mirror of https://github.com/opencv/opencv.git
Vladislav Vinogradov
12 years ago
parent
620c699456
commit
7a0d6f7733
27 changed files with 3927 additions and 3 deletions
@ -0,0 +1,33 @@ |
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if(ANDROID OR IOS) |
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ocv_module_disable(superres) |
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endif() |
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set(the_description "Super Resolution") |
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ocv_add_module(superres opencv_imgproc opencv_video OPTIONAL opencv_gpu opencv_highgui) |
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ocv_module_include_directories() |
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ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef /wd4127) |
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if(HAVE_CUDA) |
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string(REPLACE "-Wsign-promo" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") |
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ocv_source_group("Src\\Cuda" GLOB "src/cuda/*.cu") |
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ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/include" ${CUDA_INCLUDE_DIRS}) |
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file(GLOB lib_cuda "src/cuda/*.cu") |
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ocv_cuda_compile(cuda_objs ${lib_cuda}) |
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set(cuda_link_libs ${CUDA_LIBRARIES}) |
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else() |
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set(lib_cuda "") |
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set(cuda_objs "") |
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set(cuda_link_libs "") |
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endif() |
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ocv_glob_module_sources(SOURCES ${lib_cuda} ${cuda_objs}) |
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ocv_create_module(${cuda_link_libs}) |
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ocv_add_precompiled_headers(${the_module}) |
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ocv_add_accuracy_tests() |
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ocv_add_perf_tests() |
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@ -0,0 +1,84 @@ |
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Super Resolution |
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================ |
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.. highlight:: cpp |
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The Super Resolution module contains a set of functions and classes that can be used to solve the problem of resolution enhancement. There are a few methods implemented, most of them are descibed in the papers [Farsiu03]_ and [Mitzel09]_. |
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superres::SuperResolution |
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------------------------- |
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Base class for Super Resolution algorithms. |
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.. ocv:class:: superres::SuperResolution : public Algorithm, public superres::FrameSource |
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The class is only used to define the common interface for the whole family of Super Resolution algorithms. |
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superres::SuperResolution::setInput |
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----------------------------------- |
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Set input frame source for Super Resolution algorithm. |
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.. ocv:function:: void superres::SuperResolution::setInput(const Ptr<FrameSource>& frameSource) |
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:param frameSource: Input frame source |
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superres::SuperResolution::nextFrame |
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------------------------------------ |
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Process next frame from input and return output result. |
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.. ocv:function:: void superres::SuperResolution::nextFrame(OutputArray frame) |
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:param frame: Output result |
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superres::SuperResolution::collectGarbage |
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----------------------------------------- |
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Clear all inner buffers. |
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.. ocv:function:: void superres::SuperResolution::collectGarbage() |
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superres::createSuperResolution_BTVL1 |
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------------------------------------- |
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Create Bilateral TV-L1 Super Resolution. |
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.. ocv:function:: Ptr<SuperResolution> superres::createSuperResolution_BTVL1() |
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.. ocv:function:: Ptr<SuperResolution> superres::createSuperResolution_BTVL1_GPU() |
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This class implements Super Resolution algorithm described in the papers [Farsiu03]_ and [Mitzel09]_ . |
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Here are important members of the class that control the algorithm, which you can set after constructing the class instance: |
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* **int scale** Scale factor. |
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* **int iterations** Iteration count. |
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* **double tau** Asymptotic value of steepest descent method. |
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* **double lambda** Weight parameter to balance data term and smoothness term. |
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* **double alpha** Parameter of spacial distribution in Bilateral-TV. |
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* **int btvKernelSize** Kernel size of Bilateral-TV filter. |
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* **int blurKernelSize** Gaussian blur kernel size. |
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* **double blurSigma** Gaussian blur sigma. |
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* **int temporalAreaRadius** Radius of the temporal search area. |
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* **Ptr<DenseOpticalFlowExt> opticalFlow** Dense optical flow algorithm. |
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.. [Farsiu03] S. Farsiu, D. Robinson, M. Elad, P. Milanfar. Fast and robust Super-Resolution. Proc 2003 IEEE Int Conf on Image Process, pp. 291–294, 2003. |
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.. [Mitzel09] D. Mitzel, T. Pock, T. Schoenemann, D. Cremers. Video super resolution using duality based TV-L1 optical flow. DAGM, 2009. |
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@ -0,0 +1,8 @@ |
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************************** |
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superres. Super Resolution |
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************************** |
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.. toctree:: |
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:maxdepth: 2 |
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super_resolution |
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@ -0,0 +1,73 @@ |
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/*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) 2009-2011, 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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//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// 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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|
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#ifndef __OPENCV_SUPERRES_OPTICAL_FLOW_HPP__ |
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#define __OPENCV_SUPERRES_OPTICAL_FLOW_HPP__ |
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#include "opencv2/core/core.hpp" |
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namespace cv |
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{ |
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namespace superres |
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{ |
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class CV_EXPORTS DenseOpticalFlowExt : public cv::Algorithm |
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{ |
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public: |
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virtual void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2 = noArray()) = 0; |
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virtual void collectGarbage() = 0; |
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}; |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback(); |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback_GPU(); |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Simple(); |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1(); |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1_GPU(); |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Brox_GPU(); |
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_GPU(); |
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} |
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} |
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#endif // __OPENCV_SUPERRES_OPTICAL_FLOW_HPP__
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@ -0,0 +1,98 @@ |
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/*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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// 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) 2009-2011, 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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//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// 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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// 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
|
||||
// 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,
|
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// 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
|
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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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|
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#ifndef __OPENCV_SUPERRES_HPP__ |
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#define __OPENCV_SUPERRES_HPP__ |
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#include "opencv2/core/core.hpp" |
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namespace cv |
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{ |
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namespace superres |
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{ |
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CV_EXPORTS bool initModule_superres(); |
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class CV_EXPORTS FrameSource |
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{ |
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public: |
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virtual ~FrameSource(); |
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virtual void nextFrame(OutputArray frame) = 0; |
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virtual void reset() = 0; |
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}; |
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Empty(); |
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Video(const std::string& fileName); |
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Video_GPU(const std::string& fileName); |
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Camera(int deviceId = 0); |
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class CV_EXPORTS SuperResolution : public cv::Algorithm, public FrameSource |
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{ |
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public: |
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void setInput(const Ptr<FrameSource>& frameSource); |
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void nextFrame(OutputArray frame); |
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void reset(); |
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virtual void collectGarbage(); |
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protected: |
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SuperResolution(); |
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virtual void initImpl(Ptr<FrameSource>& frameSource) = 0; |
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virtual void processImpl(Ptr<FrameSource>& frameSource, OutputArray output) = 0; |
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private: |
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Ptr<FrameSource> frameSource_; |
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bool firstCall_; |
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}; |
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// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
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// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
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CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1(); |
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CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_GPU(); |
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} |
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} |
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#endif // __OPENCV_SUPERRES_HPP__
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@ -0,0 +1,3 @@ |
