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Open Source Computer Vision Library
https://opencv.org/
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1927 lines
51 KiB
1927 lines
51 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#ifdef HAVE_CUDA |
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struct ArithmTest : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int type; |
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cv::Size size; |
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cv::Mat mat1, mat2; |
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virtual void SetUp() |
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{ |
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devInfo = std::tr1::get<0>(GetParam()); |
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type = std::tr1::get<1>(GetParam()); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
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mat1 = cvtest::randomMat(rng, size, type, 1, 16, false); |
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mat2 = cvtest::randomMat(rng, size, type, 1, 16, false); |
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} |
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}; |
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//////////////////////////////////////////////////////////////////////////////// |
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// add |
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struct AddArray : ArithmTest {}; |
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TEST_P(AddArray, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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cv::add(mat1, mat2, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::add(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, AddArray, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_8UC4, CV_16UC1, CV_32SC1, CV_32FC1))); |
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struct AddScalar : ArithmTest {}; |
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TEST_P(AddScalar, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0)); |
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PRINT_PARAM(val); |
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cv::Mat dst_gold; |
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cv::add(mat1, val, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::add(cv::gpu::GpuMat(mat1), val, gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, AddScalar, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1, CV_32FC2))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// subtract |
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struct SubtractArray : ArithmTest {}; |
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TEST_P(SubtractArray, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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cv::subtract(mat1, mat2, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::subtract(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, SubtractArray, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_8UC4, CV_16UC1, CV_32SC1, CV_32FC1))); |
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struct SubtractScalar : ArithmTest {}; |
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TEST_P(SubtractScalar, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0)); |
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PRINT_PARAM(val); |
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cv::Mat dst_gold; |
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cv::subtract(mat1, val, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::subtract(cv::gpu::GpuMat(mat1), val, gpuRes); |
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gpuRes.download(dst); |
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); |
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ASSERT_LE(checkNorm(dst_gold, dst), 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, SubtractScalar, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1, CV_32FC2))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// multiply |
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struct MultiplyArray : ArithmTest {}; |
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TEST_P(MultiplyArray, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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cv::multiply(mat1, mat2, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::multiply(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, MultiplyArray, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_8UC4, CV_16UC1, CV_32SC1, CV_32FC1))); |
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struct MultiplyScalar : ArithmTest {}; |
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TEST_P(MultiplyScalar, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0)); |
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PRINT_PARAM(val); |
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cv::Mat dst_gold; |
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cv::multiply(mat1, val, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::multiply(cv::gpu::GpuMat(mat1), val, gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, MultiplyScalar, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// divide |
