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modules/cudaimgproc/test/test_bilateral_filter.cpp
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/*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 {
////////////////////////////////////////////////////////
// BilateralFilter
PARAM_TEST_CASE(BilateralFilter, cv::cuda::DeviceInfo, cv::Size, MatType)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
int kernel_size;
float sigma_color;
float sigma_spatial;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
kernel_size = 5;
sigma_color = 10.f;
sigma_spatial = 3.5f;
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(BilateralFilter, Accuracy)
{
cv::Mat src = randomMat(size, type);
src.convertTo(src, type);
cv::cuda::GpuMat dst;
cv::cuda::bilateralFilter(loadMat(src), dst, kernel_size, sigma_color, sigma_spatial);
cv::Mat dst_gold;
cv::bilateralFilter(src, dst_gold, kernel_size, sigma_color, sigma_spatial);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, BilateralFilter, testing::Combine(
ALL_DEVICES,
testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(639, 481)),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_32FC1), MatType(CV_32FC3))
));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_blend.cpp
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/*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 {
////////////////////////////////////////////////////////////////////////////
// Blend
namespace
{
template <typename T>
void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold)
{
result_gold.create(img1.size(), img1.type());
int cn = img1.channels();
for (int y = 0; y < img1.rows; ++y)
{
const float* weights1_row = weights1.ptr<float>(y);
const float* weights2_row = weights2.ptr<float>(y);
const T* img1_row = img1.ptr<T>(y);
const T* img2_row = img2.ptr<T>(y);
T* result_gold_row = result_gold.ptr<T>(y);
for (int x = 0; x < img1.cols * cn; ++x)
{
float w1 = weights1_row[x / cn];
float w2 = weights2_row[x / cn];
result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f));
}
}
}
}
PARAM_TEST_CASE(Blend, cv::cuda::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(Blend, Accuracy)
{
int depth = CV_MAT_DEPTH(type);
cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
cv::cuda::GpuMat result;
cv::cuda::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result);
cv::Mat result_gold;
if (depth == CV_8U)
blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold);
else
blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, Blend, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
WHOLE_SUBMAT));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_canny.cpp
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/*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 {
////////////////////////////////////////////////////////
// Canny
namespace
{
IMPLEMENT_PARAM_CLASS(AppertureSize, int)
IMPLEMENT_PARAM_CLASS(L2gradient, bool)
}
PARAM_TEST_CASE(Canny, cv::cuda::DeviceInfo, AppertureSize, L2gradient, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
int apperture_size;
bool useL2gradient;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
apperture_size = GET_PARAM(1);
useL2gradient = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(Canny, Accuracy)
{
cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
double low_thresh = 50.0;
double high_thresh = 100.0;
cv::Ptr<cv::cuda::CannyEdgeDetector> canny = cv::cuda::createCannyEdgeDetector(low_thresh, high_thresh, apperture_size, useL2gradient);
cv::cuda::GpuMat edges;
canny->detect(loadMat(img, useRoi), edges);
cv::Mat edges_gold;
cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient);
EXPECT_MAT_SIMILAR(edges_gold, edges, 2e-2);
}
class CannyAsyncParallelLoopBody : public cv::ParallelLoopBody
{
public:
CannyAsyncParallelLoopBody(const cv::cuda::GpuMat& d_img_, cv::cuda::GpuMat* edges_, double low_thresh_, double high_thresh_, int apperture_size_, bool useL2gradient_)
: d_img(d_img_), edges(edges_), low_thresh(low_thresh_), high_thresh(high_thresh_), apperture_size(apperture_size_), useL2gradient(useL2gradient_) {}
~CannyAsyncParallelLoopBody() {};
void operator()(const cv::Range& r) const
{
for (int i = r.start; i < r.end; i++) {
cv::cuda::Stream stream;
cv::Ptr<cv::cuda::CannyEdgeDetector> canny = cv::cuda::createCannyEdgeDetector(low_thresh, high_thresh, apperture_size, useL2gradient);
canny->detect(d_img, edges[i], stream);
stream.waitForCompletion();
}
}
protected:
const cv::cuda::GpuMat& d_img;
cv::cuda::GpuMat* edges;
double low_thresh;
double high_thresh;
int apperture_size;
bool useL2gradient;