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#include "perf_precomp.hpp" |
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CV_PERF_TEST_MAIN(superres) |
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#include "perf_precomp.hpp" |
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#ifdef __GNUC__ |
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# pragma GCC diagnostic ignored "-Wmissing-declarations" |
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# if defined __clang__ || defined __APPLE__ |
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# pragma GCC diagnostic ignored "-Wmissing-prototypes" |
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# pragma GCC diagnostic ignored "-Wextra" |
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# endif |
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#endif |
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#ifndef __OPENCV_PERF_PRECOMP_HPP__ |
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#define __OPENCV_PERF_PRECOMP_HPP__ |
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#ifdef HAVE_CVCONFIG_H |
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#include "cvconfig.h" |
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#endif |
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#include "opencv2/core/core.hpp" |
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#include "opencv2/core/gpumat.hpp" |
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#include "opencv2/ts/ts_perf.hpp" |
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#include "opencv2/superres/superres.hpp" |
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#include "opencv2/superres/optical_flow.hpp" |
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#ifdef GTEST_CREATE_SHARED_LIBRARY |
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#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined |
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#endif |
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#endif |
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@ -0,0 +1,153 @@ |
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#include "perf_precomp.hpp" |
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using namespace std; |
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using namespace std::tr1; |
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using namespace testing; |
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using namespace perf; |
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using namespace cv; |
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using namespace cv::superres; |
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using namespace cv::gpu; |
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#define GPU_SANITY_CHECK(mat, ...) \ |
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do{ \
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Mat gpu_##mat(mat); \
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SANITY_CHECK(gpu_##mat, ## __VA_ARGS__); \
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} while(0) |
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#define CPU_SANITY_CHECK(mat, ...) \ |
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do{ \
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Mat cpu_##mat(mat); \
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SANITY_CHECK(cpu_##mat, ## __VA_ARGS__); \
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} while(0) |
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namespace |
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{ |
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class OneFrameSource_CPU : public FrameSource |
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{ |
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public: |
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explicit OneFrameSource_CPU(const Mat& frame) : frame_(frame) {} |
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void nextFrame(OutputArray frame) |
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{ |
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frame.getMatRef() = frame_; |
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} |
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void reset() |
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{ |
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} |
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private: |
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Mat frame_; |
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}; |
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class OneFrameSource_GPU : public FrameSource |
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{ |
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public: |
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explicit OneFrameSource_GPU(const GpuMat& frame) : frame_(frame) {} |
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void nextFrame(OutputArray frame) |
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{ |
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frame.getGpuMatRef() = frame_; |
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} |
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void reset() |
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{ |
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} |
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private: |
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GpuMat frame_; |
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}; |
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class ZeroOpticalFlow : public DenseOpticalFlowExt |
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{ |
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public: |
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void calc(InputArray frame0, InputArray, OutputArray flow1, OutputArray flow2) |
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{ |
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cv::Size size = frame0.size(); |
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if (!flow2.needed()) |
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{ |
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flow1.create(size, CV_32FC2); |
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if (flow1.kind() == cv::_InputArray::GPU_MAT) |
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flow1.getGpuMatRef().setTo(cv::Scalar::all(0)); |
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else |
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flow1.getMatRef().setTo(cv::Scalar::all(0)); |
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} |
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else |
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{ |
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flow1.create(size, CV_32FC1); |
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flow2.create(size, CV_32FC1); |
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if (flow1.kind() == cv::_InputArray::GPU_MAT) |
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flow1.getGpuMatRef().setTo(cv::Scalar::all(0)); |
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else |
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flow1.getMatRef().setTo(cv::Scalar::all(0)); |
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if (flow2.kind() == cv::_InputArray::GPU_MAT) |
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flow2.getGpuMatRef().setTo(cv::Scalar::all(0)); |
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else |
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flow2.getMatRef().setTo(cv::Scalar::all(0)); |
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} |
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} |
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void collectGarbage() |
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{ |
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} |
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}; |
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} |
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PERF_TEST_P(Size_MatType, SuperResolution_BTVL1, |
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Combine(Values(szSmall64, szSmall128), |
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Values(MatType(CV_8UC1), MatType(CV_8UC3)))) |
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{ |
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declare.time(5 * 60); |
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const Size size = get<0>(GetParam()); |
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const int type = get<1>(GetParam()); |
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Mat frame(size, type); |
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declare.in(frame, WARMUP_RNG); |
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const int scale = 2; |
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const int iterations = 50; |
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const int temporalAreaRadius = 1; |
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Ptr<DenseOpticalFlowExt> opticalFlow(new ZeroOpticalFlow); |
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if (PERF_RUN_GPU()) |
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{ |
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Ptr<SuperResolution> superRes = createSuperResolution_BTVL1_GPU(); |
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superRes->set("scale", scale); |
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superRes->set("iterations", iterations); |
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superRes->set("temporalAreaRadius", temporalAreaRadius); |
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superRes->set("opticalFlow", opticalFlow); |
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superRes->setInput(new OneFrameSource_GPU(GpuMat(frame))); |
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GpuMat dst; |
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superRes->nextFrame(dst); |
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TEST_CYCLE_N(10) superRes->nextFrame(dst); |
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GPU_SANITY_CHECK(dst); |
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} |
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else |
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{ |
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Ptr<SuperResolution> superRes = createSuperResolution_BTVL1(); |
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superRes->set("scale", scale); |
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superRes->set("iterations", iterations); |
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superRes->set("temporalAreaRadius", temporalAreaRadius); |
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superRes->set("opticalFlow", opticalFlow); |
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superRes->setInput(new OneFrameSource_CPU(frame)); |
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Mat dst; |
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superRes->nextFrame(dst); |
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TEST_CYCLE_N(10) superRes->nextFrame(dst); |
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CPU_SANITY_CHECK(dst); |
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} |
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} |
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@ -0,0 +1,619 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
|
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// 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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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*/
|
||||
|
||||
// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
|
||||
// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
|
||||
|
||||
#include "precomp.hpp" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::superres; |
||||
using namespace cv::superres::detail; |
||||
|
||||
namespace |
||||
{ |
||||
void calcRelativeMotions(const vector<Mat>& forwardMotions, const vector<Mat>& backwardMotions, |
||||
vector<Mat>& relForwardMotions, vector<Mat>& relBackwardMotions, |
||||
int baseIdx, Size size) |
||||
{ |
||||
const int count = static_cast<int>(forwardMotions.size()); |
||||
|
||||
relForwardMotions.resize(count); |
||||
relForwardMotions[baseIdx].create(size, CV_32FC2); |
||||
relForwardMotions[baseIdx].setTo(Scalar::all(0)); |
||||
|
||||
relBackwardMotions.resize(count); |
||||
relBackwardMotions[baseIdx].create(size, CV_32FC2); |
||||
relBackwardMotions[baseIdx].setTo(Scalar::all(0)); |
||||
|
||||
for (int i = baseIdx - 1; i >= 0; --i) |
||||
{ |
||||
add(relForwardMotions[i + 1], forwardMotions[i], relForwardMotions[i]); |
||||
|
||||
add(relBackwardMotions[i + 1], backwardMotions[i + 1], relBackwardMotions[i]); |
||||
} |
||||
|
||||
for (int i = baseIdx + 1; i < count; ++i) |
||||
{ |
||||
add(relForwardMotions[i - 1], backwardMotions[i], relForwardMotions[i]); |
||||
|
||||
add(relBackwardMotions[i - 1], forwardMotions[i - 1], relBackwardMotions[i]); |
||||
} |
||||
} |
||||
|
||||
void upscaleMotions(const vector<Mat>& lowResMotions, vector<Mat>& highResMotions, int scale) |
||||
{ |
||||
highResMotions.resize(lowResMotions.size()); |
||||
|
||||
for (size_t i = 0; i < lowResMotions.size(); ++i) |
||||
{ |
||||
resize(lowResMotions[i], highResMotions[i], Size(), scale, scale, INTER_CUBIC); |
||||
multiply(highResMotions[i], Scalar::all(scale), highResMotions[i]); |
||||
} |
||||
} |
||||
|
||||
void buildMotionMaps(const Mat& forwardMotion, const Mat& backwardMotion, Mat& forwardMap, Mat& backwardMap) |
||||
{ |
||||
forwardMap.create(forwardMotion.size(), CV_32FC2); |
||||
backwardMap.create(forwardMotion.size(), CV_32FC2); |
||||
|
||||
for (int y = 0; y < forwardMotion.rows; ++y) |
||||
{ |
||||
const Point2f* forwardMotionRow = forwardMotion.ptr<Point2f>(y); |
||||
const Point2f* backwardMotionRow = backwardMotion.ptr<Point2f>(y); |
||||
Point2f* forwardMapRow = forwardMap.ptr<Point2f>(y); |
||||
Point2f* backwardMapRow = backwardMap.ptr<Point2f>(y); |
||||
|
||||
for (int x = 0; x < forwardMotion.cols; ++x) |
||||
{ |
||||
Point2f base(static_cast<float>(x), static_cast<float>(y)); |
