1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // License Agreement 11 // For Open Source Computer Vision Library 12 // 13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. 14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved. 15 // Third party copyrights are property of their respective owners. 16 // 17 // Redistribution and use in source and binary forms, with or without modification, 18 // are permitted provided that the following conditions are met: 19 // 20 // * Redistribution's of source code must retain the above copyright notice, 21 // this list of conditions and the following disclaimer. 22 // 23 // * Redistribution's in binary form must reproduce the above copyright notice, 24 // this list of conditions and the following disclaimer in the documentation 25 // and/or other materials provided with the distribution. 26 // 27 // * The name of the copyright holders may not be used to endorse or promote products 28 // derived from this software without specific prior written permission. 29 // 30 // This software is provided by the copyright holders and contributors "as is" and 31 // any express or implied warranties, including, but not limited to, the implied 32 // warranties of merchantability and fitness for a particular purpose are disclaimed. 33 // In no event shall the Intel Corporation or contributors be liable for any direct, 34 // indirect, incidental, special, exemplary, or consequential damages 35 // (including, but not limited to, procurement of substitute goods or services; 36 // loss of use, data, or profits; or business interruption) however caused 37 // and on any theory of liability, whether in contract, strict liability, 38 // or tort (including negligence or otherwise) arising in any way out of 39 // the use of this software, even if advised of the possibility of such damage. 40 // 41 //M*/ 42 43 #include "test_precomp.hpp" 44 45 #ifdef HAVE_CUDA 46 47 using namespace cvtest; 48 49 //////////////////////////////////////////////////////////////////////////////// 50 // MeanShift 51 52 struct MeanShift : testing::TestWithParam<cv::cuda::DeviceInfo> 53 { 54 cv::cuda::DeviceInfo devInfo; 55 56 cv::Mat img; 57 58 int spatialRad; 59 int colorRad; 60 61 virtual void SetUp() 62 { 63 devInfo = GetParam(); 64 65 cv::cuda::setDevice(devInfo.deviceID()); 66 67 img = readImageType("meanshift/cones.png", CV_8UC4); 68 ASSERT_FALSE(img.empty()); 69 70 spatialRad = 30; 71 colorRad = 30; 72 } 73 }; 74 75 CUDA_TEST_P(MeanShift, Filtering) 76 { 77 cv::Mat img_template; 78 if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20)) 79 img_template = readImage("meanshift/con_result.png"); 80 else 81 img_template = readImage("meanshift/con_result_CC1X.png"); 82 ASSERT_FALSE(img_template.empty()); 83 84 cv::cuda::GpuMat d_dst; 85 cv::cuda::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad); 86 87 ASSERT_EQ(CV_8UC4, d_dst.type()); 88 89 cv::Mat dst(d_dst); 90 91 cv::Mat result; 92 cv::cvtColor(dst, result, cv::COLOR_BGRA2BGR); 93 94 EXPECT_MAT_NEAR(img_template, result, 0.0); 95 } 96 97 CUDA_TEST_P(MeanShift, Proc) 98 { 99 cv::FileStorage fs; 100 if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20)) 101 fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); 102 else 103 fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); 104 ASSERT_TRUE(fs.isOpened()); 105 106 cv::Mat spmap_template; 107 fs["spmap"] >> spmap_template; 108 ASSERT_FALSE(spmap_template.empty()); 109 110 cv::cuda::GpuMat rmap_filtered; 111 cv::cuda::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); 112 113 cv::cuda::GpuMat rmap; 114 cv::cuda::GpuMat spmap; 115 cv::cuda::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad); 116 117 ASSERT_EQ(CV_8UC4, rmap.type()); 118 119 EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0); 120 EXPECT_MAT_NEAR(spmap_template, spmap, 0.0); 121 } 122 123 INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShift, ALL_DEVICES); 124 125 //////////////////////////////////////////////////////////////////////////////// 126 // MeanShiftSegmentation 127 128 namespace 129 { 130 IMPLEMENT_PARAM_CLASS(MinSize, int); 131 } 132 133 PARAM_TEST_CASE(MeanShiftSegmentation, cv::cuda::DeviceInfo, MinSize) 134 { 135 cv::cuda::DeviceInfo devInfo; 136 int minsize; 137 138 virtual void SetUp() 139 { 140 devInfo = GET_PARAM(0); 141 minsize = GET_PARAM(1); 142 143 cv::cuda::setDevice(devInfo.deviceID()); 144 } 145 }; 146 147 CUDA_TEST_P(MeanShiftSegmentation, Regression) 148 { 149 cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4); 150 ASSERT_FALSE(img.empty()); 151 152 std::ostringstream path; 153 path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize; 154 if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20)) 155 path << ".png"; 156 else 157 path << "_CC1X.png"; 158 cv::Mat dst_gold = readImage(path.str()); 159 ASSERT_FALSE(dst_gold.empty()); 160 161 cv::Mat dst; 162 cv::cuda::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize); 163 164 cv::Mat dst_rgb; 165 cv::cvtColor(dst, dst_rgb, cv::COLOR_BGRA2BGR); 166 167 EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3); 168 } 169 170 INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShiftSegmentation, testing::Combine( 171 ALL_DEVICES, 172 testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364)))); 173 174 #endif // HAVE_CUDA 175