};
#define NUM_STREAMS 128
CUDA_TEST_P(Canny, Async)
{
if (!supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_30))
{
throw SkipTestException("CUDA device doesn't support texture objects");
}
else
{
const cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const cv::cuda::GpuMat d_img_roi = loadMat(img, useRoi);
double low_thresh = 50.0;
double high_thresh = 100.0;
// Synchronous call
cv::Ptr<cv::cuda::CannyEdgeDetector> canny = cv::cuda::createCannyEdgeDetector(low_thresh, high_thresh, apperture_size, useL2gradient);
cv::cuda::GpuMat edges_gold;
canny->detect(d_img_roi, edges_gold);
// Asynchronous call
cv::cuda::GpuMat edges[NUM_STREAMS];
cv::parallel_for_(cv::Range(0, NUM_STREAMS), CannyAsyncParallelLoopBody(d_img_roi, edges, low_thresh, high_thresh, apperture_size, useL2gradient));
// Compare the results of synchronous call and asynchronous call
for (int i = 0; i < NUM_STREAMS; i++)
EXPECT_MAT_NEAR(edges_gold, edges[i], 0.0);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, Canny, testing::Combine(
ALL_DEVICES,
testing::Values(AppertureSize(3), AppertureSize(5), AppertureSize(7)),
testing::Values(L2gradient(false), L2gradient(true)),
WHOLE_SUBMAT));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_color.cpp
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modules/cudaimgproc/test/test_connectedcomponents.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test {
namespace {
// This function force a row major order for the labels
template <typename LabelT>
void normalize_labels_impl(Mat& labels) {
std::map<LabelT, LabelT> map_new_labels;
LabelT i_max_new_label = 0;
for (int r = 0; r < labels.rows; ++r) {
LabelT* const mat_row = labels.ptr<LabelT>(r);
for (int c = 0; c < labels.cols; ++c) {
LabelT iCurLabel = mat_row[c];
if (iCurLabel > 0) {
if (map_new_labels.find(iCurLabel) == map_new_labels.end()) {
map_new_labels[iCurLabel] = ++i_max_new_label;
}
mat_row[c] = map_new_labels.at(iCurLabel);
}
}
}
}
void normalize_labels(Mat& labels) {
int type = labels.type();
int depth = type & CV_MAT_DEPTH_MASK;
int chans = 1 + (type >> CV_CN_SHIFT);
CV_Assert(chans == 1);
CV_Assert(depth == CV_16U || depth == CV_16S || depth == CV_32S);
switch (depth) {
case CV_16U: normalize_labels_impl<ushort>(labels); break;
case CV_16S: normalize_labels_impl<short>(labels); break;
case CV_32S: normalize_labels_impl<int>(labels); break;
default: CV_Assert(0);
}
}
////////////////////////////////////////////////////////
// ConnectedComponents
PARAM_TEST_CASE(ConnectedComponents, cv::cuda::DeviceInfo, int, int, cv::cuda::ConnectedComponentsAlgorithmsTypes)
{
cv::cuda::DeviceInfo devInfo;
int connectivity;
int ltype;
cv::cuda::ConnectedComponentsAlgorithmsTypes algo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
connectivity = GET_PARAM(1);
ltype = GET_PARAM(2);
algo = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(ConnectedComponents, Chessboard_Even)
{
std::initializer_list<int> sizes{ 16, 16 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = cv::Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
if (connectivity == 8) {
correct_output_int = cv::Mat1i(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
}
else {
correct_output_int = cv::Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0,
0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16,
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0,
0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32,
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0,
0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48,
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0,
0, 57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64,
65, 0, 66, 0, 67, 0, 68, 0, 69, 0, 70, 0, 71, 0, 72, 0,
0, 73, 0, 74, 0, 75, 0, 76, 0, 77, 0, 78, 0, 79, 0, 80,
81, 0, 82, 0, 83, 0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0,
0, 89, 0, 90, 0, 91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96,
97, 0, 98, 0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0,
0, 105, 0, 106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112,
113, 0, 114, 0, 115, 0, 116, 0, 117, 0, 118, 0, 119, 0, 120, 0,
0, 121, 0, 122, 0, 123, 0, 124, 0, 125, 0, 126, 0, 127, 0, 128
});
}
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Chessboard_Odd)
{
std::initializer_list<int> sizes{ 15, 15 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
if (connectivity == 8) {
correct_output_int = Mat1i(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
}
else {
correct_output_int = Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8,
0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0,