||||
|
||||
forwardMapRow[x] = base + backwardMotionRow[x]; |
||||
backwardMapRow[x] = base + forwardMotionRow[x]; |
||||
} |
||||
} |
||||
} |
||||
|
||||
template <typename T> |
||||
void upscaleImpl(const Mat& src, Mat& dst, int scale) |
||||
{ |
||||
dst.create(src.rows * scale, src.cols * scale, src.type()); |
||||
dst.setTo(Scalar::all(0)); |
||||
|
||||
for (int y = 0, Y = 0; y < src.rows; ++y, Y += scale) |
||||
{ |
||||
const T* srcRow = src.ptr<T>(y); |
||||
T* dstRow = dst.ptr<T>(Y); |
||||
|
||||
for (int x = 0, X = 0; x < src.cols; ++x, X += scale) |
||||
dstRow[X] = srcRow[x]; |
||||
} |
||||
} |
||||
|
||||
void upscale(const Mat& src, Mat& dst, int scale) |
||||
{ |
||||
typedef void (*func_t)(const Mat& src, Mat& dst, int scale); |
||||
static const func_t funcs[] = |
||||
{ |
||||
0, upscaleImpl<float>, 0, upscaleImpl<Point3f> |
||||
}; |
||||
|
||||
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 ); |
||||
|
||||
const func_t func = funcs[src.channels()]; |
||||
|
||||
func(src, dst, scale); |
||||
} |
||||
|
||||
float diffSign(float a, float b) |
||||
{ |
||||
return a > b ? 1.0f : a < b ? -1.0f : 0.0f; |
||||
} |
||||
Point3f diffSign(Point3f a, Point3f b) |
||||
{ |
||||
return Point3f( |
||||
a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f, |
||||
a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f, |
||||
a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f |
||||
); |
||||
} |
||||
|
||||
void diffSign(const Mat& src1, const Mat& src2, Mat& dst) |
||||
{ |
||||
const int count = src1.cols * src1.channels(); |
||||
|
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
for (int y = 0; y < src1.rows; ++y) |
||||
{ |
||||
const float* src1Ptr = src1.ptr<float>(y); |
||||
const float* src2Ptr = src2.ptr<float>(y); |
||||
float* dstPtr = dst.ptr<float>(y); |
||||
|
||||
for (int x = 0; x < count; ++x) |
||||
dstPtr[x] = diffSign(src1Ptr[x], src2Ptr[x]); |
||||
} |
||||
} |
||||
|
||||
void calcBtvWeights(int btvKernelSize, double alpha, vector<float>& btvWeights) |
||||
{ |
||||
const size_t size = btvKernelSize * btvKernelSize; |
||||
|
||||
btvWeights.resize(size); |
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2; |
||||
const float alpha_f = static_cast<float>(alpha); |
||||
|
||||
for (int m = 0, ind = 0; m <= ksize; ++m) |
||||
{ |
||||
for (int l = ksize; l + m >= 0; --l, ++ind) |
||||
btvWeights[ind] = pow(alpha_f, std::abs(m) + std::abs(l)); |
||||
} |
||||
} |
||||
|
||||
template <typename T> |
||||
struct BtvRegularizationBody : ParallelLoopBody |
||||
{ |
||||
void operator ()(const Range& range) const; |
||||
|
||||
Mat src; |
||||
mutable Mat dst; |
||||
int ksize; |
||||
const float* btvWeights; |
||||
}; |
||||
|
||||
template <typename T> |
||||
void BtvRegularizationBody<T>::operator ()(const Range& range) const |
||||
{ |
||||
for (int i = range.start; i < range.end; ++i) |
||||
{ |
||||
const T* srcRow = src.ptr<T>(i); |
||||
T* dstRow = dst.ptr<T>(i); |
||||
|
||||
for(int j = ksize; j < src.cols - ksize; ++j) |
||||
{ |
||||
const T srcVal = srcRow[j]; |
||||
|
||||
for (int m = 0, ind = 0; m <= ksize; ++m) |
||||
{ |
||||
const T* srcRow2 = src.ptr<T>(i - m); |
||||
const T* srcRow3 = src.ptr<T>(i + m); |
||||
|
||||
for (int l = ksize; l + m >= 0; --l, ++ind) |
||||
{ |
||||
dstRow[j] += btvWeights[ind] * (diffSign(srcVal, srcRow3[j + l]) - diffSign(srcRow2[j - l], srcVal)); |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
template <typename T> |
||||
void calcBtvRegularizationImpl(const Mat& src, Mat& dst, int btvKernelSize, const vector<float>& btvWeights) |
||||
{ |
||||
dst.create(src.size(), src.type()); |
||||
dst.setTo(Scalar::all(0)); |
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2; |
||||
|
||||
BtvRegularizationBody<T> body; |
||||
|
||||
body.src = src; |
||||
body.dst = dst; |
||||
body.ksize = ksize; |
||||
body.btvWeights = &btvWeights[0]; |
||||
|
||||
parallel_for_(Range(ksize, src.rows - ksize), body); |
||||
} |
||||
|
||||
void calcBtvRegularization(const Mat& src, Mat& dst, int btvKernelSize, const vector<float>& btvWeights) |
||||
{ |
||||
typedef void (*func_t)(const Mat& src, Mat& dst, int btvKernelSize, const vector<float>& btvWeights); |
||||
static const func_t funcs[] = |
||||
{ |
||||
0, calcBtvRegularizationImpl<float>, 0, calcBtvRegularizationImpl<Point3f> |
||||
}; |
||||
|
||||
const func_t func = funcs[src.channels()]; |
||||
|
||||
func(src, dst, btvKernelSize, btvWeights); |
||||
} |
||||
|
||||
class BTVL1_Base |
||||
{ |
||||
public: |
||||
BTVL1_Base(); |
||||
|
||||
void process(const vector<Mat>& src, Mat& dst, |
||||
const vector<Mat>& forwardMotions, const vector<Mat>& backwardMotions, |
||||
int baseIdx); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
int scale_; |
||||
int iterations_; |
||||
double tau_; |
||||
double lambda_; |
||||
double alpha_; |
||||
int btvKernelSize_; |
||||
int blurKernelSize_; |
||||
double blurSigma_; |
||||
Ptr<DenseOpticalFlowExt> opticalFlow_; |
||||
|
||||
private: |
||||
Ptr<FilterEngine> filter_; |
||||
int curBlurKernelSize_; |
||||
double curBlurSigma_; |
||||
int curSrcType_; |
||||
|
||||
vector<float> btvWeights_; |
||||
int curBtvKernelSize_; |
||||
double curAlpha_; |
||||
|
||||
vector<Mat> lowResForwardMotions_; |
||||
vector<Mat> lowResBackwardMotions_; |
||||
|
||||
vector<Mat> highResForwardMotions_; |
||||
vector<Mat> highResBackwardMotions_; |
||||
|
||||
vector<Mat> forwardMaps_; |
||||
vector<Mat> backwardMaps_; |
||||
|
||||
Mat highRes_; |
||||
|
||||
Mat diffTerm_, regTerm_; |
||||
Mat a_, b_, c_; |
||||
}; |
||||
|
||||
BTVL1_Base::BTVL1_Base() |
||||
{ |
||||
scale_ = 4; |
||||
iterations_ = 180; |
||||
lambda_ = 0.03; |
||||
tau_ = 1.3; |
||||
alpha_ = 0.7; |
||||
btvKernelSize_ = 7; |
||||
blurKernelSize_ = 5; |
||||
blurSigma_ = 0.0; |
||||
opticalFlow_ = createOptFlow_Farneback(); |
||||
|
||||
curBlurKernelSize_ = -1; |
||||
curBlurSigma_ = -1.0; |
||||
curSrcType_ = -1; |
||||
|
||||
curBtvKernelSize_ = -1; |
||||
curAlpha_ = -1.0; |
||||
} |
||||
|
||||
void BTVL1_Base::process(const vector<Mat>& src, Mat& dst, const vector<Mat>& forwardMotions, const vector<Mat>& backwardMotions, int baseIdx) |
||||
{ |
||||
CV_Assert( scale_ > 1 ); |
||||
CV_Assert( iterations_ > 0 ); |
||||
CV_Assert( tau_ > 0.0 ); |
||||
CV_Assert( alpha_ > 0.0 ); |
||||
CV_Assert( btvKernelSize_ > 0 ); |
||||
CV_Assert( blurKernelSize_ > 0 ); |
||||
CV_Assert( blurSigma_ >= 0.0 ); |
||||
|
||||
// update blur filter and btv weights
|
||||
|
||||
if (filter_.empty() || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_) |
||||
{ |
||||
filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_); |
||||
curBlurKernelSize_ = blurKernelSize_; |
||||
curBlurSigma_ = blurSigma_; |
||||
curSrcType_ = src[0].type(); |
||||
} |
||||
|
||||
if (btvWeights_.empty() || btvKernelSize_ != curBtvKernelSize_ || alpha_ != curAlpha_) |
||||
{ |
||||
calcBtvWeights(btvKernelSize_, alpha_, btvWeights_); |
||||
curBtvKernelSize_ = btvKernelSize_; |
||||
curAlpha_ = alpha_; |
||||
} |
||||
|
||||
// calc high res motions
|
||||
|
||||
calcRelativeMotions(forwardMotions, backwardMotions, lowResForwardMotions_, lowResBackwardMotions_, baseIdx, src[0].size()); |
||||
|
||||
upscaleMotions(lowResForwardMotions_, highResForwardMotions_, scale_); |
||||
upscaleMotions(lowResBackwardMotions_, highResBackwardMotions_, scale_); |
||||
|
||||
forwardMaps_.resize(highResForwardMotions_.size()); |
||||
backwardMaps_.resize(highResForwardMotions_.size()); |
||||
for (size_t i = 0; i < highResForwardMotions_.size(); ++i) |
||||
buildMotionMaps(highResForwardMotions_[i], highResBackwardMotions_[i], forwardMaps_[i], backwardMaps_[i]); |
||||
|
||||
// initial estimation
|
||||
|
||||
const Size lowResSize = src[0].size(); |
||||
const Size highResSize(lowResSize.width * scale_, lowResSize.height * scale_); |
||||
|
||||
resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_CUBIC); |
||||
|
||||
// iterations
|
||||
|
||||
diffTerm_.create(highResSize, highRes_.type()); |
||||
a_.create(highResSize, highRes_.type()); |
||||
b_.create(highResSize, highRes_.type()); |
||||
c_.create(lowResSize, highRes_.type()); |
||||
|
||||
for (int i = 0; i < iterations_; ++i) |
||||
{ |
||||
diffTerm_.setTo(Scalar::all(0)); |
||||
|
||||
for (size_t k = 0; k < src.size(); ++k) |
||||
{ |
||||
// a = M * Ih
|
||||
remap(highRes_, a_, backwardMaps_[k], noArray(), INTER_NEAREST); |
||||
// b = HM * Ih
|
||||
filter_->apply(a_, b_); |
||||
// c = DHM * Ih
|
||||
resize(b_, c_, lowResSize, 0, 0, INTER_NEAREST); |
||||
|
||||
diffSign(src[k], c_, c_); |
||||
|
||||
// a = Dt * diff
|
||||
upscale(c_, a_, scale_); |
||||
// b = HtDt * diff
|
||||
filter_->apply(a_, b_); |
||||
// a = MtHtDt * diff
|
||||
remap(b_, a_, forwardMaps_[k], noArray(), INTER_NEAREST); |
||||
|
||||
add(diffTerm_, a_, diffTerm_); |
||||
} |
||||
|
||||
if (lambda_ > 0) |
||||
{ |
||||
calcBtvRegularization(highRes_, regTerm_, btvKernelSize_, btvWeights_); |
||||
addWeighted(diffTerm_, 1.0, regTerm_, -lambda_, 0.0, diffTerm_); |
||||
} |
||||
|
||||
addWeighted(highRes_, 1.0, diffTerm_, tau_, 0.0, highRes_); |
||||
} |
||||
|
||||
Rect inner(btvKernelSize_, btvKernelSize_, highRes_.cols - 2 * btvKernelSize_, highRes_.rows - 2 * btvKernelSize_); |
||||
highRes_(inner).copyTo(dst); |
||||
} |
||||
|
||||
void BTVL1_Base::collectGarbage() |
||||
{ |
||||
filter_.release(); |
||||
|
||||
lowResForwardMotions_.clear(); |
||||
lowResBackwardMotions_.clear(); |
||||
|
||||
highResForwardMotions_.clear(); |
||||
highResBackwardMotions_.clear(); |
||||
|
||||
forwardMaps_.clear(); |
||||
backwardMaps_.clear(); |
||||
|
||||
highRes_.release(); |
||||
|
||||
diffTerm_.release(); |
||||
regTerm_.release(); |
||||
a_.release(); |
||||
b_.release(); |
||||
c_.release(); |
||||
} |
||||
|
||||
////////////////////////////////////////////////////////////////////
|
||||
|
||||
class BTVL1 : public SuperResolution, private BTVL1_Base |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
BTVL1(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void initImpl(Ptr<FrameSource>& frameSource); |
||||
void processImpl(Ptr<FrameSource>& frameSource, OutputArray output); |
||||
|
||||
private: |
||||
int temporalAreaRadius_; |
||||
|
||||
void readNextFrame(Ptr<FrameSource>& frameSource); |
||||
void processFrame(int idx); |
||||
|
||||
Mat curFrame_; |
||||
Mat prevFrame_; |
||||
|
||||
vector<Mat> frames_; |
||||
vector<Mat> forwardMotions_; |
||||
vector<Mat> backwardMotions_; |
||||
vector<Mat> outputs_; |
||||
|
||||
int storePos_; |
||||
int procPos_; |
||||
int outPos_; |
||||
|
||||
vector<Mat> srcFrames_; |
||||
vector<Mat> srcForwardMotions_; |
||||
vector<Mat> srcBackwardMotions_; |
||||
Mat finalOutput_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(BTVL1, "SuperResolution.BTVL1", |
||||
obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor."); |
||||
obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count."); |
||||
obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method."); |
||||
obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term."); |
||||
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV."); |
||||
obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter."); |
||||
obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size."); |
||||
obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma."); |
||||
obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area."); |
||||
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm.")); |
||||
|
||||
BTVL1::BTVL1() |
||||
{ |
||||
temporalAreaRadius_ = 4; |
||||
} |
||||
|
||||
void BTVL1::collectGarbage() |
||||
{ |
||||
curFrame_.release(); |
||||
prevFrame_.release(); |
||||
|
||||
frames_.clear(); |
||||
forwardMotions_.clear(); |
||||
backwardMotions_.clear(); |
||||
outputs_.clear(); |
||||
|
||||
srcFrames_.clear(); |
||||
srcForwardMotions_.clear(); |
||||
srcBackwardMotions_.clear(); |
||||
finalOutput_.release(); |
||||
|
||||
SuperResolution::collectGarbage(); |
||||
BTVL1_Base::collectGarbage(); |
||||
} |
||||
|
||||
void BTVL1::initImpl(Ptr<FrameSource>& frameSource) |
||||
{ |
||||
const int cacheSize = 2 * temporalAreaRadius_ + 1; |
||||
|
||||
frames_.resize(cacheSize); |
||||
forwardMotions_.resize(cacheSize); |
||||
backwardMotions_.resize(cacheSize); |
||||
outputs_.resize(cacheSize); |
||||
|
||||
storePos_ = -1; |
||||
|
||||
for (int t = -temporalAreaRadius_; t <= temporalAreaRadius_; ++t) |
||||
readNextFrame(frameSource); |
||||
|
||||
for (int i = 0; i <= temporalAreaRadius_; ++i) |
||||
processFrame(i); |
||||
|
||||
procPos_ = temporalAreaRadius_; |
||||
outPos_ = -1; |
||||
} |
||||
|
||||
void BTVL1::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output) |
||||
{ |
||||
if (outPos_ >= storePos_) |
||||
{ |
||||
_output.release(); |
||||
return; |
||||
} |
||||
|
||||
readNextFrame(frameSource); |
||||
|
||||
if (procPos_ < storePos_) |
||||
{ |
||||
++procPos_; |
||||
processFrame(procPos_); |
||||
} |
||||
|
||||
++outPos_; |
||||
const Mat& curOutput = at(outPos_, outputs_); |
||||
|
||||
if (_output.kind() < _InputArray::OPENGL_BUFFER) |
||||
curOutput.convertTo(_output, CV_8U); |
||||
else |
||||
{ |
||||
curOutput.convertTo(finalOutput_, CV_8U); |
||||
arrCopy(finalOutput_, _output); |
||||
} |
||||
} |
||||
|
||||
void BTVL1::readNextFrame(Ptr<FrameSource>& frameSource) |
||||
{ |
||||
frameSource->nextFrame(curFrame_); |
||||
|
||||
if (curFrame_.empty()) |
||||
return; |
||||
|
||||
++storePos_; |
||||
curFrame_.convertTo(at(storePos_, frames_), CV_32F); |
||||
|
||||
if (storePos_ > 0) |
||||
{ |
||||
opticalFlow_->calc(prevFrame_, curFrame_, at(storePos_ - 1, forwardMotions_)); |
||||
opticalFlow_->calc(curFrame_, prevFrame_, at(storePos_, backwardMotions_)); |
||||
} |
||||
|
||||
curFrame_.copyTo(prevFrame_); |
||||
} |
||||
|
||||
void BTVL1::processFrame(int idx) |
||||
{ |
||||
const int startIdx = max(idx - temporalAreaRadius_, 0); |
||||
const int procIdx = idx; |
||||
const int endIdx = min(startIdx + 2 * temporalAreaRadius_, storePos_); |
||||
|
||||
const int count = endIdx - startIdx + 1; |
||||
|
||||
srcFrames_.resize(count); |
||||
srcForwardMotions_.resize(count); |
||||
srcBackwardMotions_.resize(count); |
||||
|
||||
int baseIdx = -1; |
||||
|
||||
for (int i = startIdx, k = 0; i <= endIdx; ++i, ++k) |
||||
{ |
||||
if (i == procIdx) |
||||
baseIdx = k; |
||||
|
||||
srcFrames_[k] = at(i, frames_); |
||||
|
||||
if (i < endIdx) |
||||
srcForwardMotions_[k] = at(i, forwardMotions_); |
