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