16, 0, 17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23,
0, 24, 0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0,
31, 0, 32, 0, 33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38,
0, 39, 0, 40, 0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0,
46, 0, 47, 0, 48, 0, 49, 0, 50, 0, 51, 0, 52, 0, 53,
0, 54, 0, 55, 0, 56, 0, 57, 0, 58, 0, 59, 0, 60, 0,
61, 0, 62, 0, 63, 0, 64, 0, 65, 0, 66, 0, 67, 0, 68,
0, 69, 0, 70, 0, 71, 0, 72, 0, 73, 0, 74, 0, 75, 0,
76, 0, 77, 0, 78, 0, 79, 0, 80, 0, 81, 0, 82, 0, 83,
0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0, 89, 0, 90, 0,
91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96, 0, 97, 0, 98,
0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0, 105, 0,
106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112, 0, 113
});
}
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Maxlabels_8conn_Even)
{
std::initializer_list<int> sizes{ 16, 16 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
});
correct_output_int = Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
});
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Maxlabels_8conn_Odd)
{
std::initializer_list<int> sizes{ 15, 15 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
correct_output_int = Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64
});
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Single_Row)
{
std::initializer_list<int> sizes{ 1, 15 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, { 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1 });
correct_output_int = Mat1i(sizes, { 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8 });
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Single_Column)
{
std::initializer_list<int> sizes{ 15, 1 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, { 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1 });
correct_output_int = Mat1i(sizes, { 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8 });
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Concentric_Circles)
{
string img_path = cvtest::TS::ptr()->get_data_path() + "connectedcomponents/concentric_circles.png";
string exp_path = cvtest::TS::ptr()->get_data_path() + "connectedcomponents/ccomp_exp.png";
Mat img = imread(img_path, 0);
EXPECT_FALSE(img.empty());
Mat exp = imread(exp_path, 0);
EXPECT_FALSE(exp.empty());
Mat labels;
exp.convertTo(exp, ltype);
GpuMat d_img;
GpuMat d_labels;
d_img.upload(img);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_img, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
Mat diff = labels != exp;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, ConnectedComponents, testing::Combine(
ALL_DEVICES,
testing::Values(8),
testing::Values(CV_32S),
testing::Values(cv::cuda::CCL_DEFAULT, cv::cuda::CCL_BKE)
));
}
} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_histogram.cpp
+397
View File
@@ -0,0 +1,397 @@
/*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 {
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HistEven
typedef tuple<Size, int> hist_size_to_roi_offset_params_t;
const hist_size_to_roi_offset_params_t hist_size_to_roi_offset_params[] =
{
// uchar reads only
hist_size_to_roi_offset_params_t(Size(1,32), 0),
hist_size_to_roi_offset_params_t(Size(2,32), 0),
hist_size_to_roi_offset_params_t(Size(2,32), 1),
hist_size_to_roi_offset_params_t(Size(3,32), 0),
hist_size_to_roi_offset_params_t(Size(3,32), 1),
hist_size_to_roi_offset_params_t(Size(3,32), 2),
hist_size_to_roi_offset_params_t(Size(4,32), 0),
hist_size_to_roi_offset_params_t(Size(4,32), 1),
hist_size_to_roi_offset_params_t(Size(4,32), 2),
hist_size_to_roi_offset_params_t(Size(4,32), 3),
// uchar and int reads
hist_size_to_roi_offset_params_t(Size(129,32), 0),
hist_size_to_roi_offset_params_t(Size(129,32), 1),
hist_size_to_roi_offset_params_t(Size(129,32), 2),
hist_size_to_roi_offset_params_t(Size(129,32), 3),
// int reads only
hist_size_to_roi_offset_params_t(Size(128,32), 0)
};
PARAM_TEST_CASE(HistEven, cv::cuda::DeviceInfo, hist_size_to_roi_offset_params_t)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int roiOffsetX;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = get<0>(GET_PARAM(1));
roiOffsetX = get<1>(GET_PARAM(1));
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(HistEven, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
const Rect roi = Rect(roiOffsetX, 0, src.cols - roiOffsetX, src.rows);
int hbins = 30;
float hranges[] = {50.0f, 200.0f};
cv::cuda::GpuMat hist;
cv::cuda::GpuMat srcDevice = loadMat(src);
cv::cuda::histEven(srcDevice(roi), hist, hbins, (int)hranges[0], (int)hranges[1]);