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struct DivideArray : ArithmTest {}; |
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TEST_P(DivideArray, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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cv::divide(mat1, mat2, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::divide(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 1.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, DivideArray, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_8UC4, CV_16UC1, CV_32SC1, CV_32FC1))); |
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struct DivideScalar : ArithmTest {}; |
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TEST_P(DivideScalar, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0)); |
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PRINT_PARAM(val); |
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cv::Mat dst_gold; |
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cv::divide(mat1, val, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::divide(cv::gpu::GpuMat(mat1), val, gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, DivideScalar, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// transpose |
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struct Transpose : ArithmTest {}; |
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TEST_P(Transpose, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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cv::transpose(mat1, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::transpose(cv::gpu::GpuMat(mat1), gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, Transpose, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_8UC4, CV_8SC1, CV_8SC4, CV_16UC2, CV_16SC2, CV_32SC1, CV_32SC2, CV_32FC1, CV_32FC2, CV_64FC1))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// absdiff |
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struct AbsdiffArray : ArithmTest {}; |
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TEST_P(AbsdiffArray, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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cv::absdiff(mat1, mat2, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::absdiff(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, AbsdiffArray, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_8UC4, CV_16UC1, CV_32SC1, CV_32FC1))); |
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struct AbsdiffScalar : ArithmTest {}; |
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TEST_P(AbsdiffScalar, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(size); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0)); |
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PRINT_PARAM(val); |
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cv::Mat dst_gold; |
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cv::absdiff(mat1, val, dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::absdiff(cv::gpu::GpuMat(mat1), val, gpuRes); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, AbsdiffScalar, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(CV_8UC1, CV_16UC1, CV_32SC1, CV_32FC1))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// compare |
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struct Compare : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int cmp_code; |
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cv::Size size; |
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cv::Mat mat1, mat2; |
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cv::Mat dst_gold; |
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virtual void SetUp() |
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{ |
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devInfo = std::tr1::get<0>(GetParam()); |
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cmp_code = std::tr1::get<1>(GetParam()); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
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mat1 = cvtest::randomMat(rng, size, CV_32FC1, 1, 16, false); |
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mat2 = cvtest::randomMat(rng, size, CV_32FC1, 1, 16, false); |
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cv::compare(mat1, mat2, dst_gold, cmp_code); |
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} |
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}; |
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TEST_P(Compare, Accuracy) |
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{ |
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static const char* cmp_codes[] = {"CMP_EQ", "CMP_GT", "CMP_GE", "CMP_LT", "CMP_LE", "CMP_NE"}; |
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const char* cmpCodeStr = cmp_codes[cmp_code]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(size); |
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PRINT_PARAM(cmpCodeStr); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat gpuRes; |
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cv::gpu::compare(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes, cmp_code); |
