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318 lines
12 KiB
318 lines
12 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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using namespace cv; |
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using namespace cv::cuda; |
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || !defined(HAVE_OPENCV_CUDAFILTERS) |
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Ptr<cuda::HoughCirclesDetector> cv::cuda::createHoughCirclesDetector(float, float, int, int, int, int, int) { throw_no_cuda(); return Ptr<HoughCirclesDetector>(); } |
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#else /* !defined (HAVE_CUDA) */ |
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namespace cv { namespace cuda { namespace device |
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{ |
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namespace hough |
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{ |
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int buildPointList_gpu(PtrStepSzb src, unsigned int* list); |
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} |
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namespace hough_circles |
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{ |
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void circlesAccumCenters_gpu(const unsigned int* list, int count, PtrStepi dx, PtrStepi dy, PtrStepSzi accum, int minRadius, int maxRadius, float idp); |
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int buildCentersList_gpu(PtrStepSzi accum, unsigned int* centers, int threshold); |
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int circlesAccumRadius_gpu(const unsigned int* centers, int centersCount, const unsigned int* list, int count, |
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float3* circles, int maxCircles, float dp, int minRadius, int maxRadius, int threshold, bool has20); |
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} |
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}}} |
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namespace |
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{ |
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class HoughCirclesDetectorImpl : public HoughCirclesDetector |
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{ |
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public: |
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HoughCirclesDetectorImpl(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles); |
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void detect(InputArray src, OutputArray circles, Stream& stream); |
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void setDp(float dp) { dp_ = dp; } |
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float getDp() const { return dp_; } |
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void setMinDist(float minDist) { minDist_ = minDist; } |
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float getMinDist() const { return minDist_; } |
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void setCannyThreshold(int cannyThreshold) { cannyThreshold_ = cannyThreshold; } |
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int getCannyThreshold() const { return cannyThreshold_; } |
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void setVotesThreshold(int votesThreshold) { votesThreshold_ = votesThreshold; } |
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int getVotesThreshold() const { return votesThreshold_; } |
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void setMinRadius(int minRadius) { minRadius_ = minRadius; } |
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int getMinRadius() const { return minRadius_; } |
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void setMaxRadius(int maxRadius) { maxRadius_ = maxRadius; } |
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int getMaxRadius() const { return maxRadius_; } |
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void setMaxCircles(int maxCircles) { maxCircles_ = maxCircles; } |
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int getMaxCircles() const { return maxCircles_; } |
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void write(FileStorage& fs) const |
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{ |
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writeFormat(fs); |
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fs << "name" << "HoughCirclesDetector_CUDA" |
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<< "dp" << dp_ |
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<< "minDist" << minDist_ |
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<< "cannyThreshold" << cannyThreshold_ |
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<< "votesThreshold" << votesThreshold_ |
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<< "minRadius" << minRadius_ |
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<< "maxRadius" << maxRadius_ |
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<< "maxCircles" << maxCircles_; |
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} |
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void read(const FileNode& fn) |
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{ |
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CV_Assert( String(fn["name"]) == "HoughCirclesDetector_CUDA" ); |
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dp_ = (float)fn["dp"]; |
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minDist_ = (float)fn["minDist"]; |
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cannyThreshold_ = (int)fn["cannyThreshold"]; |
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votesThreshold_ = (int)fn["votesThreshold"]; |
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minRadius_ = (int)fn["minRadius"]; |
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maxRadius_ = (int)fn["maxRadius"]; |
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maxCircles_ = (int)fn["maxCircles"]; |
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} |
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private: |
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float dp_; |
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float minDist_; |
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int cannyThreshold_; |
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int votesThreshold_; |
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int minRadius_; |
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int maxRadius_; |
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int maxCircles_; |
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GpuMat dx_, dy_; |
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GpuMat edges_; |
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GpuMat accum_; |
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Mat tt; //CPU copy of accum_ |
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GpuMat list_; |
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GpuMat result_; |
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Ptr<cuda::Filter> filterDx_; |
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Ptr<cuda::Filter> filterDy_; |
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Ptr<cuda::CannyEdgeDetector> canny_; |
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}; |
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bool centersCompare(Vec3f a, Vec3f b) {return (a[2] > b[2]);} |
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HoughCirclesDetectorImpl::HoughCirclesDetectorImpl(float dp, float minDist, int cannyThreshold, int votesThreshold, |
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int minRadius, int maxRadius, int maxCircles) : |
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dp_(dp), minDist_(minDist), cannyThreshold_(cannyThreshold), votesThreshold_(votesThreshold), |
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minRadius_(minRadius), maxRadius_(maxRadius), maxCircles_(maxCircles) |
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{ |
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canny_ = cuda::createCannyEdgeDetector(std::max(cannyThreshold_ / 2, 1), cannyThreshold_); |
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filterDx_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 1, 0); |
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filterDy_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 0, 1); |
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} |
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void HoughCirclesDetectorImpl::detect(InputArray _src, OutputArray circles, Stream& stream) |
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{ |
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// TODO : implement async version |
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(void) stream; |
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using namespace cv::cuda::device::hough; |
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using namespace cv::cuda::device::hough_circles; |
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GpuMat src = _src.getGpuMat(); |
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CV_Assert( src.type() == CV_8UC1 ); |
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CV_Assert( src.cols < std::numeric_limits<unsigned short>::max() ); |
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CV_Assert( src.rows < std::numeric_limits<unsigned short>::max() ); |
