/*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; //////////////////////////////////////////////////////////////////////////////// // Norm PARAM_TEST_CASE(Norm, cv::cuda::DeviceInfo, cv::Size, MatDepth, NormCode, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int depth; int normCode; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); depth = GET_PARAM(2); normCode = GET_PARAM(3); useRoi = GET_PARAM(4); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(Norm, Accuracy) { cv::Mat src = randomMat(size, depth); cv::Mat mask = randomMat(size, CV_8UC1, 0, 2); cv::cuda::GpuMat d_buf; double val = cv::cuda::norm(loadMat(src, useRoi), normCode, loadMat(mask, useRoi), d_buf); double val_gold = cv::norm(src, normCode, mask); EXPECT_NEAR(val_gold, val, depth < CV_32F ? 0.0 : 1.0); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Norm, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatDepth(CV_8U), MatDepth(CV_8S), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32S), MatDepth(CV_32F)), testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2), NormCode(cv::NORM_INF)), WHOLE_SUBMAT)); //////////////////////////////////////////////////////////////////////////////// // normDiff PARAM_TEST_CASE(NormDiff, cv::cuda::DeviceInfo, cv::Size, NormCode, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int normCode; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); normCode = GET_PARAM(2); useRoi = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(NormDiff, Accuracy) { cv::Mat src1 = randomMat(size, CV_8UC1); cv::Mat src2 = randomMat(size, CV_8UC1); double val = cv::cuda::norm(loadMat(src1, useRoi), loadMat(src2, useRoi), normCode); double val_gold = cv::norm(src1, src2, normCode); EXPECT_NEAR(val_gold, val, 0.0); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, NormDiff, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2), NormCode(cv::NORM_INF)), WHOLE_SUBMAT)); ////////////////////////////////////////////////////////////////////////////// // Sum namespace { template cv::Scalar absSumImpl(const cv::Mat& src) { const int cn = src.channels(); cv::Scalar sum = cv::Scalar::all(0); for (int y = 0; y < src.rows; ++y) { for (int x = 0; x < src.cols; ++x) { for (int c = 0; c < cn; ++c) sum[c] += std::abs(src.at(y, x * cn + c)); } } return sum; } cv::Scalar absSumGold(const cv::Mat& src) { typedef cv::Scalar (*func_t)(const cv::Mat& src); static const func_t funcs[] = { absSumImpl, absSumImpl, absSumImpl, absSumImpl, absSumImpl, absSumImpl, absSumImpl }; return funcs[src.depth()](src); } template cv::Scalar sqrSumImpl(const cv::Mat& src) { const int cn = src.channels(); cv::Scalar sum = cv::Scalar::all(0); for (int y = 0; y < src.rows; ++y) { for (int x = 0; x < src.cols; ++x) { for (int c = 0; c < cn; ++c) { const T val = src.at(y, x * cn + c); sum[c] += val * val; } } } return sum; } cv::Scalar sqrSumGold(const cv::Mat& src) { typedef cv::Scalar (*func_t)(const cv::Mat& src); static const func_t funcs[] = { sqrSumImpl, sqrSumImpl, sqrSumImpl, sqrSumImpl, sqrSumImpl, sqrSumImpl, sqrSumImpl }; return funcs[src.depth()](src); } } PARAM_TEST_CASE(Sum, cv::cuda::DeviceInfo, cv::Size, MatType, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int type; bool useRoi; cv::Mat src; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); type = GET_PARAM(2); useRoi = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); src = randomMat(size, type, -128.0, 128.0); } }; CUDA_TEST_P(Sum, Simple) { cv::Scalar val = cv::cuda::sum(loadMat(src, useRoi)); cv::Scalar val_gold = cv::sum(src); EXPECT_SCALAR_NEAR(val_gold, val, CV_MAT_DEPTH(type) < CV_32F ? 0.0 : 0.5); } CUDA_TEST_P(Sum, Abs) { cv::Scalar val = cv::cuda::absSum(loadMat(src, useRoi)); cv::Scalar val_gold = absSumGold(src); EXPECT_SCALAR_NEAR(val_gold, val, CV_MAT_DEPTH(type) < CV_32F ? 0.0 : 0.5); } CUDA_TEST_P(Sum, Sqr) { cv::Scalar val = cv::cuda::sqrSum(loadMat(src, useRoi)); cv::Scalar val_gold = sqrSumGold(src); EXPECT_SCALAR_NEAR(val_gold, val, CV_MAT_DEPTH(type) < CV_32F ? 