicing and his reference paper @cite Chaix2007 + +- take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which + originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is + also proposed and originates from Jeanny's discussions. More informations in the above cited + Jeanny Heraults's book. + +Code tutorial +------------- + +Please refer to the original tutorial source code in file +*opencv_folder/samples/cpp/tutorial_code/bioinspired/retina_tutorial.cpp*. + +@note do not forget that the retina model is included in the following namespace: cv::bioinspired + +To compile it, assuming OpenCV is correctly installed, use the following command. It requires the +opencv_core *(cv::Mat and friends objects management)*, opencv_highgui *(display and image/video +read)* and opencv_bioinspired *(Retina description)* libraries to compile. + +@code{.sh} +// compile +gcc retina_tutorial.cpp -o Retina_tuto -lopencv_core -lopencv_highgui -lopencv_bioinspired + +// Run commands : add 'log' as a last parameter to apply a spatial log sampling (simulates retina sampling) +// run on webcam +./Retina_tuto -video +// run on video file +./Retina_tuto -video myVideo.avi +// run on an image +./Retina_tuto -image myPicture.jpg +// run on an image with log sampling +./Retina_tuto -image myPicture.jpg log +@endcode + +Here is a code explanation : + +Retina definition is present in the bioinspired package and a simple include allows to use it. You +can rather use the specific header : *opencv2/bioinspired.hpp* if you prefer but then include the +other required openv modules : *opencv2/core.hpp* and *opencv2/highgui.hpp* +@code{.cpp} +#include "opencv2/opencv.hpp" +@endcode +Provide user some hints to run the program with a help function +@code{.cpp} +// the help procedure +static void help(std::string errorMessage) +{ + std::cout<<"Program init error : "< you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<= 3) + { + std::cout<<"RetinaDemo: processing image "<>inputFrame; + }else + { + // bad command parameter + help("bad command parameter"); + return -1; + } +@endcode +Once all input parameters are processed, a first image should have been loaded, if not, display +error and stop program : +@code{.cpp} +if (inputFrame.empty()) +{ + help("Input media could not be loaded, aborting"); + return -1; +} +@endcode +Now, everything is ready to run the retina model. I propose here to allocate a retina instance and +to manage the eventual log sampling option. The Retina constructor expects at least a cv::Size +object that shows the input data size that will have to be managed. One can activate other options +such as color and its related color multiplexing strategy (here Bayer multiplexing is chosen using +*enum cv::bioinspired::RETINA_COLOR_BAYER*). If using log sampling, the image reduction factor +(smaller output images) and log sampling strengh can be adjusted. +@code{.cpp} +// pointer to a retina object +cv::Ptr myRetina; + +// if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision) +if (useLogSampling) +{ + myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0); +} +else// -> else allocate "classical" retina : + myRetina = cv::bioinspired::createRetina(inputFrame.size()); +@endcode +Once done, the proposed code writes a default xml file that contains the default parameters of the +retina. This is useful to make your own config using this template. Here generated template xml file +is called *RetinaDefaultParameters.xml*. +@code{.cpp} +// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup" +myRetina->write("RetinaDefaultParameters.xml"); +@endcode +In the following line, the retina attempts to load another xml file called +*RetinaSpecificParameters.xml*. If you created it and introduced your own setup, it will be loaded, +in the other case, default retina parameters are used. +@code{.cpp} +// load parameters if file exists +myRetina->setup("RetinaSpecificParameters.xml"); +@endcode +It is not required here but just to show it is possible, you can reset the retina buffers to zero to +force it to forget past events. +@code{.cpp} +// reset all retina buffers (imagine you close your eyes for a long time) +myRetina->clearBuffers(); +@endcode +Now, it is time to run the retina ! First create some output buffers ready to receive the two retina +channels outputs +@code{.cpp} +// declare retina output buffers +cv::Mat retinaOutput_parvo; +cv::Mat retinaOutput_magno; +@endcode +Then, run retina in a loop, load new frames from video sequence if necessary and get retina outputs +back to dedicated buffers. +@code{.cpp} +// processing loop with no stop condition +while(true) +{ + // if using video stream, then, grabbing a new frame, else, input remains the same + if (videoCapture.isOpened()) + videoCapture>>inputFrame; + + // run retina filter on the loaded input frame + myRetina->run(inputFrame); + // Retrieve and display retina output + myRetina->getParvo(retinaOutput_parvo); + myRetina->getMagno(retinaOutput_magno); + cv::imshow("retina input", inputFrame); + cv::imshow("Retina Parvo", retinaOutput_parvo); + cv::imshow("Retina Magno", retinaOutput_magno); + cv::waitKey(10); +} +@endcode +That's done ! But if you want to secure the system, take care and manage Exceptions. The retina can +throw some when it sees irrelevant data (no input frame, wrong setup, etc.). Then, i recommend to +surround all the retina code by a try/catch system like this : +@code{.cpp} +try{ + // pointer to a retina object + cv::Ptr myRetina; + [---] + // processing loop with no stop condition + while(true) + { + [---] + } + +}catch(cv::Exception e) +{ + std::cerr<<"Error using Retina : "< + + + 1 + 1 + 7.5e-01 + 9.0e-01 + 5.7e-01 + 0.01 + 0.5 + 7. + 7.5e-01 + + 1 + 0. + 0. + 7. + 2.0e+00 + 9.5e-01 + 0. + 7. + +@endcode +Here are some hints but actually, the best parameter setup depends more on what you want to do with +the retina rather than the images input that you give to retina. Apart from the more specific case +of High Dynamic Range images (HDR) that require more specific setup for specific luminance +compression objective, the retina behaviors should be rather stable from content to content. Note +that OpenCV is able to manage such HDR format thanks to the OpenEXR images compatibility. + +Then, if the application target requires details enhancement prior to specific image processing, you +need to know if mean luminance information is required or not. If not, the the retina can cancel or +significantly reduce its energy thus giving more visibility to higher spatial frequency details. + +### Basic parameters + +The most simple parameters are the following : + +- **colorMode** : let the retina process color information (if 1) or gray scale images (if 0). In + this last case, only the first channel of the input will be processed. +- **normaliseOutput** : each channel has this parameter, if value is 1, then the considered + channel output is rescaled between 0 and 255. Take care in this case at the Magnocellular output + level (motion/transient channel detection). Residual noise will also be rescaled ! + +**Note :** using color requires color channels multiplexing/demultipexing which requires more +processing. You can expect much faster processing using gray levels : it would require around 30 +product per pixel for all the retina processes and it has recently been parallelized for multicore +architectures. + +### Photo-receptors parameters + +The following parameters act on the entry point of the retina - photo-receptors - and impact all the +following processes. These sensors are low pass spatio-temporal filters that smooth temporal and +spatial data and also adjust there sensitivity to local luminance thus improving details extraction +and high frequency noise canceling. + +- **photoreceptorsLocalAdaptationSensitivity** between 0 and 1. Values close to 1 allow high + luminance log compression effect at the photo-receptors level. Values closer to 0 give a more + linear sensitivity. Increased alone, it can burn the *Parvo (details channel)* output image. If + adjusted in collaboration with **ganglionCellsSensitivity** images can be very contrasted + whatever the local luminance there is... at the price of a naturalness decrease. +- **photoreceptorsTemporalConstant** this setups the temporal constant of the low pass filter + effect at the entry of the retina. High value lead to strong temporal smoothing effect : moving + objects are blurred and can disappear while static object are favored. But when starting the + retina processing, stable state is reached lately. +- **photoreceptorsSpatialConstant** specifies the spatial constant related to photo-receptors low + pass filter effect. This parameters specify the minimum allowed spatial signal period allowed in + the following. Typically, this filter should cut high frequency noise. Then a 0 value doesn't + cut anything noise while higher values start to cut high spatial frequencies and more and more + lower frequencies... Then, do not go to high if you wanna see some details of the input images ! + A good compromise for color images is 0.53 since this won't affect too much the color spectrum. + Higher values would lead to gray and blurred output images. + +### Horizontal cells parameters + +This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells. +It modulates photo-receptors sensitivity and completes the processing for final spectral whitening +(part of the spatial band pass effect thus favoring visual details enhancement). + +- **horizontalCellsGain** here is a critical parameter ! If you are not interested by the mean + luminance and focus on details enhancement, then, set to zero. But if you want to keep some + environment luminance data, let some low spatial frequencies pass into the system and set a + higher value (\<1). +- **hcellsTemporalConstant** similar to photo-receptors, this acts on the temporal constant of a + low pass temporal filter that smooths input data. Here, a high value generates a high retina + after effect while a lower value makes the retina more reactive. This value should be lower than + **photoreceptorsTemporalConstant** to limit strong retina after effects. +- **hcellsSpatialConstant** is the spatial constant of the low pass filter of these cells filter. + It specifies the lowest spatial frequency allowed in the following. Visually, a high value leads + to very low spatial frequencies processing and leads to salient halo effects. Lower values + reduce this effect but the limit is : do not go lower than the value of + **photoreceptorsSpatialConstant**. Those 2 parameters actually specify the spatial band-pass of + the retina. + +**NOTE** after the processing managed by the previous parameters, input data is cleaned from noise +and luminance in already partly enhanced. The following parameters act on the last processing stages +of the two outing retina signals. + +### Parvo (details channel) dedicated parameter + +- **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at + the output of this details dedicated channel. Parameter values remain between 0 and 1. Low value + tend to give a linear response while higher values enforces the remaining low contrasted areas. + +**Note :** this parameter can correct eventual burned images by favoring low energetic details of +the visual scene, even in bright areas. + +### IPL Magno (motion/transient channel) parameters + +Once image information is cleaned, this channel acts as a high pass temporal filter that only +selects signals related to transient signals (events, motion, etc.). A low pass spatial filter +smooths extracted transient data and a final logarithmic compression enhances low transient events +thus enhancing event sensitivity. + +- **parasolCells_beta** generally set to zero, can be considered as an amplifier gain at the + entry point of this processing stage. Generally set to 0. +- **parasolCells_tau** the temporal smoothing effect that can be added +- **parasolCells_k** the spatial constant of the spatial filtering effect, set it at a high value + to favor low spatial frequency signals that are lower subject to residual noise. +- **amacrinCellsTemporalCutFrequency** specifies the temporal constant of the high pass filter. + High values let slow transient events to be selected. +- **V0CompressionParameter** specifies the strength of the log compression. Similar behaviors to + previous description but here it enforces sensitivity of transient events. +- **localAdaptintegration_tau** generally set to 0, no real use here actually +- **localAdaptintegration_k** specifies the size of the area on which local adaptation is + performed. 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npXL2mTcKZaU_S{_bHWapjQ>Qc{JR7{wvdw>A1I*{bTIudWR8NW literal 0 HcmV?d00001 diff --git a/modules/cvv/tutorials/visual_debugging_introduction.markdown b/modules/cvv/tutorials/visual_debugging_introduction.markdown new file mode 100644 index 000000000..d8e7000f0 --- /dev/null +++ b/modules/cvv/tutorials/visual_debugging_introduction.markdown @@ -0,0 +1,182 @@ +Interactive Visual Debugging of Computer Vision applications {#tutorial_cvv_introduction} +============================================================ + +What is the most common way to debug computer vision applications? Usually the answer is temporary, +hacked together, custom code that must be removed from the code for release compilation. + +In this tutorial we will show how to use the visual debugging features of the **cvv** module +(*opencv2/cvv.hpp*) instead. + +Goals +----- + +In this tutorial you will learn how to: + +- Add cvv debug calls to your application +- Use the visual debug GUI +- Enable and disable the visual debug features during compilation (with zero runtime overhead when + disabled) + +Code +---- + +The example code + +- captures images (*videoio*), e.g. from a webcam, +- applies some filters to each image (*imgproc*), +- detects image features and matches them to the previous image (*features2d*). + +If the program is compiled without visual debugging (see CMakeLists.txt below) the only result is +some information printed to the command line. We want to demonstrate how much debugging or +development functionality is added by just a few lines of *cvv* commands. + +@includelineno cvv/samples/cvv_demo.cpp + +@code{.cmake} +cmake_minimum_required(VERSION 2.8) + +project(cvvisual_test) + +SET(CMAKE_PREFIX_PATH ~/software/opencv/install) + +SET(CMAKE_CXX_COMPILER "g++-4.8") +SET(CMAKE_CXX_FLAGS "-std=c++11 -O2 -pthread -Wall -Werror") + +# (un)set: cmake -DCVV_DEBUG_MODE=OFF .. +OPTION(CVV_DEBUG_MODE "cvvisual-debug-mode" ON) +if(CVV_DEBUG_MODE MATCHES ON) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DCVVISUAL_DEBUGMODE") +endif() + + +FIND_PACKAGE(OpenCV REQUIRED) +include_directories(${OpenCV_INCLUDE_DIRS}) + +add_executable(cvvt main.cpp) +target_link_libraries(cvvt + opencv_core opencv_videoio opencv_imgproc opencv_features2d + opencv_cvv +) +@endcode + +Explanation +----------- + +-# We compile the program either using the above CmakeLists.txt with Option *CVV_DEBUG_MODE=ON* + (*cmake -DCVV_DEBUG_MODE=ON*) or by adding the corresponding define *CVVISUAL_DEBUGMODE* to + our compiler (e.g. *g++ -DCVVISUAL_DEBUGMODE*). +-# The first cvv call simply shows the image (similar to *imshow*) with the imgIdString as comment. + @code{.cpp} + cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str()); + @endcode + The image is added to the overview tab in the visual debug GUI and the cvv call blocks. + + ![image](images/01_overview_single.jpg) + + The image can then be selected and viewed + + ![image](images/02_single_image_view.jpg) + + Whenever you want to continue in the code, i.e. unblock the cvv call, you can either continue + until the next cvv call (*Step*), continue until the last cvv call (*\>\>*) or run the + application until it exists (*Close*). + + We decide to press the green *Step* button. + +-# The next cvv calls are used to debug all kinds of filter operations, i.e. operations that take a + picture as input and return a picture as output. + @code{.cpp} + cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray"); + @endcode + As with every cvv call, you first end up in the overview. + + ![image](images/03_overview_two.jpg) + + We decide not to care about the conversion to gray scale and press *Step*. + @code{.cpp} + cvv::debugFilter(imgGray, imgGraySmooth, CVVISUAL_LOCATION, "smoothed"); + @endcode + If you open the filter call, you will end up in the so called "DefaultFilterView". Both images + are shown next to each other and you can (synchronized) zoom into them. + + ![image](images/04_default_filter_view.jpg) + + When you go to very high zoom levels, each pixel is annotated with its numeric values. + + ![image](images/05_default_filter_view_high_zoom.jpg) + + We press *Step* twice and have a look at the dilated image. + @code{.cpp} + cvv::debugFilter(imgEdges, imgEdgesDilated, CVVISUAL_LOCATION, "dilated edges"); + @endcode + The DefaultFilterView showing both images + + ![image](images/06_default_filter_view_edges.jpg) + + Now we use the *View* selector in the top right and select the "DualFilterView". We select + "Changed Pixels" as filter and apply it (middle image). + + ![image](images/07_dual_filter_view_edges.jpg) + + After we had a close look at these images, perhaps using different views, filters or other GUI + features, we decide to let the program run through. Therefore we press the yellow *\>\>* button. + + The program will block at + @code{.cpp} + cvv::finalShow(); + @endcode + and display the overview with everything that was passed to cvv in the meantime. + + ![image](images/08_overview_all.jpg) + +-# The cvv debugDMatch call is used in a situation where there are two images each with a set of + descriptors that are matched to each other. + + We pass both images, both sets of keypoints and their matching to the visual debug module. + @code{.cpp} + cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, allMatchIdString.c_str()); + @endcode + Since we want to have a look at matches, we use the filter capabilities (*\#type match*) in the + overview to only show match calls. + + ![image](images/09_overview_filtered_type_match.jpg) + + We want to have a closer look at one of them, e.g. to tune our parameters that use the matching. + The view has various settings how to display keypoints and matches. Furthermore, there is a + mouseover tooltip. + + ![image](images/10_line_match_view.jpg) + + We see (visual debugging!) that there are many bad matches. We decide that only 70% of the + matches should be shown - those 70% with the lowest match distance. + + ![image](images/11_line_match_view_portion_selector.jpg) + + Having successfully reduced the visual distraction, we want to see more clearly what changed + between the two images. We select the "TranslationMatchView" that shows to where the keypoint + was matched in a different way. + + ![image](images/12_translation_match_view_portion_selector.jpg) + + It is easy to see that the cup was moved to the left during the two images. + + Although, cvv is all about interactively *seeing* the computer vision bugs, this is complemented + by a "RawView" that allows to have a look at the underlying numeric data. + + ![image](images/13_raw_view.jpg) + +-# There are many more useful features contained in the cvv GUI. For instance, one can group the + overview tab. + + ![image](images/14_overview_group_by_line.jpg) + +Result +------ + +- By adding a view expressive lines to our computer vision program we can interactively debug it + through different visualizations. +- Once we are done developing/debugging we do not have to remove those lines. We simply disable + cvv debugging (*cmake -DCVV_DEBUG_MODE=OFF* or g++ without *-DCVVISUAL_DEBUGMODE*) and our + programs runs without any debug overhead. + +Enjoy computer vision! diff --git a/modules/face/include/opencv2/face.hpp b/modules/face/include/opencv2/face.hpp index be6decd5f..e64a60589 100644 --- a/modules/face/include/opencv2/face.hpp +++ b/modules/face/include/opencv2/face.hpp @@ -45,7 +45,7 @@ the use of this software, even if advised of the possibility of such damage. @defgroup face Face Recognition - @ref face_changelog -- @ref face_tutorial +- @ref tutorial_face_main */ diff --git a/modules/face/doc/tutorial.markdown b/modules/face/tutorials/face_tutorial.markdown similarity index 93% rename from modules/face/doc/tutorial.markdown rename to modules/face/tutorials/face_tutorial.markdown index a518483d4..0035bb450 100644 --- a/modules/face/doc/tutorial.markdown +++ b/modules/face/tutorials/face_tutorial.markdown @@ -1,9 +1,9 @@ -Face Recognition with OpenCV {#face_tutorial} +Face Recognition with OpenCV {#tutorial_face_main} ============================ [TOC] -Introduction {#face_tutorial_intro} +Introduction {#tutorial_face_intro} ============ [OpenCV (Open Source Computer Vision)](http://opencv.org) is a popular computer vision library @@ -36,7 +36,7 @@ users. All code in this document is released under the [BSD license](http://www.opensource.org/licenses/bsd-license), so feel free to use it for your projects. -Face Recognition {#face_tutorial_facerec} +Face Recognition {#tutorial_face_facerec} ---------------- Face recognition is an easy task for humans. Experiments in @cite Tu06 have shown, that even one to @@ -59,7 +59,7 @@ to face recognition. One of the first automated face recognition systems was des the euclidean distance between feature vectors of a probe and reference image. Such a method is robust against changes in illumination by its nature, but has a huge drawback: the accurate registration of the marker points is complicated, even with state of the art algorithms. Some of the -latest work on geometric face recognition was carried out in @cite Bru92. A 22-dimensional feature +latest work on geometric face recognition was carried out in @cite Bru92 . A 22-dimensional feature vector was used and experiments on large datasets have shown, that geometrical features alone my not carry enough information for face recognition. @@ -71,7 +71,7 @@ transformation is optimal from a reconstruction standpoint, it doesn't take any account. Imagine a situation where the variance is generated from external sources, let it be light. The axes with maximum variance do not necessarily contain any discriminative information at all, hence a classification becomes impossible. So a class-specific projection with a Linear Discriminant -Analysis was applied to face recognition in @cite BHK97. The basic idea is to minimize the variance +Analysis was applied to face recognition in @cite BHK97 . The basic idea is to minimize the variance within a class, while maximizing the variance between the classes at the same time. Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of @@ -82,7 +82,7 @@ Local Binary Patterns (@cite AHP04). It's still an open research question what's preserve spatial information when applying a local feature extraction, because spatial information is potentially useful information. -Face Database {#face_tutorial_facedb} +Face Database {#tutorial_face_facedb} ------------- Let's get some data to experiment with first. I don't want to do a toy example here. We are doing @@ -123,7 +123,7 @@ Three interesting databases are (parts of the description are quoted from same setup to take 16128 images of 28 people. The Extended Yale Facedatabase B is the merge of the two databases, which is now known as Extended Yalefacedatabase B. -### Preparing the data {#face_tutorial_prepare} +### Preparing the data {#tutorial_face_prepare} Once we have acquired some data, we'll need to read it in our program. In the demo applications I have decided to read the images from a very simple CSV file. Why? Because it's the simplest @@ -131,9 +131,9 @@ platform-independent approach I can think of. However, if you know a simpler sol about it. Basically all the CSV file needs to contain are lines composed of a filename followed by a ; followed by the label (as *integer number*), making up a line like this: -~~~ +@code{.csv} /path/to/image.ext;0 -~~~ +@endcode Let's dissect the line. /path/to/image.ext is the path to an image, probably something like this if you are in Windows: C:/faces/person0/image0.jpg. Then there is the separator ; and finally we assign @@ -143,7 +143,7 @@ same subjects (persons) should have the same label. Download the AT&T Facedatabase from AT&T Facedatabase and the corresponding CSV file from at.txt, which looks like this (file is without ... of course): -~~~ +@code{.csv} ./at/s1/1.pgm;0 ./at/s1/2.pgm;0 ... @@ -152,20 +152,20 @@ which looks like this (file is without ... of course): ... ./at/s40/1.pgm;39 ./at/s40/2.pgm;39 -~~~ +@endcode Imagine I have extracted the files to D:/data/at and have downloaded the CSV file to D:/data/at.txt. Then you would simply need to Search & Replace ./ with D:/data/. You can do that in an editor of your choice, every sufficiently advanced editor can do this. Once you have a CSV file with valid filenames and labels, you can run any of the demos by passing the path to the CSV file as parameter: -~~~ +@code{.sh} facerec_demo.exe D:/data/at.txt -~~~ +@endcode -Please, see @ref face_tutorial_appendix_csv for details on creating CSV file. +Please, see @ref tutorial_face_appendix_csv for details on creating CSV file. -Eigenfaces {#face_tutorial_eigenfaces} +Eigenfaces {#tutorial_face_eigenfaces} ---------- The problem with the image representation we are given is its high dimensionality. Two-dimensional @@ -181,7 +181,7 @@ high-dimensional dataset is often described by correlated variables and therefor meaningful dimensions account for most of the information. The PCA method finds the directions with the greatest variance in the data, called principal components. -### Algorithmic Description of Eigenfaces method {#face_tutorial_eigenfaces_algo} +### Algorithmic Description of Eigenfaces method {#tutorial_face_eigenfaces_algo} Let \f$X = \{ x_{1}, x_{2}, \ldots, x_{n} \}\f$ be a random vector with observations \f$x_i \in R^{d}\f$. @@ -237,7 +237,7 @@ The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they normalized to unit length. I don't want to turn this into a publication, so please look into @cite Duda01 for the derivation and proof of the equations. -### Eigenfaces in OpenCV {#face_tutorial_eigenfaces_use} +### Eigenfaces in OpenCV {#tutorial_face_eigenfaces_use} For the first source code example, I'll go through it with you. I am first giving you the whole source code listing, and after this we'll look at the most important lines in detail. Please note: @@ -258,7 +258,7 @@ We've already seen, that we can reconstruct a face from its lower dimensional ap let's see how many Eigenfaces are needed for a good reconstruction. I'll do a subplot with \f$10,30,\ldots,310\f$ Eigenfaces: -~~~{cpp} +@code{.cpp} // Display or save the image reconstruction at some predefined steps: for(int num_components = 10; num_components < 300; num_components+=15) { // slice the eigenvectors from the model @@ -274,7 +274,7 @@ for(int num_components = 10; num_components < 300; num_components+=15) { imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction); } } -~~~ +@endcode 10 Eigenvectors are obviously not sufficient for a good image reconstruction, 50 Eigenvectors may already be sufficient to encode important facial features. You'll get a good reconstruction with @@ -284,7 +284,7 @@ data. @cite Zhao03 is the perfect point to start researching for this: ![image](img/eigenface_reconstruction_opencv.png) -Fisherfaces {#face_tutorial_fisherfaces} +Fisherfaces {#tutorial_face_fisherfaces} ----------- The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear @@ -300,16 +300,16 @@ for an example). The Linear Discriminant Analysis performs a class-specific dimensionality reduction and was invented by the great statistician [Sir R. A. Fisher](http://en.wikipedia.org/wiki/Ronald_Fisher). He successfully used it for classifying flowers in his 1936 paper *The use of multiple measurements in -taxonomic problems* @cite Fisher36. In order to find the combination of features that separates best +taxonomic problems* @cite Fisher36 . In order to find the combination of features that separates best between classes the Linear Discriminant Analysis maximizes the ratio of between-classes to within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes should cluster tightly together, while different classes are as far away as possible from each other in the lower-dimensional representation. This was also recognized by [Belhumeur](http://www.cs.columbia.edu/~belhumeur/), [Hespanha](http://www.ece.ucsb.edu/~hespanha/) and [Kriegman](http://cseweb.ucsd.edu/~kriegman/) and so they applied a Discriminant Analysis to -face recognition in @cite BHK97. +face recognition in @cite BHK97 . -### Algorithmic Description of Fisherfaces method {#face_tutorial_fisherfaces_algo} +### Algorithmic Description of Fisherfaces method {#tutorial_face_fisherfaces_algo} Let \f$X\f$ be a random vector with samples drawn from \f$c\f$ classes: @@ -365,7 +365,7 @@ given by: \f[W = W_{fld}^{T} W_{pca}^{T}\f] -### Fisherfaces in OpenCV {#face_tutorial_fisherfaces_use} +### Fisherfaces in OpenCV {#tutorial_face_fisherfaces_use} The source code for this demo application is also available in the src folder coming with this documentation: @@ -393,7 +393,7 @@ reconstruction of the original image. For the Fisherfaces method we'll project t each of the Fisherfaces instead. So you'll have a nice visualization, which feature each of the Fisherfaces describes: -~~~{cpp} +@code{.cpp} // Display or save the image reconstruction at some predefined steps: for(int num_component = 0; num_component < min(16, W.cols); num_component++) { // Slice the Fisherface from the model: @@ -409,13 +409,13 @@ for(int num_component = 0; num_component < min(16, W.cols); num_component++) { imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction); } } -~~~ +@endcode The differences may be subtle for the human eyes, but you should be able to see some differences: ![image](img/fisherface_reconstruction_opencv.png) -Local Binary Patterns Histograms {#face_tutorial_lbph} +Local Binary Patterns Histograms {#tutorial_face_lbph} -------------------------------- Eigenfaces and Fisherfaces take a somewhat holistic approach to face recognition. You treat your @@ -461,7 +461,7 @@ literature actually used a fixed 3 x 3 neighborhood just like this: ![image](img/lbp/lbp.png) -### Algorithmic Description of LBPH method {#face_tutorial_lbph_algo} +### Algorithmic Description of LBPH method {#tutorial_face_lbph_algo} A more formal description of the LBP operator can be given as: @@ -481,7 +481,7 @@ s(x) = This description enables you to capture very fine grained details in images. In fact the authors were able to compete with state of the art results for texture classification. Soon after the operator was published it was noted, that a fixed neighborhood fails to encode details differing in -scale. So the operator was extended to use a variable neighborhood in @cite AHP04. The idea is to +scale. So the operator was extended to use a variable neighborhood in @cite AHP04 . The idea is to align an abritrary number of neighbors on a circle with a variable radius, which enables to capture the following neighborhoods: @@ -523,27 +523,27 @@ regions and extract a histogram from each. The spatially enhanced feature vector concatenating the local histograms (**not merging them**). These histograms are called *Local Binary Patterns Histograms*. -### Local Binary Patterns Histograms in OpenCV {#face_tutorial_lbph_use} +### Local Binary Patterns Histograms in OpenCV {#tutorial_face_lbph_use} The source code for this demo application is also available in the src folder coming with this documentation: @include src/facerec_lbph.cpp -Conclusion {#face_tutorial_conclusion} +Conclusion {#tutorial_face_conclusion} ---------- You've learned how to use the new FaceRecognizer in real applications. After reading the document you also know how the algorithms work, so now it's time for you to experiment with the available algorithms. Use them, improve them and let the OpenCV community participate! -Credits {#face_tutorial_credits} +Credits {#tutorial_face_credits} ------- This document wouldn't be possible without the kind permission to use the face images of the *AT&T Database of Faces* and the *Yale Facedatabase A/B*. -### The Database of Faces {#face_tutorial_credits_db} +### The Database of Faces {#tutorial_face_credits_db} __Important: when using these images, please give credit to "AT&T Laboratories, Cambridge."__ @@ -567,7 +567,7 @@ image number for that subject (between 1 and 10). A copy of the database can be retrieved from: [](http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip). -### Yale Facedatabase A {#face_tutorial_credits_yalea} +### Yale Facedatabase A {#tutorial_face_credits_yalea} *With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase @@ -579,7 +579,7 @@ w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, su (Source: [](http://cvc.yale.edu/projects/yalefaces/yalefaces.html)) -### Yale Facedatabase B {#face_tutorial_credits_yaleb} +### Yale Facedatabase B {#tutorial_face_credits_yaleb} *With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase @@ -607,14 +607,14 @@ experimental results with the cropped images, please reference the PAMI2005 pape Appendix {#face_appendix} -------- -### Creating the CSV File {#face_tutorial_appendix_csv} +### Creating the CSV File {#tutorial_face_appendix_csv} You don't really want to create the CSV file by hand. I have prepared you a little Python script `create_csv.py` (you find it at `src/create_csv.py` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (`/basepath//`): -~~~~~~ +@code{.sh} philipp@mango:~/facerec/data/at$ tree . |-- s1 @@ -630,12 +630,12 @@ philipp@mango:~/facerec/data/at$ tree | |-- 1.pgm | |-- ... | |-- 10.pgm -~~~~~~ +@endcode Then simply call `create_csv.py` with the path to the folder, just like this and you could save the output: -~~~~~~ +@code{.sh} philipp@mango:~/facerec/data$ python create_csv.py at/s13/2.pgm;0 at/s13/7.pgm;0 @@ -654,30 +654,30 @@ at/s17/9.pgm;1 at/s17/5.pgm;1 at/s17/3.pgm;1 [...] -~~~~~~ +@endcode Here is the script, if you can't find it: -@verbinclude src/create_csv.py +@verbinclude face/doc/src/create_csv.py -### Aligning Face Images {#face_tutorial_appendix_align} +### Aligning Face Images {#tutorial_face_appendix_align} An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the -face image you just need to call *CropFace(image, eye\_left, eye\_right, offset\_pct, dest\_sz)*, +face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where: -- *eye\_left* is the position of the left eye -- *eye\_right* is the position of the right eye -- *offset\_pct* is the percent of the image you want to keep next to the eyes (horizontal, +- *eye_left* is the position of the left eye +- *eye_right* is the position of the right eye +- *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction) -- *dest\_sz* is the size of the output image +- *dest_sz* is the size of the output image -If you are using the same *offset\_pct* and *dest\_sz* for your images, they are all aligned at the +If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes. -@verbinclude src/crop_face.py +@verbinclude face/doc/src/crop_face.py Imagine we are given [this photo of Arnold Schwarzenegger](http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg), which is @@ -694,6 +694,6 @@ Configuration | Cropped, Scaled, Rotated Face 0.3 (30%), 0.3 (30%), (200,200) | ![](tutorial/gender_classification/arnie_30_30_200_200.jpg) 0.2 (20%), 0.2 (20%), (70,70) | ![](tutorial/gender_classification/arnie_20_20_70_70.jpg) -### CSV for the AT&T Facedatabase {#face_tutorial_appendix_attcsv} +### CSV for the AT&T Facedatabase {#tutorial_face_appendix_attcsv} -@verbinclude etc/at.txt +@verbinclude face/doc/etc/at.txt diff --git a/modules/latentsvm/include/opencv2/latentsvm.hpp b/modules/latentsvm/include/opencv2/latentsvm.hpp index 40a153da3..18db8ca60 100644 --- a/modules/latentsvm/include/opencv2/latentsvm.hpp +++ b/modules/latentsvm/include/opencv2/latentsvm.hpp @@ -61,7 +61,7 @@ Discriminatively Trained Part Based Models for Object Detection --------------------------------------------------------------- The object detector described below has been initially proposed by P.F. Felzenszwalb in -@cite Felzenszwalb2010a. It is based on a Dalal-Triggs detector that uses a single filter on histogram +@cite Felzenszwalb2010a . It is based on a Dalal-Triggs detector that uses a single filter on histogram of oriented gradients (HOG) features to represent an object category. This detector uses a sliding window approach, where a filter is applied at all positions and scales of an image. The first innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a @@ -77,7 +77,7 @@ and scale is the maximum over components, of the score of that component model a location. The detector was dramatically speeded-up with cascade algorithm proposed by P.F. Felzenszwalb in -@cite Felzenszwalb2010b. The algorithm prunes partial hypotheses using thresholds on their scores.The +@cite Felzenszwalb2010b . The algorithm prunes partial hypotheses using thresholds on their scores.The basic idea of the algorithm is to use a hierarchy of models defined by an ordering of the original model's parts. For a model with (n+1) parts, including the root, a sequence of (n+1) models is obtained. The i-th model in this sequence is defined by the first i parts from the original model. diff --git a/modules/line_descriptor/doc/tutorial.markdown b/modules/line_descriptor/doc/tutorial.markdown deleted file mode 100644 index 2b01abf7f..000000000 --- a/modules/line_descriptor/doc/tutorial.markdown +++ /dev/null @@ -1,418 +0,0 @@ -Line Features Tutorial {#line_descriptor_tutorial} -====================== - -In this tutorial it will be shown how to: - -- use the *BinaryDescriptor* interface to extract lines and store them in *KeyLine* objects -- use the same interface to compute descriptors for every extracted line -- use the *BynaryDescriptorMatcher* to determine matches among descriptors obtained from different - images - -Lines extraction and descriptors computation --------------------------------------------- - -In the following snippet of code, it is shown how to detect lines from an image. The LSD extractor -is initialized with *LSD\_REFINE\_ADV* option; remaining parameters are left to their default -values. A mask of ones is used in order to accept all extracted lines, which, at the end, are -displayed using random colors for octave 0. - -~~~{cpp} -#include - -#include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" -#include -#include -#include - -#include - -using namespace cv; -using namespace std; - -static const char* keys = -{ "{@image_path | | Image path }" }; - -static void help() -{ - cout << "\nThis example shows the functionalities of lines extraction " << "furnished by BinaryDescriptor class\n" - << "Please, run this sample using a command in the form\n" << "./example_line_descriptor_lines_extraction " << endl; -} - -int main( int argc, char** argv ) -{ - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String image_path = parser.get( 0 ); - - if( image_path.empty() ) - { - help(); - return -1; - } - - /* load image */ - cv::Mat imageMat = imread( image_path, 1 ); - if( imageMat.data == NULL ) - { - std::cout << "Error, image could not be loaded. Please, check its path" << std::endl; - } - - /* create a ramdom binary mask */ - cv::Mat mask = Mat::ones( imageMat.size(), CV_8UC1 ); - - /* create a pointer to a BinaryDescriptor object with deafult parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* create a structure to store extracted lines */ - vector lines; - - /* extract lines */ - bd->detect( imageMat, lines, mask ); - - /* draw lines extracted from octave 0 */ - cv::Mat output = imageMat.clone(); - if( output.channels() == 1 ) - cvtColor( output, output, COLOR_GRAY2BGR ); - for ( size_t i = 0; i < lines.size(); i++ ) - { - KeyLine kl = lines[i]; - if( kl.octave == 0) - { - /* get a random color */ - int R = ( rand() % (int) ( 255 + 1 ) ); - int G = ( rand() % (int) ( 255 + 1 ) ); - int B = ( rand() % (int) ( 255 + 1 ) ); - - /* get extremes of line */ - Point pt1 = Point( kl.startPointX, kl.startPointY ); - Point pt2 = Point( kl.endPointX, kl.endPointY ); - - /* draw line */ - line( output, pt1, pt2, Scalar( B, G, R ), 5 ); - } - - } - - /* show lines on image */ - imshow( "Lines", output ); - waitKey(); -} -~~~ - -This is the result obtained for famous cameraman image: - -![alternate text](pics/lines_cameraman_edl.png) - -Another way to extract lines is using *LSDDetector* class; such class uses the LSD extractor to -compute lines. To obtain this result, it is sufficient to use the snippet code seen above, just -modifying it by the rows - -~~~{cpp} -/* create a pointer to an LSDDetector object */ -Ptr lsd = LSDDetector::createLSDDetector(); - -/* compute lines */ -std::vector keylines; -lsd->detect( imageMat, keylines, mask ); -~~~ - -Here's the result returned by LSD detector again on cameraman picture: - -![alternate text](pics/cameraman_lines2.png) - -Once keylines have been detected, it is possible to compute their descriptors as shown in the -following: - -~~~{cpp} -#include - -#include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" -#include -#include -#include - -#include - -using namespace cv; - -static const char* keys = -{ "{@image_path | | Image path }" }; - -static void help() -{ - std::cout << "\nThis example shows the functionalities of lines extraction " << "and descriptors computation furnished by BinaryDescriptor class\n" - << "Please, run this sample using a command in the form\n" << "./example_line_descriptor_compute_descriptors " - << std::endl; -} - -int main( int argc, char** argv ) -{ - /* get parameters from command line */ - CommandLineParser parser( argc, argv, keys ); - String image_path = parser.get( 0 ); - - if( image_path.empty() ) - { - help(); - return -1; - } - - /* load image */ - cv::Mat imageMat = imread( image_path, 1 ); - if( imageMat.data == NULL ) - { - std::cout << "Error, image could not be loaded. Please, check its path" << std::endl; - } - - /* create a binary mask */ - cv::Mat mask = Mat::ones( imageMat.size(), CV_8UC1 ); - - /* create a pointer to a BinaryDescriptor object with default parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* compute lines */ - std::vector keylines; - bd->detect( imageMat, keylines, mask ); - - /* compute descriptors */ - cv::Mat descriptors; - bd->compute( imageMat, keylines, descriptors ); - -} -~~~ - -Matching among descriptors --------------------------- - -If we have extracted descriptors from two different images, it is possible to search for matches -among them. One way of doing it is matching exactly a descriptor to each input query descriptor, -choosing the one at closest distance: - -~~~{cpp} -#include - -#include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" -#include -#include -#include - -#include - -using namespace cv; - -static const char* keys = -{ "{@image_path1 | | Image path 1 }" - "{@image_path2 | | Image path 2 }" }; - -static void help() -{ - std::cout << "\nThis example shows the functionalities of lines extraction " << "and descriptors computation furnished by BinaryDescriptor class\n" - << "Please, run this sample using a command in the form\n" << "./example_line_descriptor_compute_descriptors " - << "" << std::endl; - -} - -int main( int argc, char** argv ) -{ - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String image_path1 = parser.get( 0 ); - String image_path2 = parser.get( 1 ); - - if( image_path1.empty() || image_path2.empty() ) - { - help(); - return -1; - } - - /* load image */ - cv::Mat imageMat1 = imread( image_path1, 1 ); - cv::Mat imageMat2 = imread( image_path2, 1 ); - - waitKey(); - if( imageMat1.data == NULL || imageMat2.data == NULL ) - { - std::cout << "Error, images could not be loaded. Please, check their path" << std::endl; - } - - /* create binary masks */ - cv::Mat mask1 = Mat::ones( imageMat1.size(), CV_8UC1 ); - cv::Mat mask2 = Mat::ones( imageMat2.size(), CV_8UC1 ); - - /* create a pointer to a BinaryDescriptor object with default parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* compute lines */ - std::vector keylines1, keylines2; - bd->detect( imageMat1, keylines1, mask1 ); - bd->detect( imageMat2, keylines2, mask2 ); - - /* compute descriptors */ - cv::Mat descr1, descr2; - bd->compute( imageMat1, keylines1, descr1 ); - bd->compute( imageMat2, keylines2, descr2 ); - - /* create a BinaryDescriptorMatcher object */ - Ptr bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); - - /* require match */ - std::vector matches; - bdm->match( descr1, descr2, matches ); - - /* plot matches */ - cv::Mat outImg; - std::vector mask( matches.size(), 1 ); - drawLineMatches( imageMat1, keylines1, imageMat2, keylines2, matches, outImg, Scalar::all( -1 ), Scalar::all( -1 ), mask, - DrawLinesMatchesFlags::DEFAULT ); - - imshow( "Matches", outImg ); - waitKey(); -} -~~~ - -Sometimes, we could be interested in searching for the closest *k* descriptors, given an input one. -This requires to modify slightly previous code: - -~~~{cpp} -/* prepare a structure to host matches */ -std::vector > matches; - -/* require knn match */ -bdm->knnMatch( descr1, descr2, matches, 6 ); -~~~ - -In the above example, the closest 6 descriptors are returned for every query. In some cases, we -could have a search radius and look for all descriptors distant at the most *r* from input query. -Previous code must me modified: - -~~~{cpp} -/* prepare a structure to host matches */ -std::vector > matches; - -/* compute matches */ -bdm->radiusMatch( queries, matches, 30 ); -~~~ - -Here's an example om matching among descriptors extratced from original cameraman image and its -downsampled (and blurred) version: - -![alternate text](pics/matching2.png) - -Querying internal database --------------------------- - -The *BynaryDescriptorMatcher* class, owns an internal database that can be populated with -descriptors extracted from different images and queried using one of the modalities described in -previous section. Population of internal dataset can be done using the *add* function; such function -doesn't directly add new data to database, but it just stores it them locally. The real update -happens when function *train* is invoked or when any querying function is executed, since each of -them invokes *train* before querying. When queried, internal database not only returns required -descriptors, but, for every returned match, it is able to tell which image matched descriptor was -extracted from. An example of internal dataset usage is described in the following code; after -adding locally new descriptors, a radius search is invoked. This provokes local data to be -transferred to dataset, which, in turn, is then queried. - -~~~{cpp} -#include - -#include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" -#include -#include -#include - -#include -#include - -using namespace cv; - -static const std::string images[] = -{ "cameraman.jpg", "church.jpg", "church2.png", "einstein.jpg", "stuff.jpg" }; - -static const char* keys = -{ "{@image_path | | Image path }" }; - -static void help() -{ - std::cout << "\nThis example shows the functionalities of radius matching " << "Please, run this sample using a command in the form\n" - << "./example_line_descriptor_radius_matching /" << std::endl; -} - -int main( int argc, char** argv ) -{ - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String pathToImages = parser.get( 0 ); - - /* create structures for hosting KeyLines and descriptors */ - int num_elements = sizeof ( images ) / sizeof ( images[0] ); - std::vector descriptorsMat; - std::vector > linesMat; - - /*create a pointer to a BinaryDescriptor object */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* compute lines and descriptors */ - for ( int i = 0; i < num_elements; i++ ) - { - /* get path to image */ - std::stringstream image_path; - image_path << pathToImages << images[i]; - - /* load image */ - Mat loadedImage = imread( image_path.str().c_str(), 1 ); - if( loadedImage.data == NULL ) - { - std::cout << "Could not load images." << std::endl; - help(); - exit( -1 ); - } - - /* compute lines and descriptors */ - std::vector lines; - Mat computedDescr; - bd->detect( loadedImage, lines ); - bd->compute( loadedImage, lines, computedDescr ); - - descriptorsMat.push_back( computedDescr ); - linesMat.push_back( lines ); - - } - - /* compose a queries matrix */ - Mat queries; - for ( size_t j = 0; j < descriptorsMat.size(); j++ ) - { - if( descriptorsMat[j].rows >= 5 ) - queries.push_back( descriptorsMat[j].rowRange( 0, 5 ) ); - - else if( descriptorsMat[j].rows > 0 && descriptorsMat[j].rows < 5 ) - queries.push_back( descriptorsMat[j] ); - } - - std::cout << "It has been generated a matrix of " << queries.rows << " descriptors" << std::endl; - - /* create a BinaryDescriptorMatcher object */ - Ptr bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); - - /* populate matcher */ - bdm->add( descriptorsMat ); - - /* compute matches */ - std::vector > matches; - bdm->radiusMatch( queries, matches, 30 ); - - /* print matches */ - for ( size_t q = 0; q < matches.size(); q++ ) - { - for ( size_t m = 0; m < matches[q].size(); m++ ) - { - DMatch dm = matches[q][m]; - std::cout << "Descriptor: " << q << " Image: " << dm.imgIdx << " Distance: " << dm.distance << std::endl; - } - } -} -~~~ diff --git a/modules/line_descriptor/include/opencv2/line_descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor.hpp index d01d1d577..edd2fab85 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor.hpp @@ -63,8 +63,8 @@ Computation of binary descriptors --------------------------------- To obtatin a binary descriptor representing a certain line detected from a certain octave of an -image, we first compute a non-binary descriptor as described in @cite LBD. Such algorithm works on -lines extracted using EDLine detector, as explained in @cite EDL. Given a line, we consider a +image, we first compute a non-binary descriptor as described in @cite LBD . Such algorithm works on +lines extracted using EDLine detector, as explained in @cite EDL . Given a line, we consider a rectangular region centered at it and called *line support region (LSR)*. Such region is divided into a set of bands \f$\{B_1, B_2, ..., B_m\}\f$, whose length equals the one of line. diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp index 263af06ea..4688cfcba 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp @@ -854,7 +854,7 @@ std::vector octaveImages; Lines extraction methodology ---------------------------- -The lines extraction methodology described in the following is mainly based on @cite EDL. The +The lines extraction methodology described in the following is mainly based on @cite EDL . The extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm. @@ -931,7 +931,7 @@ based on *Multi-Index Hashing (MiHashing)* will be described. Multi-Index Hashing ------------------- -The theory described in this section is based on @cite MIH. Given a dataset populated with binary +The theory described in this section is based on @cite MIH . Given a dataset populated with binary codes, each code is indexed *m* times into *m* different hash tables, according to *m* substrings it has been divided into. Thus, given a query code, all the entries close to it at least in one substring are returned by search as *neighbor candidates*. Returned entries are then checked for diff --git a/modules/line_descriptor/tutorials/tutorial.markdown b/modules/line_descriptor/tutorials/tutorial.markdown new file mode 100644 index 000000000..bfff12921 --- /dev/null +++ b/modules/line_descriptor/tutorials/tutorial.markdown @@ -0,0 +1,98 @@ +Line Features Tutorial {#tutorial_line_descriptor_main} +====================== + +In this tutorial it will be shown how to: + +- use the *BinaryDescriptor* interface to extract lines and store them in *KeyLine* objects +- use the same interface to compute descriptors for every extracted line +- use the *BynaryDescriptorMatcher* to determine matches among descriptors obtained from different + images + +Lines extraction and descriptors computation +-------------------------------------------- + +In the following snippet of code, it is shown how to detect lines from an image. The LSD extractor +is initialized with *LSD\_REFINE\_ADV* option; remaining parameters are left to their default +values. A mask of ones is used in order to accept all extracted lines, which, at the end, are +displayed using random colors for octave 0. + +@includelineno line_descriptor/samples/lsd_lines_extraction.cpp + +This is the result obtained for famous cameraman image: + +![alternate text](pics/lines_cameraman_edl.png) + +Another way to extract lines is using *LSDDetector* class; such class uses the LSD extractor to +compute lines. To obtain this result, it is sufficient to use the snippet code seen above, just +modifying it by the rows + +@code{.cpp} +// create a pointer to an LSDDetector object +Ptr lsd = LSDDetector::createLSDDetector(); + +// compute lines +std::vector keylines; +lsd->detect( imageMat, keylines, mask ); +@endcode + +Here's the result returned by LSD detector again on cameraman picture: + +![alternate text](pics/cameraman_lines2.png) + +Once keylines have been detected, it is possible to compute their descriptors as shown in the +following: + +@includelineno line_descriptor/samples/compute_descriptors.cpp + +Matching among descriptors +-------------------------- + +If we have extracted descriptors from two different images, it is possible to search for matches +among them. One way of doing it is matching exactly a descriptor to each input query descriptor, +choosing the one at closest distance: + +@includelineno line_descriptor/samples/matching.cpp + +Sometimes, we could be interested in searching for the closest *k* descriptors, given an input one. +This requires to modify slightly previous code: + +@code{.cpp} +// prepare a structure to host matches +std::vector > matches; + +// require knn match +bdm->knnMatch( descr1, descr2, matches, 6 ); +@endcode + +In the above example, the closest 6 descriptors are returned for every query. In some cases, we +could have a search radius and look for all descriptors distant at the most *r* from input query. +Previous code must me modified: + +@code{.cpp} +// prepare a structure to host matches +std::vector > matches; + +// compute matches +bdm->radiusMatch( queries, matches, 30 ); +@endcode + +Here's an example om matching among descriptors extratced from original cameraman image and its +downsampled (and blurred) version: + +![alternate text](pics/matching2.png) + +Querying internal database +-------------------------- + +The *BynaryDescriptorMatcher* class, owns an internal database that can be populated with +descriptors extracted from different images and queried using one of the modalities described in +previous section. Population of internal dataset can be done using the *add* function; such function +doesn't directly add new data to database, but it just stores it them locally. The real update +happens when function *train* is invoked or when any querying function is executed, since each of +them invokes *train* before querying. When queried, internal database not only returns required +descriptors, but, for every returned match, it is able to tell which image matched descriptor was +extracted from. An example of internal dataset usage is described in the following code; after +adding locally new descriptors, a radius search is invoked. This provokes local data to be +transferred to dataset, which, in turn, is then queried. + +@includelineno line_descriptor/samples/radius_matching.cpp diff --git a/modules/optflow/include/opencv2/optflow.hpp b/modules/optflow/include/opencv2/optflow.hpp index ecca2d82b..a01b0c8ff 100644 --- a/modules/optflow/include/opencv2/optflow.hpp +++ b/modules/optflow/include/opencv2/optflow.hpp @@ -99,7 +99,7 @@ for pixel @param speed_up_thr threshold to detect point with irregular flow - where flow should be recalculated after upscale -See @cite Tao2012. And site of project - . +See @cite Tao2012 . And site of project - . @note - An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp diff --git a/modules/optflow/include/opencv2/optflow/motempl.hpp b/modules/optflow/include/opencv2/optflow/motempl.hpp index b0994fad6..aeea9e8cd 100644 --- a/modules/optflow/include/opencv2/optflow/motempl.hpp +++ b/modules/optflow/include/opencv2/optflow/motempl.hpp @@ -66,7 +66,7 @@ That is, MHI pixels where the motion occurs are set to the current timestamp , w where the motion happened last time a long time ago are cleared. The function, together with calcMotionGradient and calcGlobalOrientation , implements a motion -templates technique described in @cite Davis97 and @cite Bradski00. +templates technique described in @cite Davis97 and @cite Bradski00 . */ CV_EXPORTS_W void updateMotionHistory( InputArray silhouette, InputOutputArray mhi, double timestamp, double duration ); diff --git a/modules/reg/include/opencv2/reg/map.hpp b/modules/reg/include/opencv2/reg/map.hpp index 2ae1804bf..26b29e38e 100644 --- a/modules/reg/include/opencv2/reg/map.hpp +++ b/modules/reg/include/opencv2/reg/map.hpp @@ -45,7 +45,7 @@ The Registration module implements parametric image registration. The implemented method is direct alignment, that is, it uses directly the pixel values for calculating the registration between a pair of images, as opposed to feature-based registration. The implementation follows essentially the -corresponding part of @cite Szeliski06. +corresponding part of @cite Szeliski06 . Feature based methods have some advantages over pixel based methods when we are trying to register pictures that have been shoot under different lighting conditions or exposition times, or when the diff --git a/modules/surface_matching/include/opencv2/surface_matching.hpp b/modules/surface_matching/include/opencv2/surface_matching.hpp index eee5d2e28..dd9c71c36 100644 --- a/modules/surface_matching/include/opencv2/surface_matching.hpp +++ b/modules/surface_matching/include/opencv2/surface_matching.hpp @@ -365,7 +365,7 @@ accurate representation. However, note that number of point pair features to be quadratically increased as the complexity is O(N\^2). This is especially a concern for 32 bit systems, where large models can easily overshoot the available memory. Typically, values in the range of 0.025 - 0.05 seem adequate for most of the applications, where the default value is 0.03. -(Note that there is a difference in this paremeter with the one presented in @cite drost2010. In +(Note that there is a difference in this paremeter with the one presented in @cite drost2010 . In @cite drost2010 a uniform cuboid is used for quantization and model diameter is used for reference of sampling. In my implementation, the cuboid is a rectangular prism, and each dimension is quantized independently. I do not take reference from the diameter but along the individual dimensions. diff --git a/modules/tracking/doc/diagrams.markdown b/modules/tracking/doc/diagrams.markdown new file mode 100644 index 000000000..7ac7d6042 --- /dev/null +++ b/modules/tracking/doc/diagrams.markdown @@ -0,0 +1,225 @@ +Tracking diagrams {#tracking_diagrams} +================= + +General diagram +=============== + +@startuml{tracking_uml_general.png} + package "Tracker" + package "TrackerFeature" + package "TrackerSampler" + package "TrackerModel" + + Tracker -> TrackerModel: create + Tracker -> TrackerSampler: create + Tracker -> TrackerFeature: create +@enduml + +Tracker diagram +=============== + +@startuml{tracking_uml_tracking.png} + package "Tracker package" #DDDDDD { + + + class Algorithm + + class Tracker{ + Ptr featureSet; + Ptr sampler; + Ptr model; + --- + +static Ptr create(const string& trackerType); + +bool init(const Mat& image, const Rect& boundingBox); + +bool update(const Mat& image, Rect& boundingBox); + } + class Tracker + note right: Tracker is the general interface for each specialized trackers + class TrackerMIL{ + +static Ptr createTracker(const TrackerMIL::Params ¶meters); + +virtual ~TrackerMIL(); + } + class TrackerBoosting{ + +static Ptr createTracker(const TrackerBoosting::Params ¶meters); + +virtual ~TrackerBoosting(); + } + Algorithm <|-- Tracker : virtual inheritance + Tracker <|-- TrackerMIL + Tracker <|-- TrackerBoosting + + note "Single instance of the Tracker" as N1 + TrackerBoosting .. N1 + TrackerMIL .. N1 + } + +@enduml + +TrackerFeatureSet diagram +========================= + +@startuml{tracking_uml_feature.png} + package "TrackerFeature package" #DDDDDD { + + class TrackerFeatureSet{ + -vector > > features + -vector responses + ... + TrackerFeatureSet(); + ~TrackerFeatureSet(); + -- + +extraction(const std::vector& images); + +selection(); + +removeOutliers(); + +vector response getResponses(); + +vector > > getTrackerFeatures(); + +bool addTrackerFeature(string trackerFeatureType); + +bool addTrackerFeature(Ptr& feature); + -clearResponses(); + } + + class TrackerFeature <>{ + static Ptr = create(const string& trackerFeatureType); + compute(const std::vector& images, Mat& response); + selection(Mat& response, int npoints); + } + note bottom: Can be specialized as in table II\nA tracker can use more types of features + + class TrackerFeatureFeature2D{ + -vector keypoints + --- + TrackerFeatureFeature2D(string detectorType, string descriptorType); + ~TrackerFeatureFeature2D(); + --- + compute(const std::vector& images, Mat& response); + selection( Mat& response, int npoints); + } + class TrackerFeatureHOG{ + TrackerFeatureHOG(); + ~TrackerFeatureHOG(); + --- + compute(const std::vector& images, Mat& response); + selection(Mat& response, int npoints); + } + + TrackerFeatureSet *-- TrackerFeature + TrackerFeature <|-- TrackerFeatureHOG + TrackerFeature <|-- TrackerFeatureFeature2D + + + note "Per readability and simplicity in this diagram\n there are only two TrackerFeature but you\n can considering the implementation of the other TrackerFeature" as N1 + TrackerFeatureHOG .. N1 + TrackerFeatureFeature2D .. N1 + } + +@enduml + + +TrackerModel diagram +==================== + +@startuml{tracking_uml_model.png} + package "TrackerModel package" #DDDDDD { + + class Typedef << (T,#FF7700) >>{ + ConfidenceMap + Trajectory + } + + class TrackerModel{ + -vector confidenceMaps; + -Trajectory trajectory; + -Ptr stateEstimator; + ... + TrackerModel(); + ~TrackerModel(); + + +bool setTrackerStateEstimator(Ptr trackerStateEstimator); + +Ptr getTrackerStateEstimator(); + + +void modelEstimation(const vector& responses); + +void modelUpdate(); + +void setLastTargetState(const Ptr lastTargetState); + +void runStateEstimator(); + + +const vector& getConfidenceMaps(); + +const ConfidenceMap& getLastConfidenceMap(); + } + class TrackerTargetState <>{ + Point2f targetPosition; + --- + Point2f getTargetPosition(); + void setTargetPosition(Point2f position); + } + class TrackerTargetState + note bottom: Each tracker can create own state + + class TrackerStateEstimator <>{ + ~TrackerStateEstimator(); + static Ptr create(const String& trackeStateEstimatorType); + Ptr estimate(const vector& confidenceMaps) + void update(vector& confidenceMaps) + } + + class TrackerStateEstimatorSVM{ + TrackerStateEstimatorSVM() + ~TrackerStateEstimatorSVM() + Ptr estimate(const vector& confidenceMaps) + void update(vector& confidenceMaps) + } + class TrackerStateEstimatorMILBoosting{ + TrackerStateEstimatorMILBoosting() + ~TrackerStateEstimatorMILBoosting() + Ptr estimate(const vector& confidenceMaps) + void update(vector& confidenceMaps) + } + + TrackerModel -> TrackerStateEstimator: create + TrackerModel *-- TrackerTargetState + TrackerStateEstimator <|-- TrackerStateEstimatorMILBoosting + TrackerStateEstimator <|-- TrackerStateEstimatorSVM + } +@enduml + +TrackerSampler diagram +====================== + +@startuml{tracking_uml_sampler.png} + package "TrackerSampler package" #DDDDDD { + + class TrackerSampler{ + -vector > > samplers + -vector samples; + ... + TrackerSampler(); + ~TrackerSampler(); + +sampling(const Mat& image, Rect boundingBox); + +const vector > >& getSamplers(); + +const vector& getSamples(); + +bool addTrackerSamplerAlgorithm(String trackerSamplerAlgorithmType); + +bool addTrackerSamplerAlgorithm(Ptr& sampler); + --- + -void clearSamples(); + } + + class TrackerSamplerAlgorithm{ + ~TrackerSamplerAlgorithm(); + +static Ptr create(const String& trackerSamplerType); + +bool sampling(const Mat& image, Rect boundingBox, vector& sample); + } + note bottom: A tracker could sample the target\nor it could sample the target and the background + + + class TrackerSamplerCS{ + TrackerSamplerCS(); + ~TrackerSamplerCS(); + +bool sampling(const Mat& image, Rect boundingBox, vector& sample); + } + class TrackerSamplerCSC{ + TrackerSamplerCSC(); + ~TrackerSamplerCSC(); + +bool sampling(const Mat& image, Rect boundingBox, vector& sample); + } + + + } +@enduml diff --git a/modules/tracking/include/opencv2/tracking.hpp b/modules/tracking/include/opencv2/tracking.hpp index c4ab41fdd..86a010860 100644 --- a/modules/tracking/include/opencv2/tracking.hpp +++ b/modules/tracking/include/opencv2/tracking.hpp @@ -52,7 +52,7 @@ Long-term optical tracking API Long-term optical tracking is one of most important issue for many computer vision applications in real world scenario. The development in this area is very fragmented and this API is an unique interface useful for plug several algorithms and compare them. This work is partially based on -@cite AAM and @cite AMVOT. +@cite AAM and @cite AMVOT . This algorithms start from a bounding box of the target and with their internal representation they avoid the drift during the tracking. These long-term trackers are able to evaluate online the @@ -67,36 +67,15 @@ most likely target states). The class TrackerTargetState represents a possible s The TrackerSampler and the TrackerFeatureSet are the visual representation of the target, instead the TrackerModel is the statistical model. -A recent benchmark between these algorithms can be found in @cite OOT. +A recent benchmark between these algorithms can be found in @cite OOT -UML design: ------------ - -**General diagram** - -![General diagram](pics/package.png) - -**Tracker diagram** - -![Tracker diagram](pics/Tracker.png) - -**TrackerSampler diagram** - -![TrackerSampler diagram](pics/TrackerSampler.png) - -**TrackerFeatureSet diagram** - -![TrackerFeatureSet diagram](pics/TrackerFeature.png) - -**TrackerModel diagram** - -![TrackerModel diagram](pics/TrackerModel.png) +UML design: see @ref tracking_diagrams To see how API works, try tracker demo: @note This Tracking API has been designed with PlantUML. If you modify this API please change UML -files under modules/tracking/misc/ The following reference was used in the API +in modules/tracking/doc/tracking_diagrams.markdown. The following reference was used in the API Creating Own Tracker -------------------- diff --git a/modules/tracking/include/opencv2/tracking/tracker.hpp b/modules/tracking/include/opencv2/tracking/tracker.hpp index de1a94fff..d893b45bd 100644 --- a/modules/tracking/include/opencv2/tracking/tracker.hpp +++ b/modules/tracking/include/opencv2/tracking/tracker.hpp @@ -1073,7 +1073,7 @@ class CV_EXPORTS_W TrackerFeatureLBP : public TrackerFeature background. Multiple Instance Learning avoids the drift problem for a robust tracking. The implementation is -based on @cite MIL. +based on @cite MIL . Original code can be found here */ @@ -1105,7 +1105,7 @@ class CV_EXPORTS_W TrackerMIL : public Tracker /** @brief This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm. The classifier uses the surrounding background as negative examples in update step to avoid the -drifting problem. The implementation is based on @cite OLB. +drifting problem. The implementation is based on @cite OLB . */ class CV_EXPORTS_W TrackerBoosting : public Tracker { @@ -1137,7 +1137,7 @@ class CV_EXPORTS_W TrackerBoosting : public Tracker /** @brief Median Flow tracker implementation. -Implementation of a paper @cite MedianFlow. +Implementation of a paper @cite MedianFlow . The tracker is suitable for very smooth and predictable movements when object is visible throughout the whole sequence. It's quite and accurate for this type of problems (in particular, it was shown @@ -1168,7 +1168,7 @@ tracking, learning and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector’s -errors and updates it to avoid these errors in the future. The implementation is based on @cite TLD. +errors and updates it to avoid these errors in the future. The implementation is based on @cite TLD . The Median Flow algorithm (see cv::TrackerMedianFlow) was chosen as a tracking component in this implementation, following authors. Tracker is supposed to be able to handle rapid motions, partial diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp index b6f2909fc..5f0125776 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp @@ -64,7 +64,7 @@ namespace xfeatures2d //! @addtogroup xfeatures2d_experiment //! @{ -/** @brief Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in @cite AOV12. +/** @brief Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in @cite AOV12 . The algorithm propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is @@ -116,7 +116,7 @@ public: * BRIEF Descriptor */ -/** @brief Class for computing BRIEF descriptors described in @cite calon2010 +/** @brief Class for computing BRIEF descriptors described in @cite calon2010 . @note - A complete BRIEF extractor sample can be found at diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp index eae1bc5d0..953397255 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp @@ -54,7 +54,7 @@ namespace xfeatures2d //! @{ /** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform -(SIFT) algorithm by D. Lowe @cite Lowe04. +(SIFT) algorithm by D. Lowe @cite Lowe04 . */ class CV_EXPORTS_W SIFT : public Feature2D { @@ -84,7 +84,7 @@ public: typedef SIFT SiftFeatureDetector; typedef SIFT SiftDescriptorExtractor; -/** @brief Class for extracting Speeded Up Robust Features from an image @cite Bay06. +/** @brief Class for extracting Speeded Up Robust Features from an image @cite Bay06 . The algorithm parameters: - member int extended diff --git a/modules/ximgproc/doc/ximgproc.bib b/modules/ximgproc/doc/ximgproc.bib index 224e01340..3e1b32361 100644 --- a/modules/ximgproc/doc/ximgproc.bib +++ b/modules/ximgproc/doc/ximgproc.bib @@ -46,3 +46,12 @@ year={2010}, publisher={Springer} } + +@inproceedings{Lim2013, + title={Sketch tokens: A learned mid-level representation for contour and object detection}, + author={Lim, Joseph J and Zitnick, C Lawrence and Doll{\'a}r, Piotr}, + booktitle={Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on}, + pages={3158--3165}, + year={2013}, + organization={IEEE} +} diff --git a/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp index 60132e0f2..0228e136a 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp @@ -61,7 +61,7 @@ enum EdgeAwareFiltersList /** @brief Interface for realizations of Domain Transform filter. -For more details about this filter see @cite Gastal11. +For more details about this filter see @cite Gastal11 . */ class CV_EXPORTS_W DTFilter : public Algorithm { @@ -125,7 +125,7 @@ void dtFilter(InputArray guide, InputArray src, OutputArray dst, double sigmaSpa /** @brief Interface for realizations of Guided Filter. -For more details about this filter see @cite Kaiming10. +For more details about this filter see @cite Kaiming10 . */ class CV_EXPORTS_W GuidedFilter : public Algorithm { @@ -153,7 +153,7 @@ channels then only first 3 channels will be used. @param eps regularization term of Guided Filter. \f${eps}^2\f$ is similar to the sigma in the color space into bilateralFilter. -For more details about Guided Filter parameters, see the original article @cite Kaiming10. +For more details about Guided Filter parameters, see the original article @cite Kaiming10 . */ CV_EXPORTS_W Ptr createGuidedFilter(InputArray guide, int radius, double eps); @@ -228,7 +228,7 @@ bilateralFilter. @param adjust_outliers optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper. -For more details about Adaptive Manifold Filter parameters, see the original article @cite Gastal12. +For more details about Adaptive Manifold Filter parameters, see the original article @cite Gastal12 . @note Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same diff --git a/modules/ximgproc/include/opencv2/ximgproc/seeds.hpp b/modules/ximgproc/include/opencv2/ximgproc/seeds.hpp index c9bce4045..4db8b8f63 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/seeds.