Merge remote-tracking branch 'origin/2.4' into merge-2.4
Conflicts: modules/core/include/opencv2/core/version.hpp modules/ocl/include/opencv2/ocl/ocl.hpp modules/ocl/src/initialization.cpp modules/ocl/test/main.cpp modules/superres/CMakeLists.txt modules/superres/src/input_array_utility.cpp modules/superres/src/input_array_utility.hpp modules/superres/src/optical_flow.cpp
This commit is contained in:
Roman Donchenko
commit
feaa12a274
@ -11,7 +11,7 @@ You can store and then restore various OpenCV data structures to/from XML (http:
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Use the following procedure to write something to XML or YAML:
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#. Create new :ocv:class:`FileStorage` and open it for writing. It can be done with a single call to :ocv:func:`FileStorage::FileStorage` constructor that takes a filename, or you can use the default constructor and then call :ocv:func:`FileStorage::open`. Format of the file (XML or YAML) is determined from the filename extension (".xml" and ".yml"/".yaml", respectively)
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#. Write all the data you want using the streaming operator ``>>``, just like in the case of STL streams.
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#. Write all the data you want using the streaming operator ``<<``, just like in the case of STL streams.
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#. Close the file using :ocv:func:`FileStorage::release`. ``FileStorage`` destructor also closes the file.
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Here is an example: ::
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@ -525,7 +525,11 @@ BRISK::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& k
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bool doOrientation=true;
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if (useProvidedKeypoints)
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doOrientation = false;
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computeDescriptorsAndOrOrientation(_image, _mask, keypoints, _descriptors, true, doOrientation,
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// If the user specified cv::noArray(), this will yield false. Otherwise it will return true.
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bool doDescriptors = _descriptors.needed();
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computeDescriptorsAndOrOrientation(_image, _mask, keypoints, _descriptors, doDescriptors, doOrientation,
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useProvidedKeypoints);
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}
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@ -220,8 +220,8 @@ CV_IMPL CvCapture * cvCreateCameraCapture (int index)
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return capture;
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break;
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#endif
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#ifdef HAVE_VFW
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case CV_CAP_VFW:
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#ifdef HAVE_VFW
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capture = cvCreateCameraCapture_VFW (index);
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if (capture)
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return capture;
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@ -248,6 +248,8 @@ void cv::matchTemplate( InputArray _img, InputArray _templ, OutputArray _result,
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CV_Assert( (img.depth() == CV_8U || img.depth() == CV_32F) &&
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img.type() == templ.type() );
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CV_Assert( img.rows >= templ.rows && img.cols >= templ.cols);
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Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1);
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_result.create(corrSize, CV_32F);
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Mat result = _result.getMat();
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@ -853,6 +853,19 @@ namespace cv
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CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
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int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
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/////////////////////////////////// ML ///////////////////////////////////////////
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//! Compute closest centers for each lines in source and lable it after center's index
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// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
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CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers);
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//!Does k-means procedure on GPU
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// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
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CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
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TermCriteria criteria, int attemps, int flags, oclMat ¢ers);
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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@ -55,7 +55,7 @@ namespace cv
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namespace ocl
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{
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///////////////////////////OpenCL kernel strings///////////////////////////
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extern const char *imgproc_gfft;
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extern const char *imgproc_gftt;
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}
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}
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@ -133,7 +133,7 @@ struct Sorter<BITONIC>
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for(int passOfStage = 0; passOfStage < stage + 1; ++passOfStage)
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{
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args[4] = std::make_pair(sizeof(cl_int), (void *)&passOfStage);
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openCLExecuteKernel(cxt, &imgproc_gfft, kernelname, globalThreads, localThreads, args, -1, -1);
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openCLExecuteKernel(cxt, &imgproc_gftt, kernelname, globalThreads, localThreads, args, -1, -1);
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}
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}
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}
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@ -160,12 +160,12 @@ struct Sorter<SELECTION>
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args.push_back( std::make_pair( sizeof(cl_int), (void*)&count) );
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args.push_back( std::make_pair( lds_size, (void*)NULL) );
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openCLExecuteKernel(cxt, &imgproc_gfft, kernelname, globalThreads, localThreads, args, -1, -1);
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openCLExecuteKernel(cxt, &imgproc_gftt, kernelname, globalThreads, localThreads, args, -1, -1);
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//final
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kernelname = "sortCorners_selectionSortFinal";
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args.pop_back();
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openCLExecuteKernel(cxt, &imgproc_gfft, kernelname, globalThreads, localThreads, args, -1, -1);
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openCLExecuteKernel(cxt, &imgproc_gftt, kernelname, globalThreads, localThreads, args, -1, -1);
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}
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};
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@ -201,7 +201,7 @@ int findCorners_caller(
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size_t localThreads[3] = {16, 16, 1};
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const char * opt = mask.empty() ? "" : "-D WITH_MASK";
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openCLExecuteKernel(cxt, &imgproc_gfft, kernelname, globalThreads, localThreads, args, -1, -1, opt);
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openCLExecuteKernel(cxt, &imgproc_gftt, kernelname, globalThreads, localThreads, args, -1, -1, opt);
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return std::min(Mat(g_counter).at<int>(0), max_count);
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}
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}//unnamed namespace
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@ -319,8 +319,7 @@ namespace cv
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char clVersion[256];
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for (unsigned i = 0; i < numPlatforms; ++i)
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{
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cl_uint numsdev;
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cl_uint numsdev = 0;
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cl_int status = clGetDeviceIDs(platforms[i], devicetype, 0, NULL, &numsdev);
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if(status != CL_DEVICE_NOT_FOUND)
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openCLVerifyCall(status);
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438
modules/ocl/src/kmeans.cpp
Normal file
438
modules/ocl/src/kmeans.cpp
Normal file
@ -0,0 +1,438 @@
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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) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, 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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// @Authors
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// Xiaopeng Fu, fuxiaopeng2222@163.com
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other oclMaterials 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
|
||||
// (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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#include <iomanip>
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#include "precomp.hpp"
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using namespace cv;
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using namespace ocl;
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namespace cv
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{
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namespace ocl
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{
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////////////////////////////////////OpenCL kernel strings//////////////////////////
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extern const char *kmeans_kernel;
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}
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}
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static void generateRandomCenter(const std::vector<Vec2f>& box, float* center, RNG& rng)
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{
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size_t j, dims = box.size();
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float margin = 1.f/dims;
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for( j = 0; j < dims; j++ )
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center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
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}
