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opencv/modules/gpu/test/test_imgproc_gpu.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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 <cmath>
#include <limits>
#include "test_precomp.hpp"
using namespace cv;
using namespace std;
using namespace gpu;
class CV_GpuImageProcTest : public cvtest::BaseTest
{
public:
virtual ~CV_GpuImageProcTest() {}
protected:
void run(int);
int test8UC1 (const Mat& img);
int test8UC4 (const Mat& img);
int test32SC1(const Mat& img);
int test32FC1(const Mat& img);
virtual int test(const Mat& img) = 0;
int CheckNorm(const Mat& m1, const Mat& m2);
// Checks whether two images are similar enough using normalized
// cross-correlation as an error measure
int CheckSimilarity(const Mat& m1, const Mat& m2, float max_err=1e-3f);
};
int CV_GpuImageProcTest::test8UC1(const Mat& img)
{
cv::Mat img_C1;
cvtColor(img, img_C1, CV_BGR2GRAY);
return test(img_C1);
}
int CV_GpuImageProcTest::test8UC4(const Mat& img)
{
cv::Mat img_C4;
cvtColor(img, img_C4, CV_BGR2BGRA);
return test(img_C4);
}
int CV_GpuImageProcTest::test32SC1(const Mat& img)
{
cv::Mat img_C1;
cvtColor(img, img_C1, CV_BGR2GRAY);
img_C1.convertTo(img_C1, CV_32S);
return test(img_C1);
}
int CV_GpuImageProcTest::test32FC1(const Mat& img)
{
cv::Mat temp, img_C1;
img.convertTo(temp, CV_32F, 1.f / 255.f);
cvtColor(temp, img_C1, CV_BGR2GRAY);
return test(img_C1);
}
int CV_GpuImageProcTest::CheckNorm(const Mat& m1, const Mat& m2)
{
double ret = norm(m1, m2, NORM_INF);
if (ret < std::numeric_limits<double>::epsilon())
{
return cvtest::TS::OK;
}
else
{
ts->printf(cvtest::TS::LOG, "Norm: %f\n", ret);
return cvtest::TS::FAIL_GENERIC;
}
}
int CV_GpuImageProcTest::CheckSimilarity(const Mat& m1, const Mat& m2, float max_err)
{
Mat diff;
cv::matchTemplate(m1, m2, diff, CV_TM_CCORR_NORMED);
float err = abs(diff.at<float>(0, 0) - 1.f);
if (err > max_err)
return cvtest::TS::FAIL_INVALID_OUTPUT;
return cvtest::TS::OK;
}
void CV_GpuImageProcTest::run( int )
{
//load image
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
int testResult = cvtest::TS::OK;
//run tests
ts->printf(cvtest::TS::LOG, "\n========Start test 8UC1========\n");
if (test8UC1(img) == cvtest::TS::OK)
ts->printf(cvtest::TS::LOG, "SUCCESS\n");
else
{
ts->printf(cvtest::TS::LOG, "FAIL\n");
testResult = cvtest::TS::FAIL_GENERIC;
}
ts->printf(cvtest::TS::LOG, "\n========Start test 8UC4========\n");
if (test8UC4(img) == cvtest::TS::OK)
ts->printf(cvtest::TS::LOG, "SUCCESS\n");
else
{
ts->printf(cvtest::TS::LOG, "FAIL\n");
testResult = cvtest::TS::FAIL_GENERIC;
}
ts->printf(cvtest::TS::LOG, "\n========Start test 32SC1========\n");
if (test32SC1(img) == cvtest::TS::OK)
ts->printf(cvtest::TS::LOG, "SUCCESS\n");
else
{
ts->printf(cvtest::TS::LOG, "FAIL\n");
testResult = cvtest::TS::FAIL_GENERIC;
}
ts->printf(cvtest::TS::LOG, "\n========Start test 32FC1========\n");
if (test32FC1(img) == cvtest::TS::OK)
ts->printf(cvtest::TS::LOG, "SUCCESS\n");
else
{
ts->printf(cvtest::TS::LOG, "FAIL\n");
testResult = cvtest::TS::FAIL_GENERIC;
}
ts->set_failed_test_info(testResult);
}
////////////////////////////////////////////////////////////////////////////////
// threshold
struct CV_GpuImageThresholdTest : public CV_GpuImageProcTest
{
public:
CV_GpuImageThresholdTest() {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1 && img.type() != CV_32FC1)
{
ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
return cvtest::TS::OK;
}
const double maxVal = img.type() == CV_8UC1 ? 255 : 1.0;
cv::RNG& rng = ts->get_rng();
int res = cvtest::TS::OK;
for (int type = THRESH_BINARY; type <= THRESH_TOZERO_INV; ++type)
{
const double thresh = rng.uniform(0.0, maxVal);