cv::Mat hist_gold;
int histSize[] = {hbins};
const float* ranges[] = {hranges};
int channels[] = {0};
Mat srcRoi = src(roi);
cv::calcHist(&srcRoi, 1, channels, cv::Mat(), hist_gold, 1, histSize, ranges);
hist_gold.convertTo(hist_gold, CV_32S);
EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HistEven, testing::Combine(
ALL_DEVICES, testing::ValuesIn(hist_size_to_roi_offset_params)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// CalcHist
PARAM_TEST_CASE(CalcHist, cv::cuda::DeviceInfo, hist_size_to_roi_offset_params_t)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int roiOffsetX;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = get<0>(GET_PARAM(1));
roiOffsetX = get<1>(GET_PARAM(1));
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(CalcHist, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
const Rect roi = Rect(roiOffsetX, 0, src.cols - roiOffsetX, src.rows);
cv::cuda::GpuMat hist;
GpuMat srcDevice = loadMat(src);
cv::cuda::calcHist(srcDevice(roi), hist);
cv::Mat hist_gold;
const int hbins = 256;
const float hranges[] = {0.0f, 256.0f};
const int histSize[] = {hbins};
const float* ranges[] = {hranges};
const int channels[] = {0};
const Mat srcRoi = src(roi);
cv::calcHist(&srcRoi, 1, channels, cv::Mat(), hist_gold, 1, histSize, ranges);
hist_gold = hist_gold.reshape(1, 1);
hist_gold.convertTo(hist_gold, CV_32S);
EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, CalcHist, testing::Combine(
ALL_DEVICES, testing::ValuesIn(hist_size_to_roi_offset_params)));
PARAM_TEST_CASE(CalcHistWithMask, cv::cuda::DeviceInfo, hist_size_to_roi_offset_params_t)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int roiOffsetX;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = get<0>(GET_PARAM(1));
roiOffsetX = get<1>(GET_PARAM(1));
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(CalcHistWithMask, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
const Rect roi = Rect(roiOffsetX, 0, src.cols - roiOffsetX, src.rows);
cv::Mat mask = randomMat(size, CV_8UC1);
cv::Mat(mask, cv::Rect(0, 0, size.width / 2, size.height / 2)).setTo(0);
cv::cuda::GpuMat hist;
GpuMat srcDevice = loadMat(src);
GpuMat maskDevice = loadMat(mask);
cv::cuda::calcHist(srcDevice(roi), maskDevice(roi), hist);
cv::Mat hist_gold;
const int hbins = 256;
const float hranges[] = {0.0f, 256.0f};
const int histSize[] = {hbins};
const float* ranges[] = {hranges};
const int channels[] = {0};
const Mat srcRoi = src(roi);
cv::calcHist(&srcRoi, 1, channels, mask(roi), hist_gold, 1, histSize, ranges);
hist_gold = hist_gold.reshape(1, 1);
hist_gold.convertTo(hist_gold, CV_32S);
EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, CalcHistWithMask, testing::Combine(
ALL_DEVICES, testing::ValuesIn(hist_size_to_roi_offset_params)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// EqualizeHist
PARAM_TEST_CASE(EqualizeHist, cv::cuda::DeviceInfo, cv::Size)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(EqualizeHist, Async)
{
cv::Mat src = randomMat(size, CV_8UC1);
cv::cuda::Stream stream;
cv::cuda::GpuMat dst;
cv::cuda::equalizeHist(loadMat(src), dst, stream);
stream.waitForCompletion();
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
CUDA_TEST_P(EqualizeHist, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
cv::cuda::GpuMat dst;
cv::cuda::equalizeHist(loadMat(src), dst);
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, EqualizeHist, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES));
TEST(EqualizeHistIssue, Issue18035)
{
std::vector<std::string> imgPaths;
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/3MP.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/5MP.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/airplane.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/baboon.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/box.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/box_in_scene.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/fruits.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/fruits_ecc.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/graffiti.png");
imgPaths.push_back(std::string(cvtest::TS::ptr()->get_data_path()) + "../cv/shared/lena.png");
for (size_t i = 0; i < imgPaths.size(); ++i)
{
std::string imgPath = imgPaths[i];
cv::Mat src = cv::imread(imgPath, cv::IMREAD_GRAYSCALE);
src = src / 30;
cv::cuda::GpuMat d_src, dst;
d_src.upload(src);
cv::cuda::equalizeHist(d_src, dst);
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