0.0 : 0.5); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Sum, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, TYPES(CV_8U, CV_64F, 1, 4), WHOLE_SUBMAT)); //////////////////////////////////////////////////////////////////////////////// // MinMax PARAM_TEST_CASE(MinMax, cv::cuda::DeviceInfo, cv::Size, MatDepth, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int depth; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); depth = GET_PARAM(2); useRoi = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(MinMax, WithoutMask) { cv::Mat src = randomMat(size, depth); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { double minVal, maxVal; cv::cuda::minMax(loadMat(src), &minVal, &maxVal); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { double minVal, maxVal; cv::cuda::minMax(loadMat(src, useRoi), &minVal, &maxVal); double minVal_gold, maxVal_gold; minMaxLocGold(src, &minVal_gold, &maxVal_gold); EXPECT_DOUBLE_EQ(minVal_gold, minVal); EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); } } CUDA_TEST_P(MinMax, WithMask) { cv::Mat src = randomMat(size, depth); cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { double minVal, maxVal; cv::cuda::minMax(loadMat(src), &minVal, &maxVal, loadMat(mask)); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { double minVal, maxVal; cv::cuda::minMax(loadMat(src, useRoi), &minVal, &maxVal, loadMat(mask, useRoi)); double minVal_gold, maxVal_gold; minMaxLocGold(src, &minVal_gold, &maxVal_gold, 0, 0, mask); EXPECT_DOUBLE_EQ(minVal_gold, minVal); EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); } } CUDA_TEST_P(MinMax, NullPtr) { cv::Mat src = randomMat(size, depth); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { double minVal, maxVal; cv::cuda::minMax(loadMat(src), &minVal, 0); cv::cuda::minMax(loadMat(src), 0, &maxVal); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { double minVal, maxVal; cv::cuda::minMax(loadMat(src, useRoi), &minVal, 0); cv::cuda::minMax(loadMat(src, useRoi), 0, &maxVal); double minVal_gold, maxVal_gold; minMaxLocGold(src, &minVal_gold, &maxVal_gold, 0, 0); EXPECT_DOUBLE_EQ(minVal_gold, minVal); EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); } } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, MinMax, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, ALL_DEPTH, WHOLE_SUBMAT)); //////////////////////////////////////////////////////////////////////////////// // MinMaxLoc namespace { template void expectEqualImpl(const cv::Mat& src, cv::Point loc_gold, cv::Point loc) { EXPECT_EQ(src.at(loc_gold.y, loc_gold.x), src.at(loc.y, loc.x)); } void expectEqual(const cv::Mat& src, cv::Point loc_gold, cv::Point loc) { typedef void (*func_t)(const cv::Mat& src, cv::Point loc_gold, cv::Point loc); static const func_t funcs[] = { expectEqualImpl, expectEqualImpl, expectEqualImpl, expectEqualImpl, expectEqualImpl, expectEqualImpl, expectEqualImpl }; funcs[src.depth()](src, loc_gold, loc); } } PARAM_TEST_CASE(MinMaxLoc, cv::cuda::DeviceInfo, cv::Size, MatDepth, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int depth; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); depth = GET_PARAM(2); useRoi = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(MinMaxLoc, WithoutMask) { cv::Mat src = randomMat(size, depth); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { double minVal, maxVal; cv::Point minLoc, maxLoc; cv::cuda::minMaxLoc(loadMat(src), &minVal, &maxVal, &minLoc, &maxLoc); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { double minVal, maxVal; cv::Point minLoc, maxLoc; cv::cuda::minMaxLoc(loadMat(src, useRoi), &minVal, &maxVal, &minLoc, &maxLoc); double minVal_gold, maxVal_gold; cv::Point minLoc_gold, maxLoc_gold; minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold); EXPECT_DOUBLE_EQ(minVal_gold, minVal); EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); expectEqual(src, minLoc_gold, minLoc); expectEqual(src, maxLoc_gold, maxLoc); } } CUDA_TEST_P(MinMaxLoc, WithMask) { cv::Mat src = randomMat(size, depth); cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { double minVal, maxVal; cv::Point minLoc, maxLoc; cv::cuda::minMaxLoc(loadMat(src), &minVal, &maxVal, &minLoc, &maxLoc, loadMat(mask)); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { double minVal, maxVal; cv::Point minLoc, maxLoc; cv::cuda::minMaxLoc(loadMat(src, useRoi), &minVal, &maxVal, &minLoc, &maxLoc, loadMat(mask, useRoi)); double minVal_gold, maxVal_gold; cv::Point