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// This class is copied from matrix.cpp in core module.
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class KMeansPPDistanceComputer : public ParallelLoopBody
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{
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public:
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KMeansPPDistanceComputer( float *_tdist2,
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const float *_data,
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const float *_dist,
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int _dims,
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size_t _step,
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size_t _stepci )
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: tdist2(_tdist2),
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data(_data),
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dist(_dist),
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dims(_dims),
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step(_step),
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stepci(_stepci) { }
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void operator()( const cv::Range& range ) const
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{
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const int begin = range.start;
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const int end = range.end;
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for ( int i = begin; i<end; i++ )
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{
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tdist2[i] = std::min(normL2Sqr_(data + step*i, data + stepci, dims), dist[i]);
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}
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}
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private:
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KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
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float *tdist2;
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const float *data;
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const float *dist;
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const int dims;
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const size_t step;
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const size_t stepci;
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};
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/*
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k-means center initialization using the following algorithm:
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Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
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*/
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static void generateCentersPP(const Mat& _data, Mat& _out_centers,
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int K, RNG& rng, int trials)
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{
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int i, j, k, dims = _data.cols, N = _data.rows;
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const float* data = (float*)_data.data;
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size_t step = _data.step/sizeof(data[0]);
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std::vector<int> _centers(K);
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int* centers = &_centers[0];
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std::vector<float> _dist(N*3);
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float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
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double sum0 = 0;
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centers[0] = (unsigned)rng % N;
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for( i = 0; i < N; i++ )
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{
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dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
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sum0 += dist[i];
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}
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for( k = 1; k < K; k++ )
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{
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double bestSum = DBL_MAX;
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int bestCenter = -1;
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for( j = 0; j < trials; j++ )
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{
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double p = (double)rng*sum0, s = 0;
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for( i = 0; i < N-1; i++ )
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if( (p -= dist[i]) <= 0 )
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break;
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int ci = i;
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parallel_for_(Range(0, N),
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KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
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for( i = 0; i < N; i++ )
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{
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s += tdist2[i];
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}
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if( s < bestSum )
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{
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bestSum = s;
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bestCenter = ci;
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std::swap(tdist, tdist2);
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}
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}
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centers[k] = bestCenter;
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sum0 = bestSum;
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std::swap(dist, tdist);
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}
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for( k = 0; k < K; k++ )
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{
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const float* src = data + step*centers[k];
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float* dst = _out_centers.ptr<float>(k);
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for( j = 0; j < dims; j++ )
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dst[j] = src[j];
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}
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}
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void cv::ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers)
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{
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//if(src.clCxt -> impl -> double_support == 0 && src.type() == CV_64F)
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//{
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// CV_Error(CV_GpuNotSupported, "Selected device don't support double\r\n");
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// return;
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//}
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Context *clCxt = src.clCxt;
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int labels_step = (int)(labels.step/labels.elemSize());
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String kernelname = "distanceToCenters";
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int threadNum = src.rows > 256 ? 256 : src.rows;
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size_t localThreads[3] = {1, threadNum, 1};
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size_t globalThreads[3] = {1, src.rows, 1};
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std::vector<std::pair<size_t, const void *> > args;
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args.push_back(std::make_pair(sizeof(cl_int), (void *)&labels_step));
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args.push_back(std::make_pair(sizeof(cl_int), (void *)¢ers.rows));
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args.push_back(std::make_pair(sizeof(cl_mem), (void *)&src.data));
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args.push_back(std::make_pair(sizeof(cl_mem), (void *)&labels.data));
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args.push_back(std::make_pair(sizeof(cl_int), (void *)¢ers.cols));
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args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.rows));
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args.push_back(std::make_pair(sizeof(cl_mem), (void *)¢ers.data));
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args.push_back(std::make_pair(sizeof(cl_mem), (void*)&dists.data));
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openCLExecuteKernel(clCxt, &kmeans_kernel, kernelname, globalThreads, localThreads, args, -1, -1, NULL);
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}
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///////////////////////////////////k - means /////////////////////////////////////////////////////////
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double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
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TermCriteria criteria, int attempts, int flags, oclMat &_centers)
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{
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const int SPP_TRIALS = 3;
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bool isrow = _src.rows == 1 && _src.oclchannels() > 1;
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int N = !isrow ? _src.rows : _src.cols;
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int dims = (!isrow ? _src.cols : 1) * _src.oclchannels();
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int type = _src.depth();
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attempts = std::max(attempts, 1);
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CV_Assert(type == CV_32F && K > 0 );
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CV_Assert( N >= K );
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Mat _labels;
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if( flags & KMEANS_USE_INITIAL_LABELS )
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{
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CV_Assert( (_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
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_bestLabels.cols * _bestLabels.rows == N &&
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_bestLabels.type() == CV_32S );
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_bestLabels.download(_labels);
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}
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else
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{
|
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if( !((_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
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_bestLabels.cols * _bestLabels.rows == N &&
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_bestLabels.type() == CV_32S &&
|
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_bestLabels.isContinuous()))
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_bestLabels.create(N, 1, CV_32S);
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_labels.create(_bestLabels.size(), _bestLabels.type());
|
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}
|
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int* labels = _labels.ptr<int>();
|
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Mat data;
