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Open Source Computer Vision Library
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1200 lines
34 KiB
1200 lines
34 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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// Integral |
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PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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bool useRoi; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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useRoi = GET_PARAM(2); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(Integral, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, CV_8UC1); |
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cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi); |
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cv::gpu::integral(loadMat(src, useRoi), dst); |
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cv::Mat dst_gold; |
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cv::integral(src, dst_gold, CV_32S); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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WHOLE_SUBMAT)); |
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/////////////////////////////////////////////////////////////////////////////////////////////////////// |
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// HistEven |
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struct HistEven : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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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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} |
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}; |
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GPU_TEST_P(HistEven, Accuracy) |
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{ |
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cv::Mat img = readImage("stereobm/aloe-L.png"); |
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ASSERT_FALSE(img.empty()); |
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cv::Mat hsv; |
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cv::cvtColor(img, hsv, CV_BGR2HSV); |
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int hbins = 30; |
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float hranges[] = {0.0f, 180.0f}; |
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std::vector<cv::gpu::GpuMat> srcs; |
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cv::gpu::split(loadMat(hsv), srcs); |
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cv::gpu::GpuMat hist; |
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cv::gpu::histEven(srcs[0], hist, hbins, (int)hranges[0], (int)hranges[1]); |
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cv::MatND histnd; |
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int histSize[] = {hbins}; |
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const float* ranges[] = {hranges}; |
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int channels[] = {0}; |
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cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges); |
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cv::Mat hist_gold = histnd; |
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hist_gold = hist_gold.t(); |
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hist_gold.convertTo(hist_gold, CV_32S); |
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EXPECT_MAT_NEAR(hist_gold, hist, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, ALL_DEVICES); |
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/////////////////////////////////////////////////////////////////////////////////////////////////////// |
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// CalcHist |
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namespace |
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{ |
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void calcHistGold(const cv::Mat& src, cv::Mat& hist) |
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{ |
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hist.create(1, 256, CV_32SC1); |
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hist.setTo(cv::Scalar::all(0)); |
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int* hist_row = hist.ptr<int>(); |
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for (int y = 0; y < src.rows; ++y) |
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{ |
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const uchar* src_row = src.ptr(y); |
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for (int x = 0; x < src.cols; ++x) |
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++hist_row[src_row[x]]; |
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} |
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} |
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} |
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PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(CalcHist, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, CV_8UC1); |
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cv::gpu::GpuMat hist; |
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cv::gpu::calcHist(loadMat(src), hist); |
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cv::Mat hist_gold; |
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calcHistGold(src, hist_gold); |
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EXPECT_MAT_NEAR(hist_gold, hist, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CalcHist, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES)); |
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/////////////////////////////////////////////////////////////////////////////////////////////////////// |
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// EqualizeHist |
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PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(EqualizeHist, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, CV_8UC1); |
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cv::gpu::GpuMat dst; |
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cv::gpu::equalizeHist(loadMat(src), dst); |
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cv::Mat dst_gold; |