minLoc_gold, maxLoc_gold; minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold, mask); EXPECT_DOUBLE_EQ(minVal_gold, minVal); EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); expectEqual(src, minLoc_gold, minLoc); expectEqual(src, maxLoc_gold, maxLoc); } } CUDA_TEST_P(MinMaxLoc, NullPtr) { cv::Mat src = randomMat(size, depth); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { double minVal, maxVal; cv::Point minLoc, maxLoc; cv::cuda::minMaxLoc(loadMat(src, useRoi), &minVal, 0, 0, 0); cv::cuda::minMaxLoc(loadMat(src, useRoi), 0, &maxVal, 0, 0); cv::cuda::minMaxLoc(loadMat(src, useRoi), 0, 0, &minLoc, 0); cv::cuda::minMaxLoc(loadMat(src, useRoi), 0, 0, 0, &maxLoc); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { double minVal, maxVal; cv::Point minLoc, maxLoc; cv::cuda::minMaxLoc(loadMat(src, useRoi), &minVal, 0, 0, 0); cv::cuda::minMaxLoc(loadMat(src, useRoi), 0, &maxVal, 0, 0); cv::cuda::minMaxLoc(loadMat(src, useRoi), 0, 0, &minLoc, 0); cv::cuda::minMaxLoc(loadMat(src, useRoi), 0, 0, 0, &maxLoc); double minVal_gold, maxVal_gold; cv::Point minLoc_gold, maxLoc_gold; minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold); EXPECT_DOUBLE_EQ(minVal_gold, minVal); EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); expectEqual(src, minLoc_gold, minLoc); expectEqual(src, maxLoc_gold, maxLoc); } } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, MinMaxLoc, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, ALL_DEPTH, WHOLE_SUBMAT)); //////////////////////////////////////////////////////////////////////////// // CountNonZero PARAM_TEST_CASE(CountNonZero, cv::cuda::DeviceInfo, cv::Size, MatDepth, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int depth; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); depth = GET_PARAM(2); useRoi = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(CountNonZero, Accuracy) { cv::Mat srcBase = randomMat(size, CV_8U, 0.0, 1.5); cv::Mat src; srcBase.convertTo(src, depth); if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { cv::cuda::countNonZero(loadMat(src)); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else { int val = cv::cuda::countNonZero(loadMat(src, useRoi)); int val_gold = cv::countNonZero(src); ASSERT_EQ(val_gold, val); } } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, CountNonZero, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, ALL_DEPTH, WHOLE_SUBMAT)); ////////////////////////////////////////////////////////////////////////////// // Reduce CV_ENUM(ReduceCode, cv::REDUCE_SUM, cv::REDUCE_AVG, cv::REDUCE_MAX, cv::REDUCE_MIN) #define ALL_REDUCE_CODES testing::Values(ReduceCode(cv::REDUCE_SUM), ReduceCode(cv::REDUCE_AVG), ReduceCode(cv::REDUCE_MAX), ReduceCode(cv::REDUCE_MIN)) PARAM_TEST_CASE(Reduce, cv::cuda::DeviceInfo, cv::Size, MatDepth, Channels, ReduceCode, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int depth; int channels; int reduceOp; bool useRoi; int type; int dst_depth; int dst_type; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); depth = GET_PARAM(2); channels = GET_PARAM(3); reduceOp = GET_PARAM(4); useRoi = GET_PARAM(5); cv::cuda::setDevice(devInfo.deviceID()); type = CV_MAKE_TYPE(depth, channels); if (reduceOp == cv::REDUCE_MAX || reduceOp == cv::REDUCE_MIN) dst_depth = depth; else if (reduceOp == cv::REDUCE_SUM) dst_depth = depth == CV_8U ? CV_32S : depth < CV_64F ? CV_32F : depth; else dst_depth = depth < CV_32F ? CV_32F : depth; dst_type = CV_MAKE_TYPE(dst_depth, channels); } }; CUDA_TEST_P(Reduce, Rows) { cv::Mat src = randomMat(size, type); cv::cuda::GpuMat dst = createMat(cv::Size(src.cols, 1), dst_type, useRoi); cv::cuda::reduce(loadMat(src, useRoi), dst, 0, reduceOp, dst_depth); cv::Mat dst_gold; cv::reduce(src, dst_gold, 0, reduceOp, dst_depth); EXPECT_MAT_NEAR(dst_gold, dst, dst_depth < CV_32F ? 0.0 : 0.02); } CUDA_TEST_P(Reduce, Cols) { cv::Mat src = randomMat(size, type); cv::cuda::GpuMat dst = createMat(cv::Size(src.rows, 1), dst_type, useRoi); cv::cuda::reduce(loadMat(src, useRoi), dst, 1, reduceOp, dst_depth); cv::Mat dst_gold; cv::reduce(src, dst_gold, 1, reduceOp, dst_depth); dst_gold.cols = dst_gold.rows; dst_gold.rows = 1; dst_gold.step = dst_gold.cols * dst_gold.elemSize(); EXPECT_MAT_NEAR(dst_gold, dst, dst_depth < CV_32F ? 