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OpenCV Buildbot
2012-10-17 03:18:30 +04:00
committed by Andrey Kamaev
parent 69020da607
commit 04384a71e4
1516 changed files with 258846 additions and 258162 deletions
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modules/gpu/test/interpolation.hpp
+127 -127
View File
@@ -1,127 +1,127 @@
/*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*/
#ifndef __OPENCV_TEST_INTERPOLATION_HPP__
#define __OPENCV_TEST_INTERPOLATION_HPP__
template <typename T> T readVal(const cv::Mat& src, int y, int x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
if (border_type == cv::BORDER_CONSTANT)
return (y >= 0 && y < src.rows && x >= 0 && x < src.cols) ? src.at<T>(y, x * src.channels() + c) : cv::saturate_cast<T>(borderVal.val[c]);
return src.at<T>(cv::borderInterpolate(y, src.rows, border_type), cv::borderInterpolate(x, src.cols, border_type) * src.channels() + c);
}
template <typename T> struct NearestInterpolator
{
static T getValue(const cv::Mat& src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
return readVal<T>(src, int(y), int(x), c, border_type, borderVal);
}
};
template <typename T> struct LinearInterpolator
{
static T getValue(const cv::Mat& src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
int x1 = cvFloor(x);
int y1 = cvFloor(y);
int x2 = x1 + 1;
int y2 = y1 + 1;
float res = 0;
res += readVal<T>(src, y1, x1, c, border_type, borderVal) * ((x2 - x) * (y2 - y));
res += readVal<T>(src, y1, x2, c, border_type, borderVal) * ((x - x1) * (y2 - y));
res += readVal<T>(src, y2, x1, c, border_type, borderVal) * ((x2 - x) * (y - y1));
res += readVal<T>(src, y2, x2, c, border_type, borderVal) * ((x - x1) * (y - y1));
return cv::saturate_cast<T>(res);
}
};
template <typename T> struct CubicInterpolator
{
static float bicubicCoeff(float x_)
{
float x = fabsf(x_);
if (x <= 1.0f)
{
return x * x * (1.5f * x - 2.5f) + 1.0f;
}
else if (x < 2.0f)
{
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
}
else
{
return 0.0f;
}
}
static T getValue(const cv::Mat& src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
const float xmin = ceilf(x - 2.0f);
const float xmax = floorf(x + 2.0f);
const float ymin = ceilf(y - 2.0f);
const float ymax = floorf(y + 2.0f);
float sum = 0.0f;
float wsum = 0.0f;
for (float cy = ymin; cy <= ymax; cy += 1.0f)
{
for (float cx = xmin; cx <= xmax; cx += 1.0f)
{
const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy);
sum += w * readVal<T>(src, cvFloor(cy), cvFloor(cx), c, border_type, borderVal);
wsum += w;
}
}
float res = (!wsum)? 0 : sum / wsum;
return cv::saturate_cast<T>(res);
}
};
#endif // __OPENCV_TEST_INTERPOLATION_HPP__
/*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*/
#ifndef __OPENCV_TEST_INTERPOLATION_HPP__
#define __OPENCV_TEST_INTERPOLATION_HPP__
template <typename T> T readVal(const cv::Mat& src, int y, int x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
if (border_type == cv::BORDER_CONSTANT)
return (y >= 0 && y < src.rows && x >= 0 && x < src.cols) ? src.at<T>(y, x * src.channels() + c) : cv::saturate_cast<T>(borderVal.val[c]);
return src.at<T>(cv::borderInterpolate(y, src.rows, border_type), cv::borderInterpolate(x, src.cols, border_type) * src.channels() + c);
}
template <typename T> struct NearestInterpolator
{
static T getValue(const cv::Mat& src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
return readVal<T>(src, int(y), int(x), c, border_type, borderVal);
}
};
template <typename T> struct LinearInterpolator
{
static T getValue(const cv::Mat& src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
int x1 = cvFloor(x);
int y1 = cvFloor(y);
int x2 = x1 + 1;
int y2 = y1 + 1;
float res = 0;
res += readVal<T>(src, y1, x1, c, border_type, borderVal) * ((x2 - x) * (y2 - y));
res += readVal<T>(src, y1, x2, c, border_type, borderVal) * ((x - x1) * (y2 - y));
res += readVal<T>(src, y2, x1, c, border_type, borderVal) * ((x2 - x) * (y - y1));
res += readVal<T>(src, y2, x2, c, border_type, borderVal) * ((x - x1) * (y - y1));
return cv::saturate_cast<T>(res);
}
};
template <typename T> struct CubicInterpolator
{
static float bicubicCoeff(float x_)
{
float x = fabsf(x_);
if (x <= 1.0f)
{
return x * x * (1.5f * x - 2.5f) + 1.0f;
}
else if (x < 2.0f)
{
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
}
else
{
return 0.0f;
}
}
static T getValue(const cv::Mat& src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
const float xmin = ceilf(x - 2.0f);
const float xmax = floorf(x + 2.0f);
const float ymin = ceilf(y - 2.0f);
const float ymax = floorf(y + 2.0f);
float sum = 0.0f;
float wsum = 0.0f;
for (float cy = ymin; cy <= ymax; cy += 1.0f)
{
for (float cx = xmin; cx <= xmax; cx += 1.0f)
{
const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy);
sum += w * readVal<T>(src, cvFloor(cy), cvFloor(cx), c, border_type, borderVal);
wsum += w;
}
}
float res = (!wsum)? 0 : sum / wsum;
return cv::saturate_cast<T>(res);
}
};
#endif // __OPENCV_TEST_INTERPOLATION_HPP__
modules/gpu/test/main.cpp
+195 -195
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@@ -1,195 +1,195 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
using namespace testing;
void printOsInfo()
{
#if defined _WIN32
# if defined _WIN64
cout << "OS: Windows x64 \n" << endl;
# else
cout << "OS: Windows x32 \n" << endl;
# endif
#elif defined linux
# if defined _LP64
cout << "OS: Linux x64 \n" << endl;
# else
cout << "OS: Linux x32 \n" << endl;
# endif
#elif defined __APPLE__
# if defined _LP64
cout << "OS: Apple x64 \n" << endl;
# else
cout << "OS: Apple x32 \n" << endl;
# endif
#endif
}
void printCudaInfo()
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
cout << "OpenCV was built without CUDA support \n" << endl;
#else
int driver;
cudaDriverGetVersion(&driver);
cout << "CUDA Driver version: " << driver << '\n';
cout << "CUDA Runtime version: " << CUDART_VERSION << '\n';
cout << endl;
cout << "GPU module was compiled for the following GPU archs:" << endl;
cout << " BIN: " << CUDA_ARCH_BIN << '\n';
cout << " PTX: " << CUDA_ARCH_PTX << '\n';
cout << endl;
int deviceCount = getCudaEnabledDeviceCount();
cout << "CUDA device count: " << deviceCount << '\n';
cout << endl;
for (int i = 0; i < deviceCount; ++i)
{
DeviceInfo info(i);
cout << "Device [" << i << "] \n";
cout << "\t Name: " << info.name() << '\n';
cout << "\t Compute capability: " << info.majorVersion() << '.' << info.minorVersion()<< '\n';
cout << "\t Multi Processor Count: " << info.multiProcessorCount() << '\n';
cout << "\t Total memory: " << static_cast<int>(static_cast<int>(info.totalMemory() / 1024.0) / 1024.0) << " Mb \n";
cout << "\t Free memory: " << static_cast<int>(static_cast<int>(info.freeMemory() / 1024.0) / 1024.0) << " Mb \n";
if (!info.isCompatible())
cout << "\t !!! This device is NOT compatible with current GPU module build \n";
cout << endl;
}
#endif
}
int main(int argc, char** argv)
{
try
{
const char* keys =
"{ h help ? | | Print help}"
"{ i info | | Print information about system and exit }"
"{ device | -1 | Device on which tests will be executed (-1 means all devices) }"
"{ nvtest_output_level | none | NVidia test verbosity level (none, compact, full) }"
;
CommandLineParser cmd(argc, (const char**)argv, keys);
if (cmd.get<bool>("help"))
{
cmd.printParams();
return 0;
}
printOsInfo();
printCudaInfo();
if (cmd.get<bool>("info"))
{
return 0;
}
int device = cmd.get<int>("device");
if (device < 0)
{
DeviceManager::instance().loadAll();
cout << "Run tests on all supported devices \n" << endl;
}
else
{
DeviceManager::instance().load(device);
DeviceInfo info(device);
cout << "Run tests on device " << device << " [" << info.name() << "] \n" << endl;
}
string outputLevel = cmd.get<string>("nvtest_output_level");
if (outputLevel == "none")
nvidiaTestOutputLevel = OutputLevelNone;
else if (outputLevel == "compact")
nvidiaTestOutputLevel = OutputLevelCompact;
else if (outputLevel == "full")
nvidiaTestOutputLevel = OutputLevelFull;
TS::ptr()->init("gpu");
InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
catch (const exception& e)
{
cerr << e.what() << endl;
return -1;
}
catch (...)
{
cerr << "Unknown error" << endl;
return -1;
}
return 0;
}
#else // HAVE_CUDA
int main()
{
printf("OpenCV was built without CUDA support\n");
return 0;
}
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
using namespace testing;
void printOsInfo()
{
#if defined _WIN32
# if defined _WIN64
cout << "OS: Windows x64 \n" << endl;
# else
cout << "OS: Windows x32 \n" << endl;
# endif
#elif defined linux
# if defined _LP64
cout << "OS: Linux x64 \n" << endl;
# else
cout << "OS: Linux x32 \n" << endl;
# endif
#elif defined __APPLE__
# if defined _LP64
cout << "OS: Apple x64 \n" << endl;
# else
cout << "OS: Apple x32 \n" << endl;
# endif
#endif
}
void printCudaInfo()
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
cout << "OpenCV was built without CUDA support \n" << endl;
#else
int driver;
cudaDriverGetVersion(&driver);
cout << "CUDA Driver version: " << driver << '\n';
cout << "CUDA Runtime version: " << CUDART_VERSION << '\n';
cout << endl;
cout << "GPU module was compiled for the following GPU archs:" << endl;
cout << " BIN: " << CUDA_ARCH_BIN << '\n';
cout << " PTX: " << CUDA_ARCH_PTX << '\n';
cout << endl;
int deviceCount = getCudaEnabledDeviceCount();
cout << "CUDA device count: " << deviceCount << '\n';
cout << endl;
for (int i = 0; i < deviceCount; ++i)
{
DeviceInfo info(i);
cout << "Device [" << i << "] \n";
cout << "\t Name: " << info.name() << '\n';
cout << "\t Compute capability: " << info.majorVersion() << '.' << info.minorVersion()<< '\n';
cout << "\t Multi Processor Count: " << info.multiProcessorCount() << '\n';
cout << "\t Total memory: " << static_cast<int>(static_cast<int>(info.totalMemory() / 1024.0) / 1024.0) << " Mb \n";
cout << "\t Free memory: " << static_cast<int>(static_cast<int>(info.freeMemory() / 1024.0) / 1024.0) << " Mb \n";
if (!info.isCompatible())
cout << "\t !!! This device is NOT compatible with current GPU module build \n";
cout << endl;
}
#endif
}
int main(int argc, char** argv)
{
try
{
const char* keys =
"{ h help ? | | Print help}"
"{ i info | | Print information about system and exit }"
"{ device | -1 | Device on which tests will be executed (-1 means all devices) }"
"{ nvtest_output_level | none | NVidia test verbosity level (none, compact, full) }"
;
CommandLineParser cmd(argc, (const char**)argv, keys);
if (cmd.get<bool>("help"))
{
cmd.printParams();
return 0;
}
printOsInfo();
printCudaInfo();
if (cmd.get<bool>("info"))
{
return 0;
}
int device = cmd.get<int>("device");
if (device < 0)
{
DeviceManager::instance().loadAll();
cout << "Run tests on all supported devices \n" << endl;
}
else
{
DeviceManager::instance().load(device);
DeviceInfo info(device);
cout << "Run tests on device " << device << " [" << info.name() << "] \n" << endl;
}
string outputLevel = cmd.get<string>("nvtest_output_level");
if (outputLevel == "none")
nvidiaTestOutputLevel = OutputLevelNone;
else if (outputLevel == "compact")
nvidiaTestOutputLevel = OutputLevelCompact;
else if (outputLevel == "full")
nvidiaTestOutputLevel = OutputLevelFull;
TS::ptr()->init("gpu");
InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
catch (const exception& e)
{
cerr << e.what() << endl;
return -1;
}
catch (...)
{
cerr << "Unknown error" << endl;
return -1;
}
return 0;
}
#else // HAVE_CUDA
int main()
{
printf("OpenCV was built without CUDA support\n");
return 0;
}
#endif // HAVE_CUDA
modules/gpu/test/nvidia/NCVAutoTestLister.hpp
+140 -140
View File
@@ -1,140 +1,140 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvautotestlister_hpp_
#define _ncvautotestlister_hpp_
#include <vector>
#include "NCVTest.hpp"
#include <main_test_nvidia.h>
//enum OutputLevel
//{
// OutputLevelNone,
// OutputLevelCompact,
// OutputLevelFull
//};
class NCVAutoTestLister
{
public:
NCVAutoTestLister(std::string testSuiteName_, OutputLevel outputLevel_ = OutputLevelCompact, NcvBool bStopOnFirstFail_=false)
:
testSuiteName(testSuiteName_),
outputLevel(outputLevel_),
bStopOnFirstFail(bStopOnFirstFail_)
{
}
void add(INCVTest *test)
{
this->tests.push_back(test);
}
bool invoke()
{
Ncv32u nPassed = 0;
Ncv32u nFailed = 0;
Ncv32u nFailedMem = 0;
if (outputLevel == OutputLevelCompact)
{
printf("Test suite '%s' with %d tests\n",
testSuiteName.c_str(),
(int)(this->tests.size()));
}
for (Ncv32u i=0; i<this->tests.size(); i++)
{
INCVTest &curTest = *tests[i];
NCVTestReport curReport;
bool res = curTest.executeTest(curReport);
if (outputLevel == OutputLevelFull)
{
printf("Test %3i %16s; Consumed mem GPU = %8d, CPU = %8d; %s\n",
i,
curTest.getName().c_str(),
curReport.statsNums["MemGPU"],
curReport.statsNums["MemCPU"],
curReport.statsText["rcode"].c_str());
}
if (res)
{
nPassed++;
if (outputLevel == OutputLevelCompact)
{
printf(".");
}
}
else
{
if (!curReport.statsText["rcode"].compare("FAILED"))
{
nFailed++;
if (outputLevel == OutputLevelCompact)
{
printf("x");
}
if (bStopOnFirstFail)
{
break;
}
}
else
{
nFailedMem++;
if (outputLevel == OutputLevelCompact)
{
printf("m");
}
}
}
fflush(stdout);
}
if (outputLevel == OutputLevelCompact)
{
printf("\n");
}
if (outputLevel != OutputLevelNone)
{
printf("Test suite '%s' complete: %d total, %d passed, %d memory errors, %d failed\n\n",
testSuiteName.c_str(),
(int)(this->tests.size()),
nPassed,
nFailedMem,
nFailed);
}
bool passed = nFailed == 0 && nFailedMem == 0;
return passed;
}
~NCVAutoTestLister()
{
for (Ncv32u i=0; i<this->tests.size(); i++)
{
delete tests[i];
}
}
private:
std::string testSuiteName;
OutputLevel outputLevel;
NcvBool bStopOnFirstFail;
std::vector<INCVTest *> tests;
};
#endif // _ncvautotestlister_hpp_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvautotestlister_hpp_
#define _ncvautotestlister_hpp_
#include <vector>
#include "NCVTest.hpp"
#include <main_test_nvidia.h>
//enum OutputLevel
//{
// OutputLevelNone,
// OutputLevelCompact,
// OutputLevelFull
//};
class NCVAutoTestLister
{
public:
NCVAutoTestLister(std::string testSuiteName_, OutputLevel outputLevel_ = OutputLevelCompact, NcvBool bStopOnFirstFail_=false)
:
testSuiteName(testSuiteName_),
outputLevel(outputLevel_),
bStopOnFirstFail(bStopOnFirstFail_)
{
}
void add(INCVTest *test)
{
this->tests.push_back(test);
}
bool invoke()
{
Ncv32u nPassed = 0;
Ncv32u nFailed = 0;
Ncv32u nFailedMem = 0;
if (outputLevel == OutputLevelCompact)
{
printf("Test suite '%s' with %d tests\n",
testSuiteName.c_str(),
(int)(this->tests.size()));
}
for (Ncv32u i=0; i<this->tests.size(); i++)
{
INCVTest &curTest = *tests[i];
NCVTestReport curReport;
bool res = curTest.executeTest(curReport);
if (outputLevel == OutputLevelFull)
{
printf("Test %3i %16s; Consumed mem GPU = %8d, CPU = %8d; %s\n",
i,
curTest.getName().c_str(),
curReport.statsNums["MemGPU"],
curReport.statsNums["MemCPU"],
curReport.statsText["rcode"].c_str());
}
if (res)
{
nPassed++;
if (outputLevel == OutputLevelCompact)
{
printf(".");
}
}
else
{
if (!curReport.statsText["rcode"].compare("FAILED"))
{
nFailed++;
if (outputLevel == OutputLevelCompact)
{
printf("x");
}
if (bStopOnFirstFail)
{
break;
}
}
else
{
nFailedMem++;
if (outputLevel == OutputLevelCompact)
{
printf("m");
}
}
}
fflush(stdout);
}
if (outputLevel == OutputLevelCompact)
{
printf("\n");
}
if (outputLevel != OutputLevelNone)
{
printf("Test suite '%s' complete: %d total, %d passed, %d memory errors, %d failed\n\n",
testSuiteName.c_str(),
(int)(this->tests.size()),
nPassed,
nFailedMem,
nFailed);
}
bool passed = nFailed == 0 && nFailedMem == 0;
return passed;
}
~NCVAutoTestLister()
{
for (Ncv32u i=0; i<this->tests.size(); i++)
{
delete tests[i];
}
}
private:
std::string testSuiteName;
OutputLevel outputLevel;
NcvBool bStopOnFirstFail;
std::vector<INCVTest *> tests;
};
#endif // _ncvautotestlister_hpp_
modules/gpu/test/nvidia/NCVTest.hpp
+214 -214
View File
@@ -1,214 +1,214 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvtest_hpp_
#define _ncvtest_hpp_
#if defined _MSC_VER
# pragma warning( disable : 4201 4408 4127 4100)
#endif
#include <string>
#include <vector>
#include <map>
#include <memory>
#include <algorithm>
#include <fstream>
#include <cuda_runtime.h>
#include "NPP_staging.hpp"
struct NCVTestReport
{
std::map<std::string, Ncv32u> statsNums;
std::map<std::string, std::string> statsText;
};
class INCVTest
{
public:
virtual bool executeTest(NCVTestReport &report) = 0;
virtual std::string getName() const = 0;
virtual ~INCVTest(){}
};
class NCVTestProvider : public INCVTest
{
public:
NCVTestProvider(std::string testName_)
:
testName(testName_)
{
int devId;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&this->devProp, devId), "Error returned from cudaGetDeviceProperties", );
}
virtual bool init() = 0;
virtual bool process() = 0;
virtual bool deinit() = 0;
virtual bool toString(std::ofstream &strOut) = 0;
virtual std::string getName() const
{
return this->testName;
}
virtual ~NCVTestProvider()
{
deinitMemory();
}
virtual bool executeTest(NCVTestReport &report)
{
bool res;
report.statsText["rcode"] = "FAILED";
res = initMemory(report);
if (!res)
{
dumpToFile(report);
deinitMemory();
return false;
}
res = init();
if (!res)
{
dumpToFile(report);
deinit();
deinitMemory();
return false;
}
res = process();
if (!res)
{
dumpToFile(report);
deinit();
deinitMemory();
return false;
}
res = deinit();
if (!res)
{
dumpToFile(report);
deinitMemory();
return false;
}
deinitMemory();
report.statsText["rcode"] = "Passed";
return true;
}
protected:
cudaDeviceProp devProp;
std::auto_ptr<INCVMemAllocator> allocatorGPU;
std::auto_ptr<INCVMemAllocator> allocatorCPU;
private:
std::string testName;
bool initMemory(NCVTestReport &report)
{
this->allocatorGPU.reset(new NCVMemStackAllocator(static_cast<Ncv32u>(devProp.textureAlignment)));
this->allocatorCPU.reset(new NCVMemStackAllocator(static_cast<Ncv32u>(devProp.textureAlignment)));
if (!this->allocatorGPU.get()->isInitialized() ||
!this->allocatorCPU.get()->isInitialized())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
if (!this->process())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
Ncv32u maxGPUsize = (Ncv32u)this->allocatorGPU.get()->maxSize();
Ncv32u maxCPUsize = (Ncv32u)this->allocatorCPU.get()->maxSize();
report.statsNums["MemGPU"] = maxGPUsize;
report.statsNums["MemCPU"] = maxCPUsize;
this->allocatorGPU.reset(new NCVMemStackAllocator(NCVMemoryTypeDevice, maxGPUsize, static_cast<Ncv32u>(devProp.textureAlignment)));
this->allocatorCPU.reset(new NCVMemStackAllocator(NCVMemoryTypeHostPinned, maxCPUsize, static_cast<Ncv32u>(devProp.textureAlignment)));
if (!this->allocatorGPU.get()->isInitialized() ||
!this->allocatorCPU.get()->isInitialized())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
return true;
}
void deinitMemory()
{
this->allocatorGPU.reset();
this->allocatorCPU.reset();
}
void dumpToFile(NCVTestReport &report)
{
bool bReasonMem = (0 == report.statsText["rcode"].compare("Memory FAILED"));
std::string fname = "TestDump_";
fname += (bReasonMem ? "m_" : "") + this->testName + ".log";
std::ofstream stream(fname.c_str(), std::ios::trunc | std::ios::out);
if (!stream.is_open()) return;
stream << "NCV Test Failure Log: " << this->testName << std::endl;
stream << "====================================================" << std::endl << std::endl;
stream << "Test initialization report: " << std::endl;
for (std::map<std::string,std::string>::iterator it=report.statsText.begin();
it != report.statsText.end(); it++)
{
stream << it->first << "=" << it->second << std::endl;
}
for (std::map<std::string,Ncv32u>::iterator it=report.statsNums.begin();
it != report.statsNums.end(); it++)
{
stream << it->first << "=" << it->second << std::endl;
}
stream << std::endl;
stream << "Test initialization parameters: " << std::endl;
bool bSerializeRes = false;
try
{
bSerializeRes = this->toString(stream);
}
catch (...)
