/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 #include #include #include "gputest.hpp" using namespace cv; using namespace std; using namespace gpu; #define CHECK(pred, err) if (!(pred)) { \ ts->printf(CvTS::CONSOLE, "Fail: \"%s\" at line: %d\n", #pred, __LINE__); \ ts->set_failed_test_info(err); \ return; } class CV_GpuArithmTest : public CvTest { public: CV_GpuArithmTest(const char* test_name, const char* test_funcs) : CvTest(test_name, test_funcs) {} virtual ~CV_GpuArithmTest() {} protected: void run(int); int test(int type); virtual int test(const Mat& mat1, const Mat& mat2) = 0; int CheckNorm(const Mat& m1, const Mat& m2); int CheckNorm(const Scalar& s1, const Scalar& s2); int CheckNorm(double d1, double d2); }; int CV_GpuArithmTest::test(int type) { cv::Size sz(200, 200); cv::Mat mat1(sz, type), mat2(sz, type); cv::RNG rng(*ts->get_rng()); rng.fill(mat1, cv::RNG::UNIFORM, cv::Scalar::all(1), cv::Scalar::all(20)); rng.fill(mat2, cv::RNG::UNIFORM, cv::Scalar::all(1), cv::Scalar::all(20)); return test(mat1, mat2); } int CV_GpuArithmTest::CheckNorm(const Mat& m1, const Mat& m2) { double ret = norm(m1, m2, NORM_INF); if (ret < 1e-5) return CvTS::OK; ts->printf(CvTS::LOG, "\nNorm: %f\n", ret); return CvTS::FAIL_GENERIC; } int CV_GpuArithmTest::CheckNorm(const Scalar& s1, const Scalar& s2) { double ret0 = CheckNorm(s1[0], s2[0]), ret1 = CheckNorm(s1[1], s2[1]), ret2 = CheckNorm(s1[2], s2[2]), ret3 = CheckNorm(s1[3], s2[3]); return (ret0 == CvTS::OK && ret1 == CvTS::OK && ret2 == CvTS::OK && ret3 == CvTS::OK) ? CvTS::OK : CvTS::FAIL_GENERIC; } int CV_GpuArithmTest::CheckNorm(double d1, double d2) { double ret = ::fabs(d1 - d2); if (ret < 1e-5) return CvTS::OK; ts->printf(CvTS::LOG, "\nNorm: %f\n", ret); return CvTS::FAIL_GENERIC; } void CV_GpuArithmTest::run( int ) { int testResult = CvTS::OK; try { const int types[] = {CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1}; const char* type_names[] = {"CV_8UC1 ", "CV_8UC3 ", "CV_8UC4 ", "CV_32FC1"}; const int type_count = sizeof(types)/sizeof(types[0]); //run tests for (int t = 0; t < type_count; ++t) { ts->printf(CvTS::LOG, "Start testing %s", type_names[t]); if (CvTS::OK == test(types[t])) ts->printf(CvTS::LOG, "SUCCESS\n"); else { ts->printf(CvTS::LOG, "FAIL\n"); testResult = CvTS::FAIL_MISMATCH; } } } catch(const cv::Exception& e) { if (!check_and_treat_gpu_exception(e, ts)) throw; return; } ts->set_failed_test_info(testResult); } //////////////////////////////////////////////////////////////////////////////// // Add struct CV_GpuNppImageAddTest : public CV_GpuArithmTest { CV_GpuNppImageAddTest() : CV_GpuArithmTest( "GPU-NppImageAdd", "add" ) {} virtual int test(const Mat& mat1, const Mat& mat2) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::add(mat1, mat2, cpuRes); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::add(gpu1, gpu2, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // Sub struct CV_GpuNppImageSubtractTest : public CV_GpuArithmTest { CV_GpuNppImageSubtractTest() : CV_GpuArithmTest( "GPU-NppImageSubtract", "subtract" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::subtract(mat1, mat2, cpuRes); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::subtract(gpu1, gpu2, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // multiply struct CV_GpuNppImageMultiplyTest : public CV_GpuArithmTest { CV_GpuNppImageMultiplyTest() : CV_GpuArithmTest( "GPU-NppImageMultiply", "multiply" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::multiply(mat1, mat2, cpuRes); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::multiply(gpu1, gpu2, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // divide struct CV_GpuNppImageDivideTest : public CV_GpuArithmTest { CV_GpuNppImageDivideTest() : CV_GpuArithmTest( "GPU-NppImageDivide", "divide" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::divide(mat1, mat2, cpuRes); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::divide(gpu1, gpu2, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // transpose struct CV_GpuNppImageTransposeTest : public CV_GpuArithmTest { CV_GpuNppImageTransposeTest() : CV_GpuArithmTest( "GPU-NppImageTranspose", "transpose" ) {} int test( const Mat& mat1, const Mat& ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::transpose(mat1, cpuRes); GpuMat gpu1(mat1); GpuMat gpuRes; cv::gpu::transpose(gpu1, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // absdiff struct CV_GpuNppImageAbsdiffTest : public CV_GpuArithmTest { CV_GpuNppImageAbsdiffTest() : CV_GpuArithmTest( "GPU-NppImageAbsdiff", "absdiff" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::absdiff(mat1, mat2, cpuRes); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::absdiff(gpu1, gpu2, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // compare struct CV_GpuNppImageCompareTest : public CV_GpuArithmTest { CV_GpuNppImageCompareTest() : CV_GpuArithmTest( "GPU-NppImageCompare", "compare" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } int cmp_codes[] = {CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE}; const char* cmp_str[] = {"CMP_EQ", "CMP_GT", "CMP_GE", "CMP_LT", "CMP_LE", "CMP_NE"}; int cmp_num = sizeof(cmp_codes) / sizeof(int); int test_res = CvTS::OK; for (int i = 0; i < cmp_num; ++i) { ts->printf(CvTS::LOG, "\nCompare operation: %s\n", cmp_str[i]); cv::Mat cpuRes; cv::compare(mat1, mat2, cpuRes, cmp_codes[i]); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::compare(gpu1, gpu2, gpuRes, cmp_codes[i]); if (CheckNorm(cpuRes, gpuRes) != CvTS::OK) test_res = CvTS::FAIL_GENERIC; } return test_res; } }; //////////////////////////////////////////////////////////////////////////////// // meanStdDev struct CV_GpuNppImageMeanStdDevTest : public CV_GpuArithmTest { CV_GpuNppImageMeanStdDevTest() : CV_GpuArithmTest( "GPU-NppImageMeanStdDev", "meanStdDev" ) {} int test( const Mat& mat1, const Mat& ) { if (mat1.type() != CV_8UC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } Scalar cpumean; Scalar cpustddev; cv::meanStdDev(mat1, cpumean, cpustddev); GpuMat gpu1(mat1); Scalar gpumean; Scalar gpustddev; cv::gpu::meanStdDev(gpu1, gpumean, gpustddev); int test_res = CvTS::OK; if (CheckNorm(cpumean, gpumean) != CvTS::OK) { ts->printf(CvTS::LOG, "\nMean FAILED\n"); test_res = CvTS::FAIL_GENERIC; } if (CheckNorm(cpustddev, gpustddev) != CvTS::OK) { ts->printf(CvTS::LOG, "\nStdDev FAILED\n"); test_res = CvTS::FAIL_GENERIC; } return test_res; } }; //////////////////////////////////////////////////////////////////////////////// // norm struct CV_GpuNppImageNormTest : public CV_GpuArithmTest { CV_GpuNppImageNormTest() : CV_GpuArithmTest( "GPU-NppImageNorm", "norm" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_8UC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } int norms[] = {NORM_INF, NORM_L1, NORM_L2}; const char* norms_str[] = {"NORM_INF", "NORM_L1", "NORM_L2"}; int norms_num = sizeof(norms) / sizeof(int); int test_res = CvTS::OK; for (int i = 0; i < norms_num; ++i) { ts->printf(CvTS::LOG, "\nNorm type: %s\n", norms_str[i]); double cpu_norm = cv::norm(mat1, mat2, norms[i]); GpuMat gpu1(mat1); GpuMat gpu2(mat2); double gpu_norm = cv::gpu::norm(gpu1, gpu2, norms[i]); if (CheckNorm(cpu_norm, gpu_norm) != CvTS::OK) test_res = CvTS::FAIL_GENERIC; } return test_res; } }; //////////////////////////////////////////////////////////////////////////////// // flip struct CV_GpuNppImageFlipTest : public CV_GpuArithmTest { CV_GpuNppImageFlipTest() : CV_GpuArithmTest( "GPU-NppImageFlip", "flip" ) {} int test( const Mat& mat1, const Mat& ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } int flip_codes[] = {0, 1, -1}; const char* flip_axis[] = {"X", "Y", "Both"}; int flip_codes_num = sizeof(flip_codes) / sizeof(int); int test_res = CvTS::OK; for (int i = 0; i < flip_codes_num; ++i) { ts->printf(CvTS::LOG, "\nFlip Axis: %s\n", flip_axis[i]); Mat cpu_res; cv::flip(mat1, cpu_res, flip_codes[i]); GpuMat gpu1(mat1); GpuMat gpu_res; cv::gpu::flip(gpu1, gpu_res, flip_codes[i]); if (CheckNorm(cpu_res, gpu_res) != CvTS::OK) test_res = CvTS::FAIL_GENERIC; } return test_res; } }; //////////////////////////////////////////////////////////////////////////////// // LUT struct CV_GpuNppImageLUTTest : public CV_GpuArithmTest { CV_GpuNppImageLUTTest() : CV_GpuArithmTest( "GPU-NppImageLUT", "LUT" ) {} int test( const Mat& mat1, const Mat& ) { if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC3) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat lut(1, 256, CV_8UC1); cv::RNG rng(*ts->get_rng()); rng.fill(lut, cv::RNG::UNIFORM, cv::Scalar::all(100), cv::Scalar::all(200)); cv::Mat cpuRes; cv::LUT(mat1, lut, cpuRes); cv::gpu::GpuMat gpuRes; cv::gpu::LUT(GpuMat(mat1), lut, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // exp struct CV_GpuNppImageExpTest : public CV_GpuArithmTest { CV_GpuNppImageExpTest() : CV_GpuArithmTest( "GPU-NppImageExp", "exp" ) {} int test( const Mat& mat1, const Mat& ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::exp(mat1, cpuRes); GpuMat gpu1(mat1); GpuMat gpuRes; cv::gpu::exp(gpu1, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // log struct CV_GpuNppImageLogTest : public CV_GpuArithmTest { CV_GpuNppImageLogTest() : CV_GpuArithmTest( "GPU-NppImageLog", "log" ) {} int test( const Mat& mat1, const Mat& ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::log(mat1, cpuRes); GpuMat gpu1(mat1); GpuMat gpuRes; cv::gpu::log(gpu1, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // magnitude struct CV_GpuNppImageMagnitudeTest : public CV_GpuArithmTest { CV_GpuNppImageMagnitudeTest() : CV_GpuArithmTest( "GPU-NppImageMagnitude", "magnitude" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::magnitude(mat1, mat2, cpuRes); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::magnitude(gpu1, gpu2, gpuRes); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // phase struct CV_GpuNppImagePhaseTest : public CV_GpuArithmTest { CV_GpuNppImagePhaseTest() : CV_GpuArithmTest( "GPU-NppImagePhase", "phase" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuRes; cv::phase(mat1, mat2, cpuRes, true); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuRes; cv::gpu::phase(gpu1, gpu2, gpuRes, true); return