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/seeds.hpp @@ -54,7 +54,7 @@ namespace ximgproc //! @{ /** @brief Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels -algorithm described in @cite VBRV14. +algorithm described in @cite VBRV14 . The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy function that is based on color histograms and a boundary term, which is optional. 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+ if ( image.empty() ) + { + printf("Cannot read image file: %s\n", inFilename.c_str()); + return -1; + } + @endcode + +-# **Convert source image to [0;1] range** + @code{.cpp} + image.convertTo(image, cv::DataType::type, 1/255.0); + @endcode + +-# **Run main algorithm** + @code{.cpp} + cv::Mat edges(image.size(), image.type()); + + cv::Ptr pDollar = + cv::createStructuredEdgeDetection(modelFilename); + pDollar->detectEdges(image, edges); + @endcode + +-# **Show results** + @code{.cpp} + if ( outFilename == "" ) + { + cv::namedWindow("edges", 1); + cv::imshow("edges", edges); + + cv::waitKey(0); + } + else + cv::imwrite(outFilename, 255*edges); + @endcode + +Literature +---------- + +For more information, refer to the following papers : @cite Dollar2013 @cite Lim2013 diff --git a/modules/ximgproc/tutorials/scripts/modelConvert.m b/modules/ximgproc/tutorials/scripts/modelConvert.m new file mode 100644 index 000000000..11790bbba --- /dev/null +++ b/modules/ximgproc/tutorials/scripts/modelConvert.m @@ -0,0 +1,73 @@ +function modelConvert(model, outname) +%% script for converting Piotr's matlab model into YAML format + +outfile = fopen(outname, 'w'); + +fprintf(outfile, '%%YAML:1.0\n\n'); + +fprintf(outfile, ['options:\n'... + ' numberOfTrees: 8\n'... + ' numberOfTreesToEvaluate: 4\n'... + ' selfsimilarityGridSize: 5\n'... + ' stride: 2\n'... + ' shrinkNumber: 2\n'... + ' patchSize: 32\n'... + ' patchInnerSize: 16\n'... + ' numberOfGradientOrientations: 4\n'... + ' gradientSmoothingRadius: 0\n'... + ' regFeatureSmoothingRadius: 2\n'... + ' ssFeatureSmoothingRadius: 8\n'... + ' gradientNormalizationRadius: 4\n\n']); + +fprintf(outfile, 'childs:\n'); +printToYML(outfile, model.child', 0); + +fprintf(outfile, 'featureIds:\n'); +printToYML(outfile, model.fids', 0); + +fprintf(outfile, 'thresholds:\n'); +printToYML(outfile, model.thrs', 0); + +N = 1000; +fprintf(outfile, 'edgeBoundaries:\n'); +printToYML(outfile, model.eBnds, N); + +fprintf(outfile, 'edgeBins:\n'); +printToYML(outfile, model.eBins, N); + +fclose(outfile); +gzip(outname); + +end + +function printToYML(outfile, A, N) +%% append matrix A to outfile as +%% - [a11, a12, a13, a14, ..., a1n] +%% - [a21, a22, a23, a24, ..., a2n] +%% ... +%% +%% if size(A, 2) == 1, A is printed by N elemnent per row + + if (length(size(A)) ~= 2) + error('printToYML: second-argument matrix should have two dimensions'); + end + + if (size(A,2) ~= 1) + for i=1:size(A,1) + fprintf(outfile, ' - ['); + fprintf(outfile, '%d,', A(i, 1:end-1)); + fprintf(outfile, '%d]\n', A(i, end)); + end + else + len = length(A); + for i=1:ceil(len/N) + first = (i-1)*N + 1; + last = min(i*N, len) - 1; + + fprintf(outfile, ' - ['); + fprintf(outfile, '%d,', A(first:last)); + fprintf(outfile, '%d]\n', A(last + 1)); + end + end + fprintf(outfile, '\n'); +end \ No newline at end of file diff --git a/modules/ximgproc/tutorials/training.markdown b/modules/ximgproc/tutorials/training.markdown new file mode 100644 index 000000000..826e08e02 --- /dev/null +++ b/modules/ximgproc/tutorials/training.markdown @@ -0,0 +1,115 @@ +Structured forest training {#tutorial_ximgproc_training} +========================== + +Introduction +------------ + +In this tutorial we show how to train your own structured forest using author's initial Matlab +implementation. + +Training pipeline +----------------- + +-# Download "Piotr's Toolbox" from [link](http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html) + and put it into separate directory, e.g. PToolbox + +-# Download BSDS500 dataset from + link \ and put it into + separate directory named exactly BSR +-# Add both directory and their subdirectories to Matlab path. + +-# Download detector code from + link \ and + put it into root directory. Now you should have : + @code + . + BSR + PToolbox + models + private + Contents.m + edgesChns.m + edgesDemo.m + edgesDemoRgbd.m + edgesDetect.m + edgesEval.m + edgesEvalDir.m + edgesEvalImg.m + edgesEvalPlot.m + edgesSweeps.m + edgesTrain.m + license.txt + readme.txt + @endcode + +-# Rename models/forest/modelFinal.mat to models/forest/modelFinal.mat.backup + +-# Open edgesChns.m and comment lines 26--41. Add after commented lines the following: + @code{.cpp} + shrink=opts.shrink; + chns = single(getFeatures( im2double(I) )); + @endcode + +-# Now it is time to compile promised getFeatures. I do with the following code: + @code{.cpp} + #include + #include + + #include + #include + + #include "MxArray.hpp" // https://github.com/kyamagu/mexopencv + + class NewRFFeatureGetter : public cv::RFFeatureGetter + { + public: + NewRFFeatureGetter() : name("NewRFFeatureGetter"){} + + virtual void getFeatures(const cv::Mat &src, NChannelsMat &features, + const int gnrmRad, const int gsmthRad, + const int shrink, const int outNum, const int gradNum) const + { + // here your feature extraction code, the default one is: + // resulting features Mat should be n-channels, floating point matrix + } + + protected: + cv::String name; + }; + + MEXFUNCTION_LINKAGE void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) + { + if (nlhs != 1) mexErrMsgTxt("nlhs != 1"); + if (nrhs != 1) mexErrMsgTxt("nrhs != 1"); + + cv::Mat src = MxArray(prhs[0]).toMat(); + src.convertTo(src, cv::DataType::type); + + std::string modelFile = MxArray(prhs[1]).toString(); + NewRFFeatureGetter *pDollar = createNewRFFeatureGetter(); + + cv::Mat edges; + pDollar->getFeatures(src, edges, 4, 0, 2, 13, 4); + // you can use other numbers here + + edges.convertTo(edges, cv::DataType::type); + + plhs[0] = MxArray(edges); + } + @endcode + +-# Place compiled mex file into root dir and run edgesDemo. You will need to wait a couple of hours + after that the new model will appear inside models/forest/. + +-# The final step is converting trained model from Matlab binary format to YAML which you can use + with our ocv::StructuredEdgeDetection. For this purpose run + opencv_contrib/ximpgroc/tutorials/scripts/modelConvert(model, "model.yml") + +How to use your model +--------------------- + +Just use expanded constructor with above defined class NewRFFeatureGetter +@code{.cpp} +cv::StructuredEdgeDetection pDollar + = cv::createStructuredEdgeDetection( modelName, makePtr() ); +@endcode diff --git a/modules/xobjdetect/include/opencv2/xobjdetect.hpp b/modules/xobjdetect/include/opencv2/xobjdetect.hpp index 3086d52eb..4a76572d0 100644 --- a/modules/xobjdetect/include/opencv2/xobjdetect.hpp +++ b/modules/xobjdetect/include/opencv2/xobjdetect.hpp @@ -131,7 +131,7 @@ struct CV_EXPORTS WaldBoostParams {} }; -/** @brief WaldBoost object detector from @cite Sochman05 +/** @brief WaldBoost object detector from @cite Sochman05 . */ class CV_EXPORTS WaldBoost : public Algorithm { @@ -190,7 +190,7 @@ struct CV_EXPORTS ICFDetectorParams {} }; -/** @brief Integral Channel Features from @cite Dollar09 +/** @brief Integral Channel Features from @cite Dollar09 . */ class CV_EXPORTS ICFDetector { From 7d9bbdcaad149b0a1ba2618e30d285f98122dba0 Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Tue, 17 Feb 2015 12:19:26 +0300 Subject: [PATCH 15/32] Remove all sphinx files --- .../bioinspired/retina_model/retina_model.rst | 418 ------------ .../table_of_content_bioinspired.rst | 36 - .../table_of_content_cvv.rst | 37 -- .../visual_debugging_introduction.rst | 372 ----------- .../ximgproc/prediction/prediction.rst | 151 ----- doc/tutorials/ximgproc/training/training.rst | 115 ---- modules/bioinspired/doc/bioinspired.rst | 10 - modules/bioinspired/doc/retina.rst | 493 -------------- modules/ccalib/doc/customPattern.rst | 202 ------ modules/cvv/doc/cvv.rst | 11 - modules/cvv/doc/cvv_api.rst | 85 --- modules/cvv/doc/cvv_gui.rst | 24 - modules/datasets/doc/ar_hmdb.rst | 36 - modules/datasets/doc/ar_sports.rst | 18 - modules/datasets/doc/datasets.rst | 121 ---- modules/datasets/doc/fr_adience.rst | 20 - modules/datasets/doc/fr_lfw.rst 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modules/xphoto/doc/inpainting.rst delete mode 100644 modules/xphoto/doc/whitebalance.rst delete mode 100644 modules/xphoto/doc/xphoto.rst diff --git a/doc/tutorials/bioinspired/retina_model/retina_model.rst b/doc/tutorials/bioinspired/retina_model/retina_model.rst deleted file mode 100644 index e8527ee8b..000000000 --- a/doc/tutorials/bioinspired/retina_model/retina_model.rst +++ /dev/null @@ -1,418 +0,0 @@ -.. _Retina_Model: - -Discovering the human retina and its use for image processing -************************************************************* - -Goal -===== - -I present here a model of human retina that shows some interesting properties for image preprocessing and enhancement. -In this tutorial you will learn how to: - -.. container:: enumeratevisibleitemswithsquare - - + discover the main two channels outing from your retina - - + see the basics to use the retina model - - + discover some parameters tweaks - - -General overview -================ - -The proposed model originates from Jeanny Herault's research [herault2010]_ at `Gipsa `_. It is involved in image processing applications with `Listic `_ (code maintainer and user) lab. This is not a complete model but it already present interesting properties that can be involved for enhanced image processing experience. The model allows the following human retina properties to be used : - -* spectral whitening that has 3 important effects: high spatio-temporal frequency signals canceling (noise), mid-frequencies details enhancement and low frequencies luminance energy reduction. This *all in one* property directly allows visual signals cleaning of classical undesired distortions introduced by image sensors and input luminance range. - -* local logarithmic luminance compression allows details to be enhanced even in low light conditions. - -* decorrelation of the details information (Parvocellular output channel) and transient information (events, motion made available at the Magnocellular output channel). - -The first two points are illustrated below : - -In the figure below, the OpenEXR image sample *CrissyField.exr*, a High Dynamic Range image is shown. In order to make it visible on this web-page, the original input image is linearly rescaled to the classical image luminance range [0-255] and is converted to 8bit/channel format. Such strong conversion hides many details because of too strong local contrasts. Furthermore, noise energy is also strong and pollutes visual information. - -.. image:: images/retina_TreeHdr_small.jpg - :alt: A High dynamic range image linearly rescaled within range [0-255]. - :align: center - -In the following image, applying the ideas proposed in [benoit2010]_, as your retina does, local luminance adaptation, spatial noise removal and spectral whitening work together and transmit accurate information on lower range 8bit data channels. On this picture, noise in significantly removed, local details hidden by strong luminance contrasts are enhanced. Output image keeps its naturalness and visual content is enhanced. Color processing is based on the color multiplexing/demultiplexing method proposed in [chaix2007]_. - -.. image:: images/retina_TreeHdr_retina.jpg - :alt: A High dynamic range image compressed within range [0-255] using the retina. - :align: center - - -*Note :* image sample can be downloaded from the `OpenEXR website `_. Regarding this demonstration, before retina processing, input image has been linearly rescaled within 0-255 keeping its channels float format. 5% of its histogram ends has been cut (mostly removes wrong HDR pixels). Check out the sample *opencv/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp* for similar processing. The following demonstration will only consider classical 8bit/channel images. - -The retina model output channels -================================ - -The retina model presents two outputs that benefit from the above cited behaviors. - -* The first one is called the Parvocellular channel. It is mainly active in the foveal retina area (high resolution central vision with color sensitive photo-receptors), its aim is to provide accurate color vision for visual details remaining static on the retina. On the other hand objects moving on the retina projection are blurred. - -* The second well known channel is the Magnocellular channel. It is mainly active in the retina peripheral vision and send signals related to change events (motion, transient events, etc.). These outing signals also help visual system to focus/center retina on 'transient'/moving areas for more detailed analysis thus improving visual scene context and object classification. - -**NOTE :** regarding the proposed model, contrary to the real retina, we apply these two channels on the entire input images using the same resolution. This allows enhanced visual details and motion information to be extracted on all the considered images... but remember, that these two channels are complementary. For example, if Magnocellular channel gives strong energy in an area, then, the Parvocellular channel is certainly blurred there since there is a transient event. - -As an illustration, we apply in the following the retina model on a webcam video stream of a dark visual scene. In this visual scene, captured in an amphitheater of the university, some students are moving while talking to the teacher. - -In this video sequence, because of the dark ambiance, signal to noise ratio is low and color artifacts are present on visual features edges because of the low quality image capture tool-chain. - -.. image:: images/studentsSample_input.jpg - :alt: an input video stream extract sample - :align: center - -Below is shown the retina foveal vision applied on the entire image. In the used retina configuration, global luminance is preserved and local contrasts are enhanced. Also, signal to noise ratio is improved : since high frequency spatio-temporal noise is reduced, enhanced details are not corrupted by any enhanced noise. - -.. image:: images/studentsSample_parvo.jpg - :alt: the retina Parvocellular output. Enhanced details, luminance adaptation and noise removal. A processing tool for image analysis. - :align: center - -Below is the output of the Magnocellular output of the retina model. Its signals are strong where transient events occur. Here, a student is moving at the bottom of the image thus generating high energy. The remaining of the image is static however, it is corrupted by a strong noise. Here, the retina filters out most of the noise thus generating low false motion area 'alarms'. This channel can be used as a transient/moving areas detector : it would provide relevant information for a low cost segmentation tool that would highlight areas in which an event is occurring. - -.. image:: images/studentsSample_magno.jpg - :alt: the retina Magnocellular output. Enhanced transient signals (motion, etc.). A preprocessing tool for event detection. - :align: center - -Retina use case -=============== - -This model can be used basically for spatio-temporal video effects but also in the aim of : - -* performing texture analysis with enhanced signal to noise ratio and enhanced details robust against input images luminance ranges (check out the Parvocellular retina channel output) - -* performing motion analysis also taking benefit of the previously cited properties. - -Literature -========== -For more information, refer to the following papers : - -.. [benoit2010] Benoit A., Caplier A., Durette B., Herault, J., "Using Human Visual System Modeling For Bio-Inspired Low Level Image Processing", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773. DOI - -* Please have a look at the reference work of Jeanny Herault that you can read in his book : - -.. [herault2010] Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. - -This retina filter code includes the research contributions of phd/research collegues from which code has been redrawn by the author : - -* take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color mosaicing/demosaicing and his reference paper: - -.. [chaix2007] B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 - -* take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. More informations in the above cited Jeanny Heraults's book. - -Code tutorial -============= - -Please refer to the original tutorial source code in file *opencv_folder/samples/cpp/tutorial_code/bioinspired/retina_tutorial.cpp*. - -**Note :** do not forget that the retina model is included in the following namespace : *cv::bioinspired*. - -To compile it, assuming OpenCV is correctly installed, use the following command. It requires the opencv_core *(cv::Mat and friends objects management)*, opencv_highgui *(display and image/video read)* and opencv_bioinspired *(Retina description)* libraries to compile. - -.. code-block:: cpp - - // compile - gcc retina_tutorial.cpp -o Retina_tuto -lopencv_core -lopencv_highgui -lopencv_bioinspired - - // Run commands : add 'log' as a last parameter to apply a spatial log sampling (simulates retina sampling) - // run on webcam - ./Retina_tuto -video - // run on video file - ./Retina_tuto -video myVideo.avi - // run on an image - ./Retina_tuto -image myPicture.jpg - // run on an image with log sampling - ./Retina_tuto -image myPicture.jpg log - -Here is a code explanation : - -Retina definition is present in the bioinspired package and a simple include allows to use it. You can rather use the specific header : *opencv2/bioinspired.hpp* if you prefer but then include the other required openv modules : *opencv2/core.hpp* and *opencv2/highgui.hpp* - -.. code-block:: cpp - - #include "opencv2/opencv.hpp" - -Provide user some hints to run the program with a help function - -.. code-block:: cpp - - // the help procedure - static void help(std::string errorMessage) - { - std::cout<<"Program init error : "< you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<= 3) - { - std::cout<<"RetinaDemo: processing image "<>inputFrame; - }else - { - // bad command parameter - help("bad command parameter"); - return -1; - } - -Once all input parameters are processed, a first image should have been loaded, if not, display error and stop program : - -.. code-block:: cpp - - if (inputFrame.empty()) - { - help("Input media could not be loaded, aborting"); - return -1; - } - -Now, everything is ready to run the retina model. I propose here to allocate a retina instance and to manage the eventual log sampling option. The Retina constructor expects at least a cv::Size object that shows the input data size that will have to be managed. One can activate other options such as color and its related color multiplexing strategy (here Bayer multiplexing is chosen using *enum cv::bioinspired::RETINA_COLOR_BAYER*). If using log sampling, the image reduction factor (smaller output images) and log sampling strengh can be adjusted. - -.. code-block:: cpp - - // pointer to a retina object - cv::Ptr myRetina; - - // if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision) - if (useLogSampling) - { - myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0); - } - else// -> else allocate "classical" retina : - myRetina = cv::bioinspired::createRetina(inputFrame.size()); - -Once done, the proposed code writes a default xml file that contains the default parameters of the retina. This is useful to make your own config using this template. Here generated template xml file is called *RetinaDefaultParameters.xml*. - -.. code-block:: cpp - - // save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup" - myRetina->write("RetinaDefaultParameters.xml"); - -In the following line, the retina attempts to load another xml file called *RetinaSpecificParameters.xml*. If you created it and introduced your own setup, it will be loaded, in the other case, default retina parameters are used. - -.. code-block:: cpp - - // load parameters if file exists - myRetina->setup("RetinaSpecificParameters.xml"); - -It is not required here but just to show it is possible, you can reset the retina buffers to zero to force it to forget past events. - -.. code-block:: cpp - - // reset all retina buffers (imagine you close your eyes for a long time) - myRetina->clearBuffers(); - -Now, it is time to run the retina ! First create some output buffers ready to receive the two retina channels outputs - -.. code-block:: cpp - - // declare retina output buffers - cv::Mat retinaOutput_parvo; - cv::Mat retinaOutput_magno; - -Then, run retina in a loop, load new frames from video sequence if necessary and get retina outputs back to dedicated buffers. - -.. code-block:: cpp - - // processing loop with no stop condition - while(true) - { - // if using video stream, then, grabbing a new frame, else, input remains the same - if (videoCapture.isOpened()) - videoCapture>>inputFrame; - - // run retina filter on the loaded input frame - myRetina->run(inputFrame); - // Retrieve and display retina output - myRetina->getParvo(retinaOutput_parvo); - myRetina->getMagno(retinaOutput_magno); - cv::imshow("retina input", inputFrame); - cv::imshow("Retina Parvo", retinaOutput_parvo); - cv::imshow("Retina Magno", retinaOutput_magno); - cv::waitKey(10); - } - -That's done ! But if you want to secure the system, take care and manage Exceptions. The retina can throw some when it sees irrelevant data (no input frame, wrong setup, etc.). -Then, i recommend to surround all the retina code by a try/catch system like this : - -.. code-block:: cpp - - try{ - // pointer to a retina object - cv::Ptr myRetina; - [---] - // processing loop with no stop condition - while(true) - { - [---] - } - - }catch(cv::Exception e) - { - std::cerr<<"Error using Retina : "< - -Once done open the configuration file *RetinaDefaultParameters.xml* generated by the demo and let's have a look at it. - -.. code-block:: cpp - - - - - 1 - 1 - 7.5e-01 - 9.0e-01 - 5.7e-01 - 0.01 - 0.5 - 7. - 7.5e-01 - - 1 - 0. - 0. - 7. - 2.0e+00 - 9.5e-01 - 0. - 7. - - -Here are some hints but actually, the best parameter setup depends more on what you want to do with the retina rather than the images input that you give to retina. Apart from the more specific case of High Dynamic Range images (HDR) that require more specific setup for specific luminance compression objective, the retina behaviors should be rather stable from content to content. Note that OpenCV is able to manage such HDR format thanks to the OpenEXR images compatibility. - -Then, if the application target requires details enhancement prior to specific image processing, you need to know if mean luminance information is required or not. If not, the the retina can cancel or significantly reduce its energy thus giving more visibility to higher spatial frequency details. - - -Basic parameters ----------------- - -The most simple parameters are the following : - -* **colorMode** : let the retina process color information (if 1) or gray scale images (if 0). In this last case, only the first channel of the input will be processed. - -* **normaliseOutput** : each channel has this parameter, if value is 1, then the considered channel output is rescaled between 0 and 255. Take care in this case at the Magnocellular output level (motion/transient channel detection). Residual noise will also be rescaled ! - -**Note :** using color requires color channels multiplexing/demultipexing which requires more processing. You can expect much faster processing using gray levels : it would require around 30 product per pixel for all the retina processes and it has recently been parallelized for multicore architectures. - -Photo-receptors parameters --------------------------- - -The following parameters act on the entry point of the retina - photo-receptors - and impact all the following processes. These sensors are low pass spatio-temporal filters that smooth temporal and spatial data and also adjust there sensitivity to local luminance thus improving details extraction and high frequency noise canceling. - -* **photoreceptorsLocalAdaptationSensitivity** between 0 and 1. Values close to 1 allow high luminance log compression effect at the photo-receptors level. Values closer to 0 give a more linear sensitivity. Increased alone, it can burn the *Parvo (details channel)* output image. If adjusted in collaboration with **ganglionCellsSensitivity** images can be very contrasted whatever the local luminance there is... at the price of a naturalness decrease. - -* **photoreceptorsTemporalConstant** this setups the temporal constant of the low pass filter effect at the entry of the retina. High value lead to strong temporal smoothing effect : moving objects are blurred and can disappear while static object are favored. But when starting the retina processing, stable state is reached lately. - -* **photoreceptorsSpatialConstant** specifies the spatial constant related to photo-receptors low pass filter effect. This parameters specify the minimum allowed spatial signal period allowed in the following. Typically, this filter should cut high frequency noise. Then a 0 value doesn't cut anything noise while higher values start to cut high spatial frequencies and more and more lower frequencies... Then, do not go to high if you wanna see some details of the input images ! A good compromise for color images is 0.53 since this won't affect too much the color spectrum. Higher values would lead to gray and blurred output images. - -Horizontal cells parameters ---------------------------- - -This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells. It modulates photo-receptors sensitivity and completes the processing for final spectral whitening (part of the spatial band pass effect thus favoring visual details enhancement). - -* **horizontalCellsGain** here is a critical parameter ! If you are not interested by the mean luminance and focus on details enhancement, then, set to zero. But if you want to keep some environment luminance data, let some low spatial frequencies pass into the system and set a higher value (<1). - -* **hcellsTemporalConstant** similar to photo-receptors, this acts on the temporal constant of a low pass temporal filter that smooths input data. Here, a high value generates a high retina after effect while a lower value makes the retina more reactive. This value should be lower than **photoreceptorsTemporalConstant** to limit strong retina after effects. - -* **hcellsSpatialConstant** is the spatial constant of the low pass filter of these cells filter. It specifies the lowest spatial frequency allowed in the following. Visually, a high value leads to very low spatial frequencies processing and leads to salient halo effects. Lower values reduce this effect but the limit is : do not go lower than the value of **photoreceptorsSpatialConstant**. Those 2 parameters actually specify the spatial band-pass of the retina. - -**NOTE** after the processing managed by the previous parameters, input data is cleaned from noise and luminance in already partly enhanced. The following parameters act on the last processing stages of the two outing retina signals. - -Parvo (details channel) dedicated parameter -------------------------------------------- - -* **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at the output of this details dedicated channel. Parameter values remain between 0 and 1. Low value tend to give a linear response while higher values enforces the remaining low contrasted areas. - -**Note :** this parameter can correct eventual burned images by favoring low energetic details of the visual scene, even in bright areas. - -IPL Magno (motion/transient channel) parameters ------------------------------------------------ - -Once image information is cleaned, this channel acts as a high pass temporal filter that only selects signals related to transient signals (events, motion, etc.). A low pass spatial filter smooths extracted transient data and a final logarithmic compression enhances low transient events thus enhancing event sensitivity. - -* **parasolCells_beta** generally set to zero, can be considered as an amplifier gain at the entry point of this processing stage. Generally set to 0. - -* **parasolCells_tau** the temporal smoothing effect that can be added - -* **parasolCells_k** the spatial constant of the spatial filtering effect, set it at a high value to favor low spatial frequency signals that are lower subject to residual noise. - -* **amacrinCellsTemporalCutFrequency** specifies the temporal constant of the high pass filter. High values let slow transient events to be selected. - -* **V0CompressionParameter** specifies the strength of the log compression. Similar behaviors to previous description but here it enforces sensitivity of transient events. - -* **localAdaptintegration_tau** generally set to 0, no real use here actually - -* **localAdaptintegration_k** specifies the size of the area on which local adaptation is performed. Low values lead to short range local adaptation (higher sensitivity to noise), high values secure log compression. diff --git a/doc/tutorials/bioinspired/table_of_content_bioinspired/table_of_content_bioinspired.rst b/doc/tutorials/bioinspired/table_of_content_bioinspired/table_of_content_bioinspired.rst deleted file mode 100644 index 88869e98f..000000000 --- a/doc/tutorials/bioinspired/table_of_content_bioinspired/table_of_content_bioinspired.rst +++ /dev/null @@ -1,36 +0,0 @@ -.. _Table-Of-Content-Bioinspired: - -*bioinspired* module. Algorithms inspired from biological models ----------------------------------------------------------------- - -Here you will learn how to use additional modules of OpenCV defined in the "bioinspired" module. - - .. include:: ../../definitions/tocDefinitions.rst - -+ - .. tabularcolumns:: m{100pt} m{300pt} - .. cssclass:: toctableopencv - - =============== ====================================================== - |RetinaDemoImg| **Title:** :ref:`Retina_Model` - - *Compatibility:* > OpenCV 2.4 - - *Author:* |Author_AlexB| - - You will learn how to process images and video streams with a model of retina filter for details enhancement, spatio-temporal noise removal, luminance correction and spatio-temporal events detection. - - =============== ====================================================== - - .. |RetinaDemoImg| image:: images/retina_TreeHdr_small.jpg - :height: 90pt - :width: 90pt - - .. raw:: latex - - \pagebreak - -.. toctree:: - :hidden: - - ../retina_model/retina_model diff --git a/doc/tutorials/cvv/table_of_content_cvv/table_of_content_cvv.rst b/doc/tutorials/cvv/table_of_content_cvv/table_of_content_cvv.rst deleted file mode 100644 index b5bc04590..000000000 --- a/doc/tutorials/cvv/table_of_content_cvv/table_of_content_cvv.rst +++ /dev/null @@ -1,37 +0,0 @@ -.. _Table-Of-Content-CVV: - -*cvv* module. GUI for Interactive Visual Debugging --------------------------------------------------- - -Here you will learn how to use the cvv module to ease programming computer vision software through visual debugging aids. - - .. include:: ../../definitions/tocDefinitions.rst - -+ - .. tabularcolumns:: m{100pt} m{300pt} - .. cssclass:: toctableopencv - - =============== ====================================================== - |cvvIntro| *Title:* :ref:`Visual_Debugging_Introduction` - - *Compatibility:* > OpenCV 2.4.8 - - *Author:* |Author_Bihlmaier| - - We will learn how to debug our applications in a visual and interactive way. - - =============== ====================================================== - - .. |cvvIntro| image:: images/Visual_Debugging_Introduction_Tutorial_Cover.jpg - :height: 90pt - :width: 90pt - - - .. raw:: latex - - \pagebreak - -.. toctree:: - :hidden: - - ../visual_debugging_introduction/visual_debugging_introduction diff --git a/doc/tutorials/cvv/visual_debugging_introduction/visual_debugging_introduction.rst b/doc/tutorials/cvv/visual_debugging_introduction/visual_debugging_introduction.rst deleted file mode 100644 index 8bcaf6448..000000000 --- a/doc/tutorials/cvv/visual_debugging_introduction/visual_debugging_introduction.rst +++ /dev/null @@ -1,372 +0,0 @@ -.. _Visual_Debugging_Introduction: - -Interactive Visual Debugging of Computer Vision applications -************************************************************ - -What is the most common way to debug computer vision applications? -Usually the answer is temporary, hacked together, custom code that must be removed from the code for release compilation. - -In this tutorial we will show how to use the visual debugging features of the **cvv** module (*opencv2/cvv/cvv.hpp*) instead. - - -Goals -====== - -In this tutorial you will learn how to: - -* Add cvv debug calls to your application -* Use the visual debug GUI -* Enable and disable the visual debug features during compilation (with zero runtime overhead when disabled) - - -Code -===== - -The example code - -* captures images (*highgui*), e.g. from a webcam, -* applies some filters to each image (*imgproc*), -* detects image features and matches them to the previous image (*features2d*). - -If the program is compiled without visual debugging (see CMakeLists.txt below) the only result is some information printed to the command line. -We want to demonstrate how much debugging or development functionality is added by just a few lines of *cvv* commands. - -.. code-block:: cpp - - // system includes - #include - #include - - // library includes - #include - #include - #include - - // Visual debugging - #include - - - // helper function to convert objects that support operator<<() to std::string - template std::string toString(const T& p_arg) - { - std::stringstream ss; - - ss << p_arg; - - return ss.str(); - } - - - void - usage() - { - printf("usage: cvvt [-r WxH]\n"); - printf("-h print this help\n"); - printf("-r WxH change resolution to width W and height H\n"); - } - - - int - main(int argc, char** argv) - { - #ifdef CVVISUAL_DEBUGMODE - std::cout << "Visual debugging is ENABLED" << std::endl; - #else - std::cout << "Visual debugging is DISABLED" << std::endl; - #endif - - cv::Size* resolution = nullptr; - - // parse options - const char* optstring = "hr:"; - int opt; - while ((opt = getopt(argc, argv, optstring)) != -1) { - switch (opt) { - case 'h': - usage(); - return 0; - break; - case 'r': - { - char dummych; - resolution = new cv::Size(); - if (sscanf(optarg, "%d%c%d", &resolution->width, &dummych, &resolution->height) != 3) { - printf("%s not a valid resolution\n", optarg); - return 1; - } - } - break; - default: - usage(); - return 2; - } - } - - // setup video capture - cv::VideoCapture capture(0); - if (!capture.isOpened()) { - std::cout << "Could not open VideoCapture" << std::endl; - return 3; - } - - if (resolution) { - printf("Setting resolution to %dx%d\n", resolution->width, resolution->height); - capture.set(CV_CAP_PROP_FRAME_WIDTH, resolution->width); - capture.set(CV_CAP_PROP_FRAME_HEIGHT, resolution->height); - } - - - cv::Mat prevImgGray; - std::vector prevKeypoints; - cv::Mat prevDescriptors; - - int maxFeatureCount = 500; - cv::ORB detector(maxFeatureCount); - - cv::BFMatcher matcher(cv::NORM_HAMMING); - - for (int imgId = 0; imgId < 10; imgId++) { - // capture a frame - cv::Mat imgRead; - capture >> imgRead; - printf("%d: image captured\n", imgId); - - std::string imgIdString{"imgRead"}; - imgIdString += toString(imgId); - cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str()); - - // convert to grayscale - cv::Mat imgGray; - cv::cvtColor(imgRead, imgGray, CV_BGR2GRAY); - cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray"); - - // filter edges using Canny on smoothed image - cv::Mat imgGraySmooth; - cv::GaussianBlur(imgGray, imgGraySmooth, cv::Size(9, 9), 2, 2); - cvv::debugFilter(imgGray, imgGraySmooth, CVVISUAL_LOCATION, "smoothed"); - cv::Mat imgEdges; - cv::Canny(imgGraySmooth, imgEdges, 50, 150); - cvv::showImage(imgEdges, CVVISUAL_LOCATION, "edges"); - - // dilate edges - cv::Mat imgEdgesDilated; - cv::dilate(imgEdges, imgEdgesDilated, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(7, 7), cv::Point(3, 3))); - cvv::debugFilter(imgEdges, imgEdgesDilated, CVVISUAL_LOCATION, "dilated edges"); - - // detect ORB features - std::vector keypoints; - cv::Mat descriptors; - detector(imgGray, cv::noArray(), keypoints, descriptors); - printf("%d: detected %zd keypoints\n", imgId, keypoints.size()); - - // match them to previous image (if available) - if (!prevImgGray.empty()) { - std::vector matches; - matcher.match(prevDescriptors, descriptors, matches); - printf("%d: all matches size=%zd\n", imgId, matches.size()); - std::string allMatchIdString{"all matches "}; - allMatchIdString += toString(imgId-1) + "<->" + toString(imgId); - cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, allMatchIdString.c_str()); - - // remove worst (as defined by match distance) bestRatio quantile - double bestRatio = 0.8; - std::sort(matches.begin(), matches.end()); - matches.resize(int(bestRatio * matches.size())); - printf("%d: best matches size=%zd\n", imgId, matches.size()); - std::string bestMatchIdString{"best " + toString(bestRatio) + " matches "}; - bestMatchIdString += toString(imgId-1) + "<->" + toString(imgId); - cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, bestMatchIdString.c_str()); - } - - prevImgGray = imgGray; - prevKeypoints = keypoints; - prevDescriptors = descriptors; - } - - cvv::finalShow(); - - return 0; - } - - -.. code-block:: cmake - - cmake_minimum_required(VERSION 2.8) - - project(cvvisual_test) - - SET(CMAKE_PREFIX_PATH ~/software/opencv/install) - - SET(CMAKE_CXX_COMPILER "g++-4.8") - SET(CMAKE_CXX_FLAGS "-std=c++11 -O2 -pthread -Wall -Werror") - - # (un)set: cmake -DCVV_DEBUG_MODE=OFF .. - OPTION(CVV_DEBUG_MODE "cvvisual-debug-mode" ON) - if(CVV_DEBUG_MODE MATCHES ON) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DCVVISUAL_DEBUGMODE") - endif() - - - FIND_PACKAGE(OpenCV REQUIRED) - include_directories(${OpenCV_INCLUDE_DIRS}) - - add_executable(cvvt main.cpp) - target_link_libraries(cvvt - opencv_core opencv_highgui opencv_imgproc opencv_features2d - opencv_cvv - ) - - -Explanation -============ - -#. We compile the program either using the above CmakeLists.txt with Option *CVV_DEBUG_MODE=ON* (*cmake -DCVV_DEBUG_MODE=ON*) or by adding the corresponding define *CVVISUAL_DEBUGMODE* to our compiler (e.g. *g++ -DCVVISUAL_DEBUGMODE*). - -#. The first cvv call simply shows the image (similar to *imshow*) with the imgIdString as comment. - - .. code-block:: cpp - - cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str()); - - The image is added to the overview tab in the visual debug GUI and the cvv call blocks. - - - .. image:: images/01_overview_single.jpg - :alt: Overview with image of first cvv call - :align: center - - The image can then be selected and viewed - - .. image:: images/02_single_image_view.jpg - :alt: Display image added through cvv::showImage - :align: center - - Whenever you want to continue in the code, i.e. unblock the cvv call, you can - either continue until the next cvv call (*Step*), continue until the last cvv - call (*>>*) or run the application until it exists (*Close*). - - We decide to press the green *Step* button. - - -#. The next cvv calls are used to debug all kinds of filter operations, i.e. operations that take a picture as input and return a picture as output. - - .. code-block:: cpp - - cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray"); - - As with every cvv call, you first end up in the overview. - - .. image:: images/03_overview_two.jpg - :alt: Overview with two cvv calls after pressing Step - :align: center - - We decide not to care about the conversion to gray scale and press *Step*. - - .. code-block:: cpp - - cvv::debugFilter(imgGray, imgGraySmooth, CVVISUAL_LOCATION, "smoothed"); - - If you open the filter call, you will end up in the so called "DefaultFilterView". - Both images are shown next to each other and you can (synchronized) zoom into them. - - .. image:: images/04_default_filter_view.jpg - :alt: Default filter view displaying a gray scale image and its corresponding GaussianBlur filtered one - :align: center - - When you go to very high zoom levels, each pixel is annotated with its numeric values. - - .. image:: images/05_default_filter_view_high_zoom.jpg - :alt: Default filter view at very high zoom levels - :align: center - - We press *Step* twice and have a look at the dilated image. - - .. code-block:: cpp - - cvv::debugFilter(imgEdges, imgEdgesDilated, CVVISUAL_LOCATION, "dilated edges"); - - The DefaultFilterView showing both images - - .. image:: images/06_default_filter_view_edges.jpg - :alt: Default filter view showing an edge image and the image after dilate() - :align: center - - Now we use the *View* selector in the top right and select the "DualFilterView". - We select "Changed Pixels" as filter and apply it (middle image). - - .. image:: images/07_dual_filter_view_edges.jpg - :alt: Dual filter view showing an edge image and the image after dilate() - :align: center - - After we had a close look at these images, perhaps using different views, filters or other GUI features, we decide to let the program run through. Therefore we press the yellow *>>* button. - - The program will block at - - .. code-block:: cpp - - cvv::finalShow(); - - and display the overview with everything that was passed to cvv in the meantime. - - .. image:: images/08_overview_all.jpg - :alt: Overview displaying all cvv calls up to finalShow() - :align: center - -#. The cvv debugDMatch call is used in a situation where there are two images each with a set of descriptors that are matched to each other. - - We pass both images, both sets of keypoints and their matching to the visual debug module. - - .. code-block:: cpp - - cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, allMatchIdString.c_str()); - - Since we want to have a look at matches, we use the filter capabilities (*#type match*) in the overview to only show match calls. - - .. image:: images/09_overview_filtered_type_match.jpg - :alt: Overview displaying only match calls - :align: center - - We want to have a closer look at one of them, e.g. to tune our parameters that use the matching. - The view has various settings how to display keypoints and matches. - Furthermore, there is a mouseover tooltip. - - .. image:: images/10_line_match_view.jpg - :alt: Line match view - :align: center - - We see (visual debugging!) that there are many bad matches. - We decide that only 70% of the matches should be shown - those 70% with the lowest match distance. - - .. image:: images/11_line_match_view_portion_selector.jpg - :alt: Line match view showing the best 70% matches, i.e. lowest match distance - :align: center - - Having successfully reduced the visual distraction, we want to see more clearly what changed between the two images. - We select the "TranslationMatchView" that shows to where the keypoint was matched in a different way. - - .. image:: images/12_translation_match_view_portion_selector.jpg - :alt: Translation match view - :align: center - - It is easy to see that the cup was moved to the left during the two images. - - Although, cvv is all about interactively *seeing* the computer vision bugs, this is complemented by a "RawView" that allows to have a look at the underlying numeric data. - - .. image:: images/13_raw_view.jpg - :alt: Raw view of matches - :align: center - -#. There are many more useful features contained in the cvv GUI. For instance, one can group the overview tab. - - .. image:: images/14_overview_group_by_line.jpg - :alt: Overview grouped by call line - :align: center - - -Result -======= - -* By adding a view expressive lines to our computer vision program we can interactively debug it through different visualizations. -* Once we are done developing/debugging we do not have to remove those lines. We simply disable cvv debugging (*cmake -DCVV_DEBUG_MODE=OFF* or g++ without *-DCVVISUAL_DEBUGMODE*) and our programs runs without any debug overhead. - -Enjoy computer vision! diff --git a/doc/tutorials/ximgproc/prediction/prediction.rst b/doc/tutorials/ximgproc/prediction/prediction.rst deleted file mode 100644 index af102f68c..000000000 --- a/doc/tutorials/ximgproc/prediction/prediction.rst +++ /dev/null @@ -1,151 +0,0 @@ -.. ximgproc: - -Structured forests for fast edge detection -****************************************** - -Introduction ------------- -In this tutorial you will learn how to use structured forests for the purpose of edge detection in an image. - -Examples --------- - -.. image:: images/01.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/02.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/03.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/04.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/05.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/06.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/07.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/08.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/09.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/10.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/11.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -.. image:: images/12.jpg - :height: 238pt - :width: 750pt - :alt: First example - :align: center - -**Note :** binarization techniques like Canny edge detector are applicable - to edges produced by both algorithms (``Sobel`` and ``StructuredEdgeDetection::detectEdges``). - -Source Code ------------ - -.. literalinclude:: ../../../../modules/ximpgroc/samples/cpp/structured_edge_detection.cpp - :language: cpp - :linenos: - :tab-width: 4 - -Explanation ------------ - -1. **Load source color image** - - .. code-block:: cpp - - cv::Mat image = cv::imread(inFilename, 1); - if ( image.empty() ) - { - printf("Cannot read image file: %s\n", inFilename.c_str()); - return -1; - } - -2. **Convert source image to [0;1] range** - - .. code-block:: cpp - - image.convertTo(image, cv::DataType::type, 1/255.0); - -3. **Run main algorithm** - - .. code-block:: cpp - - cv::Mat edges(image.size(), image.type()); - - cv::Ptr pDollar = - cv::createStructuredEdgeDetection(modelFilename); - pDollar->detectEdges(image, edges); - -4. **Show results** - - .. code-block:: cpp - - if ( outFilename == "" ) - { - cv::namedWindow("edges", 1); - cv::imshow("edges", edges); - - cv::waitKey(0); - } - else - cv::imwrite(outFilename, 255*edges); - -Literature ----------- -For more information, refer to the following papers : - -.. [Dollar2013] Dollar P., Zitnick C. L., "Structured forests for fast edge detection", - IEEE International Conference on Computer Vision (ICCV), 2013, - pp. 1841-1848. `DOI `_ - -.. [Lim2013] Lim J. J., Zitnick C. L., Dollar P., "Sketch Tokens: A Learned - Mid-level Representation for Contour and Object Detection", - Comoputer Vision and Pattern Recognition (CVPR), 2013, - pp. 3158-3165. `DOI `_ diff --git a/doc/tutorials/ximgproc/training/training.rst b/doc/tutorials/ximgproc/training/training.rst deleted file mode 100644 index 20a1cfc27..000000000 --- a/doc/tutorials/ximgproc/training/training.rst +++ /dev/null @@ -1,115 +0,0 @@ -.. ximgproc: - -Structured forest training -************************** - -Introduction ------------- -In this tutorial we show how to train your own structured forest using author's initial Matlab implementation. - -Training pipeline ------------------ - -1. Download "Piotr's Toolbox" from `link `_ - and put it into separate directory, e.g. PToolbox - -2. Download BSDS500 dataset from `link ` - and put it into separate directory named exactly BSR - -3. Add both directory and their subdirectories to Matlab path. - -4. Download detector code from `link ` - and put it into root directory. Now you should have :: - - . - BSR - PToolbox - models - private - Contents.m - edgesChns.m - edgesDemo.m - edgesDemoRgbd.m - edgesDetect.m - edgesEval.m - edgesEvalDir.m - edgesEvalImg.m - edgesEvalPlot.m - edgesSweeps.m - edgesTrain.m - license.txt - readme.txt - -5. Rename models/forest/modelFinal.mat to models/forest/modelFinal.mat.backup - -6. Open edgesChns.m and comment lines 26--41. Add after commented lines the following: :: - - shrink=opts.shrink; - chns = single(getFeatures( im2double(I) )); - -7. Now it is time to compile promised getFeatures. I do with the following code: - - .. code-block:: cpp - - #include - #include - - #include - #include - - #include "MxArray.hpp" // https://github.com/kyamagu/mexopencv - - class NewRFFeatureGetter : public cv::RFFeatureGetter - { - public: - NewRFFeatureGetter() : name("NewRFFeatureGetter"){} - - virtual void getFeatures(const cv::Mat &src, NChannelsMat &features, - const int gnrmRad, const int gsmthRad, - const int shrink, const int outNum, const int gradNum) const - { - // here your feature extraction code, the default one is: - // resulting features Mat should be n-channels, floating point matrix - } - - protected: - cv::String name; - }; - - MEXFUNCTION_LINKAGE void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) - { - if (nlhs != 1) mexErrMsgTxt("nlhs != 1"); - if (nrhs != 1) mexErrMsgTxt("nrhs != 1"); - - cv::Mat src = MxArray(prhs[0]).toMat(); - src.convertTo(src, cv::DataType::type); - - std::string modelFile = MxArray(prhs[1]).toString(); - NewRFFeatureGetter *pDollar = createNewRFFeatureGetter(); - - cv::Mat edges; - pDollar->getFeatures(src, edges, 4, 0, 2, 13, 4); - // you can use other numbers here - - edges.convertTo(edges, cv::DataType::type); - - plhs[0] = MxArray(edges); - } - -8. Place compiled mex file into root dir and run edgesDemo. - You will need to wait a couple of hours after that the new model - will appear inside models/forest/. - -9. The final step is converting trained model from Matlab binary format - to YAML which you can use with our ocv::StructuredEdgeDetection. - For this purpose run opencv_contrib/doc/tutorials/ximpgroc/training/modelConvert(model, "model.yml") - -How to use your model ---------------------- - -Just use expanded constructor with above defined class NewRFFeatureGetter - - .. code-block:: cpp - - cv::StructuredEdgeDetection pDollar - = cv::createStructuredEdgeDetection( modelName, makePtr() ); diff --git a/modules/bioinspired/doc/bioinspired.rst b/modules/bioinspired/doc/bioinspired.rst deleted file mode 100644 index 0e964596b..000000000 --- a/modules/bioinspired/doc/bioinspired.rst +++ /dev/null @@ -1,10 +0,0 @@ -******************************************************************** -bioinspired. Biologically inspired vision models and derivated tools -******************************************************************** - -The module provides biological visual systems models (human visual system and others). It also provides derivated objects that take advantage of those bio-inspired models. - -.. toctree:: - :maxdepth: 2 - - Human retina documentation diff --git a/modules/bioinspired/doc/retina.rst b/modules/bioinspired/doc/retina.rst deleted file mode 100644 index 17b50330c..000000000 --- a/modules/bioinspired/doc/retina.rst +++ /dev/null @@ -1,493 +0,0 @@ -Retina : a Bio mimetic human retina model -***************************************** - -.. highlight:: cpp - -Retina -====== -.. ocv:class:: Retina : public Algorithm - -**Note** : do not forget that the retina model is included in the following namespace : *cv::bioinspired*. - -Introduction -++++++++++++ - -Class which provides the main controls to the Gipsa/Listic labs human retina model. This is a non separable spatio-temporal filter modelling the two main retina information channels : - -* foveal vision for detailled color vision : the parvocellular pathway. - -* peripheral vision for sensitive transient signals detection (motion and events) : the magnocellular pathway. - -From a general point of view, this filter whitens the image spectrum and corrects luminance thanks to local adaptation. An other important property is its hability to filter out spatio-temporal noise while enhancing details. -This model originates from Jeanny Herault work [Herault2010]_. It has been involved in Alexandre Benoit phd and his current research [Benoit2010]_, [Strat2013]_ (he currently maintains this module within OpenCV). It includes the work of other Jeanny's phd student such as [Chaix2007]_ and the log polar transformations of Barthelemy Durette described in Jeanny's book. - -**NOTES :** - -* For ease of use in computer vision applications, the two retina channels are applied homogeneously on all the input images. This does not follow the real retina topology but this can still be done using the log sampling capabilities proposed within the class. - -* Extend the retina description and code use in the tutorial/contrib section for complementary explanations. - -Preliminary illustration -++++++++++++++++++++++++ - -As a preliminary presentation, let's start with a visual example. We propose to apply the filter on a low quality color jpeg image with backlight problems. Here is the considered input... *"Well, my eyes were able to see more that this strange black shadow..."* - -.. image:: images/retinaInput.jpg - :alt: a low quality color jpeg image with backlight problems. - :align: center - -Below, the retina foveal model applied on the entire image with default parameters. Here contours are enforced, halo effects are voluntary visible with this configuration. See parameters discussion below and increase horizontalCellsGain near 1 to remove them. - -.. image:: images/retinaOutput_default.jpg - :alt: the retina foveal model applied on the entire image with default parameters. Here contours are enforced, luminance is corrected and halo effects are voluntary visible with this configuration, increase horizontalCellsGain near 1 to remove them. - :align: center - -Below, a second retina foveal model output applied on the entire image with a parameters setup focused on naturalness perception. *"Hey, i now recognize my cat, looking at the mountains at the end of the day !"