{
}
if (!bSerializeRes)
{
stream << "Couldn't retrieve object dump" << std::endl;
}
stream.flush();
}
};
#endif // _ncvtest_hpp_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvtest_hpp_
#define _ncvtest_hpp_
#if defined _MSC_VER
# pragma warning( disable : 4201 4408 4127 4100)
#endif
#include <string>
#include <vector>
#include <map>
#include <memory>
#include <algorithm>
#include <fstream>
#include <cuda_runtime.h>
#include "NPP_staging.hpp"
struct NCVTestReport
{
std::map<std::string, Ncv32u> statsNums;
std::map<std::string, std::string> statsText;
};
class INCVTest
{
public:
virtual bool executeTest(NCVTestReport &report) = 0;
virtual std::string getName() const = 0;
virtual ~INCVTest(){}
};
class NCVTestProvider : public INCVTest
{
public:
NCVTestProvider(std::string testName_)
:
testName(testName_)
{
int devId;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&this->devProp, devId), "Error returned from cudaGetDeviceProperties", );
}
virtual bool init() = 0;
virtual bool process() = 0;
virtual bool deinit() = 0;
virtual bool toString(std::ofstream &strOut) = 0;
virtual std::string getName() const
{
return this->testName;
}
virtual ~NCVTestProvider()
{
deinitMemory();
}
virtual bool executeTest(NCVTestReport &report)
{
bool res;
report.statsText["rcode"] = "FAILED";
res = initMemory(report);
if (!res)
{
dumpToFile(report);
deinitMemory();
return false;
}
res = init();
if (!res)
{
dumpToFile(report);
deinit();
deinitMemory();
return false;
}
res = process();
if (!res)
{
dumpToFile(report);
deinit();
deinitMemory();
return false;
}
res = deinit();
if (!res)
{
dumpToFile(report);
deinitMemory();
return false;
}
deinitMemory();
report.statsText["rcode"] = "Passed";
return true;
}
protected:
cudaDeviceProp devProp;
std::auto_ptr<INCVMemAllocator> allocatorGPU;
std::auto_ptr<INCVMemAllocator> allocatorCPU;
private:
std::string testName;
bool initMemory(NCVTestReport &report)
{
this->allocatorGPU.reset(new NCVMemStackAllocator(static_cast<Ncv32u>(devProp.textureAlignment)));
this->allocatorCPU.reset(new NCVMemStackAllocator(static_cast<Ncv32u>(devProp.textureAlignment)));
if (!this->allocatorGPU.get()->isInitialized() ||
!this->allocatorCPU.get()->isInitialized())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
if (!this->process())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
Ncv32u maxGPUsize = (Ncv32u)this->allocatorGPU.get()->maxSize();
Ncv32u maxCPUsize = (Ncv32u)this->allocatorCPU.get()->maxSize();
report.statsNums["MemGPU"] = maxGPUsize;
report.statsNums["MemCPU"] = maxCPUsize;
this->allocatorGPU.reset(new NCVMemStackAllocator(NCVMemoryTypeDevice, maxGPUsize, static_cast<Ncv32u>(devProp.textureAlignment)));
this->allocatorCPU.reset(new NCVMemStackAllocator(NCVMemoryTypeHostPinned, maxCPUsize, static_cast<Ncv32u>(devProp.textureAlignment)));
if (!this->allocatorGPU.get()->isInitialized() ||
!this->allocatorCPU.get()->isInitialized())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
return true;
}
void deinitMemory()
{
this->allocatorGPU.reset();
this->allocatorCPU.reset();
}
void dumpToFile(NCVTestReport &report)
{
bool bReasonMem = (0 == report.statsText["rcode"].compare("Memory FAILED"));
std::string fname = "TestDump_";
fname += (bReasonMem ? "m_" : "") + this->testName + ".log";
std::ofstream stream(fname.c_str(), std::ios::trunc | std::ios::out);
if (!stream.is_open()) return;
stream << "NCV Test Failure Log: " << this->testName << std::endl;
stream << "====================================================" << std::endl << std::endl;
stream << "Test initialization report: " << std::endl;
for (std::map<std::string,std::string>::iterator it=report.statsText.begin();
it != report.statsText.end(); it++)
{
stream << it->first << "=" << it->second << std::endl;
}
for (std::map<std::string,Ncv32u>::iterator it=report.statsNums.begin();
it != report.statsNums.end(); it++)
{
stream << it->first << "=" << it->second << std::endl;
}
stream << std::endl;
stream << "Test initialization parameters: " << std::endl;
bool bSerializeRes = false;
try
{
bSerializeRes = this->toString(stream);
}
catch (...)
{
}
if (!bSerializeRes)
{
stream << "Couldn't retrieve object dump" << std::endl;
}
stream.flush();
}
};
#endif // _ncvtest_hpp_
modules/gpu/test/nvidia/NCVTestSourceProvider.hpp
+161 -161
View File
@@ -1,161 +1,161 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvtestsourceprovider_hpp_
#define _ncvtestsourceprovider_hpp_
#include <memory>
#include "NCV.hpp"
#include <opencv2/highgui/highgui.hpp>
template <class T>
class NCVTestSourceProvider
{
public:
NCVTestSourceProvider(Ncv32u seed, T rangeLow, T rangeHigh, Ncv32u maxWidth, Ncv32u maxHeight)
:
bInit(false)
{
ncvAssertPrintReturn(rangeLow < rangeHigh, "NCVTestSourceProvider ctor:: Invalid range", );
int devId;
cudaDeviceProp devProp;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&devProp, devId), "Error returned from cudaGetDeviceProperties", );
//Ncv32u maxWpitch = alignUp(maxWidth * sizeof(T), devProp.textureAlignment);
allocatorCPU.reset(new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment)));
data.reset(new NCVMatrixAlloc<T>(*this->allocatorCPU.get(), maxWidth, maxHeight));
ncvAssertPrintReturn(data.get()->isMemAllocated(), "NCVTestSourceProvider ctor:: Matrix not allocated", );
this->dataWidth = maxWidth;
this->dataHeight = maxHeight;
srand(seed);
for (Ncv32u i=0; i<maxHeight; i++)
{
for (Ncv32u j=0; j<data.get()->stride(); j++)
{
data.get()->ptr()[i * data.get()->stride() + j] =
(T)(((1.0 * rand()) / RAND_MAX) * (rangeHigh - rangeLow) + rangeLow);
}
}
this->bInit = true;
}
NCVTestSourceProvider(std::string pgmFilename)
:
bInit(false)
{
ncvAssertPrintReturn(sizeof(T) == 1, "NCVTestSourceProvider ctor:: PGM constructor complies only with 8bit types", );
cv::Mat image = cv::imread(pgmFilename);
ncvAssertPrintReturn(!image.empty(), "NCVTestSourceProvider ctor:: PGM file error", );
int devId;
cudaDeviceProp devProp;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&devProp, devId), "Error returned from cudaGetDeviceProperties", );
allocatorCPU.reset(new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment)));
data.reset(new NCVMatrixAlloc<T>(*this->allocatorCPU.get(), image.cols, image.rows));
ncvAssertPrintReturn(data.get()->isMemAllocated(), "NCVTestSourceProvider ctor:: Matrix not allocated", );
this->dataWidth = image.cols;
this->dataHeight = image.rows;
cv::Mat hdr(image.size(), CV_8UC1, data.get()->ptr(), data.get()->pitch());
image.copyTo(hdr);
this->bInit = true;
}
NcvBool fill(NCVMatrix<T> &dst)
{
ncvAssertReturn(this->isInit() &&
dst.memType() == allocatorCPU.get()->memType(), false);
if (dst.width() == 0 || dst.height() == 0)
{
return true;
}
for (Ncv32u i=0; i<dst.height(); i++)
{
Ncv32u srcLine = i % this->dataHeight;
Ncv32u srcFullChunks = dst.width() / this->dataWidth;
for (Ncv32u j=0; j<srcFullChunks; j++)
{
memcpy(dst.ptr() + i * dst.stride() + j * this->dataWidth,
this->data.get()->ptr() + this->data.get()->stride() * srcLine,
this->dataWidth * sizeof(T));
}
Ncv32u srcLastChunk = dst.width() % this->dataWidth;
memcpy(dst.ptr() + i * dst.stride() + srcFullChunks * this->dataWidth,
this->data.get()->ptr() + this->data.get()->stride() * srcLine,
srcLastChunk * sizeof(T));
}
return true;
}
NcvBool fill(NCVVector<T> &dst)
{
ncvAssertReturn(this->isInit() &&
dst.memType() == allocatorCPU.get()->memType(), false);
if (dst.length() == 0)
{
return true;
}
Ncv32u srcLen = this->dataWidth * this->dataHeight;
Ncv32u srcFullChunks = (Ncv32u)dst.length() / srcLen;
for (Ncv32u j=0; j<srcFullChunks; j++)
{
memcpy(dst.ptr() + j * srcLen, this->data.get()->ptr(), srcLen * sizeof(T));
}
Ncv32u srcLastChunk = dst.length() % srcLen;
memcpy(dst.ptr() + srcFullChunks * srcLen, this->data.get()->ptr(), srcLastChunk * sizeof(T));
return true;
}
~NCVTestSourceProvider()
{
data.reset();
allocatorCPU.reset();
}
private:
NcvBool isInit(void)
{
return this->bInit;
}
NcvBool bInit;
std::auto_ptr< INCVMemAllocator > allocatorCPU;
std::auto_ptr< NCVMatrixAlloc<T> > data;
Ncv32u dataWidth;
Ncv32u dataHeight;
};
#endif // _ncvtestsourceprovider_hpp_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvtestsourceprovider_hpp_
#define _ncvtestsourceprovider_hpp_
#include <memory>
#include "NCV.hpp"
#include <opencv2/highgui/highgui.hpp>
template <class T>
class NCVTestSourceProvider
{
public:
NCVTestSourceProvider(Ncv32u seed, T rangeLow, T rangeHigh, Ncv32u maxWidth, Ncv32u maxHeight)
:
bInit(false)
{
ncvAssertPrintReturn(rangeLow < rangeHigh, "NCVTestSourceProvider ctor:: Invalid range", );
int devId;
cudaDeviceProp devProp;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&devProp, devId), "Error returned from cudaGetDeviceProperties", );
//Ncv32u maxWpitch = alignUp(maxWidth * sizeof(T), devProp.textureAlignment);
allocatorCPU.reset(new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment)));
data.reset(new NCVMatrixAlloc<T>(*this->allocatorCPU.get(), maxWidth, maxHeight));
ncvAssertPrintReturn(data.get()->isMemAllocated(), "NCVTestSourceProvider ctor:: Matrix not allocated", );
this->dataWidth = maxWidth;
this->dataHeight = maxHeight;
srand(seed);
for (Ncv32u i=0; i<maxHeight; i++)
{
for (Ncv32u j=0; j<data.get()->stride(); j++)
{
data.get()->ptr()[i * data.get()->stride() + j] =
(T)(((1.0 * rand()) / RAND_MAX) * (rangeHigh - rangeLow) + rangeLow);
}
}
this->bInit = true;
}
NCVTestSourceProvider(std::string pgmFilename)
:
bInit(false)
{
ncvAssertPrintReturn(sizeof(T) == 1, "NCVTestSourceProvider ctor:: PGM constructor complies only with 8bit types", );
cv::Mat image = cv::imread(pgmFilename);
ncvAssertPrintReturn(!image.empty(), "NCVTestSourceProvider ctor:: PGM file error", );
int devId;
cudaDeviceProp devProp;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&devProp, devId), "Error returned from cudaGetDeviceProperties", );
allocatorCPU.reset(new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment)));
data.reset(new NCVMatrixAlloc<T>(*this->allocatorCPU.get(), image.cols, image.rows));
ncvAssertPrintReturn(data.get()->isMemAllocated(), "NCVTestSourceProvider ctor:: Matrix not allocated", );
this->dataWidth = image.cols;
this->dataHeight = image.rows;
cv::Mat hdr(image.size(), CV_8UC1, data.get()->ptr(), data.get()->pitch());
image.copyTo(hdr);
this->bInit = true;
}
NcvBool fill(NCVMatrix<T> &dst)
{
ncvAssertReturn(this->isInit() &&
dst.memType() == allocatorCPU.get()->memType(), false);
if (dst.width() == 0 || dst.height() == 0)
{
return true;
}
for (Ncv32u i=0; i<dst.height(); i++)
{
Ncv32u srcLine = i % this->dataHeight;
Ncv32u srcFullChunks = dst.width() / this->dataWidth;
for (Ncv32u j=0; j<srcFullChunks; j++)
{
memcpy(dst.ptr() + i * dst.stride() + j * this->dataWidth,
this->data.get()->ptr() + this->data.get()->stride() * srcLine,
this->dataWidth * sizeof(T));
}
Ncv32u srcLastChunk = dst.width() % this->dataWidth;
memcpy(dst.ptr() + i * dst.stride() + srcFullChunks * this->dataWidth,
this->data.get()->ptr() + this->data.get()->stride() * srcLine,
srcLastChunk * sizeof(T));
}
return true;
}
NcvBool fill(NCVVector<T> &dst)
{
ncvAssertReturn(this->isInit() &&
dst.memType() == allocatorCPU.get()->memType(), false);
if (dst.length() == 0)
{
return true;
}
Ncv32u srcLen = this->dataWidth * this->dataHeight;
Ncv32u srcFullChunks = (Ncv32u)dst.length() / srcLen;
for (Ncv32u j=0; j<srcFullChunks; j++)
{
memcpy(dst.ptr() + j * srcLen, this->data.get()->ptr(), srcLen * sizeof(T));
}
Ncv32u srcLastChunk = dst.length() % srcLen;
memcpy(dst.ptr() + srcFullChunks * srcLen, this->data.get()->ptr(), srcLastChunk * sizeof(T));
return true;
}
~NCVTestSourceProvider()
{
data.reset();
allocatorCPU.reset();
}
private:
NcvBool isInit(void)
{
return this->bInit;
}
NcvBool bInit;
std::auto_ptr< INCVMemAllocator > allocatorCPU;
std::auto_ptr< NCVMatrixAlloc<T> > data;
Ncv32u dataWidth;
Ncv32u dataHeight;
};
#endif // _ncvtestsourceprovider_hpp_
modules/gpu/test/nvidia/TestCompact.cpp
+132 -132
View File
@@ -1,133 +1,133 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestCompact.h"
TestCompact::TestCompact(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u length_, Ncv32u badElem_, Ncv32u badElemPercentage_)
:
NCVTestProvider(testName_),
src(src_),
length(length_),
badElem(badElem_),
badElemPercentage(badElemPercentage_ > 100 ? 100 : badElemPercentage_)
{
}
bool TestCompact::toString(std::ofstream &strOut)
{
strOut << "length=" << length << std::endl;
strOut << "badElem=" << badElem << std::endl;
strOut << "badElemPercentage=" << badElemPercentage << std::endl;
return true;
}
bool TestCompact::init()
{
return true;
}
bool TestCompact::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_vecSrc(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecSrc(*this->allocatorGPU.get(), this->length);
ncvAssertReturn(d_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> h_vecDst(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecDst.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecDst(*this->allocatorGPU.get(), this->length);
ncvAssertReturn(d_vecDst.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> h_vecDst_d(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecDst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_vecSrc), false);
for (Ncv32u i=0; i<this->length; i++)
{
Ncv32u tmp = (h_vecSrc.ptr()[i]) & 0xFF;
tmp = tmp * 99 / 255;
if (tmp < this->badElemPercentage)
{
h_vecSrc.ptr()[i] = this->badElem;
}
}
NCV_SKIP_COND_END
NCVVectorAlloc<Ncv32u> h_dstLen(*this->allocatorCPU.get(), 1);
ncvAssertReturn(h_dstLen.isMemAllocated(), false);
Ncv32u bufSize;
ncvStat = nppsStCompactGetSize_32u(this->length, &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
Ncv32u h_outElemNum_h = 0;
NCV_SKIP_COND_BEGIN
ncvStat = h_vecSrc.copySolid(d_vecSrc, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppsStCompact_32u(d_vecSrc.ptr(), this->length,
d_vecDst.ptr(), h_dstLen.ptr(), this->badElem,
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_vecDst.copySolid(h_vecDst_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppsStCompact_32u_host(h_vecSrc.ptr(), this->length, h_vecDst.ptr(), &h_outElemNum_h, this->badElem);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (h_dstLen.ptr()[0] != h_outElemNum_h)
{
bLoopVirgin = false;
}
else
{
for (Ncv32u i=0; bLoopVirgin && i < h_outElemNum_h; i++)
{
if (h_vecDst.ptr()[i] != h_vecDst_d.ptr()[i])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestCompact::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestCompact.h"
TestCompact::TestCompact(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u length_, Ncv32u badElem_, Ncv32u badElemPercentage_)
:
NCVTestProvider(testName_),
src(src_),
length(length_),
badElem(badElem_),
badElemPercentage(badElemPercentage_ > 100 ? 100 : badElemPercentage_)
{
}
bool TestCompact::toString(std::ofstream &strOut)
{
strOut << "length=" << length << std::endl;
strOut << "badElem=" << badElem << std::endl;
strOut << "badElemPercentage=" << badElemPercentage << std::endl;
return true;
}
bool TestCompact::init()
{
return true;
}
bool TestCompact::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_vecSrc(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecSrc(*this->allocatorGPU.get(), this->length);
ncvAssertReturn(d_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> h_vecDst(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecDst.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecDst(*this->allocatorGPU.get(), this->length);
ncvAssertReturn(d_vecDst.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> h_vecDst_d(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecDst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_vecSrc), false);
for (Ncv32u i=0; i<this->length; i++)
{
Ncv32u tmp = (h_vecSrc.ptr()[i]) & 0xFF;
tmp = tmp * 99 / 255;
if (tmp < this->badElemPercentage)
{
h_vecSrc.ptr()[i] = this->badElem;
}
}
NCV_SKIP_COND_END
NCVVectorAlloc<Ncv32u> h_dstLen(*this->allocatorCPU.get(), 1);
ncvAssertReturn(h_dstLen.isMemAllocated(), false);
Ncv32u bufSize;
ncvStat = nppsStCompactGetSize_32u(this->length, &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
Ncv32u h_outElemNum_h = 0;
NCV_SKIP_COND_BEGIN
ncvStat = h_vecSrc.copySolid(d_vecSrc, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppsStCompact_32u(d_vecSrc.ptr(), this->length,
d_vecDst.ptr(), h_dstLen.ptr(), this->badElem,
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_vecDst.copySolid(h_vecDst_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppsStCompact_32u_host(h_vecSrc.ptr(), this->length, h_vecDst.ptr(), &h_outElemNum_h, this->badElem);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (h_dstLen.ptr()[0] != h_outElemNum_h)
{
bLoopVirgin = false;
}
else
{
for (Ncv32u i=0; bLoopVirgin && i < h_outElemNum_h; i++)
{
if (h_vecDst.ptr()[i] != h_vecDst_d.ptr()[i])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestCompact::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestCompact.h
+41 -41
View File
@@ -1,41 +1,41 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesescompact_h_
#define _testhypothesescompact_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestCompact : public NCVTestProvider
{
public:
TestCompact(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u length, Ncv32u badElem, Ncv32u badElemPercentage);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestCompact(const TestCompact&);
TestCompact& operator=(const TestCompact&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u length;
Ncv32u badElem;
Ncv32u badElemPercentage;
};
#endif // _testhypothesescompact_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesescompact_h_
#define _testhypothesescompact_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestCompact : public NCVTestProvider
{
public:
TestCompact(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u length, Ncv32u badElem, Ncv32u badElemPercentage);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestCompact(const TestCompact&);
TestCompact& operator=(const TestCompact&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u length;
Ncv32u badElem;
Ncv32u badElemPercentage;
};
#endif // _testhypothesescompact_h_
modules/gpu/test/nvidia/TestDrawRects.cpp
+167 -167
View File
@@ -1,168 +1,168 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestDrawRects.h"
#include "NCVHaarObjectDetection.hpp"
template <class T>
TestDrawRects<T>::TestDrawRects(std::string testName_, NCVTestSourceProvider<T> &src_,
NCVTestSourceProvider<Ncv32u> &src32u_,
Ncv32u width_, Ncv32u height_, Ncv32u numRects_, T color_)
:
NCVTestProvider(testName_),
src(src_),
src32u(src32u_),
width(width_),
height(height_),
numRects(numRects_),
color(color_)
{
}
template <class T>
bool TestDrawRects<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
strOut << "numRects=" << numRects << std::endl;
strOut << "color=" << color << std::endl;