PARAM_TEST_CASE(EqualizeHistExtreme, cv::cuda::DeviceInfo, cv::Size, int)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int val;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
val = GET_PARAM(2);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(EqualizeHistExtreme, Case1)
{
cv::Mat src(size, CV_8UC1, val);
cv::cuda::GpuMat dst;
cv::cuda::equalizeHist(loadMat(src), dst);
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
CUDA_TEST_P(EqualizeHistExtreme, Case2)
{
cv::Mat src = randomMat(size, CV_8UC1, val);
cv::cuda::GpuMat dst;
cv::cuda::equalizeHist(loadMat(src), dst);
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, EqualizeHistExtreme, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Range(0, 256)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// CLAHE
namespace
{
IMPLEMENT_PARAM_CLASS(ClipLimit, double)
}
PARAM_TEST_CASE(CLAHE, cv::cuda::DeviceInfo, cv::Size, ClipLimit, MatType)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
double clipLimit;
int type;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
clipLimit = GET_PARAM(2);
type = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(CLAHE, Accuracy)
{
cv::Mat src;
if (type == CV_8UC1)
src = randomMat(size, type);
else if (type == CV_16UC1)
src = randomMat(size, type, 0, 65535);
cv::Ptr<cv::cuda::CLAHE> clahe = cv::cuda::createCLAHE(clipLimit);
cv::cuda::GpuMat dst;
clahe->apply(loadMat(src), dst);
cv::Ptr<cv::CLAHE> clahe_gold = cv::createCLAHE(clipLimit);
cv::Mat dst_gold;
clahe_gold->apply(src, dst_gold);
ASSERT_MAT_NEAR(dst_gold, dst, 1.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, CLAHE, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(0.0, 5.0, 10.0, 20.0, 40.0),
testing::Values(MatType(CV_8UC1), MatType(CV_16UC1))));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_hough.cpp
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/*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 {
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughLines
PARAM_TEST_CASE(HoughLines, cv::cuda::DeviceInfo, cv::Size, UseRoi)
{
static void generateLines(cv::Mat& img)
{
img.setTo(cv::Scalar::all(0));
cv::line(img, cv::Point(20, 0), cv::Point(20, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 50), cv::Point(img.cols, 50), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 0), cv::Point(img.cols, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(img.cols, 0), cv::Point(0, img.rows), cv::Scalar::all(255));
}
static void drawLines(cv::Mat& dst, const std::vector<cv::Vec2f>& lines)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < lines.size(); ++i)
{
float rho = lines[i][0], theta = lines[i][1];
cv::Point pt1, pt2;
double a = std::cos(theta), b = std::sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cv::line(dst, pt1, pt2, cv::Scalar::all(255));
}
}
};
CUDA_TEST_P(HoughLines, Accuracy)
{
const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float rho = 1.0f;
const float theta = (float) (1.5 * CV_PI / 180.0);
const int threshold = 100;
cv::Mat src(size, CV_8UC1);
generateLines(src);
cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, threshold);
cv::cuda::GpuMat d_lines;
hough->detect(loadMat(src, useRoi), d_lines);
std::vector<cv::Vec2f> lines;
hough->downloadResults(d_lines, lines);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, lines);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughLines, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughLines Probabilistic
PARAM_TEST_CASE(HoughLinesProbabilistic, DeviceInfo, Size, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
bool useRoi;
Size size;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
useRoi = GET_PARAM(2);
cv::cuda::setDevice(devInfo.deviceID());
}
static void generateLines(cv::Mat& img)
{
img.setTo(cv::Scalar::all(0));
cv::line(img, cv::Point(10, 0), cv::Point(10, 20), cv::Scalar::all(255));
cv::line(img, cv::Point(20, 50), cv::Point(40, 50), cv::Scalar::all(255));
}
static void drawLines(cv::Mat& dst, const std::vector<cv::Vec4i>& linesSegment)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < linesSegment.size(); ++i)
{
Vec4i l = linesSegment[i];
cv::line(dst, cv::Point(l[0], l[1]), cv::Point(l[2], l[3]), cv::Scalar::all(255));
}
}
};
CUDA_TEST_P(HoughLinesProbabilistic, Accuracy)
{
const float rho = 1.0f;
const float theta = (float) (1.0 * CV_PI / 180.0);
const int minLineLength = 15;
const int maxLineGap = 8;
cv::Mat src(size, CV_8UC1);
generateLines(src);
Ptr<cv::cuda::HoughSegmentDetector> hough = cv::cuda::createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap);
cv::cuda::GpuMat d_lines;
hough->detect(loadMat(src, useRoi), d_lines);
std::vector<cv::Vec4i> linesSegment;