||||
if (i > startIdx) |
||||
srcBackwardMotions_[k] = at(i, backwardMotions_); |
||||
} |
||||
|
||||
process(srcFrames_, at(idx, outputs_), srcForwardMotions_, srcBackwardMotions_, baseIdx); |
||||
} |
||||
} |
||||
|
||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1() |
||||
{ |
||||
return new BTVL1; |
||||
} |
||||
@ -0,0 +1,580 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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*/
|
||||
|
||||
// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
|
||||
// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
|
||||
|
||||
#include "precomp.hpp" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::gpu; |
||||
using namespace cv::superres; |
||||
using namespace cv::superres::detail; |
||||
|
||||
#ifndef HAVE_CUDA |
||||
|
||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU() |
||||
{ |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<SuperResolution>(); |
||||
} |
||||
|
||||
#else // HAVE_CUDA
|
||||
|
||||
namespace btv_l1_device |
||||
{ |
||||
void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY, |
||||
PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY, |
||||
PtrStepSzf forwardMapX, PtrStepSzf forwardMapY, |
||||
PtrStepSzf backwardMapX, PtrStepSzf backwardMapY); |
||||
|
||||
template <int cn> |
||||
void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream); |
||||
|
||||
void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream); |
||||
|
||||
void loadBtvWeights(const float* weights, size_t count); |
||||
template <int cn> void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize); |
||||
} |
||||
|
||||
namespace |
||||
{ |
||||
void calcRelativeMotions(const vector<pair<GpuMat, GpuMat> >& forwardMotions, const vector<pair<GpuMat, GpuMat> >& backwardMotions, |
||||
vector<pair<GpuMat, GpuMat> >& relForwardMotions, vector<pair<GpuMat, GpuMat> >& relBackwardMotions, |
||||
int baseIdx, Size size) |
||||
{ |
||||
const int count = static_cast<int>(forwardMotions.size()); |
||||
|
||||
relForwardMotions.resize(count); |
||||
relForwardMotions[baseIdx].first.create(size, CV_32FC1); |
||||
relForwardMotions[baseIdx].first.setTo(Scalar::all(0)); |
||||
relForwardMotions[baseIdx].second.create(size, CV_32FC1); |
||||
relForwardMotions[baseIdx].second.setTo(Scalar::all(0)); |
||||
|
||||
relBackwardMotions.resize(count); |
||||
relBackwardMotions[baseIdx].first.create(size, CV_32FC1); |
||||
relBackwardMotions[baseIdx].first.setTo(Scalar::all(0)); |
||||
relBackwardMotions[baseIdx].second.create(size, CV_32FC1); |
||||
relBackwardMotions[baseIdx].second.setTo(Scalar::all(0)); |
||||
|
||||
for (int i = baseIdx - 1; i >= 0; --i) |
||||
{ |
||||
gpu::add(relForwardMotions[i + 1].first, forwardMotions[i].first, relForwardMotions[i].first); |
||||
gpu::add(relForwardMotions[i + 1].second, forwardMotions[i].second, relForwardMotions[i].second); |
||||
|
||||
gpu::add(relBackwardMotions[i + 1].first, backwardMotions[i + 1].first, relBackwardMotions[i].first); |
||||
gpu::add(relBackwardMotions[i + 1].second, backwardMotions[i + 1].second, relBackwardMotions[i].second); |
||||
} |
||||
|
||||
for (int i = baseIdx + 1; i < count; ++i) |
||||
{ |
||||
gpu::add(relForwardMotions[i - 1].first, backwardMotions[i].first, relForwardMotions[i].first); |
||||
gpu::add(relForwardMotions[i - 1].second, backwardMotions[i].second, relForwardMotions[i].second); |
||||
|
||||
gpu::add(relBackwardMotions[i - 1].first, forwardMotions[i - 1].first, relBackwardMotions[i].first); |
||||
gpu::add(relBackwardMotions[i - 1].second, forwardMotions[i - 1].second, relBackwardMotions[i].second); |
||||
} |
||||
} |
||||
|
||||
void upscaleMotions(const vector<pair<GpuMat, GpuMat> >& lowResMotions, vector<pair<GpuMat, GpuMat> >& highResMotions, int scale) |
||||
{ |
||||
highResMotions.resize(lowResMotions.size()); |
||||
|
||||
for (size_t i = 0; i < lowResMotions.size(); ++i) |
||||
{ |
||||
gpu::resize(lowResMotions[i].first, highResMotions[i].first, Size(), scale, scale, INTER_CUBIC); |
||||
gpu::resize(lowResMotions[i].second, highResMotions[i].second, Size(), scale, scale, INTER_CUBIC); |
||||
|
||||
gpu::multiply(highResMotions[i].first, Scalar::all(scale), highResMotions[i].first); |
||||
gpu::multiply(highResMotions[i].second, Scalar::all(scale), highResMotions[i].second); |
||||
} |
||||
} |
||||
|
||||
void buildMotionMaps(const pair<GpuMat, GpuMat>& forwardMotion, const pair<GpuMat, GpuMat>& backwardMotion, |
||||
pair<GpuMat, GpuMat>& forwardMap, pair<GpuMat, GpuMat>& backwardMap) |
||||
{ |
||||
forwardMap.first.create(forwardMotion.first.size(), CV_32FC1); |
||||
forwardMap.second.create(forwardMotion.first.size(), CV_32FC1); |
||||
|
||||
backwardMap.first.create(forwardMotion.first.size(), CV_32FC1); |
||||
backwardMap.second.create(forwardMotion.first.size(), CV_32FC1); |
||||
|
||||
btv_l1_device::buildMotionMaps(forwardMotion.first, forwardMotion.second, |
||||
backwardMotion.first, backwardMotion.second, |
||||
forwardMap.first, forwardMap.second, |
||||
backwardMap.first, backwardMap.second); |
||||
} |
||||
|
||||
void upscale(const GpuMat& src, GpuMat& dst, int scale, Stream& stream) |
||||
{ |
||||
typedef void (*func_t)(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream); |
||||
static const func_t funcs[] = |
||||
{ |
||||
0, btv_l1_device::upscale<1>, 0, btv_l1_device::upscale<3>, btv_l1_device::upscale<4> |
||||
}; |
||||
|
||||
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 ); |
||||
|
||||
dst.create(src.rows * scale, src.cols * scale, src.type()); |
||||
dst.setTo(Scalar::all(0)); |
||||
|
||||
const func_t func = funcs[src.channels()]; |
||||
|
||||
func(src, dst, scale, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
void diffSign(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream) |
||||
{ |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
btv_l1_device::diffSign(src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
void calcBtvWeights(int btvKernelSize, double alpha, vector<float>& btvWeights) |
||||
{ |
||||
const size_t size = btvKernelSize * btvKernelSize; |
||||
|
||||
btvWeights.resize(size); |
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2; |
||||
const float alpha_f = static_cast<float>(alpha); |
||||
|
||||
for (int m = 0, ind = 0; m <= ksize; ++m) |
||||
{ |
||||
for (int l = ksize; l + m >= 0; --l, ++ind) |
||||
btvWeights[ind] = pow(alpha_f, std::abs(m) + std::abs(l)); |
||||
} |
||||
|
||||
btv_l1_device::loadBtvWeights(&btvWeights[0], size); |
||||
} |
||||
|
||||
void calcBtvRegularization(const GpuMat& src, GpuMat& dst, int btvKernelSize) |
||||
{ |
||||
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, int ksize); |
||||
static const func_t funcs[] = |
||||
{ |
||||
0, |
||||
btv_l1_device::calcBtvRegularization<1>, |
||||
0, |
||||
btv_l1_device::calcBtvRegularization<3>, |
||||
btv_l1_device::calcBtvRegularization<4> |
||||
}; |
||||
|
||||
dst.create(src.size(), src.type()); |
||||
dst.setTo(Scalar::all(0)); |
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2; |
||||
|
||||
funcs[src.channels()](src, dst, ksize); |
||||
} |
||||
|
||||
class BTVL1_GPU_Base |
||||
{ |
||||
public: |
||||
BTVL1_GPU_Base(); |
||||
|
||||
void process(const vector<GpuMat>& src, GpuMat& dst, |
||||
const vector<pair<GpuMat, GpuMat> >& forwardMotions, const vector<pair<GpuMat, GpuMat> >& backwardMotions, |
||||
int baseIdx); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
int scale_; |
||||
int iterations_; |
||||
double lambda_; |
||||
double tau_; |
||||
double alpha_; |
||||
int btvKernelSize_; |
||||
int blurKernelSize_; |
||||
double blurSigma_; |
||||
Ptr<DenseOpticalFlowExt> opticalFlow_; |
||||
|
||||
private: |
||||
vector<Ptr<FilterEngine_GPU> > filters_; |
||||
int curBlurKernelSize_; |
||||
double curBlurSigma_; |
||||
int curSrcType_; |
||||
|
||||
vector<float> btvWeights_; |
||||
int curBtvKernelSize_; |
||||
double curAlpha_; |
||||
|
||||
vector<pair<GpuMat, GpuMat> > lowResForwardMotions_; |
||||
vector<pair<GpuMat, GpuMat> > lowResBackwardMotions_; |
||||
|
||||
vector<pair<GpuMat, GpuMat> > highResForwardMotions_; |
||||
vector<pair<GpuMat, GpuMat> > highResBackwardMotions_; |
||||
|
||||
vector<pair<GpuMat, GpuMat> > forwardMaps_; |
||||
vector<pair<GpuMat, GpuMat> > backwardMaps_; |
||||
|
||||
GpuMat highRes_; |
||||
|
||||
vector<Stream> streams_; |
||||
vector<GpuMat> diffTerms_; |
||||
vector<GpuMat> a_, b_, c_; |
||||
GpuMat regTerm_; |
||||
}; |
||||
|
||||
BTVL1_GPU_Base::BTVL1_GPU_Base() |
||||
{ |
||||
scale_ = 4; |
||||
iterations_ = 180; |
||||
lambda_ = 0.03; |
||||
tau_ = 1.3; |
||||
alpha_ = 0.7; |
||||
btvKernelSize_ = 7; |
||||
blurKernelSize_ = 5; |
||||
blurSigma_ = 0.0; |
||||
opticalFlow_ = createOptFlow_Farneback_GPU(); |
||||
|
||||
curBlurKernelSize_ = -1; |
||||
curBlurSigma_ = -1.0; |
||||
curSrcType_ = -1; |
||||
|
||||
curBtvKernelSize_ = -1; |
||||
curAlpha_ = -1.0; |
||||
} |
||||
|
||||
void BTVL1_GPU_Base::process(const vector<GpuMat>& src, GpuMat& dst, |
||||
const vector<pair<GpuMat, GpuMat> >& forwardMotions, const vector<pair<GpuMat, GpuMat> >& backwardMotions, |
||||
int baseIdx) |
||||
{ |
||||
CV_Assert( scale_ > 1 ); |
||||
CV_Assert( iterations_ > 0 ); |
||||
CV_Assert( tau_ > 0.0 ); |
||||
CV_Assert( alpha_ > 0.0 ); |
||||
CV_Assert( btvKernelSize_ > 0 && btvKernelSize_ <= 16 ); |
||||
CV_Assert( blurKernelSize_ > 0 ); |
||||
CV_Assert( blurSigma_ >= 0.0 ); |
||||
|
||||
// update blur filter and btv weights
|
||||
|
||||
if (filters_.size() != src.size() || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_) |
||||
{ |
||||
filters_.resize(src.size()); |
||||
for (size_t i = 0; i < src.size(); ++i) |
||||
filters_[i] = createGaussianFilter_GPU(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_); |
||||
curBlurKernelSize_ = blurKernelSize_; |
||||
curBlurSigma_ = blurSigma_; |
||||
curSrcType_ = src[0].type(); |
||||
} |
||||
|
||||
if (btvWeights_.empty() || btvKernelSize_ != curBtvKernelSize_ || alpha_ != curAlpha_) |
||||
{ |
||||
calcBtvWeights(btvKernelSize_, alpha_, btvWeights_); |
||||
curBtvKernelSize_ = btvKernelSize_; |
||||
curAlpha_ = alpha_; |
||||
} |
||||
|
||||
// calc motions between input frames
|
||||
|
||||
calcRelativeMotions(forwardMotions, backwardMotions, lowResForwardMotions_, lowResBackwardMotions_, baseIdx, src[0].size()); |
||||
|
||||
upscaleMotions(lowResForwardMotions_, highResForwardMotions_, scale_); |
||||
upscaleMotions(lowResBackwardMotions_, highResBackwardMotions_, scale_); |
||||
|
||||
forwardMaps_.resize(highResForwardMotions_.size()); |
||||
backwardMaps_.resize(highResForwardMotions_.size()); |
||||
for (size_t i = 0; i < highResForwardMotions_.size(); ++i) |
||||
buildMotionMaps(highResForwardMotions_[i], highResBackwardMotions_[i], forwardMaps_[i], backwardMaps_[i]); |
||||
|
||||
// initial estimation
|
||||
|
||||
const Size lowResSize = src[0].size(); |
||||
const Size highResSize(lowResSize.width * scale_, lowResSize.height * scale_); |
||||
|
||||
gpu::resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_CUBIC); |
||||
|
||||
// iterations
|
||||
|
||||
streams_.resize(src.size()); |
||||
diffTerms_.resize(src.size()); |
||||
a_.resize(src.size()); |
||||
b_.resize(src.size()); |
||||
c_.resize(src.size()); |
||||
|
||||
for (int i = 0; i < iterations_; ++i) |
||||
{ |
||||
for (size_t k = 0; k < src.size(); ++k) |
||||
{ |
||||
// a = M * Ih
|
||||
gpu::remap(highRes_, a_[k], backwardMaps_[k].first, backwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]); |
||||
// b = HM * Ih
|
||||
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1), streams_[k]); |
||||
// c = DHF * Ih
|
||||
gpu::resize(b_[k], c_[k], lowResSize, 0, 0, INTER_NEAREST, streams_[k]); |
||||
|
||||
diffSign(src[k], c_[k], c_[k], streams_[k]); |
||||
|
||||
// a = Dt * diff
|
||||
upscale(c_[k], a_[k], scale_, streams_[k]); |
||||
// b = HtDt * diff
|
||||
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1), streams_[k]); |
||||
// diffTerm = MtHtDt * diff
|
||||
gpu::remap(b_[k], diffTerms_[k], forwardMaps_[k].first, forwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]); |
||||
} |
||||
|
||||
if (lambda_ > 0) |
||||
{ |
||||
calcBtvRegularization(highRes_, regTerm_, btvKernelSize_); |
||||
gpu::addWeighted(highRes_, 1.0, regTerm_, -tau_ * lambda_, 0.0, highRes_); |
||||
} |
||||
|
||||
for (size_t k = 0; k < src.size(); ++k) |
||||
{ |
||||
streams_[k].waitForCompletion(); |
||||
gpu::addWeighted(highRes_, 1.0, diffTerms_[k], tau_, 0.0, highRes_); |
||||
} |
||||
} |
||||
|
||||
Rect inner(btvKernelSize_, btvKernelSize_, highRes_.cols - 2 * btvKernelSize_, highRes_.rows - 2 * btvKernelSize_); |
||||
highRes_(inner).copyTo(dst); |
||||
} |
||||
|
||||
void BTVL1_GPU_Base::collectGarbage() |
||||
{ |
||||
filters_.clear(); |
||||
|
||||
lowResForwardMotions_.clear(); |
||||
lowResBackwardMotions_.clear(); |
||||
|
||||
highResForwardMotions_.clear(); |
||||
highResBackwardMotions_.clear(); |
||||
|
||||
forwardMaps_.clear(); |
||||
backwardMaps_.clear(); |
||||
|
||||
highRes_.release(); |
||||
|
||||
diffTerms_.clear(); |
||||
a_.clear(); |
||||
b_.clear(); |
||||
c_.clear(); |
||||
regTerm_.release(); |
||||
} |
||||
|
||||
////////////////////////////////////////////////////////////
|
||||
|
||||
class BTVL1_GPU : public SuperResolution, private BTVL1_GPU_Base |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
BTVL1_GPU(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void initImpl(Ptr<FrameSource>& frameSource); |
||||
void processImpl(Ptr<FrameSource>& frameSource, OutputArray output); |
||||
|
||||
private: |
||||
int temporalAreaRadius_; |
||||
|
||||
void readNextFrame(Ptr<FrameSource>& frameSource); |
||||
void processFrame(int idx); |
||||
|
||||
GpuMat curFrame_; |
||||
GpuMat prevFrame_; |
||||
|
||||
vector<GpuMat> frames_; |
||||
vector<pair<GpuMat, GpuMat> > forwardMotions_; |
||||
vector<pair<GpuMat, GpuMat> > backwardMotions_; |
||||
vector<GpuMat> outputs_; |
||||
|
||||
int storePos_; |
||||
int procPos_; |
||||
int outPos_; |
||||
|
||||
vector<GpuMat> srcFrames_; |
||||
vector<pair<GpuMat, GpuMat> > srcForwardMotions_; |
||||
vector<pair<GpuMat, GpuMat> > srcBackwardMotions_; |
||||
GpuMat finalOutput_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(BTVL1_GPU, "SuperResolution.BTVL1_GPU", |
||||
obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor."); |
||||
obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count."); |
||||
obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method."); |
