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test_imgproc.cpp
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1223 lines (959 loc) · 34.5 KB
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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
using namespace cvtest;
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Integral
PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
useRoi = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Integral, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi);
cv::gpu::integral(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::integral(src, dst_gold, CV_32S);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HistEven
PARAM_TEST_CASE(HistEven, cv::gpu::DeviceInfo, cv::Size)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(HistEven, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
int hbins = 30;
float hranges[] = {50.0f, 200.0f};
cv::gpu::GpuMat hist;
cv::gpu::histEven(loadMat(src), hist, hbins, (int) hranges[0], (int) hranges[1]);
cv::Mat hist_gold;
int histSize[] = {hbins};
const float* ranges[] = {hranges};
int channels[] = {0};
cv::calcHist(&src, 1, channels, cv::Mat(), hist_gold, 1, histSize, ranges);
hist_gold = hist_gold.t();
hist_gold.convertTo(hist_gold, CV_32S);
EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// CalcHist
PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(CalcHist, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
cv::gpu::GpuMat hist;
cv::gpu::calcHist(loadMat(src), 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};
cv::calcHist(&src, 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(GPU_ImgProc, CalcHist, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// EqualizeHist
PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(EqualizeHist, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
cv::gpu::GpuMat dst;
cv::gpu::equalizeHist(loadMat(src), dst);
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 3.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// CLAHE
namespace
{
IMPLEMENT_PARAM_CLASS(ClipLimit, double)
}
PARAM_TEST_CASE(CLAHE, cv::gpu::DeviceInfo, cv::Size, ClipLimit)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
double clipLimit;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
clipLimit = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(CLAHE, Accuracy)
{
cv::Mat src = randomMat(size, CV_8UC1);
cv::Ptr<cv::gpu::CLAHE> clahe = cv::gpu::createCLAHE(clipLimit);
cv::gpu::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(GPU_ImgProc, CLAHE, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(0.0, 40.0)));
////////////////////////////////////////////////////////////////////////
// ColumnSum
PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(ColumnSum, Accuracy)
{
cv::Mat src = randomMat(size, CV_32FC1);
cv::gpu::GpuMat d_dst;
cv::gpu::columnSum(loadMat(src), d_dst);
cv::Mat dst(d_dst);
for (int j = 0; j < src.cols; ++j)
{
float gold = src.at<float>(0, j);
float res = dst.at<float>(0, j);
ASSERT_NEAR(res, gold, 1e-5);
}
for (int i = 1; i < src.rows; ++i)
{
for (int j = 0; j < src.cols; ++j)
{
float gold = src.at<float>(i, j) += src.at<float>(i - 1, j);
float res = dst.at<float>(i, j);
ASSERT_NEAR(res, gold, 1e-5);
}
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES));
////////////////////////////////////////////////////////
// Canny
namespace
{
IMPLEMENT_PARAM_CLASS(AppertureSize, int);
IMPLEMENT_PARAM_CLASS(L2gradient, bool);
}
PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi)
{
cv::gpu::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::gpu::setDevice(devInfo.deviceID());
}
};
GPU_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;
if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS))
{
try
{
cv::gpu::GpuMat edges;
cv::gpu::Canny(loadMat(img), edges, low_thresh, high_thresh, apperture_size, useL2gradient);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsNotImplemented, e.code);
}
}
else
{
cv::gpu::GpuMat edges;
cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient);
cv::Mat edges_gold;
cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient);
EXPECT_MAT_SIMILAR(edges_gold, edges, 2e-2);
}
}
#ifdef OPENCV_TINY_GPU_MODULE
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine(
ALL_DEVICES,
testing::Values(AppertureSize(3)),
testing::Values(L2gradient(false), L2gradient(true)),
WHOLE_SUBMAT));
#else
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine(
ALL_DEVICES,
testing::Values(AppertureSize(3), AppertureSize(5)),
testing::Values(L2gradient(false), L2gradient(true)),
WHOLE_SUBMAT));
#endif
////////////////////////////////////////////////////////////////////////////////
// MeanShift
struct MeanShift : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Mat img;
int spatialRad;
int colorRad;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
img = readImageType("meanshift/cones.png", CV_8UC4);
ASSERT_FALSE(img.empty());
spatialRad = 30;
colorRad = 30;
}
};
GPU_TEST_P(MeanShift, Filtering)
{
cv::Mat img_template;
if (supportFeature(devInfo, cv::gpu::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::gpu::GpuMat d_dst;
cv::gpu::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_BGRA2BGR);
EXPECT_MAT_NEAR(img_template, result, 0.0);
