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Open Source Computer Vision Library
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616 lines
16 KiB
616 lines
16 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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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., 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 GpuMaterials provided with the distribution. |
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// |
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// * The name of the copyright holders 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 bpied warranties, including, but not limited to, the bpied |
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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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//////////////////////////////////////////////////////////////////////////////// |
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// merge |
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struct Merge : 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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std::vector<cv::Mat> src; |
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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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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(20, 150), rng.uniform(20, 150)); |
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int depth = CV_MAT_DEPTH(type); |
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int num_channels = CV_MAT_CN(type); |
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src.reserve(num_channels); |
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for (int i = 0; i < num_channels; ++i) |
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src.push_back(cv::Mat(size, depth, cv::Scalar::all(i))); |
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cv::merge(src, dst_gold); |
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} |
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}; |
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TEST_P(Merge, Accuracy) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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; |
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ASSERT_NO_THROW( |
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std::vector<cv::gpu::GpuMat> dev_src; |
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cv::gpu::GpuMat dev_dst; |
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for (size_t i = 0; i < src.size(); ++i) |
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dev_src.push_back(cv::gpu::GpuMat(src[i])); |
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cv::gpu::merge(dev_src, dev_dst); |
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dev_dst.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(MatOp, Merge, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::ValuesIn(all_types()))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// split |
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struct Split : 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 src; |
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std::vector<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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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(20, 150), rng.uniform(20, 150)); |
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src.create(size, type); |
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src.setTo(cv::Scalar(1.0, 2.0, 3.0, 4.0)); |
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cv::split(src, dst_gold); |
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} |
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}; |
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TEST_P(Split, Accuracy) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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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std::vector<cv::Mat> dst; |
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ASSERT_NO_THROW( |
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std::vector<cv::gpu::GpuMat> dev_dst; |
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cv::gpu::split(cv::gpu::GpuMat(src), dev_dst); |
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dst.resize(dev_dst.size()); |
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for (size_t i = 0; i < dev_dst.size(); ++i) |
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dev_dst[i].download(dst[i]); |
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); |
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ASSERT_EQ(dst_gold.size(), dst.size()); |
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for (size_t i = 0; i < dst_gold.size(); ++i) |
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{ |
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EXPECT_MAT_NEAR(dst_gold[i], dst[i], 0.0); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(MatOp, Split, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::ValuesIn(all_types()))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// split_merge_consistency |
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struct SplitMerge : 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 orig; |
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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(20, 150), rng.uniform(20, 150)); |
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orig.create(size, type); |
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orig.setTo(cv::Scalar(1.0, 2.0, 3.0, 4.0)); |
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} |
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}; |
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TEST_P(SplitMerge, Consistency) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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 final; |
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ASSERT_NO_THROW( |
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std::vector<cv::gpu::GpuMat> dev_vec; |
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cv::gpu::GpuMat dev_final; |
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cv::gpu::split(cv::gpu::GpuMat(orig), dev_vec); |
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cv::gpu::merge(dev_vec, dev_final); |
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dev_final.download(final); |
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); |
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EXPECT_MAT_NEAR(orig, final, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(MatOp, SplitMerge, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::ValuesIn(all_types()))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// setTo |
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struct SetTo : 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 mat_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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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(20, 150), rng.uniform(20, 150)); |
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mat_gold.create(size, type); |
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} |
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}; |
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TEST_P(SetTo, Zero) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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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static cv::Scalar zero = cv::Scalar::all(0); |
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cv::Mat mat; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_mat(mat_gold); |
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mat_gold.setTo(zero); |
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dev_mat.setTo(zero); |
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dev_mat.download(mat); |
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); |
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EXPECT_MAT_NEAR(mat_gold, mat, 0.0); |
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} |
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TEST_P(SetTo, SameVal) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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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static cv::Scalar s = cv::Scalar::all(1); |
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cv::Mat mat; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_mat(mat_gold); |
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mat_gold.setTo(s); |
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dev_mat.setTo(s); |
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dev_mat.download(mat); |
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); |
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EXPECT_MAT_NEAR(mat_gold, mat, 0.0); |
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} |
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TEST_P(SetTo, DifferentVal) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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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static cv::Scalar s = cv::Scalar(1, 2, 3, 4); |
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cv::Mat mat; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_mat(mat_gold); |
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mat_gold.setTo(s); |
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dev_mat.setTo(s); |
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dev_mat.download(mat); |
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); |
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EXPECT_MAT_NEAR(mat_gold, mat, 0.0); |
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} |
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TEST_P(SetTo, Masked) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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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static cv::Scalar s = cv::Scalar(1, 2, 3, 4); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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cv::Mat mask = cvtest::randomMat(rng, mat_gold.size(), CV_8UC1, 0.0, 1.5, false); |
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cv::Mat mat; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_mat(mat_gold); |
