/*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 "test_precomp.hpp" #ifdef HAVE_CUDA using namespace cvtest; //////////////////////////////////////////////////////////////////////////////// // MatchTemplate8U CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) #define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_CCOEFF_NORMED)) namespace { IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); } PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) { cv::gpu::DeviceInfo devInfo; cv::Size size; cv::Size templ_size; int cn; int method; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); templ_size = GET_PARAM(2); cn = GET_PARAM(3); method = GET_PARAM(4); cv::gpu::setDevice(devInfo.deviceID()); } }; GPU_TEST_P(MatchTemplate8U, Accuracy) { cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn)); cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn)); cv::Ptr alg = cv::gpu::createTemplateMatching(image.type(), method); cv::gpu::GpuMat dst; alg->match(loadMat(image), loadMat(templ), dst); cv::Mat dst_gold; cv::matchTemplate(image, templ, dst_gold, method); EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), testing::Values(Channels(1), Channels(3), Channels(4)), ALL_TEMPLATE_METHODS)); //////////////////////////////////////////////////////////////////////////////// // MatchTemplate32F PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) { cv::gpu::DeviceInfo devInfo; cv::Size size; cv::Size templ_size; int cn; int method; int n, m, h, w; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); templ_size = GET_PARAM(2); cn = GET_PARAM(3); method = GET_PARAM(4); cv::gpu::setDevice(devInfo.deviceID()); } }; GPU_TEST_P(MatchTemplate32F, Regression) { cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn)); cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn)); cv::Ptr alg = cv::gpu::createTemplateMatching(image.type(), method); cv::gpu::GpuMat dst; alg->match(loadMat(image), loadMat(templ), dst); cv::Mat dst_gold; cv::matchTemplate(image, templ, dst_gold, method); EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), testing::Values(Channels(1), Channels(3), Channels(4)), testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR)))); //////////////////////////////////////////////////////////////////////////////// // MatchTemplateBlackSource PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod) { cv::gpu::DeviceInfo devInfo; int method; virtual void SetUp() { devInfo = GET_PARAM(0); method = GET_PARAM(1); cv::gpu::setDevice(devInfo.deviceID()); } }; GPU_TEST_P(MatchTemplateBlackSource, Accuracy) { cv::Mat image = readImage("matchtemplate/black.png"); ASSERT_FALSE(image.empty()); cv::Mat pattern = readImage("matchtemplate/cat.png"); ASSERT_FALSE(pattern.empty()); cv::Ptr alg = cv::gpu::createTemplateMatching(image.type(), method); cv::gpu::GpuMat d_dst; alg->match(loadMat(image), loadMat(pattern), d_dst); cv::Mat dst(d_dst); double maxValue; cv::Point maxLoc; cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc); cv::Point maxLocGold = cv::Point(284, 12); ASSERT_EQ(maxLocGold, maxLoc); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine( ALL_DEVICES, testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED)))); //////////////////////////////////////////////////////////////////////////////// // MatchTemplate_CCOEF_NORMED PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair) { cv::gpu::DeviceInfo devInfo; std::string imageName; std::string patternName; virtual void SetUp() { devInfo = GET_PARAM(0); imageName = GET_PARAM(1).first; patternName = GET_PARAM(1).second; cv::gpu::setDevice(devInfo.deviceID()); } }; GPU_TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy) { cv::Mat image = readImage(imageName); ASSERT_FALSE(image.empty()); cv::Mat pattern = readImage(patternName); ASSERT_FALSE(pattern.empty()); cv::Ptr alg = cv::gpu::createTemplateMatching(image.type(), cv::TM_CCOEFF_NORMED); cv::gpu::GpuMat d_dst; alg->match(loadMat(image), loadMat(pattern), d_dst); cv::Mat dst(d_dst); cv::Point minLoc, maxLoc; double minVal, maxVal; cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc); cv::Mat dstGold; cv::matchTemplate(image, pattern, dstGold, cv::TM_CCOEFF_NORMED); double minValGold, maxValGold; cv::Point minLocGold, maxLocGold; cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold); ASSERT_EQ(minLocGold, minLoc); ASSERT_EQ(maxLocGold, maxLoc); ASSERT_LE(maxVal, 1.0); ASSERT_GE(minVal, -1.0); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine( ALL_DEVICES, testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png"))))); //////////////////////////////////////////////////////////////////////////////// // MatchTemplate_CanFindBigTemplate struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam { cv::gpu::DeviceInfo devInfo; virtual void SetUp() { devInfo = GetParam(); cv::gpu::setDevice(devInfo.deviceID()); } }; GPU_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED) { cv::Mat scene = readImage("matchtemplate/scene.png"); ASSERT_FALSE(scene.empty()); cv::Mat templ = readImage("matchtemplate/template.png"); ASSERT_FALSE(templ.empty()); cv::Ptr alg = cv::gpu::createTemplateMatching(scene.type(), cv::TM_SQDIFF_NORMED); cv::gpu::GpuMat d_result; alg->match(loadMat(scene), loadMat(templ), d_result); cv::Mat result(d_result); double minVal; cv::Point minLoc; cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); ASSERT_GE(minVal, 0); ASSERT_LT(minVal, 1e-3); ASSERT_EQ(344, minLoc.x); ASSERT_EQ(0, minLoc.y); } GPU_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF) { cv::Mat scene = readImage("matchtemplate/scene.png"); ASSERT_FALSE(scene.empty()); cv::Mat templ = readImage("matchtemplate/template.png"); ASSERT_FALSE(templ.empty()); cv::Ptr alg = cv::gpu::createTemplateMatching(scene.type(), cv::TM_SQDIFF); cv::gpu::GpuMat d_result; alg->match(loadMat(scene), loadMat(templ), d_result); cv::Mat result(d_result); double minVal; cv::Point minLoc; cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); ASSERT_GE(minVal, 0); ASSERT_EQ(344, minLoc.x); ASSERT_EQ(0, minLoc.y); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); #endif // HAVE_CUDA