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cv::equalizeHist(src, dst_gold); |
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EXPECT_MAT_NEAR(dst_gold, dst, 3.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES)); |
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/////////////////////////////////////////////////////////////////////////////////////////////////////// |
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// CLAHE |
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namespace |
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{ |
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IMPLEMENT_PARAM_CLASS(ClipLimit, double) |
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} |
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PARAM_TEST_CASE(CLAHE, cv::gpu::DeviceInfo, cv::Size, ClipLimit) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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double clipLimit; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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clipLimit = GET_PARAM(2); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(CLAHE, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, CV_8UC1); |
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cv::Ptr<cv::gpu::CLAHE> clahe = cv::gpu::createCLAHE(clipLimit); |
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cv::gpu::GpuMat dst; |
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clahe->apply(loadMat(src), dst); |
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cv::Ptr<cv::CLAHE> clahe_gold = cv::createCLAHE(clipLimit); |
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cv::Mat dst_gold; |
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clahe_gold->apply(src, dst_gold); |
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ASSERT_MAT_NEAR(dst_gold, dst, 1.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CLAHE, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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testing::Values(0.0, 40.0))); |
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//////////////////////////////////////////////////////////////////////// |
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// ColumnSum |
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PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(ColumnSum, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, CV_32FC1); |
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cv::gpu::GpuMat d_dst; |
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cv::gpu::columnSum(loadMat(src), d_dst); |
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cv::Mat dst(d_dst); |
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for (int j = 0; j < src.cols; ++j) |
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{ |
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float gold = src.at<float>(0, j); |
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float res = dst.at<float>(0, j); |
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ASSERT_NEAR(res, gold, 1e-5); |
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} |
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for (int i = 1; i < src.rows; ++i) |
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{ |
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for (int j = 0; j < src.cols; ++j) |
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{ |
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float gold = src.at<float>(i, j) += src.at<float>(i - 1, j); |
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float res = dst.at<float>(i, j); |
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ASSERT_NEAR(res, gold, 1e-5); |
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} |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES)); |
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//////////////////////////////////////////////////////// |
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// Canny |
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namespace |
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{ |
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IMPLEMENT_PARAM_CLASS(AppertureSize, int); |
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IMPLEMENT_PARAM_CLASS(L2gradient, bool); |
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} |
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PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int apperture_size; |
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bool useL2gradient; |
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bool useRoi; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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apperture_size = GET_PARAM(1); |
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useL2gradient = GET_PARAM(2); |
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useRoi = GET_PARAM(3); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(Canny, Accuracy) |
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{ |
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cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(img.empty()); |
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double low_thresh = 50.0; |
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double high_thresh = 100.0; |
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if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS)) |
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{ |
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try |
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{ |
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cv::gpu::GpuMat edges; |
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cv::gpu::Canny(loadMat(img), edges, low_thresh, high_thresh, apperture_size, useL2gradient); |
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} |
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catch (const cv::Exception& e) |
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{ |
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ASSERT_EQ(CV_StsNotImplemented, e.code); |
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} |
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} |