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_src.download(data);
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Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
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std::vector<int> counters(K);
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std::vector<Vec2f> _box(dims);
|
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Vec2f* box = &_box[0];
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double best_compactness = DBL_MAX, compactness = 0;
|
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RNG& rng = theRNG();
|
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int a, iter, i, j, k;
|
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|
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if( criteria.type & TermCriteria::EPS )
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criteria.epsilon = std::max(criteria.epsilon, 0.);
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else
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criteria.epsilon = FLT_EPSILON;
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criteria.epsilon *= criteria.epsilon;
|
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|
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if( criteria.type & TermCriteria::COUNT )
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criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
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else
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criteria.maxCount = 100;
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|
||||
if( K == 1 )
|
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{
|
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attempts = 1;
|
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criteria.maxCount = 2;
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}
|
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|
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const float* sample = data.ptr<float>();
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for( j = 0; j < dims; j++ )
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box[j] = Vec2f(sample[j], sample[j]);
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|
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for( i = 1; i < N; i++ )
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{
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sample = data.ptr<float>(i);
|
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for( j = 0; j < dims; j++ )
|
||||
{
|
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float v = sample[j];
|
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box[j][0] = std::min(box[j][0], v);
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box[j][1] = std::max(box[j][1], v);
|
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}
|
||||
}
|
||||
|
||||
for( a = 0; a < attempts; a++ )
|
||||
{
|
||||
double max_center_shift = DBL_MAX;
|
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for( iter = 0;; )
|
||||
{
|
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swap(centers, old_centers);
|
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|
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if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
|
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{
|
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if( flags & KMEANS_PP_CENTERS )
|
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generateCentersPP(data, centers, K, rng, SPP_TRIALS);
|
||||
else
|
||||
{
|
||||
for( k = 0; k < K; k++ )
|
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generateRandomCenter(_box, centers.ptr<float>(k), rng);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
|
||||
{
|
||||
for( i = 0; i < N; i++ )
|
||||
CV_Assert( (unsigned)labels[i] < (unsigned)K );
|
||||
}
|
||||
|
||||
// compute centers
|
||||
centers = Scalar(0);
|
||||
for( k = 0; k < K; k++ )
|
||||
counters[k] = 0;
|
||||
|
||||
for( i = 0; i < N; i++ )
|
||||
{
|
||||
sample = data.ptr<float>(i);
|
||||
k = labels[i];
|
||||
float* center = centers.ptr<float>(k);
|
||||
j=0;
|
||||
#if CV_ENABLE_UNROLLED
|
||||
for(; j <= dims - 4; j += 4 )
|
||||
{
|
||||
float t0 = center[j] + sample[j];
|
||||
float t1 = center[j+1] + sample[j+1];
|
||||
|
||||
center[j] = t0;
|
||||
center[j+1] = t1;
|
||||
|
||||
t0 = center[j+2] + sample[j+2];
|
||||
t1 = center[j+3] + sample[j+3];
|
||||
|
||||
center[j+2] = t0;
|
||||
center[j+3] = t1;
|
||||
}
|
||||
#endif
|
||||
for( ; j < dims; j++ )
|
||||
center[j] += sample[j];
|
||||
counters[k]++;
|
||||
}
|
||||
|
||||
if( iter > 0 )
|
||||
max_center_shift = 0;
|
||||
|
||||
for( k = 0; k < K; k++ )
|
||||
{
|
||||
if( counters[k] != 0 )
|
||||
continue;
|
||||
|
||||
// if some cluster appeared to be empty then:
|
||||
// 1. find the biggest cluster
|
||||
// 2. find the farthest from the center point in the biggest cluster
|
||||
// 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
|
||||
int max_k = 0;
|
||||
for( int k1 = 1; k1 < K; k1++ )
|
||||
{
|
||||
if( counters[max_k] < counters[k1] )
|
||||
max_k = k1;
|
||||
}
|
||||
|
||||
double max_dist = 0;
|
||||
int farthest_i = -1;
|
||||
float* new_center = centers.ptr<float>(k);
|
||||
float* old_center = centers.ptr<float>(max_k);
|
||||
float* _old_center = temp.ptr<float>(); // normalized
|
||||
float scale = 1.f/counters[max_k];
|
||||
for( j = 0; j < dims; j++ )
|
||||
_old_center[j] = old_center[j]*scale;
|
||||
|
||||
for( i = 0; i < N; i++ )
|
||||
{
|
||||
if( labels[i] != max_k )
|
||||
continue;
|
||||
sample = data.ptr<float>(i);
|
||||
double dist = normL2Sqr_(sample, _old_center, dims);
|
||||
|
||||
if( max_dist <= dist )
|
||||
{
|
||||
max_dist = dist;
|
||||
farthest_i = i;
|
||||
}
|
||||
}
|
||||
|
||||
counters[max_k]--;
|
||||
counters[k]++;
|
||||
labels[farthest_i] = k;
|
||||
sample = data.ptr<float>(farthest_i);
|
||||
|
||||
for( j = 0; j < dims; j++ )
|
||||
{
|
||||
old_center[j] -= sample[j];
|
||||
new_center[j] += sample[j];
|
||||
}
|
||||
}
|
||||
|
||||
for( k = 0; k < K; k++ )
|
||||
{
|
||||
float* center = centers.ptr<float>(k);
|
||||
CV_Assert( counters[k] != 0 );
|
||||
|
||||
float scale = 1.f/counters[k];
|
||||
for( j = 0; j < dims; j++ )
|
||||
center[j] *= scale;
|
||||
|
||||
if( iter > 0 )
|
||||
{
|
||||
double dist = 0;
|
||||
const float* old_center = old_centers.ptr<float>(k);
|
||||
for( j = 0; j < dims; j++ )
|
||||
{
|
||||
double t = center[j] - old_center[j];
|
||||
dist += t*t;
|
||||
}
|
||||
max_center_shift = std::max(max_center_shift, dist);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
|
||||
break;
|
||||
|
||||
// assign labels
|
||||
oclMat _dists(1, N, CV_64F);
|
||||
|
||||
_bestLabels.upload(_labels);
|
||||
_centers.upload(centers);
|
||||
distanceToCenters(_dists, _bestLabels, _src, _centers);
|
||||
|
||||
Mat dists;
|
||||
_dists.download(dists);
|
||||
_bestLabels.download(_labels);
|
||||
|
||||
double* dist = dists.ptr<double>(0);
|
||||
compactness = 0;
|
||||
for( i = 0; i < N; i++ )
|
||||
{
|
||||
compactness += dist[i];
|
||||
}
|
||||
}
|
||||
|
||||
if( compactness < best_compactness )
|
||||
{
|
||||
best_compactness = compactness;
|
||||
}
|
||||
}
|
||||
|
||||
return best_compactness;
|
||||
}
|
||||
|
||||
84
modules/ocl/src/opencl/kmeans_kernel.cl
Normal file
84
modules/ocl/src/opencl/kmeans_kernel.cl
Normal file
@ -0,0 +1,84 @@
|
||||
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Xiaopeng Fu, fuxiaopeng2222@163.com
|
||||
//
|
||||
// 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 GpuMaterials 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*/
|
||||
|
||||
__kernel void distanceToCenters(
|
||||
int label_step, int K,
|
||||
__global float *src,
|
||||
__global int *labels, int dims, int rows,
|
||||
__global float *centers,
|
||||
__global float *dists)
|
||||
{
|
||||
int gid = get_global_id(1);
|
||||
|
||||
float dist, euDist, min;
|
||||
int minCentroid;
|
||||
|
||||
if(gid >= rows)
|
||||
return;
|
||||
|
||||
for(int i = 0 ; i < K; i++)
|
||||
{
|
||||
euDist = 0;
|
||||
for(int j = 0; j < dims; j++)
|
||||
{
|
||||
dist = (src[j + gid * dims]
|
||||
- centers[j + i * dims]);
|
||||
euDist += dist * dist;
|
||||
}
|
||||
|
||||
if(i == 0)
|
||||
{
|
||||
min = euDist;
|
||||
minCentroid = 0;
|
||||
}
|
||||
else if(euDist < min)
|
||||
{
|
||||
min = euDist;
|
||||
minCentroid = i;
|
||||
}
|
||||
}
|
||||
dists[gid] = min;
|
||||
labels[label_step * gid] = minCentroid;
|
||||
}
|
||||
@ -73,14 +73,12 @@ void print_info()
|
||||
#endif
|
||||
|
||||
}
|
||||
std::string workdir;
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
TS::ptr()->init("ocl");
|
||||
TS::ptr()->init(".");
|
||||
InitGoogleTest(&argc, argv);
|
||||
const char *keys =
|
||||
"{ h | false | print help message }"
|
||||
"{ w | ../../../samples/c/| set working directory i.e. -w=C:\\}"
|
||||
"{ t | gpu | set device type:i.e. -t=cpu or gpu}"
|
||||
"{ p | 0 | set platform id i.e. -p=0}"
|
||||
"{ d | 0 | set device id i.e. -d=0}";
|
||||
@ -92,7 +90,6 @@ int main(int argc, char **argv)
|
||||
cmd.printMessage();
|
||||
return 0;
|
||||
}
|
||||
workdir = cmd.get<string>("w");
|
||||
string type = cmd.get<string>("t");
|
||||
unsigned int pid = cmd.get<unsigned int>("p");
|
||||
int device = cmd.get<int>("d");
|
||||
|
||||
@ -50,7 +50,6 @@
|
||||
|
||||
using namespace cv;
|
||||
|
||||
extern std::string workdir;
|
||||
PARAM_TEST_CASE(StereoMatchBM, int, int)
|
||||
{
|
||||
int n_disp;
|
||||
@ -66,9 +65,9 @@ PARAM_TEST_CASE(StereoMatchBM, int, int)
|
||||
TEST_P(StereoMatchBM, Regression)
|
||||
{
|
||||
|
||||
Mat left_image = readImage("stereobm/aloe-L.png", IMREAD_GRAYSCALE);
|
||||
Mat right_image = readImage("stereobm/aloe-R.png", IMREAD_GRAYSCALE);
|
||||
Mat disp_gold = readImage("stereobm/aloe-disp.png", IMREAD_GRAYSCALE);
|
||||
Mat left_image = readImage("gpu/stereobm/aloe-L.png", IMREAD_GRAYSCALE);
|
||||
Mat right_image = readImage("gpu/stereobm/aloe-R.png", IMREAD_GRAYSCALE);
|
||||
Mat disp_gold = readImage("gpu/stereobm/aloe-disp.png", IMREAD_GRAYSCALE);
|
||||
ocl::oclMat d_left, d_right;
|
||||
ocl::oclMat d_disp(left_image.size(), CV_8U);
|
||||
Mat disp;
|
||||
@ -113,9 +112,9 @@ PARAM_TEST_CASE(StereoMatchBP, int, int, int, float, float, float, float)
|
||||
};
|
||||
TEST_P(StereoMatchBP, Regression)
|
||||
{
|
||||
Mat left_image = readImage("stereobp/aloe-L.png");
|
||||
Mat right_image = readImage("stereobp/aloe-R.png");
|
||||
Mat disp_gold = readImage("stereobp/aloe-disp.png", IMREAD_GRAYSCALE);
|
||||
Mat left_image = readImage("gpu/stereobp/aloe-L.png");
|
||||
Mat right_image = readImage("gpu/stereobp/aloe-R.png");
|
||||
Mat disp_gold = readImage("gpu/stereobp/aloe-disp.png", IMREAD_GRAYSCALE);
|
||||
ocl::oclMat d_left, d_right;
|
||||
ocl::oclMat d_disp;
|
||||
Mat disp;
|
||||
@ -166,9 +165,9 @@ PARAM_TEST_CASE(StereoMatchConstSpaceBP, int, int, int, int, float, float, float
|
||||
};
|
||||
TEST_P(StereoMatchConstSpaceBP, Regression)
|
||||
{
|
||||
Mat left_image = readImage("csstereobp/aloe-L.png");
|
||||
Mat right_image = readImage("csstereobp/aloe-R.png");
|
||||
Mat disp_gold = readImage("csstereobp/aloe-disp.png", IMREAD_GRAYSCALE);
|
||||
Mat left_image = readImage("gpu/csstereobp/aloe-L.png");
|
||||
Mat right_image = readImage("gpu/csstereobp/aloe-R.png");
|
||||
Mat disp_gold = readImage("gpu/csstereobp/aloe-disp.png", IMREAD_GRAYSCALE);
|
||||
|
||||
ocl::oclMat d_left, d_right;
|
||||
ocl::oclMat d_disp;
|
||||
|
||||
@ -48,7 +48,6 @@
|
||||
|
||||
////////////////////////////////////////////////////////
|
||||
// Canny
|
||||
extern std::string workdir;
|
||||
IMPLEMENT_PARAM_CLASS(AppertureSize, int);
|
||||
IMPLEMENT_PARAM_CLASS(L2gradient, bool);
|
||||
|
||||
@ -67,7 +66,7 @@ PARAM_TEST_CASE(Canny, AppertureSize, L2gradient)
|
||||
|
||||
TEST_P(Canny, Accuracy)
|
||||
{
|
||||