*. Here contours are enforced, luminance is corrected but halos are avoided with this configuration. The backlight effect is corrected and highlight details are still preserved. Then, even on a low quality jpeg image, if some luminance information remains, the retina is able to reconstruct a proper visual signal. Such configuration is also usefull for High Dynamic Range (*HDR*) images compression to 8bit images as discussed in [Benoit2010]_ and in the demonstration codes discussed below. -As shown at the end of the page, parameters change from defaults are : - -* horizontalCellsGain=0.3 - -* photoreceptorsLocalAdaptationSensitivity=ganglioncellsSensitivity=0.89. - -.. image:: images/retinaOutput_realistic.jpg - :alt: the retina foveal model applied on the entire image with 'naturalness' parameters. Here contours are enforced but are avoided with this configuration, horizontalCellsGain is 0.3 and photoreceptorsLocalAdaptationSensitivity=ganglioncellsSensitivity=0.89. - :align: center - -As observed in this preliminary demo, the retina can be settled up with various parameters, by default, as shown on the figure above, the retina strongly reduces mean luminance energy and enforces all details of the visual scene. Luminance energy and halo effects can be modulated (exagerated to cancelled as shown on the two examples). In order to use your own parameters, you can use at least one time the *write(String fs)* method which will write a proper XML file with all default parameters. Then, tweak it on your own and reload them at any time using method *setup(String fs)*. These methods update a *Retina::RetinaParameters* member structure that is described hereafter. XML parameters file samples are shown at the end of the page. - -Here is an overview of the abstract Retina interface, allocate one instance with the *createRetina* functions.:: - - namespace cv{namespace bioinspired{ - - class Retina : public Algorithm - { - public: - // parameters setup instance - struct RetinaParameters; // this class is detailled later - - // main method for input frame processing (all use method, can also perform High Dynamic Range tone mapping) - void run (InputArray inputImage); - - // specific method aiming at correcting luminance only (faster High Dynamic Range tone mapping) - void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage) - - // output buffers retreival methods - // -> foveal color vision details channel with luminance and noise correction - void getParvo (OutputArray retinaOutput_parvo); - void getParvoRAW (OutputArray retinaOutput_parvo);// retreive original output buffers without any normalisation - const Mat getParvoRAW () const;// retreive original output buffers without any normalisation - // -> peripheral monochrome motion and events (transient information) channel - void getMagno (OutputArray retinaOutput_magno); - void getMagnoRAW (OutputArray retinaOutput_magno); // retreive original output buffers without any normalisation - const Mat getMagnoRAW () const;// retreive original output buffers without any normalisation - - // reset retina buffers... equivalent to closing your eyes for some seconds - void clearBuffers (); - - // retreive input and output buffers sizes - Size getInputSize (); - Size getOutputSize (); - - // setup methods with specific parameters specification of global xml config file loading/write - void setup (String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true); - void setup (FileStorage &fs, const bool applyDefaultSetupOnFailure=true); - void setup (RetinaParameters newParameters); - struct Retina::RetinaParameters getParameters (); - const String printSetup (); - virtual void write (String fs) const; - virtual void write (FileStorage &fs) const; - void setupOPLandIPLParvoChannel (const bool colorMode=true, const bool normaliseOutput=true, const float photoreceptorsLocalAdaptationSensitivity=0.7, const float photoreceptorsTemporalConstant=0.5, const float photoreceptorsSpatialConstant=0.53, const float horizontalCellsGain=0, const float HcellsTemporalConstant=1, const float HcellsSpatialConstant=7, const float ganglionCellsSensitivity=0.7); - void setupIPLMagnoChannel (const bool normaliseOutput=true, const float parasolCells_beta=0, const float parasolCells_tau=0, const float parasolCells_k=7, const float amacrinCellsTemporalCutFrequency=1.2, const float V0CompressionParameter=0.95, const float localAdaptintegration_tau=0, const float localAdaptintegration_k=7); - void setColorSaturation (const bool saturateColors=true, const float colorSaturationValue=4.0); - void activateMovingContoursProcessing (const bool activate); - void activateContoursProcessing (const bool activate); - }; - - // Allocators - cv::Ptr createRetina (Size inputSize); - cv::Ptr createRetina (Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0); - }} // cv and bioinspired namespaces end - -.. Sample code:: - - * An example on retina tone mapping can be found at opencv_source_code/samples/cpp/OpenEXRimages_HDR_Retina_toneMapping.cpp - * An example on retina tone mapping on video input can be found at opencv_source_code/samples/cpp/OpenEXRimages_HDR_Retina_toneMapping.cpp - * A complete example illustrating the retina interface can be found at opencv_source_code/samples/cpp/retinaDemo.cpp - -Description -+++++++++++ - -Class which allows the `Gipsa `_ (preliminary work) / `Listic `_ (code maintainer and user) labs retina model to be used. This class allows human retina spatio-temporal image processing to be applied on still images, images sequences and video sequences. Briefly, here are the main human retina model properties: - -* spectral whithening (mid-frequency details enhancement) - -* high frequency spatio-temporal noise reduction (temporal noise and high frequency spatial noise are minimized) - -* low frequency luminance reduction (luminance range compression) : high luminance regions do not hide details in darker regions anymore - -* local logarithmic luminance compression allows details to be enhanced even in low light conditions - -Use : this model can be used basically for spatio-temporal video effects but also in the aim of : - -* performing texture analysis with enhanced signal to noise ratio and enhanced details robust against input images luminance ranges (check out the parvocellular retina channel output, by using the provided **getParvo** methods) - -* performing motion analysis also taking benefit of the previously cited properties (check out the magnocellular retina channel output, by using the provided **getMagno** methods) - -* general image/video sequence description using either one or both channels. An example of the use of Retina in a Bag of Words approach is given in [Strat2013]_. - -Literature -========== -For more information, refer to the following papers : - -* Model description : - -.. [Benoit2010] Benoit A., Caplier A., Durette B., Herault, J., "Using Human Visual System Modeling For Bio-Inspired Low Level Image Processing", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773. DOI - -* Model use in a Bag of Words approach : - -.. [Strat2013] Strat S., Benoit A., Lambert P., "Retina enhanced SIFT descriptors for video indexing", CBMI2013, Veszprém, Hungary, 2013. - -* Please have a look at the reference work of Jeanny Herault that you can read in his book : - -.. [Herault2010] Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. - -This retina filter code includes the research contributions of phd/research collegues from which code has been redrawn by the author : - -* take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color mosaicing/demosaicing and his reference paper: - -.. [Chaix2007] B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 - -* take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. More informations in the above cited Jeanny Heraults's book. - -* Meylan&al work on HDR tone mapping that is implemented as a specific method within the model : - -.. [Meylan2007] L. Meylan , D. Alleysson, S. Susstrunk, "A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images", Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816 - -Demos and experiments ! -======================= - -**NOTE : Complementary to the following examples, have a look at the Retina tutorial in the tutorial/contrib section for complementary explanations.** - -Take a look at the provided C++ examples provided with OpenCV : - -* **samples/cpp/retinademo.cpp** shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video. - Typical uses are (provided your OpenCV installation is situated in folder *OpenCVReleaseFolder*) - - * image processing : **OpenCVReleaseFolder/bin/retinademo -image myPicture.jpg** - - * video processing : **OpenCVReleaseFolder/bin/retinademo -video myMovie.avi** - - * webcam processing: **OpenCVReleaseFolder/bin/retinademo -video** - - **Note :** This demo generates the file *RetinaDefaultParameters.xml* which contains the default parameters of the retina. Then, rename this as *RetinaSpecificParameters.xml*, adjust the parameters the way you want and reload the program to check the effect. - - -* **samples/cpp/OpenEXRimages_HDR_Retina_toneMapping.cpp** shows how to use the retina to perform High Dynamic Range (HDR) luminance compression - - Then, take a HDR image using bracketing with your camera and generate an OpenEXR image and then process it using the demo. - - Typical use, supposing that you have the OpenEXR image such as *memorial.exr* (present in the samples/cpp/ folder) - - **OpenCVReleaseFolder/bin/OpenEXRimages_HDR_Retina_toneMapping memorial.exr [optional: 'fast']** - - Note that some sliders are made available to allow you to play with luminance compression. - - If not using the 'fast' option, then, tone mapping is performed using the full retina model [Benoit2010]_. It includes spectral whitening that allows luminance energy to be reduced. When using the 'fast' option, then, a simpler method is used, it is an adaptation of the algorithm presented in [Meylan2007]_. This method gives also good results and is faster to process but it sometimes requires some more parameters adjustement. - - -Methods description -=================== - -Here are detailled the main methods to control the retina model - -Ptr::createRetina -+++++++++++++++++++++++++ - -.. ocv:function:: Ptr createRetina(Size inputSize) -.. ocv:function:: Ptr createRetina(Size inputSize, const bool colorMode, cv::bioinspired::RETINA_COLORSAMPLINGMETHOD colorSamplingMethod = cv::bioinspired::RETINA_COLOR_BAYER, const bool useRetinaLogSampling = false, const double reductionFactor = 1.0, const double samplingStrenght = 10.0 ) - - Constructors from standardized interfaces : retreive a smart pointer to a Retina instance - - :param inputSize: the input frame size - :param colorMode: the chosen processing mode : with or without color processing - :param colorSamplingMethod: specifies which kind of color sampling will be used : - - * cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice - - * cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR... - - * cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling - - :param useRetinaLogSampling: activate retina log sampling, if true, the 2 following parameters can be used - :param reductionFactor: only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak - :param samplingStrenght: only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied - -Retina::activateContoursProcessing -++++++++++++++++++++++++++++++++++ - -.. ocv:function:: void Retina::activateContoursProcessing(const bool activate) - - Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated - - :param activate: true if Parvocellular (contours information extraction) output should be activated, false if not... if activated, the Parvocellular output can be retrieved using the **getParvo** methods - -Retina::activateMovingContoursProcessing -++++++++++++++++++++++++++++++++++++++++ - -.. ocv:function:: void Retina::activateMovingContoursProcessing(const bool activate) - - Activate/desactivate the Magnocellular pathway processing (motion information extraction), by default, it is activated - - :param activate: true if Magnocellular output should be activated, false if not... if activated, the Magnocellular output can be retrieved using the **getMagno** methods - -Retina::clearBuffers -++++++++++++++++++++ - -.. ocv:function:: void Retina::clearBuffers() - - Clears all retina buffers (equivalent to opening the eyes after a long period of eye close ;o) whatchout the temporal transition occuring just after this method call. - -Retina::getParvo -++++++++++++++++ - -.. ocv:function:: void Retina::getParvo( OutputArray retinaOutput_parvo ) -.. ocv:function:: void Retina::getParvoRAW( OutputArray retinaOutput_parvo ) -.. ocv:function:: const Mat Retina::getParvoRAW() const - - Accessor of the details channel of the retina (models foveal vision). Warning, getParvoRAW methods return buffers that are not rescaled within range [0;255] while the non RAW method allows a normalized matrix to be retrieved. - - :param retinaOutput_parvo: the output buffer (reallocated if necessary), format can be : - - * a Mat, this output is rescaled for standard 8bits image processing use in OpenCV - - * RAW methods actually return a 1D matrix (encoding is R1, R2, ... Rn, G1, G2, ..., Gn, B1, B2, ...Bn), this output is the original retina filter model output, without any quantification or rescaling. - -Retina::getMagno -++++++++++++++++ - -.. ocv:function:: void Retina::getMagno( OutputArray retinaOutput_magno ) -.. ocv:function:: void Retina::getMagnoRAW( OutputArray retinaOutput_magno ) -.. ocv:function:: const Mat Retina::getMagnoRAW() const - - Accessor of the motion channel of the retina (models peripheral vision). Warning, getMagnoRAW methods return buffers that are not rescaled within range [0;255] while the non RAW method allows a normalized matrix to be retrieved. - - :param retinaOutput_magno: the output buffer (reallocated if necessary), format can be : - - * a Mat, this output is rescaled for standard 8bits image processing use in OpenCV - - * RAW methods actually return a 1D matrix (encoding is M1, M2,... Mn), this output is the original retina filter model output, without any quantification or rescaling. - -Retina::getInputSize -++++++++++++++++++++ - -.. ocv:function:: Size Retina::getInputSize() - - Retreive retina input buffer size - - :return: the retina input buffer size - -Retina::getOutputSize -+++++++++++++++++++++ - -.. ocv:function:: Size Retina::getOutputSize() - - Retreive retina output buffer size that can be different from the input if a spatial log transformation is applied - - :return: the retina output buffer size - -Retina::printSetup -++++++++++++++++++ - -.. ocv:function:: const String Retina::printSetup() - - Outputs a string showing the used parameters setup - - :return: a string which contains formated parameters information - -Retina::run -+++++++++++ - -.. ocv:function:: void Retina::run(InputArray inputImage) - - Method which allows retina to be applied on an input image, after run, encapsulated retina module is ready to deliver its outputs using dedicated acccessors, see getParvo and getMagno methods - - :param inputImage: the input Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits) - -Retina::applyFastToneMapping -++++++++++++++++++++++++++++ - -.. ocv:function:: void Retina::applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage) - - Method which processes an image in the aim to correct its luminance : correct backlight problems, enhance details in shadows. This method is designed to perform High Dynamic Range image tone mapping (compress >8bit/pixel images to 8bit/pixel). This is a simplified version of the Retina Parvocellular model (simplified version of the run/getParvo methods call) since it does not include the spatio-temporal filter modelling the Outer Plexiform Layer of the retina that performs spectral whitening and many other stuff. However, it works great for tone mapping and in a faster way. - - Check the demos and experiments section to see examples and the way to perform tone mapping using the original retina model and the method. - - :param inputImage: the input image to process (should be coded in float format : CV_32F, CV_32FC1, CV_32F_C3, CV_32F_C4, the 4th channel won't be considered). - :param outputToneMappedImage: the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format). - -Retina::setColorSaturation -++++++++++++++++++++++++++ - -.. ocv:function:: void Retina::setColorSaturation(const bool saturateColors = true, const float colorSaturationValue = 4.0 ) - - Activate color saturation as the final step of the color demultiplexing process -> this saturation is a sigmoide function applied to each channel of the demultiplexed image. - - :param saturateColors: boolean that activates color saturation (if true) or desactivate (if false) - :param colorSaturationValue: the saturation factor : a simple factor applied on the chrominance buffers - - -Retina::setup -+++++++++++++ - -.. ocv:function:: void Retina::setup(String retinaParameterFile = "", const bool applyDefaultSetupOnFailure = true ) -.. ocv:function:: void Retina::setup(FileStorage & fs, const bool applyDefaultSetupOnFailure = true ) -.. ocv:function:: void Retina::setup(RetinaParameters newParameters) - - Try to open an XML retina parameters file to adjust current retina instance setup => if the xml file does not exist, then default setup is applied => warning, Exceptions are thrown if read XML file is not valid - - :param retinaParameterFile: the parameters filename - :param applyDefaultSetupOnFailure: set to true if an error must be thrown on error - :param fs: the open Filestorage which contains retina parameters - :param newParameters: a parameters structures updated with the new target configuration. You can retreive the current parameers structure using method *Retina::RetinaParameters Retina::getParameters()* and update it before running method *setup*. - -Retina::write -+++++++++++++ - -.. ocv:function:: void Retina::write( String fs ) const -.. ocv:function:: void Retina::write( FileStorage& fs ) const - - Write xml/yml formated parameters information - - :param fs: the filename of the xml file that will be open and writen with formatted parameters information - -Retina::setupIPLMagnoChannel -++++++++++++++++++++++++++++ - -.. ocv:function:: void Retina::setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta = 0, const float parasolCells_tau = 0, const float parasolCells_k = 7, const float amacrinCellsTemporalCutFrequency = 1.2, const float V0CompressionParameter = 0.95, const float localAdaptintegration_tau = 0, const float localAdaptintegration_k = 7 ) - - Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel this channel processes signals output from OPL processing stage in peripheral vision, it allows motion information enhancement. It is decorrelated from the details channel. See reference papers for more details. - - :param normaliseOutput: specifies if (true) output is rescaled between 0 and 255 of not (false) - :param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0 - :param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response) - :param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5 - :param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, typical value is 1.2 - :param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.95 - :param localAdaptintegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation - :param localAdaptintegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation - -Retina::setupOPLandIPLParvoChannel -++++++++++++++++++++++++++++++++++ - -.. ocv:function:: void Retina::setupOPLandIPLParvoChannel(const bool colorMode = true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity = 0.7, const float photoreceptorsTemporalConstant = 0.5, const float photoreceptorsSpatialConstant = 0.53, const float horizontalCellsGain = 0, const float HcellsTemporalConstant = 1, const float HcellsSpatialConstant = 7, const float ganglionCellsSensitivity = 0.7 ) - - Setup the OPL and IPL parvo channels (see biologocal model) OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance (low frequency energy) IPL parvo is the OPL next processing stage, it refers to a part of the Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See reference papers for more informations. - - :param colorMode: specifies if (true) color is processed of not (false) to then processing gray level image - :param normaliseOutput: specifies if (true) output is rescaled between 0 and 255 of not (false) - :param photoreceptorsLocalAdaptationSensitivity: the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases) - :param photoreceptorsTemporalConstant: the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame - :param photoreceptorsSpatialConstant: the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel - :param horizontalCellsGain: gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0 - :param HcellsTemporalConstant: the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors - :param HcellsSpatialConstant: the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model) - :param ganglionCellsSensitivity: the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.7 - - -Retina::RetinaParameters -======================== - -.. ocv:struct:: Retina::RetinaParameters - - This structure merges all the parameters that can be adjusted threw the **Retina::setup()**, **Retina::setupOPLandIPLParvoChannel** and **Retina::setupIPLMagnoChannel** setup methods - Parameters structure for better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel. :: - - class RetinaParameters{ - struct OPLandIplParvoParameters{ // Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters - OPLandIplParvoParameters():colorMode(true), - normaliseOutput(true), // specifies if (true) output is rescaled between 0 and 255 of not (false) - photoreceptorsLocalAdaptationSensitivity(0.7f), // the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases) - photoreceptorsTemporalConstant(0.5f),// the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame - photoreceptorsSpatialConstant(0.53f),// the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel - horizontalCellsGain(0.0f),//gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0 - hcellsTemporalConstant(1.f),// the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors. Reduce to 0.5 to limit retina after effects. - hcellsSpatialConstant(7.f),//the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model) - ganglionCellsSensitivity(0.7f)//the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.7 - {};// default setup - bool colorMode, normaliseOutput; - float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity; - }; - struct IplMagnoParameters{ // Inner Plexiform Layer Magnocellular channel (IplMagno) - IplMagnoParameters(): - normaliseOutput(true), //specifies if (true) output is rescaled between 0 and 255 of not (false) - parasolCells_beta(0.f), // the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0 - parasolCells_tau(0.f), //the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response) - parasolCells_k(7.f), //the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5 - amacrinCellsTemporalCutFrequency(1.2f), //the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, typical value is 1.2 - V0CompressionParameter(0.95f), the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.95 - localAdaptintegration_tau(0.f), // specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation - localAdaptintegration_k(7.f) // specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation - {};// default setup - bool normaliseOutput; - float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k; - }; - struct OPLandIplParvoParameters OPLandIplParvo; - struct IplMagnoParameters IplMagno; - }; - -Retina parameters files examples -++++++++++++++++++++++++++++++++ - -Here is the default configuration file of the retina module. It gives results such as the first retina output shown on the top of this page. - -.. code-block:: cpp - - - - - 1 - 1 - 7.5e-01 - 9.0e-01 - 5.3e-01 - 0.01 - 0.5 - 7. - 7.5e-01 - - 1 - 0. - 0. - 7. - 2.0e+00 - 9.5e-01 - 0. - 7. - - -Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page. - -.. code-block:: cpp - - - - - 1 - 1 - 8.9e-01 - 9.0e-01 - 5.3e-01 - 0.3 - 0.5 - 7. - 8.9e-01 - - 1 - 0. - 0. - 7. - 2.0e+00 - 9.5e-01 - 0. - 7. - diff --git a/modules/ccalib/doc/customPattern.rst b/modules/ccalib/doc/customPattern.rst deleted file mode 100644 index bf7c97750..000000000 --- a/modules/ccalib/doc/customPattern.rst +++ /dev/null @@ -1,202 +0,0 @@ -Custom Calibration Pattern -========================== - -.. highlight:: cpp - -CustomPattern -------------- -A custom pattern class that can be used to calibrate a camera and to further track the translation and rotation of the pattern. Defaultly it uses an ``ORB`` feature detector and a ``BruteForce-Hamming(2)`` descriptor matcher to find the location of the pattern feature points that will subsequently be used for calibration. - -.. ocv:class:: CustomPattern : public Algorithm - - -CustomPattern::CustomPattern ----------------------------- -CustomPattern constructor. - -.. ocv:function:: CustomPattern() - - -CustomPattern::create ---------------------- -A method that initializes the class and generates the necessary detectors, extractors and matchers. - -.. ocv:function:: bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray()) - - :param pattern: The image, which will be used as a pattern. If the desired pattern is part of a bigger image, you can crop it out using image(roi). - - :param boardSize: The size of the pattern in physical dimensions. These will be used to scale the points when the calibration occurs. - - :param output: A matrix that is the same as the input pattern image, but has all the feature points drawn on it. - - :return Returns whether the initialization was successful or not. Possible reason for failure may be that no feature points were detected. - -.. seealso:: - - :ocv:func:`getFeatureDetector`, - :ocv:func:`getDescriptorExtractor`, - :ocv:func:`getDescriptorMatcher` - -.. note:: - - * Determine the number of detected feature points can be done through :ocv:func:`getPatternPoints` method. - - * The feature detector, extractor and matcher cannot be changed after initialization. - - - -CustomPattern::findPattern --------------------------- -Finds the pattern in the input image - -.. ocv:function:: bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7, const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(), OutputArray H = noArray(), OutputArray pattern_corners = noArray()); - - :param image: The input image where the pattern is searched for. - - :param matched_features: A ``vector`` of the projections of calibration pattern points, matched in the image. The points correspond to the ``pattern_points``.``matched_features`` and ``pattern_points`` have the same size. - - :param pattern_points: A ``vector`` of calibration pattern points in the calibration pattern coordinate space. - - :param ratio: A ratio used to threshold matches based on D. Lowe's point ratio test. - - :param proj_error: The maximum projection error that is allowed when the found points are back projected. A lower projection error will be beneficial for eliminating mismatches. Higher values are recommended when the camera lens has greater distortions. - - :param refine_position: Whether to refine the position of the feature points with :ocv:func:`cornerSubPix`. - - :param out: An image showing the matched feature points and a contour around the estimated pattern. - - :param H: The homography transformation matrix between the pattern and the current image. - - :param pattern_corners: A ``vector`` containing the 4 corners of the found pattern. - - :return The method return whether the pattern was found or not. - - -CustomPattern::isInitialized ----------------------------- - -.. ocv:function:: bool isInitialized() - - :return If the class is initialized or not. - - -CustomPattern::getPatternPoints -------------------------------- - -.. ocv:function:: void getPatternPoints(OutputArray original_points) - - :param original_points: Fills the vector with the points found in the pattern. - - -CustomPattern::getPixelSize ---------------------------- -.. ocv:function:: double getPixelSize() - - :return Get the physical pixel size as initialized by the pattern. - - -CustomPattern::setFeatureDetector ---------------------------------- - .. ocv:function:: bool setFeatureDetector(Ptr featureDetector) - - :param featureDetector: Set a new FeatureDetector. - - :return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`. - -.. note:: - - * It is left to user discretion to select matching feature detector, extractor and matchers. Please consult the documentation for each to confirm coherence. - - -CustomPattern::setDescriptorExtractor -------------------------------------- -.. ocv:function:: bool setDescriptorExtractor(Ptr extractor) - - :param extractor: Set a new DescriptorExtractor. - - :return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`. - - -CustomPattern::setDescriptorMatcher ------------------------------------ -.. ocv:function:: bool setDescriptorMatcher(Ptr matcher) - - :param matcher: Set a new DescriptorMatcher. - - :return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`. - - -CustomPattern::getFeatureDetector ---------------------------------- -.. ocv:function:: Ptr getFeatureDetector() - - :return The used FeatureDetector. - - -CustomPattern::getDescriptorExtractor -------------------------------------- -.. ocv:function:: Ptr getDescriptorExtractor() - - :return The used DescriptorExtractor. - - -CustomPattern::getDescriptorMatcher ------------------------------------ -.. ocv:function:: Ptr getDescriptorMatcher() - - :return The used DescriptorMatcher. - - -CustomPattern::calibrate ------------------------- -Calibrates the camera. - -.. ocv:function:: double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)) - - See :ocv:func:`calibrateCamera` for parameter information. - - -CustomPattern::findRt ---------------------- -Finds the rotation and translation vectors of the pattern. - -.. ocv:function:: bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE) -.. ocv:function:: bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE) - - :param image: The image, in which the rotation and translation of the pattern will be found. - - See :ocv:func:`solvePnP` for parameter information. - - -CustomPattern::findRtRANSAC ---------------------------- -Finds the rotation and translation vectors of the pattern using RANSAC. - -.. ocv:function:: bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE) -.. ocv:function:: bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE) - - :param image: The image, in which the rotation and translation of the pattern will be found. - - See :ocv:func:`solvePnPRANSAC` for parameter information. - - -CustomPattern::drawOrientation ------------------------------- -Draws the ``(x,y,z)`` axis on the image, in the center of the pattern, showing the orientation of the pattern. - -.. ocv:function:: void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix, InputArray distCoeffs, double axis_length = 3, int axis_width = 2) - - :param image: The image, based on which the rotation and translation was calculated. The axis will be drawn in color - ``x`` - in red, ``y`` - in green, ``z`` - in blue. - - :param tvec: Translation vector. - - :param rvec: Rotation vector. - - :param cameraMatrix: The camera matrix. - - :param distCoeffs: The distortion coefficients. - - :param axis_length: The length of the axis symbol. - - :param axis_width: The width of the axis symbol. - diff --git a/modules/cvv/doc/cvv.rst b/modules/cvv/doc/cvv.rst deleted file mode 100644 index e60378f67..000000000 --- a/modules/cvv/doc/cvv.rst +++ /dev/null @@ -1,11 +0,0 @@ -********************************************************************* -cvv. GUI for Interactive Visual Debugging of Computer Vision Programs -********************************************************************* - -The module provides an interactive GUI to debug and incrementally design computer vision algorithms. The debug statements can remain in the code after development and aid in further changes because they have neglectable overhead if the program is compiled in release mode. - -.. toctree:: - :maxdepth: 2 - - CVV API Documentation - CVV GUI Documentation diff --git a/modules/cvv/doc/cvv_api.rst b/modules/cvv/doc/cvv_api.rst deleted file mode 100644 index 1cf6ed7e7..000000000 --- a/modules/cvv/doc/cvv_api.rst +++ /dev/null @@ -1,85 +0,0 @@ -CVV : the API -************* - -.. highlight:: cpp - - -Introduction -++++++++++++ - -Namespace for all functions is **cvv**, i.e. *cvv::showImage()*. - -Compilation: - -* For development, i.e. for cvv GUI to show up, compile your code using cvv with *g++ -DCVVISUAL_DEBUGMODE*. -* For release, i.e. cvv calls doing nothing, compile your code without above flag. - -See cvv tutorial for a commented example application using cvv. - - - - -API Functions -+++++++++++++ - - -showImage ---------- -Add a single image to debug GUI (similar to :imshow:`imshow <>`). - -.. ocv:function:: void showImage(InputArray img, const CallMetaData& metaData, const string& description, const string& view) - - :param img: Image to show in debug GUI. - :param metaData: Properly initialized CallMetaData struct, i.e. information about file, line and function name for GUI. Use CVVISUAL_LOCATION macro. - :param description: Human readable description to provide context to image. - :param view: Preselect view that will be used to visualize this image in GUI. Other views can still be selected in GUI later on. - - - -debugFilter ------------ -Add two images to debug GUI for comparison. Usually the input and output of some filter operation, whose result should be inspected. - -.. ocv:function:: void debugFilter(InputArray original, InputArray result, const CallMetaData& metaData, const string& description, const string& view) - - :param original: First image for comparison, e.g. filter input. - :param result: Second image for comparison, e.g. filter output. - :param metaData: See :ocv:func:`showImage` - :param description: See :ocv:func:`showImage` - :param view: See :ocv:func:`showImage` - - - -debugDMatch ------------ -Add a filled in :basicstructures:`DMatch ` to debug GUI. The matches can are visualized for interactive inspection in different GUI views (one similar to an interactive :draw_matches:`drawMatches<>`). - -.. ocv:function:: void debugDMatch(InputArray img1, std::vector keypoints1, InputArray img2, std::vector keypoints2, std::vector matches, const CallMetaData& metaData, const string& description, const string& view, bool useTrainDescriptor) - - :param img1: First image used in :basicstructures:`DMatch `. - :param keypoints1: Keypoints of first image. - :param img2: Second image used in DMatch. - :param keypoints2: Keypoints of second image. - :param metaData: See :ocv:func:`showImage` - :param description: See :ocv:func:`showImage` - :param view: See :ocv:func:`showImage` - :param useTrainDescriptor: Use :basicstructures:`DMatch `'s train descriptor index instead of query descriptor index. - - - -finalShow ---------- -This function **must** be called *once* *after* all cvv calls if any. -As an alternative create an instance of FinalShowCaller, which calls finalShow() in its destructor (RAII-style). - -.. ocv:function:: void finalShow() - - - -setDebugFlag ------------- -Enable or disable cvv for current translation unit and thread (disabled this way has higher - but still low - overhead compared to using the compile flags). - -.. ocv:function:: void setDebugFlag(bool active) - - :param active: See above diff --git a/modules/cvv/doc/cvv_gui.rst b/modules/cvv/doc/cvv_gui.rst deleted file mode 100644 index de01aff2c..000000000 --- a/modules/cvv/doc/cvv_gui.rst +++ /dev/null @@ -1,24 +0,0 @@ -CVV : the GUI -************* - -.. highlight:: cpp - - -Introduction -++++++++++++ - -For now: See cvv tutorial. - - - -Overview -++++++++ - - -Filter ------- - - - -Views -++++++++ diff --git a/modules/datasets/doc/ar_hmdb.rst b/modules/datasets/doc/ar_hmdb.rst deleted file mode 100644 index 289125022..000000000 --- a/modules/datasets/doc/ar_hmdb.rst +++ /dev/null @@ -1,36 +0,0 @@ -HMDB: A Large Human Motion Database -=================================== -.. ocv:class:: AR_hmdb - -Implements loading dataset: - -_`"HMDB: A Large Human Motion Database"`: http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/ - -.. note:: Usage - - 1. From link above download dataset files: hmdb51_org.rar & test_train_splits.rar. - - 2. Unpack them. Unpack all archives from directory: hmdb51_org/ and remove them. - - 3. To load data run: ./opencv/build/bin/example_datasets_ar_hmdb -p=/home/user/path_to_unpacked_folders/ - -Benchmark -""""""""" - -For this dataset was implemented benchmark with accuracy: 0.107407 (using precomputed HOG/HOF "STIP" features from site, averaging for 3 splits) - -To run this benchmark execute: - -.. code-block:: bash - - ./opencv/build/bin/example_datasets_ar_hmdb_benchmark -p=/home/user/path_to_unpacked_folders/ - -(precomputed features should be unpacked in the same folder: /home/user/path_to_unpacked_folders/hmdb51_org_stips/. Also unpack all archives from directory: hmdb51_org_stips/ and remove them.) - -**References:** - -.. [Kuehne11] H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: A Large Video Database for Human Motion Recognition. ICCV, 2011 - -.. [Laptev08] I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning Realistic Human Actions From Movies. CVPR, 2008 - - diff --git a/modules/datasets/doc/ar_sports.rst b/modules/datasets/doc/ar_sports.rst deleted file mode 100644 index eef86c7ff..000000000 --- a/modules/datasets/doc/ar_sports.rst +++ /dev/null @@ -1,18 +0,0 @@ -Sports-1M Dataset -================= -.. ocv:class:: AR_sports - -Implements loading dataset: - -_`"Sports-1M Dataset"`: http://cs.stanford.edu/people/karpathy/deepvideo/ - -.. note:: Usage - - 1. From link above download dataset files (git clone https://code.google.com/p/sports-1m-dataset/). - - 2. To load data run: ./opencv/build/bin/example_datasets_ar_sports -p=/home/user/path_to_downloaded_folders/ - -**References:** - -.. [KarpathyCVPR14] Andrej Karpathy and George Toderici and Sanketh Shetty and Thomas Leung and Rahul Sukthankar and Li Fei-Fei. Large-scale Video Classification with Convolutional Neural Networks. CVPR, 2014 - diff --git a/modules/datasets/doc/datasets.rst b/modules/datasets/doc/datasets.rst deleted file mode 100644 index 09d9e5038..000000000 --- a/modules/datasets/doc/datasets.rst +++ /dev/null @@ -1,121 +0,0 @@ -******************************************************* -datasets. Framework for working with different datasets -******************************************************* - -.. highlight:: cpp - -The datasets module includes classes for working with different datasets: load data, evaluate different algorithms on them, contains benchmarks, etc. - -It is planned to have: - - * basic: loading code for all datasets to help start work with them. - * next stage: quick benchmarks for all datasets to show how to solve them using OpenCV and implement evaluation code. - * finally: implement on OpenCV state-of-the-art algorithms, which solve these tasks. - -.. toctree:: - :hidden: - - ar_hmdb - ar_sports - fr_adience - fr_lfw - gr_chalearn - gr_skig - hpe_humaneva - hpe_parse - ir_affine - ir_robot - is_bsds - is_weizmann - msm_epfl - msm_middlebury - or_imagenet - or_mnist - or_sun - pd_caltech - slam_kitti - slam_tumindoor - tr_chars - tr_svt - -Action Recognition ------------------- - - :doc:`ar_hmdb` [#f1]_ - - :doc:`ar_sports` - -Face Recognition ----------------- - - :doc:`fr_adience` - - :doc:`fr_lfw` [#f1]_ - -Gesture Recognition -------------------- - - :doc:`gr_chalearn` - - :doc:`gr_skig` - -Human Pose Estimation ---------------------- - - :doc:`hpe_humaneva` - - :doc:`hpe_parse` - -Image Registration ------------------- - - :doc:`ir_affine` - - :doc:`ir_robot` - -Image Segmentation ------------------- - - :doc:`is_bsds` - - :doc:`is_weizmann` - -Multiview Stereo Matching -------------------------- - - :doc:`msm_epfl` - - :doc:`msm_middlebury` - -Object Recognition ------------------- - - :doc:`or_imagenet` - - :doc:`or_mnist` [#f2]_ - - :doc:`or_sun` - -Pedestrian Detection --------------------- - - :doc:`pd_caltech` [#f2]_ - -SLAM ----- - - :doc:`slam_kitti` - - :doc:`slam_tumindoor` - -Text Recognition ----------------- - - :doc:`tr_chars` - - :doc:`tr_svt` [#f1]_ - -*Footnotes* - - .. [#f1] Benchmark implemented - .. [#f2] Not used in Vision Challenge diff --git a/modules/datasets/doc/fr_adience.rst b/modules/datasets/doc/fr_adience.rst deleted file mode 100644 index 2bde5ecbd..000000000 --- a/modules/datasets/doc/fr_adience.rst +++ /dev/null @@ -1,20 +0,0 @@ -Adience -======= -.. ocv:class:: FR_adience - -Implements loading dataset: - -_`"Adience"`: http://www.openu.ac.il/home/hassner/Adience/data.html - -.. note:: Usage - - 1. From link above download any dataset file: faces.tar.gz\\aligned.tar.gz and files with splits: fold_0_data.txt-fold_4_data.txt, fold_frontal_0_data.txt-fold_frontal_4_data.txt. (For face recognition task another splits should be created) - - 2. Unpack dataset file to some folder and place split files into the same folder. - - 3. To load data run: ./opencv/build/bin/example_datasets_fr_adience -p=/home/user/path_to_created_folder/ - -**References:** - -.. [Eidinger] E. Eidinger, R. Enbar, and T. Hassner. Age and Gender Estimation of Unfiltered Faces - diff --git a/modules/datasets/doc/fr_lfw.rst b/modules/datasets/doc/fr_lfw.rst deleted file mode 100644 index 882c7a182..000000000 --- a/modules/datasets/doc/fr_lfw.rst +++ /dev/null @@ -1,31 +0,0 @@ -Labeled Faces in the Wild -========================= -.. ocv:class:: FR_lfw - -Implements loading dataset: - -_`"Labeled Faces in the Wild"`: http://vis-www.cs.umass.edu/lfw/ - -.. note:: Usage - - 1. From link above download any dataset file: lfw.tgz\\lfwa.tar.gz\\lfw-deepfunneled.tgz\\lfw-funneled.tgz and files with pairs: 10 test splits: pairs.txt and developer train split: pairsDevTrain.txt. - - 2. Unpack dataset file and place pairs.txt and pairsDevTrain.txt in created folder. - - 3. To load data run: ./opencv/build/bin/example_datasets_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/ - -Benchmark -""""""""" - -For this dataset was implemented benchmark with accuracy: 0.623833 +- 0.005223 (train split: pairsDevTrain.txt, dataset: lfwa) - -To run this benchmark execute: - -.. code-block:: bash - - ./opencv/build/bin/example_datasets_fr_lfw_benchmark -p=/home/user/path_to_unpacked_folder/lfw2/ - -**References:** - -.. [Huang07] G.B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. 2007 - diff --git a/modules/datasets/doc/gr_chalearn.rst b/modules/datasets/doc/gr_chalearn.rst deleted file mode 100644 index ae83faad8..000000000 --- a/modules/datasets/doc/gr_chalearn.rst +++ /dev/null @@ -1,22 +0,0 @@ -ChaLearn Looking at People -========================== -.. ocv:class:: GR_chalearn - -Implements loading dataset: - -_`"ChaLearn Looking at People"`: http://gesture.chalearn.org/ - -.. note:: Usage - - 1. Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": Train1.zip-Train5.zip, Validation1.zip-Validation3.zip (Register on site: www.codalab.org and accept the terms and conditions of competition: https://www.codalab.org/competitions/991#learn_the_details There are three mirrors for downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" works). - - 2. Unpack train archives Train1.zip-Train5.zip to folder Train/, validation archives Validation1.zip-Validation3.zip to folder Validation/ - - 3. Unpack all archives in Train/ & Validation/ in the folders with the same names, for example: Sample0001.zip to Sample0001/ - - 4. To load data run: ./opencv/build/bin/example_datasets_gr_chalearn -p=/home/user/path_to_unpacked_folders/ - -**References:** - -.. [Escalera14] S. Escalera, X. Baró, J. Gonzàlez, M.A. Bautista, M. Madadi, M. Reyes, V. Ponce-López, H.J. Escalante, J. Shotton, I. Guyon, "ChaLearn Looking at People Challenge 2014: Dataset and Results", ECCV Workshops, 2014 - diff --git a/modules/datasets/doc/gr_skig.rst b/modules/datasets/doc/gr_skig.rst deleted file mode 100644 index cdf147355..000000000 --- a/modules/datasets/doc/gr_skig.rst +++ /dev/null @@ -1,20 +0,0 @@ -Sheffield Kinect Gesture Dataset -================================ -.. ocv:class:: GR_skig - -Implements loading dataset: - -_`"Sheffield Kinect Gesture Dataset"`: http://lshao.staff.shef.ac.uk/data/SheffieldKinectGesture.htm - -.. note:: Usage - - 1. From link above download dataset files: subject1_dep.7z-subject6_dep.7z, subject1_rgb.7z-subject6_rgb.7z. - - 2. Unpack them. - - 3. To load data run: ./opencv/build/bin/example_datasets_gr_skig -p=/home/user/path_to_unpacked_folders/ - -**References:** - -.. [Liu13] L. Liu and L. Shao, “Learning Discriminative Representations from RGB-D Video Data”, In Proc. International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013. - diff --git a/modules/datasets/doc/hpe_humaneva.rst b/modules/datasets/doc/hpe_humaneva.rst deleted file mode 100644 index 9a35094c0..000000000 --- a/modules/datasets/doc/hpe_humaneva.rst +++ /dev/null @@ -1,22 +0,0 @@ -HumanEva Dataset -================ -.. ocv:class:: HPE_humaneva - -Implements loading dataset: - -_`"HumanEva Dataset"`: http://humaneva.is.tue.mpg.de - -.. note:: Usage - - 1. From link above download dataset files for HumanEva-I (tar) & HumanEva-II. - - 2. Unpack them to HumanEva_1 & HumanEva_2 accordingly. - - 3. To load data run: ./opencv/build/bin/example_datasets_hpe_humaneva -p=/home/user/path_to_unpacked_folders/ - -**References:** - -.. [Sigal10] L. Sigal, A. Balan and M. J. Black. HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion, In International Journal of Computer Vision, Vol. 87 (1-2), 2010 - -.. [Sigal06] L. Sigal and M. J. Black. HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion, Techniacl Report CS-06-08, Brown University, 2006 - diff --git a/modules/datasets/doc/hpe_parse.rst b/modules/datasets/doc/hpe_parse.rst deleted file mode 100644 index 8ac48966d..000000000 --- a/modules/datasets/doc/hpe_parse.rst +++ /dev/null @@ -1,20 +0,0 @@ -PARSE Dataset -============= -.. ocv:class:: HPE_parse - -Implements loading dataset: - -_`"PARSE Dataset"`: http://www.ics.uci.edu/~dramanan/papers/parse/ - -.. note:: Usage - - 1. From link above download dataset file: people.zip. - - 2. Unpack it. - - 3. To load data run: ./opencv/build/bin/example_datasets_hpe_parse -p=/home/user/path_to_unpacked_folder/people_all/ - -**References:** - -.. [Ramanan06] D. Ramanan "Learning to Parse Images of Articulated Bodies." Neural Info. Proc. Systems (NIPS) To appear. Dec 2006. - diff --git a/modules/datasets/doc/ir_affine.rst b/modules/datasets/doc/ir_affine.rst deleted file mode 100644 index 1bd0105ee..000000000 --- a/modules/datasets/doc/ir_affine.rst +++ /dev/null @@ -1,20 +0,0 @@ -Affine Covariant Regions Datasets -================================= -.. ocv:class:: IR_affine - -Implements loading dataset: - -_`"Affine Covariant Regions Datasets"`: http://www.robots.ox.ac.uk/~vgg/data/data-aff.html - -.. note:: Usage - - 1. From link above download dataset files: bark\\bikes\\boat\\graf\\leuven\\trees\\ubc\\wall.tar.gz. - - 2. Unpack them. - - 3. To load data, for example, for "bark", run: ./opencv/build/bin/example_datasets_ir_affine -p=/home/user/path_to_unpacked_folder/bark/ - -**References:** - -.. [Mikolajczyk05] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool. A Comparison of Affine Region Detectors. International Journal of Computer Vision, Volume 65, Number 1/2, page 43--72, 2005 - diff --git a/modules/datasets/doc/ir_robot.rst b/modules/datasets/doc/ir_robot.rst deleted file mode 100644 index 249d88e6c..000000000 --- a/modules/datasets/doc/ir_robot.rst +++ /dev/null @@ -1,20 +0,0 @@ -Robot Data Set -============== -.. ocv:class:: IR_robot - -Implements loading dataset: - -_`"Robot Data Set, Point Feature Data Set – 2010"`: http://roboimagedata.compute.dtu.dk/?page_id=24 - -.. note:: Usage - - 1. From link above download dataset files: SET001_6.tar.gz-SET055_60.tar.gz - - 2. Unpack them to one folder. - - 3. To load data run: ./opencv/build/bin/example_datasets_ir_robot -p=/home/user/path_to_unpacked_folder/ - -**References:** - -.. [aanæsinteresting] Aan{\ae}s, H. and Dahl, A.L. and Steenstrup Pedersen, K. Interesting Interest Points. International Journal of Computer Vision. 2012. - diff --git a/modules/datasets/doc/is_bsds.rst b/modules/datasets/doc/is_bsds.rst deleted file mode 100644 index b05a0f727..000000000 --- a/modules/datasets/doc/is_bsds.rst +++ /dev/null @@ -1,20 +0,0 @@ -The Berkeley Segmentation Dataset and Benchmark -=============================================== -.. ocv:class:: IS_bsds - -Implements loading dataset: - -_`"The Berkeley Segmentation Dataset and Benchmark"`: https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/ - -.. note:: Usage - - 1. From link above download dataset files: BSDS300-human.tgz & BSDS300-images.tgz. - - 2. Unpack them. - - 3. To load data run: ./opencv/build/bin/example_datasets_is_bsds -p=/home/user/path_to_unpacked_folder/BSDS300/ - -**References:** - -.. [MartinFTM01] D. Martin and C. Fowlkes and D. Tal and J. Malik. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. 2001 - diff --git a/modules/datasets/doc/is_weizmann.rst b/modules/datasets/doc/is_weizmann.rst deleted file mode 100644 index ce3e71ac6..000000000 --- a/modules/datasets/doc/is_weizmann.rst +++ /dev/null @@ -1,20 +0,0 @@ -Weizmann Segmentation Evaluation Database -========================================= -.. ocv:class:: IS_weizmann - -Implements loading dataset: - -_`"Weizmann Segmentation Evaluation Database"`: http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/ - -.. note:: Usage - - 1. From link above download dataset files: Weizmann_Seg_DB_1obj.ZIP & Weizmann_Seg_DB_2obj.ZIP. - - 2. Unpack them. - - 3. To load data, for example, for 1 object dataset, run: ./opencv/build/bin/example_datasets_is_weizmann -p=/home/user/path_to_unpacked_folder/1obj/ - -**References:** - -.. [AlpertGBB07] Sharon Alpert and Meirav Galun and Ronen Basri and Achi Brandt. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. 2007 - diff --git a/modules/datasets/doc/msm_epfl.rst b/modules/datasets/doc/msm_epfl.rst deleted file mode 100644 index 5cf21284c..000000000 --- a/modules/datasets/doc/msm_epfl.rst +++ /dev/null @@ -1,20 +0,0 @@ -EPFL Multi-View Stereo -====================== -.. ocv:class:: MSM_epfl - -Implements loading dataset: - -_`"EPFL Multi-View Stereo"`: http://cvlabwww.epfl.ch/~strecha/multiview/denseMVS.html - -.. note:: Usage - - 1. From link above download dataset files: castle_dense\\castle_dense_large\\castle_entry\\fountain\\herzjesu_dense\\herzjesu_dense_large_bounding\\cameras\\images\\p.tar.gz. - - 2. Unpack them in separate folder for each object. For example, for "fountain", in folder fountain/ : fountain_dense_bounding.tar.gz -> bounding/, fountain_dense_cameras.tar.gz -> camera/, fountain_dense_images.tar.gz -> png/, fountain_dense_p.tar.gz -> P/ - - 3. To load data, for example, for "fountain", run: ./opencv/build/bin/example_datasets_msm_epfl -p=/home/user/path_to_unpacked_folder/fountain/ - -**References:** - -.. [Strecha08] C. Strecha, W. von Hansen, L. Van Gool, P. Fua, U. Thoennessen. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery. CVPR, 2008 - diff --git a/modules/datasets/doc/msm_middlebury.rst b/modules/datasets/doc/msm_middlebury.rst deleted file mode 100644 index 6e35c5965..000000000 --- a/modules/datasets/doc/msm_middlebury.rst +++ /dev/null @@ -1,20 +0,0 @@ -Stereo – Middlebury Computer Vision -=================================== -.. ocv:class:: MSM_middlebury - -Implements loading dataset: - -_`"Stereo – Middlebury Computer Vision"`: http://vision.middlebury.edu/mview/ - -.. note:: Usage - - 1. From link above download dataset files: dino\\dinoRing\\dinoSparseRing\\temple\\templeRing\\templeSparseRing.zip - - 2. Unpack them. - - 3. To load data, for example "temple" dataset, run: ./opencv/build/bin/example_datasets_msm_middlebury -p=/home/user/path_to_unpacked_folder/temple/ - -**References:** - -.. [Seitz06] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, R. Szeliski. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR, 2006 - diff --git a/modules/datasets/doc/or_imagenet.rst b/modules/datasets/doc/or_imagenet.rst deleted file mode 100644 index b7caf7e9c..000000000 --- a/modules/datasets/doc/or_imagenet.rst +++ /dev/null @@ -1,39 +0,0 @@ -ImageNet -======== -.. ocv:class:: OR_imagenet - -Implements loading dataset: - -_`"ImageNet"`: http://www.image-net.org/ - -.. note:: Usage - - 1. From link above download dataset files: ILSVRC2010_images_train.tar\\ILSVRC2010_images_test.tar\\ILSVRC2010_images_val.tar & devkit: ILSVRC2010_devkit-1.0.tar.gz (Implemented loading of 2010 dataset as only this dataset has ground truth for test data, but structure for ILSVRC2014 is similar) - - 2. Unpack them to: some_folder/train/\\some_folder/test/\\some_folder/val & some_folder/ILSVRC2010_validation_ground_truth.txt\\some_folder/ILSVRC2010_test_ground_truth.txt. - - 3. Create file with labels: some_folder/labels.txt, for example, using :ref:`python script ` below (each file's row format: synset,labelID,description. For example: "n07751451,18,plum"). - - 4. Unpack all tar files in train. - - 5. To load data run: ./opencv/build/bin/example_datasets_or_imagenet -p=/home/user/some_folder/ - -.. _python-script: - -Python script to parse meta.mat: - -:: - - import scipy.io - meta_mat = scipy.io.loadmat("devkit-1.0/data/meta.mat") - - labels_dic = dict((m[0][1][0], m[0][0][0][0]-1) for m in meta_mat['synsets'] - label_names_dic = dict((m[0][1][0], m[0][2][0]) for m in meta_mat['synsets'] - - for label in labels_dic.keys(): - print "{0},{1},{2}".format(label, labels_dic[label], label_names_dic[label]) - -**References:** - -.. [ILSVRCarxiv14] Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. 2014 - diff --git a/modules/datasets/doc/or_mnist.rst b/modules/datasets/doc/or_mnist.rst deleted file mode 100644 index a09720d7e..000000000 --- a/modules/datasets/doc/or_mnist.rst +++ /dev/null @@ -1,20 +0,0 @@ -MNIST -===== -.. ocv:class:: OR_mnist - -Implements loading dataset: - -_`"MNIST"`: http://yann.lecun.com/exdb/mnist/ - -.. note:: Usage - - 1. From link above download dataset files: t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz, train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz. - - 2. Unpack them. - - 3. To load data run: ./opencv/build/bin/example_datasets_or_mnist -p=/home/user/path_to_unpacked_files/ - -**References:** - -.. [LeCun98a] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. - diff --git a/modules/datasets/doc/or_sun.rst b/modules/datasets/doc/or_sun.rst deleted file mode 100644 index 5a58d2cb6..000000000 --- a/modules/datasets/doc/or_sun.rst +++ /dev/null @@ -1,22 +0,0 @@ -SUN Database -============ -.. ocv:class:: OR_sun - -Implements loading dataset: - -_`"SUN Database, Scene Recognition Benchmark. SUN397"`: http://vision.cs.princeton.edu/projects/2010/SUN/ - -.. note:: Usage - - 1. From link above download dataset file: SUN397.tar & file with splits: Partitions.zip - - 2. Unpack SUN397.tar into folder: SUN397/ & Partitions.zip into folder: SUN397/Partitions/ - - 3. To load data run: ./opencv/build/bin/example_datasets_or_sun -p=/home/user/path_to_unpacked_files/SUN397/ - -**References:** - -.. [Xiao10] J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. SUN Database: Large-scale Scene Recognition from Abbey to Zoo. IEEE Conference on Computer Vision and Pattern Recognition. CVPR, 2010 - -.. [Xiao14] J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva. SUN Database: Exploring a Large Collection of Scene Categories. International Journal of Computer Vision. IJCV, 2014 - diff --git a/modules/datasets/doc/pd_caltech.rst b/modules/datasets/doc/pd_caltech.rst deleted file mode 100644 index e1c86fe29..000000000 --- a/modules/datasets/doc/pd_caltech.rst +++ /dev/null @@ -1,29 +0,0 @@ -Caltech Pedestrian Detection Benchmark -====================================== -.. ocv:class:: PD_caltech - -Implements loading dataset: - -_`"Caltech Pedestrian Detection Benchmark"`: http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/ - -.. note:: First version of Caltech Pedestrian dataset loading. - - Code to unpack all frames from seq files commented as their number is huge! - So currently load only meta information without data. - - Also ground truth isn't processed, as need to convert it from mat files first. - -.. note:: Usage - - 1. From link above download dataset files: set00.tar-set10.tar. - - 2. Unpack them to separate folder. - - 3. To load data run: ./opencv/build/bin/example_datasets_pd_caltech -p=/home/user/path_to_unpacked_folders/ - -**References:** - -.. [Dollár12] P. Dollár, C. Wojek, B. Schiele and P. Perona. Pedestrian Detection: An Evaluation of the State of the Art. PAMI, 2012. - -.. [DollárCVPR09] P. Dollár, C. Wojek, B. Schiele and P. Perona. Pedestrian Detection: A Benchmark. CVPR, 2009 - diff --git a/modules/datasets/doc/slam_kitti.rst b/modules/datasets/doc/slam_kitti.rst deleted file mode 100644 index be8d6eff2..000000000 --- a/modules/datasets/doc/slam_kitti.rst +++ /dev/null @@ -1,24 +0,0 @@ -KITTI Vision Benchmark -====================== -.. ocv:class:: SLAM_kitti - -Implements loading dataset: - -_`"KITTI Vision Benchmark"`: http://www.cvlibs.net/datasets/kitti/eval_odometry.php - -.. note:: Usage - - 1. From link above download "Odometry" dataset files: data_odometry_gray\\data_odometry_color\\data_odometry_velodyne\\data_odometry_poses\\data_odometry_calib.zip. - - 2. Unpack data_odometry_poses.zip, it creates folder dataset/poses/. After that unpack data_odometry_gray.zip, data_odometry_color.zip, data_odometry_velodyne.zip. Folder dataset/sequences/ will be created with folders 00/..21/. Each of these folders will contain: image_0/, image_1/, image_2/, image_3/, velodyne/ and files calib.txt & times.txt. These two last files will be replaced after unpacking data_odometry_calib.zip at the end. - - 3. To load data run: ./opencv/build/bin/example_datasets_slam_kitti -p=/home/user/path_to_unpacked_folder/dataset/ - -**References:** - -.. [Geiger2012CVPR] Andreas Geiger and Philip Lenz and Raquel Urtasun. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. CVPR, 2012 - -.. [Geiger2013IJRR] Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun. Vision meets Robotics: The KITTI Dataset. IJRR, 2013 - -.. [Fritsch2013ITSC] Jannik Fritsch and Tobias Kuehnl and Andreas Geiger. A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms. ITSC, 2013 - diff --git a/modules/datasets/doc/slam_tumindoor.rst b/modules/datasets/doc/slam_tumindoor.rst deleted file mode 100644 index 1b72a873c..000000000 --- a/modules/datasets/doc/slam_tumindoor.rst +++ /dev/null @@ -1,20 +0,0 @@ -TUMindoor Dataset -================= -.. ocv:class:: SLAM_tumindoor - -Implements loading dataset: - -_`"TUMindoor Dataset"`: http://www.navvis.lmt.ei.tum.de/dataset/ - -.. note:: Usage - - 1. From link above download dataset files: dslr\\info\\ladybug\\pointcloud.tar.bz2 for each dataset: 11-11-28 (1st floor)\\11-12-13 (1st floor N1)\\11-12-17a (4th floor)\\11-12-17b (3rd floor)\\11-12-17c (Ground I)\\11-12-18a (Ground II)\\11-12-18b (2nd floor) - - 2. Unpack them in separate folder for each dataset. dslr.tar.bz2 -> dslr/, info.tar.bz2 -> info/, ladybug.tar.bz2 -> ladybug/, pointcloud.tar.bz2 -> pointcloud/. - - 3. To load each dataset run: ./opencv/build/bin/example_datasets_slam_tumindoor -p=/home/user/path_to_unpacked_folders/ - -**References:** - -.. [TUMindoor] R. Huitl and G. Schroth and S. Hilsenbeck and F. Schweiger and E. Steinbach. {TUM}indoor: An Extensive Image and Point Cloud Dataset for Visual Indoor Localization and Mapping. 2012 - diff --git a/modules/datasets/doc/tr_chars.rst b/modules/datasets/doc/tr_chars.rst deleted file mode 100644 index 041db074a..000000000 --- a/modules/datasets/doc/tr_chars.rst +++ /dev/null @@ -1,22 +0,0 @@ -The Chars74K Dataset -==================== -.. ocv:class:: TR_chars - -Implements loading dataset: - -_`"The Chars74K Dataset"`: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/ - -.. note:: Usage - - 1. From link above download dataset files: EnglishFnt\\EnglishHnd\\EnglishImg\\KannadaHnd\\KannadaImg.tgz, ListsTXT.tgz. - - 2. Unpack them. - - 3. Move .m files from folder ListsTXT/ to appropriate folder. For example, English/list_English_Img.m for EnglishImg.tgz. - - 4. To load data, for example "EnglishImg", run: ./opencv/build/bin/example_datasets_tr_chars -p=/home/user/path_to_unpacked_folder/English/ - -**References:** - -.. [Campos09] T. E. de Campos, B. R. Babu and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), 2009 - diff --git a/modules/datasets/doc/tr_svt.rst b/modules/datasets/doc/tr_svt.rst deleted file mode 100644 index 348a433f5..000000000 --- a/modules/datasets/doc/tr_svt.rst +++ /dev/null @@ -1,33 +0,0 @@ -The Street View Text Dataset -============================ -.. ocv:class:: TR_svt - -Implements loading dataset: - -_`"The Street View Text Dataset"`: http://vision.ucsd.edu/~kai/svt/ - -.. note:: Usage - - 1. From link above download dataset file: svt.zip. - - 2. Unpack it. - - 3. To load data run: ./opencv/build/bin/example_datasets_tr_svt -p=/home/user/path_to_unpacked_folder/svt/svt1/ - -Benchmark -""""""""" - -For this dataset was implemented benchmark with accuracy (mean f1): 0.217 - -To run benchmark execute: - -.. code-block:: bash - - ./opencv/build/bin/example_datasets_tr_svt_benchmark -p=/home/user/path_to_unpacked_folders/svt/svt1/ - -**References:** - -.. [Wang11] Kai Wang, Boris Babenko and Serge Belongie. End-to-end Scene Text Recognition. ICCV, 2011 - -.. [Wang10] Kai Wang and Serge Belongie. Word Spotting in the Wild. ECCV, 2010 - diff --git a/modules/face/doc/face.rst b/modules/face/doc/face.rst deleted file mode 100755 index d4b21ac3c..000000000 --- a/modules/face/doc/face.rst +++ /dev/null @@ -1,10 +0,0 @@ -*************************************** -face. Face Recognition -*************************************** - -The module contains some recently added functionality that has not been stabilized, or functionality that is considered optional. - -.. toctree:: - :maxdepth: 2 - - FaceRecognizer Documentation diff --git a/modules/face/doc/facerec_api.rst b/modules/face/doc/facerec_api.rst deleted file mode 100755 index 2c1566d6f..000000000 --- a/modules/face/doc/facerec_api.rst +++ /dev/null @@ -1,412 +0,0 @@ -FaceRecognizer -============== - -.. highlight:: cpp - -.. Sample code:: - - * An example using the FaceRecognizer class can be found at opencv_source_code/samples/cpp/facerec_demo.cpp - - * (Python) An example using the FaceRecognizer class can be found at opencv_source_code/samples/python2/facerec_demo.py - -FaceRecognizer --------------- - -.. ocv:class:: FaceRecognizer : public Algorithm - -All face recognition models in OpenCV are derived from the abstract base class :ocv:class:`FaceRecognizer`, which provides -a unified access to all face recongition algorithms in OpenCV. :: - - class FaceRecognizer : public Algorithm - { - public: - //! virtual destructor - virtual ~FaceRecognizer() {} - - // Trains a FaceRecognizer. - virtual void train(InputArray src, InputArray labels) = 0; - - // Updates a FaceRecognizer. - virtual void update(InputArrayOfArrays src, InputArray labels); - - // Gets a prediction from a FaceRecognizer. - virtual int predict(InputArray src) const = 0; - - // Predicts the label and confidence for a given sample. - virtual void predict(InputArray src, int &label, double &confidence) const = 0; - - // Serializes this object to a given filename. - virtual void save(const String& filename) const; - - // Deserializes this object from a given filename. - virtual void load(const String& filename); - - // Serializes this object to a given cv::FileStorage. - virtual void save(FileStorage& fs) const = 0; - - // Deserializes this object from a given cv::FileStorage. - virtual void load(const FileStorage& fs) = 0; - - // Sets additional string info for the label - virtual void setLabelInfo(int label, const String& strInfo); - - // Gets string info by label - virtual String getLabelInfo(int label); - - // Gets labels by string info - virtual vector getLabelsByString(const String& str); - }; - - -Description -+++++++++++ - -I'll go a bit more into detail explaining :ocv:class:`FaceRecognizer`, because it doesn't look like a powerful interface at first sight. But: Every :ocv:class:`FaceRecognizer` is an :ocv:class:`Algorithm`, so you can easily get/set all model internals (if allowed by the implementation). :ocv:class:`Algorithm` is a relatively new OpenCV concept, which is available since the 2.4 release. I suggest you take a look at its description. - -:ocv:class:`Algorithm` provides the following features for all derived classes: - -* So called “virtual constructor”. That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see :ocv:func:`Algorithm::create`). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms. - -* Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with :ocv:cfunc:`cvSetCaptureProperty`, :ocv:cfunc:`cvGetCaptureProperty`, :ocv:func:`VideoCapture::set` and :ocv:func:`VideoCapture::get`. :ocv:class:`Algorithm` provides similar method where instead of integer id's you specify the parameter names as text Strings. See :ocv:func:`Algorithm::set` and :ocv:func:`Algorithm::get` for details. - -* Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time. - -Moreover every :ocv:class:`FaceRecognizer` supports the: - -* **Training** of a :ocv:class:`FaceRecognizer` with :ocv:func:`FaceRecognizer::train` on a given set of images (your face database!). - -* **Prediction** of a given sample image, that means a face. The image is given as a :ocv:class:`Mat`. - -* **Loading/Saving** the model state from/to a given XML or YAML. - -* **Setting/Getting labels info**, that is stored as a string. String labels info is useful for keeping names of the recognized people. - -.. note:: When using the FaceRecognizer interface in combination with Python, please stick to Python 2. Some underlying scripts like create_csv will not work in other versions, like Python 3. - -Setting the Thresholds -+++++++++++++++++++++++ - -Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is unknown. You might wonder, why there's no public API in :ocv:class:`FaceRecognizer` to set the threshold for the prediction, but rest assured: It's supported. It just means there's no generic way in an abstract class to provide an interface for setting/getting the thresholds of *every possible* :ocv:class:`FaceRecognizer` algorithm. The appropriate place to set the thresholds is in the constructor of the specific :ocv:class:`FaceRecognizer` and since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm` (see above), you can get/set the thresholds at runtime! - -Here is an example of setting a threshold for the Eigenfaces method, when creating the model: - -.. code-block:: cpp - - // Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0 - int num_components = 10; - double threshold = 10.0; - // Then if you want to have a cv::FaceRecognizer with a confidence threshold, - // create the concrete implementation with the appropiate parameters: - Ptr model = createEigenFaceRecognizer(num_components, threshold); - -Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to :ocv:class:`Algorithm` it's possible to set internal model thresholds during runtime. Let's see how we would set/get the prediction for the Eigenface model, we've created above: - -.. code-block:: cpp - - // The following line reads the threshold from the Eigenfaces model: - double current_threshold = model->getDouble("threshold"); - // And this line sets the threshold to 0.0: - model->set("threshold", 0.0); - -If you've set the threshold to ``0.0`` as we did above, then: - -.. code-block:: cpp - - // - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // Get a prediction from the model. Note: We've set a threshold of 0.0 above, - // since the distance is almost always larger than 0.0, you'll get -1 as - // label, which indicates, this face is unknown - int predicted_label = model->predict(img); - // ... - -is going to yield ``-1`` as predicted label, which states this face is unknown. - -Getting the name of a FaceRecognizer -+++++++++++++++++++++++++++++++++++++ - -Since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm`, you can use :ocv:func:`Algorithm::name` to get the name of a :ocv:class:`FaceRecognizer`: - -.. code-block:: cpp - - // Create a FaceRecognizer: - Ptr model = createEigenFaceRecognizer(); - // And here's how to get its name: - String name = model->name(); - - -FaceRecognizer::train ---------------------- - -Trains a FaceRecognizer with given data and associated labels. - -.. ocv:function:: void FaceRecognizer::train( InputArrayOfArrays src, InputArray labels ) = 0 - - :param src: The training images, that means the faces you want to learn. The data has to be given as a ``vector``. - - :param labels: The labels corresponding to the images have to be given either as a ``vector`` or a - -The following source code snippet shows you how to learn a Fisherfaces model on a given set of images. The images are read with :ocv:func:`imread` and pushed into a ``std::vector``. The labels of each image are stored within a ``std::vector`` (you could also use a :ocv:class:`Mat` of type `CV_32SC1`). Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label. For the available :ocv:class:`FaceRecognizer` you don't have to pay any attention to the order of the labels, just make sure same persons have the same label: - -.. code-block:: cpp - - // holds images and labels - vector images; - vector labels; - // images for first person - images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - // images for second person - images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - -Now that you have read some images, we can create a new :ocv:class:`FaceRecognizer`. In this example I'll create a Fisherfaces model and decide to keep all of the possible Fisherfaces: - -.. code-block:: cpp - - // Create a new Fisherfaces model and retain all available Fisherfaces, - // this is the most common usage of this specific FaceRecognizer: - // - Ptr model = createFisherFaceRecognizer(); - -And finally train it on the given dataset (the face images and labels): - -.. code-block:: cpp - - // This is the common interface to train all of the available cv::FaceRecognizer - // implementations: - // - model->train(images, labels); - -FaceRecognizer::update ----------------------- - -Updates a FaceRecognizer with given data and associated labels. - -.. ocv:function:: void FaceRecognizer::update( InputArrayOfArrays src, InputArray labels ) - - :param src: The training images, that means the faces you want to learn. The data has to be given as a ``vector``. - - :param labels: The labels corresponding to the images have to be given either as a ``vector`` or a - -This method updates a (probably trained) :ocv:class:`FaceRecognizer`, but only if the algorithm supports it. The Local Binary Patterns Histograms (LBPH) recognizer (see :ocv:func:`createLBPHFaceRecognizer`) can be updated. For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to re-estimate the model with :ocv:func:`FaceRecognizer::train`. In any case, a call to train empties the existing model and learns a new model, while update does not delete any model data. - -.. code-block:: cpp - - // Create a new LBPH model (it can be updated) and use the default parameters, - // this is the most common usage of this specific FaceRecognizer: - // - Ptr model = createLBPHFaceRecognizer(); - // This is the common interface to train all of the available cv::FaceRecognizer - // implementations: - // - model->train(images, labels); - // Some containers to hold new image: - vector newImages; - vector newLabels; - // You should add some images to the containers: - // - // ... - // - // Now updating the model is as easy as calling: - model->update(newImages,newLabels); - // This will preserve the old model data and extend the existing model - // with the new features extracted from newImages! - -Calling update on an Eigenfaces model (see :ocv:func:`createEigenFaceRecognizer`), which doesn't support updating, will throw an error similar to: - -.. code-block:: none - - OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305 - terminate called after throwing an instance of 'cv::Exception' - -Please note: The :ocv:class:`FaceRecognizer` does not store your training images, because this would be very memory intense and it's not the responsibility of te :ocv:class:`FaceRecognizer` to do so. The caller is responsible for maintaining the dataset, he want to work with. - -FaceRecognizer::predict ------------------------ - -.. ocv:function:: int FaceRecognizer::predict( InputArray src ) const = 0 -.. ocv:function:: void FaceRecognizer::predict( InputArray src, int & label, double & confidence ) const = 0 - - Predicts a label and associated confidence (e.g. distance) for a given input image. - - :param src: Sample image to get a prediction from. - :param label: The predicted label for the given image. - :param confidence: Associated confidence (e.g. distance) for the predicted label. - -The suffix ``const`` means that prediction does not affect the internal model -state, so the method can be safely called from within different threads. - -The following example shows how to get a prediction from a trained model: - -.. code-block:: cpp - - using namespace cv; - // Do your initialization here (create the cv::FaceRecognizer model) ... - // ... - // Read in a sample image: - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // And get a prediction from the cv::FaceRecognizer: - int predicted = model->predict(img); - -Or to get a prediction and the associated confidence (e.g. distance): - -.. code-block:: cpp - - using namespace cv; - // Do your initialization here (create the cv::FaceRecognizer model) ... - // ... - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // Some variables for the predicted label and associated confidence (e.g. distance): - int predicted_label = -1; - double predicted_confidence = 0.0; - // Get the prediction and associated confidence from the model - model->predict(img, predicted_label, predicted_confidence); - -FaceRecognizer::save --------------------- - -Saves a :ocv:class:`FaceRecognizer` and its model state. - -.. ocv:function:: void FaceRecognizer::save(const String& filename) const - - Saves this model to a given filename, either as XML or YAML. - - :param filename: The filename to store this :ocv:class:`FaceRecognizer` to (either XML/YAML). - -.. ocv:function:: void FaceRecognizer::save(FileStorage& fs) const - - Saves this model to a given :ocv:class:`FileStorage`. - - :param fs: The :ocv:class:`FileStorage` to store this :ocv:class:`FaceRecognizer` to. - - -Every :ocv:class:`FaceRecognizer` overwrites ``FaceRecognizer::save(FileStorage& fs)`` -to save the internal model state. ``FaceRecognizer::save(const String& filename)`` saves -the state of a model to the given filename. - -The suffix ``const`` means that prediction does not affect the internal model -state, so the method can be safely called from within different threads. - -FaceRecognizer::load --------------------- - -Loads a :ocv:class:`FaceRecognizer` and its model state. - -.. ocv:function:: void FaceRecognizer::load( const String& filename ) -.. ocv:function:: void FaceRecognizer::load( const FileStorage& fs ) = 0 - -Loads a persisted model and state from a given XML or YAML file . Every -:ocv:class:`FaceRecognizer` has to overwrite ``FaceRecognizer::load(FileStorage& fs)`` -to enable loading the model state. ``FaceRecognizer::load(FileStorage& fs)`` in -turn gets called by ``FaceRecognizer::load(const String& filename)``, to ease -saving a model. - -FaceRecognizer::setLabelInfo ------------------------------ - -Sets string info for the specified model's label. -.. ocv:function:: void FaceRecognizer::setLabelInfo(int label, const String& strInfo) - -The string info is replaced by the provided value if it was set before for the specified label. - -FaceRecognizer::getLabelInfo ----------------------------- - -Gets string information by label. -.. ocv:function:: String FaceRecognizer::getLabelInfo(int label) - -If an unknown label id is provided or there is no label information associated with the specified label id the method returns an empty string. - -FaceRecognizer::getLabelsByString ---------------------------------- -Gets vector of labels by string. - -.. ocv:function:: vector FaceRecognizer::getLabelsByString(const String& str) - -The function searches for the labels containing the specified sub-string in the associated string info. - -createEigenFaceRecognizer -------------------------- - -.. ocv:function:: Ptr createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX) - - :param num_components: The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. - - :param threshold: The threshold applied in the prediction. - -Notes: -++++++ - -* Training and prediction must be done on grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces. -* **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use :ocv:func:`resize` to resize the images. -* This model does not support updating. - -Model internal data: -++++++++++++++++++++ - -* ``num_components`` see :ocv:func:`createEigenFaceRecognizer`. -* ``threshold`` see :ocv:func:`createEigenFaceRecognizer`. -* ``eigenvalues`` The eigenvalues for this Principal Component Analysis (ordered descending). -* ``eigenvectors`` The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue). -* ``mean`` The sample mean calculated from the training data. -* ``projections`` The projections of the training data. -* ``labels`` The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. - -createFisherFaceRecognizer --------------------------- - -.. ocv:function:: Ptr createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX) - - :param num_components: The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes ``c`` (read: subjects, persons you want to recognize). If you leave this at the default (``0``) or set it to a value less-equal ``0`` or greater ``(c-1)``, it will be set to the correct number ``(c-1)`` automatically. - - :param threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. - -Notes: -++++++ - -* Training and prediction must be done on grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces. -* **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use :ocv:func:`resize` to resize the images. -* This model does not support updating. - -Model internal data: -++++++++++++++++++++ - -* ``num_components`` see :ocv:func:`createFisherFaceRecognizer`. -* ``threshold`` see :ocv:func:`createFisherFaceRecognizer`. -* ``eigenvalues`` The eigenvalues for this Linear Discriminant Analysis (ordered descending). -* ``eigenvectors`` The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue). -* ``mean`` The sample mean calculated from the training data. -* ``projections`` The projections of the training data. -* ``labels`` The labels corresponding to the projections. - - -createLBPHFaceRecognizer -------------------------- - -.. ocv:function:: Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX) - - :param radius: The radius used for building the Circular Local Binary Pattern. The greater the radius, the - :param neighbors: The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use `` 8`` sample points. Keep in mind: the more sample points you include, the higher the computational cost. - :param grid_x: The number of cells in the horizontal direction, ``8`` is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. - :param grid_y: The number of cells in the vertical direction, ``8`` is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. - :param threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. - -Notes: -++++++ - -* The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces. -* This model supports updating. - -Model internal data: -++++++++++++++++++++ - -* ``radius`` see :ocv:func:`createLBPHFaceRecognizer`. -* ``neighbors`` see :ocv:func:`createLBPHFaceRecognizer`. -* ``grid_x`` see :ocv:func:`createLBPHFaceRecognizer`. -* ``grid_y`` see :ocv:func:`createLBPHFaceRecognizer`. -* ``threshold`` see :ocv:func:`createLBPHFaceRecognizer`. -* ``histograms`` Local Binary Patterns Histograms calculated from the given training data (empty if none was given). -* ``labels`` Labels corresponding to the calculated Local Binary Patterns Histograms. diff --git a/modules/face/doc/facerec_changelog.rst b/modules/face/doc/facerec_changelog.rst deleted file mode 100755 index 107135818..000000000 --- a/modules/face/doc/facerec_changelog.rst +++ /dev/null @@ -1,86 +0,0 @@ -Changelog -========= - -Release 0.05 ------------- - -This library is now included in the official OpenCV distribution (from 2.4 on). -The :ocv:class`FaceRecognizer` is now an :ocv:class:`Algorithm`, which better fits into the overall -OpenCV API. - -To reduce the confusion on user side and minimize my work, libfacerec and OpenCV -have been synchronized and are now based on the same interfaces and implementation. - -The library now has an extensive documentation: - -* The API is explained in detail and with a lot of code examples. -* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``. -* A tutorial for gender classification with Fisherfaces. -* A tutorial for face recognition in videos (e.g. webcam). - - -Release highlights -++++++++++++++++++ - -* There are no single highlights to pick from, this release is a highlight itself. - -Release 0.04 ------------- - -This version is fully Windows-compatible and works with OpenCV 2.3.1. Several -bugfixes, but none influenced the recognition rate. - -Release highlights -++++++++++++++++++ - -* A whole lot of exceptions with meaningful error messages. -* A tutorial for Windows users: `http://bytefish.de/blog/opencv_visual_studio_and_libfacerec `_ - - -Release 0.03 ------------- - -Reworked the library to provide separate implementations in cpp files, because -it's the preferred way of contributing OpenCV libraries. This means the library -is not header-only anymore. Slight API changes were done, please see the -documentation for details. - -Release highlights -++++++++++++++++++ - -* New Unit Tests (for LBP Histograms) make the library more robust. -* Added more documentation. - - -Release 0.02 ------------- - -Reworked the library to provide separate implementations in cpp files, because -it's the preferred way of contributing OpenCV libraries. This means the library -is not header-only anymore. Slight API changes were done, please see the -documentation for details. - -Release highlights -++++++++++++++++++ - -* New Unit Tests (for LBP Histograms) make the library more robust. -* Added a documentation and changelog in reStructuredText. - -Release 0.01 ------------- - -Initial release as header-only library. - -Release highlights -++++++++++++++++++ - -* Colormaps for OpenCV to enhance the visualization. -* Face Recognition algorithms implemented: - - * Eigenfaces [TP91]_ - * Fisherfaces [BHK97]_ - * Local Binary Patterns Histograms [AHP04]_ - -* Added persistence facilities to store the models with a common API. -* Unit Tests (using `gtest `_). -* Providing a CMakeLists.txt to enable easy cross-platform building. diff --git a/modules/face/doc/facerec_tutorial.rst b/modules/face/doc/facerec_tutorial.rst deleted file mode 100755 index e785b86a8..000000000 --- a/modules/face/doc/facerec_tutorial.rst +++ /dev/null @@ -1,628 +0,0 @@ -Face Recognition with OpenCV -############################ - -.. contents:: Table of Contents - :depth: 3 - -Introduction -============ - -`OpenCV (Open Source Computer Vision) `_ is a popular computer vision library started by `Intel `_ in 1999. The cross-platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. In 2008 `Willow Garage `_ took over support and OpenCV 2.3.1 now comes with a programming interface to C, C++, `Python `_ and `Android `_. OpenCV is released under a BSD license so it is used in academic projects and commercial products alike. - -OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition, so you can start experimenting with face recognition right away. This document is the guide I've wished for, when I was working myself into face recognition. It shows you how to perform face recognition with :ocv:class:`FaceRecognizer` in OpenCV (with full source code listings) and gives you an introduction into the algorithms behind. I'll also show how to create the visualizations you can find in many publications, because a lot of people asked for. - -The currently available algorithms are: - -* Eigenfaces (see :ocv:func:`createEigenFaceRecognizer`) -* Fisherfaces (see :ocv:func:`createFisherFaceRecognizer`) -* Local Binary Patterns Histograms (see :ocv:func:`createLBPHFaceRecognizer`) - -You don't need to copy and paste the source code examples from this page, because they are available in the ``src`` folder coming with this documentation. If you have built OpenCV with the samples turned on, chances are good you have them compiled already! Although it might be interesting for very advanced users, I've decided to leave the implementation details out as I am afraid they confuse new users. - -All code in this document is released under the `BSD license `_, so feel free to use it for your projects. - -Face Recognition -================ - -Face recognition is an easy task for humans. Experiments in [Tu06]_ have shown, that even one to three day old babies are able to distinguish between known faces. So how hard could it be for a computer? It turns out we know little about human recognition to date. Are inner features (eyes, nose, mouth) or outer features (head shape, hairline) used for a successful face recognition? How do we analyze an image and how does the brain encode it? It was shown by `David Hubel `_ and `Torsten Wiesel `_, that our brain has specialized nerve cells responding to specific local features of a scene, such as lines, edges, angles or movement. Since we don't see the world as scattered pieces, our visual cortex must somehow combine the different sources of information into useful patterns. Automatic face recognition is all about extracting those meaningful features from an image, putting them into a useful representation and performing some kind of classification on them. - -Face recognition based on the geometric features of a face is probably the most intuitive approach to face recognition. One of the first automated face recognition systems was described in [Kanade73]_: marker points (position of eyes, ears, nose, ...) were used to build a feature vector (distance between the points, angle between them, ...). The recognition was performed by calculating the euclidean distance between feature vectors of a probe and reference image. Such a method is robust against changes in illumination by its nature, but has a huge drawback: the accurate registration of the marker points is complicated, even with state of the art algorithms. Some of the latest work on geometric face recognition was carried out in [Bru92]_. A 22-dimensional feature vector was used and experiments on large datasets have shown, that geometrical features alone my not carry enough information for face recognition. - -The Eigenfaces method described in [TP91]_ took a holistic approach to face recognition: A facial image is a point from a high-dimensional image space and a lower-dimensional representation is found, where classification becomes easy. The lower-dimensional subspace is found with Principal Component Analysis, which identifies the axes with maximum variance. While this kind of transformation is optimal from a reconstruction standpoint, it doesn't take any class labels into account. Imagine a situation where the variance is generated from external sources, let it be light. The axes with maximum variance do not necessarily contain any discriminative information at all, hence a classification becomes impossible. So a class-specific projection with a Linear Discriminant Analysis was applied to face recognition in [BHK97]_. The basic idea is to minimize the variance within a class, while maximizing the variance between the classes at the same time. - -Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of the input data only local regions of an image are described, the extracted features are (hopefully) more robust against partial occlusion, illumation and small sample size. Algorithms used for a local feature extraction are Gabor Wavelets ([Wiskott97]_), Discrete Cosinus Transform ([Messer06]_) and Local Binary Patterns ([AHP04]_). It's still an open research question what's the best way to preserve spatial information when applying a local feature extraction, because spatial information is potentially useful information. - -Face Database -============== - -Let's get some data to experiment with first. I don't want to do a toy example here. We are doing face recognition, so you'll need some face images! You can either create your own dataset or start with one of the available face databases, `http://face-rec.org/databases/ `_ gives you an up-to-date overview. Three interesting databases are (parts of the description are quoted from `http://face-rec.org `_): - -* `AT&T Facedatabase `_ The AT&T Facedatabase, sometimes also referred to as *ORL Database of Faces*, contains ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). - -* `Yale Facedatabase A `_, also known as Yalefaces. The AT&T Facedatabase is good for initial tests, but it's a fairly easy database. The Eigenfaces method already has a 97% recognition rate on it, so you won't see any great improvements with other algorithms. The Yale Facedatabase A (also known as Yalefaces) is a more appropriate dataset for initial experiments, because the recognition problem is harder. The database consists of 15 people (14 male, 1 female) each with 11 grayscale images sized :math:`320 \times 243` pixel. There are changes in the light conditions (center light, left light, right light), facial expressions (happy, normal, sad, sleepy, surprised, wink) and glasses (glasses, no-glasses). - - The original images are not cropped and aligned. Please look into the :ref:`appendixft` for a Python script, that does the job for you. - -* `Extended Yale Facedatabase B `_ The Extended Yale Facedatabase B contains 2414 images of 38 different people in its cropped version. The focus of this database is set on extracting features that are robust to illumination, the images have almost no variation in emotion/occlusion/... . I personally think, that this dataset is too large for the experiments I perform in this document. You better use the `AT&T Facedatabase `_ for intial testing. A first version of the Yale Facedatabase B was used in [BHK97]_ to see how the Eigenfaces and Fisherfaces method perform under heavy illumination changes. [Lee05]_ used the same setup to take 16128 images of 28 people. The Extended Yale Facedatabase B is the merge of the two databases, which is now known as Extended Yalefacedatabase B. - -Preparing the data -------------------- - -Once we have acquired some data, we'll need to read it in our program. In the demo applications I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this: - -.. code-block:: none - - /path/to/image.ext;0 - -Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign the label ``0`` to the image. Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label. - -Download the AT&T Facedatabase from AT&T Facedatabase and the corresponding CSV file from at.txt, which looks like this (file is without ... of course): - -.. code-block:: none - - ./at/s1/1.pgm;0 - ./at/s1/2.pgm;0 - ... - ./at/s2/1.pgm;1 - ./at/s2/2.pgm;1 - ... - ./at/s40/1.pgm;39 - ./at/s40/2.pgm;39 - -Imagine I have extracted the files to ``D:/data/at`` and have downloaded the CSV file to ``D:/data/at.txt``. Then you would simply need to Search & Replace ``./`` with ``D:/data/``. You can do that in an editor of your choice, every sufficiently advanced editor can do this. Once you have a CSV file with valid filenames and labels, you can run any of the demos by passing the path to the CSV file as parameter: - -.. code-block:: none - - facerec_demo.exe D:/data/at.txt - -Creating the CSV File -+++++++++++++++++++++ - -You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath//``): - -.. code-block:: none - - philipp@mango:~/facerec/data/at$ tree - . - |-- s1 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - |-- s2 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - ... - |-- s40 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - - -Then simply call create_csv.py with the path to the folder, just like this and you could save the output: - -.. code-block:: none - - philipp@mango:~/facerec/data$ python create_csv.py - at/s13/2.pgm;0 - at/s13/7.pgm;0 - at/s13/6.pgm;0 - at/s13/9.pgm;0 - at/s13/5.pgm;0 - at/s13/3.pgm;0 - at/s13/4.pgm;0 - at/s13/10.pgm;0 - at/s13/8.pgm;0 - at/s13/1.pgm;0 - at/s17/2.pgm;1 - at/s17/7.pgm;1 - at/s17/6.pgm;1 - at/s17/9.pgm;1 - at/s17/5.pgm;1 - at/s17/3.pgm;1 - [...] - -Please see the :ref:`appendixft` for additional informations. - -Eigenfaces -========== - -The problem with the image representation we are given is its high dimensionality. Two-dimensional :math:`p \times q` grayscale images span a :math:`m = pq`-dimensional vector space, so an image with :math:`100 \times 100` pixels lies in a :math:`10,000`-dimensional image space already. The question is: Are all dimensions equally useful for us? We can only make a decision if there's any variance in data, so what we are looking for are the components that account for most of the information. The Principal Component Analysis (PCA) was independently proposed by `Karl Pearson `_ (1901) and `Harold Hotelling `_ (1933) to turn a set of possibly correlated variables into a smaller set of uncorrelated variables. The idea is, that a high-dimensional dataset is often described by correlated variables and therefore only a few meaningful dimensions account for most of the information. The PCA method finds the directions with the greatest variance in the data, called principal components. - -Algorithmic Description ------------------------ - -Let :math:`X = \{ x_{1}, x_{2}, \ldots, x_{n} \}` be a random vector with observations :math:`x_i \in R^{d}`. - -1. Compute the mean :math:`\mu` - - .. math:: - - \mu = \frac{1}{n} \sum_{i=1}^{n} x_{i} - -2. Compute the the Covariance Matrix `S` - - .. math:: - - S = \frac{1}{n} \sum_{i=1}^{n} (x_{i} - \mu) (x_{i} - \mu)^{T}` - -3. Compute the eigenvalues :math:`\lambda_{i}` and eigenvectors :math:`v_{i}` of :math:`S` - - .. math:: - - S v_{i} = \lambda_{i} v_{i}, i=1,2,\ldots,n - -4. Order the eigenvectors descending by their eigenvalue. The :math:`k` principal components are the eigenvectors corresponding to the :math:`k` largest eigenvalues. - -The :math:`k` principal components of the observed vector :math:`x` are then given by: - -.. math:: - - y = W^{T} (x - \mu) - - -where :math:`W = (v_{1}, v_{2}, \ldots, v_{k})`. - -The reconstruction from the PCA basis is given by: - -.. math:: - - x = W y + \mu - -where :math:`W = (v_{1}, v_{2}, \ldots, v_{k})`. - - -The Eigenfaces method then performs face recognition by: - -* Projecting all training samples into the PCA subspace. -* Projecting the query image into the PCA subspace. -* Finding the nearest neighbor between the projected training images and the projected query image. - -Still there's one problem left to solve. Imagine we are given :math:`400` images sized :math:`100 \times 100` pixel. The Principal Component Analysis solves the covariance matrix :math:`S = X X^{T}`, where :math:`{size}(X) = 10000 \times 400` in our example. You would end up with a :math:`10000 \times 10000` matrix, roughly :math:`0.8 GB`. Solving this problem isn't feasible, so we'll need to apply a trick. From your linear algebra lessons you know that a :math:`M \times N` matrix with :math:`M > N` can only have :math:`N - 1` non-zero eigenvalues. So it's possible to take the eigenvalue decomposition :math:`S = X^{T} X` of size :math:`N \times N` instead: - -.. math:: - - X^{T} X v_{i} = \lambda_{i} v{i} - - -and get the original eigenvectors of :math:`S = X X^{T}` with a left multiplication of the data matrix: - -.. math:: - - X X^{T} (X v_{i}) = \lambda_{i} (X v_{i}) - -The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they need to be normalized to unit length. I don't want to turn this into a publication, so please look into [Duda01]_ for the derivation and proof of the equations. - -Eigenfaces in OpenCV --------------------- - -For the first source code example, I'll go through it with you. I am first giving you the whole source code listing, and after this we'll look at the most important lines in detail. Please note: every source code listing is commented in detail, so you should have no problems following it. - -.. literalinclude:: src/facerec_eigenfaces.cpp - :language: cpp - :linenos: - -The source code for this demo application is also available in the ``src`` folder coming with this documentation: - -* :download:`src/facerec_eigenfaces.cpp ` - - -I've used the jet colormap, so you can see how the grayscale values are distributed within the specific Eigenfaces. You can see, that the Eigenfaces do not only encode facial features, but also the illumination in the images (see the left light in Eigenface \#4, right light in Eigenfaces \#5): - -.. image:: img/eigenfaces_opencv.png - :align: center - -We've already seen, that we can reconstruct a face from its lower dimensional approximation. So let's see how many Eigenfaces are needed for a good reconstruction. I'll do a subplot with :math:`10,30,\ldots,310` Eigenfaces: - -.. code-block:: cpp - - // Display or save the image reconstruction at some predefined steps: - for(int num_components = 10; num_components < 300; num_components+=15) { - // slice the eigenvectors from the model - Mat evs = Mat(W, Range::all(), Range(0, num_components)); - Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1)); - Mat reconstruction = subspaceReconstruct(evs, mean, projection); - // Normalize the result: - reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows)); - // Display or save: - if(argc == 2) { - imshow(format("eigenface_reconstruction_%d", num_components), reconstruction); - } else { - imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction); - } - } - -10 Eigenvectors are obviously not sufficient for a good image reconstruction, 50 Eigenvectors may already be sufficient to encode important facial features. You'll get a good reconstruction with approximately 300 Eigenvectors for the AT&T Facedatabase. There are rule of thumbs how many Eigenfaces you should choose for a successful face recognition, but it heavily depends on the input data. [Zhao03]_ is the perfect point to start researching for this: - -.. image:: img/eigenface_reconstruction_opencv.png - :align: center - - -Fisherfaces -============ - -The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear combination of features that maximizes the total variance in data. While this is clearly a powerful way to represent data, it doesn't consider any classes and so a lot of discriminative information *may* be lost when throwing components away. Imagine a situation where the variance in your data is generated by an external source, let it be the light. The components identified by a PCA do not necessarily contain any discriminative information at all, so the projected samples are smeared together and a classification becomes impossible (see `http://www.bytefish.de/wiki/pca_lda_with_gnu_octave `_ for an example). - -The Linear Discriminant Analysis performs a class-specific dimensionality reduction and was invented by the great statistician `Sir R. A. Fisher `_. He successfully used it for classifying flowers in his 1936 paper *The use of multiple measurements in taxonomic problems* [Fisher36]_. In order to find the combination of features that separates best between classes the Linear Discriminant Analysis maximizes the ratio of between-classes to within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes should cluster tightly together, while different classes are as far away as possible from each other in the lower-dimensional representation. This was also recognized by `Belhumeur `_, `Hespanha `_ and `Kriegman `_ and so they applied a Discriminant Analysis to face recognition in [BHK97]_. - -Algorithmic Description ------------------------ - -Let :math:`X` be a random vector with samples drawn from :math:`c` classes: - - -.. math:: - :nowrap: - - \begin{align*} - X & = & \{X_1,X_2,\ldots,X_c\} \\ - X_i & = & \{x_1, x_2, \ldots, x_n\} - \end{align*} - - -The scatter matrices :math:`S_{B}` and `S_{W}` are calculated as: - -.. math:: - :nowrap: - - \begin{align*} - S_{B} & = & \sum_{i=1}^{c} N_{i} (\mu_i - \mu)(\mu_i - \mu)^{T} \\ - S_{W} & = & \sum_{i=1}^{c} \sum_{x_{j} \in X_{i}} (x_j - \mu_i)(x_j - \mu_i)^{T} - \end{align*} - -, where :math:`\mu` is the total mean: - -.. math:: - - \mu = \frac{1}{N} \sum_{i=1}^{N} x_i - -And :math:`\mu_i` is the mean of class :math:`i \in \{1,\ldots,c\}`: - -.. math:: - - \mu_i = \frac{1}{|X_i|} \sum_{x_j \in X_i} x_j - -Fisher's classic algorithm now looks for a projection :math:`W`, that maximizes the class separability criterion: - -.. math:: - - W_{opt} = \operatorname{arg\,max}_{W} \frac{|W^T S_B W|}{|W^T S_W W|} - - -Following [BHK97]_, a solution for this optimization problem is given by solving the General Eigenvalue Problem: - -.. math:: - :nowrap: - - \begin{align*} - S_{B} v_{i} & = & \lambda_{i} S_w v_{i} \nonumber \\ - S_{W}^{-1} S_{B} v_{i} & = & \lambda_{i} v_{i} - \end{align*} - -There's one problem left to solve: The rank of :math:`S_{W}` is at most :math:`(N-c)`, with :math:`N` samples and :math:`c` classes. In pattern recognition problems the number of samples :math:`N` is almost always samller than the dimension of the input data (the number of pixels), so the scatter matrix :math:`S_{W}` becomes singular (see [RJ91]_). In [BHK97]_ this was solved by performing a Principal Component Analysis on the data and projecting the samples into the :math:`(N-c)`-dimensional space. A Linear Discriminant Analysis was then performed on the reduced data, because :math:`S_{W}` isn't singular anymore. - -The optimization problem can then be rewritten as: - -.. math:: - :nowrap: - - \begin{align*} - W_{pca} & = & \operatorname{arg\,max}_{W} |W^T S_T W| \\ - W_{fld} & = & \operatorname{arg\,max}_{W} \frac{|W^T W_{pca}^T S_{B} W_{pca} W|}{|W^T W_{pca}^T S_{W} W_{pca} W|} - \end{align*} - -The transformation matrix :math:`W`, that projects a sample into the :math:`(c-1)`-dimensional space is then given by: - -.. math:: - - W = W_{fld}^{T} W_{pca}^{T} - -Fisherfaces in OpenCV ---------------------- - -.. literalinclude:: src/facerec_fisherfaces.cpp - :language: cpp - :linenos: - -The source code for this demo application is also available in the ``src`` folder coming with this documentation: - -* :download:`src/facerec_fisherfaces.cpp ` - - -For this example I am going to use the Yale Facedatabase A, just because the plots are nicer. Each Fisherface has the same length as an original image, thus it can be displayed as an image. The demo shows (or saves) the first, at most 16 Fisherfaces: - -.. image:: img/fisherfaces_opencv.png - :align: center - -The Fisherfaces method learns a class-specific transformation matrix, so the they do not capture illumination as obviously as the Eigenfaces method. The Discriminant Analysis instead finds the facial features to discriminate between the persons. It's important to mention, that the performance of the Fisherfaces heavily depends on the input data as well. Practically said: if you learn the Fisherfaces for well-illuminated pictures only and you try to recognize faces in bad-illuminated scenes, then method is likely to find the wrong components (just because those features may not be predominant on bad illuminated images). This is somewhat logical, since the method had no chance to learn the illumination. - -The Fisherfaces allow a reconstruction of the projected image, just like the Eigenfaces did. But since we only identified the features to distinguish between subjects, you can't expect a nice reconstruction of the original image. For the Fisherfaces method we'll project the sample image onto each of the Fisherfaces instead. So you'll have a nice visualization, which feature each of the Fisherfaces describes: - -.. code-block:: cpp - - // Display or save the image reconstruction at some predefined steps: - for(int num_component = 0; num_component < min(16, W.cols); num_component++) { - // Slice the Fisherface from the model: - Mat ev = W.col(num_component); - Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1)); - Mat reconstruction = subspaceReconstruct(ev, mean, projection); - // Normalize the result: - reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows)); - // Display or save: - if(argc == 2) { - imshow(format("fisherface_reconstruction_%d", num_component), reconstruction); - } else { - imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction); - } - } - -The differences may be subtle for the human eyes, but you should be able to see some differences: - -.. image:: img/fisherface_reconstruction_opencv.png - :align: center - - -Local Binary Patterns Histograms -================================ - -Eigenfaces and Fisherfaces take a somewhat holistic approach to face recognition. You treat your data as a vector somewhere in a high-dimensional image space. We all know high-dimensionality is bad, so a lower-dimensional subspace is identified, where (probably) useful information is preserved. The Eigenfaces approach maximizes the total scatter, which can lead to problems if the variance is generated by an external source, because components with a maximum variance over all classes aren't necessarily useful for classification (see `http://www.bytefish.de/wiki/pca_lda_with_gnu_octave `_). So to preserve some discriminative information we applied a Linear Discriminant Analysis and optimized as described in the Fisherfaces method. The Fisherfaces method worked great... at least for the constrained scenario we've assumed in our model. - -Now real life isn't perfect. You simply can't guarantee perfect light settings in your images or 10 different images of a person. So what if there's only one image for each person? Our covariance estimates for the subspace *may* be horribly wrong, so will the recognition. Remember the Eigenfaces method had a 96% recognition rate on the AT&T Facedatabase? How many images do we actually need to get such useful estimates? Here are the Rank-1 recognition rates of the Eigenfaces and Fisherfaces method on the AT&T Facedatabase, which is a fairly easy image database: - -.. image:: img/at_database_small_sample_size.png - :scale: 60% - :align: center - -So in order to get good recognition rates you'll need at least 8(+-1) images for each person and the Fisherfaces method doesn't really help here. The above experiment is a 10-fold cross validated result carried out with the facerec framework at: `https://github.com/bytefish/facerec `_. This is not a publication, so I won't back these figures with a deep mathematical analysis. Please have a look into [KM01]_ for a detailed analysis of both methods, when it comes to small training datasets. - -So some research concentrated on extracting local features from images. The idea is to not look at the whole image as a high-dimensional vector, but describe only local features of an object. The features you extract this way will have a low-dimensionality implicitly. A fine idea! But you'll soon observe the image representation we are given doesn't only suffer from illumination variations. Think of things like scale, translation or rotation in images - your local description has to be at least a bit robust against those things. Just like ``SIFT``, the Local Binary Patterns methodology has its roots in 2D texture analysis. The basic idea of Local Binary Patterns is to summarize the local structure in an image by comparing each pixel with its neighborhood. Take a pixel as center and threshold its neighbors against. If the intensity of the center pixel is greater-equal its neighbor, then denote it with 1 and 0 if not. You'll end up with a binary number for each pixel, just like 11001111. So with 8 surrounding pixels you'll end up with 2^8 possible combinations, called *Local Binary Patterns* or sometimes referred to as *LBP codes*. The first LBP operator described in literature actually used a fixed 3 x 3 neighborhood just like this: - -.. image:: img/lbp/lbp.png - :scale: 80% - :align: center - -Algorithmic Description ------------------------ - -A more formal description of the LBP operator can be given as: - -.. math:: - - LBP(x_c, y_c) = \sum_{p=0}^{P-1} 2^p s(i_p - i_c) - -, with :math:`(x_c, y_c)` as central pixel with intensity :math:`i_c`; and :math:`i_n` being the intensity of the the neighbor pixel. :math:`s` is the sign function defined as: - -.. math:: - :nowrap: - - \begin{equation} - s(x) = - \begin{cases} - 1 & \text{if $x \geq 0$}\\ - 0 & \text{else} - \end{cases} - \end{equation} - -This description enables you to capture very fine grained details in images. In fact the authors were able to compete with state of the art results for texture classification. Soon after the operator was published it was noted, that a fixed neighborhood fails to encode details differing in scale. So the operator was extended to use a variable neighborhood in [AHP04]_. The idea is to align an abritrary number of neighbors on a circle with a variable radius, which enables to capture the following neighborhoods: - -.. image:: img/lbp/patterns.png - :scale: 80% - :align: center - -For a given Point :math:`(x_c,y_c)` the position of the neighbor :math:`(x_p,y_p), p \in P` can be calculated by: - -.. math:: - :nowrap: - - \begin{align*} - x_{p} & = & x_c + R \cos({\frac{2\pi p}{P}})\\ - y_{p} & = & y_c - R \sin({\frac{2\pi p}{P}}) - \end{align*} - -Where :math:`R` is the radius of the circle and :math:`P` is the number of sample points. - -The operator is an extension to the original LBP codes, so it's sometimes called *Extended LBP* (also referred to as *Circular LBP*) . If a points coordinate on the circle doesn't correspond to image coordinates, the point get's interpolated. Computer science has a bunch of clever interpolation schemes, the OpenCV implementation does a bilinear interpolation: - -.. math:: - :nowrap: - - \begin{align*} - f(x,y) \approx \begin{bmatrix} - 1-x & x \end{bmatrix} \begin{bmatrix} - f(0,0) & f(0,1) \\ - f(1,0) & f(1,1) \end{bmatrix} \begin{bmatrix} - 1-y \\ - y \end{bmatrix}. - \end{align*} - -By definition the LBP operator is robust against monotonic gray scale transformations. We can easily verify this by looking at the LBP image of an artificially modified image (so you see what an LBP image looks like!): - -.. image:: img/lbp/lbp_yale.jpg - :scale: 60% - :align: center - -So what's left to do is how to incorporate the spatial information in the face recognition model. The representation proposed by Ahonen et. al [AHP04]_ is to divide the LBP image into :math:`m` local regions and extract a histogram from each. The spatially enhanced feature vector is then obtained by concatenating the local histograms (**not merging them**). These histograms are called *Local Binary Patterns Histograms*. - -Local Binary Patterns Histograms in OpenCV ------------------------------------------- - -.. literalinclude:: src/facerec_lbph.cpp - :language: cpp - :linenos: - -The source code for this demo application is also available in the ``src`` folder coming with this documentation: - -* :download:`src/facerec_lbph.cpp ` - -Conclusion -========== - -You've learned how to use the new :ocv:class:`FaceRecognizer` in real applications. After reading the document you also know how the algorithms work, so now it's time for you to experiment with the available algorithms. Use them, improve them and let the OpenCV community participate! - -Credits -======= - -This document wouldn't be possible without the kind permission to use the face images of the *AT&T Database of Faces* and the *Yale Facedatabase A/B*. - -The Database of Faces ---------------------- - -** Important: when using these images, please give credit to "AT&T Laboratories, Cambridge." ** - -The Database of Faces, formerly *The ORL Database of Faces*, contains a set of face images taken between April 1992 and April 1994. The database was used in the context of a face recognition project carried out in collaboration with the Speech, Vision and Robotics Group of the Cambridge University Engineering Department. - -There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). - -The files are in PGM format. The size of each image is 92x112 pixels, with 256 grey levels per pixel. The images are organised in 40 directories (one for each subject), which have names of the form sX, where X indicates the subject number (between 1 and 40). In each of these directories, there are ten different images of that subject, which have names of the form Y.pgm, where Y is the image number for that subject (between 1 and 10). - -A copy of the database can be retrieved from: `http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip `_. - -Yale Facedatabase A -------------------- - -*With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase A or the Yale Facedatabase B.* - -The Yale Face Database A (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink. (Source: `http://cvc.yale.edu/projects/yalefaces/yalefaces.html `_) - -Yale Facedatabase B --------------------- - -*With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase A or the Yale Facedatabase B.* - -The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions. The data format of this database is the same as the Yale Face Database B. Please refer to the homepage of the Yale Face Database B (or one copy of this page) for more detailed information of the data format. - -You are free to use the extended Yale Face Database B for research purposes. All publications which use this database should acknowledge the use of "the Exteded Yale Face Database B" and reference Athinodoros Georghiades, Peter Belhumeur, and David Kriegman's paper, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001, `[bibtex] `_. - -The extended database as opposed to the original Yale Face Database B with 10 subjects was first reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face Recognition under Variable Lighting, PAMI, May, 2005 `[pdf] `_." All test image data used in the experiments are manually aligned, cropped, and then re-sized to 168x192 images. If you publish your experimental results with the cropped images, please reference the PAMI2005 paper as well. (Source: `http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html `_) - -Literature -========== - -.. [AHP04] Ahonen, T., Hadid, A., and Pietikainen, M. *Face Recognition with Local Binary Patterns.* Computer Vision - ECCV 2004 (2004), 469–481. - -.. [BHK97] Belhumeur, P. N., Hespanha, J., and Kriegman, D. *Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection.* IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711–720. - -.. [Bru92] Brunelli, R., Poggio, T. *Face Recognition through Geometrical Features.* European Conference on Computer Vision (ECCV) 1992, S. 792–800. - -.. [Duda01] Duda, Richard O. and Hart, Peter E. and Stork, David G., *Pattern Classification* (2nd Edition) 2001. - -.. [Fisher36] Fisher, R. A. *The use of multiple measurements in taxonomic problems.* Annals Eugen. 7 (1936), 179–188. - -.. [GBK01] Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.J., *From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose* IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 6 (2001), 643-660. - -.. [Kanade73] Kanade, T. *Picture processing system by computer complex and recognition of human faces.* PhD thesis, Kyoto University, November 1973 - -.. [KM01] Martinez, A and Kak, A. *PCA versus LDA* IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.2, pp. 228-233, 2001. - -.. [Lee05] Lee, K., Ho, J., Kriegman, D. *Acquiring Linear Subspaces for Face Recognition under Variable Lighting.* In: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27 (2005), Nr. 5 - -.. [Messer06] Messer, K. et al. *Performance Characterisation of Face Recognition Algorithms and Their Sensitivity to Severe Illumination Changes.* In: In: ICB, 2006, S. 1–11. - -.. [RJ91] S. Raudys and A.K. Jain. *Small sample size effects in statistical pattern recognition: Recommendations for practitioneers.* - IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 3 (1991), 252-264. - -.. [Tan10] Tan, X., and Triggs, B. *Enhanced local texture feature sets for face recognition under difficult lighting conditions.* IEEE Transactions on Image Processing 19 (2010), 1635–650. - -.. [TP91] Turk, M., and Pentland, A. *Eigenfaces for recognition.* Journal of Cognitive Neuroscience 3 (1991), 71–86. - -.. [Tu06] Chiara Turati, Viola Macchi Cassia, F. S., and Leo, I. *Newborns face recognition: Role of inner and outer facial features. Child Development* 77, 2 (2006), 297–311. - -.. [Wiskott97] Wiskott, L., Fellous, J., Krüger, N., Malsburg, C. *Face Recognition By Elastic Bunch Graph Matching.* IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997), S. 775–779 - -.. [Zhao03] Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, A. Face recognition: A literature survey. ACM Computing Surveys (CSUR) 35, 4 (2003), 399–458. - -.. _appendixft: - -Appendix -======== - -Creating the CSV File ---------------------- - -You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``/src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath//``): - -.. code-block:: none - - philipp@mango:~/facerec/data/at$ tree - . - |-- s1 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - |-- s2 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - ... - |-- s40 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - - -Then simply call ``create_csv.py`` with the path to the folder, just like this and you could save the output: - -.. code-block:: none - - philipp@mango:~/facerec/data$ python create_csv.py - at/s13/2.pgm;0 - at/s13/7.pgm;0 - at/s13/6.pgm;0 - at/s13/9.pgm;0 - at/s13/5.pgm;0 - at/s13/3.pgm;0 - at/s13/4.pgm;0 - at/s13/10.pgm;0 - at/s13/8.pgm;0 - at/s13/1.pgm;0 - at/s17/2.pgm;1 - at/s17/7.pgm;1 - at/s17/6.pgm;1 - at/s17/9.pgm;1 - at/s17/5.pgm;1 - at/s17/3.pgm;1 - [...] - -Here is the script, if you can't find it: - -.. literalinclude:: ./src/create_csv.py - :language: python - :linenos: - -Aligning Face Images ---------------------- - -An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where: - -* *eye_left* is the position of the left eye -* *eye_right* is the position of the right eye -* *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction) -* *dest_sz* is the size of the output image - -If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes. - -.. literalinclude:: ./src/crop_face.py - :language: python - :linenos: - -Imagine we are given `this photo of Arnold Schwarzenegger `_, which is under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical offset and the size your scaled, rotated & cropped face should have. - -Here are some examples: - -+---------------------------------+----------------------------------------------------------------------------+ -| Configuration | Cropped, Scaled, Rotated Face | -+=================================+============================================================================+ -| 0.1 (10%), 0.1 (10%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_10_10_200_200.jpg | -+---------------------------------+----------------------------------------------------------------------------+ -| 0.2 (20%), 0.2 (20%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_20_20_200_200.jpg | -+---------------------------------+----------------------------------------------------------------------------+ -| 0.3 (30%), 0.3 (30%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_30_30_200_200.jpg | -+---------------------------------+----------------------------------------------------------------------------+ -| 0.2 (20%), 0.2 (20%), (70,70) | .. image:: ./img/tutorial/gender_classification/arnie_20_20_70_70.jpg | -+---------------------------------+----------------------------------------------------------------------------+ - -CSV for the AT&T Facedatabase ------------------------------- - -.. literalinclude:: etc/at.txt - :language: none - :linenos: diff --git a/modules/face/doc/index.rst b/modules/face/doc/index.rst deleted file mode 100755 index 03bbb5bae..000000000 --- a/modules/face/doc/index.rst +++ /dev/null @@ -1,31 +0,0 @@ -FaceRecognizer - Face Recognition with OpenCV -############################################## - -OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition. This documentation is going to explain you :doc:`the API ` in detail and it will give you a lot of help to get started (full source code examples). :doc:`Face Recognition with OpenCV ` is the definite guide to the new :ocv:class:`FaceRecognizer`. There's also a :doc:`tutorial on gender classification `, a :doc:`tutorial for face recognition in videos ` and it's shown :doc:`how to load & save your results `. - -These documents are the help I have wished for, when I was working myself into face recognition. I hope you also think the new :ocv:class:`FaceRecognizer` is a useful addition to OpenCV. - -Please issue any feature requests and/or bugs on the official OpenCV bug tracker at: - - * http://code.opencv.org/projects/opencv/issues - -Contents -======== - - -.. toctree:: - :maxdepth: 1 - - FaceRecognizer API - Guide to Face Recognition with OpenCV - Tutorial on Gender Classification - Tutorial on Face Recognition in Videos - Tutorial On Saving & Loading a FaceRecognizer - Changelog - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/modules/face/doc/tutorial/facerec_gender_classification.rst b/modules/face/doc/tutorial/facerec_gender_classification.rst deleted file mode 100755 index 95c821298..000000000 --- a/modules/face/doc/tutorial/facerec_gender_classification.rst +++ /dev/null @@ -1,233 +0,0 @@ -Gender Classification with OpenCV -================================= - -.. contents:: Table of Contents - :depth: 3 - -Introduction ------------- - -A lot of people interested in face recognition, also want to know how to perform image classification tasks like: - -* Gender Classification (Gender Detection) -* Emotion Classification (Emotion Detection) -* Glasses Classification (Glasses Detection) -* ... - -This is has become very, very easy with the new :ocv:class:`FaceRecognizer` class. In this tutorial I'll show you how to perform gender classification with OpenCV on a set of face images. You'll also learn how to align your images to enhance the recognition results. If you want to do emotion classification instead of gender classification, all you need to do is to update is your training data and the configuration you pass to the demo. - -Prerequisites --------------- - -For gender classification of faces, you'll need some images of male and female faces first. I've decided to search faces of celebrities using `Google Images `_ with the faces filter turned on (my god, they have great algorithms at `Google `_!). My database has 8 male and 5 female subjects, each with 10 images. Here are the names, if you don't know who to search: - -* Angelina Jolie -* Arnold Schwarzenegger -* Brad Pitt -* Emma Watson -* George Clooney -* Jennifer Lopez -* Johnny Depp -* Justin Timberlake -* Katy Perry -* Keanu Reeves -* Naomi Watts -* Patrick Stewart -* Tom Cruise - -Once you have acquired some images, you'll need to read them. In the demo application I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this: - -.. code-block:: none - - /path/to/image.ext;0 - -Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign a label ``0`` to the image. Think of the label as the subject (the person, the gender or whatever comes to your mind). In the gender classification scenario, the label is the gender the person has. I'll give the label ``0`` to *male* persons and the label ``1`` is for *female* subjects. So my CSV file looks like this: - -.. code-block:: none - - /home/philipp/facerec/data/gender/male/keanu_reeves/keanu_reeves_01.jpg;0 - /home/philipp/facerec/data/gender/male/keanu_reeves/keanu_reeves_02.jpg;0 - /home/philipp/facerec/data/gender/male/keanu_reeves/keanu_reeves_03.jpg;0 - ... - /home/philipp/facerec/data/gender/female/katy_perry/katy_perry_01.jpg;1 - /home/philipp/facerec/data/gender/female/katy_perry/katy_perry_02.jpg;1 - /home/philipp/facerec/data/gender/female/katy_perry/katy_perry_03.jpg;1 - ... - /home/philipp/facerec/data/gender/male/brad_pitt/brad_pitt_01.jpg;0 - /home/philipp/facerec/data/gender/male/brad_pitt/brad_pitt_02.jpg;0 - /home/philipp/facerec/data/gender/male/brad_pitt/brad_pitt_03.jpg;0 - ... - /home/philipp/facerec/data/gender/female/emma_watson/emma_watson_08.jpg;1 - /home/philipp/facerec/data/gender/female/emma_watson/emma_watson_02.jpg;1 - /home/philipp/facerec/data/gender/female/emma_watson/emma_watson_03.jpg;1 - -All images for this example were chosen to have a frontal face perspective. They have been cropped, scaled and rotated to be aligned at the eyes, just like this set of George Clooney images: - -.. image:: ../img/tutorial/gender_classification/clooney_set.png - :align: center - -You really don't want to create the CSV file by hand. And you really don't want scale, rotate & translate the images manually. I have prepared you two Python scripts ``create_csv.py`` and ``crop_face.py``, you can find them in the ``src`` folder coming with this documentation. You'll see how to use them in the :ref:`appendixfgc`. - -Fisherfaces for Gender Classification --------------------------------------- - -If you want to decide whether a person is *male* or *female*, you have to learn the discriminative features of both classes. The Eigenfaces method is based on the Principal Component Analysis, which is an unsupervised statistical model and not suitable for this task. Please see the Face Recognition tutorial for insights into the algorithms. The Fisherfaces instead yields a class-specific linear projection, so it is much better suited for the gender classification task. `http://www.bytefish.de/blog/gender_classification `_ shows the recognition rate of the Fisherfaces method for gender classification. - -The Fisherfaces method achieves a 98% recognition rate in a subject-independent cross-validation. A subject-independent cross-validation means *images of the person under test are never used for learning the model*. And could you believe it: you can simply use the facerec_fisherfaces demo, that's inlcuded in OpenCV. - -Fisherfaces in OpenCV ---------------------- - -The source code for this demo application is also available in the ``src`` folder coming with this documentation: - -* :download:`src/facerec_fisherfaces.cpp <../src/facerec_fisherfaces.cpp>` - -.. literalinclude:: ../src/facerec_fisherfaces.cpp - :language: cpp - :linenos: - -Running the Demo ----------------- - -If you are in Windows, then simply start the demo by running (from command line): - -.. code-block:: none - - facerec_fisherfaces.exe C:/path/to/your/csv.ext - -If you are in Linux, then simply start the demo by running: - -.. code-block:: none - - ./facerec_fisherfaces /path/to/your/csv.ext - -If you don't want to display the images, but save them, then pass the desired path to the demo. It works like this in Windows: - -.. code-block:: none - - facerec_fisherfaces.exe C:/path/to/your/csv.ext C:/path/to/store/results/at - -And in Linux: - -.. code-block:: none - - ./facerec_fisherfaces /path/to/your/csv.ext /path/to/store/results/at - -Results -------- - -If you run the program with your CSV file as parameter, you'll see the Fisherface that separates between male and female images. I've decided to apply a Jet colormap in this demo, so you can see which features the method identifies: - -.. image:: ../img/tutorial/gender_classification/fisherface_0.png - -The demo also shows the average face of the male and female training images you have passed: - -.. image:: ../img/tutorial/gender_classification/mean.png - -Moreover it the demo should yield the prediction for the correct gender: - -.. code-block:: none - - Predicted class = 1 / Actual class = 1. - -And for advanced users I have also shown the Eigenvalue for the Fisherface: - -.. code-block:: none - - Eigenvalue #0 = 152.49493 - -And the Fisherfaces reconstruction: - -.. image:: ../img/tutorial/gender_classification/fisherface_reconstruction_0.png - -I hope this gives you an idea how to approach gender classification and the other image classification tasks. - -.. _appendixfgc: - -Appendix --------- - -Creating the CSV File -+++++++++++++++++++++ - -You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``/src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath//``): - -.. code-block:: none - - philipp@mango:~/facerec/data/at$ tree - . - |-- s1 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - |-- s2 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - ... - |-- s40 - | |-- 1.pgm - | |-- ... - | |-- 10.pgm - - -Then simply call ``create_csv.py`` with the path to the folder, just like this and you could save the output: - -.. code-block:: none - - philipp@mango:~/facerec/data$ python create_csv.py - at/s13/2.pgm;0 - at/s13/7.pgm;0 - at/s13/6.pgm;0 - at/s13/9.pgm;0 - at/s13/5.pgm;0 - at/s13/3.pgm;0 - at/s13/4.pgm;0 - at/s13/10.pgm;0 - at/s13/8.pgm;0 - at/s13/1.pgm;0 - at/s17/2.pgm;1 - at/s17/7.pgm;1 - at/s17/6.pgm;1 - at/s17/9.pgm;1 - at/s17/5.pgm;1 - at/s17/3.pgm;1 - [...] - -Here is the script, if you can't find it: - -.. literalinclude:: ../src/create_csv.py - :language: python - :linenos: - -Aligning Face Images -++++++++++++++++++++ - -An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where: - -* *eye_left* is the position of the left eye -* *eye_right* is the position of the right eye -* *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction) -* *dest_sz* is the size of the output image - -If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes. - -.. literalinclude:: ../src/crop_face.py - :language: python - :linenos: - -Imagine we are given `this photo of Arnold Schwarzenegger `_, which is under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical offset and the size your scaled, rotated & cropped face should have. - -Here are some examples: - -+---------------------------------+----------------------------------------------------------------------------+ -| Configuration | Cropped, Scaled, Rotated Face | -+=================================+============================================================================+ -| 0.1 (10%), 0.1 (10%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_10_10_200_200.jpg | -+---------------------------------+----------------------------------------------------------------------------+ -| 0.2 (20%), 0.2 (20%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_20_20_200_200.jpg | -+---------------------------------+----------------------------------------------------------------------------+ -| 0.3 (30%), 0.3 (30%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_30_30_200_200.jpg | -+---------------------------------+----------------------------------------------------------------------------+ -| 0.2 (20%), 0.2 (20%), (70,70) | .. image:: ../img/tutorial/gender_classification/arnie_20_20_70_70.jpg | -+---------------------------------+----------------------------------------------------------------------------+ diff --git a/modules/face/doc/tutorial/facerec_save_load.rst b/modules/face/doc/tutorial/facerec_save_load.rst deleted file mode 100755 index 2d0b65dff..000000000 --- a/modules/face/doc/tutorial/facerec_save_load.rst +++ /dev/null @@ -1,46 +0,0 @@ -Saving and Loading a FaceRecognizer -=================================== - -Introduction ------------- - -Saving and loading a :ocv:class:`FaceRecognizer` is very important. Training a FaceRecognizer can be a very time-intense task, plus it's often impossible to ship the whole face database to the user of your product. The task of saving and loading a FaceRecognizer is easy with :ocv:class:`FaceRecognizer`. You only have to call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a :ocv:class:`FaceRecognizer`. - -I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces of the `AT&T Facedatabase `_, store the model to a YAML file and then load it again. - -From the loaded model, we'll get a prediction, show the mean, Eigenfaces and the image reconstruction. - -Using FaceRecognizer::save and FaceRecognizer::load ------------------------------------------------------ - -The source code for this demo application is also available in the ``src`` folder coming with this documentation: - -* :download:`src/facerec_save_load.cpp <../src/facerec_save_load.cpp>` - -.. literalinclude:: ../src/facerec_save_load.cpp - :language: cpp - :linenos: - -Results -------- - -``eigenfaces_at.yml`` then contains the model state, we'll simply look at the first 10 lines with ``head eigenfaces_at.yml``: - -.. code-block:: none - - philipp@mango:~/github/libfacerec-build$ head eigenfaces_at.yml - %YAML:1.0 - num_components: 399 - mean: !!opencv-matrix - rows: 1 - cols: 10304 - dt: d - data: [ 8.5558897243107765e+01, 8.5511278195488714e+01, - 8.5854636591478695e+01, 8.5796992481203006e+01, - 8.5952380952380949e+01, 8.6162907268170414e+01, - 8.6082706766917283e+01, 8.5776942355889716e+01, - -And here is the Reconstruction, which is the same as the original: - -.. image:: ../img/eigenface_reconstruction_opencv.png - :align: center diff --git a/modules/face/doc/tutorial/facerec_video_recognition.rst b/modules/face/doc/tutorial/facerec_video_recognition.rst deleted file mode 100755 index 76e76eebe..000000000 --- a/modules/face/doc/tutorial/facerec_video_recognition.rst +++ /dev/null @@ -1,207 +0,0 @@ -Face Recognition in Videos with OpenCV -======================================= - -.. contents:: Table of Contents - :depth: 3 - -Introduction ------------- - -Whenever you hear the term *face recognition*, you instantly think of surveillance in videos. So performing face recognition in videos (e.g. webcam) is one of the most requested features I have got. I have heard your cries, so here it is. An application, that shows you how to do face recognition in videos! For the face detection part we'll use the awesome :ocv:class:`CascadeClassifier` and we'll use :ocv:class:`FaceRecognizer` for face recognition. This example uses the Fisherfaces method for face recognition, because it is robust against large changes in illumination. - -Here is what the final application looks like. As you can see I am only writing the id of the recognized person above the detected face (by the way this id is Arnold Schwarzenegger for my data set): - -.. image:: ../img/tutorial/facerec_video/facerec_video.png - :align: center - :scale: 70% - -This demo is a basis for your research and it shows you how to implement face recognition in videos. You probably want to extend the application and make it more sophisticated: You could combine the id with the name, then show the confidence of the prediction, recognize the emotion... and and and. But before you send mails, asking what these Haar-Cascade thing is or what a CSV is: Make sure you have read the entire tutorial. It's all explained in here. If you just want to scroll down to the code, please note: - -* The available Haar-Cascades for face detection are located in the ``data`` folder of your OpenCV installation! One of the available Haar-Cascades for face detection is for example ``/path/to/opencv/data/haarcascades/haarcascade_frontalface_default.xml``. - -I encourage you to experiment with the application. Play around with the available :ocv:class:`FaceRecognizer` implementations, try the available cascades in OpenCV and see if you can improve your results! - -Prerequisites --------------- - -You want to do face recognition, so you need some face images to learn a :ocv:class:`FaceRecognizer` on. I have decided to reuse the images from the gender classification example: :doc:`facerec_gender_classification`. - -I have the following celebrities in my training data set: - -* Angelina Jolie -* Arnold Schwarzenegger -* Brad Pitt -* George Clooney -* Johnny Depp -* Justin Timberlake -* Katy Perry -* Keanu Reeves -* Patrick Stewart -* Tom Cruise - -In the demo I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this: - -.. code-block:: none - - /path/to/image.ext;0 - -Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign a label ``0`` to the image. Think of the label as the subject (the person, the gender or whatever comes to your mind). In the face recognition scenario, the label is the person this image belongs to. In the gender classification scenario, the label is the gender the person has. So my CSV file looks like this: - -.. code-block:: none - - /home/philipp/facerec/data/c/keanu_reeves/keanu_reeves_01.jpg;0 - /home/philipp/facerec/data/c/keanu_reeves/keanu_reeves_02.jpg;0 - /home/philipp/facerec/data/c/keanu_reeves/keanu_reeves_03.jpg;0 - ... - /home/philipp/facerec/data/c/katy_perry/katy_perry_01.jpg;1 - /home/philipp/facerec/data/c/katy_perry/katy_perry_02.jpg;1 - /home/philipp/facerec/data/c/katy_perry/katy_perry_03.jpg;1 - ... - /home/philipp/facerec/data/c/brad_pitt/brad_pitt_01.jpg;2 - /home/philipp/facerec/data/c/brad_pitt/brad_pitt_02.jpg;2 - /home/philipp/facerec/data/c/brad_pitt/brad_pitt_03.jpg;2 - ... - /home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_08.jpg;6 - /home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_05.jpg;6 - /home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_02.jpg;6 - /home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_03.jpg;6 - -All images for this example were chosen to have a frontal face perspective. They have been cropped, scaled and rotated to be aligned at the eyes, just like this set of George Clooney images: - -.. image:: ../img/tutorial/gender_classification/clooney_set.png - :align: center - -Face Recongition from Videos ------------------------------ - -The source code for the demo is available in the ``src`` folder coming with this documentation: - -* :download:`src/facerec_video.cpp <../src/facerec_video.cpp>` - -This demo uses the :ocv:class:`CascadeClassifier`: - -.. literalinclude:: ../src/facerec_video.cpp - :language: cpp - :linenos: - -Running the Demo ----------------- - -You'll need: - -* The path to a valid Haar-Cascade for detecting a face with a :ocv:class:`CascadeClassifier`. -* The path to a valid CSV File for learning a :ocv:class:`FaceRecognizer`. -* A webcam and its device id (you don't know the device id? Simply start from 0 on and see what happens). - -If you are in Windows, then simply start the demo by running (from command line): - -.. code-block:: none - - facerec_video.exe