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else |
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{ |
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cv::gpu::GpuMat edges; |
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cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient); |
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cv::Mat edges_gold; |
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cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient); |
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EXPECT_MAT_SIMILAR(edges_gold, edges, 2e-2); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(AppertureSize(3), AppertureSize(5)), |
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testing::Values(L2gradient(false), L2gradient(true)), |
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WHOLE_SUBMAT)); |
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//////////////////////////////////////////////////////////////////////////////// |
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// MeanShift |
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struct MeanShift : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::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::gpu::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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GPU_TEST_P(MeanShift, Filtering) |
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{ |
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cv::Mat img_template; |
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if (supportFeature(devInfo, cv::gpu::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::gpu::GpuMat d_dst; |
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cv::gpu::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_BGRA2BGR); |
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EXPECT_MAT_NEAR(img_template, result, 0.0); |
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} |
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GPU_TEST_P(MeanShift, Proc) |
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{ |
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cv::FileStorage fs; |
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if (supportFeature(devInfo, cv::gpu::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::gpu::GpuMat rmap_filtered; |
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cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); |
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cv::gpu::GpuMat rmap; |
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cv::gpu::GpuMat spmap; |
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cv::gpu::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(GPU_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::gpu::DeviceInfo, MinSize) |
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{ |
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cv::gpu::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::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_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::gpu::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::gpu::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_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(GPU_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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//////////////////////////////////////////////////////////////////////////// |
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// Blend |
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namespace |
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{ |
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template <typename T> |
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void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold) |
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{ |
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result_gold.create(img1.size(), img1.type()); |
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int cn = img1.channels(); |
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for (int y = 0; y < img1.rows; ++y) |
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{ |
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const float* weights1_row = weights1.ptr<float>(y); |
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const float* weights2_row = weights2.ptr<float>(y); |
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const T* img1_row = img1.ptr<T>(y); |
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const T* img2_row = img2.ptr<T>(y); |
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T* result_gold_row = result_gold.ptr<T>(y); |
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for (int x = 0; x < img1.cols * cn; ++x) |
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{ |
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float w1 = weights1_row[x / cn]; |
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float w2 = weights2_row[x / cn]; |
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result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f)); |
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} |
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} |
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} |
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} |
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PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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int type; |
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bool useRoi; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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type = GET_PARAM(2); |
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useRoi = GET_PARAM(3); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(Blend, Accuracy) |
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{ |
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int depth = CV_MAT_DEPTH(type); |
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cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); |
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cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); |
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cv::Mat weights1 = randomMat(size, CV_32F, 0, 1); |
