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Open Source Computer Vision Library
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260 lines
9.9 KiB
260 lines
9.9 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#ifndef __OPENCV_GPU_HPP__ |
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#define __OPENCV_GPU_HPP__ |
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#ifndef __cplusplus |
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# error gpu.hpp header must be compiled as C++ |
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#endif |
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#include "opencv2/core/gpu.hpp" |
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#if !defined(__OPENCV_BUILD) && !defined(OPENCV_GPU_SKIP_INCLUDE) |
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#include "opencv2/opencv_modules.hpp" |
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#ifdef HAVE_OPENCV_GPUARITHM |
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#include "opencv2/gpuarithm.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUWARPING |
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#include "opencv2/gpuwarping.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUFILTERS |
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#include "opencv2/gpufilters.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUIMGPROC |
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#include "opencv2/gpuimgproc.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUFEATURES2D |
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#include "opencv2/gpufeatures2d.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUOPTFLOW |
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#include "opencv2/gpuoptflow.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUBGSEGM |
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#include "opencv2/gpubgsegm.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUSTEREO |
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#include "opencv2/gpustereo.hpp" |
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#endif |
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#ifdef HAVE_OPENCV_GPUCODEC |
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#include "opencv2/gpucodec.hpp" |
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#endif |
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#endif |
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namespace cv { namespace gpu { |
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// |
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struct CV_EXPORTS HOGConfidence |
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{ |
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double scale; |
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std::vector<Point> locations; |
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std::vector<double> confidences; |
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std::vector<double> part_scores[4]; |
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}; |
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struct CV_EXPORTS HOGDescriptor |
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{ |
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enum { DEFAULT_WIN_SIGMA = -1 }; |
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enum { DEFAULT_NLEVELS = 64 }; |
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enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL }; |
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), |
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), |
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, |
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double threshold_L2hys=0.2, bool gamma_correction=true, |
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int nlevels=DEFAULT_NLEVELS); |
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size_t getDescriptorSize() const; |
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size_t getBlockHistogramSize() const; |
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void setSVMDetector(const std::vector<float>& detector); |
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static std::vector<float> getDefaultPeopleDetector(); |
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static std::vector<float> getPeopleDetector48x96(); |
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static std::vector<float> getPeopleDetector64x128(); |
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void detect(const GpuMat& img, std::vector<Point>& found_locations, |
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double hit_threshold=0, Size win_stride=Size(), |
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Size padding=Size()); |
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void detectMultiScale(const GpuMat& img, std::vector<Rect>& found_locations, |
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double hit_threshold=0, Size win_stride=Size(), |
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Size padding=Size(), double scale0=1.05, |
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int group_threshold=2); |
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void computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold, |
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Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences); |
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void computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations, |
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double hit_threshold, Size win_stride, Size padding, |
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std::vector<HOGConfidence> &conf_out, int group_threshold); |
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void getDescriptors(const GpuMat& img, Size win_stride, |
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GpuMat& descriptors, |
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int descr_format=DESCR_FORMAT_COL_BY_COL); |
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Size win_size; |
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Size block_size; |
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Size block_stride; |
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Size cell_size; |
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int nbins; |
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double win_sigma; |
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double threshold_L2hys; |
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bool gamma_correction; |
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int nlevels; |
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protected: |
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void computeBlockHistograms(const GpuMat& img); |
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void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle); |
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double getWinSigma() const; |
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bool checkDetectorSize() const; |
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static int numPartsWithin(int size, int part_size, int stride); |
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static Size numPartsWithin(Size size, Size part_size, Size stride); |
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// Coefficients of the separating plane |
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float free_coef; |
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GpuMat detector; |
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// Results of the last classification step |
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GpuMat labels, labels_buf; |
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Mat labels_host; |
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// Results of the last histogram evaluation step |
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GpuMat block_hists, block_hists_buf; |
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// Gradients conputation results |
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GpuMat grad, qangle, grad_buf, qangle_buf; |
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// returns subbuffer with required size, reallocates buffer if nessesary. |
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static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf); |
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static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf); |
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std::vector<GpuMat> image_scales; |
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}; |
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//////////////////////////// CascadeClassifier //////////////////////////// |
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// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny. |
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class CV_EXPORTS CascadeClassifier_GPU |
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{ |
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public: |
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CascadeClassifier_GPU(); |
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CascadeClassifier_GPU(const String& filename); |
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~CascadeClassifier_GPU(); |
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bool empty() const; |
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bool load(const String& filename); |
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void release(); |
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/* returns number of detected objects */ |
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size()); |
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4); |
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bool findLargestObject; |
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bool visualizeInPlace; |
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Size getClassifierSize() const; |
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private: |
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struct CascadeClassifierImpl; |
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CascadeClassifierImpl* impl; |
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struct HaarCascade; |
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struct LbpCascade; |
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friend class CascadeClassifier_GPU_LBP; |
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}; |
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//////////////////////////// Labeling //////////////////////////// |
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//!performs labeling via graph cuts of a 2D regular 4-connected graph. |
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CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, |
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GpuMat& buf, Stream& stream = Stream::Null()); |
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//!performs labeling via graph cuts of a 2D regular 8-connected graph. |
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CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight, |
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GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight, |
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GpuMat& labels, |
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GpuMat& buf, Stream& stream = Stream::Null()); |
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//! compute mask for Generalized Flood fill componetns labeling. |
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CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null()); |
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//! performs connected componnents labeling. |
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CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null()); |
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//////////////////////////// Calib3d //////////////////////////// |
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CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, |
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GpuMat& dst, Stream& stream = Stream::Null()); |
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CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, |
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const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst, |
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Stream& stream = Stream::Null()); |
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CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat, |
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const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false, |
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int num_iters=100, float max_dist=8.0, int min_inlier_count=100, |
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std::vector<int>* inliers=NULL); |
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//////////////////////////// VStab //////////////////////////// |
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//! removes points (CV_32FC2, single row matrix) with zero mask value |
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CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask); |
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CV_EXPORTS void calcWobbleSuppressionMaps( |
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int left, int idx, int right, Size size, const Mat &ml, const Mat &mr, |
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GpuMat &mapx, GpuMat &mapy); |
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}} // namespace cv { namespace gpu { |
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#endif /* __OPENCV_GPU_HPP__ */
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