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gpuRes.download(dst); |
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); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, Compare, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values((int)cv::CMP_EQ, (int)cv::CMP_GT, (int)cv::CMP_GE, (int)cv::CMP_LT, (int)cv::CMP_LE, (int)cv::CMP_NE))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// meanStdDev |
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struct MeanStdDev : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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cv::Mat mat; |
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cv::Scalar mean_gold; |
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cv::Scalar stddev_gold; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
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mat = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false); |
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cv::meanStdDev(mat, mean_gold, stddev_gold); |
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} |
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}; |
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TEST_P(MeanStdDev, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(size); |
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cv::Scalar mean; |
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cv::Scalar stddev; |
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ASSERT_NO_THROW( |
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cv::gpu::meanStdDev(cv::gpu::GpuMat(mat), mean, stddev); |
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); |
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EXPECT_NEAR(mean_gold[0], mean[0], 1e-5); |
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EXPECT_NEAR(mean_gold[1], mean[1], 1e-5); |
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EXPECT_NEAR(mean_gold[2], mean[2], 1e-5); |
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EXPECT_NEAR(mean_gold[3], mean[3], 1e-5); |
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EXPECT_NEAR(stddev_gold[0], stddev[0], 1e-5); |
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EXPECT_NEAR(stddev_gold[1], stddev[1], 1e-5); |
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EXPECT_NEAR(stddev_gold[2], stddev[2], 1e-5); |
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EXPECT_NEAR(stddev_gold[3], stddev[3], 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, MeanStdDev, testing::ValuesIn(devices())); |
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//////////////////////////////////////////////////////////////////////////////// |
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// normDiff |
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static const int norms[] = {cv::NORM_INF, cv::NORM_L1, cv::NORM_L2}; |
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static const char* norms_str[] = {"NORM_INF", "NORM_L1", "NORM_L2"}; |
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struct NormDiff : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int normIdx; |
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cv::Size size; |
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cv::Mat mat1, mat2; |
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double norm_gold; |
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virtual void SetUp() |
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{ |
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devInfo = std::tr1::get<0>(GetParam()); |
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normIdx = std::tr1::get<1>(GetParam()); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
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mat1 = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false); |
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mat2 = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false); |
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norm_gold = cv::norm(mat1, mat2, norms[normIdx]); |
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} |
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}; |
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TEST_P(NormDiff, Accuracy) |
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{ |
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const char* normStr = norms_str[normIdx]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(size); |
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PRINT_PARAM(normStr); |
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double norm; |
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ASSERT_NO_THROW( |
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norm = cv::gpu::norm(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), norms[normIdx]); |
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); |
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EXPECT_NEAR(norm_gold, norm, 1e-6); |
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} |
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INSTANTIATE_TEST_CASE_P(Arithm, NormDiff, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Range(0, 3))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// flip |