cv::Mat cpuRes;
cv::threshold(img, cpuRes, thresh, maxVal, type);
GpuMat gpu1(img);
GpuMat gpuRes;
cv::gpu::threshold(gpu1, gpuRes, thresh, maxVal, type);
if (CheckNorm(cpuRes, gpuRes) != cvtest::TS::OK)
res = cvtest::TS::FAIL_GENERIC;
}
return res;
}
};
////////////////////////////////////////////////////////////////////////////////
// resize
struct CV_GpuNppImageResizeTest : public CV_GpuImageProcTest
{
CV_GpuNppImageResizeTest() {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1 && img.type() != CV_8UC4)
{
ts->printf(cvtest::TS::LOG, "Unsupported type\n");
return cvtest::TS::OK;
}
int interpolations[] = {INTER_NEAREST, INTER_LINEAR, /*INTER_CUBIC,*/ /*INTER_LANCZOS4*/};
const char* interpolations_str[] = {"INTER_NEAREST", "INTER_LINEAR", /*"INTER_CUBIC",*/ /*"INTER_LANCZOS4"*/};
int interpolations_num = sizeof(interpolations) / sizeof(int);
int test_res = cvtest::TS::OK;
for (int i = 0; i < interpolations_num; ++i)
{
ts->printf(cvtest::TS::LOG, "Interpolation: %s\n", interpolations_str[i]);
Mat cpu_res1, cpu_res2;
cv::resize(img, cpu_res1, Size(), 2.0, 2.0, interpolations[i]);
cv::resize(cpu_res1, cpu_res2, Size(), 0.5, 0.5, interpolations[i]);
GpuMat gpu1(img), gpu_res1, gpu_res2;
cv::gpu::resize(gpu1, gpu_res1, Size(), 2.0, 2.0, interpolations[i]);
cv::gpu::resize(gpu_res1, gpu_res2, Size(), 0.5, 0.5, interpolations[i]);
if (CheckSimilarity(cpu_res2, gpu_res2) != cvtest::TS::OK)
test_res = cvtest::TS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// copyMakeBorder
struct CV_GpuNppImageCopyMakeBorderTest : public CV_GpuImageProcTest
{
CV_GpuNppImageCopyMakeBorderTest() {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1 && img.type() != CV_8UC4 && img.type() != CV_32SC1)
{
ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
return cvtest::TS::OK;
}
cv::RNG& rng = ts->get_rng();
int top = rng.uniform(1, 10);
int botton = rng.uniform(1, 10);
int left = rng.uniform(1, 10);
int right = rng.uniform(1, 10);
cv::Scalar val(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
Mat cpudst;
cv::copyMakeBorder(img, cpudst, top, botton, left, right, BORDER_CONSTANT, val);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::copyMakeBorder(gpu1, gpudst, top, botton, left, right, val);
return CheckNorm(cpudst, gpudst);
}
};
////////////////////////////////////////////////////////////////////////////////
// warpAffine
struct CV_GpuNppImageWarpAffineTest : public CV_GpuImageProcTest
{
CV_GpuNppImageWarpAffineTest() {}
int test(const Mat& img)
{
if (img.type() == CV_32SC1)
{
ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
return cvtest::TS::OK;
}
static double reflect[2][3] = { {-1, 0, 0},
{ 0, -1, 0} };
reflect[0][2] = img.cols;
reflect[1][2] = img.rows;
Mat M(2, 3, CV_64F, (void*)reflect);
int flags[] = {INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_NEAREST | WARP_INVERSE_MAP, INTER_LINEAR | WARP_INVERSE_MAP, INTER_CUBIC | WARP_INVERSE_MAP};
const char* flags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
int flags_num = sizeof(flags) / sizeof(int);
int test_res = cvtest::TS::OK;
for (int i = 0; i < flags_num; ++i)
{
ts->printf(cvtest::TS::LOG, "\nFlags: %s\n", flags_str[i]);
Mat cpudst;
cv::warpAffine(img, cpudst, M, img.size(), flags[i]);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::warpAffine(gpu1, gpudst, M, gpu1.size(), flags[i]);
// Check inner parts (ignoring 1 pixel width border)
if (CheckSimilarity(cpudst.rowRange(1, cpudst.rows - 1).colRange(1, cpudst.cols - 1),
gpudst.rowRange(1, gpudst.rows - 1).colRange(1, gpudst.cols - 1)) != cvtest::TS::OK)
test_res = cvtest::TS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// warpPerspective