cv::Mat img = readImage(workdir + "fruits.jpg", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat img = readImage("cv/shared/fruits.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(img.empty());
|
||||
|
||||
double low_thresh = 50.0;
|
||||
|
||||
162
modules/ocl/test/test_kmeans.cpp
Normal file
162
modules/ocl/test/test_kmeans.cpp
Normal file
@ -0,0 +1,162 @@
|
||||
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Erping Pang, pang_er_ping@163.com
|
||||
// Xiaopeng Fu, fuxiaopeng2222@163.com
|
||||
//
|
||||
// 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 oclMaterials 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"
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
|
||||
using namespace cvtest;
|
||||
using namespace testing;
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
#define OCL_KMEANS_USE_INITIAL_LABELS 1
|
||||
#define OCL_KMEANS_PP_CENTERS 2
|
||||
|
||||
PARAM_TEST_CASE(Kmeans, int, int, int)
|
||||
{
|
||||
int type;
|
||||
int K;
|
||||
int flags;
|
||||
cv::Mat src ;
|
||||
ocl::oclMat d_src, d_dists;
|
||||
|
||||
Mat labels, centers;
|
||||
ocl::oclMat d_labels, d_centers;
|
||||
cv::RNG rng ;
|
||||
virtual void SetUp(){
|
||||
K = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
flags = GET_PARAM(2);
|
||||
rng = TS::ptr()->get_rng();
|
||||
|
||||
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
|
||||
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
|
||||
src.create(size, type);
|
||||
int row_idx = 0;
|
||||
const int max_neighbour = MHEIGHT / K - 1;
|
||||
CV_Assert(K <= MWIDTH);
|
||||
for(int i = 0; i < K; i++ )
|
||||
{
|
||||
Mat center_row_header = src.row(row_idx);
|
||||
center_row_header.setTo(0);
|
||||
int nchannel = center_row_header.channels();
|
||||
for(int j = 0; j < nchannel; j++)
|
||||
center_row_header.at<float>(0, i*nchannel+j) = 50000.0;
|
||||
|
||||
for(int j = 0; (j < max_neighbour) ||
|
||||
(i == K-1 && j < max_neighbour + MHEIGHT%K); j ++)
|
||||
{
|
||||
Mat cur_row_header = src.row(row_idx + 1 + j);
|
||||
center_row_header.copyTo(cur_row_header);
|
||||
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), -200, 200, false);
|
||||
cur_row_header += tmpmat;
|
||||
}
|
||||
row_idx += 1 + max_neighbour;
|
||||
}
|
||||
}
|
||||
};
|
||||
TEST_P(Kmeans, Mat){
|
||||
|
||||
if(flags & KMEANS_USE_INITIAL_LABELS)
|
||||
{
|
||||
// inital a given labels
|
||||
labels.create(src.rows, 1, CV_32S);
|
||||
int *label = labels.ptr<int>();
|
||||
for(int i = 0; i < src.rows; i++)
|
||||
label[i] = rng.uniform(0, K);
|
||||
d_labels.upload(labels);
|
||||
}
|
||||
d_src.upload(src);
|
||||
|
||||
for(int j = 0; j < LOOP_TIMES; j++)
|
||||
{
|
||||
kmeans(src, K, labels,
|
||||
TermCriteria( TermCriteria::EPS + TermCriteria::MAX_ITER, 100, 0),
|
||||
1, flags, centers);
|
||||
|
||||
ocl::kmeans(d_src, K, d_labels,
|
||||
TermCriteria( TermCriteria::EPS + TermCriteria::MAX_ITER, 100, 0),
|
||||
1, flags, d_centers);
|
||||
|
||||
Mat dd_labels(d_labels);
|
||||
Mat dd_centers(d_centers);
|
||||
if(flags & KMEANS_USE_INITIAL_LABELS)
|
||||
{
|
||||
EXPECT_MAT_NEAR(labels, dd_labels, 0);
|
||||
EXPECT_MAT_NEAR(centers, dd_centers, 1e-3);
|
||||
}
|
||||
else
|
||||
{
|
||||
int row_idx = 0;
|
||||
for(int i = 0; i < K; i++)
|
||||
{
|
||||
// verify lables with ground truth resutls
|
||||
int label = labels.at<int>(row_idx);
|
||||
int header_label = dd_labels.at<int>(row_idx);
|
||||
for(int j = 0; (j < MHEIGHT/K)||(i == K-1 && j < MHEIGHT/K+MHEIGHT%K); j++)
|
||||
{
|
||||
ASSERT_NEAR(labels.at<int>(row_idx+j), label, 0);
|
||||
ASSERT_NEAR(dd_labels.at<int>(row_idx+j), header_label, 0);
|
||||
}
|
||||
|
||||
// verify centers
|
||||
float *center = centers.ptr<float>(label);
|
||||
float *header_center = dd_centers.ptr<float>(header_label);
|
||||
for(int t = 0; t < centers.cols; t++)
|
||||
ASSERT_NEAR(center[t], header_center[t], 1e-3);
|
||||
|
||||
row_idx += MHEIGHT/K;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ML, Kmeans, Combine(
|
||||
Values(3, 5, 8),
|
||||
Values(CV_32FC1, CV_32FC2, CV_32FC4),
|
||||
Values(OCL_KMEANS_USE_INITIAL_LABELS/*, OCL_KMEANS_PP_CENTERS*/)));
|
||||
|
||||
#endif
|
||||
@ -44,7 +44,7 @@ TEST_P(MomentsTest, Mat)
|
||||
{
|
||||
if(test_contours)
|
||||
{
|
||||
Mat src = imread( workdir + "../cpp/pic3.png", IMREAD_GRAYSCALE );
|
||||
Mat src = readImage( "cv/shared/pic3.png", IMREAD_GRAYSCALE );
|
||||
ASSERT_FALSE(src.empty());
|
||||
Mat canny_output;
|
||||
vector<vector<Point> > contours;
|
||||
|
||||
@ -66,11 +66,8 @@ PARAM_TEST_CASE(HOG, Size, int)
|
||||
{
|
||||
winSize = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
img_rgb = readImage(workdir + "../gpu/road.png");
|
||||
if(img_rgb.empty())
|
||||
{
|
||||
std::cout << "Couldn't read road.png" << std::endl;
|
||||
}
|
||||
img_rgb = readImage("gpu/hog/road.png");
|
||||
ASSERT_FALSE(img_rgb.empty());
|
||||
}
|
||||
};
|
||||
|
||||
@ -215,18 +212,11 @@ PARAM_TEST_CASE(Haar, int, CascadeName)
|
||||
virtual void SetUp()
|
||||
{
|
||||
flags = GET_PARAM(0);
|
||||
cascadeName = (workdir + "../../data/haarcascades/").append(GET_PARAM(1));
|
||||
if( (!cascade.load( cascadeName )) || (!cpucascade.load(cascadeName)) )
|
||||
{
|
||||
std::cout << "ERROR: Could not load classifier cascade" << std::endl;
|
||||
return;
|
||||
}
|
||||
img = readImage(workdir + "lena.jpg", IMREAD_GRAYSCALE);
|
||||
if(img.empty())
|
||||
{
|
||||
std::cout << "Couldn't read lena.jpg" << std::endl;
|
||||
return ;
|
||||
}
|
||||
cascadeName = (string(cvtest::TS::ptr()->get_data_path()) + "cv/cascadeandhog/cascades/").append(GET_PARAM(1));
|
||||
ASSERT_TRUE(cascade.load( cascadeName ));
|
||||
ASSERT_TRUE(cpucascade.load(cascadeName));
|
||||
img = readImage("cv/shared/lena.png", IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(img.empty());
|
||||
equalizeHist(img, img);
|
||||
d_img.upload(img);
|
||||
}
|
||||
|
||||
@ -75,7 +75,7 @@ PARAM_TEST_CASE(GoodFeaturesToTrack, MinDistance)
|
||||
|
||||
TEST_P(GoodFeaturesToTrack, Accuracy)
|
||||
{
|
||||
cv::Mat frame = readImage(workdir + "../gpu/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat frame = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(frame.empty());
|
||||
|
||||
int maxCorners = 1000;
|
||||
@ -146,10 +146,10 @@ PARAM_TEST_CASE(TVL1, bool)
|
||||
|
||||
TEST_P(TVL1, Accuracy)
|
||||
{
|
||||
cv::Mat frame0 = readImage(workdir + "../gpu/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(frame0.empty());
|
||||
|
||||
cv::Mat frame1 = readImage(workdir + "../gpu/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(frame1.empty());
|
||||
|
||||
cv::ocl::OpticalFlowDual_TVL1_OCL d_alg;
|
||||
@ -188,10 +188,10 @@ PARAM_TEST_CASE(Sparse, bool, bool)
|
||||
|
||||
TEST_P(Sparse, Mat)
|
||||
{
|
||||
cv::Mat frame0 = readImage(workdir + "../gpu/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
|
||||
cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
|
||||
ASSERT_FALSE(frame0.empty());
|
||||
|
||||
cv::Mat frame1 = readImage(workdir + "../gpu/rubberwhale2.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
|
||||
cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
|
||||
ASSERT_FALSE(frame1.empty());
|
||||
|
||||
cv::Mat gray_frame;
|
||||
@ -301,10 +301,10 @@ PARAM_TEST_CASE(Farneback, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
|
||||
|
||||
TEST_P(Farneback, Accuracy)
|
||||
{
|
||||
cv::Mat frame0 = imread(workdir + "/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(frame0.empty());
|
||||
|
||||
cv::Mat frame1 = imread(workdir + "/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(frame1.empty());
|
||||
|
||||
double polySigma = polyN <= 5 ? 1.1 : 1.5;
|
||||
|
||||
@ -5,5 +5,5 @@ endif()
|
||||
set(the_description "Super Resolution")
|
||||
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 -Wundef)
|
||||
ocv_define_module(superres opencv_imgproc opencv_video
|
||||
OPTIONAL opencv_highgui
|
||||
OPTIONAL opencv_highgui opencv_ocl
|
||||
opencv_gpuarithm opencv_gpufilters opencv_gpuwarping opencv_gpuimgproc opencv_gpuoptflow opencv_gpucodec)
|
||||
|
||||
@ -92,6 +92,7 @@ namespace cv
|
||||
// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
|
||||
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1();
|
||||
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_GPU();
|
||||
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_OCL();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -63,10 +63,12 @@ namespace cv
|
||||
|
||||
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1();
|
||||
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1_GPU();
|
||||
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1_OCL();
|
||||
|
||||
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Brox_GPU();
|
||||
|
||||
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_GPU();
|
||||
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_OCL();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
147
modules/superres/perf/perf_superres_ocl.cpp
Normal file
147
modules/superres/perf/perf_superres_ocl.cpp
Normal file
@ -0,0 +1,147 @@
|
||||
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, 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 "perf_precomp.hpp"
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
|
||||
#include "opencv2/ocl.hpp"
|
||||
using namespace std;
|
||||
using namespace std::tr1;
|
||||
using namespace testing;
|
||||
using namespace perf;
|
||||
using namespace cv;
|
||||
using namespace cv::superres;
|
||||
|
||||
namespace
|
||||
{
|
||||
class OneFrameSource_OCL : public FrameSource
|
||||
{
|
||||
public:
|
||||
explicit OneFrameSource_OCL(const ocl::oclMat& frame) : frame_(frame) {}
|
||||
|
||||
void nextFrame(OutputArray frame)
|
||||
{
|
||||
ocl::getOclMatRef(frame) = frame_;
|
||||
}
|
||||
void reset()
|
||||
{
|
||||
}
|
||||
|
||||
private:
|
||||
ocl::oclMat frame_;
|
||||
};
|
||||
|
||||
|
||||
class ZeroOpticalFlowOCL : public DenseOpticalFlowExt
|
||||
{
|
||||
public:
|
||||
void calc(InputArray frame0, InputArray, OutputArray flow1, OutputArray flow2)
|
||||
{
|
||||
ocl::oclMat& frame0_ = ocl::getOclMatRef(frame0);
|
||||
ocl::oclMat& flow1_ = ocl::getOclMatRef(flow1);
|
||||
ocl::oclMat& flow2_ = ocl::getOclMatRef(flow2);
|
||||
|
||||
cv::Size size = frame0_.size();
|
||||
|
||||
if(!flow2.needed())
|
||||
{
|
||||
flow1_.create(size, CV_32FC2);
|
||||
flow1_.setTo(Scalar::all(0));
|
||||
}
|
||||
else
|
||||
{
|
||||
flow1_.create(size, CV_32FC1);
|
||||
flow2_.create(size, CV_32FC1);
|
||||
|
||||
flow1_.setTo(Scalar::all(0));
|
||||
flow2_.setTo(Scalar::all(0));
|
||||
}
|
||||
}
|
||||
|
||||
void collectGarbage()
|
||||
{
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
PERF_TEST_P(Size_MatType, SuperResolution_BTVL1_OCL,
|
||||
Combine(Values(szSmall64, szSmall128),
|
||||
Values(MatType(CV_8UC1), MatType(CV_8UC3))))
|
||||
{
|
||||
std::vector<cv::ocl::Info>info;
|
||||
cv::ocl::getDevice(info);
|
||||
|
||||
declare.time(5 * 60);
|
||||
|
||||
const Size size = get<0>(GetParam());
|
||||
const int type = get<1>(GetParam());
|
||||
|
||||
Mat frame(size, type);
|
||||
declare.in(frame, WARMUP_RNG);
|
||||
|
||||
ocl::oclMat frame_ocl;
|
||||
frame_ocl.upload(frame);
|
||||
|
||||
|
||||
const int scale = 2;
|
||||
const int iterations = 50;
|
||||
const int temporalAreaRadius = 1;
|
||||
Ptr<DenseOpticalFlowExt> opticalFlowOcl(new ZeroOpticalFlowOCL);
|
||||
|
||||
Ptr<SuperResolution> superRes_ocl = createSuperResolution_BTVL1_OCL();
|
||||
|
||||
superRes_ocl->set("scale", scale);
|
||||
superRes_ocl->set("iterations", iterations);
|
||||
superRes_ocl->set("temporalAreaRadius", temporalAreaRadius);
|
||||
superRes_ocl->set("opticalFlow", opticalFlowOcl);
|
||||
|
||||
superRes_ocl->setInput(new OneFrameSource_OCL(frame_ocl));
|
||||
|
||||
ocl::oclMat dst_ocl;
|
||||
superRes_ocl->nextFrame(dst_ocl);
|
||||
|
||||
TEST_CYCLE_N(10) superRes_ocl->nextFrame(dst_ocl);
|
||||
frame_ocl.release();
|
||||
CPU_SANITY_CHECK(dst_ocl);
|
||||
}
|
||||