return true;
}
template <class T>
bool TestDrawRects<T>::init()
{
return true;
}
template <class T>
bool TestDrawRects<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img_d(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img_d.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_rects(*this->allocatorGPU.get(), this->numRects);
ncvAssertReturn(d_rects.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_rects(*this->allocatorCPU.get(), this->numRects);
ncvAssertReturn(h_rects.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
//fill vector of rectangles with random rects covering the input
NCVVectorReuse<Ncv32u> h_rects_as32u(h_rects.getSegment());
ncvAssertReturn(h_rects_as32u.isMemReused(), false);
ncvAssertReturn(this->src32u.fill(h_rects_as32u), false);
for (Ncv32u i=0; i<this->numRects; i++)
{
h_rects.ptr()[i].x = (Ncv32u)(((1.0 * h_rects.ptr()[i].x) / RAND_MAX) * (this->width-2));
h_rects.ptr()[i].y = (Ncv32u)(((1.0 * h_rects.ptr()[i].y) / RAND_MAX) * (this->height-2));
h_rects.ptr()[i].width = (Ncv32u)(((1.0 * h_rects.ptr()[i].width) / RAND_MAX) * (this->width+10 - h_rects.ptr()[i].x));
h_rects.ptr()[i].height = (Ncv32u)(((1.0 * h_rects.ptr()[i].height) / RAND_MAX) * (this->height+10 - h_rects.ptr()[i].y));
}
ncvStat = h_rects.copySolid(d_rects, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = ncvDrawRects_32u_device((Ncv32u *)d_img.ptr(), d_img.stride(), this->width, this->height,
(NcvRect32u *)d_rects.ptr(), this->numRects, this->color, 0);
}
else if (sizeof(T) == sizeof(Ncv8u))
{
ncvStat = ncvDrawRects_8u_device((Ncv8u *)d_img.ptr(), d_img.stride(), this->width, this->height,
(NcvRect32u *)d_rects.ptr(), this->numRects, (Ncv8u)this->color, 0);
}
else
{
ncvAssertPrintReturn(false, "Incorrect drawrects test instance", false);
}
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_img.copySolid(h_img_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = ncvDrawRects_32u_host((Ncv32u *)h_img.ptr(), h_img.stride(), this->width, this->height,
(NcvRect32u *)h_rects.ptr(), this->numRects, this->color);
}
else if (sizeof(T) == sizeof(Ncv8u))
{
ncvStat = ncvDrawRects_8u_host((Ncv8u *)h_img.ptr(), h_img.stride(), this->width, this->height,
(NcvRect32u *)h_rects.ptr(), this->numRects, (Ncv8u)this->color);
}
else
{
ncvAssertPrintReturn(false, "Incorrect drawrects test instance", false);
}
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_img.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width(); j++)
{
if (h_img.ptr()[h_img.stride()*i+j] != h_img_d.ptr()[h_img_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestDrawRects<T>::deinit()
{
return true;
}
template class TestDrawRects<Ncv8u>;
template class TestDrawRects<Ncv32u>;
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestDrawRects.h"
#include "NCVHaarObjectDetection.hpp"
template <class T>
TestDrawRects<T>::TestDrawRects(std::string testName_, NCVTestSourceProvider<T> &src_,
NCVTestSourceProvider<Ncv32u> &src32u_,
Ncv32u width_, Ncv32u height_, Ncv32u numRects_, T color_)
:
NCVTestProvider(testName_),
src(src_),
src32u(src32u_),
width(width_),
height(height_),
numRects(numRects_),
color(color_)
{
}
template <class T>
bool TestDrawRects<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
strOut << "numRects=" << numRects << std::endl;
strOut << "color=" << color << std::endl;
return true;
}
template <class T>
bool TestDrawRects<T>::init()
{
return true;
}
template <class T>
bool TestDrawRects<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img_d(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img_d.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_rects(*this->allocatorGPU.get(), this->numRects);
ncvAssertReturn(d_rects.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_rects(*this->allocatorCPU.get(), this->numRects);
ncvAssertReturn(h_rects.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
//fill vector of rectangles with random rects covering the input
NCVVectorReuse<Ncv32u> h_rects_as32u(h_rects.getSegment());
ncvAssertReturn(h_rects_as32u.isMemReused(), false);
ncvAssertReturn(this->src32u.fill(h_rects_as32u), false);
for (Ncv32u i=0; i<this->numRects; i++)
{
h_rects.ptr()[i].x = (Ncv32u)(((1.0 * h_rects.ptr()[i].x) / RAND_MAX) * (this->width-2));
h_rects.ptr()[i].y = (Ncv32u)(((1.0 * h_rects.ptr()[i].y) / RAND_MAX) * (this->height-2));
h_rects.ptr()[i].width = (Ncv32u)(((1.0 * h_rects.ptr()[i].width) / RAND_MAX) * (this->width+10 - h_rects.ptr()[i].x));
h_rects.ptr()[i].height = (Ncv32u)(((1.0 * h_rects.ptr()[i].height) / RAND_MAX) * (this->height+10 - h_rects.ptr()[i].y));
}
ncvStat = h_rects.copySolid(d_rects, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = ncvDrawRects_32u_device((Ncv32u *)d_img.ptr(), d_img.stride(), this->width, this->height,
(NcvRect32u *)d_rects.ptr(), this->numRects, this->color, 0);
}
else if (sizeof(T) == sizeof(Ncv8u))
{
ncvStat = ncvDrawRects_8u_device((Ncv8u *)d_img.ptr(), d_img.stride(), this->width, this->height,
(NcvRect32u *)d_rects.ptr(), this->numRects, (Ncv8u)this->color, 0);
}
else
{
ncvAssertPrintReturn(false, "Incorrect drawrects test instance", false);
}
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_img.copySolid(h_img_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = ncvDrawRects_32u_host((Ncv32u *)h_img.ptr(), h_img.stride(), this->width, this->height,
(NcvRect32u *)h_rects.ptr(), this->numRects, this->color);
}
else if (sizeof(T) == sizeof(Ncv8u))
{
ncvStat = ncvDrawRects_8u_host((Ncv8u *)h_img.ptr(), h_img.stride(), this->width, this->height,
(NcvRect32u *)h_rects.ptr(), this->numRects, (Ncv8u)this->color);
}
else
{
ncvAssertPrintReturn(false, "Incorrect drawrects test instance", false);
}
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_img.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width(); j++)
{
if (h_img.ptr()[h_img.stride()*i+j] != h_img_d.ptr()[h_img_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestDrawRects<T>::deinit()
{
return true;
}
template class TestDrawRects<Ncv8u>;
template class TestDrawRects<Ncv32u>;
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestDrawRects.h
+44 -44
View File
@@ -1,44 +1,44 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testdrawrects_h_
#define _testdrawrects_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestDrawRects : public NCVTestProvider
{
public:
TestDrawRects(std::string testName, NCVTestSourceProvider<T> &src, NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u width, Ncv32u height, Ncv32u numRects, T color);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestDrawRects(const TestDrawRects&);
TestDrawRects& operator=(const TestDrawRects&);
NCVTestSourceProvider<T> &src;
NCVTestSourceProvider<Ncv32u> &src32u;
Ncv32u width;
Ncv32u height;
Ncv32u numRects;
T color;
};
#endif // _testdrawrects_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testdrawrects_h_
#define _testdrawrects_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestDrawRects : public NCVTestProvider
{
public:
TestDrawRects(std::string testName, NCVTestSourceProvider<T> &src, NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u width, Ncv32u height, Ncv32u numRects, T color);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestDrawRects(const TestDrawRects&);
TestDrawRects& operator=(const TestDrawRects&);
NCVTestSourceProvider<T> &src;
NCVTestSourceProvider<Ncv32u> &src32u;
Ncv32u width;
Ncv32u height;
Ncv32u numRects;
T color;
};
#endif // _testdrawrects_h_
modules/gpu/test/nvidia/TestHaarCascadeApplication.cpp
+304 -304
View File
@@ -1,305 +1,305 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <float.h>
#if defined(__GNUC__) && !defined(__APPLE__)
#include <fpu_control.h>
#endif
#include "TestHaarCascadeApplication.h"
#include "NCVHaarObjectDetection.hpp"
TestHaarCascadeApplication::TestHaarCascadeApplication(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_,
std::string cascadeName_, Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
cascadeName(cascadeName_),
width(width_),
height(height_)
{
}
bool TestHaarCascadeApplication::toString(std::ofstream &strOut)
{
strOut << "cascadeName=" << cascadeName << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
bool TestHaarCascadeApplication::init()
{
return true;
}
bool TestHaarCascadeApplication::process()
{
#if defined(__APPLE)
return true;
#endif
NCVStatus ncvStat;
bool rcode = false;
Ncv32u numStages, numNodes, numFeatures;
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
NCVVectorAlloc<HaarStage64> d_HaarStages(*this->allocatorGPU.get(), numStages);
ncvAssertReturn(d_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> d_HaarNodes(*this->allocatorGPU.get(), numNodes);
ncvAssertReturn(d_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> d_HaarFeatures(*this->allocatorGPU.get(), numFeatures);
ncvAssertReturn(d_HaarFeatures.isMemAllocated(), false);
HaarClassifierCascadeDescriptor haar;
haar.ClassifierSize.width = haar.ClassifierSize.height = 1;
haar.bNeedsTiltedII = false;
haar.NumClassifierRootNodes = numNodes;
haar.NumClassifierTotalNodes = numNodes;
haar.NumFeatures = numFeatures;
haar.NumStages = numStages;
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertReturn(NCV_SUCCESS == h_HaarStages.copySolid(d_HaarStages, 0), false);
ncvAssertReturn(NCV_SUCCESS == h_HaarNodes.copySolid(d_HaarNodes, 0), false);
ncvAssertReturn(NCV_SUCCESS == h_HaarFeatures.copySolid(d_HaarFeatures, 0), false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
NCV_SKIP_COND_END
NcvSize32s srcRoi, srcIIRoi, searchRoi;
srcRoi.width = this->width;
srcRoi.height = this->height;
srcIIRoi.width = srcRoi.width + 1;
srcIIRoi.height = srcRoi.height + 1;
searchRoi.width = srcIIRoi.width - haar.ClassifierSize.width;
searchRoi.height = srcIIRoi.height - haar.ClassifierSize.height;
if (searchRoi.width <= 0 || searchRoi.height <= 0)
{
return false;
}
NcvSize32u searchRoiU(searchRoi.width, searchRoi.height);
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
Ncv32u integralWidth = this->width + 1;
Ncv32u integralHeight = this->height + 1;
NCVMatrixAlloc<Ncv32u> d_integralImage(*this->allocatorGPU.get(), integralWidth, integralHeight);
ncvAssertReturn(d_integralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_sqIntegralImage(*this->allocatorGPU.get(), integralWidth, integralHeight);
ncvAssertReturn(d_sqIntegralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_integralImage(*this->allocatorCPU.get(), integralWidth, integralHeight);
ncvAssertReturn(h_integralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_sqIntegralImage(*this->allocatorCPU.get(), integralWidth, integralHeight);
ncvAssertReturn(h_sqIntegralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> d_rectStdDev(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_rectStdDev.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> d_pixelMask(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_pixelMask.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_rectStdDev(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_rectStdDev.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_pixelMask(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_pixelMask.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_hypotheses(*this->allocatorGPU.get(), this->width * this->height);
ncvAssertReturn(d_hypotheses.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_hypotheses(*this->allocatorCPU.get(), this->width * this->height);
ncvAssertReturn(h_hypotheses.isMemAllocated(), false);
NCVStatus nppStat;
Ncv32u szTmpBufIntegral, szTmpBufSqIntegral;
nppStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &szTmpBufIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
nppStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &szTmpBufSqIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
NCVVectorAlloc<Ncv8u> d_tmpIIbuf(*this->allocatorGPU.get(), std::max(szTmpBufIntegral, szTmpBufSqIntegral));
ncvAssertReturn(d_tmpIIbuf.isMemAllocated(), false);
Ncv32u detectionsOnThisScale_d = 0;
Ncv32u detectionsOnThisScale_h = 0;
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
nppStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(),
d_integralImage.ptr(), d_integralImage.pitch(),
NcvSize32u(d_img.width(), d_img.height()),
d_tmpIIbuf.ptr(), szTmpBufIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
nppStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
NcvSize32u(d_img.width(), d_img.height()),
d_tmpIIbuf.ptr(), szTmpBufSqIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
const NcvRect32u rect(
HAAR_STDDEV_BORDER,
HAAR_STDDEV_BORDER,
haar.ClassifierSize.width - 2*HAAR_STDDEV_BORDER,
haar.ClassifierSize.height - 2*HAAR_STDDEV_BORDER);
nppStat = nppiStRectStdDev_32f_C1R(
d_integralImage.ptr(), d_integralImage.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
d_rectStdDev.ptr(), d_rectStdDev.pitch(),
NcvSize32u(searchRoi.width, searchRoi.height), rect,
1.0f, true);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
ncvStat = d_integralImage.copySolid(h_integralImage, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = d_rectStdDev.copySolid(h_rectStdDev, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
for (Ncv32u i=0; i<searchRoiU.height; i++)
{
for (Ncv32u j=0; j<h_pixelMask.stride(); j++)
{
if (j<searchRoiU.width)
{
h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = (i << 16) | j;
}
else
{
h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = OBJDET_MASK_ELEMENT_INVALID_32U;
}
}
}
ncvAssertReturn(cudaSuccess == cudaStreamSynchronize(0), false);
#if !defined(__APPLE__)
#if defined(__GNUC__)
//http://www.christian-seiler.de/projekte/fpmath/
fpu_control_t fpu_oldcw, fpu_cw;
_FPU_GETCW(fpu_oldcw); // store old cw
fpu_cw = (fpu_oldcw & ~_FPU_EXTENDED & ~_FPU_DOUBLE & ~_FPU_SINGLE) | _FPU_SINGLE;
_FPU_SETCW(fpu_cw);
// calculations here
ncvStat = ncvApplyHaarClassifierCascade_host(
h_integralImage, h_rectStdDev, h_pixelMask,
detectionsOnThisScale_h,
haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false,
searchRoiU, 1, 1.0f);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
_FPU_SETCW(fpu_oldcw); // restore old cw
#else
#ifndef _WIN64
Ncv32u fpu_oldcw, fpu_cw;
_controlfp_s(&fpu_cw, 0, 0);
fpu_oldcw = fpu_cw;
_controlfp_s(&fpu_cw, _PC_24, _MCW_PC);
#endif
ncvStat = ncvApplyHaarClassifierCascade_host(
h_integralImage, h_rectStdDev, h_pixelMask,
detectionsOnThisScale_h,
haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false,
searchRoiU, 1, 1.0f);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
#ifndef _WIN64
_controlfp_s(&fpu_cw, fpu_oldcw, _MCW_PC);
#endif
#endif
#endif
NCV_SKIP_COND_END
int devId;
ncvAssertCUDAReturn(cudaGetDevice(&devId), false);
cudaDeviceProp _devProp;
ncvAssertCUDAReturn(cudaGetDeviceProperties(&_devProp, devId), false);
ncvStat = ncvApplyHaarClassifierCascade_device(
d_integralImage, d_rectStdDev, d_pixelMask,
detectionsOnThisScale_d,
haar, h_HaarStages, d_HaarStages, d_HaarNodes, d_HaarFeatures, false,
searchRoiU, 1, 1.0f,
*this->allocatorGPU.get(), *this->allocatorCPU.get(),
_devProp, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVMatrixAlloc<Ncv32u> h_pixelMask_d(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_pixelMask_d.isMemAllocated(), false);
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
ncvStat = d_pixelMask.copySolid(h_pixelMask_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
if (detectionsOnThisScale_d != detectionsOnThisScale_h)
{
bLoopVirgin = false;
}
else
{
std::sort(h_pixelMask_d.ptr(), h_pixelMask_d.ptr() + detectionsOnThisScale_d);
for (Ncv32u i=0; i<detectionsOnThisScale_d && bLoopVirgin; i++)
{
if (h_pixelMask.ptr()[i] != h_pixelMask_d.ptr()[i])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHaarCascadeApplication::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <float.h>
#if defined(__GNUC__) && !defined(__APPLE__)
#include <fpu_control.h>
#endif
#include "TestHaarCascadeApplication.h"
#include "NCVHaarObjectDetection.hpp"
TestHaarCascadeApplication::TestHaarCascadeApplication(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_,
std::string cascadeName_, Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
cascadeName(cascadeName_),
width(width_),
height(height_)
{
}
bool TestHaarCascadeApplication::toString(std::ofstream &strOut)
{
strOut << "cascadeName=" << cascadeName << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
bool TestHaarCascadeApplication::init()
{
return true;
}
bool TestHaarCascadeApplication::process()
{
#if defined(__APPLE)
return true;
#endif
NCVStatus ncvStat;
bool rcode = false;
Ncv32u numStages, numNodes, numFeatures;
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
NCVVectorAlloc<HaarStage64> d_HaarStages(*this->allocatorGPU.get(), numStages);
ncvAssertReturn(d_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> d_HaarNodes(*this->allocatorGPU.get(), numNodes);
ncvAssertReturn(d_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> d_HaarFeatures(*this->allocatorGPU.get(), numFeatures);
ncvAssertReturn(d_HaarFeatures.isMemAllocated(), false);
HaarClassifierCascadeDescriptor haar;
haar.ClassifierSize.width = haar.ClassifierSize.height = 1;
haar.bNeedsTiltedII = false;
haar.NumClassifierRootNodes = numNodes;
haar.NumClassifierTotalNodes = numNodes;
haar.NumFeatures = numFeatures;
haar.NumStages = numStages;
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertReturn(NCV_SUCCESS == h_HaarStages.copySolid(d_HaarStages, 0), false);
ncvAssertReturn(NCV_SUCCESS == h_HaarNodes.copySolid(d_HaarNodes, 0), false);
ncvAssertReturn(NCV_SUCCESS == h_HaarFeatures.copySolid(d_HaarFeatures, 0), false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
NCV_SKIP_COND_END
NcvSize32s srcRoi, srcIIRoi, searchRoi;
srcRoi.width = this->width;
srcRoi.height = this->height;
srcIIRoi.width = srcRoi.width + 1;
srcIIRoi.height = srcRoi.height + 1;
searchRoi.width = srcIIRoi.width - haar.ClassifierSize.width;
searchRoi.height = srcIIRoi.height - haar.ClassifierSize.height;
if (searchRoi.width <= 0 || searchRoi.height <= 0)
{
return false;
}
NcvSize32u searchRoiU(searchRoi.width, searchRoi.height);
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
Ncv32u integralWidth = this->width + 1;
Ncv32u integralHeight = this->height + 1;
NCVMatrixAlloc<Ncv32u> d_integralImage(*this->allocatorGPU.get(), integralWidth, integralHeight);
ncvAssertReturn(d_integralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_sqIntegralImage(*this->allocatorGPU.get(), integralWidth, integralHeight);
ncvAssertReturn(d_sqIntegralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_integralImage(*this->allocatorCPU.get(), integralWidth, integralHeight);
ncvAssertReturn(h_integralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_sqIntegralImage(*this->allocatorCPU.get(), integralWidth, integralHeight);
