mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
341 lines
11 KiB
341 lines
11 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
|
// |
|
// By downloading, copying, installing or using the software you agree to this license. |
|
// If you do not agree to this license, do not download, install, |
|
// copy or use the software. |
|
// |
|
// |
|
// License Agreement |
|
// For Open Source Computer Vision Library |
|
// |
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
|
// Third party copyrights are property of their respective owners. |
|
// |
|
// Redistribution and use in source and binary forms, with or without modification, |
|
// are permitted provided that the following conditions are met: |
|
// |
|
// * Redistribution's of source code must retain the above copyright notice, |
|
// this list of conditions and the following disclaimer. |
|
// |
|
// * Redistribution's in binary form must reproduce the above copyright notice, |
|
// this list of conditions and the following disclaimer in the documentation |
|
// and/or other materials provided with the distribution. |
|
// |
|
// * The name of the copyright holders may not be used to endorse or promote products |
|
// derived from this software without specific prior written permission. |
|
// |
|
// This software is provided by the copyright holders and contributors "as is" and |
|
// any express or implied warranties, including, but not limited to, the implied |
|
// warranties of merchantability and fitness for a particular purpose are disclaimed. |
|
// In no event shall the Intel Corporation or contributors be liable for any direct, |
|
// indirect, incidental, special, exemplary, or consequential damages |
|
// (including, but not limited to, procurement of substitute goods or services; |
|
// loss of use, data, or profits; or business interruption) however caused |
|
// and on any theory of liability, whether in contract, strict liability, |
|
// or tort (including negligence or otherwise) arising in any way out of |
|
// the use of this software, even if advised of the possibility of such damage. |
|
// |
|
//M*/ |
|
|
|
#include "test_precomp.hpp" |
|
|
|
#ifdef HAVE_CUDA |
|
|
|
namespace opencv_test { namespace { |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplate8U |
|
|
|
CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) |
|
#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)) |
|
|
|
namespace |
|
{ |
|
IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); |
|
} |
|
|
|
PARAM_TEST_CASE(MatchTemplate8U, cv::cuda::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) |
|
{ |
|
cv::cuda::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::cuda::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
CUDA_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::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), method); |
|
|
|
cv::cuda::GpuMat dst; |
|
alg->match(loadMat(image), loadMat(templ), dst); |
|
|
|
cv::Mat dst_gold; |
|
cv::matchTemplate(image, templ, dst_gold, method); |
|
|
|
cv::Mat h_dst(dst); |
|
ASSERT_EQ(dst_gold.size(), h_dst.size()); |
|
ASSERT_EQ(dst_gold.type(), h_dst.type()); |
|
for (int y = 0; y < h_dst.rows; ++y) |
|
{ |
|
for (int x = 0; x < h_dst.cols; ++x) |
|
{ |
|
float gold_val = dst_gold.at<float>(y, x); |
|
float actual_val = dst_gold.at<float>(y, x); |
|
ASSERT_FLOAT_EQ(gold_val, actual_val) << y << ", " << x; |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(CUDA_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)), |
|
ALL_TEMPLATE_METHODS)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// MatchTemplate32F |
|
|
|
PARAM_TEST_CASE(MatchTemplate32F, cv::cuda::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) |
|
{ |
|
cv::cuda::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::cuda::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
CUDA_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::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), method); |
|
|
|
cv::cuda::GpuMat dst; |
|
alg->match(loadMat(image), loadMat(templ), dst); |
|
|
|
cv::Mat dst_gold; |
|
cv::matchTemplate(image, templ, dst_gold, method); |
|
|
|
cv::Mat h_dst(dst); |
|
ASSERT_EQ(dst_gold.size(), h_dst.size()); |
|
ASSERT_EQ(dst_gold.type(), h_dst.type()); |
|
for (int y = 0; y < h_dst.rows; ++y) |
|
{ |
|
for (int x = 0; x < h_dst.cols; ++x) |
|
{ |
|
float gold_val = dst_gold.at<float>(y, x); |
|
float actual_val = dst_gold.at<float>(y, x); |
|
ASSERT_FLOAT_EQ(gold_val, actual_val) << y << ", " << x; |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(CUDA_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::cuda::DeviceInfo, TemplateMethod) |
|
{ |
|
cv::cuda::DeviceInfo devInfo; |
|
int method; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
method = GET_PARAM(1); |
|
|
|
cv::cuda::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
CUDA_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::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), method); |
|
|
|
cv::cuda::GpuMat d_dst; |
|
alg->match(loadMat(image), loadMat(pattern), d_dst); |
|
|
|
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(CUDA_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::cuda::DeviceInfo, std::pair<std::string, std::string>) |
|
{ |
|
cv::cuda::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::cuda::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
CUDA_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::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(image.type(), cv::TM_CCOEFF_NORMED); |
|
|
|
cv::cuda::GpuMat d_dst; |
|
alg->match(loadMat(image), loadMat(pattern), d_dst); |
|
|
|
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(CUDA_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::cuda::DeviceInfo> |
|
{ |
|
cv::cuda::DeviceInfo devInfo; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GetParam(); |
|
|
|
cv::cuda::setDevice(devInfo.deviceID()); |
|
} |
|
}; |
|
|
|
CUDA_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::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(scene.type(), cv::TM_SQDIFF_NORMED); |
|
|
|
cv::cuda::GpuMat d_result; |
|
alg->match(loadMat(scene), loadMat(templ), d_result); |
|
|
|
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); |
|
} |
|
|
|
CUDA_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::Ptr<cv::cuda::TemplateMatching> alg = cv::cuda::createTemplateMatching(scene.type(), cv::TM_SQDIFF); |
|
|
|
cv::cuda::GpuMat d_result; |
|
alg->match(loadMat(scene), loadMat(templ), d_result); |
|
|
|
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(CUDA_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); |
|
|
|
|
|
}} // namespace |
|
#endif // HAVE_CUDA
|
|
|