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cv::Mat weights2 = randomMat(size, CV_32F, 0, 1); |
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cv::gpu::GpuMat result; |
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cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result); |
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cv::Mat result_gold; |
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if (depth == CV_8U) |
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blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold); |
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else |
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blendLinearGold<float>(img1, img2, weights1, weights2, result_gold); |
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EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), |
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WHOLE_SUBMAT)); |
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//////////////////////////////////////////////////////// |
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// Convolve |
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namespace |
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{ |
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void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false) |
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{ |
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// reallocate the output array if needed |
|
C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type()); |
|
cv::Size dftSize; |
|
|
|
// compute the size of DFT transform |
|
dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1); |
|
dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1); |
|
|
|
// allocate temporary buffers and initialize them with 0s |
|
cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0)); |
|
cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0)); |
|
|
|
// copy A and B to the top-left corners of tempA and tempB, respectively |
|
cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows)); |
|
A.copyTo(roiA); |
|
cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows)); |
|
B.copyTo(roiB); |
|
|
|
// now transform the padded A & B in-place; |
|
// use "nonzeroRows" hint for faster processing |
|
cv::dft(tempA, tempA, 0, A.rows); |
|
cv::dft(tempB, tempB, 0, B.rows); |
|
|
|
// multiply the spectrums; |
|
// the function handles packed spectrum representations well |
|
cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr); |
|
|
|
// transform the product back from the frequency domain. |
|
// Even though all the result rows will be non-zero, |
|
// you need only the first C.rows of them, and thus you |
|
// pass nonzeroRows == C.rows |
|
cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows); |
|
|
|
// now copy the result back to C. |
|
tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C); |
|
} |
|
|
|
IMPLEMENT_PARAM_CLASS(KSize, int); |
|
IMPLEMENT_PARAM_CLASS(Ccorr, bool); |
|
} |
|
|
|
PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
cv::Size size; |
|
int ksize; |
|
bool ccorr; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
size = GET_PARAM(1); |
|
ksize = GET_PARAM(2); |
|
ccorr = GET_PARAM(3); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(Convolve, Accuracy) |
|
{ |
|
cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0); |
|
cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0); |
|
|
|
cv::gpu::GpuMat dst; |
|
cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr); |
|
|
|
cv::Mat dst_gold; |
|
convolveDFT(src, kernel, dst_gold, ccorr); |
|
|
|
EXPECT_MAT_NEAR(dst, dst_gold, 1e-1); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Convolve, testing::Combine( |
|
ALL_DEVICES, |
|
DIFFERENT_SIZES, |
|
testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)), |
|
testing::Values(Ccorr(false), Ccorr(true)))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplate8U |
|
|
|
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED) |
|
|
|
namespace |
|
{ |
|
IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); |
|
} |
|
|
|
PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
cv::Size size; |
|
cv::Size templ_size; |
|
int cn; |
|
int method; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
size = GET_PARAM(1); |
|
templ_size = GET_PARAM(2); |
|
cn = GET_PARAM(3); |
|
method = GET_PARAM(4); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(MatchTemplate8U, Accuracy) |
|
{ |
|
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn)); |
|
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn)); |
|
|
|
cv::gpu::GpuMat dst; |
|
cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); |
|
|
|
cv::Mat dst_gold; |
|
cv::matchTemplate(image, templ, dst_gold, method); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, testing::Combine( |
|
ALL_DEVICES, |
|
DIFFERENT_SIZES, |
|
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), |
|
testing::Values(Channels(1), Channels(3), Channels(4)), |
|
TemplateMethod::all())); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplate32F |
|
|
|
PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
cv::Size size; |
|
cv::Size templ_size; |
|
int cn; |
|
int method; |
|
|
|
int n, m, h, w; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
size = GET_PARAM(1); |
|
templ_size = GET_PARAM(2); |
|
cn = GET_PARAM(3); |
|
method = GET_PARAM(4); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(MatchTemplate32F, Regression) |
|
{ |
|
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn)); |
|
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn)); |
|
|
|
cv::gpu::GpuMat dst; |
|
cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); |
|
|
|
cv::Mat dst_gold; |
|
cv::matchTemplate(image, templ, dst_gold, method); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine( |
|
ALL_DEVICES, |
|
DIFFERENT_SIZES, |
|
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), |
|
testing::Values(Channels(1), Channels(3), Channels(4)), |
|
testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR)))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplateBlackSource |
|
|
|
PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int method; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
method = GET_PARAM(1); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(MatchTemplateBlackSource, Accuracy) |