ncvAssertReturn(h_sqIntegralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> d_rectStdDev(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_rectStdDev.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> d_pixelMask(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_pixelMask.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_rectStdDev(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_rectStdDev.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_pixelMask(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_pixelMask.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_hypotheses(*this->allocatorGPU.get(), this->width * this->height);
ncvAssertReturn(d_hypotheses.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_hypotheses(*this->allocatorCPU.get(), this->width * this->height);
ncvAssertReturn(h_hypotheses.isMemAllocated(), false);
NCVStatus nppStat;
Ncv32u szTmpBufIntegral, szTmpBufSqIntegral;
nppStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &szTmpBufIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
nppStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &szTmpBufSqIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
NCVVectorAlloc<Ncv8u> d_tmpIIbuf(*this->allocatorGPU.get(), std::max(szTmpBufIntegral, szTmpBufSqIntegral));
ncvAssertReturn(d_tmpIIbuf.isMemAllocated(), false);
Ncv32u detectionsOnThisScale_d = 0;
Ncv32u detectionsOnThisScale_h = 0;
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
nppStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(),
d_integralImage.ptr(), d_integralImage.pitch(),
NcvSize32u(d_img.width(), d_img.height()),
d_tmpIIbuf.ptr(), szTmpBufIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
nppStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
NcvSize32u(d_img.width(), d_img.height()),
d_tmpIIbuf.ptr(), szTmpBufSqIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
const NcvRect32u rect(
HAAR_STDDEV_BORDER,
HAAR_STDDEV_BORDER,
haar.ClassifierSize.width - 2*HAAR_STDDEV_BORDER,
haar.ClassifierSize.height - 2*HAAR_STDDEV_BORDER);
nppStat = nppiStRectStdDev_32f_C1R(
d_integralImage.ptr(), d_integralImage.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
d_rectStdDev.ptr(), d_rectStdDev.pitch(),
NcvSize32u(searchRoi.width, searchRoi.height), rect,
1.0f, true);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
ncvStat = d_integralImage.copySolid(h_integralImage, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = d_rectStdDev.copySolid(h_rectStdDev, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
for (Ncv32u i=0; i<searchRoiU.height; i++)
{
for (Ncv32u j=0; j<h_pixelMask.stride(); j++)
{
if (j<searchRoiU.width)
{
h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = (i << 16) | j;
}
else
{
h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = OBJDET_MASK_ELEMENT_INVALID_32U;
}
}
}
ncvAssertReturn(cudaSuccess == cudaStreamSynchronize(0), false);
#if !defined(__APPLE__)
#if defined(__GNUC__)
//http://www.christian-seiler.de/projekte/fpmath/
fpu_control_t fpu_oldcw, fpu_cw;
_FPU_GETCW(fpu_oldcw); // store old cw
fpu_cw = (fpu_oldcw & ~_FPU_EXTENDED & ~_FPU_DOUBLE & ~_FPU_SINGLE) | _FPU_SINGLE;
_FPU_SETCW(fpu_cw);
// calculations here
ncvStat = ncvApplyHaarClassifierCascade_host(
h_integralImage, h_rectStdDev, h_pixelMask,
detectionsOnThisScale_h,
haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false,
searchRoiU, 1, 1.0f);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
_FPU_SETCW(fpu_oldcw); // restore old cw
#else
#ifndef _WIN64
Ncv32u fpu_oldcw, fpu_cw;
_controlfp_s(&fpu_cw, 0, 0);
fpu_oldcw = fpu_cw;
_controlfp_s(&fpu_cw, _PC_24, _MCW_PC);
#endif
ncvStat = ncvApplyHaarClassifierCascade_host(
h_integralImage, h_rectStdDev, h_pixelMask,
detectionsOnThisScale_h,
haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false,
searchRoiU, 1, 1.0f);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
#ifndef _WIN64
_controlfp_s(&fpu_cw, fpu_oldcw, _MCW_PC);
#endif
#endif
#endif
NCV_SKIP_COND_END
int devId;
ncvAssertCUDAReturn(cudaGetDevice(&devId), false);
cudaDeviceProp _devProp;
ncvAssertCUDAReturn(cudaGetDeviceProperties(&_devProp, devId), false);
ncvStat = ncvApplyHaarClassifierCascade_device(
d_integralImage, d_rectStdDev, d_pixelMask,
detectionsOnThisScale_d,
haar, h_HaarStages, d_HaarStages, d_HaarNodes, d_HaarFeatures, false,
searchRoiU, 1, 1.0f,
*this->allocatorGPU.get(), *this->allocatorCPU.get(),
_devProp, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVMatrixAlloc<Ncv32u> h_pixelMask_d(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_pixelMask_d.isMemAllocated(), false);
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
ncvStat = d_pixelMask.copySolid(h_pixelMask_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
if (detectionsOnThisScale_d != detectionsOnThisScale_h)
{
bLoopVirgin = false;
}
else
{
std::sort(h_pixelMask_d.ptr(), h_pixelMask_d.ptr() + detectionsOnThisScale_d);
for (Ncv32u i=0; i<detectionsOnThisScale_d && bLoopVirgin; i++)
{
if (h_pixelMask.ptr()[i] != h_pixelMask_d.ptr()[i])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHaarCascadeApplication::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestHaarCascadeApplication.h
+41 -41
View File
@@ -1,41 +1,41 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhaarcascadeapplication_h_
#define _testhaarcascadeapplication_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHaarCascadeApplication : public NCVTestProvider
{
public:
TestHaarCascadeApplication(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
std::string cascadeName, Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHaarCascadeApplication(const TestHaarCascadeApplication&);
TestHaarCascadeApplication& operator=(const TestHaarCascadeApplication&);
NCVTestSourceProvider<Ncv8u> &src;
std::string cascadeName;
Ncv32u width;
Ncv32u height;
};
#endif // _testhaarcascadeapplication_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhaarcascadeapplication_h_
#define _testhaarcascadeapplication_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHaarCascadeApplication : public NCVTestProvider
{
public:
TestHaarCascadeApplication(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
std::string cascadeName, Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHaarCascadeApplication(const TestHaarCascadeApplication&);
TestHaarCascadeApplication& operator=(const TestHaarCascadeApplication&);
NCVTestSourceProvider<Ncv8u> &src;
std::string cascadeName;
Ncv32u width;
Ncv32u height;
};
#endif // _testhaarcascadeapplication_h_
modules/gpu/test/nvidia/TestHaarCascadeLoader.cpp
+126 -126
View File
@@ -1,127 +1,127 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestHaarCascadeLoader.h"
#include "NCVHaarObjectDetection.hpp"
TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName_, std::string cascadeName_)
:
NCVTestProvider(testName_),
cascadeName(cascadeName_)
{
}
bool TestHaarCascadeLoader::toString(std::ofstream &strOut)
{
strOut << "cascadeName=" << cascadeName << std::endl;
return true;
}
bool TestHaarCascadeLoader::init()
{
return true;
}
bool TestHaarCascadeLoader::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u numStages, numNodes, numFeatures;
Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0;
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false);
HaarClassifierCascadeDescriptor haar;
HaarClassifierCascadeDescriptor haar_2;
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
const std::string testNvbinName = "test.nvbin";
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (
numStages_2 != numStages ||
numNodes_2 != numNodes ||
numFeatures_2 != numFeatures ||
haar.NumStages != haar_2.NumStages ||
haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes ||
haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes ||
haar.NumFeatures != haar_2.NumFeatures ||
haar.ClassifierSize.width != haar_2.ClassifierSize.width ||
haar.ClassifierSize.height != haar_2.ClassifierSize.height ||
haar.bNeedsTiltedII != haar_2.bNeedsTiltedII ||
haar.bHasStumpsOnly != haar_2.bHasStumpsOnly )
{
bLoopVirgin = false;
}
if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) ||
memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) ||
memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) )
{
bLoopVirgin = false;
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHaarCascadeLoader::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestHaarCascadeLoader.h"
#include "NCVHaarObjectDetection.hpp"
TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName_, std::string cascadeName_)
:
NCVTestProvider(testName_),
cascadeName(cascadeName_)
{
}
bool TestHaarCascadeLoader::toString(std::ofstream &strOut)
{
strOut << "cascadeName=" << cascadeName << std::endl;
return true;
}
bool TestHaarCascadeLoader::init()
{
return true;
}
bool TestHaarCascadeLoader::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u numStages, numNodes, numFeatures;
Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0;
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false);
HaarClassifierCascadeDescriptor haar;
HaarClassifierCascadeDescriptor haar_2;
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
const std::string testNvbinName = "test.nvbin";
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (
numStages_2 != numStages ||
numNodes_2 != numNodes ||
numFeatures_2 != numFeatures ||
haar.NumStages != haar_2.NumStages ||
haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes ||
haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes ||
haar.NumFeatures != haar_2.NumFeatures ||
haar.ClassifierSize.width != haar_2.ClassifierSize.width ||
haar.ClassifierSize.height != haar_2.ClassifierSize.height ||
haar.bNeedsTiltedII != haar_2.bNeedsTiltedII ||
haar.bHasStumpsOnly != haar_2.bHasStumpsOnly )
{
bLoopVirgin = false;
}
if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) ||
memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) ||
memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) )
{
bLoopVirgin = false;
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHaarCascadeLoader::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestHaarCascadeLoader.h
+34 -34
View File
@@ -1,34 +1,34 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhaarcascadeloader_h_
#define _testhaarcascadeloader_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHaarCascadeLoader : public NCVTestProvider
{
public:
TestHaarCascadeLoader(std::string testName, std::string cascadeName);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
std::string cascadeName;
};
#endif // _testhaarcascadeloader_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhaarcascadeloader_h_
#define _testhaarcascadeloader_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHaarCascadeLoader : public NCVTestProvider
{
public:
TestHaarCascadeLoader(std::string testName, std::string cascadeName);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
std::string cascadeName;
};
#endif // _testhaarcascadeloader_h_
modules/gpu/test/nvidia/TestHypothesesFilter.cpp
+179 -179
View File
@@ -1,180 +1,180 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestHypothesesFilter.h"
#include "NCVHaarObjectDetection.hpp"
TestHypothesesFilter::TestHypothesesFilter(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u numDstRects_, Ncv32u minNeighbors_, Ncv32f eps_)
:
NCVTestProvider(testName_),
src(src_),
numDstRects(numDstRects_),
minNeighbors(minNeighbors_),
eps(eps_)
{
}
bool TestHypothesesFilter::toString(std::ofstream &strOut)
{
strOut << "numDstRects=" << numDstRects << std::endl;
strOut << "minNeighbors=" << minNeighbors << std::endl;
strOut << "eps=" << eps << std::endl;
return true;
}
bool TestHypothesesFilter::init()
{
this->canvasWidth = 4096;
this->canvasHeight = 4096;
return true;
}
bool compareRects(const NcvRect32u &r1, const NcvRect32u &r2, Ncv32f eps)
{
double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5;
return std::abs((Ncv32s)r1.x - (Ncv32s)r2.x) <= delta &&
std::abs((Ncv32s)r1.y - (Ncv32s)r2.y) <= delta &&
std::abs((Ncv32s)r1.x + (Ncv32s)r1.width - (Ncv32s)r2.x - (Ncv32s)r2.width) <= delta &&
std::abs((Ncv32s)r1.y + (Ncv32s)r1.height - (Ncv32s)r2.y - (Ncv32s)r2.height) <= delta;
}
inline bool operator < (const NcvRect32u &a, const NcvRect32u &b)
{
return a.x < b.x;
}
bool TestHypothesesFilter::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_random32u(*this->allocatorCPU.get(), this->numDstRects * sizeof(NcvRect32u) / sizeof(Ncv32u));
ncvAssertReturn(h_random32u.isMemAllocated(), false);
Ncv32u srcSlotSize = 2 * this->minNeighbors + 1;
NCVVectorAlloc<NcvRect32u> h_vecSrc(*this->allocatorCPU.get(), this->numDstRects*srcSlotSize);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst_groundTruth(*this->allocatorCPU.get(), this->numDstRects);
ncvAssertReturn(h_vecDst_groundTruth.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorCPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_random32u), false);
Ncv32u randCnt = 0;
Ncv64f randVal;
for (Ncv32u i=0; i<this->numDstRects; i++)
{
h_vecDst_groundTruth.ptr()[i].x = i * this->canvasWidth / this->numDstRects + this->canvasWidth / (this->numDstRects * 4);
h_vecDst_groundTruth.ptr()[i].y = i * this->canvasHeight / this->numDstRects + this->canvasHeight / (this->numDstRects * 4);
h_vecDst_groundTruth.ptr()[i].width = this->canvasWidth / (this->numDstRects * 2);
h_vecDst_groundTruth.ptr()[i].height = this->canvasHeight / (this->numDstRects * 2);
Ncv32u numNeighbors = this->minNeighbors + 1 + (Ncv32u)(((1.0 * h_random32u.ptr()[i]) * (this->minNeighbors + 1)) / 0xFFFFFFFF);
numNeighbors = (numNeighbors > srcSlotSize) ? srcSlotSize : numNeighbors;
//fill in strong hypotheses (2 * ((1.0 * randVal) / 0xFFFFFFFF) - 1)
for (Ncv32u j=0; j<numNeighbors; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
h_vecSrc.ptr()[srcSlotSize * i + j].height = h_vecDst_groundTruth.ptr()[i].height;
}
//generate weak hypotheses (to be removed in processing)
for (Ncv32u j=numNeighbors; j<srcSlotSize; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
this->canvasWidth + h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
this->canvasHeight + h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
h_vecSrc.ptr()[srcSlotSize * i + j].height = h_vecDst_groundTruth.ptr()[i].height;
}
}
//shuffle
for (Ncv32u i=0; i<this->numDstRects*srcSlotSize-1; i++)
{
Ncv32u randValLocal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
Ncv32u secondSwap = randValLocal % (this->numDstRects*srcSlotSize-1 - i);
NcvRect32u tmp = h_vecSrc.ptr()[i + secondSwap];
h_vecSrc.ptr()[i + secondSwap] = h_vecSrc.ptr()[i];
h_vecSrc.ptr()[i] = tmp;
}
NCV_SKIP_COND_END
Ncv32u numHypothesesSrc = static_cast<Ncv32u>(h_vecSrc.length());
NCV_SKIP_COND_BEGIN
ncvStat = ncvGroupRectangles_host(h_vecSrc, numHypothesesSrc, this->minNeighbors, this->eps, NULL);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//verification
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (numHypothesesSrc != this->numDstRects)
{
bLoopVirgin = false;
}
else
{
std::vector<NcvRect32u> tmpRects(numHypothesesSrc);
memcpy(&tmpRects[0], h_vecSrc.ptr(), numHypothesesSrc * sizeof(NcvRect32u));
std::sort(tmpRects.begin(), tmpRects.end());
for (Ncv32u i=0; i<numHypothesesSrc && bLoopVirgin; i++)
{
if (!compareRects(tmpRects[i], h_vecDst_groundTruth.ptr()[i], this->eps))
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHypothesesFilter::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestHypothesesFilter.h"
#include "NCVHaarObjectDetection.hpp"
TestHypothesesFilter::TestHypothesesFilter(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u numDstRects_, Ncv32u minNeighbors_, Ncv32f eps_)
:
NCVTestProvider(testName_),
src(src_),
numDstRects(numDstRects_),
minNeighbors(minNeighbors_),
eps(eps_)
{
}
bool TestHypothesesFilter::toString(std::ofstream &strOut)
{
strOut << "numDstRects=" << numDstRects << std::endl;
strOut << "minNeighbors=" << minNeighbors << std::endl;
strOut << "eps=" << eps << std::endl;
return true;
}
bool TestHypothesesFilter::init()
{
this->canvasWidth = 4096;
this->canvasHeight = 4096;
return true;
}
bool compareRects(const NcvRect32u &r1, const NcvRect32u &r2, Ncv32f eps)
{
double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5;
return std::abs((Ncv32s)r1.x - (Ncv32s)r2.x) <= delta &&
std::abs((Ncv32s)r1.y - (Ncv32s)r2.y) <= delta &&
std::abs((Ncv32s)r1.x + (Ncv32s)r1.width - (Ncv32s)r2.x - (Ncv32s)r2.width) <= delta &&
std::abs((Ncv32s)r1.y + (Ncv32s)r1.height - (Ncv32s)r2.y - (Ncv32s)r2.height) <= delta;
}
inline bool operator < (const NcvRect32u &a, const NcvRect32u &b)
{
return a.x < b.x;
}
bool TestHypothesesFilter::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_random32u(*this->allocatorCPU.get(), this->numDstRects * sizeof(NcvRect32u) / sizeof(Ncv32u));
ncvAssertReturn(h_random32u.isMemAllocated(), false);
Ncv32u srcSlotSize = 2 * this->minNeighbors + 1;
NCVVectorAlloc<NcvRect32u> h_vecSrc(*this->allocatorCPU.get(), this->numDstRects*srcSlotSize);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst_groundTruth(*this->allocatorCPU.get(), this->numDstRects);
ncvAssertReturn(h_vecDst_groundTruth.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorCPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_random32u), false);
Ncv32u randCnt = 0;
Ncv64f randVal;
for (Ncv32u i=0; i<this->numDstRects; i++)
{
h_vecDst_groundTruth.ptr()[i].x = i * this->canvasWidth / this->numDstRects + this->canvasWidth / (this->numDstRects * 4);
h_vecDst_groundTruth.ptr()[i].y = i * this->canvasHeight / this->numDstRects + this->canvasHeight / (this->numDstRects * 4);
h_vecDst_groundTruth.ptr()[i].width = this->canvasWidth / (this->numDstRects * 2);
h_vecDst_groundTruth.ptr()[i].height = this->canvasHeight / (this->numDstRects * 2);
Ncv32u numNeighbors = this->minNeighbors + 1 + (Ncv32u)(((1.0 * h_random32u.ptr()[i]) * (this->minNeighbors + 1)) / 0xFFFFFFFF);
numNeighbors = (numNeighbors > srcSlotSize) ? srcSlotSize : numNeighbors;
//fill in strong hypotheses (2 * ((1.0 * randVal) / 0xFFFFFFFF) - 1)
for (Ncv32u j=0; j<numNeighbors; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
h_vecSrc.ptr()[srcSlotSize * i + j].height = h_vecDst_groundTruth.ptr()[i].height;
}
//generate weak hypotheses (to be removed in processing)
for (Ncv32u j=numNeighbors; j<srcSlotSize; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
this->canvasWidth + h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
this->canvasHeight + h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
h_vecSrc.ptr()[srcSlotSize * i + j].height = h_vecDst_groundTruth.ptr()[i].height;
}
}
//shuffle
for (Ncv32u i=0; i<this->numDstRects*srcSlotSize-1; i++)
{
Ncv32u randValLocal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
Ncv32u secondSwap = randValLocal % (this->numDstRects*srcSlotSize-1 - i);
NcvRect32u tmp = h_vecSrc.ptr()[i + secondSwap];
