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 // Gold implementation 51 52 namespace 53 { 54 template <typename T, template <typename> class Interpolator> 55 void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy) 56 { 57 const int cn = src.channels(); 58 59 cv::Size dsize(cv::saturate_cast<int>(src.cols * fx), cv::saturate_cast<int>(src.rows * fy)); 60 61 dst.create(dsize, src.type()); 62 63 float ifx = static_cast<float>(1.0 / fx); 64 float ify = static_cast<float>(1.0 / fy); 65 66 for (int y = 0; y < dsize.height; ++y) 67 { 68 for (int x = 0; x < dsize.width; ++x) 69 { 70 for (int c = 0; c < cn; ++c) 71 dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, y * ify, x * ifx, c, cv::BORDER_REPLICATE); 72 } 73 } 74 } 75 76 void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation) 77 { 78 typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy); 79 80 static const func_t nearest_funcs[] = 81 { 82 resizeImpl<unsigned char, NearestInterpolator>, 83 resizeImpl<signed char, NearestInterpolator>, 84 resizeImpl<unsigned short, NearestInterpolator>, 85 resizeImpl<short, NearestInterpolator>, 86 resizeImpl<int, NearestInterpolator>, 87 resizeImpl<float, NearestInterpolator> 88 }; 89 90 91 static const func_t linear_funcs[] = 92 { 93 resizeImpl<unsigned char, LinearInterpolator>, 94 resizeImpl<signed char, LinearInterpolator>, 95 resizeImpl<unsigned short, LinearInterpolator>, 96 resizeImpl<short, LinearInterpolator>, 97 resizeImpl<int, LinearInterpolator>, 98 resizeImpl<float, LinearInterpolator> 99 }; 100 101 static const func_t cubic_funcs[] = 102 { 103 resizeImpl<unsigned char, CubicInterpolator>, 104 resizeImpl<signed char, CubicInterpolator>, 105 resizeImpl<unsigned short, CubicInterpolator>, 106 resizeImpl<short, CubicInterpolator>, 107 resizeImpl<int, CubicInterpolator>, 108 resizeImpl<float, CubicInterpolator> 109 }; 110 111 static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs}; 112 113 funcs[interpolation][src.depth()](src, dst, fx, fy); 114 } 115 } 116 117 /////////////////////////////////////////////////////////////////// 118 // Test 119 120 PARAM_TEST_CASE(Resize, cv::cuda::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi) 121 { 122 cv::cuda::DeviceInfo devInfo; 123 cv::Size size; 124 double coeff; 125 int interpolation; 126 int type; 127 bool useRoi; 128 129 virtual void SetUp() 130 { 131 devInfo = GET_PARAM(0); 132 size = GET_PARAM(1); 133 type = GET_PARAM(2); 134 coeff = GET_PARAM(3); 135 interpolation = GET_PARAM(4); 136 useRoi = GET_PARAM(5); 137 138 cv::cuda::setDevice(devInfo.deviceID()); 139 } 140 }; 141 142 CUDA_TEST_P(Resize, Accuracy) 143 { 144 cv::Mat src = randomMat(size, type); 145 146 cv::cuda::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi); 147 cv::cuda::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation); 148 149 cv::Mat dst_gold; 150 resizeGold(src, dst_gold, coeff, coeff, interpolation); 151 152 EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0); 153 } 154 155 INSTANTIATE_TEST_CASE_P(CUDA_Warping, Resize, testing::Combine( 156 ALL_DEVICES, 157 DIFFERENT_SIZES, 158 testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), 159 testing::Values(0.3, 0.5, 1.5, 2.0), 160 testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)), 161 WHOLE_SUBMAT)); 162 163 ///////////////// 164 165 PARAM_TEST_CASE(ResizeSameAsHost, cv::cuda::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi) 166 { 167 cv::cuda::DeviceInfo devInfo; 168 cv::Size size; 169 double coeff; 170 int interpolation; 171 int type; 172 bool useRoi; 173 174 virtual void SetUp() 175 { 176 devInfo = GET_PARAM(0); 177 size = GET_PARAM(1); 178 type = GET_PARAM(2); 179 coeff = GET_PARAM(3); 180 interpolation = GET_PARAM(4); 181 useRoi = GET_PARAM(5); 182 183 cv::cuda::setDevice(devInfo.deviceID()); 184 } 185 }; 186 187 // downscaling only: used for classifiers 188 CUDA_TEST_P(ResizeSameAsHost, Accuracy) 189 { 190 cv::Mat src = randomMat(size, type); 191 192 cv::cuda::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi); 193 cv::cuda::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation); 194 195 cv::Mat dst_gold; 196 cv::resize(src, dst_gold, cv::Size(), coeff, coeff, interpolation); 197 198 EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0); 199 } 200 201 INSTANTIATE_TEST_CASE_P(CUDA_Warping, ResizeSameAsHost, testing::Combine( 202 ALL_DEVICES, 203 DIFFERENT_SIZES, 204 testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), 205 testing::Values(0.3, 0.5), 206 testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_AREA)), 207 WHOLE_SUBMAT)); 208 209 #endif // HAVE_CUDA 210