|
{ |
|
cv::Mat image = readImage("matchtemplate/black.png"); |
|
ASSERT_FALSE(image.empty()); |
|
|
|
cv::Mat pattern = readImage("matchtemplate/cat.png"); |
|
ASSERT_FALSE(pattern.empty()); |
|
|
|
cv::gpu::GpuMat d_dst; |
|
cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, method); |
|
|
|
cv::Mat dst(d_dst); |
|
|
|
double maxValue; |
|
cv::Point maxLoc; |
|
cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc); |
|
|
|
cv::Point maxLocGold = cv::Point(284, 12); |
|
|
|
ASSERT_EQ(maxLocGold, maxLoc); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine( |
|
ALL_DEVICES, |
|
testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED)))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplate_CCOEF_NORMED |
|
|
|
PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair<std::string, std::string>) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
std::string imageName; |
|
std::string patternName; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
imageName = GET_PARAM(1).first; |
|
patternName = GET_PARAM(1).second; |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy) |
|
{ |
|
cv::Mat image = readImage(imageName); |
|
ASSERT_FALSE(image.empty()); |
|
|
|
cv::Mat pattern = readImage(patternName); |
|
ASSERT_FALSE(pattern.empty()); |
|
|
|
cv::gpu::GpuMat d_dst; |
|
cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, CV_TM_CCOEFF_NORMED); |
|
|
|
cv::Mat dst(d_dst); |
|
|
|
cv::Point minLoc, maxLoc; |
|
double minVal, maxVal; |
|
cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc); |
|
|
|
cv::Mat dstGold; |
|
cv::matchTemplate(image, pattern, dstGold, CV_TM_CCOEFF_NORMED); |
|
|
|
double minValGold, maxValGold; |
|
cv::Point minLocGold, maxLocGold; |
|
cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold); |
|
|
|
ASSERT_EQ(minLocGold, minLoc); |
|
ASSERT_EQ(maxLocGold, maxLoc); |
|
ASSERT_LE(maxVal, 1.0); |
|
ASSERT_GE(minVal, -1.0); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine( |
|
ALL_DEVICES, |
|
testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png"))))); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplate_CanFindBigTemplate |
|
|
|
struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED) |
|
{ |
|
cv::Mat scene = readImage("matchtemplate/scene.png"); |
|
ASSERT_FALSE(scene.empty()); |
|
|
|
cv::Mat templ = readImage("matchtemplate/template.png"); |
|
ASSERT_FALSE(templ.empty()); |
|
|
|
cv::gpu::GpuMat d_result; |
|
cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF_NORMED); |
|
|
|
cv::Mat result(d_result); |
|
|
|
double minVal; |
|
cv::Point minLoc; |
|
cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); |
|
|
|
ASSERT_GE(minVal, 0); |
|
ASSERT_LT(minVal, 1e-3); |
|
ASSERT_EQ(344, minLoc.x); |
|
ASSERT_EQ(0, minLoc.y); |
|
} |
|
|
|
GPU_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF) |
|
{ |
|
cv::Mat scene = readImage("matchtemplate/scene.png"); |
|
ASSERT_FALSE(scene.empty()); |
|
|
|
cv::Mat templ = readImage("matchtemplate/template.png"); |
|
ASSERT_FALSE(templ.empty()); |
|
|
|
cv::gpu::GpuMat d_result; |
|
cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF); |
|
|
|
cv::Mat result(d_result); |
|
|
|
double minVal; |
|
cv::Point minLoc; |
|
cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); |
|
|
|
ASSERT_GE(minVal, 0); |
|
ASSERT_EQ(344, minLoc.x); |
|
ASSERT_EQ(0, minLoc.y); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); |
|
|
|
//////////////////////////////////////////////////////////////////////////// |
|
// MulSpectrums |
|
|
|
CV_FLAGS(DftFlags, 0, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT) |
|
|
|
PARAM_TEST_CASE(MulSpectrums, cv::gpu::DeviceInfo, cv::Size, DftFlags) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
cv::Size size; |
|
int flag; |
|
|
|
cv::Mat a, b; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
size = GET_PARAM(1); |
|
flag = GET_PARAM(2); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
a = randomMat(size, CV_32FC2); |
|
b = randomMat(size, CV_32FC2); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(MulSpectrums, Simple) |
|
{ |
|
cv::gpu::GpuMat c; |
|
cv::gpu::mulSpectrums(loadMat(a), loadMat(b), c, flag, false); |
|
|
|
cv::Mat c_gold; |
|
cv::mulSpectrums(a, b, c_gold, flag, false); |
|
|
|
EXPECT_MAT_NEAR(c_gold, c, 1e-2); |
|
} |
|
|
|
GPU_TEST_P(MulSpectrums, Scaled) |
|
{ |
|
float scale = 1.f / size.area(); |
|
|
|
cv::gpu::GpuMat c; |
|
cv::gpu::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false); |
|
|
|
cv::Mat c_gold; |
|
cv::mulSpectrums(a, b, c_gold, flag, false); |
|
c_gold.convertTo(c_gold, c_gold.type(), scale); |
|
|
|
EXPECT_MAT_NEAR(c_gold, c, 1e-2); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MulSpectrums, testing::Combine( |
|
ALL_DEVICES, |
|
DIFFERENT_SIZES, |
|
testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS)))); |
|
|
|
//////////////////////////////////////////////////////////////////////////// |
|
// Dft |
|
|
|
struct Dft : testing::TestWithParam<cv::gpu::DeviceInfo> |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
namespace |
|
{ |
|
void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace) |
|
{ |
|
SCOPED_TRACE(hint); |
|
|
|
cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0); |
|
|
|
cv::Mat b_gold; |
|
cv::dft(a, b_gold, flags); |
|
|
|
cv::gpu::GpuMat d_b; |
|
cv::gpu::GpuMat d_b_data; |
|
if (inplace) |
|
{ |
|
d_b_data.create(1, a.size().area(), CV_32FC2); |
|
d_b = cv::gpu::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); |
|
} |
|
cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), flags); |
|
|
|
EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); |
|
ASSERT_EQ(CV_32F, d_b.depth()); |
|
ASSERT_EQ(2, d_b.channels()); |
|
EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4); |
|
} |
|
} |
|
|
|
GPU_TEST_P(Dft, C2C) |
|
{ |
|
int cols = randomInt(2, 100); |
|
int rows = randomInt(2, 100); |
|
|
|
for (int i = 0; i < 2; ++i) |
|
{ |
|
bool inplace = i != 0; |
|
|
|
testC2C("no flags", cols, rows, 0, inplace); |
|
testC2C("no flags 0 1", cols, rows + 1, 0, inplace); |
|
testC2C("no flags 1 0", cols, rows + 1, 0, inplace); |
|
testC2C("no flags 1 1", cols + 1, rows, 0, inplace); |
|
testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace); |
|
testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace); |
|
testC2C("single col", 1, rows, 0, inplace); |
|
