342 lines
11 KiB
C++
342 lines
11 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// 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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namespace opencv_test { namespace {
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////////////////////////////////////////////////////////////////////////////////
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// MatchTemplate8U
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CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED)
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#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_CCOEFF_NORMED))
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namespace
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{
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IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size);
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}
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PARAM_TEST_CASE(MatchTemplate8U, cv::cuda::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod)
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{
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cv::cuda::DeviceInfo devInfo;
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cv::Size size;
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cv::Size templ_size;
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int cn;
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int method;
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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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templ_size = GET_PARAM(2);
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cn = GET_PARAM(3);
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method = GET_PARAM(4);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(MatchTemplate8U, Accuracy)
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{
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cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn));
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cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn));
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cv::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), method);
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cv::cuda::GpuMat dst;
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alg->match(loadMat(image), loadMat(templ), dst);
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cv::Mat dst_gold;
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cv::matchTemplate(image, templ, dst_gold, method);
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cv::Mat h_dst(dst);
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ASSERT_EQ(dst_gold.size(), h_dst.size());
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ASSERT_EQ(dst_gold.type(), h_dst.type());
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for (int y = 0; y < h_dst.rows; ++y)
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{
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for (int x = 0; x < h_dst.cols; ++x)
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{
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float gold_val = dst_gold.at<float>(y, x);
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float actual_val = dst_gold.at<float>(y, x);
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ASSERT_FLOAT_EQ(gold_val, actual_val) << y << ", " << x;
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MatchTemplate8U, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
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testing::Values(Channels(1), Channels(3), Channels(4)),
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ALL_TEMPLATE_METHODS));
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////////////////////////////////////////////////////////////////////////////////
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// MatchTemplate32F
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PARAM_TEST_CASE(MatchTemplate32F, cv::cuda::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod)
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{
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cv::cuda::DeviceInfo devInfo;
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cv::Size size;
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cv::Size templ_size;
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int cn;
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int method;
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int n, m, h, w;
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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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templ_size = GET_PARAM(2);
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cn = GET_PARAM(3);
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method = GET_PARAM(4);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(MatchTemplate32F, Regression)
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{
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cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn));
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cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn));
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cv::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), method);
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cv::cuda::GpuMat dst;
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alg->match(loadMat(image), loadMat(templ), dst);
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cv::Mat dst_gold;
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cv::matchTemplate(image, templ, dst_gold, method);
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cv::Mat h_dst(dst);
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ASSERT_EQ(dst_gold.size(), h_dst.size());
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ASSERT_EQ(dst_gold.type(), h_dst.type());
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for (int y = 0; y < h_dst.rows; ++y)
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{
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for (int x = 0; x < h_dst.cols; ++x)
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{
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float gold_val = dst_gold.at<float>(y, x);
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float actual_val = dst_gold.at<float>(y, x);
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ASSERT_FLOAT_EQ(gold_val, actual_val) << y << ", " << x;
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MatchTemplate32F, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
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testing::Values(Channels(1), Channels(3), Channels(4)),
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testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))));
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////////////////////////////////////////////////////////////////////////////////
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// MatchTemplateBlackSource
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PARAM_TEST_CASE(MatchTemplateBlackSource, cv::cuda::DeviceInfo, TemplateMethod)
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{
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cv::cuda::DeviceInfo devInfo;
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int method;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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method = 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(MatchTemplateBlackSource, Accuracy)
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{
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cv::Mat image = readImage("matchtemplate/black.png");
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ASSERT_FALSE(image.empty());
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cv::Mat pattern = readImage("matchtemplate/cat.png");
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ASSERT_FALSE(pattern.empty());
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cv::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), method);
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cv::cuda::GpuMat d_dst;
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alg->match(loadMat(image), loadMat(pattern), d_dst);
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cv::Mat dst(d_dst);
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double maxValue;
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cv::Point maxLoc;
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cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc);
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cv::Point maxLocGold = cv::Point(284, 12);
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ASSERT_EQ(maxLocGold, maxLoc);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MatchTemplateBlackSource, testing::Combine(
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ALL_DEVICES,
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testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED))));
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////////////////////////////////////////////////////////////////////////////////
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// MatchTemplate_CCOEF_NORMED
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PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::cuda::DeviceInfo, std::pair<std::string, std::string>)
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{
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cv::cuda::DeviceInfo devInfo;
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std::string imageName;
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std::string patternName;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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imageName = GET_PARAM(1).first;
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patternName = GET_PARAM(1).second;
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy)
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{
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cv::Mat image = readImage(imageName);
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ASSERT_FALSE(image.empty());
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cv::Mat pattern = readImage(patternName);
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ASSERT_FALSE(pattern.empty());
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cv::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), cv::TM_CCOEFF_NORMED);
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cv::cuda::GpuMat d_dst;
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alg->match(loadMat(image), loadMat(pattern), d_dst);
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cv::Mat dst(d_dst);
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cv::Point minLoc, maxLoc;
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double minVal, maxVal;
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cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc);
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cv::Mat dstGold;
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cv::matchTemplate(image, pattern, dstGold, cv::TM_CCOEFF_NORMED);
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double minValGold, maxValGold;
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cv::Point minLocGold, maxLocGold;
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cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold);
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ASSERT_EQ(minLocGold, minLoc);
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ASSERT_EQ(maxLocGold, maxLoc);
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ASSERT_LE(maxVal, 1.0);
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ASSERT_GE(minVal, -1.0);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine(
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ALL_DEVICES,
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testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png")))));
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////////////////////////////////////////////////////////////////////////////////
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// MatchTemplate_CanFindBigTemplate
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struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam<cv::cuda::DeviceInfo>
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{
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cv::cuda::DeviceInfo devInfo;
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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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}
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};
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CUDA_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED)
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{
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cv::Mat scene = readImage("matchtemplate/scene.png");
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ASSERT_FALSE(scene.empty());
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cv::Mat templ = readImage("matchtemplate/template.png");
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ASSERT_FALSE(templ.empty());
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cv::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(scene.type(), cv::TM_SQDIFF_NORMED);
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cv::cuda::GpuMat d_result;
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alg->match(loadMat(scene), loadMat(templ), d_result);
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cv::Mat result(d_result);
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double minVal;
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cv::Point minLoc;
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cv::minMaxLoc(result, &minVal, 0, &minLoc, 0);
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ASSERT_GE(minVal, 0);
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ASSERT_LT(minVal, 1e-3);
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ASSERT_EQ(344, minLoc.x);
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ASSERT_EQ(0, minLoc.y);
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}
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CUDA_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF)
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{
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cv::Mat scene = readImage("matchtemplate/scene.png");
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ASSERT_FALSE(scene.empty());
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cv::Mat templ = readImage("matchtemplate/template.png");
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ASSERT_FALSE(templ.empty());
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cv::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(scene.type(), cv::TM_SQDIFF);
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cv::cuda::GpuMat d_result;
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alg->match(loadMat(scene), loadMat(templ), d_result);
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cv::Mat result(d_result);
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double minVal;
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cv::Point minLoc;
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cv::minMaxLoc(result, &minVal, 0, &minLoc, 0);
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ASSERT_GE(minVal, 0);
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ASSERT_EQ(344, minLoc.x);
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ASSERT_EQ(0, minLoc.y);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES);
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}} // namespace
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#endif // HAVE_CUDA
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