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35 // loss of use, data, or profits; or business interruption) however caused 36 // and on any theory of liability, whether in contract, strict liability, 37 // or tort (including negligence or otherwise) arising in any way out of 38 // the use of this software, even if advised of the possibility of such damage. 39 // 40 //M*/ 41 42 #ifndef __OPENCV_TS_OCL_TEST_HPP__ 43 #define __OPENCV_TS_OCL_TEST_HPP__ 44 45 #include "opencv2/opencv_modules.hpp" 46 47 #include "opencv2/ts.hpp" 48 49 #include "opencv2/imgcodecs.hpp" 50 #include "opencv2/videoio.hpp" 51 #include "opencv2/highgui.hpp" 52 #include "opencv2/imgproc.hpp" 53 #include "opencv2/imgproc/types_c.h" 54 #include "opencv2/core/ocl.hpp" 55 56 namespace cvtest { 57 namespace ocl { 58 59 using namespace cv; 60 using namespace testing; 61 62 inline std::vector<UMat> ToUMat(const std::vector<Mat>& src) 63 { 64 std::vector<UMat> dst; 65 dst.resize(src.size()); 66 for (size_t i = 0; i < src.size(); ++i) 67 { 68 src[i].copyTo(dst[i]); 69 } 70 return dst; 71 } 72 73 inline UMat ToUMat(const Mat& src) 74 { 75 UMat dst; 76 src.copyTo(dst); 77 return dst; 78 } 79 80 inline UMat ToUMat(InputArray src) 81 { 82 UMat dst; 83 src.getMat().copyTo(dst); 84 return dst; 85 } 86 87 extern int test_loop_times; 88 89 #define MAX_VALUE 357 90 91 #define EXPECT_MAT_NORM(mat, eps) \ 92 do \ 93 { \ 94 EXPECT_LE(TestUtils::checkNorm1(mat), eps) \ 95 } while ((void)0, 0) 96 97 #define EXPECT_MAT_NEAR(mat1, mat2, eps) \ 98 do \ 99 { \ 100 ASSERT_EQ(mat1.type(), mat2.type()); \ 101 ASSERT_EQ(mat1.size(), mat2.size()); \ 102 EXPECT_LE(TestUtils::checkNorm2(mat1, mat2), eps) \ 103 << "Size: " << mat1.size() << std::endl; \ 104 } while ((void)0, 0) 105 106 #define EXPECT_MAT_NEAR_RELATIVE(mat1, mat2, eps) \ 107 do \ 108 { \ 109 ASSERT_EQ(mat1.type(), mat2.type()); \ 110 ASSERT_EQ(mat1.size(), mat2.size()); \ 111 EXPECT_LE(TestUtils::checkNormRelative(mat1, mat2), eps) \ 112 << "Size: " << mat1.size() << std::endl; \ 113 } while ((void)0, 0) 114 115 #define EXPECT_MAT_N_DIFF(mat1, mat2, num) \ 116 do \ 117 { \ 118 ASSERT_EQ(mat1.type(), mat2.type()); \ 119 ASSERT_EQ(mat1.size(), mat2.size()); \ 120 Mat diff; \ 121 absdiff(mat1, mat2, diff); \ 122 EXPECT_LE(countNonZero(diff.reshape(1)), num) \ 123 << "Size: " << mat1.size() << std::endl; \ 124 } while ((void)0, 0) 125 126 #define OCL_EXPECT_MATS_NEAR(name, eps) \ 127 do \ 128 { \ 129 ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \ 130 ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \ 131 EXPECT_LE(TestUtils::checkNorm2(name ## _roi, u ## name ## _roi), eps) \ 132 << "Size: " << name ## _roi.size() << std::endl; \ 133 Point _offset; \ 134 Size _wholeSize; \ 135 u ## name ## _roi.locateROI(_wholeSize, _offset); \ 136 Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \ 137 _mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \ 138 ASSERT_EQ(name.type(), u ## name.type()); \ 139 ASSERT_EQ(name.size(), u ## name.size()); \ 140 EXPECT_LE(TestUtils::checkNorm2(name, u ## name, _mask), eps) \ 141 << "Size: " << name ## _roi.size() << std::endl; \ 142 } while ((void)0, 0) 143 144 #define OCL_EXPECT_MATS_NEAR_RELATIVE(name, eps) \ 145 do \ 146 { \ 147 ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \ 148 ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \ 149 EXPECT_LE(TestUtils::checkNormRelative(name ## _roi, u ## name ## _roi), eps) \ 150 << "Size: " << name ## _roi.size() << std::endl; \ 151 Point _offset; \ 152 Size _wholeSize; \ 153 name ## _roi.locateROI(_wholeSize, _offset); \ 154 Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \ 155 _mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \ 156 ASSERT_EQ(name.type(), u ## name.type()); \ 157 ASSERT_EQ(name.size(), u ## name.size()); \ 