CheckNorm(cpuRes, gpuRes); } }; //////////////////////////////////////////////////////////////////////////////// // cartToPolar struct CV_GpuNppImageCartToPolarTest : public CV_GpuArithmTest { CV_GpuNppImageCartToPolarTest() : CV_GpuArithmTest( "GPU-NppImageCartToPolar", "cartToPolar" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuMag, cpuAngle; cv::cartToPolar(mat1, mat2, cpuMag, cpuAngle); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuMag, gpuAngle; cv::gpu::cartToPolar(gpu1, gpu2, gpuMag, gpuAngle); int magRes = CheckNorm(cpuMag, gpuMag); int angleRes = CheckNorm(cpuAngle, gpuAngle); return magRes == CvTS::OK && angleRes == CvTS::OK ? CvTS::OK : CvTS::FAIL_GENERIC; } }; //////////////////////////////////////////////////////////////////////////////// // polarToCart struct CV_GpuNppImagePolarToCartTest : public CV_GpuArithmTest { CV_GpuNppImagePolarToCartTest() : CV_GpuArithmTest( "GPU-NppImagePolarToCart", "polarToCart" ) {} int test( const Mat& mat1, const Mat& mat2 ) { if (mat1.type() != CV_32FC1) { ts->printf(CvTS::LOG, "\tUnsupported type\t"); return CvTS::OK; } cv::Mat cpuX, cpuY; cv::polarToCart(mat1, mat2, cpuX, cpuY); GpuMat gpu1(mat1); GpuMat gpu2(mat2); GpuMat gpuX, gpuY; cv::gpu::polarToCart(gpu1, gpu2, gpuX, gpuY); int xRes = CheckNorm(cpuX, gpuX); int yRes = CheckNorm(cpuY, gpuY); return xRes == CvTS::OK && yRes == CvTS::OK ? CvTS::OK : CvTS::FAIL_GENERIC; } }; //////////////////////////////////////////////////////////////////////////////// // Min max struct CV_GpuMinMaxTest: public CvTest { CV_GpuMinMaxTest(): CvTest("GPU-MinMaxTest", "minMax") {} cv::gpu::GpuMat buf; void run(int) { try { bool double_ok = gpu::hasGreaterOrEqualVersion(1, 3) && gpu::hasNativeDoubleSupport(gpu::getDevice()); int depth_end = double_ok ? CV_64F : CV_32F; for (int depth = CV_8U; depth <= depth_end; ++depth) { for (int i = 0; i < 3; ++i) { int rows = 1 + rand() % 1000; int cols = 1 + rand() % 1000; test(rows, cols, 1, depth); test_masked(rows, cols, 1, depth); } } } catch (const Exception& e) { if (!check_and_treat_gpu_exception(e, ts)) throw; return; } } void test(int rows, int cols, int cn, int depth) { cv::Mat src(rows, cols, CV_MAKE_TYPE(depth, cn)); cv::RNG rng; for (int i = 0; i < src.rows; ++i) { Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i)); rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(256)); } double minVal, maxVal; cv::Point minLoc, maxLoc; if (depth != CV_8S) { cv::minMaxLoc(src, &minVal, &maxVal, &minLoc, &maxLoc); } else { minVal = std::numeric_limits::max(); maxVal = -std::numeric_limits::max(); for (int i = 0; i < src.rows; ++i) for (int j = 0; j < src.cols; ++j) { signed char val = src.at(i, j); if (val < minVal) minVal = val; if (val > maxVal) maxVal = val; } } double minVal_, maxVal_; cv::Point minLoc_, maxLoc_; cv::gpu::minMax(cv::gpu::GpuMat(src), &minVal_, &maxVal_, cv::gpu::GpuMat(), buf); if (abs(minVal - minVal_) > 1e-3f) { ts->printf(CvTS::CONSOLE, "\nfail: minVal=%f minVal_=%f rows=%d cols=%d depth=%d cn=%d\n", minVal, minVal_, rows, cols, depth, cn); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); } if (abs(maxVal - maxVal_) > 1e-3f) { ts->printf(CvTS::CONSOLE, "\nfail: maxVal=%f maxVal_=%f rows=%d cols=%d depth=%d