h_vecSrc.ptr()[i + secondSwap] = h_vecSrc.ptr()[i];
h_vecSrc.ptr()[i] = tmp;
}
NCV_SKIP_COND_END
Ncv32u numHypothesesSrc = static_cast<Ncv32u>(h_vecSrc.length());
NCV_SKIP_COND_BEGIN
ncvStat = ncvGroupRectangles_host(h_vecSrc, numHypothesesSrc, this->minNeighbors, this->eps, NULL);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//verification
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (numHypothesesSrc != this->numDstRects)
{
bLoopVirgin = false;
}
else
{
std::vector<NcvRect32u> tmpRects(numHypothesesSrc);
memcpy(&tmpRects[0], h_vecSrc.ptr(), numHypothesesSrc * sizeof(NcvRect32u));
std::sort(tmpRects.begin(), tmpRects.end());
for (Ncv32u i=0; i<numHypothesesSrc && bLoopVirgin; i++)
{
if (!compareRects(tmpRects[i], h_vecDst_groundTruth.ptr()[i], this->eps))
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHypothesesFilter::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestHypothesesFilter.h
+44 -44
View File
@@ -1,44 +1,44 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesesfilter_h_
#define _testhypothesesfilter_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHypothesesFilter : public NCVTestProvider
{
public:
TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u numDstRects, Ncv32u minNeighbors, Ncv32f eps);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHypothesesFilter(const TestHypothesesFilter&);
TestHypothesesFilter& operator=(const TestHypothesesFilter&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u numDstRects;
Ncv32u minNeighbors;
Ncv32f eps;
Ncv32u canvasWidth;
Ncv32u canvasHeight;
};
#endif // _testhypothesesfilter_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesesfilter_h_
#define _testhypothesesfilter_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHypothesesFilter : public NCVTestProvider
{
public:
TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u numDstRects, Ncv32u minNeighbors, Ncv32f eps);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHypothesesFilter(const TestHypothesesFilter&);
TestHypothesesFilter& operator=(const TestHypothesesFilter&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u numDstRects;
Ncv32u minNeighbors;
Ncv32f eps;
Ncv32u canvasWidth;
Ncv32u canvasHeight;
};
#endif // _testhypothesesfilter_h_
modules/gpu/test/nvidia/TestHypothesesGrow.cpp
+137 -137
View File
@@ -1,138 +1,138 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestHypothesesGrow.h"
#include "NCVHaarObjectDetection.hpp"
TestHypothesesGrow::TestHypothesesGrow(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u rectWidth_, Ncv32u rectHeight_, Ncv32f rectScale_,
Ncv32u maxLenSrc_, Ncv32u lenSrc_, Ncv32u maxLenDst_, Ncv32u lenDst_)
:
NCVTestProvider(testName_),
src(src_),
rectWidth(rectWidth_),
rectHeight(rectHeight_),
rectScale(rectScale_),
maxLenSrc(maxLenSrc_),
lenSrc(lenSrc_),
maxLenDst(maxLenDst_),
lenDst(lenDst_)
{
}
bool TestHypothesesGrow::toString(std::ofstream &strOut)
{
strOut << "rectWidth=" << rectWidth << std::endl;
strOut << "rectHeight=" << rectHeight << std::endl;
strOut << "rectScale=" << rectScale << std::endl;
strOut << "maxLenSrc=" << maxLenSrc << std::endl;
strOut << "lenSrc=" << lenSrc << std::endl;
strOut << "maxLenDst=" << maxLenDst << std::endl;
strOut << "lenDst=" << lenDst << std::endl;
return true;
}
bool TestHypothesesGrow::init()
{
return true;
}
bool TestHypothesesGrow::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_vecSrc(*this->allocatorCPU.get(), this->maxLenSrc);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecSrc(*this->allocatorGPU.get(), this->maxLenSrc);
ncvAssertReturn(d_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst(*this->allocatorCPU.get(), this->maxLenDst);
ncvAssertReturn(h_vecDst.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_vecDst(*this->allocatorGPU.get(), this->maxLenDst);
ncvAssertReturn(d_vecDst.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst_d(*this->allocatorCPU.get(), this->maxLenDst);
ncvAssertReturn(h_vecDst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_vecSrc), false);
memset(h_vecDst.ptr(), 0, h_vecDst.length() * sizeof(NcvRect32u));
NCVVectorReuse<Ncv32u> h_vecDst_as32u(h_vecDst.getSegment(), lenDst * sizeof(NcvRect32u) / sizeof(Ncv32u));
ncvAssertReturn(h_vecDst_as32u.isMemReused(), false);
ncvAssertReturn(this->src.fill(h_vecDst_as32u), false);
memcpy(h_vecDst_d.ptr(), h_vecDst.ptr(), h_vecDst.length() * sizeof(NcvRect32u));
NCV_SKIP_COND_END
ncvStat = h_vecSrc.copySolid(d_vecSrc, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = h_vecDst.copySolid(d_vecDst, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
Ncv32u h_outElemNum_d = 0;
Ncv32u h_outElemNum_h = 0;
NCV_SKIP_COND_BEGIN
h_outElemNum_d = this->lenDst;
ncvStat = ncvGrowDetectionsVector_device(d_vecSrc, this->lenSrc,
d_vecDst, h_outElemNum_d, this->maxLenDst,
this->rectWidth, this->rectHeight, this->rectScale, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = d_vecDst.copySolid(h_vecDst_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
h_outElemNum_h = this->lenDst;
ncvStat = ncvGrowDetectionsVector_host(h_vecSrc, this->lenSrc,
h_vecDst, h_outElemNum_h, this->maxLenDst,
this->rectWidth, this->rectHeight, this->rectScale);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (h_outElemNum_d != h_outElemNum_h)
{
bLoopVirgin = false;
}
else
{
if (memcmp(h_vecDst.ptr(), h_vecDst_d.ptr(), this->maxLenDst * sizeof(NcvRect32u)))
{
bLoopVirgin = false;
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHypothesesGrow::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestHypothesesGrow.h"
#include "NCVHaarObjectDetection.hpp"
TestHypothesesGrow::TestHypothesesGrow(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u rectWidth_, Ncv32u rectHeight_, Ncv32f rectScale_,
Ncv32u maxLenSrc_, Ncv32u lenSrc_, Ncv32u maxLenDst_, Ncv32u lenDst_)
:
NCVTestProvider(testName_),
src(src_),
rectWidth(rectWidth_),
rectHeight(rectHeight_),
rectScale(rectScale_),
maxLenSrc(maxLenSrc_),
lenSrc(lenSrc_),
maxLenDst(maxLenDst_),
lenDst(lenDst_)
{
}
bool TestHypothesesGrow::toString(std::ofstream &strOut)
{
strOut << "rectWidth=" << rectWidth << std::endl;
strOut << "rectHeight=" << rectHeight << std::endl;
strOut << "rectScale=" << rectScale << std::endl;
strOut << "maxLenSrc=" << maxLenSrc << std::endl;
strOut << "lenSrc=" << lenSrc << std::endl;
strOut << "maxLenDst=" << maxLenDst << std::endl;
strOut << "lenDst=" << lenDst << std::endl;
return true;
}
bool TestHypothesesGrow::init()
{
return true;
}
bool TestHypothesesGrow::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_vecSrc(*this->allocatorCPU.get(), this->maxLenSrc);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecSrc(*this->allocatorGPU.get(), this->maxLenSrc);
ncvAssertReturn(d_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst(*this->allocatorCPU.get(), this->maxLenDst);
ncvAssertReturn(h_vecDst.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_vecDst(*this->allocatorGPU.get(), this->maxLenDst);
ncvAssertReturn(d_vecDst.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst_d(*this->allocatorCPU.get(), this->maxLenDst);
ncvAssertReturn(h_vecDst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_vecSrc), false);
memset(h_vecDst.ptr(), 0, h_vecDst.length() * sizeof(NcvRect32u));
NCVVectorReuse<Ncv32u> h_vecDst_as32u(h_vecDst.getSegment(), lenDst * sizeof(NcvRect32u) / sizeof(Ncv32u));
ncvAssertReturn(h_vecDst_as32u.isMemReused(), false);
ncvAssertReturn(this->src.fill(h_vecDst_as32u), false);
memcpy(h_vecDst_d.ptr(), h_vecDst.ptr(), h_vecDst.length() * sizeof(NcvRect32u));
NCV_SKIP_COND_END
ncvStat = h_vecSrc.copySolid(d_vecSrc, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = h_vecDst.copySolid(d_vecDst, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
Ncv32u h_outElemNum_d = 0;
Ncv32u h_outElemNum_h = 0;
NCV_SKIP_COND_BEGIN
h_outElemNum_d = this->lenDst;
ncvStat = ncvGrowDetectionsVector_device(d_vecSrc, this->lenSrc,
d_vecDst, h_outElemNum_d, this->maxLenDst,
this->rectWidth, this->rectHeight, this->rectScale, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = d_vecDst.copySolid(h_vecDst_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
h_outElemNum_h = this->lenDst;
ncvStat = ncvGrowDetectionsVector_host(h_vecSrc, this->lenSrc,
h_vecDst, h_outElemNum_h, this->maxLenDst,
this->rectWidth, this->rectHeight, this->rectScale);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (h_outElemNum_d != h_outElemNum_h)
{
bLoopVirgin = false;
}
else
{
if (memcmp(h_vecDst.ptr(), h_vecDst_d.ptr(), this->maxLenDst * sizeof(NcvRect32u)))
{
bLoopVirgin = false;
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHypothesesGrow::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestHypothesesGrow.h
+46 -46
View File
@@ -1,46 +1,46 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesesgrow_h_
#define _testhypothesesgrow_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHypothesesGrow : public NCVTestProvider
{
public:
TestHypothesesGrow(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u rectWidth, Ncv32u rectHeight, Ncv32f rectScale,
Ncv32u maxLenSrc, Ncv32u lenSrc, Ncv32u maxLenDst, Ncv32u lenDst);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHypothesesGrow(const TestHypothesesGrow&);
TestHypothesesGrow& operator=(const TestHypothesesGrow&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u rectWidth;
Ncv32u rectHeight;
Ncv32f rectScale;
Ncv32u maxLenSrc;
Ncv32u lenSrc;
Ncv32u maxLenDst;
Ncv32u lenDst;
};
#endif // _testhypothesesgrow_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesesgrow_h_
#define _testhypothesesgrow_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHypothesesGrow : public NCVTestProvider
{
public:
TestHypothesesGrow(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u rectWidth, Ncv32u rectHeight, Ncv32f rectScale,
Ncv32u maxLenSrc, Ncv32u lenSrc, Ncv32u maxLenDst, Ncv32u lenDst);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHypothesesGrow(const TestHypothesesGrow&);
TestHypothesesGrow& operator=(const TestHypothesesGrow&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u rectWidth;
Ncv32u rectHeight;
Ncv32f rectScale;
Ncv32u maxLenSrc;
Ncv32u lenSrc;
Ncv32u maxLenDst;
Ncv32u lenDst;
};
#endif // _testhypothesesgrow_h_
modules/gpu/test/nvidia/TestIntegralImage.cpp
+188 -188
View File
@@ -1,189 +1,189 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestIntegralImage.h"
template <class T_in, class T_out>
TestIntegralImage<T_in, T_out>::TestIntegralImage(std::string testName_, NCVTestSourceProvider<T_in> &src_,
Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_)
{
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T_in)=" << sizeof(T_in) << std::endl;
strOut << "sizeof(T_out)=" << sizeof(T_out) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::init()
{
return true;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u widthII = this->width + 1;
Ncv32u heightII = this->height + 1;
NCVMatrixAlloc<T_in> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T_in> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T_out> d_imgII(*this->allocatorGPU.get(), widthII, heightII);
ncvAssertReturn(d_imgII.isMemAllocated(), false);
NCVMatrixAlloc<T_out> h_imgII(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII.isMemAllocated(), false);
NCVMatrixAlloc<T_out> h_imgII_d(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII_d.isMemAllocated(), false);
Ncv32u bufSize;
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegralGetSize_32f32f(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegral_8u32u_C1R((Ncv8u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegral_32f32f_C1R((Ncv32f *)d_img.ptr(), d_img.pitch(),
(Ncv32f *)d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
ncvStat = d_imgII.copySolid(h_imgII_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegral_8u32u_C1R_host((Ncv8u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegral_32f32f_C1R_host((Ncv32f *)h_img.ptr(), h_img.pitch(),
(Ncv32f *)h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; bLoopVirgin && i < h_img.height() + 1; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width() + 1; j++)
{
if (sizeof(T_in) == sizeof(Ncv8u))
{
if (h_imgII.ptr()[h_imgII.stride()*i+j] != h_imgII_d.ptr()[h_imgII_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
if (fabsf((float)h_imgII.ptr()[h_imgII.stride()*i+j] - (float)h_imgII_d.ptr()[h_imgII_d.stride()*i+j]) > 0.01f)
{
bLoopVirgin = false;
}
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::deinit()
{
return true;
}
template class TestIntegralImage<Ncv8u, Ncv32u>;
template class TestIntegralImage<Ncv32f, Ncv32f>;
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestIntegralImage.h"
template <class T_in, class T_out>
TestIntegralImage<T_in, T_out>::TestIntegralImage(std::string testName_, NCVTestSourceProvider<T_in> &src_,
Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_)
{
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T_in)=" << sizeof(T_in) << std::endl;
strOut << "sizeof(T_out)=" << sizeof(T_out) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::init()
{
return true;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u widthII = this->width + 1;
Ncv32u heightII = this->height + 1;
NCVMatrixAlloc<T_in> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T_in> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T_out> d_imgII(*this->allocatorGPU.get(), widthII, heightII);
ncvAssertReturn(d_imgII.isMemAllocated(), false);
NCVMatrixAlloc<T_out> h_imgII(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII.isMemAllocated(), false);
NCVMatrixAlloc<T_out> h_imgII_d(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII_d.isMemAllocated(), false);
Ncv32u bufSize;
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegralGetSize_32f32f(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegral_8u32u_C1R((Ncv8u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegral_32f32f_C1R((Ncv32f *)d_img.ptr(), d_img.pitch(),
(Ncv32f *)d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
ncvStat = d_imgII.copySolid(h_imgII_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegral_8u32u_C1R_host((Ncv8u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegral_32f32f_C1R_host((Ncv32f *)h_img.ptr(), h_img.pitch(),
(Ncv32f *)h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; bLoopVirgin && i < h_img.height() + 1; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width() + 1; j++)
{
if (sizeof(T_in) == sizeof(Ncv8u))
{
if (h_imgII.ptr()[h_imgII.stride()*i+j] != h_imgII_d.ptr()[h_imgII_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
if (fabsf((float)h_imgII.ptr()[h_imgII.stride()*i+j] - (float)h_imgII_d.ptr()[h_imgII_d.stride()*i+j]) > 0.01f)
{
bLoopVirgin = false;
}
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::deinit()
{
return true;
}
template class TestIntegralImage<Ncv8u, Ncv32u>;
template class TestIntegralImage<Ncv32f, Ncv32f>;
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestIntegralImage.h
+40 -40
View File
@@ -1,40 +1,40 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testintegralimage_h_
#define _testintegralimage_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T_in, class T_out>
class TestIntegralImage : public NCVTestProvider
{
public:
TestIntegralImage(std::string testName, NCVTestSourceProvider<T_in> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestIntegralImage(const TestIntegralImage&);
TestIntegralImage& operator=(const TestIntegralImage&);
NCVTestSourceProvider<T_in> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testintegralimage_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testintegralimage_h_
#define _testintegralimage_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T_in, class T_out>
class TestIntegralImage : public NCVTestProvider
{
public:
TestIntegralImage(std::string testName, NCVTestSourceProvider<T_in> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestIntegralImage(const TestIntegralImage&);
TestIntegralImage& operator=(const TestIntegralImage&);
NCVTestSourceProvider<T_in> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testintegralimage_h_
modules/gpu/test/nvidia/TestIntegralImageSquared.cpp
+120 -120
View File
@@ -1,121 +1,121 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestIntegralImageSquared.h"
TestIntegralImageSquared::TestIntegralImageSquared(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_,
Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_)
{
}
bool TestIntegralImageSquared::toString(std::ofstream &strOut)
{
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
bool TestIntegralImageSquared::init()
{
return true;
}
bool TestIntegralImageSquared::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u widthSII = this->width + 1;
Ncv32u heightSII = this->height + 1;
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_imgSII(*this->allocatorGPU.get(), widthSII, heightSII);
ncvAssertReturn(d_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII_d(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII_d.isMemAllocated(), false);
Ncv32u bufSize;
ncvStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_imgSII.copySolid(h_imgSII_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; bLoopVirgin && i < h_img.height() + 1; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width() + 1; j++)
{
if (h_imgSII.ptr()[h_imgSII.stride()*i+j] != h_imgSII_d.ptr()[h_imgSII_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestIntegralImageSquared::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include "TestIntegralImageSquared.h"
TestIntegralImageSquared::TestIntegralImageSquared(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_,
Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_)
{
}
bool TestIntegralImageSquared::toString(std::ofstream &strOut)
{
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
bool TestIntegralImageSquared::init()
{
return true;
}
bool TestIntegralImageSquared::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u widthSII = this->width + 1;
Ncv32u heightSII = this->height + 1;
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_imgSII(*this->allocatorGPU.get(), widthSII, heightSII);
ncvAssertReturn(d_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII_d(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII_d.isMemAllocated(), false);
Ncv32u bufSize;
ncvStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_imgSII.copySolid(h_imgSII_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; bLoopVirgin && i < h_img.height() + 1; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width() + 1; j++)
{
if (h_imgSII.ptr()[h_imgSII.stride()*i+j] != h_imgSII_d.ptr()[h_imgSII_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestIntegralImageSquared::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestIntegralImageSquared.h
+39 -39
View File