struct CV_GpuNppImageWarpPerspectiveTest : public CV_GpuImageProcTest
{
CV_GpuNppImageWarpPerspectiveTest() {}
int test(const Mat& img)
{
if (img.type() == CV_32SC1)
{
ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
return cvtest::TS::OK;
}
static double reflect[3][3] = { { -1, 0, 0},
{ 0, -1, 0},
{ 0, 0, 1 }};
reflect[0][2] = img.cols;
reflect[1][2] = img.rows;
Mat M(3, 3, CV_64F, (void*)reflect);
int flags[] = {INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_NEAREST | WARP_INVERSE_MAP, INTER_LINEAR | WARP_INVERSE_MAP, INTER_CUBIC | WARP_INVERSE_MAP};
const char* flags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
int flags_num = sizeof(flags) / sizeof(int);
int test_res = cvtest::TS::OK;
for (int i = 0; i < flags_num; ++i)
{
ts->printf(cvtest::TS::LOG, "\nFlags: %s\n", flags_str[i]);
Mat cpudst;
cv::warpPerspective(img, cpudst, M, img.size(), flags[i]);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::warpPerspective(gpu1, gpudst, M, gpu1.size(), flags[i]);
// Check inner parts (ignoring 1 pixel width border)
if (CheckSimilarity(cpudst.rowRange(1, cpudst.rows - 1).colRange(1, cpudst.cols - 1),
gpudst.rowRange(1, gpudst.rows - 1).colRange(1, gpudst.cols - 1)) != cvtest::TS::OK)
test_res = cvtest::TS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// integral
struct CV_GpuNppImageIntegralTest : public CV_GpuImageProcTest
{
CV_GpuNppImageIntegralTest() {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1)
{
ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
return cvtest::TS::OK;
}
Mat cpusum;
cv::integral(img, cpusum, CV_32S);
GpuMat gpu1(img);
GpuMat gpusum;
cv::gpu::integral(gpu1, gpusum);
return CheckNorm(cpusum, gpusum) == cvtest::TS::OK ? cvtest::TS::OK : cvtest::TS::FAIL_GENERIC;
}
};
////////////////////////////////////////////////////////////////////////////////
// Canny
//struct CV_GpuNppImageCannyTest : public CV_GpuImageProcTest
//{
// CV_GpuNppImageCannyTest() : CV_GpuImageProcTest( "GPU-NppImageCanny", "Canny" ) {}
//
// int test(const Mat& img)
// {
// if (img.type() != CV_8UC1)
// {
// ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
// return cvtest::TS::OK;
// }
//
// const double threshold1 = 1.0, threshold2 = 10.0;
//
// Mat cpudst;
// cv::Canny(img, cpudst, threshold1, threshold2);
//
// GpuMat gpu1(img);
// GpuMat gpudst;
// cv::gpu::Canny(gpu1, gpudst, threshold1, threshold2);
//
// return CheckNorm(cpudst, gpudst);
// }
//};
////////////////////////////////////////////////////////////////////////////////
// cvtColor
class CV_GpuCvtColorTest : public cvtest::BaseTest
{
public:
CV_GpuCvtColorTest() {}
~CV_GpuCvtColorTest() {};
protected:
void run(int);
int CheckNorm(const Mat& m1, const Mat& m2);
};
int CV_GpuCvtColorTest::CheckNorm(const Mat& m1, const Mat& m2)
{
double ret = norm(m1, m2, NORM_INF);
if (ret <= 3)
{
return cvtest::TS::OK;
}
else
{
ts->printf(cvtest::TS::LOG, "\nNorm: %f\n", ret);
return cvtest::TS::FAIL_GENERIC;
}
}
void CV_GpuCvtColorTest::run( int )
{
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
int testResult = cvtest::TS::OK;
cv::Mat cpuRes;
cv::gpu::GpuMat gpuImg(img), gpuRes;
int codes[] = { CV_BGR2RGB, CV_RGB2BGRA, CV_BGRA2RGB,
CV_RGB2BGR555, CV_BGR5552BGR, CV_BGR2BGR565, CV_BGR5652RGB,
CV_RGB2YCrCb, CV_YCrCb2BGR, CV_BGR2YUV, CV_YUV2RGB,
CV_RGB2XYZ, CV_XYZ2BGR, CV_BGR2XYZ, CV_XYZ2RGB,
CV_RGB2HSV, CV_HSV2BGR, CV_BGR2HSV_FULL, CV_HSV2RGB_FULL,
CV_RGB2HLS, CV_HLS2BGR, CV_BGR2HLS_FULL, CV_HLS2RGB_FULL,
CV_RGB2GRAY, CV_GRAY2BGRA, CV_BGRA2GRAY,
CV_GRAY2BGR555, CV_BGR5552GRAY, CV_GRAY2BGR565, CV_BGR5652GRAY};
const char* codes_str[] = { "CV_BGR2RGB", "CV_RGB2BGRA", "CV_BGRA2RGB",
"CV_RGB2BGR555", "CV_BGR5552BGR", "CV_BGR2BGR565", "CV_BGR5652RGB",