||||
obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term."); |
||||
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV."); |
||||
obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter."); |
||||
obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size."); |
||||
obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma."); |
||||
obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area."); |
||||
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm.")); |
||||
|
||||
BTVL1_GPU::BTVL1_GPU() |
||||
{ |
||||
temporalAreaRadius_ = 4; |
||||
} |
||||
|
||||
void BTVL1_GPU::collectGarbage() |
||||
{ |
||||
curFrame_.release(); |
||||
prevFrame_.release(); |
||||
|
||||
frames_.clear(); |
||||
forwardMotions_.clear(); |
||||
backwardMotions_.clear(); |
||||
outputs_.clear(); |
||||
|
||||
srcFrames_.clear(); |
||||
srcForwardMotions_.clear(); |
||||
srcBackwardMotions_.clear(); |
||||
finalOutput_.release(); |
||||
|
||||
SuperResolution::collectGarbage(); |
||||
BTVL1_GPU_Base::collectGarbage(); |
||||
} |
||||
|
||||
void BTVL1_GPU::initImpl(Ptr<FrameSource>& frameSource) |
||||
{ |
||||
const int cacheSize = 2 * temporalAreaRadius_ + 1; |
||||
|
||||
frames_.resize(cacheSize); |
||||
forwardMotions_.resize(cacheSize); |
||||
backwardMotions_.resize(cacheSize); |
||||
outputs_.resize(cacheSize); |
||||
|
||||
storePos_ = -1; |
||||
|
||||
for (int t = -temporalAreaRadius_; t <= temporalAreaRadius_; ++t) |
||||
readNextFrame(frameSource); |
||||
|
||||
for (int i = 0; i <= temporalAreaRadius_; ++i) |
||||
processFrame(i); |
||||
|
||||
procPos_ = temporalAreaRadius_; |
||||
outPos_ = -1; |
||||
} |
||||
|
||||
void BTVL1_GPU::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output) |
||||
{ |
||||
if (outPos_ >= storePos_) |
||||
{ |
||||
_output.release(); |
||||
return; |
||||
} |
||||
|
||||
readNextFrame(frameSource); |
||||
|
||||
if (procPos_ < storePos_) |
||||
{ |
||||
++procPos_; |
||||
processFrame(procPos_); |
||||
} |
||||
|
||||
++outPos_; |
||||
const GpuMat& curOutput = at(outPos_, outputs_); |
||||
|
||||
if (_output.kind() == _InputArray::GPU_MAT) |
||||
curOutput.convertTo(_output.getGpuMatRef(), CV_8U); |
||||
else |
||||
{ |
||||
curOutput.convertTo(finalOutput_, CV_8U); |
||||
arrCopy(finalOutput_, _output); |
||||
} |
||||
} |
||||
|
||||
void BTVL1_GPU::readNextFrame(Ptr<FrameSource>& frameSource) |
||||
{ |
||||
frameSource->nextFrame(curFrame_); |
||||
|
||||
if (curFrame_.empty()) |
||||
return; |
||||
|
||||
++storePos_; |
||||
curFrame_.convertTo(at(storePos_, frames_), CV_32F); |
||||
|
||||
if (storePos_ > 0) |
||||
{ |
||||
pair<GpuMat, GpuMat>& forwardMotion = at(storePos_ - 1, forwardMotions_); |
||||
pair<GpuMat, GpuMat>& backwardMotion = at(storePos_, backwardMotions_); |
||||
|
||||
opticalFlow_->calc(prevFrame_, curFrame_, forwardMotion.first, forwardMotion.second); |
||||
opticalFlow_->calc(curFrame_, prevFrame_, backwardMotion.first, backwardMotion.second); |
||||
} |
||||
|
||||
curFrame_.copyTo(prevFrame_); |
||||
} |
||||
|
||||
void BTVL1_GPU::processFrame(int idx) |
||||
{ |
||||
const int startIdx = max(idx - temporalAreaRadius_, 0); |
||||
const int procIdx = idx; |
||||
const int endIdx = min(startIdx + 2 * temporalAreaRadius_, storePos_); |
||||
|
||||
const int count = endIdx - startIdx + 1; |
||||
|
||||
srcFrames_.resize(count); |
||||
srcForwardMotions_.resize(count); |
||||
srcBackwardMotions_.resize(count); |
||||
|
||||
int baseIdx = -1; |
||||
|
||||
for (int i = startIdx, k = 0; i <= endIdx; ++i, ++k) |
||||
{ |
||||
if (i == procIdx) |
||||
baseIdx = k; |
||||
|
||||
srcFrames_[k] = at(i, frames_); |
||||
|
||||
if (i < endIdx) |
||||
srcForwardMotions_[k] = at(i, forwardMotions_); |
||||
if (i > startIdx) |
||||
srcBackwardMotions_[k] = at(i, backwardMotions_); |
||||
} |
||||
|
||||
process(srcFrames_, at(idx, outputs_), srcForwardMotions_, srcBackwardMotions_, baseIdx); |
||||
} |
||||
} |
||||
|
||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU() |
||||
{ |
||||
return new BTVL1_GPU; |
||||
} |
||||
|
||||
#endif // HAVE_CUDA
|
||||
@ -0,0 +1,234 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved. |
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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 "opencv2/gpu/device/common.hpp" |
||||
#include "opencv2/gpu/device/transform.hpp" |
||||
#include "opencv2/gpu/device/vec_traits.hpp" |
||||
#include "opencv2/gpu/device/vec_math.hpp" |
||||
|
||||
using namespace cv::gpu; |
||||
using namespace cv::gpu::device; |
||||
|
||||
namespace btv_l1_device |
||||
{ |
||||
void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY, |
||||
PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY, |
||||
PtrStepSzf forwardMapX, PtrStepSzf forwardMapY, |
||||
PtrStepSzf backwardMapX, PtrStepSzf backwardMapY); |
||||
|
||||
template <int cn> |
||||
void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream); |
||||
|
||||
void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream); |
||||
|
||||
void loadBtvWeights(const float* weights, size_t count); |
||||
template <int cn> void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize); |
||||
} |
||||
|
||||
namespace btv_l1_device |
||||
{ |
||||
__global__ void buildMotionMapsKernel(const PtrStepSzf forwardMotionX, const PtrStepf forwardMotionY, |
||||
PtrStepf backwardMotionX, PtrStepf backwardMotionY, |
||||
PtrStepf forwardMapX, PtrStepf forwardMapY, |
||||
PtrStepf backwardMapX, PtrStepf backwardMapY) |
||||
{ |
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x; |
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y; |
||||
|
||||
if (x >= forwardMotionX.cols || y >= forwardMotionX.rows) |
||||
return; |
||||
|
||||
const float fx = forwardMotionX(y, x); |
||||
const float fy = forwardMotionY(y, x); |
||||
|
||||
const float bx = backwardMotionX(y, x); |
||||
const float by = backwardMotionY(y, x); |
||||
|
||||
forwardMapX(y, x) = x + bx; |
||||
forwardMapY(y, x) = y + by; |
||||
|
||||
backwardMapX(y, x) = x + fx; |
||||
backwardMapY(y, x) = y + fy; |
||||
} |
||||
|
||||
void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY, |
||||
PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY, |
||||
PtrStepSzf forwardMapX, PtrStepSzf forwardMapY, |
||||
PtrStepSzf backwardMapX, PtrStepSzf backwardMapY) |
||||
{ |
||||
const dim3 block(32, 8); |
||||
const dim3 grid(divUp(forwardMapX.cols, block.x), divUp(forwardMapX.rows, block.y)); |
||||
|
||||
buildMotionMapsKernel<<<grid, block>>>(forwardMotionX, forwardMotionY, |
||||
backwardMotionX, bacwardMotionY, |
||||
forwardMapX, forwardMapY, |
||||
backwardMapX, backwardMapY); |
||||
cudaSafeCall( cudaGetLastError() ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
|
||||
template <typename T> |
||||
__global__ void upscaleKernel(const PtrStepSz<T> src, PtrStep<T> dst, const int scale) |
||||
{ |
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x; |
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y; |
||||
|
||||
if (x >= src.cols || y >= src.rows) |
||||
return; |
||||
|
||||
dst(y * scale, x * scale) = src(y, x); |
||||
} |
||||
|
||||
template <int cn> |
||||
void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream) |
||||
{ |
||||
typedef typename TypeVec<float, cn>::vec_type src_t; |
||||
|
||||
const dim3 block(32, 8); |
||||
const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y)); |
||||
|
||||
upscaleKernel<src_t><<<grid, block, 0, stream>>>((PtrStepSz<src_t>) src, (PtrStepSz<src_t>) dst, scale); |
||||
cudaSafeCall( cudaGetLastError() ); |
||||
|
||||
if (stream == 0) |
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
|
||||
template void upscale<1>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream); |
||||
template void upscale<3>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream); |
||||
template void upscale<4>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream); |
||||
|
||||
__device__ __forceinline__ float diffSign(float a, float b) |
||||
{ |
||||
return a > b ? 1.0f : a < b ? -1.0f : 0.0f; |
||||
} |
||||
__device__ __forceinline__ float3 diffSign(const float3& a, const float3& b) |
||||
{ |
||||
return make_float3( |
||||
a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f, |
||||
a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f, |
||||
a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f |
||||
); |
||||
} |
||||
__device__ __forceinline__ float4 diffSign(const float4& a, const float4& b) |
||||
{ |
||||
return make_float4( |
||||
a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f, |
||||
a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f, |
||||
a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f, |
||||
0.0f |
||||
); |
||||
} |
||||
|
||||
struct DiffSign : binary_function<float, float, float> |
||||
{ |
||||
__device__ __forceinline__ float operator ()(float a, float b) const |
||||
{ |
||||
return diffSign(a, b); |
||||
} |
||||
}; |
||||
} |
||||
|
||||
namespace cv { namespace gpu { namespace device |
||||
{ |
||||
template <> struct TransformFunctorTraits<btv_l1_device::DiffSign> : DefaultTransformFunctorTraits<btv_l1_device::DiffSign> |
||||
{ |
||||
enum { smart_block_dim_y = 8 }; |
||||
enum { smart_shift = 4 }; |
||||
}; |
||||
}}} |
||||
|
||||
namespace btv_l1_device |
||||
{ |
||||
void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream) |
||||
{ |
||||
transform(src1, src2, dst, DiffSign(), WithOutMask(), stream); |
||||
} |
||||
|
||||
__constant__ float c_btvRegWeights[16*16]; |
||||
|
||||
template <typename T> |
||||
__global__ void calcBtvRegularizationKernel(const PtrStepSz<T> src, PtrStep<T> dst, const int ksize) |
||||
{ |
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x + ksize; |
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y + ksize; |
||||
|
||||
if (y >= src.rows - ksize || x >= src.cols - ksize) |
||||
return; |
||||
|
||||
const T srcVal = src(y, x); |
||||
|
||||
T dstVal = VecTraits<T>::all(0); |
||||
|
||||
for (int m = 0, count = 0; m <= ksize; ++m) |
||||
{ |
||||
for (int l = ksize; l + m >= 0; --l, ++count) |
||||
dstVal = dstVal + c_btvRegWeights[count] * (diffSign(srcVal, src(y + m, x + l)) - diffSign(src(y - m, x - l), srcVal)); |
||||
} |
||||
|
||||
dst(y, x) = dstVal; |
||||
} |
||||
|
||||
void loadBtvWeights(const float* weights, size_t count) |
||||
{ |
||||
cudaSafeCall( cudaMemcpyToSymbol(c_btvRegWeights, weights, count * sizeof(float)) ); |
||||
} |
||||
|
||||
template <int cn> |
||||
void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize) |
||||
{ |
||||
typedef typename TypeVec<float, cn>::vec_type src_t; |
||||
|
||||
const dim3 block(32, 8); |
||||
const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y)); |
||||
|
||||
calcBtvRegularizationKernel<src_t><<<grid, block>>>((PtrStepSz<src_t>) src, (PtrStepSz<src_t>) dst, ksize); |
||||
cudaSafeCall( cudaGetLastError() ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
|
||||
template void calcBtvRegularization<1>(PtrStepSzb src, PtrStepSzb dst, int ksize); |
||||
template void calcBtvRegularization<3>(PtrStepSzb src, PtrStepSzb dst, int ksize); |
||||
template void calcBtvRegularization<4>(PtrStepSzb src, PtrStepSzb dst, int ksize); |
||||
} |
||||
@ -0,0 +1,255 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::gpu; |
||||
using namespace cv::superres; |
||||
using namespace cv::superres::detail; |
||||
|
||||
cv::superres::FrameSource::~FrameSource() |
||||
{ |
||||
} |
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// EmptyFrameSource
|
||||
|
||||
namespace |
||||
{ |
||||
class EmptyFrameSource : public FrameSource |
||||
{ |
||||
public: |
||||
void nextFrame(OutputArray frame); |
||||
void reset(); |
||||
}; |
||||
|
||||
void EmptyFrameSource::nextFrame(OutputArray frame) |
||||
{ |
||||
frame.release(); |
||||
} |
||||
|
||||
void EmptyFrameSource::reset() |
||||
{ |
||||
} |
||||
} |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Empty() |
||||
{ |
||||
return new EmptyFrameSource; |
||||
} |
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// VideoFrameSource & CameraFrameSource
|
||||
|
||||
#ifndef HAVE_OPENCV_HIGHGUI |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video(const string& fileName) |
||||
{ |
||||
(void) fileName; |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<FrameSource>(); |
||||
} |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Camera(int deviceId) |
||||
{ |
||||
(void) deviceId; |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<FrameSource>(); |
||||
} |
||||
|
||||
#else // HAVE_OPENCV_HIGHGUI
|
||||
|
||||
namespace |
||||
{ |
||||
class CaptureFrameSource : public FrameSource |
||||
{ |
||||
public: |
||||
void nextFrame(OutputArray frame); |
||||
|
||||
protected: |
||||
VideoCapture vc_; |
||||
|
||||
private: |
||||
Mat frame_; |
||||
}; |
||||
|
||||
void CaptureFrameSource::nextFrame(OutputArray _frame) |
||||
{ |
||||
if (_frame.kind() == _InputArray::MAT) |
||||
{ |
||||
vc_ >> _frame.getMatRef(); |
||||
} |
||||
else |
||||
{ |
||||
vc_ >> frame_; |
||||
arrCopy(frame_, _frame); |
||||
} |
||||
} |
||||
|
||||
class VideoFrameSource : public CaptureFrameSource |
||||
{ |
||||
public: |
||||
VideoFrameSource(const string& fileName); |
||||
|
||||
void reset(); |
||||
|
||||
private: |
||||
string fileName_; |
||||
}; |
||||
|
||||
VideoFrameSource::VideoFrameSource(const string& fileName) : fileName_(fileName) |
||||
{ |
||||
reset(); |
||||
} |
||||
|
||||
void VideoFrameSource::reset() |
||||
{ |
||||
vc_.release(); |
||||
vc_.open(fileName_); |
||||
CV_Assert( vc_.isOpened() ); |
||||
} |
||||
|
||||
class CameraFrameSource : public CaptureFrameSource |
||||
{ |
||||
public: |
||||
CameraFrameSource(int deviceId); |
||||
|
||||
void reset(); |
||||
|
||||
private: |
||||
int deviceId_; |
||||
}; |
||||
|
||||
CameraFrameSource::CameraFrameSource(int deviceId) : deviceId_(deviceId) |
||||
{ |
||||
reset(); |
||||
} |
||||
|
||||
void CameraFrameSource::reset() |
||||
{ |
||||
vc_.release(); |
||||
vc_.open(deviceId_); |
||||
CV_Assert( vc_.isOpened() ); |
||||
} |
||||
} |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video(const string& fileName) |
||||
{ |
||||
return new VideoFrameSource(fileName); |
||||
} |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Camera(int deviceId) |
||||
{ |
||||
return new CameraFrameSource(deviceId); |
||||
} |
||||
|
||||
#endif // HAVE_OPENCV_HIGHGUI
|
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// VideoFrameSource_GPU
|
||||
|
||||
