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