std::vector<cv::Vec2f> lines;
d_lines.download(linesSegment);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, linesSegment);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
void HoughLinesProbabilisticThread(const Ptr<HoughSegmentDetector> detector, const GpuMat& imgIn, const std::vector<GpuMat>& linesOut, Stream& stream) {
for (auto& lines : linesOut)
detector->detect(imgIn, lines, stream);
stream.waitForCompletion();
}
CUDA_TEST_P(HoughLinesProbabilistic, Async)
{
constexpr int nThreads = 5;
constexpr int nIters = 5;
vector<Stream> streams(nThreads); // async test only
vector<GpuMat> imgsIn;
vector<Ptr<HoughSegmentDetector>> detectors;
vector<vector<GpuMat>> linesOut(nThreads);
const float rho = 1.0f;
const float theta = (float)(1.0 * CV_PI / 180.0);
const int minLineLength = 15;
const int maxLineGap = 8;
cv::Mat src(size, CV_8UC1);
generateLines(src);
for (int i = 0; i < nThreads; i++) {
imgsIn.push_back(loadMat(src, useRoi));
detectors.push_back(createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap));
linesOut.push_back(vector<GpuMat>(nIters));
}
vector<std::thread> thread(nThreads);
for (int i = 0; i < nThreads; i++) thread.at(i) = std::thread(HoughLinesProbabilisticThread, detectors.at(i), std::ref(imgsIn.at(i)), std::ref(linesOut.at(i)), std::ref(streams.at(i)));
for (int i = 0; i < nThreads; i++) thread.at(i).join();
for (int i = 0; i < nThreads; i++) {
std::vector<cv::Vec4i> linesSegment;
std::vector<cv::Vec2f> lines;
for (const auto& line : linesOut.at(i)) {
line.download(linesSegment);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, linesSegment);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughLinesProbabilistic, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughCircles
PARAM_TEST_CASE(HoughCircles, cv::cuda::DeviceInfo, cv::Size, UseRoi)
{
static void drawCircles(cv::Mat& dst, const std::vector<cv::Vec3f>& circles, bool fill)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < circles.size(); ++i)
cv::circle(dst, cv::Point2f(circles[i][0], circles[i][1]), (int)circles[i][2], cv::Scalar::all(255), fill ? -1 : 1);
}
};
CUDA_TEST_P(HoughCircles, Accuracy)
{
const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float dp = 2.0f;
const float minDist = 0.0f;
const int minRadius = 10;
const int maxRadius = 20;
const int cannyThreshold = 100;
const int votesThreshold = 20;
std::vector<cv::Vec3f> circles_gold(4);
circles_gold[0] = cv::Vec3i(20, 20, minRadius);
circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
cv::Mat src(size, CV_8UC1);
drawCircles(src, circles_gold, true);
cv::Ptr<cv::cuda::HoughCirclesDetector> houghCircles = cv::cuda::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
cv::cuda::GpuMat d_circles;
houghCircles->detect(loadMat(src, useRoi), d_circles);
std::vector<cv::Vec3f> circles;
d_circles.download(circles);
ASSERT_FALSE(circles.empty());
for (size_t i = 0; i < circles.size(); ++i)
{
cv::Vec3f cur = circles[i];
bool found = false;
for (size_t j = 0; j < circles_gold.size(); ++j)
{
cv::Vec3f gold = circles_gold[j];
if (std::fabs(cur[0] - gold[0]) < 5 && std::fabs(cur[1] - gold[1]) < 5 && std::fabs(cur[2] - gold[2]) < 5)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughCircles, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// GeneralizedHough
PARAM_TEST_CASE(GeneralizedHough, cv::cuda::DeviceInfo, UseRoi)
{
};
CUDA_TEST_P(GeneralizedHough, Ballard)
{
const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
const bool useRoi = GET_PARAM(1);
cv::Mat templ = readImage("../cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Point templCenter(templ.cols / 2, templ.rows / 2);
const size_t gold_count = 3;
cv::Point pos_gold[gold_count];
pos_gold[0] = cv::Point(templCenter.x + 10, templCenter.y + 10);
pos_gold[1] = cv::Point(2 * templCenter.x + 40, templCenter.y + 10);
pos_gold[2] = cv::Point(2 * templCenter.x + 40, 2 * templCenter.y + 40);
cv::Mat image(templ.rows * 3, templ.cols * 3, CV_8UC1, cv::Scalar::all(0));
for (size_t i = 0; i < gold_count; ++i)
{
cv::Rect rec(pos_gold[i].x - templCenter.x, pos_gold[i].y - templCenter.y, templ.cols, templ.rows);
cv::Mat imageROI = image(rec);
templ.copyTo(imageROI);
}
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::cuda::createGeneralizedHoughBallard();
alg->setVotesThreshold(200);
alg->setTemplate(loadMat(templ, useRoi));
cv::cuda::GpuMat d_pos;
alg->detect(loadMat(image, useRoi), d_pos);
std::vector<cv::Vec4f> pos;
d_pos.download(pos);
ASSERT_EQ(gold_count, pos.size());
for (size_t i = 0; i < gold_count; ++i)
{
cv::Point gold = pos_gold[i];
bool found = false;