#ifndef HAVE_OPENCV_GPU |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video_GPU(const string& fileName) |
||||
{ |
||||
(void) fileName; |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<FrameSource>(); |
||||
} |
||||
|
||||
#else // HAVE_OPENCV_GPU
|
||||
|
||||
namespace |
||||
{ |
||||
class VideoFrameSource_GPU : public FrameSource |
||||
{ |
||||
public: |
||||
VideoFrameSource_GPU(const string& fileName); |
||||
|
||||
void nextFrame(OutputArray frame); |
||||
void reset(); |
||||
|
||||
private: |
||||
string fileName_; |
||||
VideoReader_GPU reader_; |
||||
GpuMat frame_; |
||||
}; |
||||
|
||||
VideoFrameSource_GPU::VideoFrameSource_GPU(const string& fileName) : fileName_(fileName) |
||||
{ |
||||
reset(); |
||||
} |
||||
|
||||
void VideoFrameSource_GPU::nextFrame(OutputArray _frame) |
||||
{ |
||||
if (_frame.kind() == _InputArray::GPU_MAT) |
||||
{ |
||||
bool res = reader_.read(_frame.getGpuMatRef()); |
||||
if (!res) |
||||
_frame.release(); |
||||
} |
||||
else |
||||
{ |
||||
bool res = reader_.read(frame_); |
||||
if (!res) |
||||
_frame.release(); |
||||
else |
||||
arrCopy(frame_, _frame); |
||||
} |
||||
} |
||||
|
||||
void VideoFrameSource_GPU::reset() |
||||
{ |
||||
reader_.close(); |
||||
reader_.open(fileName_); |
||||
CV_Assert( reader_.isOpened() ); |
||||
} |
||||
} |
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video_GPU(const string& fileName) |
||||
{ |
||||
return new VideoFrameSource(fileName); |
||||
} |
||||
|
||||
#endif // HAVE_OPENCV_GPU
|
||||
@ -0,0 +1,273 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::gpu; |
||||
|
||||
Mat cv::superres::arrGetMat(InputArray arr, Mat& buf) |
||||
{ |
||||
switch (arr.kind()) |
||||
{ |
||||
case _InputArray::GPU_MAT: |
||||
arr.getGpuMat().download(buf); |
||||
return buf; |
||||
|
||||
case _InputArray::OPENGL_BUFFER: |
||||
arr.getOGlBuffer().copyTo(buf); |
||||
return buf; |
||||
|
||||
case _InputArray::OPENGL_TEXTURE: |
||||
arr.getOGlTexture2D().copyTo(buf); |
||||
return buf; |
||||
|
||||
default: |
||||
return arr.getMat(); |
||||
} |
||||
} |
||||
|
||||
GpuMat cv::superres::arrGetGpuMat(InputArray arr, GpuMat& buf) |
||||
{ |
||||
switch (arr.kind()) |
||||
{ |
||||
case _InputArray::GPU_MAT: |
||||
return arr.getGpuMat(); |
||||
|
||||
case _InputArray::OPENGL_BUFFER: |
||||
arr.getOGlBuffer().copyTo(buf); |
||||
return buf; |
||||
|
||||
case _InputArray::OPENGL_TEXTURE: |
||||
arr.getOGlTexture2D().copyTo(buf); |
||||
return buf; |
||||
|
||||
default: |
||||
buf.upload(arr.getMat()); |
||||
return buf; |
||||
} |
||||
} |
||||
|
||||
namespace |
||||
{ |
||||
void mat2mat(InputArray src, OutputArray dst) |
||||
{ |
||||
src.getMat().copyTo(dst); |
||||
} |
||||
void arr2buf(InputArray src, OutputArray dst) |
||||
{ |
||||
dst.getOGlBufferRef().copyFrom(src); |
||||
} |
||||
void arr2tex(InputArray src, OutputArray dst) |
||||
{ |
||||
dst.getOGlTexture2D().copyFrom(src); |
||||
} |
||||
void mat2gpu(InputArray src, OutputArray dst) |
||||
{ |
||||
dst.getGpuMatRef().upload(src.getMat()); |
||||
} |
||||
void buf2arr(InputArray src, OutputArray dst) |
||||
{ |
||||
src.getOGlBuffer().copyTo(dst); |
||||
} |
||||
void tex2arr(InputArray src, OutputArray dst) |
||||
{ |
||||
src.getOGlTexture2D().copyTo(dst); |
||||
} |
||||
void gpu2mat(InputArray src, OutputArray dst) |
||||
{ |
||||
GpuMat d = src.getGpuMat(); |
||||
dst.create(d.size(), d.type()); |
||||
Mat m = dst.getMat(); |
||||
d.download(m); |
||||
} |
||||
void gpu2gpu(InputArray src, OutputArray dst) |
||||
{ |
||||
src.getGpuMat().copyTo(dst.getGpuMatRef()); |
||||
} |
||||
} |
||||
|
||||
void cv::superres::arrCopy(InputArray src, OutputArray dst) |
||||
{ |
||||
typedef void (*func_t)(InputArray src, OutputArray dst); |
||||
static const func_t funcs[10][10] = |
||||
{ |
||||
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, |
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu}, |
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu}, |
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu}, |
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu}, |
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu}, |
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu}, |
||||
{0, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr}, |
||||
{0, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr}, |
||||
{0, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, arr2buf, arr2tex, gpu2gpu} |
||||
}; |
||||
|
||||
const int src_kind = src.kind() >> _InputArray::KIND_SHIFT; |
||||
const int dst_kind = dst.kind() >> _InputArray::KIND_SHIFT; |
||||
|
||||
CV_DbgAssert( src_kind >= 0 && src_kind < 10 ); |
||||
CV_DbgAssert( dst_kind >= 0 && dst_kind < 10 ); |
||||
|
||||
const func_t func = funcs[src_kind][dst_kind]; |
||||
CV_DbgAssert( func != 0 ); |
||||
|
||||
func(src, dst); |
||||
} |
||||
|
||||
namespace |
||||
{ |
||||
void convertToCn(InputArray src, OutputArray dst, int cn) |
||||
{ |
||||
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 ); |
||||
CV_Assert( cn == 1 || cn == 3 || cn == 4 ); |
||||
|
||||
static const int codes[5][5] = |
||||
{ |
||||
{-1, -1, -1, -1, -1}, |
||||
{-1, -1, -1, COLOR_GRAY2BGR, COLOR_GRAY2BGRA}, |
||||
{-1, -1, -1, -1, -1}, |
||||
{-1, COLOR_BGR2GRAY, -1, -1, COLOR_BGR2BGRA}, |
||||
{-1, COLOR_BGRA2GRAY, -1, COLOR_BGRA2BGR, -1}, |
||||
}; |
||||
|
||||
const int code = codes[src.channels()][cn]; |
||||
CV_DbgAssert( code >= 0 ); |
||||
|
||||
switch (src.kind()) |
||||
{ |
||||
case _InputArray::GPU_MAT: |
||||
#ifdef HAVE_OPENCV_GPU |
||||
gpu::cvtColor(src.getGpuMat(), dst.getGpuMatRef(), code, cn); |
||||
#else |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
#endif |
||||
break; |
||||
|
||||
default: |
||||
cvtColor(src, dst, code, cn); |
||||
break; |
||||
} |
||||
} |
||||
|
||||
void convertToDepth(InputArray src, OutputArray dst, int depth) |
||||
{ |
||||
CV_Assert( src.depth() <= CV_64F ); |
||||
CV_Assert( depth == CV_8U || depth == CV_32F ); |
||||
|
||||
static const double maxVals[] = |
||||
{ |
||||
numeric_limits<uchar>::max(), |
||||
numeric_limits<schar>::max(), |
||||
numeric_limits<ushort>::max(), |
||||
numeric_limits<short>::max(), |
||||
numeric_limits<int>::max(), |
||||
1.0, |
||||
1.0, |
||||
}; |
||||
|
||||
const double scale = maxVals[depth] / maxVals[src.depth()]; |
||||
|
||||
switch (src.kind()) |
||||
{ |
||||
case _InputArray::GPU_MAT: |
||||
src.getGpuMat().convertTo(dst.getGpuMatRef(), depth, scale); |
||||
break; |
||||
|
||||
default: |
||||
src.getMat().convertTo(dst, depth, scale); |
||||
break; |
||||
} |
||||
} |
||||
} |
||||
|
||||
Mat cv::superres::convertToType(const Mat& src, int type, Mat& buf0, Mat& buf1) |
||||
{ |
||||
if (src.type() == type) |
||||
return src; |
||||
|
||||
const int depth = CV_MAT_DEPTH(type); |
||||
const int cn = CV_MAT_CN(type); |
||||
|
||||
if (src.depth() == depth) |
||||
{ |
||||
convertToCn(src, buf0, cn); |
||||
return buf0; |
||||
} |
||||
|
||||
if (src.channels() == cn) |
||||
{ |
||||
convertToDepth(src, buf1, depth); |
||||
return buf1; |
||||
} |
||||
|
||||
convertToCn(src, buf0, cn); |
||||
convertToDepth(buf0, buf1, depth); |
||||
return buf1; |
||||
} |
||||
|
||||
GpuMat cv::superres::convertToType(const GpuMat& src, int type, GpuMat& buf0, GpuMat& buf1) |
||||
{ |
||||
if (src.type() == type) |
||||
return src; |
||||
|
||||
const int depth = CV_MAT_DEPTH(type); |
||||
const int cn = CV_MAT_CN(type); |
||||
|
||||
if (src.depth() == depth) |
||||
{ |
||||
convertToCn(src, buf0, cn); |
||||
return buf0; |
||||
} |
||||
|
||||
if (src.channels() == cn) |
||||
{ |
||||
convertToDepth(src, buf1, depth); |
||||
return buf1; |
||||
} |
||||
|
||||
convertToCn(src, buf0, cn); |
||||
convertToDepth(buf0, buf1, depth); |
||||
return buf1; |
||||
} |
||||
@ -0,0 +1,63 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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*/
|
||||
|
||||
#ifndef __OPENCV_SUPERRES_INPUT_ARRAY_UTILITY_HPP__ |
||||
#define __OPENCV_SUPERRES_INPUT_ARRAY_UTILITY_HPP__ |
||||
|
||||
#include "opencv2/core/core.hpp" |
||||
#include "opencv2/core/gpumat.hpp" |
||||
|
||||
namespace cv |
||||
{ |
||||
namespace superres |
||||
{ |
||||
CV_EXPORTS Mat arrGetMat(InputArray arr, Mat& buf); |
||||
CV_EXPORTS gpu::GpuMat arrGetGpuMat(InputArray arr, gpu::GpuMat& buf); |
||||
|
||||
CV_EXPORTS void arrCopy(InputArray src, OutputArray dst); |
||||
|
||||
CV_EXPORTS Mat convertToType(const Mat& src, int type, Mat& buf0, Mat& buf1); |
||||
CV_EXPORTS gpu::GpuMat convertToType(const gpu::GpuMat& src, int type, gpu::GpuMat& buf0, gpu::GpuMat& buf1); |
||||
} |
||||
} |
||||
|
||||
#endif // __OPENCV_SUPERRES_INPUT_ARRAY_UTILITY_HPP__
|
||||
@ -0,0 +1,721 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::gpu; |
||||
using namespace cv::superres; |
||||
using namespace cv::superres::detail; |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// CpuOpticalFlow
|
||||
|
||||
namespace |
||||
{ |
||||
class CpuOpticalFlow : public DenseOpticalFlowExt |
||||
{ |
||||
public: |
||||
explicit CpuOpticalFlow(int work_type); |
||||
|
||||
void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2); |
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
virtual void impl(const Mat& input0, const Mat& input1, OutputArray dst) = 0; |
||||
|
||||
private: |
||||
int work_type_; |
||||
Mat buf_[6]; |
||||
Mat flow_; |
||||
Mat flows_[2]; |
||||
}; |
||||
|
||||
CpuOpticalFlow::CpuOpticalFlow(int work_type) : work_type_(work_type) |
||||
{ |
||||
} |
||||
|
||||
void CpuOpticalFlow::calc(InputArray _frame0, InputArray _frame1, OutputArray _flow1, OutputArray _flow2) |
||||
{ |
||||
Mat frame0 = arrGetMat(_frame0, buf_[0]); |
||||
Mat frame1 = arrGetMat(_frame1, buf_[1]); |
||||
|
||||
CV_Assert( frame1.type() == frame0.type() ); |
||||
CV_Assert( frame1.size() == frame0.size() ); |
||||
|
||||
Mat input0 = convertToType(frame0, work_type_, buf_[2], buf_[3]); |
||||
Mat input1 = convertToType(frame1, work_type_, buf_[4], buf_[5]); |
||||
|
||||
if (!_flow2.needed() && _flow1.kind() < _InputArray::OPENGL_BUFFER) |
||||
{ |
||||
impl(input0, input1, _flow1); |
||||
return; |
||||
} |
||||
|
||||
impl(input0, input1, flow_); |
||||
|
||||
if (!_flow2.needed()) |
||||
{ |
||||
arrCopy(flow_, _flow1); |
||||
} |
||||
else |
||||
{ |
||||
split(flow_, flows_); |
||||
|
||||
arrCopy(flows_[0], _flow1); |
||||
arrCopy(flows_[1], _flow2); |
||||
} |
||||
} |
||||
|
||||
void CpuOpticalFlow::collectGarbage() |
||||
{ |
||||
for (int i = 0; i < 6; ++i) |
||||
buf_[i].release(); |
||||
flow_.release(); |
||||
flows_[0].release(); |
||||
flows_[1].release(); |
||||
} |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Farneback
|
||||
|
||||
namespace |
||||
{ |
||||
class Farneback : public CpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
Farneback(); |
||||
|
||||
protected: |
||||
void impl(const Mat& input0, const Mat& input1, OutputArray dst); |
||||
|
||||
private: |
||||
double pyrScale_; |
||||
int numLevels_; |
||||
int winSize_; |
||||
int numIters_; |
||||
int polyN_; |
||||
double polySigma_; |
||||
int flags_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(Farneback, "DenseOpticalFlowExt.Farneback", |
||||
obj.info()->addParam(obj, "pyrScale", obj.pyrScale_); |
||||
obj.info()->addParam(obj, "numLevels", obj.numLevels_); |
||||
obj.info()->addParam(obj, "winSize", obj.winSize_); |
||||
obj.info()->addParam(obj, "numIters", obj.numIters_); |
||||
obj.info()->addParam(obj, "polyN", obj.polyN_); |
||||
obj.info()->addParam(obj, "polySigma", obj.polySigma_); |
||||
obj.info()->addParam(obj, "flags", obj.flags_)); |
||||
|
||||
Farneback::Farneback() : CpuOpticalFlow(CV_8UC1) |
||||
{ |
||||
pyrScale_ = 0.5; |
||||
numLevels_ = 5; |
||||
winSize_ = 13; |
||||
numIters_ = 10; |
||||
polyN_ = 5; |
||||
polySigma_ = 1.1; |
||||
flags_ = 0; |
||||
} |
||||
|
||||
void Farneback::impl(const Mat& input0, const Mat& input1, OutputArray dst) |
||||
{ |
||||
calcOpticalFlowFarneback(input0, input1, dst, pyrScale_, numLevels_, winSize_, numIters_, polyN_, polySigma_, flags_); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Farneback() |
||||
{ |
||||
return new Farneback; |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Simple
|
||||
|
||||
namespace |
||||
{ |
||||
class Simple : public CpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
Simple(); |
||||
|
||||
protected: |
||||
void impl(const Mat& input0, const Mat& input1, OutputArray dst); |
||||
|
||||
private: |
||||
int layers_; |
||||
int averagingBlockSize_; |
||||
int maxFlow_; |
||||
double sigmaDist_; |
||||
double sigmaColor_; |
||||
int postProcessWindow_; |
||||
double sigmaDistFix_; |
||||
double sigmaColorFix_; |
||||
double occThr_; |
||||
int upscaleAveragingRadius_; |
||||
double upscaleSigmaDist_; |
||||
double upscaleSigmaColor_; |
||||
double speedUpThr_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(Simple, "DenseOpticalFlowExt.Simple", |
||||
obj.info()->addParam(obj, "layers", obj.layers_); |
||||
obj.info()->addParam(obj, "averagingBlockSize", obj.averagingBlockSize_); |
||||
obj.info()->addParam(obj, "maxFlow", obj.maxFlow_); |
||||
obj.info()->addParam(obj, "sigmaDist", obj.sigmaDist_); |
||||
obj.info()->addParam(obj, "sigmaColor", obj.sigmaColor_); |
||||
obj.info()->addParam(obj, "postProcessWindow", obj.postProcessWindow_); |
||||
obj.info()->addParam(obj, "sigmaDistFix", obj.sigmaDistFix_); |
||||
obj.info()->addParam(obj, "sigmaColorFix", obj.sigmaColorFix_); |
||||
obj.info()->addParam(obj, "occThr", obj.occThr_); |
||||
obj.info()->addParam(obj, "upscaleAveragingRadius", obj.upscaleAveragingRadius_); |
||||
obj.info()->addParam(obj, "upscaleSigmaDist", obj.upscaleSigmaDist_); |
||||
obj.info()->addParam(obj, "upscaleSigmaColor", obj.upscaleSigmaColor_); |
||||
obj.info()->addParam(obj, "speedUpThr", obj.speedUpThr_)); |
||||
|
||||
Simple::Simple() : CpuOpticalFlow(CV_8UC3) |
||||
{ |
||||
layers_ = 3; |
||||
averagingBlockSize_ = 2; |
||||
maxFlow_ = 4; |
||||
sigmaDist_ = 4.1; |
||||
sigmaColor_ = 25.5; |
||||
postProcessWindow_ = 18; |
||||
sigmaDistFix_ = 55.0; |
||||
sigmaColorFix_ = 25.5; |
||||
occThr_ = 0.35; |
||||
upscaleAveragingRadius_ = 18; |
||||
upscaleSigmaDist_ = 55.0; |
||||
upscaleSigmaColor_ = 25.5; |
||||
speedUpThr_ = 10; |
||||
} |
||||
|
||||
void Simple::impl(const Mat& _input0, const Mat& _input1, OutputArray dst) |
||||
{ |
||||
Mat input0 = _input0; |
||||
Mat input1 = _input1; |
||||
calcOpticalFlowSF(input0, input1, dst.getMatRef(), |
||||