"CV_RGB2YCrCb", "CV_YCrCb2BGR", "CV_BGR2YUV", "CV_YUV2RGB",
"CV_RGB2XYZ", "CV_XYZ2BGR", "CV_BGR2XYZ", "CV_XYZ2RGB",
"CV_RGB2HSV", "CV_HSV2RGB", "CV_BGR2HSV_FULL", "CV_HSV2RGB_FULL",
"CV_RGB2HLS", "CV_HLS2RGB", "CV_BGR2HLS_FULL", "CV_HLS2RGB_FULL",
"CV_RGB2GRAY", "CV_GRAY2BGRA", "CV_BGRA2GRAY",
"CV_GRAY2BGR555", "CV_BGR5552GRAY", "CV_GRAY2BGR565", "CV_BGR5652GRAY"};
int codes_num = sizeof(codes) / sizeof(int);
for (int i = 0; i < codes_num; ++i)
{
ts->printf(cvtest::TS::LOG, "\n%s\n", codes_str[i]);
cv::cvtColor(img, cpuRes, codes[i]);
cv::gpu::cvtColor(gpuImg, gpuRes, codes[i]);
if (CheckNorm(cpuRes, gpuRes) == cvtest::TS::OK)
ts->printf(cvtest::TS::LOG, "\nSUCCESS\n");
else
{
ts->printf(cvtest::TS::LOG, "\nFAIL\n");
testResult = cvtest::TS::FAIL_GENERIC;
}
img = cpuRes;
gpuImg = gpuRes;
}
ts->set_failed_test_info(testResult);
}
////////////////////////////////////////////////////////////////////////////////
// Histograms
class CV_GpuHistogramsTest : public cvtest::BaseTest
{
public:
CV_GpuHistogramsTest() {}
~CV_GpuHistogramsTest() {};
protected:
void run(int);
int CheckNorm(const Mat& m1, const Mat& m2)
{
double ret = norm(m1, m2, NORM_INF);
if (ret < std::numeric_limits<double>::epsilon())
{
return cvtest::TS::OK;
}
else
{
ts->printf(cvtest::TS::LOG, "\nNorm: %f\n", ret);
return cvtest::TS::FAIL_GENERIC;
}
}
};
void CV_GpuHistogramsTest::run( int )
{
//load image
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
Mat hsv;
cv::cvtColor(img, hsv, CV_BGR2HSV);
int hbins = 30;
int histSize[] = {hbins};
float hranges[] = {0, 180};
const float* ranges[] = {hranges};
MatND hist;
int channels[] = {0};
calcHist(&hsv, 1, channels, Mat(), hist, 1, histSize, ranges);
GpuMat gpuHsv(hsv);
std::vector<GpuMat> srcs;
cv::gpu::split(gpuHsv, srcs);
GpuMat gpuHist;
histEven(srcs[0], gpuHist, hbins, (int)hranges[0], (int)hranges[1]);
Mat cpuHist = hist;
cpuHist = cpuHist.t();
cpuHist.convertTo(cpuHist, CV_32S);
ts->set_failed_test_info(CheckNorm(cpuHist, gpuHist));
}
////////////////////////////////////////////////////////////////////////
// Corner Harris feature detector
struct CV_GpuCornerHarrisTest: cvtest::BaseTest
{
CV_GpuCornerHarrisTest() {}
void run(int)
{
for (int i = 0; i < 5; ++i)
{
int rows = 25 + rand() % 300, cols = 25 + rand() % 300;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, -1)) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, -1)) return;
}
}
bool compareToCpuTest(int rows, int cols, int depth, int blockSize, int apertureSize)
{
RNG rng;
cv::Mat src(rows, cols, depth);
if (depth == CV_32F)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(1));
else if (depth == CV_8U)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(256));
double k = 0.1;
cv::Mat dst_gold;
cv::gpu::GpuMat dst;
cv::Mat dsth;
int borderType;
borderType = BORDER_REFLECT101;
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, k, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-3f)
{
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d\n", i, j, a, b, apertureSize);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
borderType = BORDER_REPLICATE;
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, k, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-3f)
{
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d\n", i, j, a, b, apertureSize);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
return true;
}
};
////////////////////////////////////////////////////////////////////////
// Corner Min Eigen Val
struct CV_GpuCornerMinEigenValTest: cvtest::BaseTest
{
CV_GpuCornerMinEigenValTest() {}
void run(int)
{
for (int i = 0; i < 3; ++i)
{