for (size_t j = 0; j < pos.size(); ++j)
{
cv::Point2f p(pos[j][0], pos[j][1]);
if (::fabs(p.x - gold.x) < 2 && ::fabs(p.y - gold.y) < 2)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, GeneralizedHough, testing::Combine(
ALL_DEVICES,
WHOLE_SUBMAT));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_main.cpp
+45
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/*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"
CV_CUDA_TEST_MAIN("gpu")
modules/cudaimgproc/test/test_match_template.cpp
+341
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@@ -0,0 +1,341 @@
/*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
modules/cudaimgproc/test/test_mean_shift.cpp
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/*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 {
////////////////////////////////////////////////////////////////////////////////
// MeanShift
struct MeanShift : testing::TestWithParam<cv::cuda::DeviceInfo>
{
cv::cuda::DeviceInfo devInfo;
cv::Mat img;
int spatialRad;
int colorRad;
virtual void SetUp()
{
devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
img = readImageType("meanshift/cones.png", CV_8UC4);
ASSERT_FALSE(img.empty());
spatialRad = 30;
colorRad = 30;
}
};
CUDA_TEST_P(MeanShift, Filtering)
{
cv::Mat img_template;
if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20))
img_template = readImage("meanshift/con_result.png");
else
img_template = readImage("meanshift/con_result_CC1X.png");
ASSERT_FALSE(img_template.empty());
cv::cuda::GpuMat d_dst;
cv::cuda::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad);
ASSERT_EQ(CV_8UC4, d_dst.type());
cv::Mat dst(d_dst);
cv::Mat result;
cv::cvtColor(dst, result, cv::COLOR_BGRA2BGR);
EXPECT_MAT_NEAR(img_template, result, 0.0);
}
CUDA_TEST_P(MeanShift, Proc)
{
cv::FileStorage fs;
if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20))
fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ);
else
fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
cv::Mat spmap_template;
fs["spmap"] >> spmap_template;
ASSERT_FALSE(spmap_template.empty());
cv::cuda::GpuMat rmap_filtered;
cv::cuda::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad);
cv::cuda::GpuMat rmap;
cv::cuda::GpuMat spmap;
cv::cuda::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad);
ASSERT_EQ(CV_8UC4, rmap.type());
EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0);
EXPECT_MAT_NEAR(spmap_template, spmap, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShift, ALL_DEVICES);
////////////////////////////////////////////////////////////////////////////////
// MeanShiftSegmentation
namespace
{
IMPLEMENT_PARAM_CLASS(MinSize, int);
}
PARAM_TEST_CASE(MeanShiftSegmentation, cv::cuda::DeviceInfo, MinSize)
{
cv::cuda::DeviceInfo devInfo;
int minsize;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
minsize = GET_PARAM(1);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(MeanShiftSegmentation, Regression)
{
cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4);
ASSERT_FALSE(img.empty());
std::ostringstream path;
path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20))
path << ".png";
else
path << "_CC1X.png";
cv::Mat dst_gold = readImage(path.str());
ASSERT_FALSE(dst_gold.empty());
cv::Mat dst;
cv::cuda::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize);
cv::Mat dst_rgb;
cv::cvtColor(dst, dst_rgb, cv::COLOR_BGRA2BGR);
EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShiftSegmentation, testing::Combine(
ALL_DEVICES,
testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364))));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_moments.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test { namespace {
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Moments
CV_ENUM(MaxMomentsOrder, MomentsOrder::FIRST_ORDER_MOMENTS, MomentsOrder::SECOND_ORDER_MOMENTS, MomentsOrder::THIRD_ORDER_MOMENTS)
PARAM_TEST_CASE(Moments, cv::cuda::DeviceInfo, cv::Size, bool, MatDepth, MatDepth, UseRoi, MaxMomentsOrder)
{
DeviceInfo devInfo;
Size size;
bool isBinary;
float pcWidth = 0.6f;
int momentsType;
int imgType;
bool useRoi;
MomentsOrder order;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
isBinary = GET_PARAM(2);
momentsType = GET_PARAM(3);
imgType = GET_PARAM(4);
useRoi = GET_PARAM(5);
order = static_cast<MomentsOrder>(static_cast<int>(GET_PARAM(6)));
cv::cuda::setDevice(devInfo.deviceID());
}
static void drawCircle(cv::Mat& dst, const cv::Vec3i& circle, bool fill)
{
dst.setTo(Scalar::all(0));
cv::circle(dst, Point2i(circle[0], circle[1]), circle[2], Scalar::all(255), fill ? -1 : 1, cv::LINE_AA);
}
};