testC2C("single row", cols, 1, 0, inplace); |
|
testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace); |
|
testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace); |
|
testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace); |
|
testC2C("size 1 2", 1, 2, 0, inplace); |
|
testC2C("size 2 1", 2, 1, 0, inplace); |
|
} |
|
} |
|
|
|
namespace |
|
{ |
|
void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace) |
|
{ |
|
SCOPED_TRACE(hint); |
|
|
|
cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0); |
|
|
|
cv::gpu::GpuMat d_b, d_c; |
|
cv::gpu::GpuMat d_b_data, d_c_data; |
|
if (inplace) |
|
{ |
|
if (a.cols == 1) |
|
{ |
|
d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); |
|
d_b = cv::gpu::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); |
|
} |
|
else |
|
{ |
|
d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); |
|
d_b = cv::gpu::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); |
|
} |
|
d_c_data.create(1, a.size().area(), CV_32F); |
|
d_c = cv::gpu::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); |
|
} |
|
|
|
cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), 0); |
|
cv::gpu::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); |
|
|
|
EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); |
|
EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr()); |
|
ASSERT_EQ(CV_32F, d_c.depth()); |
|
ASSERT_EQ(1, d_c.channels()); |
|
|
|
cv::Mat c(d_c); |
|
EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5); |
|
} |
|
} |
|
|
|
GPU_TEST_P(Dft, R2CThenC2R) |
|
{ |
|
int cols = randomInt(2, 100); |
|
int rows = randomInt(2, 100); |
|
|
|
testR2CThenC2R("sanity", cols, rows, false); |
|
testR2CThenC2R("sanity 0 1", cols, rows + 1, false); |
|
testR2CThenC2R("sanity 1 0", cols + 1, rows, false); |
|
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false); |
|
testR2CThenC2R("single col", 1, rows, false); |
|
testR2CThenC2R("single col 1", 1, rows + 1, false); |
|
testR2CThenC2R("single row", cols, 1, false); |
|
testR2CThenC2R("single row 1", cols + 1, 1, false); |
|
|
|
testR2CThenC2R("sanity", cols, rows, true); |
|
testR2CThenC2R("sanity 0 1", cols, rows + 1, true); |
|
testR2CThenC2R("sanity 1 0", cols + 1, rows, true); |
|
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); |
|
testR2CThenC2R("single row", cols, 1, true); |
|
testR2CThenC2R("single row 1", cols + 1, 1, true); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Dft, ALL_DEVICES); |
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////// |
|
// CornerHarris |
|
|
|
namespace |
|
{ |
|
IMPLEMENT_PARAM_CLASS(BlockSize, int); |
|
IMPLEMENT_PARAM_CLASS(ApertureSize, int); |
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} |
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|
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PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int type; |
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int borderType; |
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int blockSize; |
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int apertureSize; |
|
|
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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type = GET_PARAM(1); |
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borderType = GET_PARAM(2); |
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blockSize = GET_PARAM(3); |
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apertureSize = GET_PARAM(4); |
|
|
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
|
|
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GPU_TEST_P(CornerHarris, Accuracy) |
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{ |
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cv::Mat src = readImageType("stereobm/aloe-L.png", type); |
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ASSERT_FALSE(src.empty()); |
|
|
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double k = randomDouble(0.1, 0.9); |
|
|
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cv::gpu::GpuMat dst; |
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cv::gpu::cornerHarris(loadMat(src), dst, blockSize, apertureSize, k, borderType); |
|
|
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cv::Mat dst_gold; |
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cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType); |
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|
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EXPECT_MAT_NEAR(dst_gold, dst, 0.02); |
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} |
|
|
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerHarris, testing::Combine( |
|
ALL_DEVICES, |
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testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), |
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testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), |
|
testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), |
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testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); |
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////// |
|
// cornerMinEigen |
|
|
|
PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
int type; |
|
int borderType; |
|
int blockSize; |
|
int apertureSize; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
type = GET_PARAM(1); |
|
borderType = GET_PARAM(2); |
|
blockSize = GET_PARAM(3); |
|
apertureSize = GET_PARAM(4); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
GPU_TEST_P(CornerMinEigen, Accuracy) |
|
{ |
|
cv::Mat src = readImageType("stereobm/aloe-L.png", type); |
|
ASSERT_FALSE(src.empty()); |
|
|
|
cv::gpu::GpuMat dst; |
|
cv::gpu::cornerMinEigenVal(loadMat(src), dst, blockSize, apertureSize, borderType); |
|
|
|
cv::Mat dst_gold; |
|
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType); |
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, 0.02); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerMinEigen, testing::Combine( |
|
ALL_DEVICES, |
|
testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), |
|
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), |
|
testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), |
|
testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); |
|
|
|
#endif // HAVE_CUDA
|
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