    - - -A Complete Sample -================= -.. literalinclude:: surface_matching/src/ppf_load_match.cpp - :language: cpp - :linenos: - :tab-width: 4 - - -Parameter Tuning ----------------- - -Surface matching module treats its parameters relative to the model diameter (diameter of the axis parallel bounding box), whenever it can. This makes the parameters independent from the model size. This is why, both model and scene cloud were subsampled such that all points have a minimum distance of :math:`RelativeSamplingStep*DimensionRange`, where :math:`DimensionRange` is the distance along a given dimension. All three dimensions are sampled in similar manner. For example, if :math:`RelativeSamplingStep` is set to 0.05 and the diameter of model is 1m (1000mm), the points sampled from the object's surface will be approximately 50 mm apart. From another point of view, if the sampling RelativeSamplingStep is set to 0.05, at most :math:`20x20x20 = 8000` model points are generated (depending on how the model fills in the volume). Consequently this results in at most 8000x8000 pairs. In practice, because the models are not uniformly distributed over a rectangular prism, much less points are to be expected. Decreasing this value, results in more model points and thus a more accurate representation. However, note that number of point pair features to be computed is now quadratically increased as the complexity is O(N^2). This is especially a concern for 32 bit systems, where large models can easily overshoot the available memory. Typically, values in the range of 0.025 - 0.05 seem adequate for most of the applications, where the default value is 0.03. (Note that there is a difference in this paremeter with the one presented in [drost2010]_. In [drost2010]_ a uniform cuboid is used for quantization and model diameter is used for reference of sampling. In my implementation, the cuboid is a rectangular prism, and each dimension is quantized independently. I do not take reference from the diameter but along the individual dimensions. - -It would very wise to remove the outliers from the model and prepare an ideal model initially. This is because, the outliers directly affect the relative computations and degrade the matching accuracy. - -During runtime stage, the scene is again sampled by :math:`RelativeSamplingStep`, as described above. However this time, only a portion of the scene points are used as reference. This portion is controlled by the parameter :math:`RelativeSceneSampleStep`, where :math:`SceneSampleStep = (int)(1.0/RelativeSceneSampleStep)`. In other words, if the :math:`RelativeSceneSampleStep = 1.0/5.0`, the subsampled scene will once again be uniformly sampled to 1/5 of the number of points. Maximum value of this parameter is 1 and increasing this parameter also increases the stability, but decreases the speed. Again, because of the initial scene-independent relative sampling, fine tuning this parameter is not a big concern. This would only be an issue when the model shape occupies a volume uniformly, or when the model shape is condensed in a tiny place within the quantization volume (e.g. The octree representation would have too much empty cells). - -:math:`RelativeDistanceStep` acts as a step of discretization over the hash table. The point pair features are quantized to be mapped to the buckets of the hashtable. This discretization involves a multiplication and a casting to the integer. Adjusting RelativeDistanceStep in theory controls the collision rate. Note that, more collisions on the hashtable results in less accurate estimations. Reducing this parameter increases the affect of quantization but starts to assign non-similar point pairs to the same bins. Increasing it however, wanes the ability to group the similar pairs. Generally, because during the sampling stage, the training model points are selected uniformly with a distance controlled by RelativeSamplingStep, RelativeDistanceStep is expected to equate to this value. Yet again, values in the range of 0.025-0.05 are sensible. This time however, when the model is dense, it is not advised to decrease this value. For noisy scenes, the value can be increased to improve the robustness of the matching against noisy points. - -References -========== - -.. [drost2010] B. Drost, S. Ilic 3D Object Detection and Localization Using Multimodal Point Pair Features Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization & Transmission (3DIMPVT), Zurich, Switzerland, October 2012 - -.. [pickyicp] Zinsser, Timo and Schmidt, Jochen and Niemann, Heinrich A refined ICP algorithm for robust 3-D correspondence estimation Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on Image Processing, IEEE. - -.. [koklimlow] Kok Lim Low, Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration Technical Report TR04-004, Department of Computer Science, University of North Carolina at Chapel Hill, February 2004 - diff --git a/modules/text/doc/erfilter.rst b/modules/text/doc/erfilter.rst deleted file mode 100644 index 54383ae3f..000000000 --- a/modules/text/doc/erfilter.rst +++ /dev/null @@ -1,239 +0,0 @@ -Scene Text Detection -==================== - -.. highlight:: cpp - -Class-specific Extremal Regions for Scene Text Detection --------------------------------------------------------- - -The scene text detection algorithm described below has been initially proposed by Lukás Neumann & Jiri Matas [Neumann12]. The main idea behind Class-specific Extremal Regions is similar to the MSER in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image. However, this technique differs from MSER in that selection of suitable ERs is done by a sequential classifier trained for character detection, i.e. dropping the stability requirement of MSERs and selecting class-specific (not necessarily stable) regions. - -The component tree of an image is constructed by thresholding by an increasing value step-by-step from 0 to 255 and then linking the obtained connected components from successive levels in a hierarchy by their inclusion relation: - -.. image:: pics/component_tree.png - :width: 100% - -The component tree may conatain a huge number of regions even for a very simple image as shown in the previous image. This number can easily reach the order of 1 x 10^6 regions for an average 1 Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm make use of a sequential classifier with two differentiated stages. - -In the first stage incrementally computable descriptors (area, perimeter, bounding box, and euler number) are computed (in O(1)) for each region r and used as features for a classifier which estimates the class-conditional probability p(r|character). Only the ERs which correspond to local maximum of the probability p(r|character) are selected (if their probability is above a global limit p_min and the difference between local maximum and local minimum is greater than a \delta_min value). - -In the second stage, the ERs that passed the first stage are classified into character and non-character classes using more informative but also more computationally expensive features. (Hole area ratio, convex hull ratio, and the number of outer boundary inflexion points). - -This ER filtering process is done in different single-channel projections of the input image in order to increase the character localization recall. - -After the ER filtering is done on each input channel, character candidates must be grouped in high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements two different grouping algorithms: the Exhaustive Search algorithm proposed in [Neumann11] for grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas in [Gomez13][Gomez14] for grouping arbitrary oriented text (see :ocv:func:`erGrouping`). - - -To see the text detector at work, have a look at the textdetection demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp - - -.. [Neumann12] Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012. The paper is available online at http://cmp.felk.cvut.cz/~neumalu1/neumann-cvpr2012.pdf - -.. [Neumann11] Neumann L., Matas J.: Text Localization in Real-world Images using Efficiently Pruned Exhaustive Search, ICDAR 2011. The paper is available online at http://cmp.felk.cvut.cz/~neumalu1/icdar2011_article.pdf - -.. [Gomez13] Gomez L. and Karatzas D.: Multi-script Text Extraction from Natural Scenes, ICDAR 2013. The paper is available online at http://158.109.8.37/files/GoK2013.pdf - -.. [Gomez14] Gomez L. and Karatzas D.: A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Scene Text Extraction, arXiv:1407.7504 [cs.CV]. The paper is available online at http://arxiv.org/abs/1407.7504 - - -ERStat ------- -.. ocv:struct:: ERStat - -The ERStat structure represents a class-specific Extremal Region (ER). - -An ER is a 4-connected set of pixels with all its grey-level values smaller than the values in its outer boundary. A class-specific ER is selected (using a classifier) from all the ER's in the component tree of the image. :: - - struct CV_EXPORTS ERStat - { - public: - //! Constructor - explicit ERStat(int level = 256, int pixel = 0, int x = 0, int y = 0); - //! Destructor - ~ERStat() { } - - //! seed point and threshold (max grey-level value) - int pixel; - int level; - - //! incrementally computable features - int area; - int perimeter; - int euler; //!< euler number - Rect rect; //!< bounding box - double raw_moments[2]; //!< order 1 raw moments to derive the centroid - double central_moments[3]; //!< order 2 central moments to construct the covariance matrix - std::deque *crossings;//!< horizontal crossings - float med_crossings; //!< median of the crossings at three different height levels - - //! 2nd stage features - float hole_area_ratio; - float convex_hull_ratio; - float num_inflexion_points; - - //! probability that the ER belongs to the class we are looking for - double probability; - - //! pointers preserving the tree structure of the component tree - ERStat* parent; - ERStat* child; - ERStat* next; - ERStat* prev; - }; - -MSERsToERStats --------------- -Converts MSER contours (vector) to ERStat regions. - -.. ocv:function:: void MSERsToERStats(InputArray image, vector< vector > &contours, vector< vector > ®ions) - - :param image: Source image ``CV_8UC1`` from which the MSERs where extracted. - :param contours: Intput vector with all the contours (vector). - :param regions: Output where the ERStat regions are stored. - -It takes as input the contours provided by the OpenCV MSER feature detector and returns as output two vectors of ERStats. This is because MSER() output contains both MSER+ and MSER- regions in a single vector, the function separates them in two different vectors (this is as if the ERStats where extracted from two different channels). - -An example of MSERsToERStats in use can be found in the text detection webcam_demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp - -computeNMChannels ------------------ -Compute the different channels to be processed independently in the N&M algorithm [Neumann12]. - -.. ocv:function:: void computeNMChannels(InputArray _src, OutputArrayOfArrays _channels, int _mode = ERFILTER_NM_RGBLGrad) - - :param _src: Source image. Must be RGB ``CV_8UC3``. - :param _channels: Output vector where computed channels are stored. - :param _mode: Mode of operation. Currently the only available options are: **ERFILTER_NM_RGBLGrad** (used by default) and **ERFILTER_NM_IHSGrad**. - -In N&M algorithm, the combination of intensity (I), hue (H), saturation (S), and gradient magnitude channels (Grad) are used in order to obtain high localization recall. This implementation also provides an alternative combination of red (R), green (G), blue (B), lightness (L), and gradient magnitude (Grad). - - -ERFilter --------- -.. ocv:class:: ERFilter : public Algorithm - -Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [Neumann12]. :: - - class CV_EXPORTS ERFilter : public Algorithm - { - public: - - //! callback with the classifier is made a class. - //! By doing it we hide SVM, Boost etc. Developers can provide their own classifiers - class CV_EXPORTS Callback - { - public: - virtual ~Callback() { } - //! The classifier must return probability measure for the region. - virtual double eval(const ERStat& stat) = 0; - }; - - /*! - the key method. Takes image on input and returns the selected regions in a vector of ERStat - only distinctive ERs which correspond to characters are selected by a sequential classifier - */ - virtual void run( InputArray image, std::vector& regions ) = 0; - - (...) - - }; - - - -ERFilter::Callback ------------------- -Callback with the classifier is made a class. By doing it we hide SVM, Boost etc. Developers can provide their own classifiers to the ERFilter algorithm. - -.. ocv:class:: ERFilter::Callback - -ERFilter::Callback::eval ------------------------- -The classifier must return probability measure for the region. - -.. ocv:function:: double ERFilter::Callback::eval(const ERStat& stat) - - :param stat: The region to be classified - -ERFilter::run -------------- -The key method of ERFilter algorithm. Takes image on input and returns the selected regions in a vector of ERStat only distinctive ERs which correspond to characters are selected by a sequential classifier - -.. ocv:function:: void ERFilter::run( InputArray image, std::vector& regions ) - - :param image: Single channel image ``CV_8UC1`` - :param regions: Output for the 1st stage and Input/Output for the 2nd. The selected Extremal Regions are stored here. - -Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier. - -createERFilterNM1 ------------------ -Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm [Neumann12]. - -.. ocv:function:: Ptr createERFilterNM1( const Ptr& cb, int thresholdDelta = 1, float minArea = 0.00025, float maxArea = 0.13, float minProbability = 0.4, bool nonMaxSuppression = true, float minProbabilityDiff = 0.1 ) - - :param cb: Callback with the classifier. Default classifier can be implicitly load with function :ocv:func:`loadClassifierNM1`, e.g. from file in samples/cpp/trained_classifierNM1.xml - :param thresholdDelta: Threshold step in subsequent thresholds when extracting the component tree - :param minArea: The minimum area (% of image size) allowed for retreived ER's - :param minArea: The maximum area (% of image size) allowed for retreived ER's - :param minProbability: The minimum probability P(er|character) allowed for retreived ER's - :param nonMaxSuppression: Whenever non-maximum suppression is done over the branch probabilities - :param minProbability: The minimum probability difference between local maxima and local minima ERs - -The component tree of the image is extracted by a threshold increased step by step from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness, number of holes, and number of horizontal crossings) are computed for each ER and used as features for a classifier which estimates the class-conditional probability P(er|character). The value of P(er|character) is tracked using the inclusion relation of ER across all thresholds and only the ERs which correspond to local maximum of the probability P(er|character) are selected (if the local maximum of the probability is above a global limit pmin and the difference between local maximum and local minimum is greater than minProbabilityDiff). - -createERFilterNM2 ------------------ -Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm [Neumann12]. - -.. ocv:function:: Ptr createERFilterNM2( const Ptr& cb, float minProbability = 0.3 ) - - :param cb: Callback with the classifier. Default classifier can be implicitly load with function :ocv:func:`loadClassifierNM2`, e.g. from file in samples/cpp/trained_classifierNM2.xml - :param minProbability: The minimum probability P(er|character) allowed for retreived ER's - -In the second stage, the ERs that passed the first stage are classified into character and non-character classes using more informative but also more computationally expensive features. The classifier uses all the features calculated in the first stage and the following additional features: hole area ratio, convex hull ratio, and number of outer inflexion points. - -loadClassifierNM1 ------------------ -Allow to implicitly load the default classifier when creating an ERFilter object. - -.. ocv:function:: Ptr loadClassifierNM1(const std::string& filename) - - :param filename: The XML or YAML file with the classifier model (e.g. trained_classifierNM1.xml) - -returns a pointer to ERFilter::Callback. - -loadClassifierNM2 ------------------ -Allow to implicitly load the default classifier when creating an ERFilter object. - -.. ocv:function:: Ptr loadClassifierNM2(const std::string& filename) - - :param filename: The XML or YAML file with the classifier model (e.g. trained_classifierNM2.xml) - -returns a pointer to ERFilter::Callback. - -erGrouping ----------- -Find groups of Extremal Regions that are organized as text blocks. - -.. ocv:function:: void erGrouping(InputArray img, InputArrayOfArrays channels, std::vector > ®ions, std::vector > &groups, std::vector &groups_rects, int method = ERGROUPING_ORIENTATION_HORIZ, const std::string& filename = std::string(), float minProbablity = 0.5) - - :param image: Original RGB or Greyscale image from wich the regions were extracted. - :param src: Vector of single channel images CV_8UC1 from wich the regions were extracted. - :param regions: Vector of ER's retreived from the ERFilter algorithm from each channel. - :param groups: The output of the algorithm is stored in this parameter as set of lists of indexes to provided regions. - :param groups_rects: The output of the algorithm are stored in this parameter as list of rectangles. - :param method: Grouping method (see the details below). Can be one of ``ERGROUPING_ORIENTATION_HORIZ``, ``ERGROUPING_ORIENTATION_ANY``. - :param filename: The XML or YAML file with the classifier model (e.g. samples/trained_classifier_erGrouping.xml). Only to use when grouping method is ``ERGROUPING_ORIENTATION_ANY``. - :param minProbability: The minimum probability for accepting a group. Only to use when grouping method is ``ERGROUPING_ORIENTATION_ANY``. - - -This function implements two different grouping algorithms: - - * **ERGROUPING_ORIENTATION_HORIZ** - - Exhaustive Search algorithm proposed in [Neumann11] for grouping horizontally aligned text. The algorithm models a verification function for all the possible ER sequences. The verification fuction for ER pairs consists in a set of threshold-based pairwise rules which compare measurements of two regions (height ratio, centroid angle, and region distance). The verification function for ER triplets creates a word text line estimate using Least Median-Squares fitting for a given triplet and then verifies that the estimate is valid (based on thresholds created during training). Verification functions for sequences larger than 3 are approximated by verifying that the text line parameters of all (sub)sequences of length 3 are consistent. - - * **ERGROUPING_ORIENTATION_ANY** - - Text grouping method proposed in [Gomez13][Gomez14] for grouping arbitrary oriented text. Regions are agglomerated by Single Linkage Clustering in a weighted feature space that combines proximity (x,y coordinates) and similarity measures (color, size, gradient magnitude, stroke width, etc.). SLC provides a dendrogram where each node represents a text group hypothesis. Then the algorithm finds the branches corresponding to text groups by traversing this dendrogram with a stopping rule that combines the output of a rotation invariant text group classifier and a probabilistic measure for hierarchical clustering validity assessment. diff --git a/modules/text/doc/ocr.rst b/modules/text/doc/ocr.rst deleted file mode 100644 index d7c56eabf..000000000 --- a/modules/text/doc/ocr.rst +++ /dev/null @@ -1,106 +0,0 @@ -Scene Text Recognition -====================== - -.. highlight:: cpp - -OCRTesseract ------------- -.. ocv:class:: OCRTesseract : public BaseOCR - -OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++. Notice that it is compiled only when tesseract-ocr is correctly installed. - -.. note:: - - * (C++) An example of OCRTesseract recognition combined with scene text detection can be found at the end_to_end_recognition demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/end_to_end_recognition.cpp - * (C++) Another example of OCRTesseract recognition combined with scene text detection can be found at the webcam_demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp - -OCRTesseract::create --------------------- -Creates an instance of the OCRTesseract class. Initializes Tesseract. - -.. ocv:function:: Ptr OCRTesseract::create(const char* datapath=NULL, const char* language=NULL, const char* char_whitelist=NULL, int oem=(int)tesseract::OEM_DEFAULT, int psmode=(int)tesseract::PSM_AUTO) - - :param datapath: the name of the parent directory of tessdata ended with "/", or NULL to use the system's default directory. - :param language: an ISO 639-3 code or NULL will default to "eng". - :param char_whitelist: specifies the list of characters used for recognition. NULL defaults to "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ". - :param oem: tesseract-ocr offers different OCR Engine Modes (OEM), by deffault tesseract::OEM_DEFAULT is used. See the tesseract-ocr API documentation for other possible values. - :param psmode: tesseract-ocr offers different Page Segmentation Modes (PSM) tesseract::PSM_AUTO (fully automatic layout analysis) is used. See the tesseract-ocr API documentation for other possible values. - -OCRTesseract::run ------------------ -Recognize text using the tesseract-ocr API. Takes image on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values. - -.. ocv:function:: void OCRTesseract::run(Mat& image, string& output_text, vector* component_rects=NULL, vector* component_texts=NULL, vector* component_confidences=NULL, int component_level=0) - - :param image: Input image ``CV_8UC1`` or ``CV_8UC3`` - :param output_text: Output text of the tesseract-ocr. - :param component_rects: If provided the method will output a list of Rects for the individual text elements found (e.g. words or text lines). - :param component_text: If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words or text lines). - :param component_confidences: If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words or text lines). - :param component_level: ``OCR_LEVEL_WORD`` (by default), or ``OCR_LEVEL_TEXT_LINE``. - -OCRHMMDecoder -------------- -.. ocv:class:: OCRHMMDecoder : public BaseOCR - -OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models. - -.. note:: - - * (C++) An example on using OCRHMMDecoder recognition combined with scene text detection can be found at the webcam_demo sample: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp - - -OCRHMMDecoder::ClassifierCallback ---------------------------------- -Callback with the character classifier is made a class. This way it hides the feature extractor and the classifier itself, so developers can write their own OCR code. - -.. ocv:class:: OCRHMMDecoder::ClassifierCallback - -The default character classifier and feature extractor can be loaded using the utility funtion ``loadOCRHMMClassifierNM`` and KNN model provided in https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRHMM_knn_model_data.xml.gz. - -OCRHMMDecoder::ClassifierCallback::eval ---------------------------------------- -The character classifier must return a (ranked list of) class(es) id('s) - -.. ocv:function:: void OCRHMMDecoder::ClassifierCallback::eval( InputArray image, std::vector& out_class, std::vector& out_confidence) - - :param image: Input image ``CV_8UC1`` or ``CV_8UC3`` with a single letter. - :param out_class: The classifier returns the character class categorical label, or list of class labels, to which the input image corresponds. - :param out_confidence: The classifier returns the probability of the input image corresponding to each classes in ``out_class``. - -OCRHMMDecoder::create ---------------------- -Creates an instance of the OCRHMMDecoder class. Initializes HMMDecoder. - -.. ocv:function:: Ptr OCRHMMDecoder::create(const Ptr classifier, const std::string& vocabulary, InputArray transition_probabilities_table, InputArray emission_probabilities_table, decoder_mode mode = OCR_DECODER_VITERBI) - - :param classifier: The character classifier with built in feature extractor. - :param vocabulary: The language vocabulary (chars when ascii english text). vocabulary.size() must be equal to the number of classes of the classifier. - :param transition_probabilities_table: Table with transition probabilities between character pairs. cols == rows == vocabulary.size(). - :param emission_probabilities_table: Table with observation emission probabilities. cols == rows == vocabulary.size(). - :param mode: HMM Decoding algorithm. Only ``OCR_DECODER_VITERBI`` is available for the moment (http://en.wikipedia.org/wiki/Viterbi_algorithm). - -OCRHMMDecoder::run ------------------- -Recognize text using HMM. Takes image on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values. - -.. ocv:function:: void OCRHMMDecoder::run(Mat& image, string& output_text, vector* component_rects=NULL, vector* component_texts=NULL, vector* component_confidences=NULL, int component_level=0) - - :param image: Input image ``CV_8UC1`` with a single text line (or word). - :param output_text: Output text. Most likely character sequence found by the HMM decoder. - :param component_rects: If provided the method will output a list of Rects for the individual text elements found (e.g. words). - :param component_text: If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words). - :param component_confidences: If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words). - :param component_level: Only ``OCR_LEVEL_WORD`` is supported. - -loadOCRHMMClassifierNM ----------------------- -Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object. - -.. ocv:function:: Ptr loadOCRHMMClassifierNM(const std::string& filename) - - :param filename: The XML or YAML file with the classifier model (e.g. OCRHMM_knn_model_data.xml) - -The default classifier is based in the scene text recognition method proposed by Lukás Neumann & Jiri Matas in [Neumann11b]. Basically, the region (contour) in the input image is normalized to a fixed size, while retaining the centroid and aspect ratio, in order to extract a feature vector based on gradient orientations along the chain-code of its perimeter. Then, the region is classified using a KNN model trained with synthetic data of rendered characters with different standard font types. - -.. [Neumann11b] Neumann L., Matas J.: Text Localization in Real-world Images using Efficiently Pruned Exhaustive Search, ICDAR 2011. The paper is available online at http://cmp.felk.cvut.cz/~neumalu1/icdar2011_article.pdf diff --git a/modules/text/doc/text.rst b/modules/text/doc/text.rst deleted file mode 100644 index 8e319f92d..000000000 --- a/modules/text/doc/text.rst +++ /dev/null @@ -1,13 +0,0 @@ -****************************************** -text. Scene Text Detection and Recognition -****************************************** - -.. highlight:: cpp - -The opencv_text module provides different algorithms for text detection and recognition in natural scene images. - -.. toctree:: - :maxdepth: 2 - - erfilter - ocr diff --git a/modules/tracking/doc/common_interfaces_tracker.rst b/modules/tracking/doc/common_interfaces_tracker.rst deleted file mode 100644 index 73960adba..000000000 --- a/modules/tracking/doc/common_interfaces_tracker.rst +++ /dev/null @@ -1,290 +0,0 @@ -Common Interfaces of Tracker -============================ - -.. highlight:: cpp - - -Tracker : Algorithm -------------------- - -.. ocv:class:: Tracker - -Base abstract class for the long-term tracker:: - - class CV_EXPORTS_W Tracker : public virtual Algorithm - { - virtual ~Tracker(); - - bool init( const Mat& image, const Rect2d& boundingBox ); - - bool update( const Mat& image, Rect2d& boundingBox ); - - static Ptr create( const String& trackerType ); - - }; - -Tracker::init -------------- - -Initialize the tracker with a know bounding box that surrounding the target - -.. ocv:function:: bool Tracker::init( const Mat& image, const Rect2d& boundingBox ) - - :param image: The initial frame - - :param boundingBox: The initial boundig box - - :return: True if initialization went succesfully, false otherwise - - -Tracker::update ---------------- - -Update the tracker, find the new most likely bounding box for the target - -.. ocv:function:: bool Tracker::update( const Mat& image, Rect2d& boundingBox ) - - :param image: The current frame - - :param boundingBox: The boundig box that represent the new target location, if true was returned, not modified otherwise - - :return: True means that target was located and false means that tracker cannot locate target in current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed missing from the frame (say, out of sight) - - -Tracker::create ---------------- - -Creates a tracker by its name. - -.. ocv:function:: static Ptr Tracker::create( const String& trackerType ) - - :param trackerType: Tracker type - -The following detector types are supported: - -* ``"MIL"`` -- :ocv:class:`TrackerMIL` - -* ``"BOOSTING"`` -- :ocv:class:`TrackerBoosting` - -Creating Own Tracker --------------------- - -If you want create a new tracker, here's what you have to do. First, decide on the name of the class for the tracker (to meet the existing style, -we suggest something with prefix "tracker", e.g. trackerMIL, trackerBoosting) -- we shall refer to this choice as to "classname" in subsequent. Also, -you should decide upon the name of the tracker, is it will be known to user (the current style suggests using all capitals, say MIL or BOOSTING) -- -we'll call it a "name". - -* Declare your tracker in ``include/opencv2/tracking/tracker.hpp``. - Your tracker should inherit from :ocv:class:`Tracker` (please, see the example below). You should declare the specialized ``Param`` - structure, where you probably will want to put the data, needed to initialize your tracker. Also don't forget to put the - BOILERPLATE_CODE(name,classname) macro inside the class declaration. That macro will generate static ``createTracker()`` function, which - we'll talk about later. You should get something similar to :: - - class CV_EXPORTS_W TrackerMIL : public Tracker - { - public: - struct CV_EXPORTS Params - { - Params(); - //parameters for sampler - float samplerInitInRadius; // radius for gathering positive instances during init - int samplerInitMaxNegNum; // # negative samples to use during init - float samplerSearchWinSize; // size of search window - float samplerTrackInRadius; // radius for gathering positive instances during tracking - int samplerTrackMaxPosNum; // # positive samples to use during tracking - int samplerTrackMaxNegNum; // # negative samples to use during tracking - int featureSetNumFeatures; // #features - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - - of course, you can also add any additional methods of your choice. It should be pointed out, however, that it is not expected to have a constructor - declared, as creation should be done via the corresponding ``createTracker()`` method. -* In ``src/tracker.cpp`` file add BOILERPLATE_CODE(name,classname) line to the body of ``Tracker::create()`` method you will find there, like :: - - Ptr Tracker::create( const String& trackerType ) - { - BOILERPLATE_CODE("BOOSTING",TrackerBoosting); - BOILERPLATE_CODE("MIL",TrackerMIL); - return Ptr(); - } -* Finally, you should implement the function with signature :: - - Ptr classname::createTracker(const classname::Params ¶meters){ - ... - } - - That function can (and probably will) return a pointer to some derived class of "classname", which will probably have a real constructor. - -Every tracker has three component :ocv:class:`TrackerSampler`, :ocv:class:`TrackerFeatureSet` and :ocv:class:`TrackerModel`. -The first two are instantiated from Tracker base class, instead the last component is abstract, so you must implement your TrackerModel. - -TrackerSampler -.............. - -TrackerSampler is already instantiated, but you should define the sampling algorithm and add the classes (or single class) to TrackerSampler. -You can choose one of the ready implementation as TrackerSamplerCSC or you can implement your sampling method, in this case -the class must inherit :ocv:class:`TrackerSamplerAlgorithm`. Fill the samplingImpl method that writes the result in "sample" output argument. - -Example of creating specialized TrackerSamplerAlgorithm ``TrackerSamplerCSC`` : :: - - class CV_EXPORTS_W TrackerSamplerCSC : public TrackerSamplerAlgorithm - { - public: - TrackerSamplerCSC( const TrackerSamplerCSC::Params ¶meters = TrackerSamplerCSC::Params() ); - ~TrackerSamplerCSC(); - ... - - protected: - bool samplingImpl( const Mat& image, Rect boundingBox, std::vector& sample ); - ... - - }; - -Example of adding TrackerSamplerAlgorithm to TrackerSampler : :: - - //sampler is the TrackerSampler - Ptr CSCSampler = new TrackerSamplerCSC( CSCparameters ); - if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) ) - return false; - - //or add CSC sampler with default parameters - //sampler->addTrackerSamplerAlgorithm( "CSC" ); - -.. seealso:: - - :ocv:class:`TrackerSamplerCSC`, :ocv:class:`TrackerSamplerAlgorithm` - - -TrackerFeatureSet -................. - -TrackerFeatureSet is already instantiated (as first) , but you should define what kinds of features you'll use in your tracker. -You can use multiple feature types, so you can add a ready implementation as :ocv:class:`TrackerFeatureHAAR` in your TrackerFeatureSet or develop your own implementation. -In this case, in the computeImpl method put the code that extract the features and -in the selection method optionally put the code for the refinement and selection of the features. - -Example of creating specialized TrackerFeature ``TrackerFeatureHAAR`` : :: - - class CV_EXPORTS_W TrackerFeatureHAAR : public TrackerFeature - { - public: - TrackerFeatureHAAR( const TrackerFeatureHAAR::Params ¶meters = TrackerFeatureHAAR::Params() ); - ~TrackerFeatureHAAR(); - void selection( Mat& response, int npoints ); - ... - - protected: - bool computeImpl( const std::vector& images, Mat& response ); - ... - - }; - -Example of adding TrackerFeature to TrackerFeatureSet : :: - - //featureSet is the TrackerFeatureSet - Ptr trackerFeature = new TrackerFeatureHAAR( HAARparameters ); - featureSet->addTrackerFeature( trackerFeature ); - -.. seealso:: - - :ocv:class:`TrackerFeatureHAAR`, :ocv:class:`TrackerFeatureSet` - -TrackerModel -............ - -TrackerModel is abstract, so in your implementation you must develop your TrackerModel that inherit from :ocv:class:`TrackerModel`. -Fill the method for the estimation of the state "modelEstimationImpl", that estimates the most likely target location, -see [AAM]_ table I (ME) for further information. Fill "modelUpdateImpl" in order to update the model, see [AAM]_ table I (MU). -In this class you can use the :c:type:`ConfidenceMap` and :c:type:`Trajectory` to storing the model. The first represents the model on the all -possible candidate states and the second represents the list of all estimated states. - -Example of creating specialized TrackerModel ``TrackerMILModel`` : :: - - class TrackerMILModel : public TrackerModel - { - public: - TrackerMILModel( const Rect& boundingBox ); - ~TrackerMILModel(); - ... - - protected: - void modelEstimationImpl( const std::vector& responses ); - void modelUpdateImpl(); - ... - - }; - -And add it in your Tracker : :: - - bool TrackerMIL::initImpl( const Mat& image, const Rect2d& boundingBox ) - { - ... - //model is the general TrackerModel field od the general Tracker - model = new TrackerMILModel( boundingBox ); - ... - } - - -In the last step you should define the TrackerStateEstimator based on your implementation or you can use one of ready class as :ocv:class:`TrackerStateEstimatorMILBoosting`. -It represent the statistical part of the model that estimates the most likely target state. - -Example of creating specialized TrackerStateEstimator ``TrackerStateEstimatorMILBoosting`` : :: - - class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator - { - class TrackerMILTargetState : public TrackerTargetState - { - ... - }; - - public: - TrackerStateEstimatorMILBoosting( int nFeatures = 250 ); - ~TrackerStateEstimatorMILBoosting(); - ... - - protected: - Ptr estimateImpl( const std::vector& confidenceMaps ); - void updateImpl( std::vector& confidenceMaps ); - ... - - }; - -And add it in your TrackerModel : :: - - //model is the TrackerModel of your Tracker - Ptr stateEstimator = new TrackerStateEstimatorMILBoosting( params.featureSetNumFeatures ); - model->setTrackerStateEstimator( stateEstimator ); - -.. seealso:: - - :ocv:class:`TrackerModel`, :ocv:class:`TrackerStateEstimatorMILBoosting`, :ocv:class:`TrackerTargetState` - - -During this step, you should define your TrackerTargetState based on your implementation. :ocv:class:`TrackerTargetState` base class has only the bounding box (upper-left position, width and height), you can -enrich it adding scale factor, target rotation, etc. - -Example of creating specialized TrackerTargetState ``TrackerMILTargetState`` : :: - - class TrackerMILTargetState : public TrackerTargetState - { - public: - TrackerMILTargetState( const Point2f& position, int targetWidth, int targetHeight, bool foreground, const Mat& features ); - ~TrackerMILTargetState(); - ... - - private: - bool isTarget; - Mat targetFeatures; - ... - - }; - - -Try it -...... - -To try your tracker you can use the demo at https://github.com/lenlen/opencv/blob/tracking_api/samples/cpp/tracker.cpp. - -The first argument is the name of the tracker and the second is a video source. diff --git a/modules/tracking/doc/common_interfaces_tracker_feature_set.rst b/modules/tracking/doc/common_interfaces_tracker_feature_set.rst deleted file mode 100644 index 496a1dfe2..000000000 --- a/modules/tracking/doc/common_interfaces_tracker_feature_set.rst +++ /dev/null @@ -1,343 +0,0 @@ -Common Interfaces of TrackerFeatureSet -====================================== - -.. highlight:: cpp - - -TrackerFeatureSet ------------------ - -Class that manages the extraction and selection of features - -[AAM]_ Feature Extraction and Feature Set Refinement (Feature Processing and Feature Selection). See table I and section III C -[AMVOT]_ Appearance modelling -> Visual representation (Table II, section 3.1 - 3.2) - -.. ocv:class:: TrackerFeatureSet - -TrackerFeatureSet class:: - - class CV_EXPORTS_W TrackerFeatureSet - { - public: - - TrackerFeatureSet(); - ~TrackerFeatureSet(); - - void extraction( const std::vector& images ); - void selection(); - void removeOutliers(); - - bool addTrackerFeature( String trackerFeatureType ); - bool addTrackerFeature( Ptr& feature ); - - const std::vector > >& getTrackerFeature() const; - const std::vector& getResponses() const; - - }; - - -TrackerFeatureSet is an aggregation of :ocv:class:`TrackerFeature` - -.. seealso:: - - :ocv:class:`TrackerFeature` - -TrackerFeatureSet::extraction ------------------------------ - -Extract features from the images collection - -.. ocv:function:: void TrackerFeatureSet::extraction( const std::vector& images ) - - :param images: The input images - -TrackerFeatureSet::selection ----------------------------- - -Identify most effective features for all feature types (optional) - -.. ocv:function:: void TrackerFeatureSet::selection() - -TrackerFeatureSet::removeOutliers ---------------------------------- - -Remove outliers for all feature types (optional) - -.. ocv:function:: void TrackerFeatureSet::removeOutliers() - -TrackerFeatureSet::addTrackerFeature ------------------------------------- - -Add TrackerFeature in the collection. Return true if TrackerFeature is added, false otherwise - -.. ocv:function:: bool TrackerFeatureSet::addTrackerFeature( String trackerFeatureType ) - - :param trackerFeatureType: The TrackerFeature name - -.. ocv:function:: bool TrackerFeatureSet::addTrackerFeature( Ptr& feature ) - - :param feature: The TrackerFeature class - - -The modes available now: - -* ``"HAAR"`` -- Haar Feature-based - -The modes that will be available soon: - -* ``"HOG"`` -- Histogram of Oriented Gradients features - -* ``"LBP"`` -- Local Binary Pattern features - -* ``"FEATURE2D"`` -- All types of Feature2D - -Example ``TrackerFeatureSet::addTrackerFeature`` : :: - - //sample usage: - - Ptr trackerFeature = new TrackerFeatureHAAR( HAARparameters ); - featureSet->addTrackerFeature( trackerFeature ); - - //or add CSC sampler with default parameters - //featureSet->addTrackerFeature( "HAAR" ); - - -.. note:: If you use the second method, you must initialize the TrackerFeature - -TrackerFeatureSet::getTrackerFeature ------------------------------------- - -Get the TrackerFeature collection (TrackerFeature name, TrackerFeature pointer) - -.. ocv:function:: const std::vector > >& TrackerFeatureSet::getTrackerFeature() const - -TrackerFeatureSet::getResponses -------------------------------- - -Get the responses - -.. ocv:function:: const std::vector& TrackerFeatureSet::getResponses() const - -.. note:: Be sure to call extraction before getResponses - -Example ``TrackerFeatureSet::getResponses`` : :: - - //get the patches from sampler - std::vector detectSamples = sampler->getSamples(); - - if( detectSamples.empty() ) - return false; - - //features extraction - featureSet->extraction( detectSamples ); - - //get responses - std::vector response = featureSet->getResponses(); - -TrackerFeature --------------- - -Abstract base class for TrackerFeature that represents the feature. - -.. ocv:class:: TrackerFeature - -TrackerFeature class:: - - class CV_EXPORTS_W TrackerFeature - { - public: - virtual ~TrackerFeature(); - - static Ptr create( const String& trackerFeatureType ); - - void compute( const std::vector& images, Mat& response ); - - virtual void selection( Mat& response, int npoints ) = 0; - - String getClassName() const; - }; - -TrackerFeature::create ----------------------- - -Create TrackerFeature by tracker feature type - -.. ocv:function:: static Ptr TrackerFeature::create( const String& trackerFeatureType ) - - :param trackerFeatureType: The TrackerFeature name - -The modes available now: - -* ``"HAAR"`` -- Haar Feature-based - -The modes that will be available soon: - -* ``"HOG"`` -- Histogram of Oriented Gradients features - -* ``"LBP"`` -- Local Binary Pattern features - -* ``"FEATURE2D"`` -- All types of Feature2D - -TrackerFeature::compute ------------------------ - -Compute the features in the images collection - -.. ocv:function:: void TrackerFeature::compute( const std::vector& images, Mat& response ) - - :param images: The images - - :param response: The output response - -TrackerFeature::selection -------------------------- - -Identify most effective features - -.. ocv:function:: void TrackerFeature::selection( Mat& response, int npoints ) - - :param response: Collection of response for the specific TrackerFeature - - :param npoints: Max number of features - -.. note:: This method modifies the response parameter - -TrackerFeature::getClassName ----------------------------- - -Get the name of the specific TrackerFeature - -.. ocv:function:: String TrackerFeature::getClassName() const - -Specialized TrackerFeature -========================== - -In [AAM]_ table I and section III C are described the most known features type. At moment only :ocv:class:`TrackerFeatureHAAR` is implemented. - -TrackerFeatureHAAR : TrackerFeature ------------------------------------ - -TrackerFeature based on HAAR features, used by TrackerMIL and many others algorithms - -.. ocv:class:: TrackerFeatureHAAR - -TrackerFeatureHAAR class:: - - class CV_EXPORTS_W TrackerFeatureHAAR : TrackerFeature - { - public: - - TrackerFeatureHAAR( const TrackerFeatureHAAR::Params ¶meters = TrackerFeatureHAAR::Params() ); - ~TrackerFeatureHAAR(); - - void selection( Mat& response, int npoints ); - bool extractSelected( const std::vector selFeatures, const std::vector& images, Mat& response ); - std::vector >& getMeanSigmaPairs(); - bool swapFeature( int source, int target ); - bool swapFeature( int id, CvHaarEvaluator::FeatureHaar& feature ); - CvHaarEvaluator::FeatureHaar& getFeatureAt( int id ); - }; - -.. note:: HAAR features implementation is copied from apps/traincascade and modified according to MIL implementation - -TrackerFeatureHAAR::Params --------------------------- - -.. ocv:struct:: TrackerFeatureHAAR::Params - -List of TrackerFeatureHAAR parameters:: - - struct CV_EXPORTS Params - { - Params(); - int numFeatures; // # of rects - Size rectSize; // rect size - bool isIntegral; // true if input images are integral, false otherwise - }; - -TrackerFeatureHAAR::TrackerFeatureHAAR --------------------------------------- - -Constructor - -.. ocv:function:: TrackerFeatureHAAR::TrackerFeatureHAAR( const TrackerFeatureHAAR::Params ¶meters = TrackerFeatureHAAR::Params() ) - - :param parameters: TrackerFeatureHAAR parameters :ocv:struct:`TrackerFeatureHAAR::Params` - - -TrackerFeatureHAAR::selection ------------------------------ - -Identify most effective features - -.. ocv:function:: void TrackerFeatureHAAR::selection( Mat& response, int npoints ) - - :param response: Collection of response for the specific TrackerFeature - - :param npoints: Max number of features - -.. note:: This method modifies the response parameter - -TrackerFeatureHAAR::extractSelected ------------------------------------ - -Compute the features only for the selected indices in the images collection - -.. ocv:function:: bool TrackerFeatureHAAR::extractSelected( const std::vector selFeatures, const std::vector& images, Mat& response ) - - :param selFeatures: indices of selected features - - :param images: The images - - :param response: Collection of response for the specific TrackerFeature - -TrackerFeatureHAAR::getMeanSigmaPairs -------------------------------------- - -Get the list of mean/sigma. Return the list of mean/sigma - -.. ocv:function:: std::vector >& TrackerFeatureHAAR::getMeanSigmaPairs() - -TrackerFeatureHAAR::swapFeature -------------------------------- - -Swap the feature in position source with the feature in position target - -.. ocv:function:: bool TrackerFeatureHAAR::swapFeature( int source, int target ) - - :param source: The source position - - :param target: The target position - -Swap the feature in position id with the feature input - -.. ocv:function:: bool TrackerFeatureHAAR::swapFeature( int id, CvHaarEvaluator::FeatureHaar& feature ) - - :param id: The position - - :param feature: The feature - -TrackerFeatureHAAR::getFeatureAt --------------------------------- - -Get the feature in position id - -.. ocv:function:: CvHaarEvaluator::FeatureHaar& TrackerFeatureHAAR::getFeatureAt( int id ) - - :param id: The position - - -TrackerFeatureHOG ------------------ - -TODO To be implemented - -TrackerFeatureLBP ------------------ - -TODO To be implemented - -TrackerFeatureFeature2d ------------------------ - -TODO To be implemented diff --git a/modules/tracking/doc/common_interfaces_tracker_model.rst b/modules/tracking/doc/common_interfaces_tracker_model.rst deleted file mode 100644 index ef0696f0d..000000000 --- a/modules/tracking/doc/common_interfaces_tracker_model.rst +++ /dev/null @@ -1,506 +0,0 @@ -Common Interfaces of TrackerModel -================================= - -.. highlight:: cpp - -ConfidenceMap -------------- - -Represents the model of the target at frame :math:`k` (all states and scores) - -[AAM]_ The set of the pair :math:`\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle` - -.. c:type:: ConfidenceMap - -ConfidenceMap:: - - typedef std::vector, float> > ConfidenceMap; - -.. seealso:: - - :ocv:class:`TrackerTargetState` - -Trajectory ----------- - -Represents the estimate states for all frames - -[AAM]_ :math:`x_{k}` is the trajectory of the target up to time :math:`k` - -.. c:type:: Trajectory - -Trajectory:: - - typedef std::vector > Trajectory; - -.. seealso:: - - :ocv:class:`TrackerTargetState` - -TrackerTargetState ------------------- - -Abstract base class for TrackerTargetState that represents a possible state of the target. - -[AAM]_ :math:`\hat{x}^{i}_{k}` all the states candidates. - -Inherits this class with your Target state - -.. ocv:class:: TrackerTargetState - -TrackerTargetState class:: - - class CV_EXPORTS_W TrackerTargetState - { - public: - virtual ~TrackerTargetState(){}; - - Point2f getTargetPosition() const; - void setTargetPosition( const Point2f& position ); - - int getTargetWidth() const; - void setTargetWidth( int width ); - - int getTargetHeight() const; - void setTargetHeight( int height ); - - }; - -In own implementation you can add scale variation, width, height, orientation, etc. - - -TrackerStateEstimator ---------------------- - -Abstract base class for TrackerStateEstimator that estimates the most likely target state. - -[AAM]_ State estimator - -[AMVOT]_ Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid) - -.. ocv:class:: TrackerStateEstimator - -TrackerStateEstimator class:: - - class CV_EXPORTS_W TrackerStateEstimator - { - public: - virtual ~TrackerStateEstimator(); - - static Ptr create( const String& trackeStateEstimatorType ); - - Ptr estimate( const std::vector& confidenceMaps ); - void update( std::vector& confidenceMaps ); - - String getClassName() const; - - }; - -TrackerStateEstimator::create ------------------------------ - -Create TrackerStateEstimator by tracker state estimator type - -.. ocv:function:: static Ptr TrackerStateEstimator::create( const String& trackeStateEstimatorType ) - - :param trackeStateEstimatorType: The TrackerStateEstimator name - -The modes available now: - -* ``"BOOSTING"`` -- Boosting-based discriminative appearance models. See [AMVOT]_ section 4.4 - -The modes available soon: - -* ``"SVM"`` -- SVM-based discriminative appearance models. See [AMVOT]_ section 4.5 - -TrackerStateEstimator::estimate -------------------------------- - -Estimate the most likely target state, return the estimated state - -.. ocv:function:: Ptr TrackerStateEstimator::estimate( const std::vector& confidenceMaps ) - - :param confidenceMaps: The overall appearance model as a list of :c:type:`ConfidenceMap` - -TrackerStateEstimator::update ------------------------------ - -Update the ConfidenceMap with the scores - -.. ocv:function:: void TrackerStateEstimator::update( std::vector& confidenceMaps ) - - :param confidenceMaps: The overall appearance model as a list of :c:type:`ConfidenceMap` - -TrackerStateEstimator::getClassName ------------------------------------ - -Get the name of the specific TrackerStateEstimator - -.. ocv:function:: String TrackerStateEstimator::getClassName() const - -TrackerModel ------------- - -Abstract class that represents the model of the target. It must be instantiated by specialized tracker - -[AAM]_ Ak - -Inherits this with your TrackerModel - -.. ocv:class:: TrackerModel - -TrackerModel class:: - - class CV_EXPORTS_W TrackerModel - { - public: - - TrackerModel(); - virtual ~TrackerModel(); - - void modelEstimation( const std::vector& responses ); - void modelUpdate(); - bool runStateEstimator(); - - bool setTrackerStateEstimator( Ptr trackerStateEstimator ); - void setLastTargetState( const Ptr& lastTargetState ); - - Ptr getLastTargetState() const; - const std::vector& getConfidenceMaps() const; - const ConfidenceMap& getLastConfidenceMap() const; - Ptr getTrackerStateEstimator() const; - }; - -TrackerModel::modelEstimation ------------------------------ - -Estimate the most likely target location - -[AAM]_ ME, Model Estimation table I - -.. ocv:function:: void TrackerModel::modelEstimation( const std::vector& responses ) - - :param responses: Features extracted from :ocv:class:`TrackerFeatureSet` - - -TrackerModel::modelUpdate -------------------------- - -Update the model - -[AAM]_ MU, Model Update table I - -.. ocv:function:: void TrackerModel::modelUpdate() - - -TrackerModel::runStateEstimator -------------------------------- - -Run the TrackerStateEstimator, return true if is possible to estimate a new state, false otherwise - -.. ocv:function:: bool TrackerModel::runStateEstimator() - -TrackerModel::setTrackerStateEstimator --------------------------------------- - -Set TrackerEstimator, return true if the tracker state estimator is added, false otherwise - -.. ocv:function:: bool TrackerModel::setTrackerStateEstimator( Ptr trackerStateEstimator ) - - :param trackerStateEstimator: The :ocv:class:`TrackerStateEstimator` - -.. note:: You can add only one :ocv:class:`TrackerStateEstimator` - -TrackerModel::setLastTargetState --------------------------------- - -Set the current :ocv:class:`TrackerTargetState` in the :c:type:`Trajectory` - -.. ocv:function:: void TrackerModel::setLastTargetState( const Ptr& lastTargetState ) - - :param lastTargetState: The current :ocv:class:`TrackerTargetState` - - -TrackerModel::getLastTargetState --------------------------------- - -Get the last :ocv:class:`TrackerTargetState` from :c:type:`Trajectory` - -.. ocv:function:: Ptr TrackerModel::getLastTargetState() const - - -TrackerModel::getConfidenceMaps -------------------------------- - -Get the list of the :c:type:`ConfidenceMap` - -.. ocv:function:: const std::vector& TrackerModel::getConfidenceMaps() const - -TrackerModel::getLastConfidenceMap ----------------------------------- - -Get the last :c:type:`ConfidenceMap` for the current frame - -.. ocv:function:: const ConfidenceMap& TrackerModel::getLastConfidenceMap() const - -TrackerModel::getTrackerStateEstimator --------------------------------------- - -Get the :ocv:class:`TrackerStateEstimator` - -.. ocv:function:: Ptr TrackerModel::getTrackerStateEstimator() const - -Specialized TrackerStateEstimator -================================= - -In [AMVOT]_ Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid) are described the most known statistical model. - -At moment :ocv:class:`TrackerStateEstimatorMILBoosting` and :ocv:class:`TrackerStateEstimatorAdaBoosting` are implemented. - -TrackerStateEstimatorMILBoosting : TrackerStateEstimator --------------------------------------------------------- - -TrackerStateEstimator based on Boosting - -.. ocv:class:: TrackerStateEstimatorMILBoosting - -TrackerStateEstimatorMILBoosting class:: - - class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator - { - public: - class TrackerMILTargetState : public TrackerTargetState - { - ... - }; - TrackerStateEstimatorMILBoosting( int nFeatures = 250 ); - ~TrackerStateEstimatorMILBoosting(); - - void setCurrentConfidenceMap( ConfidenceMap& confidenceMap ); - }; - -TrackerMILTargetState : TrackerTargetState ------------------------------------------- - -Implementation of the target state for TrackerMILTargetState - -.. ocv:class:: TrackerMILTargetState - -TrackerMILTargetState class:: - - class TrackerMILTargetState : public TrackerTargetState - { - public: - TrackerMILTargetState( const Point2f& position, int targetWidth, int targetHeight, bool foreground, const Mat& features ); - ~TrackerMILTargetState(){}; - - void setTargetFg( bool foreground ); - void setFeatures( const Mat& features ); - bool isTargetFg() const; - Mat getFeatures() const; - }; - -TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg --------------------------------------------------------------------- - -Set label: true for target foreground, false for background - -.. ocv:function:: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg( bool foreground ) - - :param foreground: Label for background/foreground - -TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures --------------------------------------------------------------------- - -Set the features extracted from :ocv:class:`TrackerFeatureSet` - -.. ocv:function:: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures( const Mat& features ) - - :param features: The features extracted - -TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg -------------------------------------------------------------------- - -Get the label. Return true for target foreground, false for background - -.. ocv:function:: bool TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg() const - -TrackerStateEstimatorMILBoosting::TrackerMILTargetState::getFeatures --------------------------------------------------------------------- - -Get the features extracted - -.. ocv:function:: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures( const Mat& features ) - -TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting ------------------------------------------------------------------- - -Constructor - -.. ocv:function:: TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting( int nFeatures=250 ) - - :param nFeatures: Number of features for each sample - -TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap ---------------------------------------------------------- - -Set the current confidenceMap - -.. ocv:function:: void TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap( ConfidenceMap& confidenceMap ) - - :param confidenceMap: The current :c:type:`ConfidenceMap` - -TrackerStateEstimatorAdaBoosting : TrackerStateEstimator --------------------------------------------------------- - -TrackerStateEstimatorAdaBoosting based on ADA-Boosting - -.. ocv:class:: TrackerStateEstimatorAdaBoosting - -TrackerStateEstimatorAdaBoosting class:: - - class CV_EXPORTS_W TrackerStateEstimatorAdaBoosting : public TrackerStateEstimator - { - public: - class TrackerAdaBoostingTargetState : public TrackerTargetState - { - ... - }; - TrackerStateEstimatorAdaBoosting( int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI, const std::vector >& meanSigma ); - ~TrackerStateEstimatorAdaBoosting(); - - Rect getSampleROI() const; - void setSampleROI( const Rect& ROI ); - void setCurrentConfidenceMap( ConfidenceMap& confidenceMap ); - std::vector