/*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 "perf_precomp.hpp" using namespace std; using namespace testing; using namespace perf; ////////////////////////////////////////////////////////////////////// // HoughLines namespace { struct Vec4iComparator { bool operator()(const cv::Vec4i& a, const cv::Vec4i b) const { if (a[0] != b[0]) return a[0] < b[0]; else if(a[1] != b[1]) return a[1] < b[1]; else if(a[2] != b[2]) return a[2] < b[2]; else return a[3] < b[3]; } }; struct Vec3fComparator { bool operator()(const cv::Vec3f& a, const cv::Vec3f b) const { if(a[0] != b[0]) return a[0] < b[0]; else if(a[1] != b[1]) return a[1] < b[1]; else return a[2] < b[2]; } }; struct Vec2fComparator { bool operator()(const cv::Vec2f& a, const cv::Vec2f b) const { if(a[0] != b[0]) return a[0] < b[0]; else return a[1] < b[1]; } }; } PERF_TEST_P(Sz, HoughLines, GPU_TYPICAL_MAT_SIZES) { declare.time(30.0); const cv::Size size = GetParam(); const float rho = 1.0f; const float theta = static_cast(CV_PI / 180.0); const int threshold = 300; cv::Mat src(size, CV_8UC1, cv::Scalar::all(0)); cv::line(src, cv::Point(0, 100), cv::Point(src.cols, 100), cv::Scalar::all(255), 1); cv::line(src, cv::Point(0, 200), cv::Point(src.cols, 200), cv::Scalar::all(255), 1); cv::line(src, cv::Point(0, 400), cv::Point(src.cols, 400), cv::Scalar::all(255), 1); cv::line(src, cv::Point(100, 0), cv::Point(100, src.rows), cv::Scalar::all(255), 1); cv::line(src, cv::Point(200, 0), cv::Point(200, src.rows), cv::Scalar::all(255), 1); cv::line(src, cv::Point(400, 0), cv::Point(400, src.rows), cv::Scalar::all(255), 1); if (PERF_RUN_GPU()) { const cv::gpu::GpuMat d_src(src); cv::gpu::GpuMat d_lines; cv::Ptr hough = cv::gpu::createHoughLinesDetector(rho, theta, threshold); TEST_CYCLE() hough->detect(d_src, d_lines); cv::Mat gpu_lines(d_lines.row(0)); cv::Vec2f* begin = gpu_lines.ptr(0); cv::Vec2f* end = begin + gpu_lines.cols; std::sort(begin, end, Vec2fComparator()); SANITY_CHECK(gpu_lines); } else { std::vector cpu_lines; TEST_CYCLE() cv::HoughLines(src, cpu_lines, rho, theta, threshold); SANITY_CHECK(cpu_lines); } } ////////////////////////////////////////////////////////////////////// // HoughLinesP DEF_PARAM_TEST_1(Image, std::string); PERF_TEST_P(Image, HoughLinesP, testing::Values("cv/shared/pic5.png", "stitching/a1.png")) { declare.time(30.0); const std::string fileName = getDataPath(GetParam()); const float rho = 1.0f; const float theta = static_cast(CV_PI / 180.0); const int threshold = 100; const int minLineLenght = 50; const int maxLineGap = 5; const cv::Mat image = cv::imread(fileName, cv::IMREAD_GRAYSCALE); ASSERT_FALSE(image.empty()); cv::Mat mask; cv::Canny(image, mask, 50, 100); if (PERF_RUN_GPU()) { const cv::gpu::GpuMat d_mask(mask); cv::gpu::GpuMat d_lines; cv::Ptr hough = cv::gpu::createHoughSegmentDetector(rho, theta, minLineLenght, maxLineGap); TEST_CYCLE() hough->detect(d_mask, d_lines); cv::Mat gpu_lines(d_lines); cv::Vec4i* begin = gpu_lines.ptr(); cv::Vec4i* end = begin + gpu_lines.cols; std::sort(begin, end, Vec4iComparator()); SANITY_CHECK(gpu_lines); } else { std::vector cpu_lines; TEST_CYCLE() cv::HoughLinesP(mask, cpu_lines, rho, theta, threshold, minLineLenght, maxLineGap); SANITY_CHECK(cpu_lines); } } ////////////////////////////////////////////////////////////////////// // HoughCircles DEF_PARAM_TEST(Sz_Dp_MinDist, cv::Size, float, float); PERF_TEST_P(Sz_Dp_MinDist, HoughCircles, Combine(GPU_TYPICAL_MAT_SIZES, Values(1.0f, 2.0f, 4.0f), Values(1.0f))) { declare.time(30.0); const cv::Size size = GET_PARAM(0); const float dp = GET_PARAM(1); const