158 EXPECT_LE(TestUtils::checkNormRelative(name, u ## name, _mask), eps) \ 159 << "Size: " << name ## _roi.size() << std::endl; \ 160 } while ((void)0, 0) 161 162 //for sparse matrix 163 #define OCL_EXPECT_MATS_NEAR_RELATIVE_SPARSE(name, eps) \ 164 do \ 165 { \ 166 ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \ 167 ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \ 168 EXPECT_LE(TestUtils::checkNormRelativeSparse(name ## _roi, u ## name ## _roi), eps) \ 169 << "Size: " << name ## _roi.size() << std::endl; \ 170 Point _offset; \ 171 Size _wholeSize; \ 172 name ## _roi.locateROI(_wholeSize, _offset); \ 173 Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \ 174 _mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \ 175 ASSERT_EQ(name.type(), u ## name.type()); \ 176 ASSERT_EQ(name.size(), u ## name.size()); \ 177 EXPECT_LE(TestUtils::checkNormRelativeSparse(name, u ## name, _mask), eps) \ 178 << "Size: " << name ## _roi.size() << std::endl; \ 179 } while ((void)0, 0) 180 181 #define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \ 182 do \ 183 { \ 184 ASSERT_EQ(mat1.type(), mat2.type()); \ 185 ASSERT_EQ(mat1.size(), mat2.size()); \ 186 EXPECT_LE(checkSimilarity(mat1, mat2), eps) \ 187 << "Size: " << mat1.size() << std::endl; \ 188 } while ((void)0, 0) 189 190 using perf::MatDepth; 191 using perf::MatType; 192 193 #define OCL_RNG_SEED 123456 194 195 struct CV_EXPORTS TestUtils 196 { 197 cv::RNG rng; 198 199 TestUtils() 200 { 201 rng = cv::RNG(OCL_RNG_SEED); 202 } 203 204 int randomInt(int minVal, int maxVal) 205 { 206 return rng.uniform(minVal, maxVal); 207 } 208 209 double randomDouble(double minVal, double maxVal) 210 { 211 return rng.uniform(minVal, maxVal); 212 } 213 214 double randomDoubleLog(double minVal, double maxVal) 215 { 216 double logMin = log((double)minVal + 1); 217 double logMax = log((double)maxVal + 1); 218 double pow = rng.uniform(logMin, logMax); 219 double v = exp(pow) - 1; 220 CV_Assert(v >= minVal && (v < maxVal || (v == minVal && v == maxVal))); 221 return v; 222 } 223 224 Size randomSize(int minVal, int maxVal) 225 { 226 #if 1 227 return cv::Size((int)randomDoubleLog(minVal, maxVal), (int)randomDoubleLog(minVal, maxVal)); 228 #else 229 return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal)); 230 #endif 231 } 232 233 Size randomSize(int minValX, int maxValX, int minValY, int maxValY) 234 { 235 #if 1 236 return cv::Size((int)randomDoubleLog(minValX, maxValX), (int)randomDoubleLog(minValY, maxValY)); 237 #else 238 return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal)); 239 #endif 240 } 241 242 Scalar randomScalar(double minVal, double maxVal) 243 { 244 return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal)); 245 } 246 247 Mat randomMat(Size size, int type, double minVal, double maxVal, bool useRoi = false) 248 { 249 RNG dataRng(rng.next()); 250 return cvtest::randomMat(dataRng, size, type, minVal, maxVal, useRoi); 251 } 252 253 struct Border 254 { 255 int top, bot, lef, rig; 256 }; 257 258 Border randomBorder(int minValue = 0, int maxValue = MAX_VALUE) 259 { 260 Border border = { 261 (int)randomDoubleLog(minValue, maxValue), 262 (int)randomDoubleLog(minValue, maxValue), 263 (int)randomDoubleLog(minValue, maxValue), 264 (int)randomDoubleLog(minValue, maxValue) 265 }; 266 return border; 267 } 268 269 void randomSubMat(Mat& whole, Mat& subMat, const Size& roiSize, const Border& border, int type, double minVal, double maxVal) 270 { 271 Size wholeSize = Size(roiSize.width + border.lef + border.rig, roiSize.height + border.top + border.bot); 272 whole = randomMat(wholeSize, type, minVal, maxVal, false); 273 subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height)); 274 } 275 276 // If the two vectors are not equal, it will return the difference in vector size 277 // Else it will return (total diff of each 1 and 2 rects covered pixels)/(total 1 rects covered pixels) 278 // The smaller, the better matched 279 static double checkRectSimilarity(const cv::Size & sz, std::vector<cv::Rect>& ob1, std::vector<cv::Rect>& ob2); 280 281 //! read image from testdata folder. 