computeSelectedWeakClassifier(); - std::vector computeReplacedClassifier(); - std::vector computeSwappedClassifier(); - void setMeanSigmaPair( const std::vector >& meanSigmaPair ); - }; - -TrackerAdaBoostingTargetState : TrackerTargetState --------------------------------------------------- - -Implementation of the target state for TrackerAdaBoostingTargetState - -.. ocv:class:: TrackerAdaBoostingTargetState - -TrackerAdaBoostingTargetState class:: - - class TrackerAdaBoostingTargetState : public TrackerTargetState - { - public: - TrackerAdaBoostingTargetState( const Point2f& position, int width, int height, bool foreground, const Mat& responses ); - ~TrackerAdaBoostingTargetState(){}; - - void setTargetResponses( const Mat& responses ); - void setTargetFg( bool foreground ); - Mat getTargetResponses() const; - bool isTargetFg() const; - }; - -TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg ----------------------------------------------------------------------------- - -Set label: true for target foreground, false for background - -.. ocv:function:: void TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg( bool foreground ) - - :param foreground: Label for background/foreground - -TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses ------------------------------------------------------------------------------------ - -Set the features extracted from :ocv:class:`TrackerFeatureSet` - -.. ocv:function:: void TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses( const Mat& responses ) - - :param responses: The features extracted - -TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg ---------------------------------------------------------------------------- - -Get the label. Return true for target foreground, false for background - -.. ocv:function:: bool TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg() const - -TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses ------------------------------------------------------------------------------------ - -Get the features extracted - -.. ocv:function:: Mat TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses() - -TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting ------------------------------------------------------------------- - -Constructor - -.. ocv:function:: TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting( int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI, const std::vector >& meanSigma ) - - :param numClassifer: Number of base classifiers - - :param initIterations: Number of iterations in the initialization - - :param nFeatures: Number of features/weak classifiers - - :param patchSize: tracking rect - - :param ROI: initial ROI - - :param meanSigma: pairs of mean/sigma - -TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap ---------------------------------------------------------- - -Set the current confidenceMap - -.. ocv:function:: void TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap( ConfidenceMap& confidenceMap ) - - :param confidenceMap: The current :c:type:`ConfidenceMap` - -TrackerStateEstimatorAdaBoosting::getSampleROI ----------------------------------------------- - -Get the sampling ROI - -.. ocv:function:: Rect TrackerStateEstimatorAdaBoosting::getSampleROI() const - -TrackerStateEstimatorAdaBoosting::setSampleROI ----------------------------------------------- - -Set the sampling ROI - -.. ocv:function:: void TrackerStateEstimatorAdaBoosting::setSampleROI( const Rect& ROI ) - - :param ROI: the sampling ROI - -TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier ---------------------------------------------------------------- - -Get the list of the selected weak classifiers for the classification step - -.. ocv:function:: std::vector TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier() - -TrackerStateEstimatorAdaBoosting::computeReplacedClassifier ------------------------------------------------------------ - -Get the list of the weak classifiers that should be replaced - -.. ocv:function:: std::vector TrackerStateEstimatorAdaBoosting::computeReplacedClassifier() - -TrackerStateEstimatorAdaBoosting::computeSwappedClassifier ----------------------------------------------------------- - -Get the list of the weak classifiers that replace those to be replaced - -.. ocv:function:: std::vector TrackerStateEstimatorAdaBoosting::computeSwappedClassifier() - -TrackerStateEstimatorAdaBoosting::setMeanSigmaPair --------------------------------------------------- - -Set the mean/sigma to instantiate possibly new classifiers - -.. ocv:function:: void TrackerStateEstimatorAdaBoosting::setMeanSigmaPair( const std::vector >& meanSigmaPair ) - - :param meanSigmaPair: the mean/sigma pairs diff --git a/modules/tracking/doc/common_interfaces_tracker_sampler.rst b/modules/tracking/doc/common_interfaces_tracker_sampler.rst deleted file mode 100644 index 349331bd8..000000000 --- a/modules/tracking/doc/common_interfaces_tracker_sampler.rst +++ /dev/null @@ -1,347 +0,0 @@ -Common Interfaces of TrackerSampler -=================================== - -.. highlight:: cpp - - -TrackerSampler --------------- - -Class that manages the sampler in order to select regions for the update the model of the tracker - -[AAM]_ Sampling e Labeling. See table I and section III B - -.. ocv:class:: TrackerSampler - -TrackerSampler class:: - - class CV_EXPORTS_W TrackerSampler - { - public: - - TrackerSampler(); - ~TrackerSampler(); - - void sampling( const Mat& image, Rect boundingBox ); - - const std::vector > >& getSamplers() const; - const std::vector& getSamples() const; - - bool addTrackerSamplerAlgorithm( String trackerSamplerAlgorithmType ); - bool addTrackerSamplerAlgorithm( Ptr& sampler ); - - }; - - -TrackerSampler is an aggregation of :ocv:class:`TrackerSamplerAlgorithm` - -.. seealso:: - - :ocv:class:`TrackerSamplerAlgorithm` - -TrackerSampler::sampling ------------------------- - -Computes the regions starting from a position in an image - -.. ocv:function:: void TrackerSampler::sampling( const Mat& image, Rect boundingBox ) - - :param image: The current frame - - :param boundingBox: The bounding box from which regions can be calculated - - -TrackerSampler::getSamplers ---------------------------- - -Return the collection of the :ocv:class:`TrackerSamplerAlgorithm` - -.. ocv:function:: const std::vector > >& TrackerSampler::getSamplers() const - - -TrackerSampler::getSamples --------------------------- - -Return the samples from all :ocv:class:`TrackerSamplerAlgorithm`, [AAM]_ Fig. 1 variable Sk - -.. ocv:function:: const std::vector& TrackerSampler::getSamples() const - -TrackerSampler::addTrackerSamplerAlgorithm ------------------------------------------- - -Add TrackerSamplerAlgorithm in the collection. -Return true if sampler is added, false otherwise - -.. ocv:function:: bool TrackerSampler::addTrackerSamplerAlgorithm( String trackerSamplerAlgorithmType ) - - :param trackerSamplerAlgorithmType: The TrackerSamplerAlgorithm name - -.. ocv:function:: bool TrackerSampler::addTrackerSamplerAlgorithm( Ptr& sampler ) - - :param sampler: The TrackerSamplerAlgorithm class - - -The modes available now: - -* ``"CSC"`` -- Current State Center - -* ``"CS"`` -- Current State - -* ``"PF"`` -- Particle Filtering - -Example ``TrackerSamplerAlgorithm::addTrackerSamplerAlgorithm`` : :: - - //sample usage: - - TrackerSamplerCSC::Params CSCparameters; - Ptr CSCSampler = new TrackerSamplerCSC( CSCparameters ); - - if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) ) - return false; - - //or add CSC sampler with default parameters - //sampler->addTrackerSamplerAlgorithm( "CSC" ); - - -.. note:: If you use the second method, you must initialize the TrackerSamplerAlgorithm - - -TrackerSamplerAlgorithm ------------------------ - -Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler. - -.. ocv:class:: TrackerSamplerAlgorithm - -TrackerSamplerAlgorithm class:: - - class CV_EXPORTS_W TrackerSamplerAlgorithm - { - public: - - virtual ~TrackerSamplerAlgorithm(); - - static Ptr create( const String& trackerSamplerType ); - - bool sampling( const Mat& image, Rect boundingBox, std::vector& sample ); - String getClassName() const; - }; - -TrackerSamplerAlgorithm::create -------------------------------- - -Create TrackerSamplerAlgorithm by tracker sampler type. - -.. ocv:function:: static Ptr TrackerSamplerAlgorithm::create( const String& trackerSamplerType ) - - :param trackerSamplerType: The trackerSamplerType name - -The modes available now: - -* ``"CSC"`` -- Current State Center - -* ``"CS"`` -- Current State - - -TrackerSamplerAlgorithm::sampling ---------------------------------- - -Computes the regions starting from a position in an image. Return true if samples are computed, false otherwise - -.. ocv:function:: bool TrackerSamplerAlgorithm::sampling( const Mat& image, Rect boundingBox, std::vector& sample ) - - :param image: The current frame - - :param boundingBox: The bounding box from which regions can be calculated - - :sample: The computed samples [AAM]_ Fig. 1 variable Sk - -TrackerSamplerAlgorithm::getClassName -------------------------------------- - -Get the name of the specific TrackerSamplerAlgorithm - -.. ocv:function:: String TrackerSamplerAlgorithm::getClassName() const - -Specialized TrackerSamplerAlgorithm -=================================== - -In [AAM]_ table I there are described the most known sampling strategies. At moment :ocv:class:`TrackerSamplerCSC` and :ocv:class:`TrackerSamplerCS` are implemented. Beside these, there is :ocv:class:`TrackerSamplerPF`, sampler based on particle filtering. - -TrackerSamplerCSC : TrackerSamplerAlgorithm -------------------------------------------- - -TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL - -.. ocv:class:: TrackerSamplerCSC - -TrackerSamplerCSC class:: - - - class CV_EXPORTS_W TrackerSamplerCSC - { - public: - - TrackerSamplerCSC( const TrackerSamplerCSC::Params ¶meters = TrackerSamplerCSC::Params() ); - void setMode( int samplingMode ); - - ~TrackerSamplerCSC(); - }; - - -TrackerSamplerCSC::Params -------------------------- - -.. ocv:struct:: TrackerSamplerCSC::Params - -List of TrackerSamplerCSC parameters:: - - struct CV_EXPORTS Params - { - Params(); - float initInRad; // radius for gathering positive instances during init - float trackInPosRad; // radius for gathering positive instances during tracking - float searchWinSize; // size of search window - int initMaxNegNum; // # negative samples to use during init - int trackMaxPosNum; // # positive samples to use during training - int trackMaxNegNum; // # negative samples to use during training - }; - - -TrackerSamplerCSC::TrackerSamplerCSC ------------------------------------- - -Constructor - -.. ocv:function:: TrackerSamplerCSC::TrackerSamplerCSC( const TrackerSamplerCSC::Params ¶meters = TrackerSamplerCSC::Params() ) - - :param parameters: TrackerSamplerCSC parameters :ocv:struct:`TrackerSamplerCSC::Params` - -TrackerSamplerCSC::setMode --------------------------- - -Set the sampling mode of TrackerSamplerCSC - -.. ocv:function:: void TrackerSamplerCSC::setMode( int samplingMode ) - - :param samplingMode: The sampling mode - -The modes are: - -* ``"MODE_INIT_POS = 1"`` -- for the positive sampling in initialization step -* ``"MODE_INIT_NEG = 2"`` -- for the negative sampling in initialization step -* ``"MODE_TRACK_POS = 3"`` -- for the positive sampling in update step -* ``"MODE_TRACK_NEG = 4"`` -- for the negative sampling in update step -* ``"MODE_DETECT = 5"`` -- for the sampling in detection step - -TrackerSamplerCS : TrackerSamplerAlgorithm -------------------------------------------- - -TrackerSampler based on CS (current state), used by algorithm TrackerBoosting - -.. ocv:class:: TrackerSamplerCS - -TrackerSamplerCS class:: - - - class CV_EXPORTS_W TrackerSamplerCS - { - public: - - TrackerSamplerCS( const TrackerSamplerCS::Params ¶meters = TrackerSamplerCS::Params() ); - void setMode( int samplingMode ); - - ~TrackerSamplerCS(); - }; - - -TrackerSamplerCS::Params -------------------------- - -.. ocv:struct:: TrackerSamplerCS::Params - -List of TrackerSamplerCS parameters:: - - struct CV_EXPORTS Params - { - Params(); - float overlap; //overlapping for the search windows - float searchFactor; //search region parameter - }; - - -TrackerSamplerCS::TrackerSamplerCS ------------------------------------- - -Constructor - -.. ocv:function:: TrackerSamplerCS::TrackerSamplerCS( const TrackerSamplerCS::Params ¶meters = TrackerSamplerCS::Params() ) - - :param parameters: TrackerSamplerCS parameters :ocv:struct:`TrackerSamplerCS::Params` - -TrackerSamplerCS::setMode --------------------------- - -Set the sampling mode of TrackerSamplerCS - -.. ocv:function:: void TrackerSamplerCS::setMode( int samplingMode ) - - :param samplingMode: The sampling mode - -The modes are: - -* ``"MODE_POSITIVE = 1"`` -- for the positive sampling -* ``"MODE_NEGATIVE = 2"`` -- for the negative sampling -* ``"MODE_CLASSIFY = 3"`` -- for the sampling in classification step - -TrackerSamplerPF : TrackerSamplerAlgorithm -------------------------------------------- - -This sampler is based on particle filtering. In principle, it can be thought of as performing some sort of optimization (and indeed, this -tracker uses opencv's ``optim`` module), where tracker seeks to find the rectangle in given frame, which is the most *"similar"* to the initial -rectangle (the one, given through the constructor). - -The optimization performed is stochastic and somehow resembles genetic algorithms, where on each new ``image`` received (submitted via ``TrackerSamplerPF::sampling()``) we start with the region bounded by ``boundingBox``, then generate several "perturbed" boxes, take the ones most similar to the original. This selection round is repeated several times. At the end, we hope that only the most promising box remaining, and these are combined to produce the subrectangle of ``image``, which is put as a sole element in array ``sample``. - -It should be noted, that the definition of "similarity" between two rectangles is based on comparing their histograms. As experiments show, tracker is *not* very succesfull if target is assumed to strongly change its dimensions. - -.. ocv:class:: TrackerSamplerPF - -TrackerSamplerPF class:: - - class CV_EXPORTS_W TrackerSamplerPF : public TrackerSamplerAlgorithm{ - public: - TrackerSamplerPF(const Mat& chosenRect,const TrackerSamplerPF::Params ¶meters = TrackerSamplerPF::Params()); - void sampling( const Mat& image, Rect boundingBox, std::vector& sample ); //inherited from TrackerSamplerAlgorithmTrackerSamplerAlgorithm - }; - - -TrackerSamplerPF::Params -------------------------- - -.. ocv:struct:: TrackerSamplerPF::Params - -This structure contains all the parameters that can be varied during the course of sampling algorithm. Below is the structure exposed, -together with its members briefly explained with reference to the above discussion on algorithm's working. - -:: - - struct CV_EXPORTS Params - { - Params(); - int iterationNum; //number of selection rounds - int particlesNum; //number of "perturbed" boxes on each round - double alpha; //with each new round we exponentially decrease the amount of "perturbing" we allow (like in simulated annealing) - //and this very alpha controls how fast annealing happens, ie. how fast perturbing decreases - Mat_ std; //initial values for perturbing (1-by-4 array, as each rectangle is given by 4 values -- coordinates of opposite vertices, - //hence we have 4 values to perturb) - }; - -TrackerSamplerPF::TrackerSamplerPF ------------------------------------- - -Constructor - -.. ocv:function:: TrackerSamplerPF(const Mat& chosenRect,const TrackerSamplerPF::Params ¶meters = TrackerSamplerPF::Params()) - - :param chosenRect: Initial rectangle, that is supposed to contain target we'd like to track. diff --git a/modules/tracking/doc/tracker_algorithms.rst b/modules/tracking/doc/tracker_algorithms.rst deleted file mode 100644 index dee541a26..000000000 --- a/modules/tracking/doc/tracker_algorithms.rst +++ /dev/null @@ -1,226 +0,0 @@ -Tracker Algorithms -================== - -.. highlight:: cpp - -The following algorithms are implemented at the moment. - -.. [MIL] B Babenko, M-H Yang, and S Belongie, Visual Tracking with Online Multiple Instance Learning, In CVPR, 2009 - -.. [OLB] H Grabner, M Grabner, and H Bischof, Real-time tracking via on-line boosting, In Proc. BMVC, volume 1, pages 47– 56, 2006 - -.. [MedianFlow] Z. Kalal, K. Mikolajczyk, and J. Matas, “Forward-Backward Error: Automatic Detection of Tracking Failures,” International Conference on Pattern Recognition, 2010, pp. 23-26. - -.. [TLD] Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-Learning-Detection,” Pattern Analysis and Machine Intelligence 2011. - -TrackerBoosting ---------------- - -This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm. -The classifier uses the surrounding background as negative examples in update step to avoid the drifting problem. The implementation is based on -[OLB]_. - -.. ocv:class:: TrackerBoosting - -Implementation of TrackerBoosting from :ocv:class:`Tracker`:: - - class CV_EXPORTS_W TrackerBoosting : public Tracker - { - public: - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - static Ptr createTracker(const trackerBoosting::Params ¶meters=trackerBoosting::Params()); - virtual ~trackerBoosting(){}; - - protected: - bool initImpl( const Mat& image, const Rect2d& boundingBox ); - bool updateImpl( const Mat& image, Rect2d& boundingBox ); - }; - -TrackerBoosting::Params ------------------------------------------------------------------------ - -.. ocv:struct:: TrackerBoosting::Params - -List of BOOSTING parameters:: - - struct CV_EXPORTS Params - { - Params(); - int numClassifiers; //the number of classifiers to use in a OnlineBoosting algorithm - float samplerOverlap; //search region parameters to use in a OnlineBoosting algorithm - float samplerSearchFactor; // search region parameters to use in a OnlineBoosting algorithm - int iterationInit; //the initial iterations - int featureSetNumFeatures; // #features - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - -TrackerBoosting::createTracker ------------------------------------------------------------------------ - -Constructor - -.. ocv:function:: Ptr TrackerBoosting::createTracker(const trackerBoosting::Params ¶meters=trackerBoosting::Params()) - - :param parameters: BOOSTING parameters :ocv:struct:`TrackerBoosting::Params` - -TrackerMIL ----------------------- - -The MIL algorithm trains a classifier in an online manner to separate the object from the background. Multiple Instance Learning avoids the drift problem for a robust tracking. The implementation is based on [MIL]_. - -Original code can be found here http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml - -.. ocv:class:: TrackerMIL - -Implementation of TrackerMIL from :ocv:class:`Tracker`:: - - class CV_EXPORTS_W TrackerMIL : public Tracker - { - public: - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - static Ptr createTracker(const trackerMIL::Params ¶meters=trackerMIL::Params()); - virtual ~trackerMIL(){}; - - protected: - bool initImpl( const Mat& image, const Rect2d& boundingBox ); - bool updateImpl( const Mat& image, Rect2d& boundingBox ); - }; - -TrackerMIL::Params ------------------- - -.. ocv:struct:: TrackerMIL::Params - -List of MIL parameters:: - - struct CV_EXPORTS Params - { - Params(); - //parameters for sampler - float samplerInitInRadius; // radius for gathering positive instances during init - int samplerInitMaxNegNum; // # negative samples to use during init - float samplerSearchWinSize; // size of search window - float samplerTrackInRadius; // radius for gathering positive instances during tracking - int samplerTrackMaxPosNum; // # positive samples to use during tracking - int samplerTrackMaxNegNum; // # negative samples to use during tracking - - int featureSetNumFeatures; // # features - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - -TrackerMIL::createTracker -------------------------------- - -Constructor - -.. ocv:function:: Ptr TrackerMIL::createTracker(const trackerMIL::Params ¶meters=trackerMIL::Params()) - - :param parameters: MIL parameters :ocv:struct:`TrackerMIL::Params` - -TrackerMedianFlow ----------------------- - -Implementation of a paper "Forward-Backward Error: Automatic Detection of Tracking Failures" by Z. Kalal, K. Mikolajczyk -and Jiri Matas. The implementation is based on [MedianFlow]_. - -The tracker is suitable for very smooth and predictable movements when object is visible throughout the whole sequence. It's quite and -accurate for this type of problems (in particular, it was shown by authors to outperform MIL). During the implementation period the code at -http://www.aonsquared.co.uk/node/5, the courtesy of the author Arthur Amarra, was used for the reference purpose. - -.. ocv:class:: TrackerMedianFlow - -Implementation of TrackerMedianFlow from :ocv:class:`Tracker`:: - - class CV_EXPORTS_W TrackerMedianFlow : public Tracker - { - public: - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - static Ptr createTracker(const trackerMedianFlow::Params ¶meters=trackerMedianFlow::Params()); - virtual ~trackerMedianFlow(){}; - - protected: - bool initImpl( const Mat& image, const Rect2d& boundingBox ); - bool updateImpl( const Mat& image, Rect2d& boundingBox ); - }; - -TrackerMedianFlow::Params ------------------------------------- - -.. ocv:struct:: TrackerMedianFlow::Params - -List of MedianFlow parameters:: - - struct CV_EXPORTS Params - { - Params(); - int pointsInGrid; //square root of number of keypoints used; increase it to trade - //accurateness for speed; default value is sensible and recommended - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - -TrackerMedianFlow::createTracker ------------------------------------ - -Constructor - -.. ocv:function:: Ptr TrackerMedianFlow::createTracker(const trackerMedianFlow::Params ¶meters=trackerMedianFlow::Params()) - - :param parameters: Median Flow parameters :ocv:struct:`TrackerMedianFlow::Params` - -TrackerTLD ----------------------- - -TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector’s errors and updates it to avoid these errors in the future. The implementation is based on [TLD]_. - -The Median Flow algorithm (see above) was chosen as a tracking component in this implementation, following authors. Tracker is supposed to be able -to handle rapid motions, partial occlusions, object absence etc. - -.. ocv:class:: TrackerTLD - -Implementation of TrackerTLD from :ocv:class:`Tracker`:: - - class CV_EXPORTS_W TrackerTLD : public Tracker - { - public: - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - static Ptr createTracker(const trackerTLD::Params ¶meters=trackerTLD::Params()); - virtual ~trackerTLD(){}; - - protected: - bool initImpl( const Mat& image, const Rect2d& boundingBox ); - bool updateImpl( const Mat& image, Rect2d& boundingBox ); - }; - -TrackerTLD::Params ------------------------- - -.. ocv:struct:: TrackerTLD::Params - -List of TLD parameters:: - - struct CV_EXPORTS Params - { - Params(); - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - -TrackerTLD::createTracker -------------------------------- - -Constructor - -.. ocv:function:: Ptr TrackerTLD::createTracker(const trackerTLD::Params ¶meters=trackerTLD::Params()) - - :param parameters: TLD parameters :ocv:struct:`TrackerTLD::Params` diff --git a/modules/tracking/doc/tracking.rst b/modules/tracking/doc/tracking.rst deleted file mode 100644 index e6cb7861f..000000000 --- a/modules/tracking/doc/tracking.rst +++ /dev/null @@ -1,63 +0,0 @@ -****************************************** -tracking. Tracking API -****************************************** - -.. highlight:: cpp - - -Long-term optical tracking API ------------------------------- -Long-term optical tracking is one of most important issue for many computer vision applications in real world scenario. -The development in this area is very fragmented and this API is an unique interface useful for plug several algorithms and compare them. -This work is partially based on [AAM]_ and [AMVOT]_. - -This algorithms start from a bounding box of the target and with their internal representation they avoid the drift during the tracking. -These long-term trackers are able to evaluate online the quality of the location of the target in the new frame, without ground truth. - -There are three main components: the TrackerSampler, the TrackerFeatureSet and the TrackerModel. The first component is the object that computes the patches over the frame based on the last target location. -The TrackerFeatureSet is the class that manages the Features, is possible plug many kind of these (HAAR, HOG, LBP, Feature2D, etc). -The last component is the internal representation of the target, it is the appearence model. It stores all state candidates and compute the trajectory (the most likely target states). The class TrackerTargetState represents a possible state of the target. -The TrackerSampler and the TrackerFeatureSet are the visual representation of the target, instead the TrackerModel is the statistical model. - -A recent benchmark between these algorithms can be found in [OOT]_. - -.. only:: plantuml - - UML design: - ----------- - - .. toctree:: - - uml/package - uml/Tracker - uml/TrackerSampler - uml/TrackerFeature - uml/TrackerModel - - -To see how API works, try tracker demo: -https://github.com/lenlen/opencv/blob/tracking_api/samples/cpp/tracker.cpp - - -.. note:: This Tracking API has been designed with PlantUML. If you modify this API please change UML files under modules/tracking/doc/uml/ - -The following reference was used in the API - -.. [AAM] S Salti, A Cavallaro, L Di Stefano, Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation, IEEE Transactions on Image Processing, Vol. 21, Issue 10, October 2012, pp. 4334-4348 - -.. [AMVOT] X Li, W Hu, C Shen, Z Zhang, A Dick, A van den Hengel, A Survey of Appearance Models in Visual Object Tracking, ACM Transactions on Intelligent Systems and Technology (TIST), 2013 - -.. [OOT] Yi Wu and Jongwoo Lim and Ming-Hsuan Yang, Online Object Tracking: A Benchmark, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 - - -Tracker classes: ----------------- - -.. toctree:: - :maxdepth: 2 - - tracker_algorithms - common_interfaces_tracker - common_interfaces_tracker_sampler - common_interfaces_tracker_feature_set - common_interfaces_tracker_model diff --git a/modules/tracking/doc/uml/Tracker.rst b/modules/tracking/doc/uml/Tracker.rst deleted file mode 100644 index 6c2cd113d..000000000 --- a/modules/tracking/doc/uml/Tracker.rst +++ /dev/null @@ -1,40 +0,0 @@ -Tracker diagram -=============== - -.. uml:: - - ..@startuml - package "Tracker package" #DDDDDD { - - - class Algorithm - - class Tracker{ - Ptr featureSet; - Ptr sampler; - Ptr model; - --- - +static Ptr create(const string& trackerType); - +bool init(const Mat& image, const Rect& boundingBox); - +bool update(const Mat& image, Rect& boundingBox); - } - class Tracker - note right: Tracker is the general interface for each specialized trackers - class TrackerMIL{ - +static Ptr createTracker(const TrackerMIL::Params ¶meters); - +virtual ~TrackerMIL(); - } - class TrackerBoosting{ - +static Ptr createTracker(const TrackerBoosting::Params ¶meters); - +virtual ~TrackerBoosting(); - } - Algorithm <|-- Tracker : virtual inheritance - Tracker <|-- TrackerMIL - Tracker <|-- TrackerBoosting - - note "Single instance of the Tracker" as N1 - TrackerBoosting .. N1 - TrackerMIL .. N1 - } - - ..@enduml diff --git a/modules/tracking/doc/uml/TrackerFeature.rst b/modules/tracking/doc/uml/TrackerFeature.rst deleted file mode 100644 index efa6d5a0d..000000000 --- a/modules/tracking/doc/uml/TrackerFeature.rst +++ /dev/null @@ -1,60 +0,0 @@ -TrackerFeatureSet diagram -========================= - -.. uml:: - - ..@startuml - package "TrackerFeature package" #DDDDDD { - - class TrackerFeatureSet{ - -vector > > features - -vector responses - ... - TrackerFeatureSet(); - ~TrackerFeatureSet(); - -- - +extraction(const std::vector& images); - +selection(); - +removeOutliers(); - +vector response getResponses(); - +vector > > getTrackerFeatures(); - +bool addTrackerFeature(string trackerFeatureType); - +bool addTrackerFeature(Ptr& feature); - -clearResponses(); - } - - class TrackerFeature <>{ - static Ptr = create(const string& trackerFeatureType); - compute(const std::vector& images, Mat& response); - selection(Mat& response, int npoints); - } - note bottom: Can be specialized as in table II\nA tracker can use more types of features - - class TrackerFeatureFeature2D{ - -vector keypoints - --- - TrackerFeatureFeature2D(string detectorType, string descriptorType); - ~TrackerFeatureFeature2D(); - --- - compute(const std::vector& images, Mat& response); - selection( Mat& response, int npoints); - } - class TrackerFeatureHOG{ - TrackerFeatureHOG(); - ~TrackerFeatureHOG(); - --- - compute(const std::vector& images, Mat& response); - selection(Mat& response, int npoints); - } - - TrackerFeatureSet *-- TrackerFeature - TrackerFeature <|-- TrackerFeatureHOG - TrackerFeature <|-- TrackerFeatureFeature2D - - - note "Per readability and simplicity in this diagram\n there are only two TrackerFeature but you\n can considering the implementation of the other TrackerFeature" as N1 - TrackerFeatureHOG .. N1 - TrackerFeatureFeature2D .. N1 - } - - ..@enduml diff --git a/modules/tracking/doc/uml/TrackerModel.rst b/modules/tracking/doc/uml/TrackerModel.rst deleted file mode 100644 index ba853d7d0..000000000 --- a/modules/tracking/doc/uml/TrackerModel.rst +++ /dev/null @@ -1,67 +0,0 @@ -TrackerModel diagram -==================== - -.. uml:: - - ..@startuml - package "TrackerModel package" #DDDDDD { - - class Typedef << (T,#FF7700) >>{ - ConfidenceMap - Trajectory - } - - class TrackerModel{ - -vector confidenceMaps; - -Trajectory trajectory; - -Ptr stateEstimator; - ... - TrackerModel(); - ~TrackerModel(); - - +bool setTrackerStateEstimator(Ptr trackerStateEstimator); - +Ptr getTrackerStateEstimator(); - - +void modelEstimation(const vector& responses); - +void modelUpdate(); - +void setLastTargetState(const Ptr lastTargetState); - +void runStateEstimator(); - - +const vector& getConfidenceMaps(); - +const ConfidenceMap& getLastConfidenceMap(); - } - class TrackerTargetState <>{ - Point2f targetPosition; - --- - Point2f getTargetPosition(); - void setTargetPosition(Point2f position); - } - class TrackerTargetState - note bottom: Each tracker can create own state - - class TrackerStateEstimator <>{ - ~TrackerStateEstimator(); - static Ptr create(const String& trackeStateEstimatorType); - Ptr estimate(const vector& confidenceMaps) - void update(vector& confidenceMaps) - } - - class TrackerStateEstimatorSVM{ - TrackerStateEstimatorSVM() - ~TrackerStateEstimatorSVM() - Ptr estimate(const vector& confidenceMaps) - void update(vector& confidenceMaps) - } - class TrackerStateEstimatorMILBoosting{ - TrackerStateEstimatorMILBoosting() - ~TrackerStateEstimatorMILBoosting() - Ptr estimate(const vector& confidenceMaps) - void update(vector& confidenceMaps) - } - - TrackerModel -> TrackerStateEstimator: create - TrackerModel *-- TrackerTargetState - TrackerStateEstimator <|-- TrackerStateEstimatorMILBoosting - TrackerStateEstimator <|-- TrackerStateEstimatorSVM - } - ..@enduml diff --git a/modules/tracking/doc/uml/TrackerSampler.rst b/modules/tracking/doc/uml/TrackerSampler.rst deleted file mode 100644 index f2efe03c6..000000000 --- a/modules/tracking/doc/uml/TrackerSampler.rst +++ /dev/null @@ -1,45 +0,0 @@ -TrackerSampler diagram -====================== - -.. uml:: - - ..@startuml - package "TrackerSampler package" #DDDDDD { - - class TrackerSampler{ - -vector > > samplers - -vector samples; - ... - TrackerSampler(); - ~TrackerSampler(); - +sampling(const Mat& image, Rect boundingBox); - +const vector > >& getSamplers(); - +const vector& getSamples(); - +bool addTrackerSamplerAlgorithm(String trackerSamplerAlgorithmType); - +bool addTrackerSamplerAlgorithm(Ptr& sampler); - --- - -void clearSamples(); - } - - class TrackerSamplerAlgorithm{ - ~TrackerSamplerAlgorithm(); - +static Ptr create(const String& trackerSamplerType); - +bool sampling(const Mat& image, Rect boundingBox, vector& sample); - } - note bottom: A tracker could sample the target\nor it could sample the target and the background - - - class TrackerSamplerCS{ - TrackerSamplerCS(); - ~TrackerSamplerCS(); - +bool sampling(const Mat& image, Rect boundingBox, vector& sample); - } - class TrackerSamplerCSC{ - TrackerSamplerCSC(); - ~TrackerSamplerCSC(); - +bool sampling(const Mat& image, Rect boundingBox, vector& sample); - } - - - } - ..@enduml diff --git a/modules/tracking/doc/uml/package.rst b/modules/tracking/doc/uml/package.rst deleted file mode 100644 index da258d4c7..000000000 --- a/modules/tracking/doc/uml/package.rst +++ /dev/null @@ -1,15 +0,0 @@ -General diagram -=============== - -.. uml:: - - ..@startuml - package "Tracker" - package "TrackerFeature" - package "TrackerSampler" - package "TrackerModel" - - Tracker -> TrackerModel: create - Tracker -> TrackerSampler: create - Tracker -> TrackerFeature: create - ..@enduml diff --git a/modules/xfeatures2d/doc/extra_features.rst b/modules/xfeatures2d/doc/extra_features.rst deleted file mode 100644 index ca3296f7b..000000000 --- a/modules/xfeatures2d/doc/extra_features.rst +++ /dev/null @@ -1,93 +0,0 @@ -Experimental 2D Features Algorithms -=================================== - -This section describes experimental algorithms for 2d feature detection. - -StarFeatureDetector -------------------- -.. ocv:class:: StarFeatureDetector : public FeatureDetector - -The class implements the keypoint detector introduced by [Agrawal08]_, synonym of ``StarDetector``. :: - - class StarFeatureDetector : public FeatureDetector - { - public: - StarFeatureDetector( int maxSize=16, int responseThreshold=30, - int lineThresholdProjected = 10, - int lineThresholdBinarized=8, int suppressNonmaxSize=5 ); - virtual void read( const FileNode& fn ); - virtual void write( FileStorage& fs ) const; - protected: - ... - }; - -.. [Agrawal08] Agrawal, M., Konolige, K., & Blas, M. R. (2008). Censure: Center surround extremas for realtime feature detection and matching. In Computer Vision–ECCV 2008 (pp. 102-115). Springer Berlin Heidelberg. - - -BriefDescriptorExtractor ------------------------- -.. ocv:class:: BriefDescriptorExtractor : public DescriptorExtractor - -Class for computing BRIEF descriptors described in a paper of Calonder M., Lepetit V., -Strecha C., Fua P. *BRIEF: Binary Robust Independent Elementary Features* , -11th European Conference on Computer Vision (ECCV), Heraklion, Crete. LNCS Springer, September 2010. :: - - class BriefDescriptorExtractor : public DescriptorExtractor - { - public: - static const int PATCH_SIZE = 48; - static const int KERNEL_SIZE = 9; - - // bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes. - BriefDescriptorExtractor( int bytes = 32 ); - - virtual void read( const FileNode& ); - virtual void write( FileStorage& ) const; - virtual int descriptorSize() const; - virtual int descriptorType() const; - virtual int defaultNorm() const; - protected: - ... - }; - -.. note:: - - * A complete BRIEF extractor sample can be found at opencv_source_code/samples/cpp/brief_match_test.cpp - - -FREAK ------ -.. ocv:class:: FREAK : public DescriptorExtractor - -Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in [AOV12]_. The algorithm propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are competitive alternatives to existing keypoints in particular for embedded applications. - -.. [AOV12] A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award Winner. - -.. note:: - - * An example on how to use the FREAK descriptor can be found at opencv_source_code/samples/cpp/freak_demo.cpp - -FREAK::FREAK ------------- -The FREAK constructor - -.. ocv:function:: FREAK::FREAK( bool orientationNormalized=true, bool scaleNormalized=true, float patternScale=22.0f, int nOctaves=4, const vector& selectedPairs=vector() ) - - :param orientationNormalized: Enable orientation normalization. - :param scaleNormalized: Enable scale normalization. - :param patternScale: Scaling of the description pattern. - :param nOctaves: Number of octaves covered by the detected keypoints. - :param selectedPairs: (Optional) user defined selected pairs indexes, - -FREAK::selectPairs ------------------- -Select the 512 best description pair indexes from an input (grayscale) image set. FREAK is available with a set of pairs learned off-line. Researchers can run a training process to learn their own set of pair. For more details read section 4.2 in: A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. - -We notice that for keypoint matching applications, image content has little effect on the selected pairs unless very specific what does matter is the detector type (blobs, corners,...) and the options used (scale/rotation invariance,...). Reduce corrThresh if not enough pairs are selected (43 points --> 903 possible pairs) - -.. ocv:function:: vector FREAK::selectPairs(const vector& images, vector >& keypoints, const double corrThresh = 0.7, bool verbose = true) - - :param images: Grayscale image input set. - :param keypoints: Set of detected keypoints - :param corrThresh: Correlation threshold. - :param verbose: Prints pair selection informations. diff --git a/modules/xfeatures2d/doc/nonfree_features.rst b/modules/xfeatures2d/doc/nonfree_features.rst deleted file mode 100644 index 0ad058c0f..000000000 --- a/modules/xfeatures2d/doc/nonfree_features.rst +++ /dev/null @@ -1,252 +0,0 @@ -Non-free 2D Features Algorithms -================================= - -This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are known to be patented. Use them at your own risk. - -SIFT ----- -.. ocv:class:: SIFT : public Feature2D - -Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04]_. - -.. [Lowe04] Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004. - - -SIFT::SIFT ----------- -The SIFT constructors. - -.. ocv:function:: SIFT::SIFT( int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6) - -.. ocv:pyfunction:: cv2.SIFT([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> - - :param nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast) - - :param nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution. - - :param contrastThreshold: The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. - - :param edgeThreshold: The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the ``edgeThreshold``, the less features are filtered out (more features are retained). - - :param sigma: The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number. - - -SIFT::operator () ------------------ -Extract features and computes their descriptors using SIFT algorithm - -.. ocv:function:: void SIFT::operator()(InputArray img, InputArray mask, vector& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) - -.. ocv:pyfunction:: cv2.SIFT.detect(image[, mask]) -> keypoints - -.. ocv:pyfunction:: cv2.SIFT.compute(image, keypoints[, descriptors]) -> keypoints, descriptors - -.. ocv:pyfunction:: cv2.SIFT.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors - - :param img: Input 8-bit grayscale image - - :param mask: Optional input mask that marks the regions where we should detect features. - - :param keypoints: The input/output vector of keypoints - - :param descriptors: The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them. - - :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors. - -.. note:: Python API provides three functions. First one finds keypoints only. Second function computes the descriptors based on the keypoints we provide. Third function detects the keypoints and computes their descriptors. If you want both keypoints and descriptors, directly use third function as ``kp, des = cv2.SIFT.detectAndCompute(image, None)`` - -SURF ----- -.. ocv:class:: SURF : public Feature2D - - Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters: - - .. ocv:member:: int extended - - * 0 means that the basic descriptors (64 elements each) shall be computed - * 1 means that the extended descriptors (128 elements each) shall be computed - - .. ocv:member:: int upright - - * 0 means that detector computes orientation of each feature. - * 1 means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting ``upright=1``. - - .. ocv:member:: double hessianThreshold - - Threshold for the keypoint detector. Only features, whose hessian is larger than ``hessianThreshold`` are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast. - - .. ocv:member:: int nOctaves - - The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it. - - .. ocv:member:: int nOctaveLayers - - The number of images within each octave of a gaussian pyramid. It is set to 2 by default. - - -.. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006 - -.. note:: - - * An example using the SURF feature detector can be found at opencv_source_code/samples/cpp/generic_descriptor_match.cpp - * Another example using the SURF feature detector, extractor and matcher can be found at opencv_source_code/samples/cpp/matcher_simple.cpp - -SURF::SURF ----------- -The SURF extractor constructors. - -.. ocv:function:: SURF::SURF() - -.. ocv:function:: SURF::SURF( double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=true, bool upright=false ) - -.. ocv:pyfunction:: cv2.SURF([hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) -> - - :param hessianThreshold: Threshold for hessian keypoint detector used in SURF. - - :param nOctaves: Number of pyramid octaves the keypoint detector will use. - - :param nOctaveLayers: Number of octave layers within each octave. - - :param extended: Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors). - - :param upright: Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation). - - -SURF::operator() ----------------- -Detects keypoints and computes SURF descriptors for them. - -.. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector& keypoints) const -.. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) - -.. ocv:pyfunction:: cv2.SURF.detect(image[, mask]) -> keypoints -.. ocv:pyfunction:: cv2.SURF.compute(image, keypoints[, descriptors]) -> keypoints, descriptors -.. ocv:pyfunction:: cv2.SURF.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors - -.. ocv:pyfunction:: cv2.SURF.detectAndCompute(image[, mask]) -> keypoints, descriptors - -.. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params ) - - :param image: Input 8-bit grayscale image - - :param mask: Optional input mask that marks the regions where we should detect features. - - :param keypoints: The input/output vector of keypoints - - :param descriptors: The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them. - - :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors. - - :param storage: Memory storage for the output keypoints and descriptors in OpenCV 1.x API. - - :param params: SURF algorithm parameters in OpenCV 1.x API. - -The function is parallelized with the TBB library. - -If you are using the C version, make sure you call ``cv::initModule_xfeatures2d()`` from ``xfeatures2d/nonfree.hpp``. - - -cuda::SURF_CUDA ---------------- -.. ocv:class:: cuda::SURF_CUDA - -Class used for extracting Speeded Up Robust Features (SURF) from an image. :: - - class SURF_CUDA - { - public: - enum KeypointLayout - { - X_ROW = 0, - Y_ROW, - LAPLACIAN_ROW, - OCTAVE_ROW, - SIZE_ROW, - ANGLE_ROW, - HESSIAN_ROW, - ROWS_COUNT - }; - - //! the default constructor - SURF_CUDA(); - //! the full constructor taking all the necessary parameters - explicit SURF_CUDA(double _hessianThreshold, int _nOctaves=4, - int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f); - - //! returns the descriptor size in float's (64 or 128) - int descriptorSize() const; - - //! upload host keypoints to device memory - void uploadKeypoints(const vector& keypoints, - GpuMat& keypointsGPU); - //! download keypoints from device to host memory - void downloadKeypoints(const GpuMat& keypointsGPU, - vector& keypoints); - - //! download descriptors from device to host memory - void downloadDescriptors(const GpuMat& descriptorsGPU, - vector& descriptors); - - void operator()(const GpuMat& img, const GpuMat& mask, - GpuMat& keypoints); - - void operator()(const GpuMat& img, const GpuMat& mask, - GpuMat& keypoints, GpuMat& descriptors, - bool useProvidedKeypoints = false, - bool calcOrientation = true); - - void operator()(const GpuMat& img, const GpuMat& mask, - std::vector& keypoints); - - void operator()(const GpuMat& img, const GpuMat& mask, - std::vector& keypoints, GpuMat& descriptors, - bool useProvidedKeypoints = false, - bool calcOrientation = true); - - void operator()(const GpuMat& img, const GpuMat& mask, - std::vector& keypoints, - std::vector& descriptors, - bool useProvidedKeypoints = false, - bool calcOrientation = true); - - void releaseMemory(); - - // SURF parameters - double hessianThreshold; - int nOctaves; - int nOctaveLayers; - bool extended; - bool upright; - - //! max keypoints = keypointsRatio * img.size().area() - float keypointsRatio; - - GpuMat sum, mask1, maskSum, intBuffer; - - GpuMat det, trace; - - GpuMat maxPosBuffer; - }; - - -The class ``SURF_CUDA`` implements Speeded Up Robust Features descriptor. There is a fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option). But the descriptors can also be computed for the user-specified keypoints. Only 8-bit grayscale images are supported. - -The class ``SURF_CUDA`` can store results in the GPU and CPU memory. It provides functions to convert results between CPU and GPU version ( ``uploadKeypoints``, ``downloadKeypoints``, ``downloadDescriptors`` ). The format of CPU results is the same as ``SURF`` results. GPU results are stored in ``GpuMat``. The ``keypoints`` matrix is :math:`\texttt{nFeatures} \times 7` matrix with the ``CV_32FC1`` type. - -* ``keypoints.ptr(X_ROW)[i]`` contains x coordinate of the i-th feature. -* ``keypoints.ptr(Y_ROW)[i]`` contains y coordinate of the i-th feature. -* ``keypoints.ptr(LAPLACIAN_ROW)[i]`` contains the laplacian sign of the i-th feature. -* ``keypoints.ptr(OCTAVE_ROW)[i]`` contains the octave of the i-th feature. -* ``keypoints.ptr(SIZE_ROW)[i]`` contains the size of the i-th feature. -* ``keypoints.ptr(ANGLE_ROW)[i]`` contain orientation of the i-th feature. -* ``keypoints.ptr(HESSIAN_ROW)[i]`` contains the response of the i-th feature. - -The ``descriptors`` matrix is :math:`\texttt{nFeatures} \times \texttt{descriptorSize}` matrix with the ``CV_32FC1`` type. - -The class ``SURF_CUDA`` uses some buffers and provides access to it. All buffers can be safely released between function calls. - -.. seealso:: :ocv:class:`SURF` - -.. note:: - - * An example for using the SURF keypoint matcher on GPU can be found at opencv_source_code/samples/gpu/surf_keypoint_matcher.cpp diff --git a/modules/xfeatures2d/doc/xfeatures2d.rst b/modules/xfeatures2d/doc/xfeatures2d.rst deleted file mode 100644 index a690f0922..000000000 --- a/modules/xfeatures2d/doc/xfeatures2d.rst +++ /dev/null @@ -1,11 +0,0 @@ -***************************************** -xfeatures2d. Extra 2D Features Framework -***************************************** - -.. highlight:: cpp - -.. toctree:: - :maxdepth: 2 - - extra_features - nonfree_features diff --git a/modules/ximgproc/doc/edge_aware_filters.rst b/modules/ximgproc/doc/edge_aware_filters.rst deleted file mode 100644 index 9f822f908..000000000 --- a/modules/ximgproc/doc/edge_aware_filters.rst +++ /dev/null @@ -1,259 +0,0 @@ -.. highlight:: cpp - -Domain Transform filter -==================================== - -This section describes interface for Domain Transform filter. -For more details about this filter see [Gastal11]_ and References_. - -DTFilter ------------------------------------- -.. ocv:class:: DTFilter : public Algorithm - -Interface for realizations of Domain Transform filter. - -createDTFilter ------------------------------------- -Factory method, create instance of :ocv:class:`DTFilter` and produce initialization routines. - -.. ocv:function:: Ptr createDTFilter(InputArray guide, double sigmaSpatial, double sigmaColor, int mode = DTF_NC, int numIters = 3) - -.. ocv:pyfunction:: cv2.createDTFilter(guide, sigmaSpatial, sigmaColor[, mode[, numIters]]) -> instance - - :param guide: guided image (used to build transformed distance, which describes edge structure of guided image). - :param sigmaSpatial: :math:`{\sigma}_H` parameter in the original article, it's similar to the sigma in the coordinate space into :ocv:func:`bilateralFilter`. - :param sigmaColor: :math:`{\sigma}_r` parameter in the original article, it's similar to the sigma in the color space into :ocv:func:`bilateralFilter`. - :param mode: one form three modes ``DTF_NC``, ``DTF_RF`` and ``DTF_IC`` which corresponds to three modes for filtering 2D signals in the article. - :param numIters: optional number of iterations used for filtering, 3 is quite enough. - -For more details about Domain Transform filter parameters, see the original article [Gastal11]_ and `Domain Transform filter homepage `_. - -DTFilter::filter ------------------------------------- -Produce domain transform filtering operation on source image. - -.. ocv:function:: void DTFilter::filter(InputArray src, OutputArray dst, int dDepth = -1) - -.. ocv:pyfunction:: cv2.DTFilter.filter(src, dst[, dDepth]) -> None - - :param src: filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. - :param dst: destination image. - :param dDepth: optional depth of the output image. ``dDepth`` can be set to -1, which will be equivalent to ``src.depth()``. - -dtFilter ------------------------------------- -Simple one-line Domain Transform filter call. -If you have multiple images to filter with the same guided image then use :ocv:class:`DTFilter` interface to avoid extra computations on initialization stage. - -.. ocv:function:: void dtFilter(InputArray guide, InputArray src, OutputArray dst, double sigmaSpatial, double sigmaColor, int mode = DTF_NC, int numIters = 3) - -.. ocv:pyfunction:: cv2.dtFilter(guide, src, sigmaSpatial, sigmaColor[, mode[, numIters]]) -> None - - :param guide: guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. - :param src: filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. - :param sigmaSpatial: :math:`{\sigma}_H` parameter in the original article, it's similar to the sigma in the coordinate space into :ocv:func:`bilateralFilter`. - :param sigmaColor: :math:`{\sigma}_r` parameter in the original article, it's similar to the sigma in the color space into :ocv:func:`bilateralFilter`. - :param mode: one form three modes ``DTF_NC``, ``DTF_RF`` and ``DTF_IC`` which corresponds to three modes for filtering 2D signals in the article. - :param numIters: optional number of iterations used for filtering, 3 is quite enough. - -.. seealso:: :ocv:func:`bilateralFilter`, :ocv:func:`guidedFilter`, :ocv:func:`amFilter` - -Guided Filter -==================================== - -This section describes interface for Guided Filter. -For more details about this filter see [Kaiming10]_ and References_. - -GuidedFilter ------------------------------------- -.. ocv:class:: GuidedFilter : public Algorithm - -Interface for realizations of Guided Filter. - -createGuidedFilter ------------------------------------- -Factory method, create instance of :ocv:class:`GuidedFilter` and produce initialization routines. - -.. ocv:function:: Ptr createGuidedFilter(InputArray guide, int radius, double eps) - -.. ocv:pyfunction:: cv2.createGuidedFilter(guide, radius, eps) -> instance - - :param guide: guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used. - :param radius: radius of Guided Filter. - :param eps: regularization term of Guided Filter. :math:`{eps}^2` is similar to the sigma in the color space into :ocv:func:`bilateralFilter`. - -For more details about Guided Filter parameters, see the original article [Kaiming10]_. - -GuidedFilter::filter ------------------------------------- -Apply Guided Filter to the filtering image. - -.. ocv:function:: void GuidedFilter::filter(InputArray src, OutputArray dst, int dDepth = -1) - -.. ocv:pyfunction:: cv2.GuidedFilter.filter(src, dst[, dDepth]) -> None - - :param src: filtering image with any numbers of channels. - :param dst: output image. - :param dDepth: optional depth of the output image. ``dDepth`` can be set to -1, which will be equivalent to ``src.depth()``. - -guidedFilter ------------------------------------- -Simple one-line Guided Filter call. -If you have multiple images to filter with the same guided image then use :ocv:class:`GuidedFilter` interface to avoid extra computations on initialization stage. - -.. ocv:function:: void guidedFilter(InputArray guide, InputArray src, OutputArray dst, int radius, double eps, int dDepth = -1) - -.. ocv:pyfunction:: cv2.guidedFilter(guide, src, dst, radius, eps, [, dDepth]) -> None - - :param guide: guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used. - :param src: filtering image with any numbers of channels. - :param dst: output image. - :param radius: radius of Guided Filter. - :param eps: regularization term of Guided Filter. :math:`{eps}^2` is similar to the sigma in the color space into :ocv:func:`bilateralFilter`. - :param dDepth: optional depth of the output image. - -.. seealso:: :ocv:func:`bilateralFilter`, :ocv:func:`dtFilter`, :ocv:func:`amFilter` - -Adaptive Manifold Filter -==================================== - -This section describes interface for Adaptive Manifold Filter. - -For more details about this filter see [Gastal12]_ and References_. - -AdaptiveManifoldFilter ------------------------------------- -.. ocv:class:: AdaptiveManifoldFilter : public Algorithm - - Interface for Adaptive Manifold Filter realizations. - - Below listed optional parameters which may be set up with :ocv:func:`Algorithm::set` function. - - .. ocv:member:: double sigma_s = 16.0 - - Spatial standard deviation. - - .. ocv:member:: double sigma_r = 0.2 - - Color space standard deviation. - - .. ocv:member:: int tree_height = -1 - - Height of the manifold tree (default = -1 : automatically computed). - - .. ocv:member:: int num_pca_iterations = 1 - - Number of iterations to computed the eigenvector. - - .. ocv:member:: bool adjust_outliers = false - - Specify adjust outliers using Eq. 9 or not. - - .. ocv:member:: bool use_RNG = true - - Specify use random number generator to compute eigenvector or not. - -createAMFilter ------------------------------------- -Factory method, create instance of :ocv:class:`AdaptiveManifoldFilter` and produce some initialization routines. - -.. ocv:function:: Ptr createAMFilter(double sigma_s, double sigma_r, bool adjust_outliers = false) - -.. ocv:pyfunction:: cv2.createAMFilter(sigma_s, sigma_r, adjust_outliers) -> instance - - :param sigma_s: spatial standard deviation. - :param sigma_r: color space standard deviation, it is similar to the sigma in the color space into :ocv:func:`bilateralFilter`. - :param adjust_outliers: optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper. - -For more details about Adaptive Manifold Filter parameters, see the original article [Gastal12]_. - -.. note:: - Joint images with `CV_8U` and `CV_16U` depth converted to images with `CV_32F` depth and [0; 1] color range before processing. - Hence color space sigma `sigma_r` must be in [0; 1] range, unlike same sigmas in :ocv:func:`bilateralFilter` and :ocv:func:`dtFilter` functions. - -AdaptiveManifoldFilter::filter ------------------------------------- -Apply high-dimensional filtering using adaptive manifolds. - -.. ocv:function:: void AdaptiveManifoldFilter::filter(InputArray src, OutputArray dst, InputArray joint = noArray()) - -.. ocv:pyfunction:: cv2.AdaptiveManifoldFilter.filter(src, dst[, joint]) -> None - - :param src: filtering image with any numbers of channels. - :param dst: output image. - :param joint: optional joint (also called as guided) image with any numbers of channels. - -amFilter ------------------------------------- -Simple one-line Adaptive Manifold Filter call. - -.. ocv:function:: void amFilter(InputArray joint, InputArray src, OutputArray dst, double sigma_s, double sigma_r, bool adjust_outliers = false) - -.. ocv:pyfunction:: cv2.amFilter(joint, src, dst, sigma_s, sigma_r, [, adjust_outliers]) -> None - - :param joint: joint (also called as guided) image or array of images with any numbers of channels. - :param src: filtering image with any numbers of channels. - :param dst: output image. - :param sigma_s: spatial standard deviation. - :param sigma_r: color space standard deviation, it is similar to the sigma in the color space into :ocv:func:`bilateralFilter`. - :param adjust_outliers: optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper. - -.. note:: - Joint images with `CV_8U` and `CV_16U` depth converted to images with `CV_32F` depth and [0; 1] color range before processing. - Hence color space sigma `sigma_r` must be in [0; 1] range, unlike same sigmas in :ocv:func:`bilateralFilter` and :ocv:func:`dtFilter` functions. - -.. seealso:: :ocv:func:`bilateralFilter`, :ocv:func:`dtFilter`, :ocv:func:`guidedFilter` - -Joint Bilateral Filter -==================================== - -jointBilateralFilter ------------------------------------- -Applies the joint bilateral filter to an image. - -.. ocv:function:: void jointBilateralFilter(InputArray joint, InputArray src, OutputArray dst, int d, double sigmaColor, double sigmaSpace, int borderType = BORDER_DEFAULT) - -.. ocv:pyfunction:: cv2.jointBilateralFilter(joint, src, dst, d, sigmaColor, sigmaSpace, [, borderType]) -> None - - :param joint: Joint 8-bit or floating-point, 1-channel or 3-channel image. - - :param src: Source 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint image. - - :param dst: Destination image of the same size and type as ``src`` . - - :param d: Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from ``sigmaSpace`` . - - :param sigmaColor: Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see ``sigmaSpace`` ) will be mixed together, resulting in larger areas of semi-equal color. - - :param sigmaSpace: Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see ``sigmaColor`` ). When ``d>0`` , it specifies the neighborhood size regardless of ``sigmaSpace`` . Otherwise, ``d`` is proportional to ``sigmaSpace`` . - -.. note:: :ocv:func:`bilateralFilter` and :ocv:func:`jointBilateralFilter` use L1 norm to compute difference between colors. - -.. seealso:: :ocv:func:`bilateralFilter`, :ocv:func:`amFilter` - -References -========== - -.. [Gastal11] E. Gastal and M. Oliveira, "Domain Transform for Edge-Aware Image and Video Processing", Proceedings of SIGGRAPH, 2011, vol. 30, pp. 69:1 - 69:12. - - The paper is available `online `__. - - -.. [Gastal12] E. Gastal and M. Oliveira, "Adaptive manifolds for real-time high-dimensional filtering," Proceedings of SIGGRAPH, 2012, vol. 31, pp. 33:1 - 33:13. - - The paper is available `online `__. - - -.. [Kaiming10] Kaiming He et. al., "Guided Image Filtering," ECCV 2010, pp. 1 - 14. - - The paper is available `online `__. - - -.. [Tomasi98] Carlo Tomasi and Roberto Manduchi, “Bilateral filtering for gray and color images,” in Computer Vision, 1998. Sixth International Conference on . IEEE, 1998, pp. 839– 846. - - The paper is available `online `__. - - -.. [Ziyang13] Ziyang Ma et al., "Constant Time Weighted Median Filtering for Stereo Matching and Beyond," ICCV, 2013, pp. 49 - 56. - - The paper is available `online `__. diff --git a/modules/ximgproc/doc/structured_edge_detection.rst b/modules/ximgproc/doc/structured_edge_detection.rst deleted file mode 100644 index 912d3c6ce..000000000 --- a/modules/ximgproc/doc/structured_edge_detection.rst +++ /dev/null @@ -1,67 +0,0 @@ -Structured forests for fast edge detection -****************************************** - -.. highlight:: cpp - -This module contains implementations of modern structured edge detection algorithms, -i.e. algorithms which somehow takes into account pixel affinities in natural images. - -StructuredEdgeDetection ------------------------ - -.. ocv:class:: StructuredEdgeDetection : public Algorithm - -Class implementing edge detection algorithm from [Dollar2013]_ :: - - /*! \class StructuredEdgeDetection - Prediction part of [P. Dollar and C. L. Zitnick. Structured Forests for Fast Edge Detection, 2013]. - */ - class CV_EXPORTS_W StructuredEdgeDetection : public Algorithm - { - public: - - /*! - * The function detects edges in src and draw them to dst - * - * \param src : source image (RGB, float, in [0;1]) to detect edges - * \param dst : destination image (grayscale, float, in [0;1]) - * where edges are drawn - */ - CV_WRAP virtual void detectEdges(const Mat src, Mat dst) = 0; - }; - - /*! - * The only available constructor loading data from model file - * - * \param model : name of the file where the model is stored - */ - CV_EXPORTS_W Ptr createStructuredEdgeDetection(const String &model); - -StructuredEdgeDetection::detectEdges -++++++++++++++++++++++++++++++++++++ -.. ocv:function:: void detectEdges(const Mat src, Mat dst) - - The function detects edges in src and draw them to dst. The algorithm underlies this function - is much more robust to texture presence, than common approaches, e.g. Sobel - - :param src: source image (RGB, float, in [0;1]) to detect edges - :param dst: destination image (grayscale, float, in [0;1]) - where edges are drawn - -.. seealso:: - - :ocv:func:`Sobel`, - :ocv:func:`Canny` - -createStructuredEdgeDetection -+++++++++++++++++++++++++++++ -.. ocv:function:: Ptr createStructuredEdgeDetection(String model) - - The only available constructor - - :param model: model file name - - -.. [Dollar2013] P. Dollár, C. L. Zitnick, "Structured forests for fast edge detection", - IEEE International Conference on Computer Vision (ICCV), 2013, - pp. 1841-1848. `DOI `_ diff --git a/modules/ximgproc/doc/superpixels.rst b/modules/ximgproc/doc/superpixels.rst deleted file mode 100644 index 682cc745f..000000000 --- a/modules/ximgproc/doc/superpixels.rst +++ /dev/null @@ -1,118 +0,0 @@ -Superpixels -=========== - -SuperpixelSEEDS ---------------- -.. ocv:class:: SuperpixelSEEDS : public Algorithm - -Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm described in [VBRV14]_. The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy function that is based on color histograms and a boundary term, which is optional. The energy function encourages superpixels to be of the same color, and if the boundary term is activated, the superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the solution. The algorithm runs in real-time using a single CPU. - -.. [VBRV14] Michael Van den Bergh, Xavier Boix, Gemma Roig, Luc Van Gool: SEEDS: Superpixels Extracted via Energy-Driven Sampling. International Journal of Computer Vision (IJCV), 2014. - -.. highlight:: cpp - - -SuperpixelSEEDS::createSuperpixelSEEDS() ----------------------------------------- -Initializes a SuperpixelSEEDS object. - -.. ocv:function:: SuperpixelSEEDS::createSuperpixelSEEDS(int image_width, int image_height, int image_channels, int num_superpixels, int num_levels, int use_prior = 2, int histogram_bins=5, bool double_step = false) - -.. ocv:pyfunction:: cv2.SuperpixelSEEDS.createSuperpixelSEEDS(image_width, image_height, image_channels, num_superpixels, num_levels, use_prior = 2, histogram_bins=5, double_step = false) -> - - - :param image_width: Image width. - - :param image_height: Image height. - - :param image_channels: Number of channels of the image. - - :param num_superpixels: Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number. - - :param num_levels: Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. - - :param prior: enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5]. - - :param histogram_bins: Number of histogram bins. - - :param double_step: If true, iterate each block level twice for higher accuracy. - -The function initializes a SuperpixelSEEDS object for the input ``image``. It stores the parameters of the image: ``image_width``, ``image_height`` and ``image_channels``. It also sets the parameters of the SEEDS superpixel algorithm, which are: ``num_superpixels``, ``num_levels``, ``use_prior``, ``histogram_bins`` and ``double_step``. - -The number of levels in ``num_levels`` defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure. - - -.. image:: pics/superpixels_blocks.png - - -SuperpixelSEEDS::iterate() --------------------------- -Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelSEEDS object. This function can be called again for other images without the need of initializing the algorithm with createSuperpixelSEEDS(). This save the computational cost of allocating memory for all the structures of the algorithm. - -.. ocv:function:: void SuperpixelSEEDS::iterate(InputArray img, int num_iterations=4) - -.. ocv:pyfunction:: cv2.SuperpixelSEEDS.iterate(image, num_iterations) - - - :param