layers_, |
||||
averagingBlockSize_, |
||||
maxFlow_, |
||||
sigmaDist_, |
||||
sigmaColor_, |
||||
postProcessWindow_, |
||||
sigmaDistFix_, |
||||
sigmaColorFix_, |
||||
occThr_, |
||||
upscaleAveragingRadius_, |
||||
upscaleSigmaDist_, |
||||
upscaleSigmaColor_, |
||||
speedUpThr_); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Simple() |
||||
{ |
||||
return new Simple; |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// DualTVL1
|
||||
|
||||
namespace |
||||
{ |
||||
class DualTVL1 : public CpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
DualTVL1(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void impl(const Mat& input0, const Mat& input1, OutputArray dst); |
||||
|
||||
private: |
||||
double tau_; |
||||
double lambda_; |
||||
double theta_; |
||||
int nscales_; |
||||
int warps_; |
||||
double epsilon_; |
||||
int iterations_; |
||||
bool useInitialFlow_; |
||||
|
||||
Ptr<DenseOpticalFlow> alg_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(DualTVL1, "DenseOpticalFlowExt.DualTVL1", |
||||
obj.info()->addParam(obj, "tau", obj.tau_); |
||||
obj.info()->addParam(obj, "lambda", obj.lambda_); |
||||
obj.info()->addParam(obj, "theta", obj.theta_); |
||||
obj.info()->addParam(obj, "nscales", obj.nscales_); |
||||
obj.info()->addParam(obj, "warps", obj.warps_); |
||||
obj.info()->addParam(obj, "epsilon", obj.epsilon_); |
||||
obj.info()->addParam(obj, "iterations", obj.iterations_); |
||||
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_)); |
||||
|
||||
DualTVL1::DualTVL1() : CpuOpticalFlow(CV_8UC1) |
||||
{ |
||||
alg_ = cv::createOptFlow_DualTVL1(); |
||||
tau_ = alg_->getDouble("tau"); |
||||
lambda_ = alg_->getDouble("lambda"); |
||||
theta_ = alg_->getDouble("theta"); |
||||
nscales_ = alg_->getInt("nscales"); |
||||
warps_ = alg_->getInt("warps"); |
||||
epsilon_ = alg_->getDouble("epsilon"); |
||||
iterations_ = alg_->getInt("iterations"); |
||||
useInitialFlow_ = alg_->getBool("useInitialFlow"); |
||||
} |
||||
|
||||
void DualTVL1::impl(const Mat& input0, const Mat& input1, OutputArray dst) |
||||
{ |
||||
alg_->set("tau", tau_); |
||||
alg_->set("lambda", lambda_); |
||||
alg_->set("theta", theta_); |
||||
alg_->set("nscales", nscales_); |
||||
alg_->set("warps", warps_); |
||||
alg_->set("epsilon", epsilon_); |
||||
alg_->set("iterations", iterations_); |
||||
alg_->set("useInitialFlow", useInitialFlow_); |
||||
|
||||
alg_->calc(input0, input1, dst); |
||||
} |
||||
|
||||
void DualTVL1::collectGarbage() |
||||
{ |
||||
alg_->collectGarbage(); |
||||
CpuOpticalFlow::collectGarbage(); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1() |
||||
{ |
||||
return new DualTVL1; |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// GpuOpticalFlow
|
||||
|
||||
#ifndef HAVE_OPENCV_GPU |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Farneback_GPU() |
||||
{ |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<DenseOpticalFlowExt>(); |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1_GPU() |
||||
{ |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<DenseOpticalFlowExt>(); |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Brox_GPU() |
||||
{ |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<DenseOpticalFlowExt>(); |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_PyrLK_GPU() |
||||
{ |
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); |
||||
return Ptr<DenseOpticalFlowExt>(); |
||||
} |
||||
|
||||
#else // HAVE_OPENCV_GPU
|
||||
|
||||
namespace |
||||
{ |
||||
class GpuOpticalFlow : public DenseOpticalFlowExt |
||||
{ |
||||
public: |
||||
explicit GpuOpticalFlow(int work_type); |
||||
|
||||
void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2); |
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
virtual void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2) = 0; |
||||
|
||||
private: |
||||
int work_type_; |
||||
GpuMat buf_[6]; |
||||
GpuMat u_, v_, flow_; |
||||
}; |
||||
|
||||
GpuOpticalFlow::GpuOpticalFlow(int work_type) : work_type_(work_type) |
||||
{ |
||||
} |
||||
|
||||
void GpuOpticalFlow::calc(InputArray _frame0, InputArray _frame1, OutputArray _flow1, OutputArray _flow2) |
||||
{ |
||||
GpuMat frame0 = arrGetGpuMat(_frame0, buf_[0]); |
||||
GpuMat frame1 = arrGetGpuMat(_frame1, buf_[1]); |
||||
|
||||
CV_Assert( frame1.type() == frame0.type() ); |
||||
CV_Assert( frame1.size() == frame0.size() ); |
||||
|
||||
GpuMat input0 = convertToType(frame0, work_type_, buf_[2], buf_[3]); |
||||
GpuMat input1 = convertToType(frame1, work_type_, buf_[4], buf_[5]); |
||||
|
||||
if (_flow2.needed() && _flow1.kind() == _InputArray::GPU_MAT && _flow2.kind() == _InputArray::GPU_MAT) |
||||
{ |
||||
impl(input0, input1, _flow1.getGpuMatRef(), _flow2.getGpuMatRef()); |
||||
return; |
||||
} |
||||
|
||||
impl(input0, input1, u_, v_); |
||||
|
||||
if (_flow2.needed()) |
||||
{ |
||||
arrCopy(u_, _flow1); |
||||
arrCopy(v_, _flow2); |
||||
} |
||||
else |
||||
{ |
||||
GpuMat src[] = {u_, v_}; |
||||
merge(src, 2, flow_); |
||||
arrCopy(flow_, _flow1); |
||||
} |
||||
} |
||||
|
||||
void GpuOpticalFlow::collectGarbage() |
||||
{ |
||||
for (int i = 0; i < 6; ++i) |
||||
buf_[i].release(); |
||||
u_.release(); |
||||
v_.release(); |
||||
flow_.release(); |
||||
} |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Brox_GPU
|
||||
|
||||
namespace |
||||
{ |
||||
class Brox_GPU : public GpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
Brox_GPU(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2); |
||||
|
||||
private: |
||||
double alpha_; |
||||
double gamma_; |
||||
double scaleFactor_; |
||||
int innerIterations_; |
||||
int outerIterations_; |
||||
int solverIterations_; |
||||
|
||||
BroxOpticalFlow alg_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(Brox_GPU, "DenseOpticalFlowExt.Brox_GPU", |
||||
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Flow smoothness"); |
||||
obj.info()->addParam(obj, "gamma", obj.gamma_, false, 0, 0, "Gradient constancy importance"); |
||||
obj.info()->addParam(obj, "scaleFactor", obj.scaleFactor_, false, 0, 0, "Pyramid scale factor"); |
||||
obj.info()->addParam(obj, "innerIterations", obj.innerIterations_, false, 0, 0, "Number of lagged non-linearity iterations (inner loop)"); |
||||
obj.info()->addParam(obj, "outerIterations", obj.outerIterations_, false, 0, 0, "Number of warping iterations (number of pyramid levels)"); |
||||
obj.info()->addParam(obj, "solverIterations", obj.solverIterations_, false, 0, 0, "Number of linear system solver iterations")); |
||||
|
||||
Brox_GPU::Brox_GPU() : GpuOpticalFlow(CV_32FC1), alg_(0.197f, 50.0f, 0.8f, 10, 77, 10) |
||||
{ |
||||
alpha_ = alg_.alpha; |
||||
gamma_ = alg_.gamma; |
||||
scaleFactor_ = alg_.scale_factor; |
||||
innerIterations_ = alg_.inner_iterations; |
||||
outerIterations_ = alg_.outer_iterations; |
||||
solverIterations_ = alg_.solver_iterations; |
||||
} |
||||
|
||||
void Brox_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2) |
||||
{ |
||||
alg_.alpha = static_cast<float>(alpha_); |
||||
alg_.gamma = static_cast<float>(gamma_); |
||||
alg_.scale_factor = static_cast<float>(scaleFactor_); |
||||
alg_.inner_iterations = innerIterations_; |
||||
alg_.outer_iterations = outerIterations_; |
||||
alg_.solver_iterations = solverIterations_; |
||||
|
||||
alg_(input0, input1, dst1, dst2); |
||||
} |
||||
|
||||
void Brox_GPU::collectGarbage() |
||||
{ |
||||
alg_.buf.release(); |
||||
GpuOpticalFlow::collectGarbage(); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Brox_GPU() |
||||
{ |
||||
return new Brox_GPU; |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// PyrLK_GPU
|
||||
|
||||
namespace |
||||
{ |
||||
class PyrLK_GPU : public GpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
PyrLK_GPU(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2); |
||||
|
||||
private: |
||||
int winSize_; |
||||
int maxLevel_; |
||||
int iterations_; |
||||
|
||||
PyrLKOpticalFlow alg_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(PyrLK_GPU, "DenseOpticalFlowExt.PyrLK_GPU", |
||||
obj.info()->addParam(obj, "winSize", obj.winSize_); |
||||
obj.info()->addParam(obj, "maxLevel", obj.maxLevel_); |
||||
obj.info()->addParam(obj, "iterations", obj.iterations_)); |
||||
|
||||
PyrLK_GPU::PyrLK_GPU() : GpuOpticalFlow(CV_8UC1) |
||||
{ |
||||
winSize_ = alg_.winSize.width; |
||||
maxLevel_ = alg_.maxLevel; |
||||
iterations_ = alg_.iters; |
||||
} |
||||
|
||||
void PyrLK_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2) |
||||
{ |
||||
alg_.winSize.width = winSize_; |
||||
alg_.winSize.height = winSize_; |
||||
alg_.maxLevel = maxLevel_; |
||||
alg_.iters = iterations_; |
||||
|
||||
alg_.dense(input0, input1, dst1, dst2); |
||||
} |
||||
|
||||
void PyrLK_GPU::collectGarbage() |
||||
{ |
||||
alg_.releaseMemory(); |
||||
GpuOpticalFlow::collectGarbage(); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_PyrLK_GPU() |
||||
{ |
||||
return new PyrLK_GPU; |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Farneback_GPU
|
||||
|
||||
namespace |
||||
{ |
||||
class Farneback_GPU : public GpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
Farneback_GPU(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2); |
||||
|
||||
private: |
||||
double pyrScale_; |
||||
int numLevels_; |
||||
int winSize_; |
||||
int numIters_; |
||||
int polyN_; |
||||
double polySigma_; |
||||
int flags_; |
||||
|
||||
FarnebackOpticalFlow alg_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(Farneback_GPU, "DenseOpticalFlowExt.Farneback_GPU", |
||||
obj.info()->addParam(obj, "pyrScale", obj.pyrScale_); |
||||
obj.info()->addParam(obj, "numLevels", obj.numLevels_); |
||||
obj.info()->addParam(obj, "winSize", obj.winSize_); |
||||
obj.info()->addParam(obj, "numIters", obj.numIters_); |
||||
obj.info()->addParam(obj, "polyN", obj.polyN_); |
||||
obj.info()->addParam(obj, "polySigma", obj.polySigma_); |
||||
obj.info()->addParam(obj, "flags", obj.flags_)); |
||||
|
||||
Farneback_GPU::Farneback_GPU() : GpuOpticalFlow(CV_8UC1) |
||||
{ |
||||
pyrScale_ = alg_.pyrScale; |
||||
numLevels_ = alg_.numLevels; |
||||
winSize_ = alg_.winSize; |
||||
numIters_ = alg_.numIters; |
||||
polyN_ = alg_.polyN; |
||||
polySigma_ = alg_.polySigma; |
||||
flags_ = alg_.flags; |
||||
} |
||||
|
||||
void Farneback_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2) |
||||
{ |
||||
alg_.pyrScale = pyrScale_; |
||||
alg_.numLevels = numLevels_; |
||||
alg_.winSize = winSize_; |
||||
alg_.numIters = numIters_; |
||||
alg_.polyN = polyN_; |
||||
alg_.polySigma = polySigma_; |
||||
alg_.flags = flags_; |
||||
|
||||
alg_(input0, input1, dst1, dst2); |
||||
} |
||||
|
||||
void Farneback_GPU::collectGarbage() |
||||
{ |
||||
alg_.releaseMemory(); |
||||
GpuOpticalFlow::collectGarbage(); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Farneback_GPU() |
||||
{ |
||||
return new Farneback_GPU; |
||||
} |
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// DualTVL1_GPU
|
||||
|
||||
namespace |
||||
{ |
||||
class DualTVL1_GPU : public GpuOpticalFlow |
||||
{ |
||||
public: |
||||
AlgorithmInfo* info() const; |
||||
|
||||
DualTVL1_GPU(); |
||||
|
||||
void collectGarbage(); |
||||
|
||||
protected: |
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2); |
||||
|
||||
private: |
||||
double tau_; |
||||
double lambda_; |
||||
double theta_; |
||||
int nscales_; |
||||
int warps_; |
||||
double epsilon_; |
||||
int iterations_; |
||||
bool useInitialFlow_; |
||||
|
||||
OpticalFlowDual_TVL1_GPU alg_; |
||||
}; |
||||
|
||||
CV_INIT_ALGORITHM(DualTVL1_GPU, "DenseOpticalFlowExt.DualTVL1_GPU", |
||||
obj.info()->addParam(obj, "tau", obj.tau_); |
||||
obj.info()->addParam(obj, "lambda", obj.lambda_); |
||||
obj.info()->addParam(obj, "theta", obj.theta_); |
||||
obj.info()->addParam(obj, "nscales", obj.nscales_); |
||||
obj.info()->addParam(obj, "warps", obj.warps_); |
||||
obj.info()->addParam(obj, "epsilon", obj.epsilon_); |
||||
obj.info()->addParam(obj, "iterations", obj.iterations_); |
||||
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_)); |
||||
|
||||
DualTVL1_GPU::DualTVL1_GPU() : GpuOpticalFlow(CV_8UC1) |
||||
{ |
||||
tau_ = alg_.tau; |
||||
lambda_ = alg_.lambda; |
||||
theta_ = alg_.theta; |
||||
nscales_ = alg_.nscales; |
||||
warps_ = alg_.warps; |
||||
epsilon_ = alg_.epsilon; |
||||
iterations_ = alg_.iterations; |
||||
useInitialFlow_ = alg_.useInitialFlow; |
||||
} |
||||
|
||||
void DualTVL1_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2) |
||||
{ |
||||
alg_.tau = tau_; |
||||
alg_.lambda = lambda_; |
||||
alg_.theta = theta_; |
||||
alg_.nscales = nscales_; |
||||
alg_.warps = warps_; |
||||
alg_.epsilon = epsilon_; |
||||
alg_.iterations = iterations_; |
||||
alg_.useInitialFlow = useInitialFlow_; |
||||
|
||||
alg_(input0, input1, dst1, dst2); |
||||
} |
||||
|
||||
void DualTVL1_GPU::collectGarbage() |
||||
{ |
||||
alg_.collectGarbage(); |
||||
GpuOpticalFlow::collectGarbage(); |
||||
} |
||||
} |
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1_GPU() |
||||
{ |
||||
return new DualTVL1_GPU; |
||||
} |
||||
|
||||
#endif // HAVE_OPENCV_GPU
|
||||
@ -0,0 +1,43 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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" |
||||
@ -0,0 +1,78 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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*/
|
||||
|
||||
#ifndef __OPENCV_PRECOMP_H__ |
||||
#define __OPENCV_PRECOMP_H__ |
||||
|
||||
#include <vector> |
||||
#include <limits> |
||||
|
||||
#ifdef HAVE_CVCONFIG_H |
||||
#include "cvconfig.h" |
||||
#endif |
||||
|
||||
#include "opencv2/opencv_modules.hpp" |
||||
#include "opencv2/core/core.hpp" |
||||
#include "opencv2/core/gpumat.hpp" |
||||
#include "opencv2/core/opengl_interop.hpp" |
||||
#include "opencv2/core/internal.hpp" |
||||
#include "opencv2/imgproc/imgproc.hpp" |
||||
#include "opencv2/video/tracking.hpp" |
||||
|
||||
#ifdef HAVE_OPENCV_GPU |
||||
#include "opencv2/gpu/gpu.hpp" |
||||
#ifdef HAVE_CUDA |
||||
#include "opencv2/gpu/stream_accessor.hpp" |
||||
#endif |
||||
#endif |
||||
|
||||
#ifdef HAVE_OPENCV_HIGHGUI |
||||
#include "opencv2/highgui/highgui.hpp" |
||||
#endif |
||||
|
||||
#include "opencv2/superres/superres.hpp" |
||||
#include "opencv2/superres/optical_flow.hpp" |
||||
#include "input_array_utility.hpp" |
||||
|
||||
#include "ring_buffer.hpp" |
||||
|
||||
#endif /* __OPENCV_PRECOMP_H__ */ |
||||
@ -0,0 +1,79 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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*/
|
||||
|