282 static cv::Mat readImage(const String &fileName, int flags = cv::IMREAD_COLOR); 283 static cv::Mat readImageType(const String &fname, int type); 284 285 static double checkNorm1(InputArray m, InputArray mask = noArray()); 286 static double checkNorm2(InputArray m1, InputArray m2, InputArray mask = noArray()); 287 static double checkSimilarity(InputArray m1, InputArray m2); 288 static void showDiff(InputArray _src, InputArray _gold, InputArray _actual, double eps, bool alwaysShow); 289 290 static inline double checkNormRelative(InputArray m1, InputArray m2, InputArray mask = noArray()) 291 { 292 return cvtest::norm(m1.getMat(), m2.getMat(), cv::NORM_INF, mask) / 293 std::max((double)std::numeric_limits<float>::epsilon(), 294 (double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF))); 295 } 296 297 static inline double checkNormRelativeSparse(InputArray m1, InputArray m2, InputArray mask = noArray()) 298 { 299 double norm_inf = cvtest::norm(m1.getMat(), m2.getMat(), cv::NORM_INF, mask); 300 double norm_rel = norm_inf / 301 std::max((double)std::numeric_limits<float>::epsilon(), 302 (double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF))); 303 return std::min(norm_inf, norm_rel); 304 } 305 306 }; 307 308 #define TEST_DECLARE_INPUT_PARAMETER(name) Mat name, name ## _roi; UMat u ## name, u ## name ## _roi 309 #define TEST_DECLARE_OUTPUT_PARAMETER(name) TEST_DECLARE_INPUT_PARAMETER(name) 310 311 #define UMAT_UPLOAD_INPUT_PARAMETER(name) \ 312 do \ 313 { \ 314 name.copyTo(u ## name); \ 315 Size _wholeSize; Point ofs; name ## _roi.locateROI(_wholeSize, ofs); \ 316 u ## name ## _roi = u ## name(Rect(ofs.x, ofs.y, name ## _roi.size().width, name ## _roi.size().height)); \ 317 } while ((void)0, 0) 318 319 #define UMAT_UPLOAD_OUTPUT_PARAMETER(name) UMAT_UPLOAD_INPUT_PARAMETER(name) 320 321 template <typename T> 322 struct CV_EXPORTS TSTestWithParam : public TestUtils, public ::testing::TestWithParam<T> 323 { 324 325 }; 326 327 #define PARAM_TEST_CASE(name, ...) struct name : public TSTestWithParam< std::tr1::tuple< __VA_ARGS__ > > 328 329 #define GET_PARAM(k) std::tr1::get< k >(GetParam()) 330 331 #ifndef IMPLEMENT_PARAM_CLASS 332 #define IMPLEMENT_PARAM_CLASS(name, type) \ 333 class name \ 334 { \ 335 public: \ 336 name ( type arg = type ()) : val_(arg) {} \ 337 operator type () const {return val_;} \ 338 private: \ 339 type val_; \ 340 }; \ 341 inline void PrintTo( name param, std::ostream* os) \ 342 { \ 343 *os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \ 344 } 345 346 IMPLEMENT_PARAM_CLASS(Channels, int) 347 #endif // IMPLEMENT_PARAM_CLASS 348 349 #define OCL_TEST_P TEST_P 350 #define OCL_TEST_F(name, ...) typedef name OCL_##name; TEST_F(OCL_##name, __VA_ARGS__) 351 #define OCL_TEST(name, ...) TEST(OCL_##name, __VA_ARGS__) 352 353 #define OCL_OFF(fn) cv::ocl::setUseOpenCL(false); fn 354 #define OCL_ON(fn) cv::ocl::setUseOpenCL(true); fn 355 356 #define OCL_ALL_DEPTHS Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F) 357 #define OCL_ALL_CHANNELS Values(1, 2, 3, 4) 358 359 CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA) 360 CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV) 361 CV_ENUM(BorderType, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101) 362 363 #define OCL_INSTANTIATE_TEST_CASE_P(prefix, test_case_name, generator) \ 364 INSTANTIATE_TEST_CASE_P(OCL_ ## prefix, test_case_name, generator) 365 366 } } // namespace cvtest::ocl 367 368 #endif // __OPENCV_TS_OCL_TEST_HPP__ 369