cn=%d\n", maxVal, maxVal_, rows, cols, depth, cn); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); } } void test_masked(int rows, int cols, int cn, int depth) { cv::Mat src(rows, cols, CV_MAKE_TYPE(depth, cn)); cv::RNG rng; for (int i = 0; i < src.rows; ++i) { Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i)); rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(256)); } cv::Mat mask(src.size(), CV_8U); rng.fill(mask, RNG::UNIFORM, Scalar(0), Scalar(2)); double minVal, maxVal; cv::Point minLoc, maxLoc; Mat src_ = src.reshape(1); if (depth != CV_8S) { cv::minMaxLoc(src_, &minVal, &maxVal, &minLoc, &maxLoc, mask); } else { // OpenCV's minMaxLoc doesn't support CV_8S type minVal = std::numeric_limits::max(); maxVal = -std::numeric_limits::max(); for (int i = 0; i < src_.rows; ++i) for (int j = 0; j < src_.cols; ++j) { char val = src_.at(i, j); if (mask.at(i, j)) { if (val < minVal) minVal = val; } if (mask.at(i, j)) { if (val > maxVal) maxVal = val; } } } double minVal_, maxVal_; cv::Point minLoc_, maxLoc_; cv::gpu::minMax(cv::gpu::GpuMat(src), &minVal_, &maxVal_, cv::gpu::GpuMat(mask), buf); if (abs(minVal - minVal_) > 1e-3f) { ts->printf(CvTS::CONSOLE, "\nfail: minVal=%f minVal_=%f rows=%d cols=%d depth=%d cn=%d\n", minVal, minVal_, rows, cols, depth, cn); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); } if (abs(maxVal - maxVal_) > 1e-3f) { ts->printf(CvTS::CONSOLE, "\nfail: maxVal=%f maxVal_=%f rows=%d cols=%d depth=%d cn=%d\n", maxVal, maxVal_, rows, cols, depth, cn); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); } } }; //////////////////////////////////////////////////////////////////////////////// // Min max loc struct CV_GpuMinMaxLocTest: public CvTest { CV_GpuMinMaxLocTest(): CvTest("GPU-MinMaxLocTest", "minMaxLoc") {} GpuMat valbuf, locbuf; void run(int) { try { bool double_ok = gpu::hasGreaterOrEqualVersion(1, 3) && gpu::hasNativeDoubleSupport(gpu::getDevice()); int depth_end = double_ok ? CV_64F : CV_32F; for (int depth = CV_8U; depth <= depth_end; ++depth) { int rows = 1, cols = 3; test(rows, cols, depth); for (int i = 0; i < 4; ++i) { int rows = 1 + rand() % 1000; int cols = 1 + rand() % 1000; test(rows, cols, depth); } } } catch (const Exception& e) { if (!check_and_treat_gpu_exception(e, ts)) throw; return; } } void test(int rows, int cols, int depth) { cv::Mat src(rows, cols, depth); cv::RNG rng; for (int i = 0; i < src.rows; ++i) { Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i)); rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(256)); } cv::Mat mask(src.size(), CV_8U); rng.fill(mask, RNG::UNIFORM, Scalar(0), Scalar(2)); double minVal, maxVal; cv::Point minLoc, maxLoc; if (depth != CV_8S) cv::minMaxLoc(src, &minVal, &maxVal, &minLoc, &maxLoc, mask); else { // OpenCV's minMaxLoc doesn't support CV_8S type minVal = std::numeric_limits::max(); maxVal = -std::numeric_limits::max(); for (int i = 0; i < src.rows; ++i) for (int j = 0; j < src.cols; ++j) { char val = src.at(i, j); if (mask.at(i, j)) { if (val < minVal) { minVal = val; minLoc = cv::Point(j, i); } if (val > maxVal) { maxVal = val; maxLoc = cv::Point(j, i); } } } } double minVal_, maxVal_; cv::Point minLoc_, maxLoc_; cv::gpu::minMaxLoc(cv::gpu::GpuMat(src), &minVal_, &maxVal_, &minLoc_, &maxLoc_, cv::gpu::GpuMat(mask), valbuf, locbuf); CHECK(minVal == minVal_, CvTS::FAIL_INVALID_OUTPUT); CHECK(maxVal == maxVal_, CvTS::FAIL_INVALID_OUTPUT); CHECK(0 == memcmp(src.ptr(minLoc.y) + minLoc.x * src.elemSize(), src.ptr(minLoc_.y) + minLoc_.x * src.elemSize(), src.elemSize()), CvTS::FAIL_INVALID_OUTPUT); CHECK(0 == memcmp(src.ptr(maxLoc.y) + maxLoc.x * src.elemSize(), src.ptr(maxLoc_.y) + maxLoc_.x * src.elemSize(), src.elemSize()), CvTS::FAIL_INVALID_OUTPUT); } }; //////////////////////////////////////////////////////////////////////////// // Count non zero struct CV_GpuCountNonZeroTest: CvTest { CV_GpuCountNonZeroTest(): CvTest("GPU-CountNonZeroTest", "countNonZero") {} void run(int) { try { int depth_end; if (cv::gpu::hasNativeDoubleSupport(cv::gpu::getDevice())) depth_end = CV_64F; else depth_end = CV_32F; for (int depth = CV_8U; depth <= CV_32F; ++depth) { for (int i = 0; i < 4; ++i) { int rows = 1 + rand() % 1000; int cols = 1 + rand() % 1000; test(rows, cols, depth); } } } catch (const Exception& e) { if (!check_and_treat_gpu_exception(e, ts)) throw; return; } } void test(int rows, int cols, int depth) { cv::Mat src(rows, cols, depth); cv::RNG rng; if (depth == 5) rng.fill(src, RNG::UNIFORM, Scalar(-1000.f), Scalar(1000.f)); else if (depth == 6) rng.fill(src, RNG::UNIFORM, Scalar(-1000.), Scalar(1000.)); else for (int i = 0; i < src.rows; ++i) { Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i)); rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(256)); } int n_gold = cv::countNonZero(src); int n = cv::gpu::countNonZero(cv::gpu::GpuMat(src)); if (n != n_gold) { ts->printf(CvTS::LOG, "%d %d %d %d %d\n", n, n_gold, depth, cols, rows); n_gold = cv::countNonZero(src); } CHECK(n == n_gold, CvTS::FAIL_INVALID_OUTPUT); } }; ////////////////////////////////////////////////////////////////////////////// // sum struct CV_GpuSumTest: CvTest { CV_GpuSumTest(): CvTest("GPU-SumTest", "sum") {} void run(int) { try { Mat src; Scalar a, b; double max_err = 1e-5; int typemax = CV_32F; for (int type = CV_8U; type <= typemax; ++type) { gen(1 + rand() % 500, 1 + rand() % 500, CV_MAKETYPE(type, 2), src); a = sum(src); b = sum(GpuMat(src)); if (abs(a[0] - b[0]) + abs(a[1] - b[1]) > src.size().area() * max_err) { ts->printf(CvTS::CONSOLE, "2 cols: %d, rows: %d, expected: %f, actual: %f\n", src.cols, src.rows, a[1], b[1]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } gen(1 + rand() % 500, 1 + rand() % 500, CV_MAKETYPE(type, 3), src); a = sum(src); b = sum(GpuMat(src)); if (abs(a[0] - b[0]) + abs(a[1] - b[1]) + abs(a[2] - b[2])> src.size().area() * max_err) { ts->printf(CvTS::CONSOLE, "3 cols: %d, rows: %d, expected: %f, actual: %f\n", src.cols, src.rows, a[2], b[2]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } gen(1 + rand() % 500, 1 + rand() % 500, CV_MAKETYPE(type, 4), src); a = sum(src); b = sum(GpuMat(src)); if (abs(a[0] - b[0]) + abs(a[1] - b[1]) + abs(a[2] - b[2]) + abs(a[3] - b[3])> src.size().area() * max_err) { ts->printf(CvTS::CONSOLE, "4 cols: %d, rows: %d, expected: %f, actual: %f\n", src.cols, src.rows, a[3], b[3]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } gen(1 + rand() % 500, 1 + rand() % 500, type, src); a = sum(src); b = sum(GpuMat(src)); if (abs(a[0] - b[0]) > src.size().area() * max_err) { ts->printf(CvTS::CONSOLE, "cols: %d, rows: %d, expected: %f, actual: %f\n", src.cols, src.rows, a[0], b[0]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } if (type != CV_8S) { gen(1 + rand() % 200, 1 + rand() % 200, CV_MAKETYPE(type, 1), src); b = sqrSum(GpuMat(src)); Mat sqrsrc; multiply(src, src, sqrsrc); a = sum(sqrsrc); if (abs(a[0] - b[0]) > src.size().area() * max_err) { ts->printf(CvTS::CONSOLE, "type: %d, cols: %d, rows: %d, expected: %f, actual: %f\n", type, src.cols, src.rows, a[0], b[0]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } gen(1 + rand() % 200, 1 + rand() % 200, CV_MAKETYPE(type, 2), src); b = sqrSum(GpuMat(src)); multiply(src, src, sqrsrc); a = sum(sqrsrc); if (abs(a[0] - b[0]) + abs(a[1] - b[1])> src.size().area() * max_err * 2) { ts->printf(CvTS::CONSOLE, "type: %d, cols: %d, rows: %d, expected: %f, actual: %f\n", type, src.cols, src.rows, a[0], b[0]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } gen(1 + rand() % 200, 1 + rand() % 200, CV_MAKETYPE(type, 3), src); b = sqrSum(GpuMat(src)); multiply(src, src, sqrsrc); a = sum(sqrsrc); if (abs(a[0] - b[0]) + abs(a[1] - b[1]) + abs(a[2] - b[2])> src.size().area() * max_err * 3) { ts->printf(CvTS::CONSOLE, "type: %d, cols: %d, rows: %d, expected: %f, actual: %f\n", type, src.cols, src.rows, a[0], b[0]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } gen(1 + rand() % 200, 1 + rand() % 200, CV_MAKETYPE(type, 4), src); b = sqrSum(GpuMat(src)); multiply(src, src, sqrsrc); a = sum(sqrsrc); if (abs(a[0] - b[0]) + abs(a[1] - b[1]) + abs(a[2] - b[2]) + abs(a[3] - b[3])> src.size().area() * max_err * 4) { ts->printf(CvTS::CONSOLE, "type: %d, cols: %d, rows: %d, expected: %f, actual: %f\n", type, src.cols, src.rows, a[0], b[0]); ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT); return; } } } } catch (const Exception& e) { if (!check_and_treat_gpu_exception(e, ts)) throw; return; } } void gen(int cols, int rows, int type, Mat& m) { m.create(rows, cols, type); RNG rng; rng.fill(m, RNG::UNIFORM, Scalar::all(0), Scalar::all(16)); } }; ///////////////////////////////////////////////////////////////////////////// /////////////////// tests registration ///////////////////////////////////// ///////////////////////////////////////////////////////////////////////////// // If we comment some tests, we may foget/miss to uncomment it after. // Placing all test definitions in one place // makes us know about what tests are commented. CV_GpuNppImageAddTest CV_GpuNppImageAdd_test; CV_GpuNppImageSubtractTest CV_GpuNppImageSubtract_test; CV_GpuNppImageMultiplyTest CV_GpuNppImageMultiply_test; CV_GpuNppImageDivideTest CV_GpuNppImageDivide_test; CV_GpuNppImageTransposeTest CV_GpuNppImageTranspose_test; CV_GpuNppImageAbsdiffTest CV_GpuNppImageAbsdiff_test; CV_GpuNppImageCompareTest CV_GpuNppImageCompare_test; CV_GpuNppImageMeanStdDevTest CV_GpuNppImageMeanStdDev_test; CV_GpuNppImageNormTest CV_GpuNppImageNorm_test; CV_GpuNppImageFlipTest CV_GpuNppImageFlip_test; CV_GpuNppImageLUTTest CV_GpuNppImageLUT_test; CV_GpuNppImageExpTest CV_GpuNppImageExp_test; CV_GpuNppImageLogTest CV_GpuNppImageLog_test; CV_GpuNppImageMagnitudeTest CV_GpuNppImageMagnitude_test; CV_GpuNppImagePhaseTest CV_GpuNppImagePhase_test; CV_GpuNppImageCartToPolarTest CV_GpuNppImageCartToPolar_test; CV_GpuNppImagePolarToCartTest CV_GpuNppImagePolarToCart_test; CV_GpuMinMaxTest CV_GpuMinMaxTest_test; CV_GpuMinMaxLocTest CV_GpuMinMaxLocTest_test; CV_GpuCountNonZeroTest CV_CountNonZero_test; CV_GpuSumTest CV_GpuSum_test;