int rows = 25 + rand() % 300, cols = 25 + rand() % 300;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, -1)) return;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, -1)) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
}
}
bool compareToCpuTest(int rows, int cols, int depth, int blockSize, int apertureSize)
{
RNG rng;
cv::Mat src(rows, cols, depth);
if (depth == CV_32F)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(1));
else if (depth == CV_8U)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(256));
cv::Mat dst_gold;
cv::gpu::GpuMat dst;
cv::Mat dsth;
int borderType;
borderType = BORDER_REFLECT101;
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-2f)
{
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d %d\n", i, j, a, b, apertureSize, blockSize);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
borderType = BORDER_REPLICATE;
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-2f)
{
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d %d\n", i, j, a, b, apertureSize, blockSize);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
return true;
}
};
struct CV_GpuColumnSumTest: cvtest::BaseTest
{
CV_GpuColumnSumTest() {}
void run(int)
{
int cols = 375;
int rows = 1072;
Mat src(rows, cols, CV_32F);
RNG rng(1);
rng.fill(src, RNG::UNIFORM, Scalar(0), Scalar(1));
GpuMat d_dst;
columnSum(GpuMat(src), d_dst);
Mat dst = d_dst;
for (int j = 0; j < src.cols; ++j)
{
float a = src.at<float>(0, j);
float b = dst.at<float>(0, j);
if (fabs(a - b) > 0.5f)
{
ts->printf(cvtest::TS::CONSOLE, "big diff at %d %d: %f %f\n", 0, j, a, b);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
}
for (int i = 1; i < src.rows; ++i)
{
for (int j = 0; j < src.cols; ++j)
{
float a = src.at<float>(i, j) += src.at<float>(i - 1, j);
float b = dst.at<float>(i, j);
if (fabs(a - b) > 0.5f)
{
ts->printf(cvtest::TS::CONSOLE, "big diff at %d %d: %f %f\n", i, j, a, b);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
};
struct CV_GpuNormTest : cvtest::BaseTest
{
CV_GpuNormTest() {}
void run(int)
{
RNG rng(0);
int rows = rng.uniform(1, 500);
int cols = rng.uniform(1, 500);
for (int cn = 1; cn <= 4; ++cn)
{
test(NORM_L1, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L1, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L1, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L1, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L1, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L1, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
test(NORM_L2, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L2, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L2, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L2, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L2, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L2, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
test(NORM_INF, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_INF, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_INF, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_INF, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_INF, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_INF, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
}
}
void gen(Mat& mat, int rows, int cols, int type, Scalar low, Scalar high)
{
mat.create(rows, cols, type);
RNG rng(0);
rng.fill(mat, RNG::UNIFORM, low, high);
}
void test(int norm_type, int rows, int cols, int depth, int cn, Scalar low, Scalar high)
{
int type = CV_MAKE_TYPE(depth, cn);
Mat src;
gen(src, rows, cols, type, low, high);
double gold = norm(src, norm_type);
double mine = norm(GpuMat(src), norm_type);
if (abs(gold - mine) > 1e-3)
{
ts->printf(cvtest::TS::CONSOLE, "failed test: gold=%f, mine=%f, norm_type=%d, rows=%d, "