#endif
|
||||
748
modules/superres/src/btv_l1_ocl.cpp
Normal file
748
modules/superres/src/btv_l1_ocl.cpp
Normal file
@ -0,0 +1,748 @@
|
||||
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Jin Ma, jin@multicorewareinc.com
|
||||
// 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"
|
||||
|
||||
#if !defined(HAVE_OPENCL) || !defined(HAVE_OPENCV_OCL)
|
||||
|
||||
cv::Ptr<cv::superres::SuperResolution> cv::superres::createSuperResolution_BTVL1_OCL()
|
||||
{
|
||||
CV_Error(cv::Error::StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<SuperResolution>();
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::ocl;
|
||||
using namespace cv::superres;
|
||||
using namespace cv::superres::detail;
|
||||
|
||||
namespace cv
|
||||
{
|
||||
namespace ocl
|
||||
{
|
||||
extern const char* superres_btvl1;
|
||||
|
||||
float* btvWeights_ = NULL;
|
||||
size_t btvWeights_size = 0;
|
||||
}
|
||||
}
|
||||
|
||||
namespace btv_l1_device_ocl
|
||||
{
|
||||
void buildMotionMaps(const oclMat& forwardMotionX, const oclMat& forwardMotionY,
|
||||
const oclMat& backwardMotionX, const oclMat& bacwardMotionY,
|
||||
oclMat& forwardMapX, oclMat& forwardMapY,
|
||||
oclMat& backwardMapX, oclMat& backwardMapY);
|
||||
|
||||
void upscale(const oclMat& src, oclMat& dst, int scale);
|
||||
|
||||
float diffSign(float a, float b);
|
||||
|
||||
Point3f diffSign(Point3f a, Point3f b);
|
||||
|
||||
void diffSign(const oclMat& src1, const oclMat& src2, oclMat& dst);
|
||||
|
||||
void calcBtvRegularization(const oclMat& src, oclMat& dst, int ksize);
|
||||
}
|
||||
|
||||
void btv_l1_device_ocl::buildMotionMaps(const oclMat& forwardMotionX, const oclMat& forwardMotionY,
|
||||
const oclMat& backwardMotionX, const oclMat& backwardMotionY,
|
||||
oclMat& forwardMapX, oclMat& forwardMapY,
|
||||
oclMat& backwardMapX, oclMat& backwardMapY)
|
||||
{
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {forwardMapX.cols, forwardMapX.rows, 1};
|
||||
|
||||
int forwardMotionX_step = (int)(forwardMotionX.step/forwardMotionX.elemSize());
|
||||
int forwardMotionY_step = (int)(forwardMotionY.step/forwardMotionY.elemSize());
|
||||
int backwardMotionX_step = (int)(backwardMotionX.step/backwardMotionX.elemSize());
|
||||
int backwardMotionY_step = (int)(backwardMotionY.step/backwardMotionY.elemSize());
|
||||
int forwardMapX_step = (int)(forwardMapX.step/forwardMapX.elemSize());
|
||||
int forwardMapY_step = (int)(forwardMapY.step/forwardMapY.elemSize());
|
||||
int backwardMapX_step = (int)(backwardMapX.step/backwardMapX.elemSize());
|
||||
int backwardMapY_step = (int)(backwardMapY.step/backwardMapY.elemSize());
|
||||
|
||||
String kernel_name = "buildMotionMapsKernel";
|
||||
vector< pair<size_t, const void*> > args;
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&forwardMotionX.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&forwardMotionY.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&backwardMotionX.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&backwardMotionY.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&forwardMapX.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&forwardMapY.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&backwardMapX.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&backwardMapY.data));
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&forwardMotionX.rows));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&forwardMotionY.cols));
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&forwardMotionX_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&forwardMotionY_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&backwardMotionX_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&backwardMotionY_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&forwardMapX_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&forwardMapY_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&backwardMapX_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&backwardMapY_step));
|
||||
|
||||
openCLExecuteKernel(clCxt, &superres_btvl1, kernel_name, global_thread, local_thread, args, -1, -1);
|
||||
}
|
||||
|
||||
void btv_l1_device_ocl::upscale(const oclMat& src, oclMat& dst, int scale)
|
||||
{
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {src.cols, src.rows, 1};
|
||||
|
||||
int src_step = (int)(src.step/src.elemSize());
|
||||
int dst_step = (int)(dst.step/dst.elemSize());
|
||||
|
||||
String kernel_name = "upscaleKernel";
|
||||
vector< pair<size_t, const void*> > args;
|
||||
|
||||
int cn = src.oclchannels();
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&src.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&dst.data));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&dst_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src.rows));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src.cols));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&scale));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&cn));
|
||||
|
||||
openCLExecuteKernel(clCxt, &superres_btvl1, kernel_name, global_thread, local_thread, args, -1, -1);
|
||||
|
||||
}
|
||||
|
||||
float btv_l1_device_ocl::diffSign(float a, float b)
|
||||
{
|
||||
return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
|
||||
}
|
||||
|
||||
Point3f btv_l1_device_ocl::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 btv_l1_device_ocl::diffSign(const oclMat& src1, const oclMat& src2, oclMat& dst)
|
||||
{
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
oclMat src1_ = src1.reshape(1);
|
||||
oclMat src2_ = src2.reshape(1);
|
||||
oclMat dst_ = dst.reshape(1);
|
||||
|
||||
int src1_step = (int)(src1_.step/src1_.elemSize());
|
||||
int src2_step = (int)(src2_.step/src2_.elemSize());
|
||||
int dst_step = (int)(dst_.step/dst_.elemSize());
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {src1_.cols, src1_.rows, 1};
|
||||
|
||||
String kernel_name = "diffSignKernel";
|
||||
vector< pair<size_t, const void*> > args;
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&src1_.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&src2_.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&dst_.data));
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src1_.rows));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src1_.cols));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&dst_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src1_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src2_step));
|
||||
|
||||
openCLExecuteKernel(clCxt, &superres_btvl1, kernel_name, global_thread, local_thread, args, -1, -1);
|
||||
}
|
||||
|
||||
void btv_l1_device_ocl::calcBtvRegularization(const oclMat& src, oclMat& dst, int ksize)
|
||||
{
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
oclMat src_ = src.reshape(1);
|
||||
oclMat dst_ = dst.reshape(1);
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {src.cols, src.rows, 1};
|
||||
|
||||
int src_step = (int)(src_.step/src_.elemSize());
|
||||
int dst_step = (int)(dst_.step/dst_.elemSize());
|
||||
|
||||
String kernel_name = "calcBtvRegularizationKernel";
|
||||
vector< pair<size_t, const void*> > args;
|
||||
|
||||
int cn = src.oclchannels();
|
||||
|
||||
cl_mem c_btvRegWeights;
|
||||
size_t count = btvWeights_size * sizeof(float);
|
||||
c_btvRegWeights = openCLCreateBuffer(clCxt, CL_MEM_READ_ONLY, count);
|
||||
int cl_safe_check = clEnqueueWriteBuffer((cl_command_queue)clCxt->oclCommandQueue(), c_btvRegWeights, 1, 0, count, btvWeights_, 0, NULL, NULL);
|
||||
CV_Assert(cl_safe_check == CL_SUCCESS);
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&src_.data));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&dst_.data));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&dst_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src.rows));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&src.cols));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&ksize));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&cn));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&c_btvRegWeights));
|
||||
|
||||
openCLExecuteKernel(clCxt, &superres_btvl1, kernel_name, global_thread, local_thread, args, -1, -1);
|
||||
cl_safe_check = clReleaseMemObject(c_btvRegWeights);
|
||||
CV_Assert(cl_safe_check == CL_SUCCESS);
|
||||
}
|
||||
|
||||
namespace
|
||||
{
|
||||
void calcRelativeMotions(const vector<pair<oclMat, oclMat> >& forwardMotions, const vector<pair<oclMat, oclMat> >& backwardMotions,
|
||||
vector<pair<oclMat, oclMat> >& relForwardMotions, vector<pair<oclMat, oclMat> >& 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)
|
||||
{
|
||||
ocl::add(relForwardMotions[i + 1].first, forwardMotions[i].first, relForwardMotions[i].first);
|
||||
ocl::add(relForwardMotions[i + 1].second, forwardMotions[i].second, relForwardMotions[i].second);
|
||||
|
||||
ocl::add(relBackwardMotions[i + 1].first, backwardMotions[i + 1].first, relBackwardMotions[i].first);
|
||||
ocl::add(relBackwardMotions[i + 1].second, backwardMotions[i + 1].second, relBackwardMotions[i].second);
|
||||
}
|
||||
|
||||
for (int i = baseIdx + 1; i < count; ++i)
|
||||
{
|
||||
ocl::add(relForwardMotions[i - 1].first, backwardMotions[i].first, relForwardMotions[i].first);
|
||||
ocl::add(relForwardMotions[i - 1].second, backwardMotions[i].second, relForwardMotions[i].second);
|
||||
|
||||
ocl::add(relBackwardMotions[i - 1].first, forwardMotions[i - 1].first, relBackwardMotions[i].first);
|
||||
ocl::add(relBackwardMotions[i - 1].second, forwardMotions[i - 1].second, relBackwardMotions[i].second);
|
||||
}
|
||||
}
|
||||
|
||||
void upscaleMotions(const vector<pair<oclMat, oclMat> >& lowResMotions, vector<pair<oclMat, oclMat> >& highResMotions, int scale)
|
||||
{
|
||||
highResMotions.resize(lowResMotions.size());
|
||||
|
||||
for (size_t i = 0; i < lowResMotions.size(); ++i)
|
||||
{
|
||||
ocl::resize(lowResMotions[i].first, highResMotions[i].first, Size(), scale, scale, INTER_LINEAR);
|
||||
ocl::resize(lowResMotions[i].second, highResMotions[i].second, Size(), scale, scale, INTER_LINEAR);
|
||||
|
||||
ocl::multiply(scale, highResMotions[i].first, highResMotions[i].first);
|
||||
ocl::multiply(scale, highResMotions[i].second, highResMotions[i].second);
|
||||
}
|
||||
}
|
||||
|
||||
void buildMotionMaps(const pair<oclMat, oclMat>& forwardMotion, const pair<oclMat, oclMat>& backwardMotion,
|
||||
pair<oclMat, oclMat>& forwardMap, pair<oclMat, oclMat>& 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_ocl::buildMotionMaps(forwardMotion.first, forwardMotion.second,
|
||||
backwardMotion.first, backwardMotion.second,
|
||||
forwardMap.first, forwardMap.second,
|
||||
backwardMap.first, backwardMap.second);
|
||||
}
|
||||
|
||||
void upscale(const oclMat& src, oclMat& dst, int scale)
|
||||
{
|
||||
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));
|
||||
|
||||
btv_l1_device_ocl::upscale(src, dst, scale);
|
||||
}
|
||||
|
||||
void diffSign(const oclMat& src1, const oclMat& src2, oclMat& dst)
|
||||
{
|
||||
dst.create(src1.size(), src1.type());
|
||||
|
||||
btv_l1_device_ocl::diffSign(src1, src2, dst);
|
||||
}
|
||||
|
||||
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));
|
||||
}
|
||||
|
||||
btvWeights_ = &btvWeights[0];
|
||||
btvWeights_size = size;
|
||||
}
|
||||
|
||||
void calcBtvRegularization(const oclMat& src, oclMat& dst, int btvKernelSize)
|
||||
{
|
||||
dst.create(src.size(), src.type());
|
||||
dst.setTo(Scalar::all(0));
|
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2;
|
||||
|
||||
btv_l1_device_ocl::calcBtvRegularization(src, dst, ksize);
|
||||
}
|
||||
|
||||
class BTVL1_OCL_Base
|
||||
{
|
||||
public:
|
||||
BTVL1_OCL_Base();
|
||||
|
||||
void process(const vector<oclMat>& src, oclMat& dst,
|
||||
const vector<pair<oclMat, oclMat> >& forwardMotions, const vector<pair<oclMat, oclMat> >& 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<cv::ocl::FilterEngine_GPU> > filters_;
|
||||
int curBlurKernelSize_;
|
||||
double curBlurSigma_;