@@ -1,39 +1,39 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testintegralimagesquared_h_
#define _testintegralimagesquared_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestIntegralImageSquared : public NCVTestProvider
{
public:
TestIntegralImageSquared(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestIntegralImageSquared(const TestIntegralImageSquared&);
TestIntegralImageSquared& operator=(const TestIntegralImageSquared&);
NCVTestSourceProvider<Ncv8u> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testintegralimagesquared_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testintegralimagesquared_h_
#define _testintegralimagesquared_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestIntegralImageSquared : public NCVTestProvider
{
public:
TestIntegralImageSquared(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestIntegralImageSquared(const TestIntegralImageSquared&);
TestIntegralImageSquared& operator=(const TestIntegralImageSquared&);
NCVTestSourceProvider<Ncv8u> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testintegralimagesquared_h_
modules/gpu/test/nvidia/TestRectStdDev.cpp
+183 -183
View File
@@ -1,184 +1,184 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestRectStdDev.h"
TestRectStdDev::TestRectStdDev(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_,
Ncv32u width_, Ncv32u height_, NcvRect32u rect_, Ncv32f scaleFactor_,
NcvBool bTextureCache_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_),
rect(rect_),
scaleFactor(scaleFactor_),
bTextureCache(bTextureCache_)
{
}
bool TestRectStdDev::toString(std::ofstream &strOut)
{
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
strOut << "rect=[" << rect.x << ", " << rect.y << ", " << rect.width << ", " << rect.height << "]\n";
strOut << "scaleFactor=" << scaleFactor << std::endl;
strOut << "bTextureCache=" << bTextureCache << std::endl;
return true;
}
bool TestRectStdDev::init()
{
return true;
}
bool TestRectStdDev::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32s _normWidth = (Ncv32s)this->width - this->rect.x - this->rect.width + 1;
Ncv32s _normHeight = (Ncv32s)this->height - this->rect.y - this->rect.height + 1;
if (_normWidth <= 0 || _normHeight <= 0)
{
return true;
}
Ncv32u normWidth = (Ncv32u)_normWidth;
Ncv32u normHeight = (Ncv32u)_normHeight;
NcvSize32u szNormRoi(normWidth, normHeight);
Ncv32u widthII = this->width + 1;
Ncv32u heightII = this->height + 1;
Ncv32u widthSII = this->width + 1;
Ncv32u heightSII = this->height + 1;
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> d_imgII(*this->allocatorGPU.get(), widthII, heightII);
ncvAssertReturn(d_imgII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_imgII(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_imgSII(*this->allocatorGPU.get(), widthSII, heightSII);
ncvAssertReturn(d_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> d_norm(*this->allocatorGPU.get(), normWidth, normHeight);
ncvAssertReturn(d_norm.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_norm(*this->allocatorCPU.get(), normWidth, normHeight);
ncvAssertReturn(h_norm.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_norm_d(*this->allocatorCPU.get(), normWidth, normHeight);
ncvAssertReturn(h_norm_d.isMemAllocated(), false);
Ncv32u bufSizeII, bufSizeSII;
ncvStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &bufSizeII, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
ncvStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &bufSizeSII, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
Ncv32u bufSize = bufSizeII > bufSizeSII ? bufSizeII : bufSizeSII;
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(),
d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStRectStdDev_32f_C1R(d_imgII.ptr(), d_imgII.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
d_norm.ptr(), d_norm.pitch(),
szNormRoi, this->rect,
this->scaleFactor,
this->bTextureCache);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_norm.copySolid(h_norm_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStIntegral_8u32u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStRectStdDev_32f_C1R_host(h_imgII.ptr(), h_imgII.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
h_norm.ptr(), h_norm.pitch(),
szNormRoi, this->rect,
this->scaleFactor);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_norm.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_norm.width(); j++)
{
Ncv64f absErr = fabs(h_norm.ptr()[h_norm.stride()*i+j] - h_norm_d.ptr()[h_norm_d.stride()*i+j]);
Ncv64f relErr = absErr / h_norm.ptr()[h_norm.stride()*i+j];
if (relErr > relEPS)
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestRectStdDev::deinit()
{
return true;
}
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestRectStdDev.h"
TestRectStdDev::TestRectStdDev(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_,
Ncv32u width_, Ncv32u height_, NcvRect32u rect_, Ncv32f scaleFactor_,
NcvBool bTextureCache_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_),
rect(rect_),
scaleFactor(scaleFactor_),
bTextureCache(bTextureCache_)
{
}
bool TestRectStdDev::toString(std::ofstream &strOut)
{
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
strOut << "rect=[" << rect.x << ", " << rect.y << ", " << rect.width << ", " << rect.height << "]\n";
strOut << "scaleFactor=" << scaleFactor << std::endl;
strOut << "bTextureCache=" << bTextureCache << std::endl;
return true;
}
bool TestRectStdDev::init()
{
return true;
}
bool TestRectStdDev::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32s _normWidth = (Ncv32s)this->width - this->rect.x - this->rect.width + 1;
Ncv32s _normHeight = (Ncv32s)this->height - this->rect.y - this->rect.height + 1;
if (_normWidth <= 0 || _normHeight <= 0)
{
return true;
}
Ncv32u normWidth = (Ncv32u)_normWidth;
Ncv32u normHeight = (Ncv32u)_normHeight;
NcvSize32u szNormRoi(normWidth, normHeight);
Ncv32u widthII = this->width + 1;
Ncv32u heightII = this->height + 1;
Ncv32u widthSII = this->width + 1;
Ncv32u heightSII = this->height + 1;
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> d_imgII(*this->allocatorGPU.get(), widthII, heightII);
ncvAssertReturn(d_imgII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_imgII(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_imgSII(*this->allocatorGPU.get(), widthSII, heightSII);
ncvAssertReturn(d_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> d_norm(*this->allocatorGPU.get(), normWidth, normHeight);
ncvAssertReturn(d_norm.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_norm(*this->allocatorCPU.get(), normWidth, normHeight);
ncvAssertReturn(h_norm.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_norm_d(*this->allocatorCPU.get(), normWidth, normHeight);
ncvAssertReturn(h_norm_d.isMemAllocated(), false);
Ncv32u bufSizeII, bufSizeSII;
ncvStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &bufSizeII, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
ncvStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &bufSizeSII, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
Ncv32u bufSize = bufSizeII > bufSizeSII ? bufSizeII : bufSizeSII;
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(),
d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStRectStdDev_32f_C1R(d_imgII.ptr(), d_imgII.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
d_norm.ptr(), d_norm.pitch(),
szNormRoi, this->rect,
this->scaleFactor,
this->bTextureCache);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_norm.copySolid(h_norm_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStIntegral_8u32u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStRectStdDev_32f_C1R_host(h_imgII.ptr(), h_imgII.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
h_norm.ptr(), h_norm.pitch(),
szNormRoi, this->rect,
this->scaleFactor);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_norm.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_norm.width(); j++)
{
Ncv64f absErr = fabs(h_norm.ptr()[h_norm.stride()*i+j] - h_norm_d.ptr()[h_norm_d.stride()*i+j]);
Ncv64f relErr = absErr / h_norm.ptr()[h_norm.stride()*i+j];
if (relErr > relEPS)
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestRectStdDev::deinit()
{
return true;
}
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestRectStdDev.h
+44 -44
View File
@@ -1,44 +1,44 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testrectstddev_h_
#define _testrectstddev_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestRectStdDev : public NCVTestProvider
{
public:
TestRectStdDev(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height, NcvRect32u rect, Ncv32f scaleFactor,
NcvBool bTextureCache);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestRectStdDev(const TestRectStdDev&);
TestRectStdDev& operator=(const TestRectStdDev&);
NCVTestSourceProvider<Ncv8u> &src;
Ncv32u width;
Ncv32u height;
NcvRect32u rect;
Ncv32f scaleFactor;
NcvBool bTextureCache;
};
#endif // _testrectstddev_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testrectstddev_h_
#define _testrectstddev_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestRectStdDev : public NCVTestProvider
{
public:
TestRectStdDev(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height, NcvRect32u rect, Ncv32f scaleFactor,
NcvBool bTextureCache);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestRectStdDev(const TestRectStdDev&);
TestRectStdDev& operator=(const TestRectStdDev&);
NCVTestSourceProvider<Ncv8u> &src;
Ncv32u width;
Ncv32u height;
NcvRect32u rect;
Ncv32f scaleFactor;
NcvBool bTextureCache;
};
#endif // _testrectstddev_h_
modules/gpu/test/nvidia/TestResize.cpp
+164 -164
View File
@@ -1,165 +1,165 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestResize.h"
template <class T>
TestResize<T>::TestResize(std::string testName_, NCVTestSourceProvider<T> &src_,
Ncv32u width_, Ncv32u height_, Ncv32u scaleFactor_, NcvBool bTextureCache_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_),
scaleFactor(scaleFactor_),
bTextureCache(bTextureCache_)
{
}
template <class T>
bool TestResize<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "scaleFactor=" << scaleFactor << std::endl;
strOut << "bTextureCache=" << bTextureCache << std::endl;
return true;
}
template <class T>
bool TestResize<T>::init()
{
return true;
}
template <class T>
bool TestResize<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32s smallWidth = this->width / this->scaleFactor;
Ncv32s smallHeight = this->height / this->scaleFactor;
if (smallWidth == 0 || smallHeight == 0)
{
return true;
}
NcvSize32u srcSize(this->width, this->height);
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> d_small(*this->allocatorGPU.get(), smallWidth, smallHeight);
ncvAssertReturn(d_small.isMemAllocated(), false);
NCVMatrixAlloc<T> h_small(*this->allocatorCPU.get(), smallWidth, smallHeight);
ncvAssertReturn(h_small.isMemAllocated(), false);
NCVMatrixAlloc<T> h_small_d(*this->allocatorCPU.get(), smallWidth, smallHeight);
ncvAssertReturn(h_small_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
NCV_SKIP_COND_END
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStDecimate_32u_C1R((Ncv32u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_small.ptr(), d_small.pitch(),
srcSize, this->scaleFactor,
this->bTextureCache);
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStDecimate_64u_C1R((Ncv64u *)d_img.ptr(), d_img.pitch(),
(Ncv64u *)d_small.ptr(), d_small.pitch(),
srcSize, this->scaleFactor,
this->bTextureCache);
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_small.copySolid(h_small_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStDecimate_32u_C1R_host((Ncv32u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_small.ptr(), h_small.pitch(),
srcSize, this->scaleFactor);
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStDecimate_64u_C1R_host((Ncv64u *)h_img.ptr(), h_img.pitch(),
(Ncv64u *)h_small.ptr(), h_small.pitch(),
srcSize, this->scaleFactor);
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_small.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_small.width(); j++)
{
if (h_small.ptr()[h_small.stride()*i+j] != h_small_d.ptr()[h_small_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestResize<T>::deinit()
{
return true;
}
template class TestResize<Ncv32u>;
template class TestResize<Ncv64u>;
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestResize.h"
template <class T>
TestResize<T>::TestResize(std::string testName_, NCVTestSourceProvider<T> &src_,
Ncv32u width_, Ncv32u height_, Ncv32u scaleFactor_, NcvBool bTextureCache_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_),
scaleFactor(scaleFactor_),
bTextureCache(bTextureCache_)
{
}
template <class T>
bool TestResize<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "scaleFactor=" << scaleFactor << std::endl;
strOut << "bTextureCache=" << bTextureCache << std::endl;
return true;
}
template <class T>
bool TestResize<T>::init()
{
return true;
}
template <class T>
bool TestResize<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32s smallWidth = this->width / this->scaleFactor;
Ncv32s smallHeight = this->height / this->scaleFactor;
if (smallWidth == 0 || smallHeight == 0)
{
return true;
}
NcvSize32u srcSize(this->width, this->height);
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> d_small(*this->allocatorGPU.get(), smallWidth, smallHeight);
ncvAssertReturn(d_small.isMemAllocated(), false);
NCVMatrixAlloc<T> h_small(*this->allocatorCPU.get(), smallWidth, smallHeight);
ncvAssertReturn(h_small.isMemAllocated(), false);
NCVMatrixAlloc<T> h_small_d(*this->allocatorCPU.get(), smallWidth, smallHeight);
ncvAssertReturn(h_small_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
NCV_SKIP_COND_END
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStDecimate_32u_C1R((Ncv32u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_small.ptr(), d_small.pitch(),
srcSize, this->scaleFactor,
this->bTextureCache);
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStDecimate_64u_C1R((Ncv64u *)d_img.ptr(), d_img.pitch(),
(Ncv64u *)d_small.ptr(), d_small.pitch(),
srcSize, this->scaleFactor,
this->bTextureCache);
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_small.copySolid(h_small_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStDecimate_32u_C1R_host((Ncv32u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_small.ptr(), h_small.pitch(),
srcSize, this->scaleFactor);
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStDecimate_64u_C1R_host((Ncv64u *)h_img.ptr(), h_img.pitch(),
(Ncv64u *)h_small.ptr(), h_small.pitch(),
srcSize, this->scaleFactor);
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_small.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_small.width(); j++)
{
if (h_small.ptr()[h_small.stride()*i+j] != h_small_d.ptr()[h_small_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestResize<T>::deinit()
{
return true;
}
template class TestResize<Ncv32u>;
template class TestResize<Ncv64u>;
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestResize.h
+42 -42
View File
@@ -1,42 +1,42 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testresize_h_
#define _testresize_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestResize : public NCVTestProvider
{
public:
TestResize(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height, Ncv32u scaleFactor, NcvBool bTextureCache);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestResize(const TestResize&);
TestResize& operator=(const TestResize&);
NCVTestSourceProvider<T> &src;
Ncv32u width;
Ncv32u height;
Ncv32u scaleFactor;
NcvBool bTextureCache;
};
#endif // _testresize_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testresize_h_
#define _testresize_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestResize : public NCVTestProvider
{
public:
TestResize(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height, Ncv32u scaleFactor, NcvBool bTextureCache);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestResize(const TestResize&);
TestResize& operator=(const TestResize&);
NCVTestSourceProvider<T> &src;
Ncv32u width;
Ncv32u height;
Ncv32u scaleFactor;
NcvBool bTextureCache;
};
#endif // _testresize_h_
modules/gpu/test/nvidia/TestTranspose.cpp
+151 -151
View File
@@ -1,152 +1,152 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestTranspose.h"
template <class T>
TestTranspose<T>::TestTranspose(std::string testName_, NCVTestSourceProvider<T> &src_,
Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_)
{
}
template <class T>
bool TestTranspose<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
return true;
}
template <class T>
bool TestTranspose<T>::init()
{
return true;
}
template <class T>
bool TestTranspose<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
NcvSize32u srcSize(this->width, this->height);
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> d_dst(*this->allocatorGPU.get(), this->height, this->width);
ncvAssertReturn(d_dst.isMemAllocated(), false);
NCVMatrixAlloc<T> h_dst(*this->allocatorCPU.get(), this->height, this->width);
ncvAssertReturn(h_dst.isMemAllocated(), false);
NCVMatrixAlloc<T> h_dst_d(*this->allocatorCPU.get(), this->height, this->width);
ncvAssertReturn(h_dst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
NCV_SKIP_COND_END
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStTranspose_32u_C1R((Ncv32u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_dst.ptr(), d_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStTranspose_64u_C1R((Ncv64u *)d_img.ptr(), d_img.pitch(),
(Ncv64u *)d_dst.ptr(), d_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else
{
ncvAssertPrintReturn(false, "Incorrect transpose test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_dst.copySolid(h_dst_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStTranspose_32u_C1R_host((Ncv32u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_dst.ptr(), h_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStTranspose_64u_C1R_host((Ncv64u *)h_img.ptr(), h_img.pitch(),
(Ncv64u *)h_dst.ptr(), h_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < this->width; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < this->height; j++)
{
if (h_dst.ptr()[h_dst.stride()*i+j] != h_dst_d.ptr()[h_dst_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestTranspose<T>::deinit()
{
return true;
}
template class TestTranspose<Ncv32u>;
template class TestTranspose<Ncv64u>;
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#if !defined CUDA_DISABLER
#include <math.h>
#include "TestTranspose.h"
template <class T>
TestTranspose<T>::TestTranspose(std::string testName_, NCVTestSourceProvider<T> &src_,
Ncv32u width_, Ncv32u height_)
:
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_)
{
}
template <class T>
bool TestTranspose<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
return true;
}
template <class T>
bool TestTranspose<T>::init()