|
|
|
struct Flip : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
int flip_code; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
flip_code = std::tr1::get<2>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, type, 1, 255, false); |
|
|
|
cv::flip(mat, dst_gold, flip_code); |
|
} |
|
}; |
|
|
|
TEST_P(Flip, Accuracy) |
|
{ |
|
static const char* flip_axis[] = {"Both", "X", "Y"}; |
|
const char* flipAxisStr = flip_axis[flip_code + 1]; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
PRINT_PARAM(flipAxisStr); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::flip(cv::gpu::GpuMat(mat), gpu_res, flip_code); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Flip, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8UC1, CV_8UC4), |
|
testing::Values(0, 1, -1))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// LUT |
|
|
|
struct LUT : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
cv::Mat lut; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, type, 1, 255, false); |
|
lut = cvtest::randomMat(rng, cv::Size(256, 1), CV_8UC1, 100, 200, false); |
|
|
|
cv::LUT(mat, lut, dst_gold); |
|
} |
|
}; |
|
|
|
TEST_P(LUT, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::LUT(cv::gpu::GpuMat(mat), lut, gpu_res); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, LUT, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8UC1, CV_8UC3))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// exp |
|
|
|
struct Exp : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, CV_32FC1, -10.0, 2.0, false); |
|
|
|
cv::exp(mat, dst_gold); |
|
} |
|
}; |
|
|
|
TEST_P(Exp, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::exp(cv::gpu::GpuMat(mat), gpu_res); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Exp, testing::ValuesIn(devices())); |
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// pow |
|
|
|
struct Pow : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
double power; |
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
//size = cv::Size(2, 2); |
|
|
|
mat = cvtest::randomMat(rng, size, type, 0.0, 100.0, false); |
|
|
|
if (mat.depth() == CV_32F) |
|
power = rng.uniform(1.2f, 3.f); |
|
else |
|
{ |
|
int ipower = rng.uniform(2, 8); |
|
power = (float)ipower; |
|
} |
|
cv::pow(mat, power, dst_gold); |
|
} |
|
}; |
|
|
|
TEST_P(Pow, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
PRINT_PARAM(power); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::pow(cv::gpu::GpuMat(mat), power, gpu_res); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
/*std::cout << mat << std::endl << std::endl; |
|
std::cout << dst << std::endl << std::endl; |
|
std::cout << dst_gold << std::endl;*/ |
|
EXPECT_MAT_NEAR(dst_gold, dst, 2); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Pow, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_32F, CV_32FC3))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// log |
|
|
|
struct Log : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false); |
|
|
|
cv::log(mat, dst_gold); |
|
} |
|
}; |
|
|
|
TEST_P(Log, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::log(cv::gpu::GpuMat(mat), gpu_res); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Log, testing::ValuesIn(devices())); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// magnitude |
|
|
|
struct Magnitude : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
cv::Size size; |
|
cv::Mat mat1, mat2; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat1 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false); |
|
mat2 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false); |
|
|
|
cv::magnitude(mat1, mat2, dst_gold); |
|
} |
|
}; |
|
|
|
TEST_P(Magnitude, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::magnitude(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpu_res); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 1e-4); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Magnitude, testing::ValuesIn(devices())); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// phase |
|
|
|
struct Phase : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
cv::Size size; |
|
cv::Mat mat1, mat2; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat1 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false); |
|
mat2 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false); |
|
|
|
cv::phase(mat1, mat2, dst_gold); |
|
} |
|
}; |
|
|
|
TEST_P(Phase, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpu_res; |
|
|
|
cv::gpu::phase(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpu_res); |
|
|
|
gpu_res.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 1e-3); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Phase, testing::ValuesIn(devices())); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// cartToPolar |
|
|
|
struct CartToPolar : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
cv::Size size; |
|
cv::Mat mat1, mat2; |
|
|
|
cv::Mat mag_gold; |
|
cv::Mat angle_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat1 = cvtest::randomMat(rng, size, CV_32FC1, -100.0, 100.0, false); |
|
mat2 = cvtest::randomMat(rng, size, CV_32FC1, -100.0, 100.0, false); |
|
|
|
cv::cartToPolar(mat1, mat2, mag_gold, angle_gold); |