img: Input image. Supported formats: CV_8U, CV_16U, CV_32F. Image size & number of channels must match with the initialized image size & channels with the function createSuperpixelSEEDS(). It should be in HSV or Lab color space. Lab is a bit better, but also slower. - - :param num_iterations: Number of pixel level iterations. Higher number improves the result. - -The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries from large to smaller size, finalizing with proposing pixel updates. An illustrative example can be seen below. - -.. image:: pics/superpixels_blocks2.png - -SuperpixelSEEDS::getNumberOfSuperpixels() ------------------------------------------ -Calculates the superpixel segmentation on a given image stored in SuperpixelSEEDS object. - -.. ocv:function:: void SuperpixelSEEDS::getNumberOfSuperpixels(InputArray img, int num_iterations=4) - -.. ocv:pyfunction:: cv2.SuperpixelSEEDS.getNumberOfSuperpixels(img, num_iterations=4) - - - :param img: Input image. Supported formats: CV_8U, CV_16U, CV_32F image size & number of channels must match with the initialized image size & channels with the function createSuperpixelSEEDS(). - - :param num_iterations: Number of pixel level iterations. Higher number improves the result. - -The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelSEEDS(). - - -SuperpixelSEEDS::getLabels() ----------------------------- -Returns the segmentation labeling of the image. Each label represents a superpixel, and each pixel is assigned to one superpixel label. - -.. ocv:function:: void SuperpixelSEEDS::getLabels(OutputArray labels_out) - -.. ocv:pyfunction:: cv2.SuperpixelSEEDS.getLabels(labels_out) - - - :param labels_out: Return: A CV_32UC1 integer array containing the labels of the superpixel segmentation. The labels are in the range [0, getNumberOfSuperpixels()]. - -The function returns an image with ssthe labels of the superpixel segmentation. The labels are in the range [0, getNumberOfSuperpixels()]. - - -SuperpixelSEEDS::getLabelContourMask() --------------------------------------- -Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object. - -.. ocv:function:: void SuperpixelSEEDS::getLabelContourMask(OutputArray image, bool thick_line = false) - -.. ocv:pyfunction:: cv2.SuperpixelSEEDS.getLabelContourMask(image, thick_line = false) - - :param image: Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise. - - :param thick_line: If false, the border is only one pixel wide, otherwise all pixels at the border are masked. - -The function return the boundaries of the superpixel segmentation. - - -.. note:: - - * (Python) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/samples/python2/seeds.py - - * (cpp) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command line argument, the static image will be used instead of the webcam. - - * It will show a window with the video from the webcam with the superpixel boundaries marked in red (see below). Use Space to switch between different output modes. At the top of the window there are 4 sliders, from which the user can change on-the-fly the number of superpixels, the number of block levels, the strength of the boundary prior term to modify the shape, and the number of iterations at pixel level. This is useful to play with the parameters and set them to the user convenience. In the console the frame-rate of the algorithm is indicated. - -.. image:: pics/superpixels_demo.png diff --git a/modules/ximgproc/doc/ximgproc.rst b/modules/ximgproc/doc/ximgproc.rst deleted file mode 100644 index 88abbc5ee..000000000 --- a/modules/ximgproc/doc/ximgproc.rst +++ /dev/null @@ -1,12 +0,0 @@ -******************************************** -ximgproc. Extended Image Processing -******************************************** - -.. highlight:: cpp - -.. toctree:: - :maxdepth: 2 - - structured_edge_detection - edge_aware_filters - superpixels diff --git a/modules/xobjdetect/doc/integral_channel_features.rst b/modules/xobjdetect/doc/integral_channel_features.rst deleted file mode 100644 index f7db13169..000000000 --- a/modules/xobjdetect/doc/integral_channel_features.rst +++ /dev/null @@ -1,236 +0,0 @@ -.. highlight:: cpp - -Integral Channel Features Detector -================================== - -This section describes classes for object detection using WaldBoost and Integral -Channel Features from [Šochman05]_ and [Dollár09]_. - -computeChannels ---------------- - -Compute channels for integral channel features evaluation - -.. ocv:function:: void computeChannels(InputArray image, vector& channels) - - :param image: image for which channels should be computed - :param channels: output array for computed channels - -FeatureEvaluator ----------------- - -Feature evaluation interface - -.. ocv:class:: FeatureEvaluator : public Algorithm - -FeatureEvaluator::setChannels ------------------------------ - -Set channels for feature evaluation - -.. ocv:function:: void FeatureEvaluator::setChannels(InputArrayOfArrays channels) - - :param channels: array of channels to be set - -FeatureEvaluator::setPosition ------------------------------ - -Set window position to sample features with shift. By default position is (0, 0). - -.. ocv:function:: void FeatureEvaluator::setPosition(Size position) - - :param position: position to be set - -FeatureEvaluator::evaluate --------------------------- - -Evaluate feature value with given index for current channels and window position. - -.. ocv:function:: int FeatureEvaluator::evaluate(size_t feature_ind) const - - :param feature_ind: index of feature to be evaluated - -FeatureEvaluator::evaluateAll ------------------------------ - -Evaluate all features for current channels and window position. - -.. ocv:function:: void FeatureEvaluator::evaluateAll(OutputArray feature_values) - - :param feature_values: matrix-column of evaluated feature values - - -generateFeatures ----------------- - -Generate integral features. Returns vector of features. - -.. ocv:function:: vector > generateFeatures(Size window_size, const string& type, int count=INT_MAX, int channel_count=10) - - :param window_size: size of window in which features should be evaluated - :param type: feature type. Can be "icf" or "acf" - :param count: number of features to generate. - :param channel_count: number of feature channels - -createFeatureEvaluator ----------------------- - -Construct feature evaluator. - -.. ocv:function:: Ptr createFeatureEvaluator(const vector>& features, const string& type) - - :param features: features for evaluation - :param type: feature type. Can be "icf" or "acf" - - -WaldBoostParams ---------------- - -Parameters for WaldBoost. weak_count — number of weak learners, alpha — cascade -thresholding param. - -:: - - struct CV_EXPORTS WaldBoostParams - { - int weak_count; - float alpha; - - WaldBoostParams(): weak_count(100), alpha(0.02f) - {} - }; - -WaldBoost ---------- - -.. ocv:class:: WaldBoost : public Algorithm - -WaldBoost::train ----------------- - -Train WaldBoost cascade for given data. Returns feature indices chosen for -cascade. Feature enumeration starts from 0. - -.. ocv:function:: vector WaldBoost::train(const Mat& data, const Mat& labels) - - :param data: matrix of feature values, size M x N, one feature per row - :param labels: matrix of samples class labels, size 1 x N. Labels can be from {-1, +1} - -WaldBoost::predict ------------------- - -Predict objects class given object that can compute object features. Returns -unnormed confidence value — measure of confidence that object is from class +1. - -.. ocv:function:: float WaldBoost::predict(const Ptr& feature_evaluator) const - - :param feature_evaluator: object that can compute features by demand - -WaldBoost::write ----------------- - -Write WaldBoost to FileStorage - -.. ocv:function:: void WaldBoost::write(FileStorage& fs) - - :param fs: FileStorage for output - -WaldBoost::read ---------------- - -Write WaldBoost to FileNode - -.. ocv:function:: void WaldBoost::read(const FileNode& node) - - :param node: FileNode for reading - -createWaldBoost ---------------- - -Construct WaldBoost object. - -.. ocv:function:: Ptr createWaldBoost(const WaldBoostParams& params = WaldBoostParams()) - -ICFDetectorParams ------------------ - -Params for ICFDetector training. - -:: - - struct CV_EXPORTS ICFDetectorParams - { - int feature_count; - int weak_count; - int model_n_rows; - int model_n_cols; - int bg_per_image; - std::string features_type; - float alpha; - bool is_grayscale; - bool use_fast_log; - - ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100), - model_n_rows(56), model_n_cols(56), bg_per_image(5), - alpha(0.02), is_grayscale(false), use_fast_log(false) - {} - }; - -ICFDetector ------------ - -.. ocv:class:: ICFDetector - -ICFDetector::train ------------------- - -Train detector. - -.. ocv:function:: void ICFDetector::train(const std::vector& pos_filenames, const std::vector& bg_filenames, ICFDetectorParams params = ICFDetectorParams()) - - :param pos_path: path to folder with images of objects (wildcards like ``/my/path/*.png`` are allowed) - :param bg_path: path to folder with background images - :param params: parameters for detector training - -ICFDetector::detect -------------------- - -Detect objects on image. - -.. ocv:function:: void ICFDetector::detect(const Mat& image, vector& objects, float scaleFactor, Size minSize, Size maxSize, float threshold, int slidingStep, std::vector& values) - -.. ocv:function:: detect(const Mat& img, std::vector& objects, float minScaleFactor, float maxScaleFactor, float factorStep, float threshold, int slidingStep, std::vector& values) - - :param image: image for detection - :param objects: output array of bounding boxes - :param scaleFactor: scale between layers in detection pyramid - :param minSize: min size of objects in pixels - :param maxSize: max size of objects in pixels - :param minScaleFactor: min factor by which the image will be resized - :param maxScaleFactor: max factor by which the image will be resized - :param factorStep: scaling factor is incremented each pyramid layer according to this parameter - :param slidingStep: sliding window step - :param values: output vector with values of positive samples - -ICFDetector::write ------------------- - -Write detector to FileStorage. - -.. ocv:function:: void ICFDetector::write(FileStorage& fs) const - - :param fs: FileStorage for output - -ICFDetector::read ------------------ - -Write ICFDetector to FileNode - -.. ocv:function:: void ICFDetector::read(const FileNode& node) - - :param node: FileNode for reading - - -.. [Šochman05] J. Šochman and J. Matas. WaldBoost – Learning for Time Constrained Sequential Detection", CVPR, 2005. The paper is available `online `__. - -.. [Dollár09] P. Dollár, Z. Tu, P. Perona and S. Belongie. "Integral Channel Features", BMCV 2009. The paper is available `online `__. diff --git a/modules/xobjdetect/doc/xobjdetect.rst b/modules/xobjdetect/doc/xobjdetect.rst deleted file mode 100644 index 6b32b5a01..000000000 --- a/modules/xobjdetect/doc/xobjdetect.rst +++ /dev/null @@ -1,10 +0,0 @@ -*************************************** -xobjdetect. Extended object detection. -*************************************** - -.. highlight:: cpp - -.. toctree:: - :maxdepth: 2 - - integral_channel_features diff --git a/modules/xphoto/doc/denoising.rst b/modules/xphoto/doc/denoising.rst deleted file mode 100644 index 74e03c3b2..000000000 --- a/modules/xphoto/doc/denoising.rst +++ /dev/null @@ -1,20 +0,0 @@ -Image denoising techniques -************************** - -.. highlight:: cpp - -dctDenoising ------------- -.. ocv:function:: void dctDenoising(const Mat &src, Mat &dst, const float sigma) - - The function implements simple dct-based denoising, - link: http://www.ipol.im/pub/art/2011/ys-dct/. - - :param src: source image - :param dst: destination image - :param sigma: expected noise standard deviation - :param psize: size of block side where dct is computed - -.. seealso:: - - :ocv:func:`fastNlMeansDenoising` diff --git a/modules/xphoto/doc/inpainting.rst b/modules/xphoto/doc/inpainting.rst deleted file mode 100644 index 58f9577db..000000000 --- a/modules/xphoto/doc/inpainting.rst +++ /dev/null @@ -1,20 +0,0 @@ -Single image inpainting -*********************** - -.. highlight:: cpp - -Inpainting ----------- -.. ocv:function:: void inpaint(const Mat &src, const Mat &mask, Mat &dst, const int algorithmType) - - The function implements different single-image inpainting algorithms. - - :param src: source image, it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces. - :param mask: mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted - :param dst: destination image - :param algorithmType: expected noise standard deviation - * INPAINT_SHIFTMAP: This algorithm searches for dominant correspondences (transformations) of image patches and tries to seamlessly fill-in the area to be inpainted using this transformations. Look in the original paper [He2012]_ for details. - - .. [He2012] K. He, J. Sun., "Statistics of Patch Offsets for Image Completion", - IEEE European Conference on Computer Vision (ICCV), 2012, - pp. 16-29. `DOI `_ \ No newline at end of file diff --git a/modules/xphoto/doc/whitebalance.rst b/modules/xphoto/doc/whitebalance.rst deleted file mode 100644 index 81fa4e04e..000000000 --- a/modules/xphoto/doc/whitebalance.rst +++ /dev/null @@ -1,22 +0,0 @@ -Automatic white balance correction -********************************** - -.. highlight:: cpp - -balanceWhite ------------- - -.. ocv:function:: void balanceWhite(const Mat &src, Mat &dst, const int algorithmType, const float inputMin = 0.0f, const float inputMax = 255.0f, const float outputMin = 0.0f, const float outputMax = 255.0f) - - The function implements different algorithm of automatic white balance, i.e. it tries to map image's white color to perceptual white (this can be violated due to specific illumination or camera settings). - - :param algorithmType: type of the algorithm to use. Use WHITE_BALANCE_SIMPLE to perform smart histogram adjustments (ignoring 4% pixels with minimal and maximal values) for each channel. - :param inputMin: minimum value in the input image - :param inputMax: maximum value in the input image - :param outputMin: minimum value in the output image - :param outputMax: maximum value in the output image - -.. seealso:: - - :ocv:func:`cvtColor`, - :ocv:func:`equalizeHist` diff --git a/modules/xphoto/doc/xphoto.rst b/modules/xphoto/doc/xphoto.rst deleted file mode 100644 index 028362fd2..000000000 --- a/modules/xphoto/doc/xphoto.rst +++ /dev/null @@ -1,10 +0,0 @@ -*********************************************** -xphoto. Additional photo processing algorithms -*********************************************** - -.. toctree:: - :maxdepth: 2 - - Color balance - Denoising - Inpainting From 380e6111ec01ce512911addb5761767ebce0191c Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Tue, 17 Feb 2015 12:48:34 +0300 Subject: [PATCH 16/32] Doc files reorganized --- modules/face/doc/etc/at.txt | 0 modules/face/doc/src/facerec_demo.cpp | 0 modules/face/samples/etc/at.txt | 400 ++++++++++++++++++ .../face/{doc => samples}/src/CMakeLists.txt | 0 .../face/{doc => samples}/src/create_csv.py | 0 .../face/{doc => samples}/src/crop_face.py | 0 modules/face/samples/src/facerec_demo.cpp | 169 ++++++++ .../src/facerec_eigenfaces.cpp | 0 .../src/facerec_fisherfaces.cpp | 0 .../{doc => samples}/src/facerec_lbph.cpp | 0 .../src/facerec_save_load.cpp | 0 .../{doc => samples}/src/facerec_video.cpp | 0 modules/face/tutorials/face_tutorial.markdown | 14 +- .../facerec_video/facerec_video.png | Bin .../arnie_10_10_200_200.jpg | Bin .../arnie_20_20_200_200.jpg | Bin .../arnie_20_20_70_70.jpg | Bin .../arnie_30_30_200_200.jpg | Bin .../gender_classification/clooney_set.png | Bin .../gender_classification/fisherface_0.png | Bin .../fisherface_reconstruction_0.png | Bin .../gender_classification/mean.png | Bin .../img/at_database_small_sample_size.png | Bin .../img/eigenface_reconstruction_opencv.png | Bin .../img/eigenfaces_opencv.png | Bin .../img/fisherface_reconstruction_opencv.png | Bin .../img/fisherfaces_opencv.png | Bin .../face/{doc => tutorials}/img/lbp/lbp.png | Bin .../{doc => tutorials}/img/lbp/lbp_yale.jpg | Bin .../{doc => tutorials}/img/lbp/patterns.png | Bin .../pics => img}/gsoc_forg_matches.jpg | Bin .../pics => img}/outline.jpg | Bin .../pics => img}/snapshot27.jpg | Bin .../surface_matching/src/ppf_load_match.cpp | 146 ------- .../include/opencv2/surface_matching.hpp | 6 +- 35 files changed, 579 insertions(+), 156 deletions(-) mode change 100755 => 100644 modules/face/doc/etc/at.txt mode change 100755 => 100644 modules/face/doc/src/facerec_demo.cpp create mode 100755 modules/face/samples/etc/at.txt rename modules/face/{doc => samples}/src/CMakeLists.txt (100%) rename modules/face/{doc => samples}/src/create_csv.py (100%) rename modules/face/{doc => samples}/src/crop_face.py (100%) create mode 100755 modules/face/samples/src/facerec_demo.cpp rename modules/face/{doc => samples}/src/facerec_eigenfaces.cpp (100%) rename modules/face/{doc => samples}/src/facerec_fisherfaces.cpp (100%) rename modules/face/{doc => samples}/src/facerec_lbph.cpp (100%) rename modules/face/{doc => samples}/src/facerec_save_load.cpp (100%) rename modules/face/{doc => samples}/src/facerec_video.cpp (100%) rename modules/face/{doc/img/tutorial => tutorials}/facerec_video/facerec_video.png (100%) rename modules/face/{doc/img/tutorial => tutorials}/gender_classification/arnie_10_10_200_200.jpg (100%) rename modules/face/{doc/img/tutorial => tutorials}/gender_classification/arnie_20_20_200_200.jpg 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similarity index 100% rename from modules/face/doc/src/CMakeLists.txt rename to modules/face/samples/src/CMakeLists.txt diff --git a/modules/face/doc/src/create_csv.py b/modules/face/samples/src/create_csv.py similarity index 100% rename from modules/face/doc/src/create_csv.py rename to modules/face/samples/src/create_csv.py diff --git a/modules/face/doc/src/crop_face.py b/modules/face/samples/src/crop_face.py similarity index 100% rename from modules/face/doc/src/crop_face.py rename to modules/face/samples/src/crop_face.py diff --git a/modules/face/samples/src/facerec_demo.cpp b/modules/face/samples/src/facerec_demo.cpp new file mode 100755 index 000000000..d90f2b2a2 --- /dev/null +++ b/modules/face/samples/src/facerec_demo.cpp @@ -0,0 +1,169 @@ +/* + * Copyright (c) 2011. Philipp Wagner . + * Released to public domain under terms of the BSD Simplified license. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of the organization nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * See + */ + +#include "opencv2/core.hpp" +#include "opencv2/face.hpp" +#include "opencv2/highgui.hpp" + +#include +#include +#include + +using namespace cv; +using namespace cv::face; +using namespace std; + +static Mat norm_0_255(InputArray _src) { + Mat src = _src.getMat(); + // Create and return normalized image: + Mat dst; + switch(src.channels()) { + case 1: + cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1); + break; + case 3: + cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3); + break; + default: + src.copyTo(dst); + break; + } + return dst; +} + +static void read_csv(const string& filename, vector& images, vector& labels, char separator = ';') { + std::ifstream file(filename.c_str(), ifstream::in); + if (!file) { + string error_message = "No valid input file was given, please check the given filename."; + CV_Error(CV_StsBadArg, error_message); + } + string line, path, classlabel; + while (getline(file, line)) { + stringstream liness(line); + getline(liness, path, separator); + getline(liness, classlabel); + if(!path.empty() && !classlabel.empty()) { + images.push_back(imread(path, 0)); + labels.push_back(atoi(classlabel.c_str())); + } + } +} + +int main(int argc, const char *argv[]) { + // Check for valid command line arguments, print usage + // if no arguments were given. + if (argc != 2) { + cout << "usage: " << argv[0] << " " << endl; + exit(1); + } + // Get the path to your CSV. + string fn_csv = string(argv[1]); + // These vectors hold the images and corresponding labels. + vector images; + vector labels; + // Read in the data. This can fail if no valid + // input filename is given. + try { + read_csv(fn_csv, images, labels); + } catch (cv::Exception& e) { + cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl; + // nothing more we can do + exit(1); + } + // Quit if there are not enough images for this demo. + if(images.size() <= 1) { + string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!"; + CV_Error(CV_StsError, error_message); + } + // Get the height from the first image. We'll need this + // later in code to reshape the images to their original + // size: + int height = images[0].rows; + // The following lines simply get the last images from + // your dataset and remove it from the vector. This is + // done, so that the training data (which we learn the + // cv::FaceRecognizer on) and the test data we test + // the model with, do not overlap. + Mat testSample = images[images.size() - 1]; + int testLabel = labels[labels.size() - 1]; + images.pop_back(); + labels.pop_back(); + // The following lines create an Eigenfaces model for + // face recognition and train it with the images and + // labels read from the given CSV file. + // This here is a full PCA, if you just want to keep + // 10 principal components (read Eigenfaces), then call + // the factory method like this: + // + // cv::createEigenFaceRecognizer(10); + // + // If you want to create a FaceRecognizer with a + // confidennce threshold, call it with: + // + // cv::createEigenFaceRecognizer(10, 123.0); + // + Ptr model = createFisherFaceRecognizer(); + model->train(images, labels); + // The following line predicts the label of a given + // test image: + int predictedLabel = model->predict(testSample); + // + // To get the confidence of a prediction call the model with: + // + // int predictedLabel = -1; + // double confidence = 0.0; + // model->predict(testSample, predictedLabel, confidence); + // + string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel); + cout << result_message << endl; + // Sometimes you'll need to get/set internal model data, + // which isn't exposed by the public cv::FaceRecognizer. + // Since each cv::FaceRecognizer is derived from a + // cv::Algorithm, you can query the data. + // + // First we'll use it to set the threshold of the FaceRecognizer + // to 0.0 without retraining the model. This can be useful if + // you are evaluating the model: + // + model->set("threshold", 0.0); + // Now the threshold of this model is set to 0.0. A prediction + // now returns -1, as it's impossible to have a distance below + // it + predictedLabel = model->predict(testSample); + cout << "Predicted class = " << predictedLabel << endl; + // Here is how to get the eigenvalues of this Eigenfaces model: + Mat eigenvalues = model->getMat("eigenvalues"); + // And we can do the same to display the Eigenvectors (read Eigenfaces): + Mat W = model->getMat("eigenvectors"); + // From this we will display the (at most) first 10 Eigenfaces: + for (int i = 0; i < min(10, W.cols); i++) { + string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at(i)); + cout << msg << endl; + // get eigenvector #i + Mat ev = W.col(i).clone(); + // Reshape to original size & normalize to [0...255] for imshow. + Mat grayscale = norm_0_255(ev.reshape(1, height)); + // Show the image & apply a Jet colormap for better sensing. + Mat cgrayscale; + applyColorMap(grayscale, cgrayscale, COLORMAP_JET); + imshow(format("%d", i), cgrayscale); + } + waitKey(0); + + return 0; +} diff --git a/modules/face/doc/src/facerec_eigenfaces.cpp b/modules/face/samples/src/facerec_eigenfaces.cpp similarity index 100% rename from modules/face/doc/src/facerec_eigenfaces.cpp rename to modules/face/samples/src/facerec_eigenfaces.cpp diff --git a/modules/face/doc/src/facerec_fisherfaces.cpp b/modules/face/samples/src/facerec_fisherfaces.cpp similarity index 100% rename from modules/face/doc/src/facerec_fisherfaces.cpp rename to modules/face/samples/src/facerec_fisherfaces.cpp diff --git a/modules/face/doc/src/facerec_lbph.cpp b/modules/face/samples/src/facerec_lbph.cpp similarity index 100% rename from modules/face/doc/src/facerec_lbph.cpp rename to modules/face/samples/src/facerec_lbph.cpp diff --git a/modules/face/doc/src/facerec_save_load.cpp b/modules/face/samples/src/facerec_save_load.cpp similarity index 100% rename from modules/face/doc/src/facerec_save_load.cpp rename to modules/face/samples/src/facerec_save_load.cpp diff --git a/modules/face/doc/src/facerec_video.cpp b/modules/face/samples/src/facerec_video.cpp similarity index 100% rename from modules/face/doc/src/facerec_video.cpp rename to modules/face/samples/src/facerec_video.cpp diff --git a/modules/face/tutorials/face_tutorial.markdown b/modules/face/tutorials/face_tutorial.markdown index 0035bb450..ceb686477 100644 --- a/modules/face/tutorials/face_tutorial.markdown +++ b/modules/face/tutorials/face_tutorial.markdown @@ -658,7 +658,7 @@ at/s17/3.pgm;1 Here is the script, if you can't find it: -@verbinclude face/doc/src/create_csv.py +@verbinclude face/samples/src/create_csv.py ### Aligning Face Images {#tutorial_face_appendix_align} @@ -677,7 +677,7 @@ where: If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes. -@verbinclude face/doc/src/crop_face.py +@verbinclude face/samples/src/crop_face.py Imagine we are given [this photo of Arnold Schwarzenegger](http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg), which is @@ -689,11 +689,11 @@ Here are some examples: Configuration | Cropped, Scaled, Rotated Face --------------------------------|------------------------------------------------------------------ -0.1 (10%), 0.1 (10%), (200,200) | ![](tutorial/gender_classification/arnie_10_10_200_200.jpg) -0.2 (20%), 0.2 (20%), (200,200) | ![](tutorial/gender_classification/arnie_20_20_200_200.jpg) -0.3 (30%), 0.3 (30%), (200,200) | ![](tutorial/gender_classification/arnie_30_30_200_200.jpg) -0.2 (20%), 0.2 (20%), (70,70) | ![](tutorial/gender_classification/arnie_20_20_70_70.jpg) +0.1 (10%), 0.1 (10%), (200,200) | ![](tutorials/gender_classification/arnie_10_10_200_200.jpg) +0.2 (20%), 0.2 (20%), (200,200) | ![](tutorials/gender_classification/arnie_20_20_200_200.jpg) +0.3 (30%), 0.3 (30%), (200,200) | ![](tutorials/gender_classification/arnie_30_30_200_200.jpg) +0.2 (20%), 0.2 (20%), (70,70) | ![](tutorials/gender_classification/arnie_20_20_70_70.jpg) ### CSV for the AT&T Facedatabase {#tutorial_face_appendix_attcsv} -@verbinclude face/doc/etc/at.txt +@verbinclude face/samples/etc/at.txt diff --git a/modules/face/doc/img/tutorial/facerec_video/facerec_video.png b/modules/face/tutorials/facerec_video/facerec_video.png similarity index 100% rename from modules/face/doc/img/tutorial/facerec_video/facerec_video.png rename to modules/face/tutorials/facerec_video/facerec_video.png diff --git a/modules/face/doc/img/tutorial/gender_classification/arnie_10_10_200_200.jpg b/modules/face/tutorials/gender_classification/arnie_10_10_200_200.jpg similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/arnie_10_10_200_200.jpg rename to modules/face/tutorials/gender_classification/arnie_10_10_200_200.jpg diff --git a/modules/face/doc/img/tutorial/gender_classification/arnie_20_20_200_200.jpg b/modules/face/tutorials/gender_classification/arnie_20_20_200_200.jpg similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/arnie_20_20_200_200.jpg rename to modules/face/tutorials/gender_classification/arnie_20_20_200_200.jpg diff --git a/modules/face/doc/img/tutorial/gender_classification/arnie_20_20_70_70.jpg b/modules/face/tutorials/gender_classification/arnie_20_20_70_70.jpg similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/arnie_20_20_70_70.jpg rename to modules/face/tutorials/gender_classification/arnie_20_20_70_70.jpg diff --git a/modules/face/doc/img/tutorial/gender_classification/arnie_30_30_200_200.jpg b/modules/face/tutorials/gender_classification/arnie_30_30_200_200.jpg similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/arnie_30_30_200_200.jpg rename to modules/face/tutorials/gender_classification/arnie_30_30_200_200.jpg diff --git a/modules/face/doc/img/tutorial/gender_classification/clooney_set.png b/modules/face/tutorials/gender_classification/clooney_set.png similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/clooney_set.png rename to modules/face/tutorials/gender_classification/clooney_set.png diff --git a/modules/face/doc/img/tutorial/gender_classification/fisherface_0.png b/modules/face/tutorials/gender_classification/fisherface_0.png similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/fisherface_0.png rename to modules/face/tutorials/gender_classification/fisherface_0.png diff --git a/modules/face/doc/img/tutorial/gender_classification/fisherface_reconstruction_0.png b/modules/face/tutorials/gender_classification/fisherface_reconstruction_0.png similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/fisherface_reconstruction_0.png rename to modules/face/tutorials/gender_classification/fisherface_reconstruction_0.png diff --git a/modules/face/doc/img/tutorial/gender_classification/mean.png b/modules/face/tutorials/gender_classification/mean.png similarity index 100% rename from modules/face/doc/img/tutorial/gender_classification/mean.png rename to modules/face/tutorials/gender_classification/mean.png diff --git a/modules/face/doc/img/at_database_small_sample_size.png b/modules/face/tutorials/img/at_database_small_sample_size.png similarity index 100% rename from modules/face/doc/img/at_database_small_sample_size.png rename to modules/face/tutorials/img/at_database_small_sample_size.png diff --git a/modules/face/doc/img/eigenface_reconstruction_opencv.png b/modules/face/tutorials/img/eigenface_reconstruction_opencv.png similarity index 100% rename from modules/face/doc/img/eigenface_reconstruction_opencv.png rename to modules/face/tutorials/img/eigenface_reconstruction_opencv.png diff --git a/modules/face/doc/img/eigenfaces_opencv.png b/modules/face/tutorials/img/eigenfaces_opencv.png similarity index 100% rename from modules/face/doc/img/eigenfaces_opencv.png rename to modules/face/tutorials/img/eigenfaces_opencv.png diff --git a/modules/face/doc/img/fisherface_reconstruction_opencv.png b/modules/face/tutorials/img/fisherface_reconstruction_opencv.png similarity index 100% rename from modules/face/doc/img/fisherface_reconstruction_opencv.png rename to modules/face/tutorials/img/fisherface_reconstruction_opencv.png diff --git a/modules/face/doc/img/fisherfaces_opencv.png b/modules/face/tutorials/img/fisherfaces_opencv.png similarity index 100% rename from modules/face/doc/img/fisherfaces_opencv.png rename to modules/face/tutorials/img/fisherfaces_opencv.png diff --git a/modules/face/doc/img/lbp/lbp.png b/modules/face/tutorials/img/lbp/lbp.png similarity index 100% rename from modules/face/doc/img/lbp/lbp.png rename to modules/face/tutorials/img/lbp/lbp.png diff --git a/modules/face/doc/img/lbp/lbp_yale.jpg b/modules/face/tutorials/img/lbp/lbp_yale.jpg similarity index 100% rename from modules/face/doc/img/lbp/lbp_yale.jpg rename to modules/face/tutorials/img/lbp/lbp_yale.jpg diff --git a/modules/face/doc/img/lbp/patterns.png b/modules/face/tutorials/img/lbp/patterns.png similarity index 100% rename from modules/face/doc/img/lbp/patterns.png rename to modules/face/tutorials/img/lbp/patterns.png diff --git a/modules/surface_matching/doc/surface_matching/pics/gsoc_forg_matches.jpg b/modules/surface_matching/doc/img/gsoc_forg_matches.jpg similarity index 100% rename from modules/surface_matching/doc/surface_matching/pics/gsoc_forg_matches.jpg rename to modules/surface_matching/doc/img/gsoc_forg_matches.jpg diff --git a/modules/surface_matching/doc/surface_matching/pics/outline.jpg b/modules/surface_matching/doc/img/outline.jpg similarity index 100% rename from modules/surface_matching/doc/surface_matching/pics/outline.jpg rename to modules/surface_matching/doc/img/outline.jpg diff --git a/modules/surface_matching/doc/surface_matching/pics/snapshot27.jpg b/modules/surface_matching/doc/img/snapshot27.jpg similarity index 100% rename from modules/surface_matching/doc/surface_matching/pics/snapshot27.jpg rename to modules/surface_matching/doc/img/snapshot27.jpg diff --git a/modules/surface_matching/doc/surface_matching/src/ppf_load_match.cpp b/modules/surface_matching/doc/surface_matching/src/ppf_load_match.cpp deleted file mode 100644 index 303e8080b..000000000 --- a/modules/surface_matching/doc/surface_matching/src/ppf_load_match.cpp +++ /dev/null @@ -1,146 +0,0 @@ -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -// Author: Tolga Birdal - -#include "opencv2/surface_matching.hpp" -#include -#include "opencv2/surface_matching/ppf_helpers.hpp" -#include "opencv2/core/utility.hpp" - -using namespace std; -using namespace cv; -using namespace ppf_match_3d; - -static void help(const string& errorMessage) -{ - cout << "Program init error : "<< errorMessage << endl; - cout << "\nUsage : ppf_matching [input model file] [input scene file]"<< endl; - cout << "\nPlease start again with new parameters"<< endl; -} - -int main(int argc, char** argv) -{ - // welcome message - cout << "****************************************************" << endl; - cout << "* Surface Matching demonstration : demonstrates the use of surface matching" - " using point pair features." << endl; - cout << "* The sample loads a model and a scene, where the model lies in a different" - " pose than the training.\n* It then trains the model and searches for it in the" - " input scene. The detected poses are further refined by ICP\n* and printed to the " - " standard output." << endl; - cout << "****************************************************" << endl; - - if (argc < 3) - { - help("Not enough input arguments"); - exit(1); - } - -#if (defined __x86_64__ || defined _M_X64) - cout << "Running on 64 bits" << endl; -#else - cout << "Running on 32 bits" << endl; -#endif - -#ifdef _OPENMP - cout << "Running with OpenMP" << endl; -#else - cout << "Running without OpenMP and without TBB" << endl; -#endif - - string modelFileName = (string)argv[1]; - string sceneFileName = (string)argv[2]; - - Mat pc = loadPLYSimple(modelFileName.c_str(), 1); - - // Now train the model - cout << "Training..." << endl; - int64 tick1 = cv::getTickCount(); - ppf_match_3d::PPF3DDetector detector(0.025, 0.05); - detector.trainModel(pc); - int64 tick2 = cv::getTickCount(); - cout << endl << "Training complete in " - << (double)(tick2-tick1)/ cv::getTickFrequency() - << " sec" << endl << "Loading model..." << endl; - - // Read the scene - Mat pcTest = loadPLYSimple(sceneFileName.c_str(), 1); - - // Match the model to the scene and get the pose - cout << endl << "Starting matching..." << endl; - vector results; - tick1 = cv::getTickCount(); - detector.match(pcTest, results, 1.0/40.0, 0.05); - tick2 = cv::getTickCount(); - cout << endl << "PPF Elapsed Time " << - (tick2-tick1)/cv::getTickFrequency() << " sec" << endl; - - // Get only first N results - int N = 2; - vector resultsSub(results.begin(),results.begin()+N); - - // Create an instance of ICP - ICP icp(100, 0.005f, 2.5f, 8); - int64 t1 = cv::getTickCount(); - - // Register for all selected poses - cout << endl << "Performing ICP on " << N << " poses..." << endl; - icp.registerModelToScene(pc, pcTest, resultsSub); - int64 t2 = cv::getTickCount(); - - cout << endl << "ICP Elapsed Time " << - (t2-t1)/cv::getTickFrequency() << " sec" << endl; - - cout << "Poses: " << endl; - // debug first five poses - for (size_t i=0; iprintPose(); - if (i==0) - { - Mat pct = transformPCPose(pc, result->pose); - writePLY(pct, "para6700PCTrans.ply"); - } - } - - return 0; - -} diff --git a/modules/surface_matching/include/opencv2/surface_matching.hpp b/modules/surface_matching/include/opencv2/surface_matching.hpp index dd9c71c36..6cf5f9dae 100644 --- a/modules/surface_matching/include/opencv2/surface_matching.hpp +++ b/modules/surface_matching/include/opencv2/surface_matching.hpp @@ -111,7 +111,7 @@ point sampling, I will be leaving that aside now in order to respect the general methods (Typically for such algorithms training on a CAD model is not needed, and a point cloud would be sufficient). Below is the outline of the entire algorithm: -![Outline of the Algorithm](surface_matching/pics/outline.jpg) +![Outline of the Algorithm](img/outline.jpg) As explained, the algorithm relies on the extraction and indexing of point pair features, which are defined as follows: @@ -337,11 +337,11 @@ Results This section is dedicated to the results of surface matching (point-pair-feature matching and a following ICP refinement): -![Several matches of a single frog model using ppf + icp](surface_matching/pics/gsoc_forg_matches.jpg) +![Several matches of a single frog model using ppf + icp](img/gsoc_forg_matches.jpg) Matches of different models for Mian dataset is presented below: -![Matches of different models for Mian dataset](surface_matching/pics/snapshot27.jpg) +![Matches of different models for Mian dataset](img/snapshot27.jpg) You might checkout the video on [youTube here](http://www.youtube.com/watch?v=uFnqLFznuZU). From 04dfaaf9350c342e6c288bcdaab81dfee24eec6b Mon Sep 17 00:00:00 2001 From: berak Date: Fri, 20 Feb 2015 14:55:26 +0100 Subject: [PATCH 17/32] add a 'split' train method to datasets/lfw_benckmark --- modules/datasets/samples/fr_lfw_benchmark.cpp | 79 +++++++++++++------ 1 file changed, 57 insertions(+), 22 deletions(-) diff --git a/modules/datasets/samples/fr_lfw_benchmark.cpp b/modules/datasets/samples/fr_lfw_benchmark.cpp index 9f578b54a..7ec939e98 100644 --- a/modules/datasets/samples/fr_lfw_benchmark.cpp +++ b/modules/datasets/samples/fr_lfw_benchmark.cpp @@ -78,7 +78,9 @@ int main(int argc, const char *argv[]) { const char *keys = "{ help h usage ? | | show this message }" - "{ path p |true| path to dataset (lfw2 folder) }"; + "{ path p |true| path to dataset (lfw2 folder) }" + "{ train t |dev | train method: 'dev'(pairsDevTrain.txt) or 'split'(pairs.txt) }"; + CommandLineParser parser(argc, argv, keys); string path(parser.get("path")); if (parser.has("help") || path=="true") @@ -86,6 +88,7 @@ int main(int argc, const char *argv[]) parser.printMessage(); return -1; } + string trainMethod(parser.get("train")); // These vectors hold the images and corresponding labels. vector images; @@ -97,24 +100,12 @@ int main(int argc, const char *argv[]) unsigned int numSplits = dataset->getNumSplits(); printf("splits number: %u\n", numSplits); - printf("train size: %u\n", (unsigned int)dataset->getTrain().size()); + if (trainMethod == "dev") + printf("train size: %u\n", (unsigned int)dataset->getTrain().size()); + else + printf("train size: %u\n", (numSplits-1) * (unsigned int)dataset->getTest().size()); printf("test size: %u\n", (unsigned int)dataset->getTest().size()); - for (unsigned int i=0; igetTrain().size(); ++i) - { - FR_lfwObj *example = static_cast(dataset->getTrain()[i].get()); - - int currNum = getLabel(example->image1); - Mat img = imread(path+example->image1, IMREAD_GRAYSCALE); - images.push_back(img); - labels.push_back(currNum); - - currNum = getLabel(example->image2); - img = imread(path+example->image2, IMREAD_GRAYSCALE); - images.push_back(img); - labels.push_back(currNum); - } - // 2200 pairsDevTrain, first split: correct: 373, from: 600 -> 62.1667% Ptr model = createLBPHFaceRecognizer(); // 2200 pairsDevTrain, first split: correct: correct: 369, from: 600 -> 61.5% @@ -122,14 +113,58 @@ int main(int argc, const char *argv[]) // 2200 pairsDevTrain, first split: correct: 372, from: 600 -> 62% //Ptr model = createFisherFaceRecognizer(); - model->train(images, labels); - //string saveModelPath = "face-rec-model.txt"; - //cout << "Saving the trained model to " << saveModelPath << endl; - //model->save(saveModelPath); + if (trainMethod == "dev") // train on personsDevTrain.txt + { + for (unsigned int i=0; igetTrain().size(); ++i) + { + FR_lfwObj *example = static_cast(dataset->getTrain()[i].get()); + + int currNum = getLabel(example->image1); + Mat img = imread(path+example->image1, IMREAD_GRAYSCALE); + images.push_back(img); + labels.push_back(currNum); + + currNum = getLabel(example->image2); + img = imread(path+example->image2, IMREAD_GRAYSCALE); + images.push_back(img); + labels.push_back(currNum); + } + model->train(images, labels); + //string saveModelPath = "face-rec-model.txt"; + //cout << "Saving the trained model to " << saveModelPath << endl; + //model->save(saveModelPath); + } vector p; for (unsigned int j=0; j > &curr = dataset->getTest(j2); + for (unsigned int i=0; i(curr[i].get()); + + int currNum = getLabel(example->image1); + Mat img = imread(path+example->image1, IMREAD_GRAYSCALE); + images.push_back(img); + labels.push_back(currNum); + + currNum = getLabel(example->image2); + img = imread(path+example->image2, IMREAD_GRAYSCALE); + images.push_back(img); + labels.push_back(currNum); + } + } + model->train(images, labels); + } + unsigned int incorrect = 0, correct = 0; vector < Ptr > &curr = dataset->getTest(j); for (unsigned int i=0; i Date: Tue, 24 Feb 2015 18:38:52 +0300 Subject: [PATCH 18/32] Java and python wrappers for contrib modules --- modules/bgsegm/CMakeLists.txt | 2 +- modules/bioinspired/CMakeLists.txt | 2 +- modules/ccalib/CMakeLists.txt | 2 +- modules/cvv/CMakeLists.txt | 2 +- modules/datasets/CMakeLists.txt | 2 +- modules/face/CMakeLists.txt | 2 +- modules/latentsvm/CMakeLists.txt | 2 +- modules/line_descriptor/CMakeLists.txt | 4 +--- modules/optflow/CMakeLists.txt | 2 +- modules/reg/CMakeLists.txt | 2 +- modules/rgbd/CMakeLists.txt | 2 +- modules/saliency/CMakeLists.txt | 2 +- modules/surface_matching/CMakeLists.txt | 2 +- modules/text/CMakeLists.txt | 2 +- modules/xfeatures2d/CMakeLists.txt | 2 +- modules/ximgproc/CMakeLists.txt | 2 +- modules/xobjdetect/CMakeLists.txt | 2 +- modules/xphoto/CMakeLists.txt | 2 +- 18 files changed, 18 insertions(+), 20 deletions(-) diff --git a/modules/bgsegm/CMakeLists.txt b/modules/bgsegm/CMakeLists.txt index a497fb2c9..14152ba96 100644 --- a/modules/bgsegm/CMakeLists.txt +++ b/modules/bgsegm/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Background Segmentation Algorithms") -ocv_define_module(bgsegm opencv_core opencv_imgproc opencv_video opencv_highgui) +ocv_define_module(bgsegm opencv_core opencv_imgproc opencv_video opencv_highgui WRAP python) diff --git a/modules/bioinspired/CMakeLists.txt b/modules/bioinspired/CMakeLists.txt index c800d33ff..1d6233f33 100644 --- a/modules/bioinspired/CMakeLists.txt +++ b/modules/bioinspired/CMakeLists.txt @@ -1,3 +1,3 @@ set(the_description "Biologically inspired algorithms") ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef) -ocv_define_module(bioinspired opencv_core OPTIONAL opencv_highgui opencv_ocl) +ocv_define_module(bioinspired opencv_core OPTIONAL opencv_highgui opencv_ocl WRAP java) diff --git a/modules/ccalib/CMakeLists.txt b/modules/ccalib/CMakeLists.txt index add019d78..5700a1204 100644 --- a/modules/ccalib/CMakeLists.txt +++ b/modules/ccalib/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Custom Calibration Pattern") -ocv_define_module(ccalib opencv_core opencv_imgproc opencv_calib3d opencv_features2d) +ocv_define_module(ccalib opencv_core opencv_imgproc opencv_calib3d opencv_features2d WRAP python) diff --git a/modules/cvv/CMakeLists.txt b/modules/cvv/CMakeLists.txt index 2bcbb1b7a..6c3224192 100644 --- a/modules/cvv/CMakeLists.txt +++ b/modules/cvv/CMakeLists.txt @@ -17,4 +17,4 @@ foreach(dt5_dep Core Gui Widgets) endforeach() set(the_description "Debug visualization framework") -ocv_define_module(cvv opencv_core opencv_imgproc opencv_features2d ${CVV_LIBRARIES}) +ocv_define_module(cvv opencv_core opencv_imgproc opencv_features2d ${CVV_LIBRARIES} WRAP python) diff --git a/modules/datasets/CMakeLists.txt b/modules/datasets/CMakeLists.txt index 0102b7a92..47afccbb8 100644 --- a/modules/datasets/CMakeLists.txt +++ b/modules/datasets/CMakeLists.txt @@ -1,4 +1,4 @@ set(the_description "datasets framework") -ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann opencv_text) +ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann opencv_text WRAP python) ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267) # flann, Win64 diff --git a/modules/face/CMakeLists.txt b/modules/face/CMakeLists.txt index fe02ecb11..7dc833a25 100644 --- a/modules/face/CMakeLists.txt +++ b/modules/face/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Face recognition etc") -ocv_define_module(face opencv_core opencv_imgproc) +ocv_define_module(face opencv_core opencv_imgproc WRAP python) diff --git a/modules/latentsvm/CMakeLists.txt b/modules/latentsvm/CMakeLists.txt index 7c1ae441d..8653df9c0 100644 --- a/modules/latentsvm/CMakeLists.txt +++ b/modules/latentsvm/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Object Detection") -ocv_define_module(latentsvm opencv_core opencv_imgproc opencv_objdetect OPTIONAL opencv_highgui) +ocv_define_module(latentsvm opencv_core opencv_imgproc opencv_objdetect OPTIONAL opencv_highgui WRAP python) diff --git a/modules/line_descriptor/CMakeLists.txt b/modules/line_descriptor/CMakeLists.txt index 91a54ce3e..d0b0b4b46 100644 --- a/modules/line_descriptor/CMakeLists.txt +++ b/modules/line_descriptor/CMakeLists.txt @@ -1,4 +1,2 @@ set(the_description "Line descriptor") -ocv_define_module(line_descriptor opencv_features2d opencv_imgproc opencv_highgui) - - +ocv_define_module(line_descriptor opencv_features2d opencv_imgproc opencv_highgui WRAP python) diff --git a/modules/optflow/CMakeLists.txt b/modules/optflow/CMakeLists.txt index 5916b2b76..9887a3828 100644 --- a/modules/optflow/CMakeLists.txt +++ b/modules/optflow/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Optical Flow Algorithms") -ocv_define_module(optflow opencv_core opencv_imgproc opencv_video opencv_highgui) +ocv_define_module(optflow opencv_core opencv_imgproc opencv_video opencv_highgui WRAP python) diff --git a/modules/reg/CMakeLists.txt b/modules/reg/CMakeLists.txt index 8bb987706..cb928cf88 100644 --- a/modules/reg/CMakeLists.txt +++ b/modules/reg/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Image Registration") -ocv_define_module(reg opencv_imgproc opencv_core) +ocv_define_module(reg opencv_imgproc opencv_core WRAP python) diff --git a/modules/rgbd/CMakeLists.txt b/modules/rgbd/CMakeLists.txt index 9a347c185..f7c6b17e7 100644 --- a/modules/rgbd/CMakeLists.txt +++ b/modules/rgbd/CMakeLists.txt @@ -1,3 +1,3 @@ set(the_description "RGBD algorithms") ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef) -ocv_define_module(rgbd opencv_core opencv_calib3d opencv_highgui opencv_imgproc) +ocv_define_module(rgbd opencv_core opencv_calib3d opencv_highgui opencv_imgproc WRAP python) diff --git a/modules/saliency/CMakeLists.txt b/modules/saliency/CMakeLists.txt index e2eda6568..31f09f986 100644 --- a/modules/saliency/CMakeLists.txt +++ b/modules/saliency/CMakeLists.txt @@ -1,3 +1,3 @@ set(the_description "Saliency API") set(OPENCV_MODULE_IS_PART_OF_WORLD OFF) -ocv_define_module(saliency opencv_imgproc opencv_highgui opencv_features2d) +ocv_define_module(saliency opencv_imgproc opencv_highgui opencv_features2d WRAP python) diff --git a/modules/surface_matching/CMakeLists.txt b/modules/surface_matching/CMakeLists.txt index 9421b355c..8887bbcd7 100644 --- a/modules/surface_matching/CMakeLists.txt +++ b/modules/surface_matching/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "3D point features") -ocv_define_module(surface_matching opencv_core opencv_flann) +ocv_define_module(surface_matching opencv_core opencv_flann WRAP python) diff --git a/modules/text/CMakeLists.txt b/modules/text/CMakeLists.txt index 3324bac65..1e0a8e54d 100644 --- a/modules/text/CMakeLists.txt +++ b/modules/text/CMakeLists.txt @@ -18,7 +18,7 @@ include_directories(${Tesseract_INCLUDE_DIR}) endif() set(the_description "Text Detection and Recognition") -ocv_define_module(text opencv_ml opencv_highgui opencv_imgproc opencv_core opencv_features2d) +ocv_define_module(text opencv_ml opencv_highgui opencv_imgproc opencv_core opencv_features2d WRAP python) if(${Tesseract_FOUND}) target_link_libraries(opencv_text ${Tesseract_LIBS}) diff --git a/modules/xfeatures2d/CMakeLists.txt b/modules/xfeatures2d/CMakeLists.txt index 2dd2ce87f..2cac3e69b 100644 --- a/modules/xfeatures2d/CMakeLists.txt +++ b/modules/xfeatures2d/CMakeLists.txt @@ -1,3 +1,3 @@ set(the_description "Contributed/Experimental Algorithms for Salient 2D Features Detection") ocv_define_module(xfeatures2d opencv_core opencv_imgproc opencv_features2d opencv_calib3d opencv_shape opencv_highgui opencv_videoio opencv_ml - OPTIONAL opencv_cudaarithm) + OPTIONAL opencv_cudaarithm WRAP python) diff --git a/modules/ximgproc/CMakeLists.txt b/modules/ximgproc/CMakeLists.txt index 8aa4b6444..5fe74ef09 100644 --- a/modules/ximgproc/CMakeLists.txt +++ b/modules/ximgproc/CMakeLists.txt @@ -1,5 +1,5 @@ set(the_description "Extended image processing module. It includes edge-aware filters and etc.") set(OPENCV_MODULE_IS_PART_OF_WORLD OFF) -ocv_define_module(ximgproc opencv_imgproc opencv_core opencv_highgui) +ocv_define_module(ximgproc opencv_imgproc opencv_core opencv_highgui WRAP python) target_link_libraries(opencv_ximgproc) diff --git a/modules/xobjdetect/CMakeLists.txt b/modules/xobjdetect/CMakeLists.txt index 82488a70e..8d78c8a9f 100644 --- a/modules/xobjdetect/CMakeLists.txt +++ b/modules/xobjdetect/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Object detection algorithms") -ocv_define_module(xobjdetect opencv_core opencv_imgproc opencv_highgui) +ocv_define_module(xobjdetect opencv_core opencv_imgproc opencv_highgui WRAP python) diff --git a/modules/xphoto/CMakeLists.txt b/modules/xphoto/CMakeLists.txt index 31d04beb2..44a1e8a0c 100644 --- a/modules/xphoto/CMakeLists.txt +++ b/modules/xphoto/CMakeLists.txt @@ -1,2 +1,2 @@ set(the_description "Addon to basic photo module") -ocv_define_module(xphoto opencv_core opencv_imgproc OPTIONAL opencv_photo opencv_highgui opencv_photo) \ No newline at end of file +ocv_define_module(xphoto opencv_core opencv_imgproc OPTIONAL opencv_photo opencv_highgui opencv_photo WRAP python) From b07c6d71a4047cafc182652f48a664936acc8783 Mon Sep 17 00:00:00 2001 From: berak Date: Wed, 4 Mar 2015 10:12:15 +0100 Subject: [PATCH 19/32] fix missing delete[] calls in or_mnist.cpp --- modules/datasets/src/or_mnist.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modules/datasets/src/or_mnist.cpp b/modules/datasets/src/or_mnist.cpp index e85266c1e..1e206f306 100644 --- a/modules/datasets/src/or_mnist.cpp +++ b/modules/datasets/src/or_mnist.cpp @@ -84,6 +84,7 @@ void OR_mnistImp::loadDatasetPart(const string &imagesFile, const string &labels fclose(f); if (num*imageSize != res) { + delete[] images; return; } f = fopen(labelsFile.c_str(), "rb"); @@ -93,6 +94,8 @@ void OR_mnistImp::loadDatasetPart(const string &imagesFile, const string &labels fclose(f); if (num != res) { + delete[] images; + delete[] labels; return; } From afa9e9338959a1f41119fa86fb7deb01d9e983a1 Mon Sep 17 00:00:00 2001 From: Auron-X Date: Mon, 16 Mar 2015 16:39:27 +0900 Subject: [PATCH 20/32] Bug fix #1 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The bug appears on the initialization step, if the initial bounding box width was larger than height. The problem was in function calculating a binary code for ensemble classifiers. In this function “measurement” values were used in wrong order (h1,w1,h2,w2; instead of: w1,w2,h1,h2), generating a access violation error on “patch” data access by the reason that width>height and pointer to data is calculated linearly data.step*height+width --- modules/tracking/src/tld_utils.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/tracking/src/tld_utils.cpp b/modules/tracking/src/tld_utils.cpp index df9c9b709..c5c85bd32 100644 --- a/modules/tracking/src/tld_utils.cpp +++ b/modules/tracking/src/tld_utils.cpp @@ -359,8 +359,8 @@ int TLDEnsembleClassifier::code(const uchar* data, int rowstep) const for( int i = 0; i < (int)measurements.size(); i++ ) { position = position << 1; - if( *(data + rowstep * measurements[i].val[0] + measurements[i].val[1]) < - *(data + rowstep * measurements[i].val[2] + measurements[i].val[3]) ) + if( *(data + rowstep * measurements[i].val[2] + measurements[i].val[0]) < + *(data + rowstep * measurements[i].val[3] + measurements[i].val[1]) ) { position++; } From 05405e3dca8f5c168589bbdb8e1dab700d1f6b6d Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Wed, 18 Mar 2015 17:37:37 +0300 Subject: [PATCH 21/32] Replaced CV_PURE_PROPERTY macros with the code --- modules/face/include/opencv2/face/facerec.hpp | 49 +++++++++++++------ modules/rgbd/include/opencv2/rgbd.hpp | 30 ++++++------ .../include/opencv2/ximgproc/edge_filter.hpp | 44 ++++++++++++----- 3 files changed, 80 insertions(+), 43 deletions(-) diff --git a/modules/face/include/opencv2/face/facerec.hpp b/modules/face/include/opencv2/face/facerec.hpp index ddb933997..e5f9863dc 100644 --- a/modules/face/include/opencv2/face/facerec.hpp +++ b/modules/face/include/opencv2/face/facerec.hpp @@ -20,13 +20,19 @@ namespace cv { namespace face { class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer { public: - CV_PURE_PROPERTY(int, NumComponents) - CV_PURE_PROPERTY(double, Threshold) - CV_PURE_PROPERTY_RO(std::vector, Projections) - CV_PURE_PROPERTY_RO(cv::Mat, Labels) - CV_PURE_PROPERTY_RO(cv::Mat, EigenValues) - CV_PURE_PROPERTY_RO(cv::Mat, EigenVectors) - CV_PURE_PROPERTY_RO(cv::Mat, Mean) + /** @see setNumComponents */ + virtual int getNumComponents() const = 0; + /** @copybrief getNumComponents @see getNumComponents */ + virtual void setNumComponents(int val) = 0; + /** @see setThreshold */ + virtual double getThreshold() const = 0; + /** @copybrief getThreshold @see getThreshold */ + virtual void setThreshold(double val) = 0; + virtual std::vector getProjections() const = 0; + virtual cv::Mat getLabels() const = 0; + virtual cv::Mat getEigenValues() const = 0; + virtual cv::Mat getEigenVectors() const = 0; + virtual cv::Mat getMean() const = 0; }; /** @@ -95,13 +101,28 @@ CV_EXPORTS_W Ptr createFisherFaceRecognizer(int num_compone class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer { public: - CV_PURE_PROPERTY(int, GridX) - CV_PURE_PROPERTY(int, GridY) - CV_PURE_PROPERTY(int, Radius) - CV_PURE_PROPERTY(int, Neighbors) - CV_PURE_PROPERTY(double, Threshold) - CV_PURE_PROPERTY_RO(std::vector, Histograms) - CV_PURE_PROPERTY_RO(cv::Mat, Labels) + /** @see setGridX */ + virtual int getGridX() const = 0; + /** @copybrief getGridX @see getGridX */ + virtual void setGridX(int val) = 0; + /** @see setGridY */ + virtual int getGridY() const = 0; + /** @copybrief getGridY @see getGridY */ + virtual void setGridY(int val) = 0; + /** @see setRadius */ + virtual int getRadius() const = 0; + /** @copybrief getRadius @see getRadius */ + virtual void setRadius(int val) = 0; + /** @see setNeighbors */ + virtual int getNeighbors() const = 0; + /** @copybrief getNeighbors @see getNeighbors */ + virtual void setNeighbors(int val) = 0; + /** @see setThreshold */ + virtual double getThreshold() const = 0; + /** @copybrief getThreshold @see getThreshold */ + virtual void setThreshold(double val) = 0; + virtual std::vector getHistograms() const = 0; + virtual cv::Mat getLabels() const = 0; }; /** diff --git a/modules/rgbd/include/opencv2/rgbd.hpp b/modules/rgbd/include/opencv2/rgbd.hpp index 9684bb1c1..863228cb5 100644 --- a/modules/rgbd/include/opencv2/rgbd.hpp +++ b/modules/rgbd/include/opencv2/rgbd.hpp @@ -47,7 +47,7 @@ namespace cv { namespace rgbd -{ +{ //! @addtogroup rgbd //! @{ @@ -431,7 +431,7 @@ namespace rgbd /** Method to compute a transformation from the source frame to the destination one. * Some odometry algorithms do not used some data of frames (eg. ICP does not use images). * In such case corresponding arguments can be set as empty Mat. - * The method returns true if all internal computions were possible (e.g. there were enough correspondences, + * The method returns true if all internal computions were possible (e.g. there were enough correspondences, * system of equations has a solution, etc) and resulting transformation satisfies some test if it's provided * by the Odometry inheritor implementation (e.g. thresholds for maximum translation and rotation). * @param srcImage Image data of the source frame (CV_8UC1) @@ -466,16 +466,14 @@ namespace rgbd static Ptr create(const String & odometryType); - //TODO: which properties are common for all Odometry successors? - CV_PURE_PROPERTY_S(cv::Mat, CameraMatrix) -// CV_PURE_PROPERTY(double, MinDepth) -// CV_PURE_PROPERTY(double, MaxDepth) -// CV_PURE_PROPERTY(double, MaxDepthDiff) -// CV_PURE_PROPERTY_S(cv::Mat, IterationCounts) -// CV_PURE_PROPERTY(double, MaxPointsPart) - CV_PURE_PROPERTY(int, TransformType) -// CV_PURE_PROPERTY(double, MaxTranslation) -// CV_PURE_PROPERTY(double, MaxRotation) + /** @see setCameraMatrix */ + virtual cv::Mat getCameraMatrix() const = 0; + /** @copybrief getCameraMatrix @see getCameraMatrix */ + virtual void setCameraMatrix(const cv::Mat &val) = 0; + /** @see setTransformType */ + virtual int getTransformType() const = 0; + /** @copybrief getTransformType @see getTransformType */ + virtual void setTransformType(int val) = 0; protected: virtual void @@ -486,7 +484,7 @@ namespace rgbd const Mat& initRt) const = 0; }; - /** Odometry based on the paper "Real-Time Visual Odometry from Dense RGB-D Images", + /** Odometry based on the paper "Real-Time Visual Odometry from Dense RGB-D Images", * F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011. */ class CV_EXPORTS RgbdOdometry: public Odometry @@ -546,7 +544,7 @@ namespace rgbd double maxTranslation, maxRotation; }; - /** Odometry based on the paper "KinectFusion: Real-Time Dense Surface Mapping and Tracking", + /** Odometry based on the paper "KinectFusion: Real-Time Dense Surface Mapping and Tracking", * Richard A. Newcombe, Andrew Fitzgibbon, at al, SIGGRAPH, 2011. */ class ICPOdometry: public Odometry @@ -669,8 +667,8 @@ namespace rgbd mutable Ptr normalsComputer; }; - /** Warp the image: compute 3d points from the depth, transform them using given transformation, - * then project color point cloud to an image plane. + /** Warp the image: compute 3d points from the depth, transform them using given transformation, + * then project color point cloud to an image plane. * This function can be used to visualize results of the Odometry algorithm. * @param image The image (of CV_8UC1 or CV_8UC3 type) * @param depth The depth (of type used in depthTo3d fuction) diff --git a/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp index 4eef0b5db..ad6a4bee2 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp @@ -2,26 +2,26 @@ * By downloading, copying, installing or using the software you agree to this license. * If you do not agree to this license, do not download, install, * copy or use the software. - * - * + * + * * License Agreement * For Open Source Computer Vision Library * (3 - clause BSD License) - * + * * Redistribution and use in source and binary forms, with or without modification, * are permitted provided that the following conditions are met : - * + * * *Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. - * + * * * Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and / or other materials provided with the distribution. - * + * * * Neither the names of the copyright holders nor the names of the contributors * may be used to endorse or promote products derived from this software * without specific prior written permission. - * + * * This software is provided by the copyright holders and contributors "as is" and * any express or implied warranties, including, but not limited to, the implied * warranties of merchantability and fitness for a particular purpose are disclaimed. @@ -217,12 +217,30 @@ public: CV_WRAP static Ptr create(); - CV_PURE_PROPERTY(double, SigmaS) - CV_PURE_PROPERTY(double, SigmaR) - CV_PURE_PROPERTY(int, TreeHeight) - CV_PURE_PROPERTY(int, PCAIterations) - CV_PURE_PROPERTY(bool, AdjustOutliers) - CV_PURE_PROPERTY(bool, UseRNG) + /** @see setSigmaS */ + virtual double getSigmaS() const = 0; + /** @copybrief getSigmaS @see getSigmaS */ + virtual void setSigmaS(double val) = 0; + /** @see setSigmaR */ + virtual double getSigmaR() const = 0; + /** @copybrief getSigmaR @see getSigmaR */ + virtual void setSigmaR(double val) = 0; + /** @see setTreeHeight */ + virtual int getTreeHeight() const = 0; + /** @copybrief getTreeHeight @see getTreeHeight */ + virtual void setTreeHeight(int val) = 0; + /** @see setPCAIterations */ + virtual int getPCAIterations() const = 0; + /** @copybrief getPCAIterations @see getPCAIterations */ + virtual void setPCAIterations(int val) = 0; + /** @see setAdjustOutliers */ + virtual bool getAdjustOutliers() const = 0; + /** @copybrief getAdjustOutliers @see getAdjustOutliers */ + virtual void setAdjustOutliers(bool val) = 0; + /** @see setUseRNG */ + virtual bool getUseRNG() const = 0; + /** @copybrief getUseRNG @see getUseRNG */ + virtual void setUseRNG(bool val) = 0; }; /** @brief Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines. From da7ff82d1915df254c9aed688a3bfba7825cc63c Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Wed, 18 Mar 2015 17:56:47 +0300 Subject: [PATCH 22/32] Replaced CV_IMPL_PROPERTY macros in public headers --- modules/rgbd/include/opencv2/rgbd.hpp | 415 ++++++++++++++++-- .../saliency/saliencySpecializedClasses.hpp | 63 ++- 2 files changed, 424 insertions(+), 54 deletions(-) diff --git a/modules/rgbd/include/opencv2/rgbd.hpp b/modules/rgbd/include/opencv2/rgbd.hpp index 863228cb5..62431c80c 100644 --- a/modules/rgbd/include/opencv2/rgbd.hpp +++ b/modules/rgbd/include/opencv2/rgbd.hpp @@ -150,12 +150,54 @@ namespace rgbd void initialize() const; - CV_IMPL_PROPERTY(int, Rows, rows_) - CV_IMPL_PROPERTY(int, Cols, cols_) - CV_IMPL_PROPERTY(int, WindowSize, window_size_) - CV_IMPL_PROPERTY(int, Depth, depth_) - CV_IMPL_PROPERTY_S(cv::Mat, K, K_) - CV_IMPL_PROPERTY(int, Method, method_) + int getRows() const + { + return rows_; + } + void setRows(int val) + { + rows_ = val; + } + int getCols() const + { + return cols_; + } + void setCols(int val) + { + cols_ = val; + } + int getWindowSize() const + { + return window_size_; + } + void setWindowSize(int val) + { + window_size_ = val; + } + int getDepth() const + { + return depth_; + } + void setDepth(int val) + { + depth_ = val; + } + cv::Mat getK() const + { + return K_; + } + void setK(const cv::Mat &val) + { + K_ = val; + } + int getMethod() const + { + return method_; + } + void setMethod(int val) + { + method_ = val; + } protected: void @@ -213,9 +255,30 @@ namespace rgbd void initialize() const; - CV_IMPL_PROPERTY(int, WindowSize, window_size_) - CV_IMPL_PROPERTY(int, Depth, depth_) - CV_IMPL_PROPERTY(int, Method, method_) + int getWindowSize() const + { + return window_size_; + } + void setWindowSize(int val) + { + window_size_ = val; + } + int getDepth() const + { + return depth_; + } + void setDepth(int val) + { + depth_ = val; + } + int getMethod() const + { + return method_; + } + void setMethod(int val) + { + method_ = val; + } protected: void @@ -305,13 +368,62 @@ namespace rgbd void operator()(InputArray points3d, OutputArray mask, OutputArray plane_coefficients); - CV_IMPL_PROPERTY(int, BlockSize, block_size_) - CV_IMPL_PROPERTY(int, MinSize, min_size_) - CV_IMPL_PROPERTY(int, Method, method_) - CV_IMPL_PROPERTY(double, Threshold, threshold_) - CV_IMPL_PROPERTY(double, SensorErrorA, sensor_error_a_) - CV_IMPL_PROPERTY(double, SensorErrorB, sensor_error_b_) - CV_IMPL_PROPERTY(double, SensorErrorC, sensor_error_c_) + int getBlockSize() const + { + return block_size_; + } + void setBlockSize(int val) + { + block_size_ = val; + } + int getMinSize() const + { + return min_size_; + } + void setMinSize(int val) + { + min_size_ = val; + } + int getMethod() const + { + return method_; + } + void setMethod(int val) + { + method_ = val; + } + double getThreshold() const + { + return threshold_; + } + void setThreshold(double val) + { + threshold_ = val; + } + double getSensorErrorA() const + { + return sensor_error_a_; + } + void setSensorErrorA(double val) + { + sensor_error_a_ = val; + } + double getSensorErrorB() const + { + return sensor_error_b_; + } + void setSensorErrorB(double val) + { + sensor_error_b_ = val; + } + double getSensorErrorC() const + { + return sensor_error_c_; + } + void setSensorErrorC(double val) + { + sensor_error_c_ = val; + } private: /** The method to use to compute the planes */ @@ -510,16 +622,86 @@ namespace rgbd virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - CV_IMPL_PROPERTY_S(cv::Mat, CameraMatrix, cameraMatrix) - CV_IMPL_PROPERTY(double, MinDepth, minDepth) - CV_IMPL_PROPERTY(double, MaxDepth, maxDepth) - CV_IMPL_PROPERTY(double, MaxDepthDiff, maxDepthDiff) - CV_IMPL_PROPERTY_S(cv::Mat, IterationCounts, iterCounts) - CV_IMPL_PROPERTY_S(cv::Mat, MinGradientMagnitudes, minGradientMagnitudes) - CV_IMPL_PROPERTY(double, MaxPointsPart, maxPointsPart) - CV_IMPL_PROPERTY(int, TransformType, transformType) - CV_IMPL_PROPERTY(double, MaxTranslation, maxTranslation) - CV_IMPL_PROPERTY(double, MaxRotation, maxRotation) + cv::Mat getCameraMatrix() const + { + return cameraMatrix; + } + void setCameraMatrix(const cv::Mat &val) + { + cameraMatrix = val; + } + double getMinDepth() const + { + return minDepth; + } + void setMinDepth(double val) + { + minDepth = val; + } + double getMaxDepth() const + { + return maxDepth; + } + void setMaxDepth(double val) + { + maxDepth = val; + } + double getMaxDepthDiff() const + { + return maxDepthDiff; + } + void setMaxDepthDiff(double val) + { + maxDepthDiff = val; + } + cv::Mat getIterationCounts() const + { + return iterCounts; + } + void setIterationCounts(const cv::Mat &val) + { + iterCounts = val; + } + cv::Mat getMinGradientMagnitudes() const + { + return minGradientMagnitudes; + } + void setMinGradientMagnitudes(const cv::Mat &val) + { + minGradientMagnitudes = val; + } + double getMaxPointsPart() const + { + return maxPointsPart; + } + void setMaxPointsPart(double val) + { + maxPointsPart = val; + } + int getTransformType() const + { + return transformType; + } + void setTransformType(int val) + { + transformType = val; + } + double getMaxTranslation() const + { + return maxTranslation; + } + void setMaxTranslation(double val) + { + maxTranslation = val; + } + double getMaxRotation() const + { + return maxRotation; + } + void setMaxRotation(double val) + { + maxRotation = val; + } protected: virtual void @@ -567,16 +749,82 @@ namespace rgbd virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - CV_IMPL_PROPERTY_S(cv::Mat, CameraMatrix, cameraMatrix) - CV_IMPL_PROPERTY(double, MinDepth, minDepth) - CV_IMPL_PROPERTY(double, MaxDepth, maxDepth) - CV_IMPL_PROPERTY(double, MaxDepthDiff, maxDepthDiff) - CV_IMPL_PROPERTY_S(cv::Mat, IterationCounts, iterCounts) - CV_IMPL_PROPERTY(double, MaxPointsPart, maxPointsPart) - CV_IMPL_PROPERTY(int, TransformType, transformType) - CV_IMPL_PROPERTY(double, MaxTranslation, maxTranslation) - CV_IMPL_PROPERTY(double, MaxRotation, maxRotation) - CV_IMPL_PROPERTY_RO(Ptr, NormalsComputer, normalsComputer) + cv::Mat getCameraMatrix() const + { + return cameraMatrix; + } + void setCameraMatrix(const cv::Mat &val) + { + cameraMatrix = val; + } + double getMinDepth() const + { + return minDepth; + } + void setMinDepth(double val) + { + minDepth = val; + } + double getMaxDepth() const + { + return maxDepth; + } + void setMaxDepth(double val) + { + maxDepth = val; + } + double getMaxDepthDiff() const + { + return maxDepthDiff; + } + void setMaxDepthDiff(double val) + { + maxDepthDiff = val; + } + cv::Mat getIterationCounts() const + { + return iterCounts; + } + void setIterationCounts(const cv::Mat &val) + { + iterCounts = val; + } + double getMaxPointsPart() const + { + return maxPointsPart; + } + void setMaxPointsPart(double val) + { + maxPointsPart = val; + } + int getTransformType() const + { + return transformType; + } + void setTransformType(int val) + { + transformType = val; + } + double getMaxTranslation() const + { + return maxTranslation; + } + void setMaxTranslation(double val) + { + maxTranslation = val; + } + double getMaxRotation() const + { + return maxRotation; + } + void setMaxRotation(double val) + { + maxRotation = val; + } + Ptr getNormalsComputer() const + { + return normalsComputer; + } protected: virtual void @@ -629,17 +877,90 @@ namespace rgbd virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - CV_IMPL_PROPERTY_S(cv::Mat, CameraMatrix, cameraMatrix) - CV_IMPL_PROPERTY(double, MinDepth, minDepth) - CV_IMPL_PROPERTY(double, MaxDepth, maxDepth) - CV_IMPL_PROPERTY(double, MaxDepthDiff, maxDepthDiff) - CV_IMPL_PROPERTY(double, MaxPointsPart, maxPointsPart) - CV_IMPL_PROPERTY_S(cv::Mat, IterationCounts, iterCounts) - CV_IMPL_PROPERTY_S(cv::Mat, MinGradientMagnitudes, minGradientMagnitudes) - CV_IMPL_PROPERTY(int, TransformType, transformType) - CV_IMPL_PROPERTY(double, MaxTranslation, maxTranslation) - CV_IMPL_PROPERTY(double, MaxRotation, maxRotation) - CV_IMPL_PROPERTY_RO(Ptr, NormalsComputer, normalsComputer) + cv::Mat getCameraMatrix() const + { + return cameraMatrix; + } + void setCameraMatrix(const cv::Mat &val) + { + cameraMatrix = val; + } + double getMinDepth() const + { + return minDepth; + } + void setMinDepth(double val) + { + minDepth = val; + } + double getMaxDepth() const + { + return maxDepth; + } + void setMaxDepth(double val) + { + maxDepth = val; + } + double getMaxDepthDiff() const + { + return maxDepthDiff; + } + void setMaxDepthDiff(double val) + { + maxDepthDiff = val; + } + double getMaxPointsPart() const + { + return maxPointsPart; + } + void setMaxPointsPart(double val) + { + maxPointsPart = val; + } + cv::Mat getIterationCounts() const + { + return iterCounts; + } + void setIterationCounts(const cv::Mat &val) + { + iterCounts = val; + } + cv::Mat getMinGradientMagnitudes() const + { + return minGradientMagnitudes; + } + void setMinGradientMagnitudes(const cv::Mat &val) + { + minGradientMagnitudes = val; + } + int getTransformType() const + { + return transformType; + } + void setTransformType(int val) + { + transformType = val; + } + double getMaxTranslation() const + { + return maxTranslation; + } + void setMaxTranslation(double val) + { + maxTranslation = val; + } + double getMaxRotation() const + { + return maxRotation; + } + void setMaxRotation(double val) + { + maxRotation = val; + } + Ptr getNormalsComputer() const + { + return normalsComputer; + } protected: virtual void diff --git a/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp b/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp index 326d91e20..ab63f153f 100644 --- a/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp +++ b/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp @@ -76,8 +76,22 @@ public: void read( const FileNode& fn ); void write( FileStorage& fs ) const; - CV_IMPL_PROPERTY(int, ImageWidth, resImWidth) - CV_IMPL_PROPERTY(int, ImageHeight, resImHeight) + int getImageWidth() const + { + return resImWidth; + } + inline void setImageWidth(int val) + { + resImWidth = val; + } + int getImageHeight() const + { + return resImHeight; + } + void setImageHeight(int val) + { + resImHeight = val; + } protected: bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ); @@ -114,8 +128,22 @@ public: */ bool init(); - CV_IMPL_PROPERTY(int, ImageWidth, imageWidth) - CV_IMPL_PROPERTY(int, ImageHeight, imageHeight) + int getImageWidth() const + { + return imageWidth; + } + inline void setImageWidth(int val) + { + imageWidth = val; + } + int getImageHeight() const + { + return imageHeight; + } + void setImageHeight(int val) + { + imageHeight = val; + } protected: /** @brief Performs all the operations and calls all internal functions necessary for the accomplishment of the @@ -205,9 +233,30 @@ public: */ void setBBResDir( std::string resultsDir ); - CV_IMPL_PROPERTY(double, Base, _base) - CV_IMPL_PROPERTY(int, NSS, _NSS) - CV_IMPL_PROPERTY(int, W, _W) + double getBase() const + { + return _base; + } + inline void setBase(double val) + { + _base = val; + } + int getNSS() const + { + return _NSS; + } + void setNSS(int val) + { + _NSS = val; + } + int getW() const + { + return _W; + } + void setW(int val) + { + _W = val; + } protected: /** @brief Performs all the operations and calls all internal functions necessary for the From 872366245eeb5f61a4b5c4c997a87979655a030a Mon Sep 17 00:00:00 2001 From: pmkalshetti Date: Thu, 19 Mar 2015 20:57:12 +0530 Subject: [PATCH 23/32] Link Correction Corrected Link for corresponding dataset --- modules/datasets/include/opencv2/datasets/dataset.