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mat_gold.setTo(s, mask); |
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dev_mat.setTo(s, cv::gpu::GpuMat(mask)); |
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dev_mat.download(mat); |
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); |
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EXPECT_MAT_NEAR(mat_gold, mat, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(MatOp, SetTo, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::ValuesIn(all_types()))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// copyTo |
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struct CopyTo : 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 src; |
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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(20, 150), rng.uniform(20, 150)); |
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src = cvtest::randomMat(rng, size, type, 0.0, 127.0, false); |
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} |
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}; |
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TEST_P(CopyTo, WithoutMask) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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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src.copyTo(dst_gold); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_src(src); |
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cv::gpu::GpuMat dev_dst; |
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dev_src.copyTo(dev_dst); |
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dev_dst.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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TEST_P(CopyTo, Masked) |
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{ |
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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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::Mat mask = cvtest::randomMat(rng, src.size(), CV_8UC1, 0.0, 2.0, false); |
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cv::Mat dst_gold(src.size(), src.type(), cv::Scalar::all(0)); |
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src.copyTo(dst_gold, mask); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_src(src); |
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cv::gpu::GpuMat dev_dst(src.size(), src.type(), cv::Scalar::all(0)); |
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dev_src.copyTo(dev_dst, cv::gpu::GpuMat(mask)); |
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dev_dst.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(MatOp, CopyTo, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::ValuesIn(all_types()))); |
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//////////////////////////////////////////////////////////////////////////////// |
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// convertTo |
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struct ConvertTo : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> > |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int depth1; |
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int depth2; |
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cv::Size size; |
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cv::Mat src; |
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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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depth1 = std::tr1::get<1>(GetParam()); |
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depth2 = std::tr1::get<2>(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(20, 150), rng.uniform(20, 150)); |
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src = cvtest::randomMat(rng, size, depth1, 0.0, 127.0, false); |
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} |
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}; |
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TEST_P(ConvertTo, WithoutScaling) |
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{ |
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if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(depth1); |
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PRINT_TYPE(depth2); |
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PRINT_PARAM(size); |
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cv::Mat dst_gold; |
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src.convertTo(dst_gold, depth2); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_src(src); |
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cv::gpu::GpuMat dev_dst; |
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dev_src.convertTo(dev_dst, depth2); |
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dev_dst.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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TEST_P(ConvertTo, WithScaling) |
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{ |
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if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE)) |
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return; |
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PRINT_PARAM(devInfo); |
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PRINT_TYPE(depth1); |
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PRINT_TYPE(depth2); |
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PRINT_PARAM(size); |
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cv::RNG& rng = cvtest::TS::ptr()->get_rng(); |
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const double a = rng.uniform(0.0, 1.0); |
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const double b = rng.uniform(-10.0, 10.0); |
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PRINT_PARAM(a); |
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PRINT_PARAM(b); |
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cv::Mat dst_gold; |
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src.convertTo(dst_gold, depth2, a, b); |
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cv::Mat dst; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_src(src); |
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cv::gpu::GpuMat dev_dst; |
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dev_src.convertTo(dev_dst, depth2, a, b); |
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dev_dst.download(dst); |
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); |
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const double eps = depth2 < CV_32F ? 1 : 1e-4; |
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EXPECT_MAT_NEAR(dst_gold, dst, eps); |
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} |
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INSTANTIATE_TEST_CASE_P(MatOp, ConvertTo, testing::Combine( |
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testing::ValuesIn(devices()), |
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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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// async |
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struct Async : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::gpu::CudaMem src; |
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cv::Mat dst_gold0; |
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cv::Mat dst_gold1; |
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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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int rows = rng.uniform(100, 200); |
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int cols = rng.uniform(100, 200); |
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src = cv::gpu::CudaMem(cv::Mat::zeros(rows, cols, CV_8UC1)); |
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dst_gold0 = cv::Mat(rows, cols, CV_8UC1, cv::Scalar::all(255)); |
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dst_gold1 = cv::Mat(rows, cols, CV_8UC1, cv::Scalar::all(128)); |
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} |
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}; |
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TEST_P(Async, Accuracy) |
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{ |
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PRINT_PARAM(devInfo); |
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cv::Mat dst0, dst1; |
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ASSERT_NO_THROW( |
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cv::gpu::CudaMem cpudst0; |
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cv::gpu::CudaMem cpudst1; |
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cv::gpu::GpuMat gpusrc; |
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cv::gpu::GpuMat gpudst0; |
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cv::gpu::GpuMat gpudst1(src.rows, src.cols, CV_8UC1); |
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cv::gpu::Stream stream0; |
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cv::gpu::Stream stream1; |
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stream0.enqueueUpload(src, gpusrc); |
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cv::gpu::bitwise_not(gpusrc, gpudst0, cv::gpu::GpuMat(), stream0); |
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stream0.enqueueDownload(gpudst0, cpudst0); |
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stream1.enqueueMemSet(gpudst1, cv::Scalar::all(128)); |
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stream1.enqueueDownload(gpudst1, cpudst1); |
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stream0.waitForCompletion(); |
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stream1.waitForCompletion(); |
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dst0 = cpudst0.createMatHeader(); |
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dst1 = cpudst1.createMatHeader(); |
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); |
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EXPECT_MAT_NEAR(dst_gold0, dst0, 0.0); |
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EXPECT_MAT_NEAR(dst_gold1, dst1, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(MatOp, Async, testing::ValuesIn(devices())); |
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#endif // HAVE_CUDA
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