|
} |
|
}; |
|
|
|
TEST_P(CartToPolar, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat mag, angle; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpuMag; |
|
cv::gpu::GpuMat gpuAngle; |
|
|
|
cv::gpu::cartToPolar(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuMag, gpuAngle); |
|
|
|
gpuMag.download(mag); |
|
gpuAngle.download(angle); |
|
); |
|
|
|
EXPECT_MAT_NEAR(mag_gold, mag, 1e-4); |
|
EXPECT_MAT_NEAR(angle_gold, angle, 1e-3); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, CartToPolar, testing::ValuesIn(devices())); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// polarToCart |
|
|
|
struct PolarToCart : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
cv::Size size; |
|
cv::Mat mag; |
|
cv::Mat angle; |
|
|
|
cv::Mat x_gold; |
|
cv::Mat y_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mag = cvtest::randomMat(rng, size, CV_32FC1, -100.0, 100.0, false); |
|
angle = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 2.0 * CV_PI, false); |
|
|
|
cv::polarToCart(mag, angle, x_gold, y_gold); |
|
} |
|
}; |
|
|
|
TEST_P(PolarToCart, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat x, y; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat gpuX; |
|
cv::gpu::GpuMat gpuY; |
|
|
|
cv::gpu::polarToCart(cv::gpu::GpuMat(mag), cv::gpu::GpuMat(angle), gpuX, gpuY); |
|
|
|
gpuX.download(x); |
|
gpuY.download(y); |
|
); |
|
|
|
EXPECT_MAT_NEAR(x_gold, x, 1e-4); |
|
EXPECT_MAT_NEAR(y_gold, y, 1e-4); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, PolarToCart, testing::ValuesIn(devices())); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// minMax |
|
|
|
struct MinMax : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
cv::Mat mask; |
|
|
|
double minVal_gold; |
|
double maxVal_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, type, 0.0, 127.0, false); |
|
mask = cvtest::randomMat(rng, size, CV_8UC1, 0, 2, false); |
|
|
|
if (type != CV_8S) |
|
{ |
|
cv::minMaxLoc(mat, &minVal_gold, &maxVal_gold, 0, 0, mask); |
|
} |
|
else |
|
{ |
|
// OpenCV's minMaxLoc doesn't support CV_8S type |
|
minVal_gold = std::numeric_limits<double>::max(); |
|
maxVal_gold = -std::numeric_limits<double>::max(); |
|
for (int i = 0; i < mat.rows; ++i) |
|
{ |
|
const signed char* mat_row = mat.ptr<signed char>(i); |
|
const unsigned char* mask_row = mask.ptr<unsigned char>(i); |
|
for (int j = 0; j < mat.cols; ++j) |
|
{ |
|
if (mask_row[j]) |
|
{ |
|
signed char val = mat_row[j]; |
|
if (val < minVal_gold) minVal_gold = val; |
|
if (val > maxVal_gold) maxVal_gold = val; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
}; |
|
|
|
TEST_P(MinMax, Accuracy) |
|
{ |
|
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
double minVal, maxVal; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::minMax(cv::gpu::GpuMat(mat), &minVal, &maxVal, cv::gpu::GpuMat(mask)); |
|
); |
|
|
|
EXPECT_DOUBLE_EQ(minVal_gold, minVal); |
|
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, MinMax, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// minMaxLoc |
|
|
|
struct MinMaxLoc : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
cv::Mat mask; |
|
|
|
double minVal_gold; |
|
double maxVal_gold; |
|
cv::Point minLoc_gold; |
|
cv::Point maxLoc_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, type, 0.0, 127.0, false); |
|
mask = cvtest::randomMat(rng, size, CV_8UC1, 0, 2, false); |
|
|
|
if (type != CV_8S) |
|
{ |
|
cv::minMaxLoc(mat, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold, mask); |
|
} |
|
else |
|
{ |
|
// OpenCV's minMaxLoc doesn't support CV_8S type |
|
minVal_gold = std::numeric_limits<double>::max(); |
|
maxVal_gold = -std::numeric_limits<double>::max(); |
|
for (int i = 0; i < mat.rows; ++i) |
|
{ |
|
const signed char* mat_row = mat.ptr<signed char>(i); |
|
const unsigned char* mask_row = mask.ptr<unsigned char>(i); |
|
for (int j = 0; j < mat.cols; ++j) |
|
{ |
|
if (mask_row[j]) |
|
{ |
|
signed char val = mat_row[j]; |
|
if (val < minVal_gold) { minVal_gold = val; minLoc_gold = cv::Point(j, i); } |
|
if (val > maxVal_gold) { maxVal_gold = val; maxLoc_gold = cv::Point(j, i); } |
|
} |
|
} |
|
} |
|
} |
|
} |
|
}; |
|
|
|
TEST_P(MinMaxLoc, Accuracy) |
|
{ |
|
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
double minVal, maxVal; |
|
cv::Point minLoc, maxLoc; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::minMaxLoc(cv::gpu::GpuMat(mat), &minVal, &maxVal, &minLoc, &maxLoc, cv::gpu::GpuMat(mask)); |
|
); |
|
|
|
EXPECT_DOUBLE_EQ(minVal_gold, minVal); |
|
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal); |
|
|
|
int cmpMinVals = memcmp(mat.data + minLoc_gold.y * mat.step + minLoc_gold.x * mat.elemSize(), |
|
mat.data + minLoc.y * mat.step + minLoc.x * mat.elemSize(), |
|
mat.elemSize()); |
|
int cmpMaxVals = memcmp(mat.data + maxLoc_gold.y * mat.step + maxLoc_gold.x * mat.elemSize(), |
|
mat.data + maxLoc.y * mat.step + maxLoc.x * mat.elemSize(), |
|
mat.elemSize()); |
|
|
|
EXPECT_EQ(0, cmpMinVals); |
|
EXPECT_EQ(0, cmpMaxVals); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, MinMaxLoc, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F))); |
|
|
|
//////////////////////////////////////////////////////////////////////////// |
|
// countNonZero |
|
|
|
