Open Source Computer Vision Library
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229 lines
8.7 KiB
229 lines
8.7 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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// Authors: |
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// * Peter Andreas Entschev, peter@entschev.com |
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// |
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//M*/ |
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#include "precomp.hpp" |
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#include "opencl_kernels.hpp" |
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using namespace cv; |
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using namespace cv::ocl; |
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cv::ocl::FAST_OCL::FAST_OCL(int _threshold, bool _nonmaxSupression, double _keypointsRatio) : |
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nonmaxSupression(_nonmaxSupression), threshold(_threshold), keypointsRatio(_keypointsRatio), count_(0) |
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{ |
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} |
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void cv::ocl::FAST_OCL::operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints) |
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{ |
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if (image.empty()) |
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return; |
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(*this)(image, mask, d_keypoints_); |
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downloadKeypoints(d_keypoints_, keypoints); |
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} |
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void cv::ocl::FAST_OCL::downloadKeypoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints) |
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{ |
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if (d_keypoints.empty()) |
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return; |
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Mat h_keypoints(d_keypoints); |
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convertKeypoints(h_keypoints, keypoints); |
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} |
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void cv::ocl::FAST_OCL::convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints) |
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{ |
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if (h_keypoints.empty()) |
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return; |
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CV_Assert(h_keypoints.rows == ROWS_COUNT && h_keypoints.elemSize() == 4); |
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int npoints = h_keypoints.cols; |
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keypoints.resize(npoints); |
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const float* loc_x = h_keypoints.ptr<float>(X_ROW); |
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const float* loc_y = h_keypoints.ptr<float>(Y_ROW); |
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const float* response_row = h_keypoints.ptr<float>(RESPONSE_ROW); |
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for (int i = 0; i < npoints; ++i) |
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{ |
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KeyPoint kp(loc_x[i], loc_y[i], static_cast<float>(FEATURE_SIZE), -1, response_row[i]); |
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keypoints[i] = kp; |
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} |
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} |
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void cv::ocl::FAST_OCL::operator ()(const oclMat& img, const oclMat& mask, oclMat& keypoints) |
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{ |
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calcKeyPointsLocation(img, mask); |
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keypoints.cols = getKeyPoints(keypoints); |
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} |
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int cv::ocl::FAST_OCL::calcKeyPointsLocation(const oclMat& img, const oclMat& mask) |
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{ |
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CV_Assert(img.type() == CV_8UC1); |
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CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == img.size())); |
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int maxKeypoints = static_cast<int>(keypointsRatio * img.size().area()); |
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ensureSizeIsEnough(ROWS_COUNT, maxKeypoints, CV_32SC1, kpLoc_); |
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kpLoc_.setTo(Scalar::all(0)); |
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if (nonmaxSupression) |
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{ |
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ensureSizeIsEnough(img.size(), CV_32SC1, score_); |
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score_.setTo(Scalar::all(0)); |
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} |
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count_ = calcKeypointsOCL(img, mask, maxKeypoints); |
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count_ = std::min(count_, maxKeypoints); |
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return count_; |
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} |
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int cv::ocl::FAST_OCL::calcKeypointsOCL(const oclMat& img, const oclMat& mask, int maxKeypoints) |
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{ |
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size_t localThreads[3] = {16, 16, 1}; |
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size_t globalThreads[3] = {divUp(img.cols - 6, localThreads[0]) * localThreads[0], |
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divUp(img.rows - 6, localThreads[1]) * localThreads[1], |
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1}; |
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Context *clCxt = Context::getContext(); |
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String kernelName = (mask.empty()) ? "calcKeypoints" : "calcKeypointsWithMask"; |
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std::vector< std::pair<size_t, const void *> > args; |
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int counter = 0; |
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int err = CL_SUCCESS; |
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cl_mem counterCL = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(), |
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CL_MEM_COPY_HOST_PTR, sizeof(int), |
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&counter, &err); |
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int kpLocStep = kpLoc_.step / kpLoc_.elemSize(); |
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int scoreStep = score_.step / score_.elemSize(); |
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int nms = (nonmaxSupression) ? 1 : 0; |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data)); |
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if (!mask.empty()) args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mask.data)); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&kpLoc_.data)); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&score_.data)); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&counterCL)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&nms)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&maxKeypoints)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&threshold)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.step)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.rows)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.cols)); |
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if (!mask.empty()) args.push_back( std::make_pair( sizeof(cl_int), (void *)&mask.step)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&kpLocStep)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&scoreStep)); |
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openCLExecuteKernel(clCxt, &featdetect_fast, kernelName, globalThreads, localThreads, args, -1, -1); |
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openCLSafeCall(clEnqueueReadBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(), |
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counterCL, CL_TRUE, 0, sizeof(int), &counter, 0, NULL, NULL)); |
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openCLSafeCall(clReleaseMemObject(counterCL)); |
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return counter; |
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} |
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int cv::ocl::FAST_OCL::nonmaxSupressionOCL(oclMat& keypoints) |
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{ |
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size_t localThreads[3] = {256, 1, 1}; |
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size_t globalThreads[3] = {count_, 1, 1}; |
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Context *clCxt = Context::getContext(); |
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String kernelName = "nonmaxSupression"; |
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std::vector< std::pair<size_t, const void *> > args; |
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int counter = 0; |
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int err = CL_SUCCESS; |
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cl_mem counterCL = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(), |
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CL_MEM_COPY_HOST_PTR, sizeof(int), |
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&counter, &err); |
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int kpLocStep = kpLoc_.step / kpLoc_.elemSize(); |
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int sStep = score_.step / score_.elemSize(); |
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int kStep = keypoints.step / keypoints.elemSize(); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&kpLoc_.data)); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&score_.data)); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data)); |
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&counterCL)); |
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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( sizeof(cl_int), (void *)&kpLocStep)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&sStep)); |
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&kStep)); |
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openCLExecuteKernel(clCxt, &featdetect_fast, kernelName, globalThreads, localThreads, args, -1, -1); |
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openCLSafeCall(clEnqueueReadBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(), |
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counterCL, CL_TRUE, 0, sizeof(int), &counter, 0, NULL, NULL)); |
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openCLSafeCall(clReleaseMemObject(counterCL)); |
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return counter; |
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} |
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int cv::ocl::FAST_OCL::getKeyPoints(oclMat& keypoints) |
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{ |
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if (count_ == 0) |
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return 0; |
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if (nonmaxSupression) |
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{ |
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ensureSizeIsEnough(ROWS_COUNT, count_, CV_32FC1, keypoints); |
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return nonmaxSupressionOCL(keypoints); |
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} |
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kpLoc_.convertTo(keypoints, CV_32FC1); |
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Mat k = keypoints; |
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return count_; |
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} |
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void cv::ocl::FAST_OCL::release() |
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{ |
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kpLoc_.release(); |
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score_.release(); |
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d_keypoints_.release(); |
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}
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