bool Equal(const double m0, const double m1, const double absPcErr) {
if (absPcErr == 0) return m0 == m1;
if (m0 == 0) {
if (m1 < absPcErr) return true;
else return false;
}
const double pcDiff = abs(m0 - m1) / m1;
return pcDiff < absPcErr;
}
void CheckMoments(const cv::Moments m0, const cv::Moments m1, const MomentsOrder order, const int momentsType) {
double absPcErr = momentsType == CV_64F ? 0 : 5e-7;
ASSERT_TRUE(Equal(m0.m00, m1.m00, absPcErr)) << "m0.m00: " << m0.m00 << ", m1.m00: " << m1.m00 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m10, m1.m10, absPcErr)) << "m0.m10: " << m0.m10 << ", m1.m10: " << m1.m10 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m01, m1.m01, absPcErr)) << "m0.m01: " << m0.m01 << ", m1.m01: " << m1.m01 << ", absPcErr: " << absPcErr;
if (static_cast<int>(order) >= static_cast<int>(MomentsOrder::SECOND_ORDER_MOMENTS)) {
ASSERT_TRUE(Equal(m0.m20, m1.m20, absPcErr)) << "m0.m20: " << m0.m20 << ", m1.m20: " << m1.m20 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m11, m1.m11, absPcErr)) << "m0.m11: " << m0.m11 << ", m1.m11: " << m1.m11 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m02, m1.m02, absPcErr)) << "m0.m02: " << m0.m02 << ", m1.m02: " << m1.m02 << ", absPcErr: " << absPcErr;
}
if (static_cast<int>(order) >= static_cast<int>(MomentsOrder::THIRD_ORDER_MOMENTS)) {
ASSERT_TRUE(Equal(m0.m30, m1.m30, absPcErr)) << "m0.m30: " << m0.m30 << ", m1.m30: " << m1.m30 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m21, m1.m21, absPcErr)) << "m0.m21: " << m0.m21 << ", m1.m21: " << m1.m21 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m12, m1.m12, absPcErr)) << "m0.m12: " << m0.m12 << ", m1.m12: " << m1.m12 << ", absPcErr: " << absPcErr;
ASSERT_TRUE(Equal(m0.m03, m1.m03, absPcErr)) << "m0.m03: " << m0.m03 << ", m1.m03: " << m1.m03 << ", absPcErr: " << absPcErr;
}
}
CUDA_TEST_P(Moments, Accuracy)
{
Mat imgHost(size, imgType);
const Rect roi = useRoi ? Rect(1, 0, imgHost.cols - 2, imgHost.rows) : Rect(0, 0, imgHost.cols, imgHost.rows);
const Vec3i circle(size.width / 2, size.height / 2, static_cast<int>(static_cast<float>(size.width/2) * pcWidth));
drawCircle(imgHost, circle, true);
const GpuMat imgDevice(imgHost);
const int nMoments = numMoments(order);
setBufferPoolUsage(true);
setBufferPoolConfig(getDevice(), nMoments * ((momentsType == CV_64F) ? sizeof(double) : sizeof(float)), 1);
const cv::Moments moments = cuda::moments(imgDevice(roi), isBinary, order, momentsType);
Mat imgHostFloat; imgHost(roi).convertTo(imgHostFloat, CV_32F);
const cv::Moments momentsGs = cv::moments(imgHostFloat, isBinary);
CheckMoments(momentsGs, moments, order, momentsType);
}
CUDA_TEST_P(Moments, Async)
{
Stream stream;
const int nMoments = numMoments(order);
GpuMat momentsDevice(1, nMoments, momentsType);
Mat imgHost(size, imgType);
const Rect roi = useRoi ? Rect(1, 0, imgHost.cols - 2, imgHost.rows) : Rect(0, 0, imgHost.cols, imgHost.rows);
const Vec3i circle(size.width / 2, size.height / 2, static_cast<int>(static_cast<float>(size.width/2) * pcWidth));
drawCircle(imgHost, circle, true);
const GpuMat imgDevice(imgHost);
cuda::spatialMoments(imgDevice(roi), momentsDevice, isBinary, order, momentsType, stream);
HostMem momentsHost(1, nMoments, momentsType);
momentsDevice.download(momentsHost, stream);
stream.waitForCompletion();
const cv::Moments moments = convertSpatialMoments(momentsHost.createMatHeader(), order, momentsType);
Mat imgHostAdjustedType = imgHost(roi);
if (imgType != CV_8U && imgType != CV_32F)
imgHost(roi).convertTo(imgHostAdjustedType, CV_32F);
const cv::Moments momentsGs = cv::moments(imgHostAdjustedType, isBinary);
CheckMoments(momentsGs, moments, order, momentsType);
}
#define SIZES DIFFERENT_SIZES
#define GRAYSCALE_BINARY testing::Bool()
#define MOMENTS_TYPE testing::Values(MatDepth(CV_32F), MatDepth(CV_64F))
#define IMG_TYPE ALL_DEPTH
#define USE_ROI WHOLE_SUBMAT
#define MOMENTS_ORDER testing::Values(MaxMomentsOrder(MomentsOrder::FIRST_ORDER_MOMENTS), MaxMomentsOrder(MomentsOrder::SECOND_ORDER_MOMENTS), MaxMomentsOrder(MomentsOrder::THIRD_ORDER_MOMENTS))
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, Moments, testing::Combine(ALL_DEVICES, SIZES, GRAYSCALE_BINARY, MOMENTS_TYPE, IMG_TYPE, USE_ROI, MOMENTS_ORDER));
}} // namespace
#endif // HAVE_CUDA
modules/cudaimgproc/test/test_precomp.hpp
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/*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*/
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_test.hpp"
#include "opencv2/cudaimgproc.hpp"
#include "opencv2/geometry.hpp"
#include "cvconfig.h"
#include <thread>
#endif