struct CountNonZero : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
int n_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
cv::Mat matBase = cvtest::randomMat(rng, size, CV_8U, 0.0, 1.0, false); |
|
matBase.convertTo(mat, type); |
|
|
|
n_gold = cv::countNonZero(mat); |
|
} |
|
}; |
|
|
|
TEST_P(CountNonZero, Accuracy) |
|
{ |
|
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
int n; |
|
|
|
ASSERT_NO_THROW( |
|
n = cv::gpu::countNonZero(cv::gpu::GpuMat(mat)); |
|
); |
|
|
|
ASSERT_EQ(n_gold, n); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, CountNonZero, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F))); |
|
|
|
////////////////////////////////////////////////////////////////////////////// |
|
// sum |
|
|
|
struct Sum : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Scalar sum_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, CV_8U, 0.0, 10.0, false); |
|
|
|
sum_gold = cv::sum(mat); |
|
} |
|
}; |
|
|
|
TEST_P(Sum, Accuracy) |
|
{ |
|
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Scalar sum; |
|
|
|
ASSERT_NO_THROW( |
|
sum = cv::gpu::sum(cv::gpu::GpuMat(mat)); |
|
); |
|
|
|
EXPECT_NEAR(sum[0], sum_gold[0], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[1], sum_gold[1], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[2], sum_gold[2], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[3], sum_gold[3], mat.size().area() * 1e-5); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Sum, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F))); |
|
|
|
struct AbsSum : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Scalar sum_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, CV_8U, 0.0, 10.0, false); |
|
|
|
sum_gold = cv::norm(mat, cv::NORM_L1); |
|
} |
|
}; |
|
|
|
TEST_P(AbsSum, Accuracy) |
|
{ |
|
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Scalar sum; |
|
|
|
ASSERT_NO_THROW( |
|
sum = cv::gpu::absSum(cv::gpu::GpuMat(mat)); |
|
); |
|
|
|
EXPECT_NEAR(sum[0], sum_gold[0], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[1], sum_gold[1], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[2], sum_gold[2], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[3], sum_gold[3], mat.size().area() * 1e-5); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, AbsSum, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F))); |
|
|
|
struct SqrSum : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Scalar sum_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat = cvtest::randomMat(rng, size, CV_8U, 0.0, 10.0, false); |
|
|
|
cv::Mat sqrmat; |
|
cv::multiply(mat, mat, sqrmat); |
|
sum_gold = cv::sum(sqrmat); |
|
} |
|
}; |
|
|
|
TEST_P(SqrSum, Accuracy) |
|
{ |
|
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Scalar sum; |
|
|
|
ASSERT_NO_THROW( |
|
sum = cv::gpu::sqrSum(cv::gpu::GpuMat(mat)); |
|
); |
|
|
|
EXPECT_NEAR(sum[0], sum_gold[0], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[1], sum_gold[1], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[2], sum_gold[2], mat.size().area() * 1e-5); |
|
EXPECT_NEAR(sum[3], sum_gold[3], mat.size().area() * 1e-5); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, SqrSum, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F))); |
|
|
|
////////////////////////////////////////////////////////////////////////////// |
|
// bitwise |
|
|
|
struct BitwiseNot : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat.create(size, type); |
|
|
|
for (int i = 0; i < mat.rows; ++i) |
|
{ |
|
cv::Mat row(1, static_cast<int>(mat.cols * mat.elemSize()), CV_8U, (void*)mat.ptr(i)); |
|
rng.fill(row, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
} |
|
|
|
dst_gold = ~mat; |
|
} |
|
}; |
|
|
|
TEST_P(BitwiseNot, Accuracy) |
|
{ |
|
if (mat.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat dev_dst; |
|
|
|
cv::gpu::bitwise_not(cv::gpu::GpuMat(mat), dev_dst); |
|
|
|
dev_dst.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseNot, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::ValuesIn(all_types()))); |
|
|
|
struct BitwiseOr : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat1; |
|
cv::Mat mat2; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat1.create(size, type); |
|
mat2.create(size, type); |
|
|
|
for (int i = 0; i < mat1.rows; ++i) |
|
{ |
|
cv::Mat row1(1, static_cast<int>(mat1.cols * mat1.elemSize()), CV_8U, (void*)mat1.ptr(i)); |
|
rng.fill(row1, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
|
|
cv::Mat row2(1, static_cast<int>(mat2.cols * mat2.elemSize()), CV_8U, (void*)mat2.ptr(i)); |
|
rng.fill(row2, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
} |
|
|
|
dst_gold = mat1 | mat2; |
|
} |
|
}; |
|
|
|
TEST_P(BitwiseOr, Accuracy) |
|
{ |
|
if (mat1.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat dev_dst; |
|
|
|
cv::gpu::bitwise_or(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), dev_dst); |
|
|
|
dev_dst.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseOr, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::ValuesIn(all_types()))); |
|
|
|
struct BitwiseAnd : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat1; |
|
cv::Mat mat2; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat1.create(size, type); |
|
mat2.create(size, type); |
|
|
|
for (int i = 0; i < mat1.rows; ++i) |