}
GPU_TEST_P(MeanShift, Proc)
{
cv::FileStorage fs;
if (supportFeature(devInfo, cv::gpu::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::gpu::GpuMat rmap_filtered;
cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad);
cv::gpu::GpuMat rmap;
cv::gpu::GpuMat spmap;
cv::gpu::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(GPU_ImgProc, MeanShift, ALL_DEVICES);
////////////////////////////////////////////////////////////////////////////////
// MeanShiftSegmentation
namespace
{
IMPLEMENT_PARAM_CLASS(MinSize, int);
}
PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize)
{
cv::gpu::DeviceInfo devInfo;
int minsize;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
minsize = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_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::gpu::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::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize);
cv::Mat dst_rgb;
cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR);
EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine(
ALL_DEVICES,
testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364))));
////////////////////////////////////////////////////////////////////////////
// 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::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::gpu::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::gpu::setDevice(devInfo.deviceID());
}
};
GPU_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::gpu::GpuMat result;
cv::gpu::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(GPU_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));
#ifdef HAVE_CUFFT
////////////////////////////////////////////////////////
// Convolve
namespace
{
void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false)
{
// reallocate the output array if needed
C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type());
cv::Size dftSize;
// compute the size of DFT transform
dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1);
dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1);
// allocate temporary buffers and initialize them with 0s
cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0));
cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0));
// copy A and B to the top-left corners of tempA and tempB, respectively
cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows));
A.copyTo(roiA);
cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows));
B.copyTo(roiB);
// now transform the padded A & B in-place;
// use "nonzeroRows" hint for faster processing
cv::dft(tempA, tempA, 0, A.rows);
cv::dft(tempB, tempB, 0, B.rows);
// multiply the spectrums;
// the function handles packed spectrum representations well
cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr);
// transform the product back from the frequency domain.
// Even though all the result rows will be non-zero,
// you need only the first C.rows of them, and thus you
// pass nonzeroRows == C.rows
cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows);
// now copy the result back to C.
tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C);
}
IMPLEMENT_PARAM_CLASS(KSize, int);
IMPLEMENT_PARAM_CLASS(Ccorr, bool);
}
PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int ksize;
bool ccorr;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
ksize = GET_PARAM(2);
ccorr = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Convolve, Accuracy)
{
cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0);
cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0);
cv::gpu::GpuMat dst;
cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr);
cv::Mat dst_gold;
convolveDFT(src, kernel, dst_gold, ccorr);
EXPECT_MAT_NEAR(dst, dst_gold, 1e-1);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Convolve, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)),
testing::Values(Ccorr(false), Ccorr(true))));
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate8U
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
namespace
{
IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size);
}
PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Size templ_size;
int cn;
int method;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
templ_size = GET_PARAM(2);
cn = GET_PARAM(3);
method = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(MatchTemplate8U, DISABLED_Accuracy)
{
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn));
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn));
cv::gpu::GpuMat dst;
cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method);
cv::Mat dst_gold;
cv::matchTemplate(image, templ, dst_gold, method);
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(GPU_ImgProc, MatchTemplate8U, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
testing::Values(Channels(1), Channels(3), Channels(4)),
TemplateMethod::all()));
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate32F
PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Size templ_size;
int cn;
int method;
int n, m, h, w;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
templ_size = GET_PARAM(2);
cn = GET_PARAM(3);
method = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(MatchTemplate32F, DISABLED_Regression)
{