||||
#ifndef __RING_BUFFER_HPP__ |
||||
#define __RING_BUFFER_HPP__ |
||||
|
||||
#include "precomp.hpp" |
||||
|
||||
namespace cv |
||||
{ |
||||
namespace superres |
||||
{ |
||||
namespace detail |
||||
{ |
||||
template <typename T, class A> |
||||
inline const T& at(int index, const std::vector<T, A>& items) |
||||
{ |
||||
const int len = static_cast<int>(items.size()); |
||||
if (index < 0) |
||||
index -= ((index - len + 1) / len) * len; |
||||
if (index >= len) |
||||
index %= len; |
||||
return items[index]; |
||||
} |
||||
|
||||
template <typename T, class A> |
||||
inline T& at(int index, std::vector<T, A>& items) |
||||
{ |
||||
const int len = static_cast<int>(items.size()); |
||||
if (index < 0) |
||||
index -= ((index - len + 1) / len) * len; |
||||
if (index >= len) |
||||
index %= len; |
||||
return items[index]; |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
#endif // __RING_BUFFER_HPP__
|
||||
@ -0,0 +1,85 @@ |
||||
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., 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" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::superres; |
||||
|
||||
bool cv::superres::initModule_superres() |
||||
{ |
||||
return !createSuperResolution_BTVL1().empty(); |
||||
} |
||||
|
||||
cv::superres::SuperResolution::SuperResolution() |
||||
{ |
||||
frameSource_ = createFrameSource_Empty(); |
||||
firstCall_ = true; |
||||
} |
||||
|
||||
void cv::superres::SuperResolution::setInput(const Ptr<FrameSource>& frameSource) |
||||
{ |
||||
frameSource_ = frameSource; |
||||
firstCall_ = true; |
||||
} |
||||
|
||||
void cv::superres::SuperResolution::nextFrame(OutputArray frame) |
||||
{ |
||||
if (firstCall_) |
||||
{ |
||||
initImpl(frameSource_); |
||||
firstCall_ = false; |
||||
} |
||||
|
||||
processImpl(frameSource_, frame); |
||||
} |
||||
|
||||
void cv::superres::SuperResolution::reset() |
||||
{ |
||||
frameSource_->reset(); |
||||
firstCall_ = true; |
||||
} |
||||
|
||||
void cv::superres::SuperResolution::collectGarbage() |
||||
{ |
||||
} |
||||
@ -0,0 +1,3 @@ |
||||
#include "test_precomp.hpp" |
||||
|
||||
CV_TEST_MAIN("superres") |
||||
@ -0,0 +1 @@ |
||||
#include "test_precomp.hpp" |
||||
@ -0,0 +1,23 @@ |
||||
#ifdef __GNUC__ |
||||
# pragma GCC diagnostic ignored "-Wmissing-declarations" |
||||
# if defined __clang__ || defined __APPLE__ |
||||
# pragma GCC diagnostic ignored "-Wmissing-prototypes" |
||||
# pragma GCC diagnostic ignored "-Wextra" |
||||
# endif |
||||
#endif |
||||
|
||||
#ifndef __OPENCV_TEST_PRECOMP_HPP__ |
||||
#define __OPENCV_TEST_PRECOMP_HPP__ |
||||
|
||||
#ifdef HAVE_CVCONFIG_H |
||||
#include "cvconfig.h" |
||||
#endif |
||||
|
||||
#include "opencv2/opencv_modules.hpp" |
||||
#include "opencv2/core/core.hpp" |
||||
#include "opencv2/imgproc/imgproc.hpp" |
||||
#include "opencv2/ts/ts.hpp" |
||||
#include "opencv2/superres/superres.hpp" |
||||
#include "input_array_utility.hpp" |
||||
|
||||
#endif |
||||
@ -0,0 +1,236 @@ |
||||
#include "test_precomp.hpp" |
||||
|
||||
class AllignedFrameSource : public cv::superres::FrameSource |
||||
{ |
||||
public: |
||||
AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale); |
||||
|
||||
void nextFrame(cv::OutputArray frame); |
||||
void reset(); |
||||
|
||||
private: |
||||
cv::Ptr<cv::superres::FrameSource> base_; |
||||
cv::Mat origFrame_; |
||||
int scale_; |
||||
}; |
||||
|
||||
AllignedFrameSource::AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) : |
||||
base_(base), scale_(scale) |
||||
{ |
||||
CV_Assert( !base_.empty() ); |
||||
} |
||||
|
||||
void AllignedFrameSource::nextFrame(cv::OutputArray frame) |
||||
{ |
||||
base_->nextFrame(origFrame_); |
||||
|
||||
if (origFrame_.rows % scale_ == 0 && origFrame_.cols % scale_ == 0) |
||||
{ |
||||
cv::superres::arrCopy(origFrame_, frame); |
||||
} |
||||
else |
||||
{ |
||||
cv::Rect ROI(0, 0, (origFrame_.cols / scale_) * scale_, (origFrame_.rows / scale_) * scale_); |
||||
cv::superres::arrCopy(origFrame_(ROI), frame); |
||||
} |
||||
} |
||||
|
||||
void AllignedFrameSource::reset() |
||||
{ |
||||
base_->reset(); |
||||
} |
||||
|
||||
class DegradeFrameSource : public cv::superres::FrameSource |
||||
{ |
||||
public: |
||||
DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale); |
||||
|
||||
void nextFrame(cv::OutputArray frame); |
||||
void reset(); |
||||
|
||||
private: |
||||
cv::Ptr<cv::superres::FrameSource> base_; |
||||
cv::Mat origFrame_; |
||||
cv::Mat blurred_; |
||||
cv::Mat deg_; |
||||
double iscale_; |
||||
}; |
||||
|
||||
DegradeFrameSource::DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) : |
||||
base_(base), iscale_(1.0 / scale) |
||||
{ |
||||
CV_Assert( !base_.empty() ); |
||||
} |
||||
|
||||
void addGaussNoise(cv::Mat& image, double sigma) |
||||
{ |
||||
cv::Mat noise(image.size(), CV_32FC(image.channels())); |
||||
cvtest::TS::ptr()->get_rng().fill(noise, cv::RNG::NORMAL, 0.0, sigma); |
||||
|
||||
cv::addWeighted(image, 1.0, noise, 1.0, 0.0, image, image.depth()); |
||||
} |
||||
|
||||
void addSpikeNoise(cv::Mat& image, int frequency) |
||||
{ |
||||
cv::Mat_<uchar> mask(image.size(), 0); |
||||
|
||||
for (int y = 0; y < mask.rows; ++y) |
||||
{ |
||||
for (int x = 0; x < mask.cols; ++x) |
||||
{ |
||||
if (cvtest::TS::ptr()->get_rng().uniform(0, frequency) < 1) |
||||
mask(y, x) = 255; |
||||
} |
||||
} |
||||
|
||||
image.setTo(cv::Scalar::all(255), mask); |
||||
} |
||||
|
||||
void DegradeFrameSource::nextFrame(cv::OutputArray frame) |
||||
{ |
||||
base_->nextFrame(origFrame_); |
||||
|
||||
cv::GaussianBlur(origFrame_, blurred_, cv::Size(5, 5), 0); |
||||
cv::resize(blurred_, deg_, cv::Size(), iscale_, iscale_, cv::INTER_NEAREST); |
||||
|
||||
addGaussNoise(deg_, 10.0); |
||||
addSpikeNoise(deg_, 500); |
||||
|
||||
cv::superres::arrCopy(deg_, frame); |
||||
} |
||||
|
||||
void DegradeFrameSource::reset() |
||||
{ |
||||
base_->reset(); |
||||
} |
||||
|
||||
double MSSIM(const cv::Mat& i1, const cv::Mat& i2) |
||||
{ |
||||
const double C1 = 6.5025; |
||||
const double C2 = 58.5225; |
||||
|
||||
const int depth = CV_32F; |
||||
|
||||
cv::Mat I1, I2; |
||||
i1.convertTo(I1, depth); |
||||
i2.convertTo(I2, depth); |
||||
|
||||
cv::Mat I2_2 = I2.mul(I2); // I2^2
|
||||
cv::Mat I1_2 = I1.mul(I1); // I1^2
|
||||
cv::Mat I1_I2 = I1.mul(I2); // I1 * I2
|
||||
|
||||
cv::Mat mu1, mu2; |
||||
cv::GaussianBlur(I1, mu1, cv::Size(11, 11), 1.5); |
||||
cv::GaussianBlur(I2, mu2, cv::Size(11, 11), 1.5); |
||||
|
||||
cv::Mat mu1_2 = mu1.mul(mu1); |
||||
cv::Mat mu2_2 = mu2.mul(mu2); |
||||
cv::Mat mu1_mu2 = mu1.mul(mu2); |
||||
|
||||
cv::Mat sigma1_2, sigma2_2, sigma12; |
||||
|
||||
cv::GaussianBlur(I1_2, sigma1_2, cv::Size(11, 11), 1.5); |
||||
sigma1_2 -= mu1_2; |
||||
|
||||
cv::GaussianBlur(I2_2, sigma2_2, cv::Size(11, 11), 1.5); |
||||
sigma2_2 -= mu2_2; |
||||
|
||||
cv::GaussianBlur(I1_I2, sigma12, cv::Size(11, 11), 1.5); |
||||
sigma12 -= mu1_mu2; |
||||
|
||||
cv::Mat t1, t2; |
||||
cv::Mat numerator; |
||||
cv::Mat denominator; |
||||
|
||||
// t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
|
||||
t1 = 2 * mu1_mu2 + C1; |
||||
t2 = 2 * sigma12 + C2; |
||||
numerator = t1.mul(t2); |
||||
|
||||
// t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
|
||||
t1 = mu1_2 + mu2_2 + C1; |
||||
t2 = sigma1_2 + sigma2_2 + C2; |
||||
denominator = t1.mul(t2); |
||||
|
||||
// ssim_map = numerator./denominator;
|
||||
cv::Mat ssim_map; |
||||
cv::divide(numerator, denominator, ssim_map); |
||||
|
||||
// mssim = average of ssim map
|
||||
cv::Scalar mssim = cv::mean(ssim_map); |
||||
|
||||
if (i1.channels() == 1) |
||||
return mssim[0]; |
||||
|
||||
return (mssim[0] + mssim[1] + mssim[3]) / 3; |
||||
} |
||||
|
||||
class SuperResolution : public testing::Test |
||||
{ |
||||
public: |
||||
void RunTest(cv::Ptr<cv::superres::SuperResolution> superRes); |
||||
}; |
||||
|
||||
void SuperResolution::RunTest(cv::Ptr<cv::superres::SuperResolution> superRes) |
||||
{ |
||||
const std::string inputVideoName = cvtest::TS::ptr()->get_data_path() + "car.avi"; |
||||
const int scale = 2; |
||||
const int iterations = 100; |
||||
const int temporalAreaRadius = 2; |
||||
|
||||
ASSERT_FALSE( superRes.empty() ); |
||||
|
||||
const int btvKernelSize = superRes->getInt("btvKernelSize"); |
||||
|
||||
superRes->set("scale", scale); |
||||
superRes->set("iterations", iterations); |
||||
superRes->set("temporalAreaRadius", temporalAreaRadius); |
||||
|
||||
cv::Ptr<cv::superres::FrameSource> goldSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale)); |
||||
cv::Ptr<cv::superres::FrameSource> lowResSource(new DegradeFrameSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale), scale)); |
||||
|
||||
// skip first frame
|
||||
cv::Mat frame; |
||||
|
||||
lowResSource->nextFrame(frame); |
||||
goldSource->nextFrame(frame); |
||||
|
||||
cv::Rect inner(btvKernelSize, btvKernelSize, frame.cols - 2 * btvKernelSize, frame.rows - 2 * btvKernelSize); |
||||
|
||||
superRes->setInput(lowResSource); |
||||
|
||||
double srAvgMSSIM = 0.0; |
||||
const int count = 10; |
||||
|
||||
cv::Mat goldFrame, superResFrame; |
||||
for (int i = 0; i < count; ++i) |
||||
{ |
||||
goldSource->nextFrame(goldFrame); |
||||
ASSERT_FALSE( goldFrame.empty() ); |
||||
|
||||
superRes->nextFrame(superResFrame); |
||||
ASSERT_FALSE( superResFrame.empty() ); |
||||
|
||||
const double srMSSIM = MSSIM(goldFrame(inner), superResFrame); |
||||
|
||||
srAvgMSSIM += srMSSIM; |
||||
} |
||||
|
||||
srAvgMSSIM /= count; |
||||
|
||||
EXPECT_GE( srAvgMSSIM, 0.5 ); |
||||
} |
||||
|
||||
TEST_F(SuperResolution, BTVL1) |
||||
{ |
||||
RunTest(cv::superres::createSuperResolution_BTVL1()); |
||||
} |
||||
|
||||
#if defined(HAVE_OPENCV_GPU) && defined(HAVE_CUDA) |
||||
|
||||
TEST_F(SuperResolution, BTVL1_GPU) |
||||
{ |
||||
RunTest(cv::superres::createSuperResolution_BTVL1_GPU()); |
||||
} |
||||
|
||||
#endif |
||||
@ -0,0 +1,152 @@ |
||||
#include <iostream> |
||||
#include <iomanip> |
||||
#include <string> |
||||
#include "opencv2/core/core.hpp" |
||||
#include "opencv2/highgui/highgui.hpp" |
||||
#include "opencv2/imgproc/imgproc.hpp" |
||||
#include "opencv2/contrib/contrib.hpp" |
||||
#include "opencv2/superres/superres.hpp" |
||||
#include "opencv2/superres/optical_flow.hpp" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::superres; |
||||
|
||||
#define MEASURE_TIME(op) \ |
||||
{ \
|
||||
TickMeter tm; \
|
||||
tm.start(); \
|
||||
op; \
|
||||
tm.stop(); \
|
||||
cout << tm.getTimeSec() << " sec" << endl; \
|
||||
} |
||||
|
||||
static Ptr<DenseOpticalFlowExt> createOptFlow(const string& name, bool useGpu) |
||||
{ |
||||
if (name == "farneback") |
||||
{ |
||||
if (useGpu) |
||||
return createOptFlow_Farneback_GPU(); |
||||
else |
||||
return createOptFlow_Farneback(); |
||||
} |
||||
else if (name == "simple") |
||||
return createOptFlow_Simple(); |
||||
else if (name == "tvl1") |
||||
{ |
||||
if (useGpu) |
||||
return createOptFlow_DualTVL1_GPU(); |
||||
else |
||||
return createOptFlow_DualTVL1(); |
||||
} |
||||
else if (name == "brox") |
||||
return createOptFlow_Brox_GPU(); |
||||
else if (name == "pyrlk") |
||||
return createOptFlow_PyrLK_GPU(); |
||||
else |
||||
{ |
||||
cerr << "Incorrect Optical Flow algorithm - " << name << endl; |
||||
exit(-1); |
||||
} |
||||
|
||||
return Ptr<DenseOpticalFlowExt>(); |
||||
} |
||||
|
||||
int main(int argc, const char* argv[]) |
||||
{ |
||||
CommandLineParser cmd(argc, argv, |
||||
"{ v | video | | Input video }" |
||||
"{ o | output | | Output video }" |
||||
"{ s | scale | 4 | Scale factor }" |
||||
"{ i | iterations | 180 | Iteration count }" |
||||
"{ t | temporal | 4 | Radius of the temporal search area }" |
||||
"{ f | flow | farneback | Optical flow algorithm (farneback, simple, tvl1, brox, pyrlk) }" |
||||
"{ gpu | gpu | false | Use GPU }" |
||||
"{ h | help | false | Print help message }" |
||||
); |
||||
|
||||
if (cmd.get<bool>("help")) |
||||
{ |
||||
cout << "This sample demonstrates Super Resolution algorithms for video sequence" << endl; |
||||
cmd.printParams(); |
||||
return 0; |
||||
} |
||||
|
||||
const string inputVideoName = cmd.get<string>("video"); |
||||
const string outputVideoName = cmd.get<string>("output"); |
||||
const int scale = cmd.get<int>("scale"); |
||||
const int iterations = cmd.get<int>("iterations"); |
||||
const int temporalAreaRadius = cmd.get<int>("temporal"); |
||||
const string optFlow = cmd.get<string>("flow"); |
||||
const bool useGpu = cmd.get<bool>("gpu"); |
||||
|
||||
Ptr<SuperResolution> superRes; |
||||
if (useGpu) |
||||
superRes = createSuperResolution_BTVL1_GPU(); |
||||
else |
||||
superRes = createSuperResolution_BTVL1(); |
||||
|
||||
superRes->set("scale", scale); |
||||
superRes->set("iterations", iterations); |
||||
superRes->set("temporalAreaRadius", temporalAreaRadius); |
||||
superRes->set("opticalFlow", createOptFlow(optFlow, useGpu)); |
||||
|
||||
Ptr<FrameSource> frameSource; |
||||
if (useGpu) |
||||
{ |
||||
// Try to use gpu Video Decoding
|
||||
try |
||||
{ |
||||
frameSource = createFrameSource_Video_GPU(inputVideoName); |
||||
Mat frame; |
||||
frameSource->nextFrame(frame); |
||||
} |
||||
catch (const cv::Exception&) |
||||
{ |
||||
frameSource.release(); |
||||
} |
||||
} |
||||
if (frameSource.empty()) |
||||
frameSource = createFrameSource_Video(inputVideoName); |
||||
|
||||
// skip first frame, it is usually corrupted
|
||||
{ |
||||
Mat frame; |
||||
frameSource->nextFrame(frame); |
||||
cout << "Input : " << inputVideoName << " " << frame.size() << endl; |
||||
cout << "Scale factor : " << scale << endl; |
||||
cout << "Iterations : " << iterations << endl; |
||||
cout << "Temporal radius : " << temporalAreaRadius << endl; |
||||
cout << "Optical Flow : " << optFlow << endl; |
||||
cout << "Mode : " << (useGpu ? "GPU" : "CPU") << endl; |
||||
} |
||||
|
||||
superRes->setInput(frameSource); |
||||
|
||||
VideoWriter writer; |
||||
|
||||
for (int i = 0;; ++i) |
||||
{ |
||||
cout << '[' << setw(3) << i << "] : "; |
||||
|
||||
Mat result; |
||||
MEASURE_TIME(superRes->nextFrame(result)); |
||||
|
||||
if (result.empty()) |
||||
break; |
||||
|
||||
imshow("Super Resolution", result); |
||||
|
||||
if (waitKey(1000) > 0) |
||||
break; |
||||
|
||||
if (!outputVideoName.empty()) |
||||
{ |
||||
if (!writer.isOpened()) |
||||
writer.open(outputVideoName, CV_FOURCC('X', 'V', 'I', 'D'), 25.0, result.size()); |
||||
writer << result; |
||||
} |
||||
} |
||||
|
||||
return 0; |
||||
} |
||||
Loading…
Reference in new issue