|
||||
int curSrcType_;
|
||||
|
||||
vector<float> btvWeights_;
|
||||
int curBtvKernelSize_;
|
||||
double curAlpha_;
|
||||
|
||||
vector<pair<oclMat, oclMat> > lowResForwardMotions_;
|
||||
vector<pair<oclMat, oclMat> > lowResBackwardMotions_;
|
||||
|
||||
vector<pair<oclMat, oclMat> > highResForwardMotions_;
|
||||
vector<pair<oclMat, oclMat> > highResBackwardMotions_;
|
||||
|
||||
vector<pair<oclMat, oclMat> > forwardMaps_;
|
||||
vector<pair<oclMat, oclMat> > backwardMaps_;
|
||||
|
||||
oclMat highRes_;
|
||||
|
||||
vector<oclMat> diffTerms_;
|
||||
vector<oclMat> a_, b_, c_;
|
||||
oclMat regTerm_;
|
||||
};
|
||||
|
||||
BTVL1_OCL_Base::BTVL1_OCL_Base()
|
||||
{
|
||||
scale_ = 4;
|
||||
iterations_ = 180;
|
||||
lambda_ = 0.03;
|
||||
tau_ = 1.3;
|
||||
alpha_ = 0.7;
|
||||
btvKernelSize_ = 7;
|
||||
blurKernelSize_ = 5;
|
||||
blurSigma_ = 0.0;
|
||||
opticalFlow_ = createOptFlow_DualTVL1_OCL();
|
||||
|
||||
curBlurKernelSize_ = -1;
|
||||
curBlurSigma_ = -1.0;
|
||||
curSrcType_ = -1;
|
||||
|
||||
curBtvKernelSize_ = -1;
|
||||
curAlpha_ = -1.0;
|
||||
}
|
||||
|
||||
void BTVL1_OCL_Base::process(const vector<oclMat>& src, oclMat& dst,
|
||||
const vector<pair<oclMat, oclMat> >& forwardMotions, const vector<pair<oclMat, oclMat> >& 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] = cv::ocl::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_);
|
||||
|
||||
ocl::resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_LINEAR);
|
||||
|
||||
// iterations
|
||||
|
||||
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)
|
||||
{
|
||||
diffTerms_[k].create(highRes_.size(), highRes_.type());
|
||||
a_[k].create(highRes_.size(), highRes_.type());
|
||||
b_[k].create(highRes_.size(), highRes_.type());
|
||||
c_[k].create(lowResSize, highRes_.type());
|
||||
|
||||
// a = M * Ih
|
||||
ocl::remap(highRes_, a_[k], backwardMaps_[k].first, backwardMaps_[k].second, INTER_NEAREST, BORDER_CONSTANT, Scalar());
|
||||
// b = HM * Ih
|
||||
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1));
|
||||
// c = DHF * Ih
|
||||
ocl::resize(b_[k], c_[k], lowResSize, 0, 0, INTER_NEAREST);
|
||||
|
||||
diffSign(src[k], c_[k], c_[k]);
|
||||
|
||||
// a = Dt * diff
|
||||
upscale(c_[k], a_[k], scale_);
|
||||
// b = HtDt * diff
|
||||
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1));
|
||||
// diffTerm = MtHtDt * diff
|
||||
ocl::remap(b_[k], diffTerms_[k], forwardMaps_[k].first, forwardMaps_[k].second, INTER_NEAREST, BORDER_CONSTANT, Scalar());
|
||||
}
|
||||
|
||||
if (lambda_ > 0)
|
||||
{
|
||||
calcBtvRegularization(highRes_, regTerm_, btvKernelSize_);
|
||||
ocl::addWeighted(highRes_, 1.0, regTerm_, -tau_ * lambda_, 0.0, highRes_);
|
||||
}
|
||||
|
||||
for (size_t k = 0; k < src.size(); ++k)
|
||||
{
|
||||
ocl::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_OCL_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_OCL : public SuperResolution, private BTVL1_OCL_Base
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
BTVL1_OCL();
|
||||
|
||||
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);
|
||||
|
||||
oclMat curFrame_;
|
||||
oclMat prevFrame_;
|
||||
|
||||
vector<oclMat> frames_;
|
||||
vector<pair<oclMat, oclMat> > forwardMotions_;
|
||||
vector<pair<oclMat, oclMat> > backwardMotions_;
|
||||
vector<oclMat> outputs_;
|
||||
|
||||
int storePos_;
|
||||
int procPos_;
|
||||
int outPos_;
|
||||
|
||||
vector<oclMat> srcFrames_;
|
||||
vector<pair<oclMat, oclMat> > srcForwardMotions_;
|
||||
vector<pair<oclMat, oclMat> > srcBackwardMotions_;
|
||||
oclMat finalOutput_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(BTVL1_OCL, "SuperResolution.BTVL1_OCL",
|
||||
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_OCL::BTVL1_OCL()
|
||||
{
|
||||
temporalAreaRadius_ = 4;
|
||||
}
|
||||
|
||||
void BTVL1_OCL::collectGarbage()
|
||||
{
|
||||
curFrame_.release();
|
||||
prevFrame_.release();
|
||||
|
||||
frames_.clear();
|
||||
forwardMotions_.clear();
|
||||
backwardMotions_.clear();
|
||||
outputs_.clear();
|
||||
|
||||
srcFrames_.clear();
|
||||
srcForwardMotions_.clear();
|
||||
srcBackwardMotions_.clear();
|
||||
finalOutput_.release();
|
||||
|
||||
SuperResolution::collectGarbage();
|
||||
BTVL1_OCL_Base::collectGarbage();
|
||||
}
|
||||
|
||||
void BTVL1_OCL::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_OCL::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output)
|
||||
{
|
||||
if (outPos_ >= storePos_)
|
||||
{
|
||||
if(_output.kind() == _InputArray::OCL_MAT)
|
||||
{
|
||||
getOclMatRef(_output).release();
|
||||
}
|
||||
else
|
||||
{
|
||||
_output.release();
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
readNextFrame(frameSource);
|
||||
|
||||
if (procPos_ < storePos_)
|
||||
{
|
||||
++procPos_;
|
||||
processFrame(procPos_);
|
||||
}
|
||||
|
||||
++outPos_;
|
||||
const oclMat& curOutput = at(outPos_, outputs_);
|
||||
|
||||
if (_output.kind() == _InputArray::OCL_MAT)
|
||||
curOutput.convertTo(getOclMatRef(_output), CV_8U);
|
||||
else
|
||||
{
|
||||
curOutput.convertTo(finalOutput_, CV_8U);
|
||||
arrCopy(finalOutput_, _output);
|
||||
}
|
||||
}
|
||||
|
||||
void BTVL1_OCL::readNextFrame(Ptr<FrameSource>& frameSource)
|
||||
{
|
||||
curFrame_.release();
|
||||
frameSource->nextFrame(curFrame_);
|
||||
|
||||
if (curFrame_.empty())
|
||||
return;
|
||||
|
||||
++storePos_;
|
||||
curFrame_.convertTo(at(storePos_, frames_), CV_32F);
|
||||
|
||||
if (storePos_ > 0)
|
||||
{
|
||||
pair<oclMat, oclMat>& forwardMotion = at(storePos_ - 1, forwardMotions_);
|
||||
pair<oclMat, oclMat>& 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_OCL::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_OCL()
|
||||
{
|
||||
return new BTVL1_OCL;
|
||||
}
|
||||
#endif
|
||||
@ -118,11 +118,23 @@ namespace
|
||||
{
|
||||
vc_ >> _frame.getMatRef();
|
||||
}
|
||||
else
|
||||
else if(_frame.kind() == _InputArray::GPU_MAT)
|
||||
{
|
||||
vc_ >> frame_;
|
||||
arrCopy(frame_, _frame);
|
||||
}
|
||||
else if(_frame.kind() == _InputArray::OCL_MAT)
|
||||
{
|
||||
vc_ >> frame_;
|
||||
if(!frame_.empty())
|
||||
{
|
||||
arrCopy(frame_, _frame);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
//should never get here
|
||||
}
|
||||
}
|
||||
|
||||
class VideoFrameSource : public CaptureFrameSource
|
||||
|
||||
@ -108,30 +108,59 @@ namespace
|
||||
{
|
||||
src.getGpuMat().copyTo(dst.getGpuMatRef());
|
||||
}
|
||||
#ifdef HAVE_OPENCV_OCL
|
||||
void ocl2mat(InputArray src, OutputArray dst)
|
||||
{
|
||||
dst.getMatRef() = (Mat)ocl::getOclMatRef(src);
|
||||
}
|
||||
void mat2ocl(InputArray src, OutputArray dst)
|
||||
{
|
||||
Mat m = src.getMat();
|
||||
ocl::getOclMatRef(dst) = (ocl::oclMat)m;
|
||||
}
|
||||
void ocl2ocl(InputArray src, OutputArray dst)
|
||||
{
|
||||
ocl::getOclMatRef(src).copyTo(ocl::getOclMatRef(dst));
|
||||
}
|
||||
#else
|
||||
void ocl2mat(InputArray, OutputArray)
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "The called functionality is disabled for current build or platform");;
|
||||
}
|
||||
void mat2ocl(InputArray, OutputArray)
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "The called functionality is disabled for current build or platform");;
|
||||
}
|
||||
void ocl2ocl(InputArray, OutputArray)
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
void cv::superres::arrCopy(InputArray src, OutputArray dst)
|
||||
{
|
||||
typedef void (*func_t)(InputArray src, OutputArray dst);
|
||||
static const func_t funcs[10][10] =
|
||||
static const func_t funcs[11][11] =
|
||||
{
|
||||
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu},
|
||||
{0, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, 0 /*buf2arr*/, buf2arr},
|
||||
{0, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/},
|
||||
{0, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, arr2buf, 0 /*arr2tex*/, gpu2gpu}
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu, mat2ocl},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu, mat2ocl},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu, mat2ocl},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu, mat2ocl},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu, mat2ocl},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0 /*arr2tex*/, mat2gpu, mat2ocl},
|
||||
{0, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, 0 /*buf2arr*/, buf2arr, 0 },
|
||||
{0, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0 /*tex2arr*/, 0},
|
||||
{0, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, arr2buf, 0 /*arr2tex*/, gpu2gpu, 0 },
|
||||
{0, ocl2mat, ocl2mat, ocl2mat, ocl2mat, ocl2mat, ocl2mat, 0, 0, 0, ocl2ocl}
|
||||
};
|
||||
|
||||
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 );
|
||||
CV_DbgAssert( src_kind >= 0 && src_kind < 11 );
|
||||
CV_DbgAssert( dst_kind >= 0 && dst_kind < 11 );
|
||||
|
||||
const func_t func = funcs[src_kind][dst_kind];
|
||||
CV_DbgAssert( func != 0 );
|
||||
@ -173,7 +202,6 @@ namespace
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
void convertToDepth(InputArray src, OutputArray dst, int depth)
|
||||
{
|
||||
CV_Assert( src.depth() <= CV_64F );
|
||||
@ -254,3 +282,70 @@ GpuMat cv::superres::convertToType(const GpuMat& src, int type, GpuMat& buf0, Gp
|
||||
convertToDepth(buf0, buf1, depth);
|
||||
return buf1;
|
||||
}
|
||||
#ifdef HAVE_OPENCV_OCL
|
||||
namespace
|
||||
{
|
||||
// TODO(pengx17): remove these overloaded functions until IntputArray fully supports oclMat
|
||||
void convertToCn(const ocl::oclMat& src, ocl::oclMat& 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 );
|
||||
|
||||
ocl::cvtColor(src, dst, code, cn);
|
||||
}
|
||||
void convertToDepth(const ocl::oclMat& src, ocl::oclMat& dst, int depth)
|
||||
{
|
||||
CV_Assert( src.depth() <= CV_64F );
|
||||
CV_Assert( depth == CV_8U || depth == CV_32F );
|
||||
|
||||
static const double maxVals[] =
|
||||
{
|
||||
std::numeric_limits<uchar>::max(),
|
||||
std::numeric_limits<schar>::max(),
|
||||
std::numeric_limits<ushort>::max(),
|
||||
std::numeric_limits<short>::max(),
|
||||
std::numeric_limits<int>::max(),
|
||||
1.0,
|
||||
1.0,
|
||||
};
|
||||
const double scale = maxVals[depth] / maxVals[src.depth()];
|
||||
src.convertTo(dst, depth, scale);
|
||||
}
|
||||
}
|
||||
ocl::oclMat cv::superres::convertToType(const ocl::oclMat& src, int type, ocl::oclMat& buf0, ocl::oclMat& 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;
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -45,6 +45,9 @@
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/core/gpu.hpp"
|
||||
#ifdef HAVE_OPENCV_OCL
|
||||
#include "opencv2/ocl.hpp"
|
||||
#endif
|
||||
|
||||
namespace cv
|
||||
{
|
||||
@ -57,6 +60,10 @@ namespace cv
|
||||
|
||||
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);
|
||||
|
||||
#ifdef HAVE_OPENCV_OCL
|
||||
CV_EXPORTS ocl::oclMat convertToType(const ocl::oclMat& src, int type, ocl::oclMat& buf0, ocl::oclMat& buf1);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
261
modules/superres/src/opencl/superres_btvl1.cl
Normal file
261
modules/superres/src/opencl/superres_btvl1.cl
Normal file
@ -0,0 +1,261 @@
|
||||
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Jin Ma jin@multicorewareinc.com
|
||||
//
|
||||
// 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 oclMaterials 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*/
|
||||
|
||||
__kernel void buildMotionMapsKernel(__global float* forwardMotionX,
|
||||
__global float* forwardMotionY,
|
||||
__global float* backwardMotionX,
|
||||
__global float* backwardMotionY,
|
||||
__global float* forwardMapX,
|
||||
__global float* forwardMapY,
|
||||
__global float* backwardMapX,
|
||||
__global float* backwardMapY,
|
||||
int forwardMotionX_row,
|
||||
int forwardMotionX_col,
|
||||
int forwardMotionX_step,
|
||||
int forwardMotionY_step,
|
||||
int backwardMotionX_step,
|
||||
int backwardMotionY_step,
|
||||
int forwardMapX_step,
|
||||
int forwardMapY_step,
|
||||
int backwardMapX_step,
|
||||