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CV_Assert( dp_ > 0 ); |
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CV_Assert( minRadius_ > 0 && maxRadius_ > minRadius_ ); |
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CV_Assert( cannyThreshold_ > 0 ); |
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CV_Assert( votesThreshold_ > 0 ); |
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CV_Assert( maxCircles_ > 0 ); |
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const float idp = 1.0f / dp_; |
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filterDx_->apply(src, dx_); |
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filterDy_->apply(src, dy_); |
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canny_->setLowThreshold(std::max(cannyThreshold_ / 2, 1)); |
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canny_->setHighThreshold(cannyThreshold_); |
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canny_->detect(dx_, dy_, edges_); |
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ensureSizeIsEnough(2, src.size().area(), CV_32SC1, list_); |
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unsigned int* srcPoints = list_.ptr<unsigned int>(0); |
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unsigned int* centers = list_.ptr<unsigned int>(1); |
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const int pointsCount = buildPointList_gpu(edges_, srcPoints); |
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if (pointsCount == 0) |
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{ |
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circles.release(); |
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return; |
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} |
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ensureSizeIsEnough(cvCeil(src.rows * idp) + 2, cvCeil(src.cols * idp) + 2, CV_32SC1, accum_); |
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accum_.setTo(Scalar::all(0)); |
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circlesAccumCenters_gpu(srcPoints, pointsCount, dx_, dy_, accum_, minRadius_, maxRadius_, idp); |
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accum_.download(tt); |
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int centersCount = buildCentersList_gpu(accum_, centers, votesThreshold_); |
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if (centersCount == 0) |
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{ |
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circles.release(); |
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return; |
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} |
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if (minDist_ > 1) |
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{ |
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AutoBuffer<ushort2> oldBuf_(centersCount); |
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AutoBuffer<ushort2> newBuf_(centersCount); |
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int newCount = 0; |
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ushort2* oldBuf = oldBuf_; |
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ushort2* newBuf = newBuf_; |
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cudaSafeCall( cudaMemcpy(oldBuf, centers, centersCount * sizeof(ushort2), cudaMemcpyDeviceToHost) ); |
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const int cellSize = cvRound(minDist_); |
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const int gridWidth = (src.cols + cellSize - 1) / cellSize; |
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const int gridHeight = (src.rows + cellSize - 1) / cellSize; |
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std::vector< std::vector<ushort2> > grid(gridWidth * gridHeight); |
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const float minDist2 = minDist_ * minDist_; |
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std::vector<Vec3f> sortBuf; |
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for(int i=0; i<centersCount; i++){ |
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Vec3f temp; |
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temp[0] = oldBuf[i].x; |
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temp[1] = oldBuf[i].y; |
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temp[2] = tt.at<int>(temp[1]+1, temp[0]+1); |
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sortBuf.push_back(temp); |
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} |
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std::sort(sortBuf.begin(), sortBuf.end(), centersCompare); |
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for (int i = 0; i < centersCount; ++i) |
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{ |
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ushort2 p; |
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p.x = sortBuf[i][0]; |
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p.y = sortBuf[i][1]; |
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bool good = true; |
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int xCell = static_cast<int>(p.x / cellSize); |
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int yCell = static_cast<int>(p.y / cellSize); |
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int x1 = xCell - 1; |
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int y1 = yCell - 1; |
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int x2 = xCell + 1; |
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int y2 = yCell + 1; |
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// boundary check |
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x1 = std::max(0, x1); |
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y1 = std::max(0, y1); |
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x2 = std::min(gridWidth - 1, x2); |
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y2 = std::min(gridHeight - 1, y2); |
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for (int yy = y1; yy <= y2; ++yy) |
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{ |
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for (int xx = x1; xx <= x2; ++xx) |
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{ |
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std::vector<ushort2>& m = grid[yy * gridWidth + xx]; |
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for(size_t j = 0; j < m.size(); ++j) |
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{ |
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float dx = (float)(p.x - m[j].x); |
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float dy = (float)(p.y - m[j].y); |
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if (dx * dx + dy * dy < minDist2) |
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{ |
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good = false; |
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goto break_out; |
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} |
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} |
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} |
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} |
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break_out: |
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if(good) |
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{ |
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grid[yCell * gridWidth + xCell].push_back(p); |
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newBuf[newCount++] = p; |
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} |
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} |
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cudaSafeCall( cudaMemcpy(centers, newBuf, newCount * sizeof(unsigned int), cudaMemcpyHostToDevice) ); |
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centersCount = newCount; |
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} |
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ensureSizeIsEnough(1, maxCircles_, CV_32FC3, result_); |
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int circlesCount = circlesAccumRadius_gpu(centers, centersCount, srcPoints, pointsCount, result_.ptr<float3>(), maxCircles_, |
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dp_, minRadius_, maxRadius_, votesThreshold_, deviceSupports(FEATURE_SET_COMPUTE_20)); |
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if (circlesCount == 0) |
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{ |
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circles.release(); |
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return; |
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} |
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result_.cols = circlesCount; |
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result_.copyTo(circles); |
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} |
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} |
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Ptr<HoughCirclesDetector> cv::cuda::createHoughCirclesDetector(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles) |
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{ |
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return makePtr<HoughCirclesDetectorImpl>(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius, maxCircles); |
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} |
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#endif /* !defined (HAVE_CUDA) */
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