mirror of
https://github.com/opencv/opencv.git
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1716 lines
45 KiB
C++
1716 lines
45 KiB
C++
/*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_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_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_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_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_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_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_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_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_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_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;
|
|
cv::Mat mat1, mat2;
|
|
|
|
double norm_gold;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = std::tr1::get<0>(GetParam());
|
|
normIdx = 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 = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false);
|
|
mat2 = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false);
|
|
|
|
norm_gold = cv::norm(mat1, mat2, norms[normIdx]);
|
|
}
|
|
};
|
|
|
|
TEST_P(NormDiff, Accuracy)
|
|
{
|
|
const char* normStr = norms_str[normIdx];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(size);
|
|
PRINT_PARAM(normStr);
|
|
|
|
double norm;
|
|
|
|
ASSERT_NO_THROW(
|
|
norm = cv::gpu::norm(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), norms[normIdx]);
|
|
);
|
|
|
|
EXPECT_NEAR(norm_gold, norm, 1e-6);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Arithm, NormDiff, testing::Combine(
|
|
testing::ValuesIn(devices()),
|
|
testing::Range(0, 3)));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// 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, 1);
|
|
}
|
|
|
|
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);
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|
|
|
dev_dst.download(dst);
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|
);
|
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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|
}
|
|
|
|
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())));
|
|
|
|
#endif // HAVE_CUDA
|