float minDist = GET_PARAM(2); const int minRadius = 10; const int maxRadius = 30; const int cannyThreshold = 100; const int votesThreshold = 15; cv::Mat src(size, CV_8UC1, cv::Scalar::all(0)); cv::circle(src, cv::Point(100, 100), 20, cv::Scalar::all(255), -1); cv::circle(src, cv::Point(200, 200), 25, cv::Scalar::all(255), -1); cv::circle(src, cv::Point(200, 100), 25, cv::Scalar::all(255), -1); if (PERF_RUN_GPU()) { const cv::gpu::GpuMat d_src(src); cv::gpu::GpuMat d_circles; cv::Ptr houghCircles = cv::gpu::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius); TEST_CYCLE() houghCircles->detect(d_src, d_circles); cv::Mat gpu_circles(d_circles); cv::Vec3f* begin = gpu_circles.ptr(0); cv::Vec3f* end = begin + gpu_circles.cols; std::sort(begin, end, Vec3fComparator()); SANITY_CHECK(gpu_circles); } else { std::vector cpu_circles; TEST_CYCLE() cv::HoughCircles(src, cpu_circles, cv::HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius); SANITY_CHECK(cpu_circles); } } ////////////////////////////////////////////////////////////////////// // GeneralizedHough PERF_TEST_P(Sz, GeneralizedHoughBallard, GPU_TYPICAL_MAT_SIZES) { declare.time(10); const cv::Size imageSize = GetParam(); const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(templ.empty()); cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0)); templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows))); cv::Mat edges; cv::Canny(image, edges, 50, 100); cv::Mat dx, dy; cv::Sobel(image, dx, CV_32F, 1, 0); cv::Sobel(image, dy, CV_32F, 0, 1); if (PERF_RUN_GPU()) { cv::Ptr alg = cv::gpu::createGeneralizedHoughBallard(); const cv::gpu::GpuMat d_edges(edges); const cv::gpu::GpuMat d_dx(dx); const cv::gpu::GpuMat d_dy(dy); cv::gpu::GpuMat positions; alg->setTemplate(cv::gpu::GpuMat(templ)); TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions); GPU_SANITY_CHECK(positions); } else { cv::Ptr alg = cv::createGeneralizedHoughBallard(); cv::Mat positions; alg->setTemplate(templ); TEST_CYCLE() alg->detect(edges, dx, dy, positions); CPU_SANITY_CHECK(positions); } } PERF_TEST_P(Sz, GeneralizedHoughGuil, GPU_TYPICAL_MAT_SIZES) { declare.time(10); const cv::Size imageSize = GetParam(); const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(templ.empty()); cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0)); templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows))); cv::RNG rng(123456789); const int objCount = rng.uniform(5, 15); for (int i = 0; i < objCount; ++i) { double scale = rng.uniform(0.7, 1.3); bool rotate = 1 == rng.uniform(0, 2); cv::Mat obj; cv::resize(templ, obj, cv::Size(), scale, scale); if (rotate) obj = obj.t(); cv::Point pos; pos.x = rng.uniform(0, image.cols - obj.cols); pos.y = rng.uniform(0, image.rows - obj.rows); cv::Mat roi = image(cv::Rect(pos, obj.size())); cv::add(roi, obj, roi); } cv::Mat edges; cv::Canny(image, edges, 50, 100); cv::Mat dx, dy; cv::Sobel(image, dx, CV_32F, 1, 0); cv::Sobel(image, dy, CV_32F, 0, 1); if (PERF_RUN_GPU()) { cv::Ptr alg = cv::gpu::createGeneralizedHoughGuil(); alg->setMaxAngle(90.0); alg->setAngleStep(2.0); const cv::gpu::GpuMat d_edges(edges); const cv::gpu::GpuMat d_dx(dx); const cv::gpu::GpuMat d_dy(dy); cv::gpu::GpuMat positions; alg->setTemplate(cv::gpu::GpuMat(templ)); TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions); GPU_SANITY_CHECK(positions); } else { cv::Ptr alg = cv::createGeneralizedHoughGuil(); alg->setMaxAngle(90.0); alg->setAngleStep(2.0); cv::Mat positions; alg->setTemplate(templ); TEST_CYCLE() alg->detect(edges, dx, dy, positions); CPU_SANITY_CHECK(positions); } }