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Open Source Computer Vision Library
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174 lines
5.5 KiB
174 lines
5.5 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 materials 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 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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using namespace cvtest; |
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//////////////////////////////////////////////////////////////////////////////// |
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// MeanShift |
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struct MeanShift : testing::TestWithParam<cv::cuda::DeviceInfo> |
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{ |
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cv::cuda::DeviceInfo devInfo; |
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cv::Mat img; |
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int spatialRad; |
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int colorRad; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::cuda::setDevice(devInfo.deviceID()); |
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img = readImageType("meanshift/cones.png", CV_8UC4); |
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ASSERT_FALSE(img.empty()); |
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spatialRad = 30; |
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colorRad = 30; |
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} |
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}; |
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CUDA_TEST_P(MeanShift, Filtering) |
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{ |
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cv::Mat img_template; |
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if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20)) |
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img_template = readImage("meanshift/con_result.png"); |
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else |
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img_template = readImage("meanshift/con_result_CC1X.png"); |
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ASSERT_FALSE(img_template.empty()); |
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cv::cuda::GpuMat d_dst; |
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cv::cuda::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad); |
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ASSERT_EQ(CV_8UC4, d_dst.type()); |
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cv::Mat dst(d_dst); |
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cv::Mat result; |
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cv::cvtColor(dst, result, cv::COLOR_BGRA2BGR); |
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EXPECT_MAT_NEAR(img_template, result, 0.0); |
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} |
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CUDA_TEST_P(MeanShift, Proc) |
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{ |
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cv::FileStorage fs; |
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if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20)) |
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fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); |
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else |
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fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); |
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ASSERT_TRUE(fs.isOpened()); |
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cv::Mat spmap_template; |
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fs["spmap"] >> spmap_template; |
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ASSERT_FALSE(spmap_template.empty()); |
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cv::cuda::GpuMat rmap_filtered; |
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cv::cuda::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); |
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cv::cuda::GpuMat rmap; |
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cv::cuda::GpuMat spmap; |
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cv::cuda::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad); |
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ASSERT_EQ(CV_8UC4, rmap.type()); |
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EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0); |
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EXPECT_MAT_NEAR(spmap_template, spmap, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShift, ALL_DEVICES); |
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//////////////////////////////////////////////////////////////////////////////// |
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// MeanShiftSegmentation |
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namespace |
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{ |
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IMPLEMENT_PARAM_CLASS(MinSize, int); |
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} |
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PARAM_TEST_CASE(MeanShiftSegmentation, cv::cuda::DeviceInfo, MinSize) |
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{ |
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cv::cuda::DeviceInfo devInfo; |
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int minsize; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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minsize = GET_PARAM(1); |
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cv::cuda::setDevice(devInfo.deviceID()); |
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} |
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}; |
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CUDA_TEST_P(MeanShiftSegmentation, Regression) |
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{ |
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cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4); |
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ASSERT_FALSE(img.empty()); |
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std::ostringstream path; |
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path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize; |
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if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20)) |
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path << ".png"; |
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else |
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path << "_CC1X.png"; |
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cv::Mat dst_gold = readImage(path.str()); |
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ASSERT_FALSE(dst_gold.empty()); |
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cv::Mat dst; |
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cv::cuda::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize); |
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cv::Mat dst_rgb; |
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cv::cvtColor(dst, dst_rgb, cv::COLOR_BGRA2BGR); |
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EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3); |
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} |
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShiftSegmentation, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364)))); |
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#endif // HAVE_CUDA
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