|
{ |
|
cv::Mat row1(1, static_cast<int>(mat1.cols * mat1.elemSize()), CV_8U, (void*)mat1.ptr(i)); |
|
rng.fill(row1, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
|
|
cv::Mat row2(1, static_cast<int>(mat2.cols * mat2.elemSize()), CV_8U, (void*)mat2.ptr(i)); |
|
rng.fill(row2, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
} |
|
|
|
dst_gold = mat1 & mat2; |
|
} |
|
}; |
|
|
|
TEST_P(BitwiseAnd, Accuracy) |
|
{ |
|
if (mat1.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat dev_dst; |
|
|
|
cv::gpu::bitwise_and(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), dev_dst); |
|
|
|
dev_dst.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseAnd, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::ValuesIn(all_types()))); |
|
|
|
struct BitwiseXor : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
|
|
cv::Size size; |
|
cv::Mat mat1; |
|
cv::Mat mat2; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
mat1.create(size, type); |
|
mat2.create(size, type); |
|
|
|
for (int i = 0; i < mat1.rows; ++i) |
|
{ |
|
cv::Mat row1(1, static_cast<int>(mat1.cols * mat1.elemSize()), CV_8U, (void*)mat1.ptr(i)); |
|
rng.fill(row1, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
|
|
cv::Mat row2(1, static_cast<int>(mat2.cols * mat2.elemSize()), CV_8U, (void*)mat2.ptr(i)); |
|
rng.fill(row2, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255)); |
|
} |
|
|
|
dst_gold = mat1 ^ mat2; |
|
} |
|
}; |
|
|
|
TEST_P(BitwiseXor, Accuracy) |
|
{ |
|
if (mat1.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(size); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat dev_dst; |
|
|
|
cv::gpu::bitwise_xor(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), dev_dst); |
|
|
|
dev_dst.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseXor, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::ValuesIn(all_types()))); |
|
|
|
////////////////////////////////////////////////////////////////////////////// |
|
// addWeighted |
|
|
|
struct AddWeighted : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int, int> > |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type1; |
|
int type2; |
|
int dtype; |
|
|
|
cv::Size size; |
|
cv::Mat src1; |
|
cv::Mat src2; |
|
double alpha; |
|
double beta; |
|
double gamma; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type1 = std::tr1::get<1>(GetParam()); |
|
type2 = std::tr1::get<2>(GetParam()); |
|
dtype = std::tr1::get<3>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)); |
|
|
|
src1 = cvtest::randomMat(rng, size, type1, 0.0, 255.0, false); |
|
src2 = cvtest::randomMat(rng, size, type2, 0.0, 255.0, false); |
|
|
|
alpha = rng.uniform(-10.0, 10.0); |
|
beta = rng.uniform(-10.0, 10.0); |
|
gamma = rng.uniform(-10.0, 10.0); |
|
|
|
cv::addWeighted(src1, alpha, src2, beta, gamma, dst_gold, dtype); |
|
} |
|
}; |
|
|
|
TEST_P(AddWeighted, Accuracy) |
|
{ |
|
if ((src1.depth() == CV_64F || src2.depth() == CV_64F || dst_gold.depth() == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
|
return; |
|
|
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type1); |
|
PRINT_TYPE(type2); |
|
PRINT_TYPE(dtype); |
|
PRINT_PARAM(size); |
|
PRINT_PARAM(alpha); |
|
PRINT_PARAM(beta); |
|
PRINT_PARAM(gamma); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat dev_dst; |
|
|
|
cv::gpu::addWeighted(cv::gpu::GpuMat(src1), alpha, cv::gpu::GpuMat(src2), beta, gamma, dev_dst, dtype); |
|
|
|
dev_dst.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, dtype < CV_32F ? 1.0 : 1e-12); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, AddWeighted, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::ValuesIn(types(CV_8U, CV_64F, 1, 1)), |
|
testing::ValuesIn(types(CV_8U, CV_64F, 1, 1)), |
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testing::ValuesIn(types(CV_8U, CV_64F, 1, 1)))); |
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|
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////////////////////////////////////////////////////////////////////////////// |
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// reduce |
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|
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struct Reduce : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int, int> > |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int type; |
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int dim; |
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int reduceOp; |
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|
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cv::Size size; |
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cv::Mat src; |
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|
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cv::Mat dst_gold; |
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|
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virtual void SetUp() |
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{ |
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devInfo = std::tr1::get<0>(GetParam()); |
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type = std::tr1::get<1>(GetParam()); |
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dim = std::tr1::get<2>(GetParam()); |