{
return true;
}
template <class T>
bool TestTranspose<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
NcvSize32u srcSize(this->width, this->height);
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> d_dst(*this->allocatorGPU.get(), this->height, this->width);
ncvAssertReturn(d_dst.isMemAllocated(), false);
NCVMatrixAlloc<T> h_dst(*this->allocatorCPU.get(), this->height, this->width);
ncvAssertReturn(h_dst.isMemAllocated(), false);
NCVMatrixAlloc<T> h_dst_d(*this->allocatorCPU.get(), this->height, this->width);
ncvAssertReturn(h_dst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
NCV_SKIP_COND_END
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStTranspose_32u_C1R((Ncv32u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_dst.ptr(), d_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStTranspose_64u_C1R((Ncv64u *)d_img.ptr(), d_img.pitch(),
(Ncv64u *)d_dst.ptr(), d_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else
{
ncvAssertPrintReturn(false, "Incorrect transpose test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_dst.copySolid(h_dst_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStTranspose_32u_C1R_host((Ncv32u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_dst.ptr(), h_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStTranspose_64u_C1R_host((Ncv64u *)h_img.ptr(), h_img.pitch(),
(Ncv64u *)h_dst.ptr(), h_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < this->width; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < this->height; j++)
{
if (h_dst.ptr()[h_dst.stride()*i+j] != h_dst_d.ptr()[h_dst_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestTranspose<T>::deinit()
{
return true;
}
template class TestTranspose<Ncv32u>;
template class TestTranspose<Ncv64u>;
#endif /* CUDA_DISABLER */
modules/gpu/test/nvidia/TestTranspose.h
+41 -41
View File
@@ -1,41 +1,41 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testtranspose_h_
#define _testtranspose_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestTranspose : public NCVTestProvider
{
public:
TestTranspose(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestTranspose(const TestTranspose&);
TestTranspose& operator=(const TestTranspose&);
NCVTestSourceProvider<T> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testtranspose_h_
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testtranspose_h_
#define _testtranspose_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestTranspose : public NCVTestProvider
{
public:
TestTranspose(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestTranspose(const TestTranspose&);
TestTranspose& operator=(const TestTranspose&);
NCVTestSourceProvider<T> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testtranspose_h_
modules/gpu/test/nvidia/main_nvidia.cpp
+441 -441
View File
@@ -1,442 +1,442 @@
#if defined _MSC_VER && _MSC_VER >= 1200
# pragma warning (disable : 4408 4201 4100)
#endif
#if !defined CUDA_DISABLER
#include <cstdio>
#include "NCV.hpp"
#include "NCVHaarObjectDetection.hpp"
#include "TestIntegralImage.h"
#include "TestIntegralImageSquared.h"
#include "TestRectStdDev.h"
#include "TestResize.h"
#include "TestCompact.h"
#include "TestTranspose.h"
#include "TestDrawRects.h"
#include "TestHypothesesGrow.h"
#include "TestHypothesesFilter.h"
#include "TestHaarCascadeLoader.h"
#include "TestHaarCascadeApplication.h"
#include "NCVAutoTestLister.hpp"
#include "NCVTestSourceProvider.hpp"
#include <main_test_nvidia.h>
static std::string path;
namespace {
template <class T_in, class T_out>
void generateIntegralTests(NCVAutoTestLister &testLister,
NCVTestSourceProvider<T_in> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=1.0; _i<maxWidth; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "LinIntImgW%dH%d", i, 2);
testLister.add(new TestIntegralImage<T_in, T_out>(testName, src, i, 2));
}
for (Ncv32f _i=1.0; _i<maxHeight; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "LinIntImgW%dH%d", 2, i);
testLister.add(new TestIntegralImage<T_in, T_out>(testName, src, 2, i));
}
testLister.add(new TestIntegralImage<T_in, T_out>("LinIntImg_VGA", src, 640, 480));
}
void generateSquaredIntegralTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=1.0; _i<maxWidth; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "SqIntImgW%dH%d", i, 32);
testLister.add(new TestIntegralImageSquared(testName, src, i, 32));
}
for (Ncv32f _i=1.0; _i<maxHeight; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "SqIntImgW%dH%d", 32, i);
testLister.add(new TestIntegralImageSquared(testName, src, 32, i));
}
testLister.add(new TestIntegralImageSquared("SqLinIntImg_VGA", src, 640, 480));
}
void generateRectStdDevTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
NcvRect32u rect(1,1,18,18);
for (Ncv32f _i=32; _i<maxHeight/2 && _i < maxWidth/2; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "RectStdDevW%dH%d", i*2, i);
testLister.add(new TestRectStdDev(testName, src, i*2, i, rect, 1, true));
testLister.add(new TestRectStdDev(testName, src, i*2, i, rect, 1.5, false));
testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 1, false));
testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 2.5, true));
}
testLister.add(new TestRectStdDev("RectStdDev_VGA", src, 640, 480, rect, 1, true));
}
template <class T>
void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
for (Ncv32u i=1; i<480; i+=3)
{
char testName[80];
sprintf(testName, "TestResize_VGA_s%d", i);
testLister.add(new TestResize<T>(testName, src, 640, 480, i, true));
testLister.add(new TestResize<T>(testName, src, 640, 480, i, false));
}
for (Ncv32u i=1; i<1080; i+=5)
{
char testName[80];
sprintf(testName, "TestResize_1080_s%d", i);
testLister.add(new TestResize<T>(testName, src, 1920, 1080, i, true));
testLister.add(new TestResize<T>(testName, src, 1920, 1080, i, false));
}
}
void generateNPPSTVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
//compaction
for (Ncv32f _i=256.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xFFFFFFFF, 30));
}
for (Ncv32u i=1; i<260; i++)
{
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 70));
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 0));
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 100));
}
for (Ncv32u i=256*256-256; i<256*256+257; i++)
{
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xFFFFFFFF, 40));
}
for (Ncv32u i=256*256*256-10; i<256*256*256+10; i++)
{
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0x00000000, 2));
}
}
template <class T>
void generateTransposeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
for (int i=2; i<64; i+=4)
{
for (int j=2; j<64; j+=4)
{
char testName[80];
sprintf(testName, "TestTranspose_%dx%d", i, j);
testLister.add(new TestTranspose<T>(testName, src, i, j));
}
}
for (int i=1; i<128; i+=1)
{
for (int j=1; j<2; j+=1)
{
char testName[80];
sprintf(testName, "TestTranspose_%dx%d", i, j);
testLister.add(new TestTranspose<T>(testName, src, i, j));
}
}
testLister.add(new TestTranspose<T>("TestTranspose_VGA", src, 640, 480));
testLister.add(new TestTranspose<T>("TestTranspose_HD1080", src, 1920, 1080));
//regression tests
testLister.add(new TestTranspose<T>("TestTranspose_reg_0", src, 1072, 375));
}
template <class T>
void generateDrawRectsTests(NCVAutoTestLister &testLister,
NCVTestSourceProvider<T> &src,
NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=16.0; _i<maxWidth; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
Ncv32u j = maxHeight * i / maxWidth;
if (!j) continue;
char testName[80];
sprintf(testName, "DrawRectsW%dH%d", i, j);
if (sizeof(T) == sizeof(Ncv32u))
{
testLister.add(new TestDrawRects<T>(testName, src, src32u, i, j, i*j/1000+1, (T)0xFFFFFFFF));
}
else if (sizeof(T) == sizeof(Ncv8u))
{
testLister.add(new TestDrawRects<T>(testName, src, src32u, i, j, i*j/1000+1, (T)0xFF));
}
else
{
ncvAssertPrintCheck(false, "Attempted to instantiate non-existing DrawRects test suite");
}
}
//test VGA
testLister.add(new TestDrawRects<T>("DrawRects_VGA", src, src32u, 640, 480, 640*480/1000, (T)0xFF));
}
void generateVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
//growth
for (Ncv32f _i=10.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "VectorGrow%d", i);
testLister.add(new TestHypothesesGrow(testName, src, 20, 20, 2.2f, i, i/2, i, i/4));
testLister.add(new TestHypothesesGrow(testName, src, 10, 42, 1.2f, i, i, i, 0));
}
testLister.add(new TestHypothesesGrow("VectorGrow01b", src, 10, 42, 1.2f, 10, 0, 10, 1));
testLister.add(new TestHypothesesGrow("VectorGrow11b", src, 10, 42, 1.2f, 10, 1, 10, 1));
testLister.add(new TestHypothesesGrow("VectorGrow10b", src, 10, 42, 1.2f, 10, 1, 10, 0));
testLister.add(new TestHypothesesGrow("VectorGrow00b", src, 10, 42, 1.2f, 10, 0, 10, 0));
}
void generateHypothesesFiltrationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
for (Ncv32f _i=1.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "HypFilter%d", i);
testLister.add(new TestHypothesesFilter(testName, src, i, 3, 0.2f));
testLister.add(new TestHypothesesFilter(testName, src, i, 0, 0.2f));
testLister.add(new TestHypothesesFilter(testName, src, i, 1, 0.1f));
}
}
void generateHaarLoaderTests(NCVAutoTestLister &testLister)
{
testLister.add(new TestHaarCascadeLoader("haarcascade_eye.xml", path + "haarcascade_eye.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt.xml", path + "haarcascade_frontalface_alt.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt2.xml", path + "haarcascade_frontalface_alt2.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt_tree.xml", path + "haarcascade_frontalface_alt_tree.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_eye_tree_eyeglasses.xml", path + "haarcascade_eye_tree_eyeglasses.xml"));
}
void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
(void)maxHeight;
for (Ncv32u i=20; i<512; i+=11)
{
for (Ncv32u j=20; j<128; j+=5)
{
char testName[80];
sprintf(testName, "HaarAppl%d_%d", i, j);
testLister.add(new TestHaarCascadeApplication(testName, src, path + "haarcascade_frontalface_alt.xml", j, i));
}
}
for (Ncv32f _i=20.0; _i<maxWidth; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "HaarAppl%d", i);
testLister.add(new TestHaarCascadeApplication(testName, src, path + "haarcascade_frontalface_alt.xml", i, i));
}
}
static void devNullOutput(const std::string& msg)
{
(void)msg;
}
bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path.c_str();
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerII("NPPST Integral Image", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
NCVTestSourceProvider<Ncv32f> testSrcRandom_32f(2010, -1.0f, 1.0f, 4096, 4096);
generateIntegralTests<Ncv8u, Ncv32u>(testListerII, testSrcRandom_8u, 4096, 4096);
generateIntegralTests<Ncv32f, Ncv32f>(testListerII, testSrcRandom_32f, 4096, 4096);
return testListerII.invoke();
}
}
bool nvidia_NPPST_Squared_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerSII("NPPST Squared Integral Image", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
generateSquaredIntegralTests(testListerSII, testSrcRandom_8u, 4096, 4096);
return testListerSII.invoke();
}
bool nvidia_NPPST_RectStdDev(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerRStdDev("NPPST RectStdDev", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
generateRectStdDevTests(testListerRStdDev, testSrcRandom_8u, 4096, 4096);
return testListerRStdDev.invoke();
}
bool nvidia_NPPST_Resize(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerResize("NPPST Resize", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv64u> testSrcRandom_64u(2010, 0, -1, 4096, 4096);
generateResizeTests(testListerResize, testSrcRandom_32u);
generateResizeTests(testListerResize, testSrcRandom_64u);
return testListerResize.invoke();
}
bool nvidia_NPPST_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerNPPSTVectorOperations("NPPST Vector Operations", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
generateNPPSTVectorTests(testListerNPPSTVectorOperations, testSrcRandom_32u, 4096*4096);
return testListerNPPSTVectorOperations.invoke();
}
bool nvidia_NPPST_Transpose(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerTranspose("NPPST Transpose", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv64u> testSrcRandom_64u(2010, 0, -1, 4096, 4096);
generateTransposeTests(testListerTranspose, testSrcRandom_32u);
generateTransposeTests(testListerTranspose, testSrcRandom_64u);
return testListerTranspose.invoke();
}
bool nvidia_NCV_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerVectorOperations("Vector Operations", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
generateVectorTests(testListerVectorOperations, testSrcRandom_32u, 4096*4096);
return testListerVectorOperations.invoke();
}
bool nvidia_NCV_Haar_Cascade_Loader(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerHaarLoader("Haar Cascade Loader", outputLevel);
generateHaarLoaderTests(testListerHaarLoader);
return testListerHaarLoader.invoke();
}
bool nvidia_NCV_Haar_Cascade_Application(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerHaarAppl("Haar Cascade Application", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcFacesVGA_8u(path + "group_1_640x480_VGA.pgm");
generateHaarApplicationTests(testListerHaarAppl, testSrcFacesVGA_8u, 1280, 720);
return testListerHaarAppl.invoke();
}
bool nvidia_NCV_Hypotheses_Filtration(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerHypFiltration("Hypotheses Filtration", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
generateHypothesesFiltrationTests(testListerHypFiltration, testSrcRandom_32u, 1024);
return testListerHypFiltration.invoke();
}
bool nvidia_NCV_Visualization(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerVisualize("Visualization", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, RAND_MAX, 4096, 4096);
generateDrawRectsTests(testListerVisualize, testSrcRandom_8u, testSrcRandom_32u, 4096, 4096);
generateDrawRectsTests(testListerVisualize, testSrcRandom_32u, testSrcRandom_32u, 4096, 4096);
return testListerVisualize.invoke();
}
#if defined _MSC_VER && _MSC_VER >= 1200
# pragma warning (disable : 4408 4201 4100)
#endif
#if !defined CUDA_DISABLER
#include <cstdio>
#include "NCV.hpp"
#include "NCVHaarObjectDetection.hpp"
#include "TestIntegralImage.h"
#include "TestIntegralImageSquared.h"
#include "TestRectStdDev.h"
#include "TestResize.h"
#include "TestCompact.h"
#include "TestTranspose.h"
#include "TestDrawRects.h"
#include "TestHypothesesGrow.h"
#include "TestHypothesesFilter.h"
#include "TestHaarCascadeLoader.h"
#include "TestHaarCascadeApplication.h"
#include "NCVAutoTestLister.hpp"
#include "NCVTestSourceProvider.hpp"
#include <main_test_nvidia.h>
static std::string path;
namespace {
template <class T_in, class T_out>
void generateIntegralTests(NCVAutoTestLister &testLister,
NCVTestSourceProvider<T_in> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=1.0; _i<maxWidth; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "LinIntImgW%dH%d", i, 2);
testLister.add(new TestIntegralImage<T_in, T_out>(testName, src, i, 2));
}
for (Ncv32f _i=1.0; _i<maxHeight; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "LinIntImgW%dH%d", 2, i);
testLister.add(new TestIntegralImage<T_in, T_out>(testName, src, 2, i));
}
testLister.add(new TestIntegralImage<T_in, T_out>("LinIntImg_VGA", src, 640, 480));
}
void generateSquaredIntegralTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=1.0; _i<maxWidth; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "SqIntImgW%dH%d", i, 32);
testLister.add(new TestIntegralImageSquared(testName, src, i, 32));
}
for (Ncv32f _i=1.0; _i<maxHeight; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "SqIntImgW%dH%d", 32, i);
testLister.add(new TestIntegralImageSquared(testName, src, 32, i));
}
testLister.add(new TestIntegralImageSquared("SqLinIntImg_VGA", src, 640, 480));
}
void generateRectStdDevTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
NcvRect32u rect(1,1,18,18);
for (Ncv32f _i=32; _i<maxHeight/2 && _i < maxWidth/2; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "RectStdDevW%dH%d", i*2, i);
testLister.add(new TestRectStdDev(testName, src, i*2, i, rect, 1, true));
testLister.add(new TestRectStdDev(testName, src, i*2, i, rect, 1.5, false));
testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 1, false));
testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 2.5, true));
}
testLister.add(new TestRectStdDev("RectStdDev_VGA", src, 640, 480, rect, 1, true));
}
template <class T>
void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
for (Ncv32u i=1; i<480; i+=3)
{
char testName[80];
sprintf(testName, "TestResize_VGA_s%d", i);
testLister.add(new TestResize<T>(testName, src, 640, 480, i, true));
testLister.add(new TestResize<T>(testName, src, 640, 480, i, false));
}
for (Ncv32u i=1; i<1080; i+=5)
{
char testName[80];
sprintf(testName, "TestResize_1080_s%d", i);
testLister.add(new TestResize<T>(testName, src, 1920, 1080, i, true));
testLister.add(new TestResize<T>(testName, src, 1920, 1080, i, false));
}
}
void generateNPPSTVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
//compaction
for (Ncv32f _i=256.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xFFFFFFFF, 30));
}
for (Ncv32u i=1; i<260; i++)
{
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 70));
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 0));
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 100));
}
for (Ncv32u i=256*256-256; i<256*256+257; i++)
{
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xFFFFFFFF, 40));
}
for (Ncv32u i=256*256*256-10; i<256*256*256+10; i++)
{
char testName[80];
sprintf(testName, "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0x00000000, 2));
}
}
template <class T>
void generateTransposeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
for (int i=2; i<64; i+=4)
{
for (int j=2; j<64; j+=4)
{
char testName[80];
sprintf(testName, "TestTranspose_%dx%d", i, j);
testLister.add(new TestTranspose<T>(testName, src, i, j));
}
}
for (int i=1; i<128; i+=1)
{
for (int j=1; j<2; j+=1)
{
char testName[80];
sprintf(testName, "TestTranspose_%dx%d", i, j);
testLister.add(new TestTranspose<T>(testName, src, i, j));
}
}
testLister.add(new TestTranspose<T>("TestTranspose_VGA", src, 640, 480));
testLister.add(new TestTranspose<T>("TestTranspose_HD1080", src, 1920, 1080));
//regression tests
testLister.add(new TestTranspose<T>("TestTranspose_reg_0", src, 1072, 375));
}
template <class T>
void generateDrawRectsTests(NCVAutoTestLister &testLister,
NCVTestSourceProvider<T> &src,
NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=16.0; _i<maxWidth; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
Ncv32u j = maxHeight * i / maxWidth;
if (!j) continue;
char testName[80];
sprintf(testName, "DrawRectsW%dH%d", i, j);
if (sizeof(T) == sizeof(Ncv32u))
{
testLister.add(new TestDrawRects<T>(testName, src, src32u, i, j, i*j/1000+1, (T)0xFFFFFFFF));