"cols=%d, depth=%d, cn=%d\n", gold, mine, norm_type, rows, cols, depth, cn);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
}
}
};
////////////////////////////////////////////////////////////////////////////////
// reprojectImageTo3D
class CV_GpuReprojectImageTo3DTest : public cvtest::BaseTest
{
public:
CV_GpuReprojectImageTo3DTest() {}
protected:
void run(int)
{
Mat disp(320, 240, CV_8UC1);
RNG& rng = ts->get_rng();
rng.fill(disp, RNG::UNIFORM, Scalar(5), Scalar(30));
Mat Q(4, 4, CV_32FC1);
rng.fill(Q, RNG::UNIFORM, Scalar(0.1), Scalar(1));
Mat cpures;
GpuMat gpures;
reprojectImageTo3D(disp, cpures, Q, false);
reprojectImageTo3D(GpuMat(disp), gpures, Q);
Mat temp = gpures;
for (int y = 0; y < cpures.rows; ++y)
{
const Vec3f* cpu_row = cpures.ptr<Vec3f>(y);
const Vec4f* gpu_row = temp.ptr<Vec4f>(y);
for (int x = 0; x < cpures.cols; ++x)
{
Vec3f a = cpu_row[x];
Vec4f b = gpu_row[x];
if (fabs(a[0] - b[0]) > 1e-5 || fabs(a[1] - b[1]) > 1e-5 || fabs(a[2] - b[2]) > 1e-5)
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
};
TEST(threshold, accuracy) { CV_GpuImageThresholdTest test; test.safe_run(); }
TEST(resize, accuracy) { CV_GpuNppImageResizeTest test; test.safe_run(); }
TEST(copyMakeBorder, accuracy) { CV_GpuNppImageCopyMakeBorderTest test; test.safe_run(); }
TEST(warpAffine, accuracy) { CV_GpuNppImageWarpAffineTest test; test.safe_run(); }
TEST(warpPerspective, accuracy) { CV_GpuNppImageWarpPerspectiveTest test; test.safe_run(); }
TEST(integral, accuracy) { CV_GpuNppImageIntegralTest test; test.safe_run(); }
TEST(cvtColor, accuracy) { CV_GpuCvtColorTest test; test.safe_run(); }
TEST(histograms, accuracy) { CV_GpuHistogramsTest test; test.safe_run(); }
TEST(cornerHearris, accuracy) { CV_GpuCornerHarrisTest test; test.safe_run(); }
TEST(minEigen, accuracy) { CV_GpuCornerMinEigenValTest test; test.safe_run(); }
TEST(columnSum, accuracy) { CV_GpuColumnSumTest test; test.safe_run(); }
TEST(norm, accuracy) { CV_GpuNormTest test; test.safe_run(); }
TEST(reprojectImageTo3D, accuracy) { CV_GpuReprojectImageTo3DTest test; test.safe_run(); }
TEST(downsample, accuracy_on_8U)
{
RNG& rng = cvtest::TS::ptr()->get_rng();
Size size(200 + cvtest::randInt(rng) % 1000, 200 + cvtest::randInt(rng) % 1000);
Mat src = cvtest::randomMat(rng, size, CV_8U, 0, 255, false);
for (int k = 2; k <= 5; ++k)
{
GpuMat d_dst;
downsample(GpuMat(src), d_dst, k);
Size dst_gold_size((src.cols + k - 1) / k, (src.rows + k - 1) / k);
ASSERT_EQ(dst_gold_size.width, d_dst.cols)
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
ASSERT_EQ(dst_gold_size.height, d_dst.rows)
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
Mat dst = d_dst;
for (int y = 0; y < dst.rows; ++y)
for (int x = 0; x < dst.cols; ++x)
ASSERT_EQ(src.at<uchar>(y * k, x * k), dst.at<uchar>(y, x))
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
}
}
TEST(downsample, accuracy_on_32F)
{
RNG& rng = cvtest::TS::ptr()->get_rng();
Size size(200 + cvtest::randInt(rng) % 1000, 200 + cvtest::randInt(rng) % 1000);
Mat src = cvtest::randomMat(rng, size, CV_32F, 0, 1, false);
for (int k = 2; k <= 5; ++k)
{
GpuMat d_dst;
downsample(GpuMat(src), d_dst, k);
Size dst_gold_size((src.cols + k - 1) / k, (src.rows + k - 1) / k);
ASSERT_EQ(dst_gold_size.width, d_dst.cols)
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
ASSERT_EQ(dst_gold_size.height, d_dst.rows)
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
Mat dst = d_dst;
for (int y = 0; y < dst.rows; ++y)
for (int x = 0; x < dst.cols; ++x)
ASSERT_FLOAT_EQ(src.at<float>(y * k, x * k), dst.at<float>(y, x))
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
}
}
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