int backwardMapY_step
|
||||
)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if(x < forwardMotionX_col && y < forwardMotionX_row)
|
||||
{
|
||||
float fx = forwardMotionX[y * forwardMotionX_step + x];
|
||||
float fy = forwardMotionY[y * forwardMotionY_step + x];
|
||||
|
||||
float bx = backwardMotionX[y * backwardMotionX_step + x];
|
||||
float by = backwardMotionY[y * backwardMotionY_step + x];
|
||||
|
||||
forwardMapX[y * forwardMapX_step + x] = x + bx;
|
||||
forwardMapY[y * forwardMapY_step + x] = y + by;
|
||||
|
||||
backwardMapX[y * backwardMapX_step + x] = x + fx;
|
||||
backwardMapY[y * backwardMapY_step + x] = y + fy;
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void upscaleKernel(__global float* src,
|
||||
__global float* dst,
|
||||
int src_step,
|
||||
int dst_step,
|
||||
int src_row,
|
||||
int src_col,
|
||||
int scale,
|
||||
int channels
|
||||
)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if(x < src_col && y < src_row)
|
||||
{
|
||||
if(channels == 1)
|
||||
{
|
||||
dst[y * scale * dst_step + x * scale] = src[y * src_step + x];
|
||||
}else if(channels == 3)
|
||||
{
|
||||
dst[y * channels * scale * dst_step + 3 * x * scale + 0] = src[y * channels * src_step + 3 * x + 0];
|
||||
dst[y * channels * scale * dst_step + 3 * x * scale + 1] = src[y * channels * src_step + 3 * x + 1];
|
||||
dst[y * channels * scale * dst_step + 3 * x * scale + 2] = src[y * channels * src_step + 3 * x + 2];
|
||||
}else
|
||||
{
|
||||
dst[y * channels * scale * dst_step + 4 * x * scale + 0] = src[y * channels * src_step + 4 * x + 0];
|
||||
dst[y * channels * scale * dst_step + 4 * x * scale + 1] = src[y * channels * src_step + 4 * x + 1];
|
||||
dst[y * channels * scale * dst_step + 4 * x * scale + 2] = src[y * channels * src_step + 4 * x + 2];
|
||||
dst[y * channels * scale * dst_step + 4 * x * scale + 3] = src[y * channels * src_step + 4 * x + 3];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
float diffSign(float a, float b)
|
||||
{
|
||||
return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
|
||||
}
|
||||
|
||||
float3 diffSign3(float3 a, float3 b)
|
||||
{
|
||||
float3 pos;
|
||||
pos.x = a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f;
|
||||
pos.y = a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f;
|
||||
pos.z = a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f;
|
||||
return pos;
|
||||
}
|
||||
|
||||
float4 diffSign4(float4 a, float4 b)
|
||||
{
|
||||
float4 pos;
|
||||
pos.x = a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f;
|
||||
pos.y = a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f;
|
||||
pos.z = a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f;
|
||||
pos.w = 0.0f;
|
||||
return pos;
|
||||
}
|
||||
|
||||
__kernel void diffSignKernel(__global float* src1,
|
||||
__global float* src2,
|
||||
__global float* dst,
|
||||
int src1_row,
|
||||
int src1_col,
|
||||
int dst_step,
|
||||
int src1_step,
|
||||
int src2_step)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if(x < src1_col && y < src1_row)
|
||||
{
|
||||
dst[y * dst_step + x] = diffSign(src1[y * src1_step + x], src2[y * src2_step + x]);
|
||||
}
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
|
||||
__kernel void calcBtvRegularizationKernel(__global float* src,
|
||||
__global float* dst,
|
||||
int src_step,
|
||||
int dst_step,
|
||||
int src_row,
|
||||
int src_col,
|
||||
int ksize,
|
||||
int channels,
|
||||
__global float* c_btvRegWeights
|
||||
)
|
||||
{
|
||||
int x = get_global_id(0) + ksize;
|
||||
int y = get_global_id(1) + ksize;
|
||||
|
||||
if ((y < src_row - ksize) && (x < src_col - ksize))
|
||||
{
|
||||
if(channels == 1)
|
||||
{
|
||||
const float srcVal = src[y * src_step + x];
|
||||
float dstVal = 0.0f;
|
||||
|
||||
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) * src_step + (x + l)]) - diffSign(src[(y - m) * src_step + (x - l)], srcVal));
|
||||
}
|
||||
dst[y * dst_step + x] = dstVal;
|
||||
}else if(channels == 3)
|
||||
{
|
||||
float3 srcVal;
|
||||
srcVal.x = src[y * src_step + 3 * x + 0];
|
||||
srcVal.y = src[y * src_step + 3 * x + 1];
|
||||
srcVal.z = src[y * src_step + 3 * x + 2];
|
||||
|
||||
float3 dstVal;
|
||||
dstVal.x = 0.0f;
|
||||
dstVal.y = 0.0f;
|
||||
dstVal.z = 0.0f;
|
||||
|
||||
for (int m = 0, count = 0; m <= ksize; ++m)
|
||||
{
|
||||
for (int l = ksize; l + m >= 0; --l, ++count)
|
||||
{
|
||||
float3 src1;
|
||||
src1.x = src[(y + m) * src_step + 3 * (x + l) + 0];
|
||||
src1.y = src[(y + m) * src_step + 3 * (x + l) + 1];
|
||||
src1.z = src[(y + m) * src_step + 3 * (x + l) + 2];
|
||||
|
||||
float3 src2;
|
||||
src2.x = src[(y - m) * src_step + 3 * (x - l) + 0];
|
||||
src2.y = src[(y - m) * src_step + 3 * (x - l) + 1];
|
||||
src2.z = src[(y - m) * src_step + 3 * (x - l) + 2];
|
||||
|
||||
dstVal = dstVal + c_btvRegWeights[count] * (diffSign3(srcVal, src1) - diffSign3(src2, srcVal));
|
||||
}
|
||||
}
|
||||
dst[y * dst_step + 3 * x + 0] = dstVal.x;
|
||||
dst[y * dst_step + 3 * x + 1] = dstVal.y;
|
||||
dst[y * dst_step + 3 * x + 2] = dstVal.z;
|
||||
}else
|
||||
{
|
||||
float4 srcVal;
|
||||
srcVal.x = src[y * src_step + 4 * x + 0];//r type =float
|
||||
srcVal.y = src[y * src_step + 4 * x + 1];//g
|
||||
srcVal.z = src[y * src_step + 4 * x + 2];//b
|
||||
srcVal.w = src[y * src_step + 4 * x + 3];//a
|
||||
|
||||
float4 dstVal;
|
||||
dstVal.x = 0.0f;
|
||||
dstVal.y = 0.0f;
|
||||
dstVal.z = 0.0f;
|
||||
dstVal.w = 0.0f;
|
||||
|
||||
for (int m = 0, count = 0; m <= ksize; ++m)
|
||||
{
|
||||
for (int l = ksize; l + m >= 0; --l, ++count)
|
||||
{
|
||||
float4 src1;
|
||||
src1.x = src[(y + m) * src_step + 4 * (x + l) + 0];
|
||||
src1.y = src[(y + m) * src_step + 4 * (x + l) + 1];
|
||||
src1.z = src[(y + m) * src_step + 4 * (x + l) + 2];
|
||||
src1.w = src[(y + m) * src_step + 4 * (x + l) + 3];
|
||||
|
||||
float4 src2;
|
||||
src2.x = src[(y - m) * src_step + 4 * (x - l) + 0];
|
||||
src2.y = src[(y - m) * src_step + 4 * (x - l) + 1];
|
||||
src2.z = src[(y - m) * src_step + 4 * (x - l) + 2];
|
||||
src2.w = src[(y - m) * src_step + 4 * (x - l) + 3];
|
||||
|
||||
dstVal = dstVal + c_btvRegWeights[count] * (diffSign4(srcVal, src1) - diffSign4(src2, srcVal));
|
||||
|
||||
}
|
||||
}
|
||||
dst[y * dst_step + 4 * x + 0] = dstVal.x;
|
||||
dst[y * dst_step + 4 * x + 1] = dstVal.y;
|
||||
dst[y * dst_step + 4 * x + 2] = dstVal.z;
|
||||
dst[y * dst_step + 4 * x + 3] = dstVal.w;
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -718,3 +718,195 @@ Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1_GPU()
|
||||
}
|
||||
|
||||
#endif // HAVE_OPENCV_GPUOPTFLOW
|
||||
#ifdef HAVE_OPENCV_OCL
|
||||
|
||||
namespace
|
||||
{
|
||||
class oclOpticalFlow : public DenseOpticalFlowExt
|
||||
{
|
||||
public:
|
||||
explicit oclOpticalFlow(int work_type);
|
||||
|
||||
void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2);
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
virtual void impl(const cv::ocl::oclMat& input0, const cv::ocl::oclMat& input1, cv::ocl::oclMat& dst1, cv::ocl::oclMat& dst2) = 0;
|
||||
|
||||
private:
|
||||
int work_type_;
|
||||
cv::ocl::oclMat buf_[6];
|
||||
cv::ocl::oclMat u_, v_, flow_;
|
||||
};
|
||||
|
||||
oclOpticalFlow::oclOpticalFlow(int work_type) : work_type_(work_type)
|
||||
{
|
||||
}
|
||||
|
||||
void oclOpticalFlow::calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2)
|
||||
{
|
||||
ocl::oclMat& _frame0 = ocl::getOclMatRef(frame0);
|
||||
ocl::oclMat& _frame1 = ocl::getOclMatRef(frame1);
|
||||
ocl::oclMat& _flow1 = ocl::getOclMatRef(flow1);
|
||||
ocl::oclMat& _flow2 = ocl::getOclMatRef(flow2);
|
||||
|
||||
CV_Assert( _frame1.type() == _frame0.type() );
|
||||
CV_Assert( _frame1.size() == _frame0.size() );
|
||||
|
||||
cv::ocl::oclMat input0_ = convertToType(_frame0, work_type_, buf_[2], buf_[3]);
|
||||
cv::ocl::oclMat input1_ = convertToType(_frame1, work_type_, buf_[4], buf_[5]);
|
||||
|
||||
impl(input0_, input1_, u_, v_);//go to tvl1 algorithm
|
||||
|
||||
u_.copyTo(_flow1);
|
||||
v_.copyTo(_flow2);
|
||||
}
|
||||
|
||||
void oclOpticalFlow::collectGarbage()
|
||||
{
|
||||
for (int i = 0; i < 6; ++i)
|
||||
buf_[i].release();
|
||||
u_.release();
|
||||
v_.release();
|
||||
flow_.release();
|
||||
}
|
||||
}
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// PyrLK_OCL
|
||||
|
||||
namespace
|
||||
{
|
||||
class PyrLK_OCL : public oclOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
PyrLK_OCL();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const ocl::oclMat& input0, const ocl::oclMat& input1, ocl::oclMat& dst1, ocl::oclMat& dst2);
|
||||
|
||||
private:
|
||||
int winSize_;
|
||||
int maxLevel_;
|
||||
int iterations_;
|
||||
|
||||
ocl::PyrLKOpticalFlow alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(PyrLK_OCL, "DenseOpticalFlowExt.PyrLK_OCL",
|
||||
obj.info()->addParam(obj, "winSize", obj.winSize_);
|
||||
obj.info()->addParam(obj, "maxLevel", obj.maxLevel_);
|
||||
obj.info()->addParam(obj, "iterations", obj.iterations_));
|
||||
|
||||
PyrLK_OCL::PyrLK_OCL() : oclOpticalFlow(CV_8UC1)
|
||||
{
|
||||
winSize_ = alg_.winSize.width;
|
||||
maxLevel_ = alg_.maxLevel;
|
||||
iterations_ = alg_.iters;
|
||||
}
|
||||
|
||||
void PyrLK_OCL::impl(const cv::ocl::oclMat& input0, const cv::ocl::oclMat& input1, cv::ocl::oclMat& dst1, cv::ocl::oclMat& dst2)
|
||||
{
|
||||
alg_.winSize.width = winSize_;
|
||||
alg_.winSize.height = winSize_;
|
||||
alg_.maxLevel = maxLevel_;
|
||||
alg_.iters = iterations_;
|
||||
|
||||
alg_.dense(input0, input1, dst1, dst2);
|
||||
}
|
||||
|
||||
void PyrLK_OCL::collectGarbage()
|
||||
{
|
||||
alg_.releaseMemory();
|
||||
oclOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_PyrLK_OCL()
|
||||
{
|
||||
return new PyrLK_OCL;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// DualTVL1_OCL
|
||||
|
||||
namespace
|
||||
{
|
||||
class DualTVL1_OCL : public oclOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
DualTVL1_OCL();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const cv::ocl::oclMat& input0, const cv::ocl::oclMat& input1, cv::ocl::oclMat& dst1, cv::ocl::oclMat& dst2);
|
||||
|
||||
private:
|
||||
double tau_;
|
||||
double lambda_;
|
||||
double theta_;
|
||||
int nscales_;
|
||||
int warps_;
|
||||
double epsilon_;
|
||||
int iterations_;
|
||||
bool useInitialFlow_;
|
||||
|
||||
ocl::OpticalFlowDual_TVL1_OCL alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(DualTVL1_OCL, "DenseOpticalFlowExt.DualTVL1_OCL",
|
||||
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_OCL::DualTVL1_OCL() : oclOpticalFlow(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_OCL::impl(const cv::ocl::oclMat& input0, const cv::ocl::oclMat& input1, cv::ocl::oclMat& dst1, cv::ocl::oclMat& 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_OCL::collectGarbage()
|
||||
{
|
||||
alg_.collectGarbage();
|
||||
oclOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1_OCL()
|
||||
{
|
||||
return new DualTVL1_OCL;
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
@ -81,6 +81,10 @@
|
||||
# include "opencv2/gpucodec.hpp"
|
||||
#endif
|
||||
|
||||
#ifdef HAVE_OPENCV_OCL
|
||||
#include "opencv2/ocl/private/util.hpp"
|
||||
#endif
|
||||
|
||||
#ifdef HAVE_OPENCV_HIGHGUI
|
||||
#include "opencv2/highgui.hpp"
|
||||
#endif
|
||||
|
||||
@ -274,5 +274,12 @@ TEST_F(SuperResolution, BTVL1_GPU)
|
||||
{
|
||||
RunTest(cv::superres::createSuperResolution_BTVL1_GPU());
|
||||
}
|
||||
|
||||
#endif
|
||||
#if defined(HAVE_OPENCV_OCL) && defined(HAVE_OPENCL)
|
||||
TEST_F(SuperResolution, BTVL1_OCL)
|
||||
{
|
||||
std::vector<cv::ocl::Info> infos;
|
||||
cv::ocl::getDevice(infos);
|
||||
RunTest(cv::superres::createSuperResolution_BTVL1_OCL());
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -551,6 +551,13 @@ int main(int argc, char **argv) \
|
||||
return RUN_ALL_TESTS(); \
|
||||
}
|
||||
|
||||
// This usually only makes sense in perf tests with several implementations,
|
||||
// some of which are not available.