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/datasets/include/opencv2/datasets/dataset.hpp b/modules/datasets/include/opencv2/datasets/dataset.hpp index 6ac524b64..43540578b 100644 --- a/modules/datasets/include/opencv2/datasets/dataset.hpp +++ b/modules/datasets/include/opencv2/datasets/dataset.hpp @@ -282,7 +282,7 @@ Usage: Implements loading dataset: -"EPFL Multi-View Stereo": +"EPFL Multi-View Stereo": Usage: -# From link above download dataset files: From 8f9dd88c324eff529026382fbf0d14cdc30f4977 Mon Sep 17 00:00:00 2001 From: Dikay900 Date: Sat, 29 Nov 2014 17:16:48 +0100 Subject: [PATCH 24/32] delete private.hpp include in sample code and corresponding documentation --- modules/line_descriptor/samples/compute_descriptors.cpp | 1 - modules/line_descriptor/samples/knn_matching.cpp | 1 - modules/line_descriptor/samples/lines_extraction.cpp | 1 - modules/line_descriptor/samples/lsd_lines_extraction.cpp | 1 - modules/line_descriptor/samples/matching.cpp | 1 - modules/line_descriptor/samples/radius_matching.cpp | 1 - 6 files changed, 6 deletions(-) diff --git a/modules/line_descriptor/samples/compute_descriptors.cpp b/modules/line_descriptor/samples/compute_descriptors.cpp index 07097fc6d..d2bdef469 100644 --- a/modules/line_descriptor/samples/compute_descriptors.cpp +++ b/modules/line_descriptor/samples/compute_descriptors.cpp @@ -42,7 +42,6 @@ #include #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" #include #include #include diff --git a/modules/line_descriptor/samples/knn_matching.cpp b/modules/line_descriptor/samples/knn_matching.cpp index 664eb3a1e..361813308 100644 --- a/modules/line_descriptor/samples/knn_matching.cpp +++ b/modules/line_descriptor/samples/knn_matching.cpp @@ -42,7 +42,6 @@ #include #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" #include #include #include diff --git a/modules/line_descriptor/samples/lines_extraction.cpp b/modules/line_descriptor/samples/lines_extraction.cpp index 95b5990f9..03b0124dc 100644 --- a/modules/line_descriptor/samples/lines_extraction.cpp +++ b/modules/line_descriptor/samples/lines_extraction.cpp @@ -42,7 +42,6 @@ #include #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" #include #include #include diff --git a/modules/line_descriptor/samples/lsd_lines_extraction.cpp b/modules/line_descriptor/samples/lsd_lines_extraction.cpp index da659a52e..4c1923e01 100644 --- a/modules/line_descriptor/samples/lsd_lines_extraction.cpp +++ b/modules/line_descriptor/samples/lsd_lines_extraction.cpp @@ -42,7 +42,6 @@ #include #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" #include #include #include diff --git a/modules/line_descriptor/samples/matching.cpp b/modules/line_descriptor/samples/matching.cpp index d02530f24..6eb669c38 100644 --- a/modules/line_descriptor/samples/matching.cpp +++ b/modules/line_descriptor/samples/matching.cpp @@ -42,7 +42,6 @@ #include #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" #include #include #include diff --git a/modules/line_descriptor/samples/radius_matching.cpp b/modules/line_descriptor/samples/radius_matching.cpp index fe71457e9..7d43b8424 100644 --- a/modules/line_descriptor/samples/radius_matching.cpp +++ b/modules/line_descriptor/samples/radius_matching.cpp @@ -42,7 +42,6 @@ #include #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" #include #include #include From 9bc8514fd94ab1ef9bc7fcc4d94828f259b5d07a Mon Sep 17 00:00:00 2001 From: Dikay900 Date: Sat, 6 Dec 2014 15:07:24 +0100 Subject: [PATCH 25/32] fix issue for visual studio versions which arent standarized for the c99 standard --- .../include/opencv2/line_descriptor/descriptor.hpp | 6 ++++++ modules/line_descriptor/src/types.hpp | 4 ++++ 2 files changed, 10 insertions(+) diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp index a06d43bb0..c8d32f142 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp @@ -45,7 +45,13 @@ #include #include #include + +#if defined _MSC_VER && _MSC_VER <= 1700 +#include +#else #include +#endif + #include #include diff --git a/modules/line_descriptor/src/types.hpp b/modules/line_descriptor/src/types.hpp index ac55a9d8e..7a80a82a5 100644 --- a/modules/line_descriptor/src/types.hpp +++ b/modules/line_descriptor/src/types.hpp @@ -40,7 +40,11 @@ // //M*/ +#if defined _MSC_VER && _MSC_VER <= 1700 +#include +#else #include +#endif #ifndef __OPENCV_TYPES_HPP #define __OPENCV_TYPES_HPP From 361dff4ee43c99a51ccdc915fbdcb29e01bc25cf Mon Sep 17 00:00:00 2001 From: Dikay900 Date: Fri, 28 Nov 2014 19:49:58 +0100 Subject: [PATCH 26/32] fix already defined macro variable --- modules/line_descriptor/src/binary_descriptor.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/line_descriptor/src/binary_descriptor.cpp b/modules/line_descriptor/src/binary_descriptor.cpp index 2d2929f9c..f5739b661 100644 --- a/modules/line_descriptor/src/binary_descriptor.cpp +++ b/modules/line_descriptor/src/binary_descriptor.cpp @@ -756,7 +756,7 @@ int BinaryDescriptor::OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines ) /* some variables' declarations */ float rho1, rho2, tempValue; - float direction, near, length; + float direction, diffNear, length; unsigned int octaveID, lineIDInOctave; /*more than one octave image, organize lines in scale space. @@ -784,8 +784,8 @@ int BinaryDescriptor::OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines ) /*nearThreshold depends on the distance of the image coordinate origin to current line. *so nearThreshold = rho1 * nearThresholdRatio, where nearThresholdRatio = 1-cos(10*pi/180) = 0.0152*/ tempValue = (float) ( rho1 * 0.0152 ); - float nearThreshold = ( tempValue > 6 ) ? ( tempValue ) : 6; - nearThreshold = ( nearThreshold < 12 ) ? nearThreshold : 12; + float diffNearThreshold = ( tempValue > 6 ) ? ( tempValue ) : 6; + diffNearThreshold = ( diffNearThreshold < 12 ) ? diffNearThreshold : 12; /* compute scaled lenght of current line */ dx = fabs( edLineVec_[octaveCount]->lineEndpoints_[lineCurId][0] - edLineVec_[octaveCount]->lineEndpoints_[lineCurId][2] ); //x1-x2 @@ -831,10 +831,10 @@ int BinaryDescriptor::OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines ) /* get known term from equation to be compared */ rho2 = (float) ( scale[octaveID] * fabs( edLineVec_[octaveID]->lineEquations_[lineIDInOctave][2] ) ); /* compute difference between known ters */ - near = fabs( rho1 - rho2 ); + diffNear = fabs( rho1 - rho2 ); /* two lines are not close in the image */ - if( near > nearThreshold ) + if( diffNear > diffNearThreshold ) { continue; } From 0ea70a46f5789b54fa0b83520ad11fcd7c8a43d3 Mon Sep 17 00:00:00 2001 From: Auron-X Date: Wed, 25 Mar 2015 15:57:48 +0900 Subject: [PATCH 27/32] Unused header Unused header --- modules/tracking/src/precomp.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/tracking/src/precomp.hpp b/modules/tracking/src/precomp.hpp index 876306f0d..1cad0a5f0 100644 --- a/modules/tracking/src/precomp.hpp +++ b/modules/tracking/src/precomp.hpp @@ -44,6 +44,6 @@ #include "opencv2/tracking.hpp" #include "opencv2/core/utility.hpp" -#include "opencv2/core/private.hpp" +//#include "opencv2/core/private.hpp" #endif From f430f808a26a326dcc8af585341c5df14ab8906b Mon Sep 17 00:00:00 2001 From: Auron-X Date: Wed, 25 Mar 2015 16:40:37 +0900 Subject: [PATCH 28/32] Bug fix #2 The bug has the same origin as: https://github.com/Itseez/opencv_contrib/pull/187 The problem was in "fast" version of function generating a binary code for ensemble classifier posterior probability calculation. Bug was eliminated by fixing the order of "values" in offsets calculating function --- modules/tracking/src/tld_utils.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/tracking/src/tld_utils.cpp b/modules/tracking/src/tld_utils.cpp index c5c85bd32..e534072ba 100644 --- a/modules/tracking/src/tld_utils.cpp +++ b/modules/tracking/src/tld_utils.cpp @@ -302,8 +302,8 @@ void TLDEnsembleClassifier::prepareClassifier(int rowstep) lastStep_ = rowstep; for( int i = 0; i < (int)offset.size(); i++ ) { - offset[i].x = rowstep * measurements[i].val[0] + measurements[i].val[1]; - offset[i].y = rowstep * measurements[i].val[2] + measurements[i].val[3]; + offset[i].x = rowstep * measurements[i].val[2] + measurements[i].val[0]; + offset[i].y = rowstep * measurements[i].val[3] + measurements[i].val[1]; } } } From 758968588232a420633402df16f0a20c2d16699b Mon Sep 17 00:00:00 2001 From: jiaolong_cvc228 Date: Sat, 21 Mar 2015 17:07:19 +0100 Subject: [PATCH 29/32] corrected the testing data path for latentsvm detector and some typos --- modules/latentsvm/src/_lsvmc_function.h | 10 ++-- modules/latentsvm/src/_lsvmc_latentsvm.h | 40 ++++++------- modules/latentsvm/src/_lsvmc_matching.h | 6 +- modules/latentsvm/src/_lsvmc_parser.h | 2 +- modules/latentsvm/src/_lsvmc_routine.h | 12 ++-- modules/latentsvm/src/_lsvmc_types.h | 16 ++--- .../latentsvm/src/lsvmc_featurepyramid.cpp | 22 +++---- modules/latentsvm/src/lsvmc_function.cpp | 8 +-- modules/latentsvm/src/lsvmc_latentsvm.cpp | 58 +++++++++---------- .../latentsvm/src/lsvmc_latentsvmdetector.cpp | 50 ++++++++-------- modules/latentsvm/src/lsvmc_matching.cpp | 6 +- modules/latentsvm/src/lsvmc_parser.cpp | 50 ++++++++-------- modules/latentsvm/src/lsvmc_routine.cpp | 22 +++---- .../test/test_latentsvmdetector_cascade.cpp | 17 +++--- modules/latentsvm/test/test_main.cpp | 2 +- 15 files changed, 161 insertions(+), 160 deletions(-) diff --git a/modules/latentsvm/src/_lsvmc_function.h b/modules/latentsvm/src/_lsvmc_function.h index 5fa1f2bc7..d1a42bed6 100644 --- a/modules/latentsvm/src/_lsvmc_function.h +++ b/modules/latentsvm/src/_lsvmc_function.h @@ -49,10 +49,10 @@ namespace cv namespace lsvm { -float calcM (int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, const CvLSVMFilterObjectCaskade *filter); -float calcM_PCA (int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, const CvLSVMFilterObjectCaskade *filter); -float calcM_PCA_cash(int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, const CvLSVMFilterObjectCaskade *filter, float * cashM, int * maskM, int step); -float calcFine (const CvLSVMFilterObjectCaskade *filter, int di, int dj); +float calcM (int k,int di,int dj, const CvLSVMFeaturePyramidCascade * H, const CvLSVMFilterObjectCascade *filter); +float calcM_PCA (int k,int di,int dj, const CvLSVMFeaturePyramidCascade * H, const CvLSVMFilterObjectCascade *filter); +float calcM_PCA_cash(int k,int di,int dj, const CvLSVMFeaturePyramidCascade * H, const CvLSVMFilterObjectCascade *filter, float * cashM, int * maskM, int step); +float calcFine (const CvLSVMFilterObjectCascade *filter, int di, int dj); } } -#endif \ No newline at end of file +#endif diff --git a/modules/latentsvm/src/_lsvmc_latentsvm.h b/modules/latentsvm/src/_lsvmc_latentsvm.h index 61029567e..f92428706 100644 --- a/modules/latentsvm/src/_lsvmc_latentsvm.h +++ b/modules/latentsvm/src/_lsvmc_latentsvm.h @@ -61,29 +61,29 @@ namespace lsvm // (pyramid constructed both contrast and non-contrast image) ////////////////////////////////////////////////////////////// -void FeaturePyramid32(CvLSVMFeaturePyramidCaskade* H, int maxX, int maxY); +void FeaturePyramid32(CvLSVMFeaturePyramidCascade* H, int maxX, int maxY); /* -// Creation PSA feature pyramid +// Creation PCA feature pyramid // // API -// featurePyramid* createPSA_FeaturePyramid(featurePyramid* H); +// featurePyramid* createPCA_FeaturePyramid(featurePyramid* H); // INPUT // H - feature pyramid // OUTPUT // RESULT -// PSA feature pyramid +// PCA feature pyramid */ -CvLSVMFeaturePyramidCaskade* createPCA_FeaturePyramid(CvLSVMFeaturePyramidCaskade* H, - CvLatentSvmDetectorCaskade* detector, +CvLSVMFeaturePyramidCascade* createPCA_FeaturePyramid(CvLSVMFeaturePyramidCascade* H, + CvLatentSvmDetectorCascade* detector, int maxX, int maxY); /* // Getting feature pyramid // // API -// int getFeaturePyramid(IplImage * image, const CvLSVMFilterObjectCaskade **all_F, +// int getFeaturePyramid(IplImage * image, const CvLSVMFilterObjectCascade **all_F, const int n_f, const int lambda, const int k, const int startX, const int startY, @@ -101,7 +101,7 @@ CvLSVMFeaturePyramidCaskade* createPCA_FeaturePyramid(CvLSVMFeaturePyramidCaskad // RESULT // Error status */ -int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramidCaskade **maps); +int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramidCascade **maps); /* // Getting feature map for the selected subimage @@ -116,7 +116,7 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramidCaskade **maps); // RESULT // Error status */ -int getFeatureMaps(const IplImage * image, const int k, CvLSVMFeatureMapCaskade **map); +int getFeatureMaps(const IplImage * image, const int k, CvLSVMFeatureMapCascade **map); /* @@ -132,7 +132,7 @@ int getFeatureMaps(const IplImage * image, const int k, CvLSVMFeatureMapCaskade // RESULT // Error status */ -int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa); +int normalizeAndTruncate(CvLSVMFeatureMapCascade *map, const float alfa); /* // Feature map reduction @@ -148,7 +148,7 @@ int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa); // RESULT // Error status */ -int PCAFeatureMaps(CvLSVMFeatureMapCaskade *map); +int PCAFeatureMaps(CvLSVMFeatureMapCascade *map); ////////////////////////////////////////////////////////////// // search object @@ -227,7 +227,7 @@ int clippingBoxes(int width, int height, // RESULT // Feature pyramid with nullable border */ -CvLSVMFeaturePyramidCaskade* createFeaturePyramidWithBorder(IplImage *image, +CvLSVMFeaturePyramidCascade* createFeaturePyramidWithBorder(IplImage *image, int maxXBorder, int maxYBorder); /* @@ -235,7 +235,7 @@ CvLSVMFeaturePyramidCaskade* createFeaturePyramidWithBorder(IplImage *image, // // API // int searchObjectThresholdSomeComponents(const featurePyramid *H, - const CvLSVMFilterObjectCaskade **filters, + const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, const float *b, float scoreThreshold, CvPoint **points, CvPoint **oppPoints, @@ -255,9 +255,9 @@ CvLSVMFeaturePyramidCaskade* createFeaturePyramidWithBorder(IplImage *image, // RESULT // Error status */ -int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramidCaskade *H, - const CvLSVMFeaturePyramidCaskade *H_PCA, - const CvLSVMFilterObjectCaskade **filters, +int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramidCascade *H, + const CvLSVMFeaturePyramidCascade *H_PCA, + const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, const float *b, float scoreThreshold, CvPoint **points, CvPoint **oppPoints, @@ -294,7 +294,7 @@ int getOppositePoint(CvPoint point, // // API // int showRootFilterBoxes(const IplImage *image, - const CvLSVMFilterObjectCaskade *filter, + const CvLSVMFilterObjectCascade *filter, CvPoint *points, int *levels, int kPoints, CvScalar color, int thickness, int line_type, int shift); @@ -314,7 +314,7 @@ int getOppositePoint(CvPoint point, // Error status */ int showRootFilterBoxes(IplImage *image, - const CvLSVMFilterObjectCaskade *filter, + const CvLSVMFilterObjectCascade *filter, CvPoint *points, int *levels, int kPoints, CvScalar color, int thickness, int line_type, int shift); @@ -324,7 +324,7 @@ int showRootFilterBoxes(IplImage *image, // // API // int showPartFilterBoxes(const IplImage *image, - const CvLSVMFilterObjectCaskade *filter, + const CvLSVMFilterObjectCascade *filter, CvPoint *points, int *levels, int kPoints, CvScalar color, int thickness, int line_type, int shift); @@ -345,7 +345,7 @@ int showRootFilterBoxes(IplImage *image, // Error status */ int showPartFilterBoxes(IplImage *image, - const CvLSVMFilterObjectCaskade **filters, + const CvLSVMFilterObjectCascade **filters, int n, CvPoint **partsDisplacement, int *levels, int kPoints, CvScalar color, int thickness, diff --git a/modules/latentsvm/src/_lsvmc_matching.h b/modules/latentsvm/src/_lsvmc_matching.h index 3175d6047..ef2001b7d 100644 --- a/modules/latentsvm/src/_lsvmc_matching.h +++ b/modules/latentsvm/src/_lsvmc_matching.h @@ -85,7 +85,7 @@ int computeBorderSize(int maxXBorder, int maxYBorder, int *bx, int *by); // RESULT // Error status */ -int addNullableBorder(CvLSVMFeatureMapCaskade *map, int bx, int by); +int addNullableBorder(CvLSVMFeatureMapCascade *map, int bx, int by); /* // Perform non-maximum suppression algorithm (described in original paper) @@ -117,12 +117,12 @@ int nonMaximumSuppression(int numBoxes, const CvPoint *points, float overlapThreshold, int *numBoxesOut, CvPoint **pointsOut, CvPoint **oppositePointsOut, float **scoreOut); -int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents, +int getMaxFilterDims(const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, unsigned int *maxXBorder, unsigned int *maxYBorder); //} -int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents, +int getMaxFilterDims(const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, unsigned int *maxXBorder, unsigned int *maxYBorder); } diff --git a/modules/latentsvm/src/_lsvmc_parser.h b/modules/latentsvm/src/_lsvmc_parser.h index eb9a7f52a..0652a080a 100644 --- a/modules/latentsvm/src/_lsvmc_parser.h +++ b/modules/latentsvm/src/_lsvmc_parser.h @@ -116,7 +116,7 @@ namespace lsvm const char *modelPath,// model path // output parametrs - CvLSVMFilterObjectCaskade ***filters, + CvLSVMFilterObjectCascade ***filters, int *kFilters, int *kComponents, int **kPartFilters, diff --git a/modules/latentsvm/src/_lsvmc_routine.h b/modules/latentsvm/src/_lsvmc_routine.h index 1e251e4a0..a3380a8fe 100644 --- a/modules/latentsvm/src/_lsvmc_routine.h +++ b/modules/latentsvm/src/_lsvmc_routine.h @@ -58,18 +58,18 @@ namespace lsvm // with all fields including arrays // Error status is return value ////////////////////////////////////////////////////////////// -int allocFilterObject(CvLSVMFilterObjectCaskade **obj, const int sizeX, const int sizeY, +int allocFilterObject(CvLSVMFilterObjectCascade **obj, const int sizeX, const int sizeY, const int p); -int freeFilterObject (CvLSVMFilterObjectCaskade **obj); +int freeFilterObject (CvLSVMFilterObjectCascade **obj); -int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX, const int sizeY, +int allocFeatureMapObject(CvLSVMFeatureMapCascade **obj, const int sizeX, const int sizeY, const int p); -int freeFeatureMapObject (CvLSVMFeatureMapCaskade **obj); +int freeFeatureMapObject (CvLSVMFeatureMapCascade **obj); -int allocFeaturePyramidObject(CvLSVMFeaturePyramidCaskade **obj, +int allocFeaturePyramidObject(CvLSVMFeaturePyramidCascade **obj, const int countLevel); -int freeFeaturePyramidObject (CvLSVMFeaturePyramidCaskade **obj); +int freeFeaturePyramidObject (CvLSVMFeaturePyramidCascade **obj); } } diff --git a/modules/latentsvm/src/_lsvmc_types.h b/modules/latentsvm/src/_lsvmc_types.h index 9c15ba0ae..9764394d6 100644 --- a/modules/latentsvm/src/_lsvmc_types.h +++ b/modules/latentsvm/src/_lsvmc_types.h @@ -96,7 +96,7 @@ typedef struct{ int sizeY; int numFeatures; float *map; -} CvLSVMFeatureMapCaskade; +} CvLSVMFeatureMapCascade; // DataType: STRUCT featurePyramid // @@ -104,8 +104,8 @@ typedef struct{ // pyramid - array of pointers to feature map at different levels typedef struct{ int numLevels; - CvLSVMFeatureMapCaskade **pyramid; -} CvLSVMFeaturePyramidCaskade; + CvLSVMFeatureMapCascade **pyramid; +} CvLSVMFeaturePyramidCascade; // DataType: STRUCT filterDisposition // The structure stores preliminary results in optimization process @@ -147,7 +147,7 @@ typedef struct CvLSVMFilterPosition // k - component of feature vector in cell (i, j) // END OF FILTER DESCRIPTION -typedef struct CvLSVMFilterObjectCaskade{ +typedef struct CvLSVMFilterObjectCascade{ CvLSVMFilterPosition V; float fineFunction[4]; int sizeX; @@ -159,7 +159,7 @@ typedef struct CvLSVMFilterObjectCaskade{ float Hypothesis_PCA, Deformation_PCA; int deltaX; int deltaY; -} CvLSVMFilterObjectCaskade; +} CvLSVMFilterObjectCascade; // data type: STRUCT CvLatentSvmDetector // structure contains internal representation of trained Latent SVM detector @@ -170,17 +170,17 @@ typedef struct CvLSVMFilterObjectCaskade{ // b - biases for all model components // score_threshold - confidence level threshold -typedef struct CvLatentSvmDetectorCaskade +typedef struct CvLatentSvmDetectorCascade { int num_filters; int num_components; int* num_part_filters; - CvLSVMFilterObjectCaskade** filters; + CvLSVMFilterObjectCascade** filters; float* b; float score_threshold; float *pca; int pca_size; -} CvLatentSvmDetectorCaskade; +} CvLatentSvmDetectorCascade; } } #endif diff --git a/modules/latentsvm/src/lsvmc_featurepyramid.cpp b/modules/latentsvm/src/lsvmc_featurepyramid.cpp index 7dbfc1e58..b5414085a 100644 --- a/modules/latentsvm/src/lsvmc_featurepyramid.cpp +++ b/modules/latentsvm/src/lsvmc_featurepyramid.cpp @@ -64,7 +64,7 @@ namespace lsvm int getPathOfFeaturePyramid(IplImage * image, float step, int numStep, int startIndex, - int sideLength, CvLSVMFeaturePyramidCaskade **maps); + int sideLength, CvLSVMFeaturePyramidCascade **maps); /* // Getting feature map for the selected subimage @@ -79,7 +79,7 @@ int getPathOfFeaturePyramid(IplImage * image, // RESULT // Error status */ -int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMapCaskade **map) +int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMapCascade **map) { int sizeX, sizeY; int p, px, stringSize; @@ -289,7 +289,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMapCaskade * // RESULT // Error status */ -int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa) +int normalizeAndTruncate(CvLSVMFeatureMapCascade *map, const float alfa) { int i,j, ii; int sizeX, sizeY, p, pos, pp, xp, pos1, pos2; @@ -413,7 +413,7 @@ int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa) // RESULT // Error status */ -int PCAFeatureMaps(CvLSVMFeatureMapCaskade *map) +int PCAFeatureMaps(CvLSVMFeatureMapCascade *map) { int i,j, ii, jj, k; int sizeX, sizeY, p, pp, xp, yp, pos1, pos2; @@ -485,9 +485,9 @@ int PCAFeatureMaps(CvLSVMFeatureMapCaskade *map) int getPathOfFeaturePyramid(IplImage * image, float step, int numStep, int startIndex, - int sideLength, CvLSVMFeaturePyramidCaskade **maps) + int sideLength, CvLSVMFeaturePyramidCascade **maps) { - CvLSVMFeatureMapCaskade *map; + CvLSVMFeatureMapCascade *map; IplImage *scaleTmp; float scale; int i; @@ -513,11 +513,11 @@ public: float step; int startIndex; int sideLength; - CvLSVMFeaturePyramidCaskade **maps; + CvLSVMFeaturePyramidCascade **maps; void operator() (const Range& range) const { - CvLSVMFeatureMapCaskade *map; + CvLSVMFeatureMapCascade *map; IplImage *scaleTmp; float scale; int err; @@ -538,7 +538,7 @@ public: int getPathOfFeaturePyramid_TBB(IplImage * image, float step, int numStep, int startIndex, - int sideLength, CvLSVMFeaturePyramidCaskade **maps) + int sideLength, CvLSVMFeaturePyramidCascade **maps) { PathOfFeaturePyramid str; str.step = step; @@ -557,7 +557,7 @@ int getPathOfFeaturePyramid_TBB(IplImage * image, // Getting feature pyramid // // API -// int getFeaturePyramid(IplImage * image, const CvLSVMFilterObjectCaskade **all_F, +// int getFeaturePyramid(IplImage * image, const CvLSVMFilterObjectCascade **all_F, const int n_f, const int lambda, const int k, const int startX, const int startY, @@ -569,7 +569,7 @@ int getPathOfFeaturePyramid_TBB(IplImage * image, // RESULT // Error status */ -int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramidCaskade **maps) +int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramidCascade **maps) { IplImage *imgResize; float step; diff --git a/modules/latentsvm/src/lsvmc_function.cpp b/modules/latentsvm/src/lsvmc_function.cpp index a515db286..28a1b9a5c 100644 --- a/modules/latentsvm/src/lsvmc_function.cpp +++ b/modules/latentsvm/src/lsvmc_function.cpp @@ -46,7 +46,7 @@ namespace cv namespace lsvm { -float calcM (int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, const CvLSVMFilterObjectCaskade *filter){ +float calcM (int k,int di,int dj, const CvLSVMFeaturePyramidCascade * H, const CvLSVMFilterObjectCascade *filter){ int i, j; float m = 0.0f; for(j = dj; j < dj + filter->sizeY; j++){ @@ -57,7 +57,7 @@ float calcM (int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, cons } return m; } -float calcM_PCA(int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, const CvLSVMFilterObjectCaskade *filter){ +float calcM_PCA(int k,int di,int dj, const CvLSVMFeaturePyramidCascade * H, const CvLSVMFilterObjectCascade *filter){ int i, j; float m = 0.0f; for(j = dj; j < dj + filter->sizeY; j++){ @@ -69,7 +69,7 @@ float calcM_PCA(int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, cons return m; } -float calcM_PCA_cash(int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, const CvLSVMFilterObjectCaskade *filter, float * cashM, int * maskM, int step){ +float calcM_PCA_cash(int k,int di,int dj, const CvLSVMFeaturePyramidCascade * H, const CvLSVMFilterObjectCascade *filter, float * cashM, int * maskM, int step){ int i, j, n; float m = 0.0f; float tmp1, tmp2, tmp3, tmp4; @@ -119,7 +119,7 @@ float calcM_PCA_cash(int k,int di,int dj, const CvLSVMFeaturePyramidCaskade * H, } return m; } -float calcFine (const CvLSVMFilterObjectCaskade *filter, int di, int dj){ +float calcFine (const CvLSVMFilterObjectCascade *filter, int di, int dj){ return filter->fineFunction[0] * di + filter->fineFunction[1] * dj + filter->fineFunction[2] * di * di + filter->fineFunction[3] * dj * dj; } diff --git a/modules/latentsvm/src/lsvmc_latentsvm.cpp b/modules/latentsvm/src/lsvmc_latentsvm.cpp index e2d65945d..813bb5af6 100644 --- a/modules/latentsvm/src/lsvmc_latentsvm.cpp +++ b/modules/latentsvm/src/lsvmc_latentsvm.cpp @@ -58,17 +58,17 @@ namespace lsvm int estimateBoxes(CvPoint *points, int *levels, int kPoints, int sizeX, int sizeY, CvPoint **oppositePoints); -int searchObjectThreshold(const CvLSVMFeaturePyramidCaskade *H, - const CvLSVMFeaturePyramidCaskade *H_PCA, - const CvLSVMFilterObjectCaskade **all_F, int n, +int searchObjectThreshold(const CvLSVMFeaturePyramidCascade *H, + const CvLSVMFeaturePyramidCascade *H_PCA, + const CvLSVMFilterObjectCascade **all_F, int n, float b, int maxXBorder, int maxYBorder, float scoreThreshold, CvPoint **points, int **levels, int *kPoints, float **score, CvPoint ***partsDisplacement); -void FeaturePyramid32(CvLSVMFeaturePyramidCaskade* H, int maxX, int maxY){ - CvLSVMFeatureMapCaskade *H32; +void FeaturePyramid32(CvLSVMFeaturePyramidCascade* H, int maxX, int maxY){ + CvLSVMFeatureMapCascade *H32; int i, j, k, l; int p = H->pyramid[0]->numFeatures; @@ -92,8 +92,8 @@ void FeaturePyramid32(CvLSVMFeaturePyramidCaskade* H, int maxX, int maxY){ } } -CvLSVMFeaturePyramidCaskade* createPCA_FeaturePyramid(CvLSVMFeaturePyramidCaskade* H, CvLatentSvmDetectorCaskade* detector, int maxX, int maxY){ - CvLSVMFeaturePyramidCaskade *H_PCA; +CvLSVMFeaturePyramidCascade* createPCA_FeaturePyramid(CvLSVMFeaturePyramidCascade* H, CvLatentSvmDetectorCascade* detector, int maxX, int maxY){ + CvLSVMFeaturePyramidCascade *H_PCA; int i, j, k, l; int max_l = detector->pca_size; int p = H->pyramid[0]->numFeatures; @@ -239,13 +239,13 @@ int clippingBoxes(int width, int height, // RESULT // Feature pyramid with nullable border */ -CvLSVMFeaturePyramidCaskade* createFeaturePyramidWithBorder(IplImage *image, +CvLSVMFeaturePyramidCascade* createFeaturePyramidWithBorder(IplImage *image, int maxXBorder, int maxYBorder) { int opResult; int bx, by; int level; - CvLSVMFeaturePyramidCaskade *H; + CvLSVMFeaturePyramidCascade *H; // Obtaining feature pyramid opResult = getFeaturePyramid(image, &H); @@ -303,7 +303,7 @@ int estimateBoxes(CvPoint *points, int *levels, int kPoints, // // API // int searchObjectThreshold(const featurePyramid *H, - const CvLSVMFilterObjectCaskade **all_F, int n, + const CvLSVMFilterObjectCascade **all_F, int n, float b, int maxXBorder, int maxYBorder, float scoreThreshold, @@ -329,9 +329,9 @@ int estimateBoxes(CvPoint *points, int *levels, int kPoints, // RESULT // Error status */ -int searchObjectThreshold(const CvLSVMFeaturePyramidCaskade *H, - const CvLSVMFeaturePyramidCaskade *H_PCA, - const CvLSVMFilterObjectCaskade **all_F, int n, +int searchObjectThreshold(const CvLSVMFeaturePyramidCascade *H, + const CvLSVMFeaturePyramidCascade *H_PCA, + const CvLSVMFilterObjectCascade **all_F, int n, float b, int maxXBorder, int maxYBorder, float scoreThreshold, @@ -630,7 +630,7 @@ int getOppositePoint(CvPoint point, // // API // int showRootFilterBoxes(const IplImage *image, - const CvLSVMFilterObjectCaskade *filter, + const CvLSVMFilterObjectCascade *filter, CvPoint *points, int *levels, int kPoints, CvScalar color, int thickness, int line_type, int shift); @@ -650,7 +650,7 @@ int getOppositePoint(CvPoint point, // Error status */ int showRootFilterBoxes(IplImage *image, - const CvLSVMFilterObjectCaskade *filter, + const CvLSVMFilterObjectCascade *filter, CvPoint *points, int *levels, int kPoints, CvScalar color, int thickness, int line_type, int shift) @@ -679,7 +679,7 @@ int showRootFilterBoxes(IplImage *image, // // API // int showPartFilterBoxes(const IplImage *image, - const CvLSVMFilterObjectCaskade *filter, + const CvLSVMFilterObjectCascade *filter, CvPoint *points, int *levels, int kPoints, CvScalar color, int thickness, int line_type, int shift); @@ -700,7 +700,7 @@ int showRootFilterBoxes(IplImage *image, // Error status */ int showPartFilterBoxes(IplImage *image, - const CvLSVMFilterObjectCaskade **filters, + const CvLSVMFilterObjectCascade **filters, int n, CvPoint **partsDisplacement, int *levels, int kPoints, CvScalar color, int thickness, @@ -770,7 +770,7 @@ int showBoxes(IplImage *img, //// Computation maximum filter size for each dimension //// //// API -//// int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents, +//// int getMaxFilterDims(const CvLSVMFilterObjectCascade **filters, int kComponents, // const int *kPartFilters, // unsigned int *maxXBorder, unsigned int *maxYBorder); //// INPUT @@ -784,7 +784,7 @@ int showBoxes(IplImage *img, //// RESULT //// Error status //*/ -//int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents, +//int getMaxFilterDims(const CvLSVMFilterObjectCascade **filters, int kComponents, // const int *kPartFilters, // unsigned int *maxXBorder, unsigned int *maxYBorder) //{ @@ -812,9 +812,9 @@ int showBoxes(IplImage *img, class PathOfModel :public ParallelLoopBody{ int *componentIndex; - const CvLSVMFeaturePyramidCaskade *H; - const CvLSVMFeaturePyramidCaskade *H_PCA; - const CvLSVMFilterObjectCaskade **filters; + const CvLSVMFeaturePyramidCascade *H; + const CvLSVMFeaturePyramidCascade *H_PCA; + const CvLSVMFilterObjectCascade **filters; const int *kPartFilters; const float *b; unsigned int maxXBorder, maxYBorder; @@ -826,9 +826,9 @@ class PathOfModel :public ParallelLoopBody{ public: PathOfModel( int *_componentIndex, - const CvLSVMFeaturePyramidCaskade *_H, - const CvLSVMFeaturePyramidCaskade *_H_PCA, - const CvLSVMFilterObjectCaskade **_filters, + const CvLSVMFeaturePyramidCascade *_H, + const CvLSVMFeaturePyramidCascade *_H_PCA, + const CvLSVMFilterObjectCascade **_filters, const int *_kPartFilters, const float *_b, unsigned int _maxXBorder, unsigned int _maxYBorder, @@ -878,7 +878,7 @@ public: // // API // int searchObjectThresholdSomeComponents(const featurePyramid *H, - const CvLSVMFilterObjectCaskade **filters, + const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, const float *b, float scoreThreshold, CvPoint **points, CvPoint **oppPoints, @@ -898,9 +898,9 @@ public: // RESULT // Error status */ -int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramidCaskade *H, - const CvLSVMFeaturePyramidCaskade *H_PCA, - const CvLSVMFilterObjectCaskade **filters, +int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramidCascade *H, + const CvLSVMFeaturePyramidCascade *H_PCA, + const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, const float *b, float scoreThreshold, CvPoint **points, CvPoint **oppPoints, diff --git a/modules/latentsvm/src/lsvmc_latentsvmdetector.cpp b/modules/latentsvm/src/lsvmc_latentsvmdetector.cpp index e24495fb8..731d5e981 100644 --- a/modules/latentsvm/src/lsvmc_latentsvmdetector.cpp +++ b/modules/latentsvm/src/lsvmc_latentsvmdetector.cpp @@ -51,10 +51,10 @@ std::string extractModelName( const std::string& filename ); const int pca_size = 31; -CvLatentSvmDetectorCaskade* cvLoadLatentSvmDetectorCaskade(const char* filename); -void cvReleaseLatentSvmDetectorCaskade(CvLatentSvmDetectorCaskade** detector); -CvSeq* cvLatentSvmDetectObjectsCaskade(IplImage* image, - CvLatentSvmDetectorCaskade* detector, +CvLatentSvmDetectorCascade* cvLoadLatentSvmDetectorCascade(const char* filename); +void cvReleaseLatentSvmDetectorCascade(CvLatentSvmDetectorCascade** detector); +CvSeq* cvLatentSvmDetectObjectsCascade(IplImage* image, + CvLatentSvmDetectorCascade* detector, CvMemStorage* storage, float overlap_threshold); @@ -62,17 +62,17 @@ CvSeq* cvLatentSvmDetectObjectsCaskade(IplImage* image, // load trained detector from a file // // API -// CvLatentSvmDetectorCaskade* cvLoadLatentSvmDetector(const char* filename); +// CvLatentSvmDetectorCascade* cvLoadLatentSvmDetector(const char* filename); // INPUT // filename - path to the file containing the parameters of // - trained Latent SVM detector // OUTPUT // trained Latent SVM detector in internal representation */ -CvLatentSvmDetectorCaskade* cvLoadLatentSvmDetectorCaskade(const char* filename) +CvLatentSvmDetectorCascade* cvLoadLatentSvmDetectorCascade(const char* filename) { - CvLatentSvmDetectorCaskade* detector = 0; - CvLSVMFilterObjectCaskade** filters = 0; + CvLatentSvmDetectorCascade* detector = 0; + CvLSVMFilterObjectCascade** filters = 0; int kFilters = 0; int kComponents = 0; int* kPartFilters = 0; @@ -84,7 +84,7 @@ CvLatentSvmDetectorCaskade* cvLoadLatentSvmDetectorCaskade(const char* filename) err_code = loadModel(filename, &filters, &kFilters, &kComponents, &kPartFilters, &b, &scoreThreshold, &PCAcoeff); if (err_code != LATENT_SVM_OK) return 0; - detector = (CvLatentSvmDetectorCaskade*)malloc(sizeof(CvLatentSvmDetectorCaskade)); + detector = (CvLatentSvmDetectorCascade*)malloc(sizeof(CvLatentSvmDetectorCascade)); detector->filters = filters; detector->b = b; detector->num_components = kComponents; @@ -98,15 +98,15 @@ CvLatentSvmDetectorCaskade* cvLoadLatentSvmDetectorCaskade(const char* filename) } /* -// release memory allocated for CvLatentSvmDetectorCaskade structure +// release memory allocated for CvLatentSvmDetectorCascade structure // // API -// void cvReleaseLatentSvmDetector(CvLatentSvmDetectorCaskade** detector); +// void cvReleaseLatentSvmDetector(CvLatentSvmDetectorCascade** detector); // INPUT -// detector - CvLatentSvmDetectorCaskade structure to be released +// detector - CvLatentSvmDetectorCascade structure to be released // OUTPUT */ -void cvReleaseLatentSvmDetectorCaskade(CvLatentSvmDetectorCaskade** detector) +void cvReleaseLatentSvmDetectorCascade(CvLatentSvmDetectorCascade** detector) { free((*detector)->b); free((*detector)->num_part_filters); @@ -127,7 +127,7 @@ void cvReleaseLatentSvmDetectorCaskade(CvLatentSvmDetectorCaskade** detector) // // API // CvSeq* cvLatentSvmDetectObjects(const IplImage* image, -// CvLatentSvmDetectorCaskade* detector, +// CvLatentSvmDetectorCascade* detector, // CvMemStorage* storage, // float overlap_threshold = 0.5f); // INPUT @@ -139,13 +139,13 @@ void cvReleaseLatentSvmDetectorCaskade(CvLatentSvmDetectorCaskade** detector) // OUTPUT // sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures) */ -CvSeq* cvLatentSvmDetectObjectsCaskade(IplImage* image, - CvLatentSvmDetectorCaskade* detector, +CvSeq* cvLatentSvmDetectObjectsCascade(IplImage* image, + CvLatentSvmDetectorCascade* detector, CvMemStorage* storage, float overlap_threshold) { - CvLSVMFeaturePyramidCaskade *H = 0; - CvLSVMFeaturePyramidCaskade *H_PCA = 0; + CvLSVMFeaturePyramidCascade *H = 0; + CvLSVMFeaturePyramidCascade *H_PCA = 0; CvPoint *points = 0, *oppPoints = 0; int kPoints = 0; float *score = 0; @@ -161,18 +161,18 @@ CvSeq* cvLatentSvmDetectObjectsCaskade(IplImage* image, cvCvtColor(image, image, CV_BGR2RGB); // Getting maximum filter dimensions - getMaxFilterDims((const CvLSVMFilterObjectCaskade**)(detector->filters), detector->num_components, + getMaxFilterDims((const CvLSVMFilterObjectCascade**)(detector->filters), detector->num_components, detector->num_part_filters, &maxXBorder, &maxYBorder); // Create feature pyramid with nullable border H = createFeaturePyramidWithBorder(image, maxXBorder, maxYBorder); - // Create PSA feature pyramid + // Create PCA feature pyramid H_PCA = createPCA_FeaturePyramid(H, detector, maxXBorder, maxYBorder); FeaturePyramid32(H, maxXBorder, maxYBorder); // Search object - error = searchObjectThresholdSomeComponents(H, H_PCA,(const CvLSVMFilterObjectCaskade**)(detector->filters), + error = searchObjectThresholdSomeComponents(H, H_PCA,(const CvLSVMFilterObjectCascade**)(detector->filters), detector->num_components, detector->num_part_filters, detector->b, detector->score_threshold, &points, &oppPoints, &score, &kPoints); if (error != LATENT_SVM_OK) @@ -226,7 +226,7 @@ public: size_t getClassCount() const; private: - std::vector detectors; + std::vector detectors; std::vector classNames; }; @@ -250,7 +250,7 @@ LSVMDetectorImpl::LSVMDetectorImpl( const std::vector& filenames, c if( filename.length() < 5 || filename.substr(filename.length()-4, 4) != ".xml" ) continue; - CvLatentSvmDetectorCaskade* detector = cvLoadLatentSvmDetectorCaskade( filename.c_str() ); + CvLatentSvmDetectorCascade* detector = cvLoadLatentSvmDetectorCascade( filename.c_str() ); if( detector ) { detectors.push_back( detector ); @@ -267,7 +267,7 @@ LSVMDetectorImpl::LSVMDetectorImpl( const std::vector& filenames, c LSVMDetectorImpl::~LSVMDetectorImpl() { for(size_t i = 0; i < detectors.size(); i++) - cv::lsvm::cvReleaseLatentSvmDetectorCaskade(&detectors[i]); + cv::lsvm::cvReleaseLatentSvmDetectorCascade(&detectors[i]); } bool LSVMDetectorImpl::isEmpty() const @@ -313,7 +313,7 @@ void LSVMDetectorImpl::detect( cv::Mat const &image, { IplImage image_ipl = image; CvMemStorage* storage = cvCreateMemStorage(0); - CvSeq* detections = cv::lsvm::cvLatentSvmDetectObjectsCaskade( &image_ipl, (CvLatentSvmDetectorCaskade*)(detectors[classID]), storage, overlapThreshold); + CvSeq* detections = cv::lsvm::cvLatentSvmDetectObjectsCascade( &image_ipl, (CvLatentSvmDetectorCascade*)(detectors[classID]), storage, overlapThreshold); // convert results objectDetections.reserve( objectDetections.size() + detections->total ); diff --git a/modules/latentsvm/src/lsvmc_matching.cpp b/modules/latentsvm/src/lsvmc_matching.cpp index 3795d7e4a..4470c2193 100644 --- a/modules/latentsvm/src/lsvmc_matching.cpp +++ b/modules/latentsvm/src/lsvmc_matching.cpp @@ -91,7 +91,7 @@ int computeBorderSize(int maxXBorder, int maxYBorder, int *bx, int *by) // RESULT // Error status */ -int addNullableBorder(CvLSVMFeatureMapCaskade *map, int bx, int by) +int addNullableBorder(CvLSVMFeatureMapCascade *map, int bx, int by) { int sizeX, sizeY, i, j, k; float *new_map; @@ -124,7 +124,7 @@ int addNullableBorder(CvLSVMFeatureMapCaskade *map, int bx, int by) // Computation maximum filter size for each dimension // // API -// int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents, +// int getMaxFilterDims(const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, unsigned int *maxXBorder, unsigned int *maxYBorder); // INPUT @@ -138,7 +138,7 @@ int addNullableBorder(CvLSVMFeatureMapCaskade *map, int bx, int by) // RESULT // Error status */ -int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents, +int getMaxFilterDims(const CvLSVMFilterObjectCascade **filters, int kComponents, const int *kPartFilters, unsigned int *maxXBorder, unsigned int *maxYBorder) { diff --git a/modules/latentsvm/src/lsvmc_parser.cpp b/modules/latentsvm/src/lsvmc_parser.cpp index b606b4f31..d551e9314 100644 --- a/modules/latentsvm/src/lsvmc_parser.cpp +++ b/modules/latentsvm/src/lsvmc_parser.cpp @@ -79,26 +79,26 @@ int isHYPOTHES (char *str); int isDEFORM (char *str); int getTeg (char *str); -void addFilter(CvLSVMFilterObjectCaskade *** model, int *last, int *max); +void addFilter(CvLSVMFilterObjectCascade *** model, int *last, int *max); -void parserCascadeThresholds (FILE * xmlf, CvLSVMFilterObjectCaskade * model); +void parserCascadeThresholds (FILE * xmlf, CvLSVMFilterObjectCascade * model); -void parserRFilter (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCaskade * model, float *b); +void parserRFilter (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCascade * model, float *b); -void parserV (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCaskade * model); +void parserV (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCascade * model); -void parserD (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCaskade * model); +void parserD (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCascade * model); -void parserPFilter (FILE * xmlf, int p, int pca, int /*N_path*/, CvLSVMFilterObjectCaskade * model); +void parserPFilter (FILE * xmlf, int p, int pca, int /*N_path*/, CvLSVMFilterObjectCascade * model); -void parserPFilterS (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCaskade *** model, int *last, int *max); +void parserPFilterS (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCascade *** model, int *last, int *max); -void parserComp (FILE * xmlf, int p, int pca, int *N_comp, CvLSVMFilterObjectCaskade *** model, float *b, int *last, int *max); +void parserComp (FILE * xmlf, int p, int pca, int *N_comp, CvLSVMFilterObjectCascade *** model, float *b, int *last, int *max); -void parserModel(FILE * xmlf, CvLSVMFilterObjectCaskade *** model, int *last, int *max, int **comp, float **b, int *count, float * score, float** PCAcoeff); +void parserModel(FILE * xmlf, CvLSVMFilterObjectCascade *** model, int *last, int *max, int **comp, float **b, int *count, float * score, float** PCAcoeff); void LSVMparser(const char * filename, - CvLSVMFilterObjectCaskade *** model, + CvLSVMFilterObjectCascade *** model, int *last, int *max, int **comp, @@ -336,21 +336,21 @@ int getTeg(char *str){ return sum; } -void addFilter(CvLSVMFilterObjectCaskade *** model, int *last, int *max) +void addFilter(CvLSVMFilterObjectCascade *** model, int *last, int *max) { - CvLSVMFilterObjectCaskade ** nmodel; + CvLSVMFilterObjectCascade ** nmodel; int i; (*last) ++; if((*last) >= (*max)){ (*max) += 10; - nmodel = (CvLSVMFilterObjectCaskade **)malloc(sizeof(CvLSVMFilterObjectCaskade *) * (*max)); + nmodel = (CvLSVMFilterObjectCascade **)malloc(sizeof(CvLSVMFilterObjectCascade *) * (*max)); for(i = 0; i < *last; i++){ nmodel[i] = (* model)[i]; } free(* model); (*model) = nmodel; } - (*model) [(*last)] = (CvLSVMFilterObjectCaskade *)malloc(sizeof(CvLSVMFilterObjectCaskade)); + (*model) [(*last)] = (CvLSVMFilterObjectCascade *)malloc(sizeof(CvLSVMFilterObjectCascade)); (*model) [(*last)]->Hypothesis = 0.0f; (*model) [(*last)]->Deformation = 0.0f; (*model) [(*last)]->Hypothesis_PCA = 0.0f; @@ -359,7 +359,7 @@ void addFilter(CvLSVMFilterObjectCaskade *** model, int *last, int *max) } //############################################## -void parserCascadeThresholds (FILE * xmlf, CvLSVMFilterObjectCaskade * model){ +void parserCascadeThresholds (FILE * xmlf, CvLSVMFilterObjectCascade * model){ int st = 0; int tag; int tagVal; @@ -438,7 +438,7 @@ void parserCascadeThresholds (FILE * xmlf, CvLSVMFilterObjectCaskade * model){ } //############################################## -void parserRFilter (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCaskade * model, float *b){ +void parserRFilter (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCascade * model, float *b){ int st = 0; int sizeX = 0, sizeY = 0; int tag; @@ -548,7 +548,7 @@ void parserRFilter (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCaskade * mo } } -void parserV (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCaskade * model){ +void parserV (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCascade * model){ int st = 0; int tag; int tagVal; @@ -611,7 +611,7 @@ void parserV (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCaskade * model){ } } } -void parserD (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCaskade * model){ +void parserD (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCascade * model){ int st = 0; int tag; int tagVal; @@ -700,7 +700,7 @@ void parserD (FILE * xmlf, int /*p*/, CvLSVMFilterObjectCaskade * model){ } } -void parserPFilter (FILE * xmlf, int p, int pca, int /*N_path*/, CvLSVMFilterObjectCaskade * model){ +void parserPFilter (FILE * xmlf, int p, int pca, int /*N_path*/, CvLSVMFilterObjectCascade * model){ int st = 0; int sizeX = 0, sizeY = 0; int tag; @@ -806,7 +806,7 @@ void parserPFilter (FILE * xmlf, int p, int pca, int /*N_path*/, CvLSVMFilterOb } } } -void parserPFilterS (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCaskade *** model, int *last, int *max){ +void parserPFilterS (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCascade *** model, int *last, int *max){ int st = 0; int N_path = 0; int tag; @@ -850,7 +850,7 @@ void parserPFilterS (FILE * xmlf, int p, int pca, CvLSVMFilterObjectCaskade *** } } } -void parserComp (FILE * xmlf, int p, int pca, int *N_comp, CvLSVMFilterObjectCaskade *** model, float *b, int *last, int *max){ +void parserComp (FILE * xmlf, int p, int pca, int *N_comp, CvLSVMFilterObjectCascade *** model, float *b, int *last, int *max){ int st = 0; int tag; int tagVal; @@ -895,7 +895,7 @@ void parserComp (FILE * xmlf, int p, int pca, int *N_comp, CvLSVMFilterObjectCas } } } -void parserModel(FILE * xmlf, CvLSVMFilterObjectCaskade *** model, int *last, int *max, int **comp, float **b, int *count, float * score, float** PCAcoeff){ +void parserModel(FILE * xmlf, CvLSVMFilterObjectCascade *** model, int *last, int *max, int **comp, float **b, int *count, float * score, float** PCAcoeff){ int p = 0, pca = 0; int N_comp = 0; int * cmp; @@ -1023,7 +1023,7 @@ void parserModel(FILE * xmlf, CvLSVMFilterObjectCaskade *** model, int *last, in } void LSVMparser(const char * filename, - CvLSVMFilterObjectCaskade *** model, + CvLSVMFilterObjectCascade *** model, int *last, int *max, int **comp, @@ -1040,7 +1040,7 @@ void LSVMparser(const char * filename, (*max) = 10; (*last) = -1; - (*model) = (CvLSVMFilterObjectCaskade ** )malloc((sizeof(CvLSVMFilterObjectCaskade * )) * (*max)); + (*model) = (CvLSVMFilterObjectCascade ** )malloc((sizeof(CvLSVMFilterObjectCascade * )) * (*max)); //printf("parse : %s\n", filename); xmlf = fopen(filename, "rb"); @@ -1075,7 +1075,7 @@ void LSVMparser(const char * filename, int loadModel( const char *modelPath, - CvLSVMFilterObjectCaskade ***filters, + CvLSVMFilterObjectCascade ***filters, int *kFilters, int *kComponents, int **kPartFilters, diff --git a/modules/latentsvm/src/lsvmc_routine.cpp b/modules/latentsvm/src/lsvmc_routine.cpp index 1c7152c9f..1a751be64 100644 --- a/modules/latentsvm/src/lsvmc_routine.cpp +++ b/modules/latentsvm/src/lsvmc_routine.cpp @@ -45,11 +45,11 @@ namespace cv { namespace lsvm { -int allocFilterObject(CvLSVMFilterObjectCaskade **obj, const int sizeX, +int allocFilterObject(CvLSVMFilterObjectCascade **obj, const int sizeX, const int sizeY, const int numFeatures) { int i; - (*obj) = (CvLSVMFilterObjectCaskade *)malloc(sizeof(CvLSVMFilterObjectCaskade)); + (*obj) = (CvLSVMFilterObjectCascade *)malloc(sizeof(CvLSVMFilterObjectCascade)); (*obj)->sizeX = sizeX; (*obj)->sizeY = sizeY; (*obj)->numFeatures = numFeatures; @@ -68,7 +68,7 @@ int allocFilterObject(CvLSVMFilterObjectCaskade **obj, const int sizeX, } return LATENT_SVM_OK; } -int freeFilterObject (CvLSVMFilterObjectCaskade **obj) +int freeFilterObject (CvLSVMFilterObjectCascade **obj) { if(*obj == NULL) return LATENT_SVM_MEM_NULL; free((*obj)->H); @@ -77,11 +77,11 @@ int freeFilterObject (CvLSVMFilterObjectCaskade **obj) return LATENT_SVM_OK; } -int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX, +int allocFeatureMapObject(CvLSVMFeatureMapCascade **obj, const int sizeX, const int sizeY, const int numFeatures) { int i; - (*obj) = (CvLSVMFeatureMapCaskade *)malloc(sizeof(CvLSVMFeatureMapCaskade)); + (*obj) = (CvLSVMFeatureMapCascade *)malloc(sizeof(CvLSVMFeatureMapCascade)); (*obj)->sizeX = sizeX; (*obj)->sizeY = sizeY; (*obj)->numFeatures = numFeatures; @@ -93,7 +93,7 @@ int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX, } return LATENT_SVM_OK; } -int freeFeatureMapObject (CvLSVMFeatureMapCaskade **obj) +int freeFeatureMapObject (CvLSVMFeatureMapCascade **obj) { if(*obj == NULL) return LATENT_SVM_MEM_NULL; free((*obj)->map); @@ -102,17 +102,17 @@ int freeFeatureMapObject (CvLSVMFeatureMapCaskade **obj) return LATENT_SVM_OK; } -int allocFeaturePyramidObject(CvLSVMFeaturePyramidCaskade **obj, +int allocFeaturePyramidObject(CvLSVMFeaturePyramidCascade **obj, const int numLevels) { - (*obj) = (CvLSVMFeaturePyramidCaskade *)malloc(sizeof(CvLSVMFeaturePyramidCaskade)); + (*obj) = (CvLSVMFeaturePyramidCascade *)malloc(sizeof(CvLSVMFeaturePyramidCascade)); (*obj)->numLevels = numLevels; - (*obj)->pyramid = (CvLSVMFeatureMapCaskade **)malloc( - sizeof(CvLSVMFeatureMapCaskade *) * numLevels); + (*obj)->pyramid = (CvLSVMFeatureMapCascade **)malloc( + sizeof(CvLSVMFeatureMapCascade *) * numLevels); return LATENT_SVM_OK; } -int freeFeaturePyramidObject (CvLSVMFeaturePyramidCaskade **obj) +int freeFeaturePyramidObject (CvLSVMFeaturePyramidCascade **obj) { int i; if(*obj == NULL) return LATENT_SVM_MEM_NULL; diff --git a/modules/latentsvm/test/test_latentsvmdetector_cascade.cpp b/modules/latentsvm/test/test_latentsvmdetector_cascade.cpp index a819dd3d9..dbbdbccf2 100644 --- a/modules/latentsvm/test/test_latentsvmdetector_cascade.cpp +++ b/modules/latentsvm/test/test_latentsvmdetector_cascade.cpp @@ -56,7 +56,7 @@ using namespace cv; const float score_thr = 0.05f; -class LatentSVMDetectorCaskadeTest : public cvtest::BaseTest +class LatentSVMDetectorCascadeTest : public cvtest::BaseTest { protected: void run(int); @@ -125,15 +125,16 @@ bool compareResults( const std::vector& cal return true; } -void LatentSVMDetectorCaskadeTest::run( int /* start_from */) +void LatentSVMDetectorCascadeTest::run( int /* start_from */) { - std::string img_path_cat = std::string(ts->get_data_path()) + "cat.png"; - std::string img_path_cars = std::string(ts->get_data_path()) + "cars.png"; + std::string test_data_path = ts->get_data_path() + "latentsvmdetector/"; + std::string img_path_cat = test_data_path + "cat.png"; + std::string img_path_cars = test_data_path + "cars.png"; - std::string model_path_cat = std::string(ts->get_data_path()) + "models_VOC2007_cascade/cat.xml"; - std::string model_path_car = std::string(ts->get_data_path()) + "models_VOC2007_cascade/car.xml"; + std::string model_path_cat = test_data_path + "models_VOC2007_cascade/cat.xml"; + std::string model_path_car = test_data_path + "models_VOC2007_cascade/car.xml"; - std::string true_res_path = std::string(ts->get_data_path()) + "results_cascade.xml"; + std::string true_res_path = test_data_path + "results_cascade.xml"; #ifdef HAVE_TBB @@ -214,4 +215,4 @@ void LatentSVMDetectorCaskadeTest::run( int /* start_from */) ts->set_failed_test_info( cvtest::TS::OK); } -TEST(Objdetect_LatentSVMDetectorCaskade_cpp, regression) { LatentSVMDetectorCaskadeTest test; test.safe_run(); } +TEST(Objdetect_LatentSVMDetectorCascade_cpp, regression) { LatentSVMDetectorCascadeTest test; test.safe_run(); } diff --git a/modules/latentsvm/test/test_main.cpp b/modules/latentsvm/test/test_main.cpp index 6d0975e24..6b2499344 100644 --- a/modules/latentsvm/test/test_main.cpp +++ b/modules/latentsvm/test/test_main.cpp @@ -1,3 +1,3 @@ #include "test_precomp.hpp" -CV_TEST_MAIN("latentsvm") +CV_TEST_MAIN("cv") From aa11ac48f13fe0e12bd08b1b4a4d3fc0eb9d1884 Mon Sep 17 00:00:00 2001 From: Maksim Shabunin Date: Thu, 26 Mar 2015 11:39:11 +0300 Subject: [PATCH 30/32] Wrap face module with python and java --- modules/face/include/opencv2/face/facerec.hpp | 42 +++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/modules/face/include/opencv2/face/facerec.hpp b/modules/face/include/opencv2/face/facerec.hpp index e5f9863dc..40f62f165 100644 --- a/modules/face/include/opencv2/face/facerec.hpp +++ b/modules/face/include/opencv2/face/facerec.hpp @@ -21,18 +21,18 @@ class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer { public: /** @see setNumComponents */ - virtual int getNumComponents() const = 0; + CV_WRAP virtual int getNumComponents() const = 0; /** @copybrief getNumComponents @see getNumComponents */ - virtual void setNumComponents(int val) = 0; + CV_WRAP virtual void setNumComponents(int val) = 0; /** @see setThreshold */ - virtual double getThreshold() const = 0; + CV_WRAP virtual double getThreshold() const = 0; /** @copybrief getThreshold @see getThreshold */ - virtual void setThreshold(double val) = 0; - virtual std::vector getProjections() const = 0; - virtual cv::Mat getLabels() const = 0; - virtual cv::Mat getEigenValues() const = 0; - virtual cv::Mat getEigenVectors() const = 0; - virtual cv::Mat getMean() const = 0; + CV_WRAP virtual void setThreshold(double val) = 0; + CV_WRAP virtual std::vector getProjections() const = 0; + CV_WRAP virtual cv::Mat getLabels() const = 0; + CV_WRAP virtual cv::Mat getEigenValues() const = 0; + CV_WRAP virtual cv::Mat getEigenVectors() const = 0; + CV_WRAP virtual cv::Mat getMean() const = 0; }; /** @@ -102,27 +102,27 @@ class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer { public: /** @see setGridX */ - virtual int getGridX() const = 0; + CV_WRAP virtual int getGridX() const = 0; /** @copybrief getGridX @see getGridX */ - virtual void setGridX(int val) = 0; + CV_WRAP virtual void setGridX(int val) = 0; /** @see setGridY */ - virtual int getGridY() const = 0; + CV_WRAP virtual int getGridY() const = 0; /** @copybrief getGridY @see getGridY */ - virtual void setGridY(int val) = 0; + CV_WRAP virtual void setGridY(int val) = 0; /** @see setRadius */ - virtual int getRadius() const = 0; + CV_WRAP virtual int getRadius() const = 0; /** @copybrief getRadius @see getRadius */ - virtual void setRadius(int val) = 0; + CV_WRAP virtual void setRadius(int val) = 0; /** @see setNeighbors */ - virtual int getNeighbors() const = 0; + CV_WRAP virtual int getNeighbors() const = 0; /** @copybrief getNeighbors @see getNeighbors */ - virtual void setNeighbors(int val) = 0; + CV_WRAP virtual void setNeighbors(int val) = 0; /** @see setThreshold */ - virtual double getThreshold() const = 0; + CV_WRAP virtual double getThreshold() const = 0; /** @copybrief getThreshold @see getThreshold */ - virtual void setThreshold(double val) = 0; - virtual std::vector getHistograms() const = 0; - virtual cv::Mat getLabels() const = 0; + CV_WRAP virtual void setThreshold(double val) = 0; + CV_WRAP virtual std::vector getHistograms() const = 0; + CV_WRAP virtual cv::Mat getLabels() const = 0; }; /** From dd9531a5eb16d450e4a21feedb2ba805a92bf445 Mon Sep 17 00:00:00 2001 From: Vladimir Date: Thu, 26 Mar 2015 22:11:02 +0900 Subject: [PATCH 31/32] Unused header deleted --- modules/tracking/src/precomp.hpp | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/tracking/src/precomp.hpp b/modules/tracking/src/precomp.hpp index 1cad0a5f0..98fbce135 100644 --- a/modules/tracking/src/precomp.hpp +++ b/modules/tracking/src/precomp.hpp @@ -44,6 +44,5 @@ #include "opencv2/tracking.hpp" #include "opencv2/core/utility.hpp" -//#include "opencv2/core/private.hpp" #endif From 97cf5ade023466d90861b696322a1e479a5bab0c Mon Sep 17 00:00:00 2001 From: Alfonso Sanchez-Beato Date: Fri, 3 Apr 2015 12:15:57 +0200 Subject: [PATCH 32/32] Adapt reg sample to changes in features detectors Adapt to changes in the interface of feature detectors, using now Feature2D interface. --- modules/reg/samples/map_test.cpp | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/modules/reg/samples/map_test.cpp b/modules/reg/samples/map_test.cpp index 38fa00b7d..3e8846de2 100644 --- a/modules/reg/samples/map_test.cpp +++ b/modules/reg/samples/map_test.cpp @@ -289,23 +289,25 @@ static void calcHomographyFeature(const Mat& image1, const Mat& image2) else image2.copyTo(gray_image2); - //-- Step 1: Detect the keypoints using SURF Detector + //-- Step 1: Detect the keypoints using SIFT or SURF Detector +#ifdef USE_SIFT + Ptr features = SIFT::create(); +#else int minHessian = 400; - - SurfFeatureDetector detector(minHessian); + Ptr features = SURF::create(minHessian); +#endif std::vector keypoints_object, keypoints_scene; - detector.detect(gray_image1, keypoints_object); - detector.detect(gray_image2, keypoints_scene); + features->detect(gray_image1, keypoints_object); + features->detect(gray_image2, keypoints_scene); //-- Step 2: Calculate descriptors (feature vectors) - SurfDescriptorExtractor extractor; Mat descriptors_object, descriptors_scene; - extractor.compute(gray_image1, keypoints_object, descriptors_object); - extractor.compute(gray_image2, keypoints_scene, descriptors_scene); + features->compute(gray_image1, keypoints_object, descriptors_object); + features->compute(gray_image2, keypoints_scene, descriptors_scene); //-- Step 3: Matching descriptor vectors using FLANN matcher FlannBasedMatcher matcher; @@ -431,4 +433,3 @@ int main(void) return 0; } -

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