0.0 : 0.02); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Reduce, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatDepth(CV_8U), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32F), MatDepth(CV_64F)), ALL_CHANNELS, ALL_REDUCE_CODES, WHOLE_SUBMAT)); ////////////////////////////////////////////////////////////////////////////// // Normalize PARAM_TEST_CASE(Normalize, cv::cuda::DeviceInfo, cv::Size, MatDepth, NormCode, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int type; int norm_type; bool useRoi; double alpha; double beta; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); type = GET_PARAM(2); norm_type = GET_PARAM(3); useRoi = GET_PARAM(4); cv::cuda::setDevice(devInfo.deviceID()); alpha = 1; beta = 0; } }; CUDA_TEST_P(Normalize, WithOutMask) { cv::Mat src = randomMat(size, type); cv::cuda::GpuMat dst = createMat(size, type, useRoi); cv::cuda::normalize(loadMat(src, useRoi), dst, alpha, beta, norm_type, type); cv::Mat dst_gold; cv::normalize(src, dst_gold, alpha, beta, norm_type, type); EXPECT_MAT_NEAR(dst_gold, dst, 1e-6); } CUDA_TEST_P(Normalize, WithMask) { cv::Mat src = randomMat(size, type); cv::Mat mask = randomMat(size, CV_8UC1, 0, 2); cv::cuda::GpuMat dst = createMat(size, type, useRoi); dst.setTo(cv::Scalar::all(0)); cv::cuda::normalize(loadMat(src, useRoi), dst, alpha, beta, norm_type, type, loadMat(mask, useRoi)); cv::Mat dst_gold(size, type); dst_gold.setTo(cv::Scalar::all(0)); cv::normalize(src, dst_gold, alpha, beta, norm_type, type, mask); EXPECT_MAT_NEAR(dst_gold, dst, 1e-6); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Normalize, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, ALL_DEPTH, testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2), NormCode(cv::NORM_INF), NormCode(cv::NORM_MINMAX)), WHOLE_SUBMAT)); //////////////////////////////////////////////////////////////////////////////// // MeanStdDev PARAM_TEST_CASE(MeanStdDev, cv::cuda::DeviceInfo, cv::Size, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); useRoi = GET_PARAM(2); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(MeanStdDev, Accuracy) { cv::Mat src = randomMat(size, CV_8UC1); if (!supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_13)) { try { cv::Scalar mean; cv::Scalar stddev; cv::cuda::meanStdDev(loadMat(src, useRoi), mean, stddev); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsNotImplemented, e.code); } } else { cv::Scalar mean; cv::Scalar stddev; cv::cuda::meanStdDev(loadMat(src, useRoi), mean, stddev); cv::Scalar mean_gold; cv::Scalar stddev_gold; cv::meanStdDev(src, mean_gold, stddev_gold); EXPECT_SCALAR_NEAR(mean_gold, mean, 1e-5); EXPECT_SCALAR_NEAR(stddev_gold, stddev, 1e-5); } } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, MeanStdDev, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, WHOLE_SUBMAT)); /////////////////////////////////////////////////////////////////////////////////////////////////////// // Integral PARAM_TEST_CASE(Integral, cv::cuda::DeviceInfo, cv::Size, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); useRoi = GET_PARAM(2); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(Integral, Accuracy) { cv::Mat src = randomMat(size, CV_8UC1); cv::cuda::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi); cv::cuda::integral(loadMat(src, useRoi), dst); cv::Mat dst_gold; cv::integral(src, dst_gold, CV_32S); EXPECT_MAT_NEAR(dst_gold, dst, 0.0); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Integral, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, WHOLE_SUBMAT)); /////////////////////////////////////////////////////////////////////////////////////////////////////// // IntegralSqr PARAM_TEST_CASE(IntegralSqr, cv::cuda::DeviceInfo, cv::Size, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); useRoi = GET_PARAM(2); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(IntegralSqr, Accuracy) { cv::Mat src = randomMat(size, CV_8UC1); cv::cuda::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_64FC1, useRoi); cv::cuda::sqrIntegral(loadMat(src, useRoi), dst); cv::Mat dst_gold, temp; cv::integral(src, temp, dst_gold); EXPECT_MAT_NEAR(dst_gold, dst, 0.0); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, IntegralSqr, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, WHOLE_SUBMAT)); #endif // HAVE_CUDA