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reduceOp = std::tr1::get<3>(GetParam()); |
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|
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cv::gpu::setDevice(devInfo.deviceID()); |
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|
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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size = cv::Size(rng.uniform(100, 400), rng.uniform(100, 400)); |
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src = cvtest::randomMat(rng, size, type, 0.0, 255.0, false); |
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|
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cv::reduce(src, dst_gold, dim, reduceOp, reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? CV_32F : CV_MAT_DEPTH(type)); |
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|
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if (dim == 1) |
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{ |
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dst_gold.cols = dst_gold.rows; |
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dst_gold.rows = 1; |
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dst_gold.step = dst_gold.cols * dst_gold.elemSize(); |
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} |
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} |
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}; |
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|
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TEST_P(Reduce, Accuracy) |
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{ |
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static const char* reduceOpStrs[] = {"CV_REDUCE_SUM", "CV_REDUCE_AVG", "CV_REDUCE_MAX", "CV_REDUCE_MIN"}; |
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const char* reduceOpStr = reduceOpStrs[reduceOp]; |
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|
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(type); |
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PRINT_PARAM(dim); |
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PRINT_PARAM(reduceOpStr); |
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PRINT_PARAM(size); |
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|
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cv::Mat dst; |
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|
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_dst; |
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|
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cv::gpu::reduce(cv::gpu::GpuMat(src), dev_dst, dim, reduceOp, reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? CV_32F : CV_MAT_DEPTH(type)); |
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|
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dev_dst.download(dst); |
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); |
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|
|
double norm = reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? 1e-1 : 0.0; |
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EXPECT_MAT_NEAR(dst_gold, dst, norm); |
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} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, Reduce, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32FC1, CV_32FC3, CV_32FC4), |
|
testing::Values(0, 1), |
|
testing::Values((int)CV_REDUCE_SUM, (int)CV_REDUCE_AVG, (int)CV_REDUCE_MAX, (int)CV_REDUCE_MIN))); |
|
|
|
////////////////////////////////////////////////////////////////////////////// |
|
// gemm |
|
|
|
struct GEMM : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> > |
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{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
int flags; |
|
|
|
int size; |
|
cv::Mat src1; |
|
cv::Mat src2; |
|
cv::Mat src3; |
|
double alpha; |
|
double beta; |
|
|
|
cv::Mat dst_gold; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = std::tr1::get<0>(GetParam()); |
|
type = std::tr1::get<1>(GetParam()); |
|
flags = std::tr1::get<2>(GetParam()); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
|
|
|
size = rng.uniform(100, 500); |
|
|
|
src1 = cvtest::randomMat(rng, cv::Size(size, size), type, -10.0, 10.0, false); |
|
src2 = cvtest::randomMat(rng, cv::Size(size, size), type, -10.0, 10.0, false); |
|
src3 = cvtest::randomMat(rng, cv::Size(size, size), type, -10.0, 10.0, false); |
|
alpha = rng.uniform(-10.0, 10.0); |
|
beta = rng.uniform(-10.0, 10.0); |
|
|
|
cv::gemm(src1, src2, alpha, src3, beta, dst_gold, flags); |
|
} |
|
}; |
|
|
|
TEST_P(GEMM, Accuracy) |
|
{ |
|
PRINT_PARAM(devInfo); |
|
PRINT_TYPE(type); |
|
PRINT_PARAM(flags); |
|
|
|
cv::Mat dst; |
|
|
|
ASSERT_NO_THROW( |
|
cv::gpu::GpuMat dev_dst; |
|
|
|
cv::gpu::gemm(cv::gpu::GpuMat(src1), cv::gpu::GpuMat(src2), alpha, cv::gpu::GpuMat(src3), beta, dev_dst, flags); |
|
|
|
dev_dst.download(dst); |
|
); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 1e-1); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, GEMM, testing::Combine( |
|
testing::ValuesIn(devices()), |
|
testing::Values(CV_32FC1, CV_32FC2), |
|
testing::Values(0, (int)cv::GEMM_1_T, (int)cv::GEMM_2_T, (int)cv::GEMM_3_T))); |
|
|
|
#endif // HAVE_CUDA
|
|
|