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn));
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn));
cv::gpu::GpuMat dst;
cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method);
cv::Mat dst_gold;
cv::matchTemplate(image, templ, dst_gold, method);
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(GPU_ImgProc, MatchTemplate32F, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
testing::Values(Channels(1), Channels(3), Channels(4)),
testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))));
////////////////////////////////////////////////////////////////////////////////
// MatchTemplateBlackSource
PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod)
{
cv::gpu::DeviceInfo devInfo;
int method;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
method = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(MatchTemplateBlackSource, DISABLED_Accuracy)
{
cv::Mat image = readImage("matchtemplate/black.png");
ASSERT_FALSE(image.empty());
cv::Mat pattern = readImage("matchtemplate/cat.png");
ASSERT_FALSE(pattern.empty());
cv::gpu::GpuMat d_dst;
cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, method);
cv::Mat dst(d_dst);
double maxValue;
cv::Point maxLoc;
cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc);
cv::Point maxLocGold = cv::Point(284, 12);
ASSERT_EQ(maxLocGold, maxLoc);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine(
ALL_DEVICES,
testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED))));
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate_CCOEF_NORMED
PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair<std::string, std::string>)
{
cv::gpu::DeviceInfo devInfo;
std::string imageName;
std::string patternName;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
imageName = GET_PARAM(1).first;
patternName = GET_PARAM(1).second;
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(MatchTemplate_CCOEF_NORMED, DISABLED_Accuracy)
{
cv::Mat image = readImage(imageName);
ASSERT_FALSE(image.empty());
cv::Mat pattern = readImage(patternName);
ASSERT_FALSE(pattern.empty());
cv::gpu::GpuMat d_dst;
cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, CV_TM_CCOEFF_NORMED);
cv::Mat dst(d_dst);
cv::Point minLoc, maxLoc;
double minVal, maxVal;
cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc);
cv::Mat dstGold;
cv::matchTemplate(image, pattern, dstGold, CV_TM_CCOEFF_NORMED);
double minValGold, maxValGold;
cv::Point minLocGold, maxLocGold;
cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold);
ASSERT_EQ(minLocGold, minLoc);
ASSERT_EQ(maxLocGold, maxLoc);
ASSERT_LE(maxVal, 1.0);
ASSERT_GE(minVal, -1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine(
ALL_DEVICES,
testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png")))));
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate_CanFindBigTemplate
struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(MatchTemplate_CanFindBigTemplate, DISABLED_SQDIFF_NORMED)
{
cv::Mat scene = readImage("matchtemplate/scene.png");
ASSERT_FALSE(scene.empty());
cv::Mat templ = readImage("matchtemplate/template.png");
ASSERT_FALSE(templ.empty());
cv::gpu::GpuMat d_result;
cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF_NORMED);
cv::Mat result(d_result);
double minVal;
cv::Point minLoc;
cv::minMaxLoc(result, &minVal, 0, &minLoc, 0);
ASSERT_GE(minVal, 0);
ASSERT_LT(minVal, 1e-3);
ASSERT_EQ(344, minLoc.x);
ASSERT_EQ(0, minLoc.y);
}
GPU_TEST_P(MatchTemplate_CanFindBigTemplate, DISABLED_SQDIFF)
{
cv::Mat scene = readImage("matchtemplate/scene.png");
ASSERT_FALSE(scene.empty());
cv::Mat templ = readImage("matchtemplate/template.png");
ASSERT_FALSE(templ.empty());
cv::gpu::GpuMat d_result;
cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF);
cv::Mat result(d_result);
double minVal;
cv::Point minLoc;
cv::minMaxLoc(result, &minVal, 0, &minLoc, 0);
ASSERT_GE(minVal, 0);
ASSERT_EQ(344, minLoc.x);
ASSERT_EQ(0, minLoc.y);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES);
////////////////////////////////////////////////////////////////////////////
// MulSpectrums
CV_FLAGS(DftFlags, 0, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)
PARAM_TEST_CASE(MulSpectrums, cv::gpu::DeviceInfo, cv::Size, DftFlags)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int flag;
cv::Mat a, b;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
flag = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
a = randomMat(size, CV_32FC2);
b = randomMat(size, CV_32FC2);
}
};
GPU_TEST_P(MulSpectrums, Simple)
{
cv::gpu::GpuMat c;
cv::gpu::mulSpectrums(loadMat(a), loadMat(b), c, flag, false);
cv::Mat c_gold;
cv::mulSpectrums(a, b, c_gold, flag, false);
EXPECT_MAT_NEAR(c_gold, c, 1e-2);
}
GPU_TEST_P(MulSpectrums, Scaled)
{
float scale = 1.f / size.area();
cv::gpu::GpuMat c;
cv::gpu::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false);
cv::Mat c_gold;
cv::mulSpectrums(a, b, c_gold, flag, false);
c_gold.convertTo(c_gold, c_gold.type(), scale);
EXPECT_MAT_NEAR(c_gold, c, 1e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MulSpectrums, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS))));
////////////////////////////////////////////////////////////////////////////
// Dft
struct Dft : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;