|
||||
#define CV_TEST_FAIL_NO_IMPL() do { \
|
||||
::testing::Test::RecordProperty("custom_status", "noimpl"); \
|
||||
FAIL() << "No equivalent implementation."; \
|
||||
} while (0)
|
||||
|
||||
#endif
|
||||
|
||||
#include "opencv2/ts/ts_perf.hpp"
|
||||
|
||||
@ -13,10 +13,17 @@ class TestInfo(object):
|
||||
self.name = xmlnode.getAttribute("name")
|
||||
self.value_param = xmlnode.getAttribute("value_param")
|
||||
self.type_param = xmlnode.getAttribute("type_param")
|
||||
if xmlnode.getElementsByTagName("failure"):
|
||||
|
||||
custom_status = xmlnode.getAttribute("custom_status")
|
||||
failures = xmlnode.getElementsByTagName("failure")
|
||||
|
||||
if len(custom_status) > 0:
|
||||
self.status = custom_status
|
||||
elif len(failures) > 0:
|
||||
self.status = "failed"
|
||||
else:
|
||||
self.status = xmlnode.getAttribute("status")
|
||||
|
||||
if self.name.startswith("DISABLED_"):
|
||||
self.status = "disabled"
|
||||
self.fixture = self.fixture.replace("DISABLED_", "")
|
||||
|
||||
@ -64,6 +64,10 @@
|
||||
Name for the sheet. If this parameter is missing, the name of sheet's directory
|
||||
will be used.
|
||||
|
||||
* 'sheet_properties': [(string, string)]
|
||||
List of arbitrary (key, value) pairs that somehow describe the sheet. Will be
|
||||
dumped into the first row of the sheet in string form.
|
||||
|
||||
Note that all keys are optional, although to get useful results, you'll want to
|
||||
specify at least 'configurations' and 'configuration_matchers'.
|
||||
|
||||
@ -100,6 +104,7 @@ bad_speedup_style = xlwt.easyxf('font: color red', num_format_str='#0.00')
|
||||
no_speedup_style = no_time_style
|
||||
error_speedup_style = xlwt.easyxf('pattern: pattern solid, fore_color orange')
|
||||
header_style = xlwt.easyxf('font: bold true; alignment: horizontal centre, vertical top, wrap True')
|
||||
subheader_style = xlwt.easyxf('alignment: horizontal centre, vertical top')
|
||||
|
||||
class Collector(object):
|
||||
def __init__(self, config_match_func, include_unmatched):
|
||||
@ -189,6 +194,8 @@ def main():
|
||||
arg_parser.add_argument('-c', '--config', metavar='CONF', help='global configuration file')
|
||||
arg_parser.add_argument('--include-unmatched', action='store_true',
|
||||
help='include results from XML files that were not recognized by configuration matchers')
|
||||
arg_parser.add_argument('--show-times-per-pixel', action='store_true',
|
||||
help='for tests that have an image size parameter, show per-pixel time, as well as total time')
|
||||
|
||||
args = arg_parser.parse_args()
|
||||
|
||||
@ -231,24 +238,64 @@ def main():
|
||||
|
||||
sheet = wb.add_sheet(sheet_conf.get('sheet_name', os.path.basename(os.path.abspath(sheet_path))))
|
||||
|
||||
sheet.row(0).height = 800
|
||||
sheet_properties = sheet_conf.get('sheet_properties', [])
|
||||
|
||||
sheet.write(0, 0, 'Properties:')
|
||||
|
||||
sheet.write(0, 1,
|
||||
'N/A' if len(sheet_properties) == 0 else
|
||||
' '.join(str(k) + '=' + repr(v) for (k, v) in sheet_properties))
|
||||
|
||||
sheet.row(2).height = 800
|
||||
sheet.panes_frozen = True
|
||||
sheet.remove_splits = True
|
||||
sheet.horz_split_pos = 1
|
||||
sheet.horz_split_first_visible = 1
|
||||
|
||||
sheet_comparisons = sheet_conf.get('comparisons', [])
|
||||
|
||||
for i, w in enumerate([2000, 15000, 2500, 2000, 15000]
|
||||
+ (len(config_names) + 1 + len(sheet_comparisons)) * [4000]):
|
||||
sheet.col(i).width = w
|
||||
row = 2
|
||||
|
||||
for i, caption in enumerate(['Module', 'Test', 'Image\nsize', 'Data\ntype', 'Parameters']
|
||||
+ config_names + [None]
|
||||
+ [comp['to'] + '\nvs\n' + comp['from'] for comp in sheet_comparisons]):
|
||||
sheet.row(0).write(i, caption, header_style)
|
||||
col = 0
|
||||
|
||||
row = 1
|
||||
for (w, caption) in [
|
||||
(2500, 'Module'),
|
||||
(10000, 'Test'),
|
||||
(2500, 'Image\nsize'),
|
||||
(2000, 'Data\ntype'),
|
||||
(7500, 'Other parameters')]:
|
||||
sheet.col(col).width = w
|
||||
if args.show_times_per_pixel:
|
||||
sheet.write_merge(row, row + 1, col, col, caption, header_style)
|
||||
else:
|
||||
sheet.write(row, col, caption, header_style)
|
||||
col += 1
|
||||
|
||||
for config_name in config_names:
|
||||
if args.show_times_per_pixel:
|
||||
sheet.col(col).width = 3000
|
||||
sheet.col(col + 1).width = 3000
|
||||
sheet.write_merge(row, row, col, col + 1, config_name, header_style)
|
||||
sheet.write(row + 1, col, 'total, ms', subheader_style)
|
||||
sheet.write(row + 1, col + 1, 'per pixel, ns', subheader_style)
|
||||
col += 2
|
||||
else:
|
||||
sheet.col(col).width = 4000
|
||||
sheet.write(row, col, config_name, header_style)
|
||||
col += 1
|
||||
|
||||
col += 1 # blank column between configurations and comparisons
|
||||
|
||||
for comp in sheet_comparisons:
|
||||
sheet.col(col).width = 4000
|
||||
caption = comp['to'] + '\nvs\n' + comp['from']
|
||||
if args.show_times_per_pixel:
|
||||
sheet.write_merge(row, row + 1, col, col, caption, header_style)
|
||||
else:
|
||||
sheet.write(row, col, caption, header_style)
|
||||
|
||||
row += 2 if args.show_times_per_pixel else 1
|
||||
|
||||
sheet.horz_split_pos = row
|
||||
sheet.horz_split_first_visible = row
|
||||
|
||||
module_colors = sheet_conf.get('module_colors', {})
|
||||
module_styles = {module: xlwt.easyxf('pattern: pattern solid, fore_color {}'.format(color))
|
||||
@ -259,21 +306,49 @@ def main():
|
||||
sheet.write(row, 0, module, module_styles.get(module, xlwt.Style.default_style))
|
||||
sheet.write(row, 1, test)
|
||||
|
||||
param_list = param[1:-1].split(", ")
|
||||
sheet.write(row, 2, next(ifilter(re_image_size.match, param_list), None))
|
||||
sheet.write(row, 3, next(ifilter(re_data_type.match, param_list), None))
|
||||
param_list = param[1:-1].split(', ') if param.startswith('(') and param.endswith(')') else [param]
|
||||
|
||||
sheet.row(row).write(4, param)
|
||||
for i, c in enumerate(config_names):
|
||||
image_size = next(ifilter(re_image_size.match, param_list), None)
|
||||
if image_size is not None:
|
||||
sheet.write(row, 2, image_size)
|
||||
del param_list[param_list.index(image_size)]
|
||||
|
||||
data_type = next(ifilter(re_data_type.match, param_list), None)
|
||||
if data_type is not None:
|
||||
sheet.write(row, 3, data_type)
|
||||
del param_list[param_list.index(data_type)]
|
||||
|
||||
sheet.row(row).write(4, ' | '.join(param_list))
|
||||
|
||||
col = 5
|
||||
|
||||
for c in config_names:
|
||||
if c in configs:
|
||||
sheet.write(row, 5 + i, configs[c], time_style)
|
||||
sheet.write(row, col, configs[c], time_style)
|
||||
else:
|
||||
sheet.write(row, 5 + i, None, no_time_style)
|
||||
sheet.write(row, col, None, no_time_style)
|
||||
col += 1
|
||||
if args.show_times_per_pixel:
|
||||
sheet.write(row, col,
|
||||
xlwt.Formula(
|
||||
'''
|
||||
{0} * 1000000 / (
|
||||
VALUE(MID({1}; 1; SEARCH("x"; {1}) - 1))
|
||||
* VALUE(MID({1}; SEARCH("x"; {1}) + 1; LEN({1})))
|
||||
)
|
||||
'''.replace('\n', '').replace(' ', '').format(
|
||||
xlwt.Utils.rowcol_to_cell(row, col - 1),
|
||||
xlwt.Utils.rowcol_to_cell(row, 2)
|
||||
)
|
||||
),
|
||||
time_style)
|
||||
col += 1
|
||||
|
||||
for i, comp in enumerate(sheet_comparisons):
|
||||
col += 1 # blank column
|
||||
|
||||
for comp in sheet_comparisons:
|
||||
cmp_from = configs.get(comp["from"])
|
||||
cmp_to = configs.get(comp["to"])
|
||||
col = 5 + len(config_names) + 1 + i
|
||||
|
||||
if isinstance(cmp_from, numbers.Number) and isinstance(cmp_to, numbers.Number):
|
||||
try:
|
||||
@ -286,6 +361,8 @@ def main():
|
||||
else:
|
||||
sheet.write(row, col, None, no_speedup_style)
|
||||
|
||||
col += 1
|
||||
|
||||
row += 1
|
||||
if row % 1000 == 0: sheet.flush_row_data()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user