}
else if (sizeof(T) == sizeof(Ncv8u))
{
testLister.add(new TestDrawRects<T>(testName, src, src32u, i, j, i*j/1000+1, (T)0xFF));
}
else
{
ncvAssertPrintCheck(false, "Attempted to instantiate non-existing DrawRects test suite");
}
}
//test VGA
testLister.add(new TestDrawRects<T>("DrawRects_VGA", src, src32u, 640, 480, 640*480/1000, (T)0xFF));
}
void generateVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
//growth
for (Ncv32f _i=10.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "VectorGrow%d", i);
testLister.add(new TestHypothesesGrow(testName, src, 20, 20, 2.2f, i, i/2, i, i/4));
testLister.add(new TestHypothesesGrow(testName, src, 10, 42, 1.2f, i, i, i, 0));
}
testLister.add(new TestHypothesesGrow("VectorGrow01b", src, 10, 42, 1.2f, 10, 0, 10, 1));
testLister.add(new TestHypothesesGrow("VectorGrow11b", src, 10, 42, 1.2f, 10, 1, 10, 1));
testLister.add(new TestHypothesesGrow("VectorGrow10b", src, 10, 42, 1.2f, 10, 1, 10, 0));
testLister.add(new TestHypothesesGrow("VectorGrow00b", src, 10, 42, 1.2f, 10, 0, 10, 0));
}
void generateHypothesesFiltrationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
for (Ncv32f _i=1.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "HypFilter%d", i);
testLister.add(new TestHypothesesFilter(testName, src, i, 3, 0.2f));
testLister.add(new TestHypothesesFilter(testName, src, i, 0, 0.2f));
testLister.add(new TestHypothesesFilter(testName, src, i, 1, 0.1f));
}
}
void generateHaarLoaderTests(NCVAutoTestLister &testLister)
{
testLister.add(new TestHaarCascadeLoader("haarcascade_eye.xml", path + "haarcascade_eye.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt.xml", path + "haarcascade_frontalface_alt.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt2.xml", path + "haarcascade_frontalface_alt2.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt_tree.xml", path + "haarcascade_frontalface_alt_tree.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_eye_tree_eyeglasses.xml", path + "haarcascade_eye_tree_eyeglasses.xml"));
}
void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
(void)maxHeight;
for (Ncv32u i=20; i<512; i+=11)
{
for (Ncv32u j=20; j<128; j+=5)
{
char testName[80];
sprintf(testName, "HaarAppl%d_%d", i, j);
testLister.add(new TestHaarCascadeApplication(testName, src, path + "haarcascade_frontalface_alt.xml", j, i));
}
}
for (Ncv32f _i=20.0; _i<maxWidth; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf(testName, "HaarAppl%d", i);
testLister.add(new TestHaarCascadeApplication(testName, src, path + "haarcascade_frontalface_alt.xml", i, i));
}
}
static void devNullOutput(const std::string& msg)
{
(void)msg;
}
bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path.c_str();
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerII("NPPST Integral Image", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
NCVTestSourceProvider<Ncv32f> testSrcRandom_32f(2010, -1.0f, 1.0f, 4096, 4096);
generateIntegralTests<Ncv8u, Ncv32u>(testListerII, testSrcRandom_8u, 4096, 4096);
generateIntegralTests<Ncv32f, Ncv32f>(testListerII, testSrcRandom_32f, 4096, 4096);
return testListerII.invoke();
}
}
bool nvidia_NPPST_Squared_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerSII("NPPST Squared Integral Image", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
generateSquaredIntegralTests(testListerSII, testSrcRandom_8u, 4096, 4096);
return testListerSII.invoke();
}
bool nvidia_NPPST_RectStdDev(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerRStdDev("NPPST RectStdDev", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
generateRectStdDevTests(testListerRStdDev, testSrcRandom_8u, 4096, 4096);
return testListerRStdDev.invoke();
}
bool nvidia_NPPST_Resize(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerResize("NPPST Resize", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv64u> testSrcRandom_64u(2010, 0, -1, 4096, 4096);
generateResizeTests(testListerResize, testSrcRandom_32u);
generateResizeTests(testListerResize, testSrcRandom_64u);
return testListerResize.invoke();
}
bool nvidia_NPPST_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerNPPSTVectorOperations("NPPST Vector Operations", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
generateNPPSTVectorTests(testListerNPPSTVectorOperations, testSrcRandom_32u, 4096*4096);
return testListerNPPSTVectorOperations.invoke();
}
bool nvidia_NPPST_Transpose(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerTranspose("NPPST Transpose", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv64u> testSrcRandom_64u(2010, 0, -1, 4096, 4096);
generateTransposeTests(testListerTranspose, testSrcRandom_32u);
generateTransposeTests(testListerTranspose, testSrcRandom_64u);
return testListerTranspose.invoke();
}
bool nvidia_NCV_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerVectorOperations("Vector Operations", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
generateVectorTests(testListerVectorOperations, testSrcRandom_32u, 4096*4096);
return testListerVectorOperations.invoke();
}
bool nvidia_NCV_Haar_Cascade_Loader(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerHaarLoader("Haar Cascade Loader", outputLevel);
generateHaarLoaderTests(testListerHaarLoader);
return testListerHaarLoader.invoke();
}
bool nvidia_NCV_Haar_Cascade_Application(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerHaarAppl("Haar Cascade Application", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcFacesVGA_8u(path + "group_1_640x480_VGA.pgm");
generateHaarApplicationTests(testListerHaarAppl, testSrcFacesVGA_8u, 1280, 720);
return testListerHaarAppl.invoke();
}
bool nvidia_NCV_Hypotheses_Filtration(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerHypFiltration("Hypotheses Filtration", outputLevel);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
generateHypothesesFiltrationTests(testListerHypFiltration, testSrcRandom_32u, 1024);
return testListerHypFiltration.invoke();
}
bool nvidia_NCV_Visualization(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerVisualize("Visualization", outputLevel);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, RAND_MAX, 4096, 4096);
generateDrawRectsTests(testListerVisualize, testSrcRandom_8u, testSrcRandom_32u, 4096, 4096);
generateDrawRectsTests(testListerVisualize, testSrcRandom_32u, testSrcRandom_32u, 4096, 4096);
return testListerVisualize.invoke();
}
#endif /* CUDA_DISABLER */
modules/gpu/test/test_calib3d.cpp
+349 -349
View File
@@ -1,349 +1,349 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
//////////////////////////////////////////////////////////////////////////
// StereoBM
struct StereoBM : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(StereoBM, Regression)
{
cv::Mat left_image = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
cv::Mat right_image = readImage("stereobm/aloe-R.png", cv::IMREAD_GRAYSCALE);
cv::Mat disp_gold = readImage("stereobm/aloe-disp.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
cv::gpu::StereoBM_GPU bm(0, 128, 19);
cv::gpu::GpuMat disp;
bm(loadMat(left_image), loadMat(right_image), disp);
EXPECT_MAT_NEAR(disp_gold, disp, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoBM, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////////
// StereoBeliefPropagation
struct StereoBeliefPropagation : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(StereoBeliefPropagation, Regression)
{
cv::Mat left_image = readImage("stereobp/aloe-L.png");
cv::Mat right_image = readImage("stereobp/aloe-R.png");
cv::Mat disp_gold = readImage("stereobp/aloe-disp.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
cv::gpu::StereoBeliefPropagation bp(64, 8, 2, 25, 0.1f, 15, 1, CV_16S);
cv::gpu::GpuMat disp;
bp(loadMat(left_image), loadMat(right_image), disp);
cv::Mat h_disp(disp);
h_disp.convertTo(h_disp, disp_gold.depth());
EXPECT_MAT_NEAR(disp_gold, h_disp, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoBeliefPropagation, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////////
// StereoConstantSpaceBP
struct StereoConstantSpaceBP : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(StereoConstantSpaceBP, Regression)
{
cv::Mat left_image = readImage("csstereobp/aloe-L.png");
cv::Mat right_image = readImage("csstereobp/aloe-R.png");
cv::Mat disp_gold;
if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
disp_gold = readImage("csstereobp/aloe-disp.png", cv::IMREAD_GRAYSCALE);
else
disp_gold = readImage("csstereobp/aloe-disp_CC1X.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
cv::gpu::StereoConstantSpaceBP csbp(128, 16, 4, 4);
cv::gpu::GpuMat disp;
csbp(loadMat(left_image), loadMat(right_image), disp);
cv::Mat h_disp(disp);
h_disp.convertTo(h_disp, disp_gold.depth());
EXPECT_MAT_NEAR(disp_gold, h_disp, 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoConstantSpaceBP, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// transformPoints
struct TransformPoints : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(TransformPoints, Accuracy)
{
cv::Mat src = randomMat(cv::Size(1000, 1), CV_32FC3, 0, 10);
cv::Mat rvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::Mat tvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::gpu::GpuMat dst;
cv::gpu::transformPoints(loadMat(src), rvec, tvec, dst);
ASSERT_EQ(src.size(), dst.size());
ASSERT_EQ(src.type(), dst.type());
cv::Mat h_dst(dst);
cv::Mat rot;
cv::Rodrigues(rvec, rot);
for (int i = 0; i < h_dst.cols; ++i)
{
cv::Point3f res = h_dst.at<cv::Point3f>(0, i);
cv::Point3f p = src.at<cv::Point3f>(0, i);
cv::Point3f res_gold(
rot.at<float>(0, 0) * p.x + rot.at<float>(0, 1) * p.y + rot.at<float>(0, 2) * p.z + tvec.at<float>(0, 0),
rot.at<float>(1, 0) * p.x + rot.at<float>(1, 1) * p.y + rot.at<float>(1, 2) * p.z + tvec.at<float>(0, 1),
rot.at<float>(2, 0) * p.x + rot.at<float>(2, 1) * p.y + rot.at<float>(2, 2) * p.z + tvec.at<float>(0, 2));
ASSERT_POINT3_NEAR(res_gold, res, 1e-5);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, TransformPoints, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// ProjectPoints
struct ProjectPoints : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ProjectPoints, Accuracy)
{
cv::Mat src = randomMat(cv::Size(1000, 1), CV_32FC3, 0, 10);
cv::Mat rvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::Mat tvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::Mat camera_mat = randomMat(cv::Size(3, 3), CV_32F, 0.5, 1);
camera_mat.at<float>(0, 1) = 0.f;
camera_mat.at<float>(1, 0) = 0.f;
camera_mat.at<float>(2, 0) = 0.f;
camera_mat.at<float>(2, 1) = 0.f;
cv::gpu::GpuMat dst;
cv::gpu::projectPoints(loadMat(src), rvec, tvec, camera_mat, cv::Mat(), dst);
ASSERT_EQ(1, dst.rows);
ASSERT_EQ(MatType(CV_32FC2), MatType(dst.type()));
std::vector<cv::Point2f> dst_gold;
cv::projectPoints(src, rvec, tvec, camera_mat, cv::Mat(1, 8, CV_32F, cv::Scalar::all(0)), dst_gold);
ASSERT_EQ(dst_gold.size(), static_cast<size_t>(dst.cols));
cv::Mat h_dst(dst);
for (size_t i = 0; i < dst_gold.size(); ++i)
{
cv::Point2f res = h_dst.at<cv::Point2f>(0, (int)i);
cv::Point2f res_gold = dst_gold[i];
ASSERT_LE(cv::norm(res_gold - res) / cv::norm(res_gold), 1e-3f);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, ProjectPoints, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// SolvePnPRansac
struct SolvePnPRansac : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(SolvePnPRansac, Accuracy)
{
cv::Mat object = randomMat(cv::Size(5000, 1), CV_32FC3, 0, 100);
cv::Mat camera_mat = randomMat(cv::Size(3, 3), CV_32F, 0.5, 1);
camera_mat.at<float>(0, 1) = 0.f;
camera_mat.at<float>(1, 0) = 0.f;
camera_mat.at<float>(2, 0) = 0.f;
camera_mat.at<float>(2, 1) = 0.f;
std::vector<cv::Point2f> image_vec;
cv::Mat rvec_gold;
cv::Mat tvec_gold;
rvec_gold = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
tvec_gold = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::projectPoints(object, rvec_gold, tvec_gold, camera_mat, cv::Mat(1, 8, CV_32F, cv::Scalar::all(0)), image_vec);
cv::Mat rvec, tvec;
std::vector<int> inliers;
cv::gpu::solvePnPRansac(object, cv::Mat(1, (int)image_vec.size(), CV_32FC2, &image_vec[0]),
camera_mat, cv::Mat(1, 8, CV_32F, cv::Scalar::all(0)),
rvec, tvec, false, 200, 2.f, 100, &inliers);
ASSERT_LE(cv::norm(rvec - rvec_gold), 1e-3);
ASSERT_LE(cv::norm(tvec - tvec_gold), 1e-3);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, SolvePnPRansac, ALL_DEVICES);
////////////////////////////////////////////////////////////////////////////////
// reprojectImageTo3D
PARAM_TEST_CASE(ReprojectImageTo3D, cv::gpu::DeviceInfo, cv::Size, MatDepth, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ReprojectImageTo3D, Accuracy)
{
cv::Mat disp = randomMat(size, depth, 5.0, 30.0);
cv::Mat Q = randomMat(cv::Size(4, 4), CV_32FC1, 0.1, 1.0);
cv::gpu::GpuMat dst;
cv::gpu::reprojectImageTo3D(loadMat(disp, useRoi), dst, Q, 3);
cv::Mat dst_gold;
cv::reprojectImageTo3D(disp, dst_gold, Q, false);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, ReprojectImageTo3D, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatDepth(CV_8U), MatDepth(CV_16S)),
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
//////////////////////////////////////////////////////////////////////////
// StereoBM
struct StereoBM : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(StereoBM, Regression)
{
cv::Mat left_image = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
cv::Mat right_image = readImage("stereobm/aloe-R.png", cv::IMREAD_GRAYSCALE);
cv::Mat disp_gold = readImage("stereobm/aloe-disp.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
cv::gpu::StereoBM_GPU bm(0, 128, 19);
cv::gpu::GpuMat disp;
bm(loadMat(left_image), loadMat(right_image), disp);
EXPECT_MAT_NEAR(disp_gold, disp, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoBM, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////////
// StereoBeliefPropagation
struct StereoBeliefPropagation : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(StereoBeliefPropagation, Regression)
{
cv::Mat left_image = readImage("stereobp/aloe-L.png");
cv::Mat right_image = readImage("stereobp/aloe-R.png");
cv::Mat disp_gold = readImage("stereobp/aloe-disp.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
cv::gpu::StereoBeliefPropagation bp(64, 8, 2, 25, 0.1f, 15, 1, CV_16S);
cv::gpu::GpuMat disp;
bp(loadMat(left_image), loadMat(right_image), disp);
cv::Mat h_disp(disp);
h_disp.convertTo(h_disp, disp_gold.depth());
EXPECT_MAT_NEAR(disp_gold, h_disp, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoBeliefPropagation, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////////
// StereoConstantSpaceBP
struct StereoConstantSpaceBP : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(StereoConstantSpaceBP, Regression)
{
cv::Mat left_image = readImage("csstereobp/aloe-L.png");
cv::Mat right_image = readImage("csstereobp/aloe-R.png");
cv::Mat disp_gold;
if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
disp_gold = readImage("csstereobp/aloe-disp.png", cv::IMREAD_GRAYSCALE);
else
disp_gold = readImage("csstereobp/aloe-disp_CC1X.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
cv::gpu::StereoConstantSpaceBP csbp(128, 16, 4, 4);
cv::gpu::GpuMat disp;
csbp(loadMat(left_image), loadMat(right_image), disp);
cv::Mat h_disp(disp);
h_disp.convertTo(h_disp, disp_gold.depth());
EXPECT_MAT_NEAR(disp_gold, h_disp, 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoConstantSpaceBP, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// transformPoints
struct TransformPoints : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(TransformPoints, Accuracy)
{
cv::Mat src = randomMat(cv::Size(1000, 1), CV_32FC3, 0, 10);
cv::Mat rvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::Mat tvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::gpu::GpuMat dst;
cv::gpu::transformPoints(loadMat(src), rvec, tvec, dst);
ASSERT_EQ(src.size(), dst.size());
ASSERT_EQ(src.type(), dst.type());
cv::Mat h_dst(dst);
cv::Mat rot;
cv::Rodrigues(rvec, rot);
for (int i = 0; i < h_dst.cols; ++i)
{
cv::Point3f res = h_dst.at<cv::Point3f>(0, i);
cv::Point3f p = src.at<cv::Point3f>(0, i);
cv::Point3f res_gold(
rot.at<float>(0, 0) * p.x + rot.at<float>(0, 1) * p.y + rot.at<float>(0, 2) * p.z + tvec.at<float>(0, 0),
rot.at<float>(1, 0) * p.x + rot.at<float>(1, 1) * p.y + rot.at<float>(1, 2) * p.z + tvec.at<float>(0, 1),
rot.at<float>(2, 0) * p.x + rot.at<float>(2, 1) * p.y + rot.at<float>(2, 2) * p.z + tvec.at<float>(0, 2));
ASSERT_POINT3_NEAR(res_gold, res, 1e-5);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, TransformPoints, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// ProjectPoints
struct ProjectPoints : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ProjectPoints, Accuracy)
{
cv::Mat src = randomMat(cv::Size(1000, 1), CV_32FC3, 0, 10);
cv::Mat rvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::Mat tvec = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::Mat camera_mat = randomMat(cv::Size(3, 3), CV_32F, 0.5, 1);
camera_mat.at<float>(0, 1) = 0.f;
camera_mat.at<float>(1, 0) = 0.f;
camera_mat.at<float>(2, 0) = 0.f;
camera_mat.at<float>(2, 1) = 0.f;
cv::gpu::GpuMat dst;
cv::gpu::projectPoints(loadMat(src), rvec, tvec, camera_mat, cv::Mat(), dst);
ASSERT_EQ(1, dst.rows);
ASSERT_EQ(MatType(CV_32FC2), MatType(dst.type()));
std::vector<cv::Point2f> dst_gold;
cv::projectPoints(src, rvec, tvec, camera_mat, cv::Mat(1, 8, CV_32F, cv::Scalar::all(0)), dst_gold);
ASSERT_EQ(dst_gold.size(), static_cast<size_t>(dst.cols));
cv::Mat h_dst(dst);
for (size_t i = 0; i < dst_gold.size(); ++i)
{
cv::Point2f res = h_dst.at<cv::Point2f>(0, (int)i);
cv::Point2f res_gold = dst_gold[i];
ASSERT_LE(cv::norm(res_gold - res) / cv::norm(res_gold), 1e-3f);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, ProjectPoints, ALL_DEVICES);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// SolvePnPRansac
struct SolvePnPRansac : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(SolvePnPRansac, Accuracy)
{
cv::Mat object = randomMat(cv::Size(5000, 1), CV_32FC3, 0, 100);
cv::Mat camera_mat = randomMat(cv::Size(3, 3), CV_32F, 0.5, 1);
camera_mat.at<float>(0, 1) = 0.f;
camera_mat.at<float>(1, 0) = 0.f;
camera_mat.at<float>(2, 0) = 0.f;
camera_mat.at<float>(2, 1) = 0.f;
std::vector<cv::Point2f> image_vec;
cv::Mat rvec_gold;
cv::Mat tvec_gold;
rvec_gold = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
tvec_gold = randomMat(cv::Size(3, 1), CV_32F, 0, 1);
cv::projectPoints(object, rvec_gold, tvec_gold, camera_mat, cv::Mat(1, 8, CV_32F, cv::Scalar::all(0)), image_vec);
cv::Mat rvec, tvec;
std::vector<int> inliers;
cv::gpu::solvePnPRansac(object, cv::Mat(1, (int)image_vec.size(), CV_32FC2, &image_vec[0]),
camera_mat, cv::Mat(1, 8, CV_32F, cv::Scalar::all(0)),
rvec, tvec, false, 200, 2.f, 100, &inliers);
ASSERT_LE(cv::norm(rvec - rvec_gold), 1e-3);
ASSERT_LE(cv::norm(tvec - tvec_gold), 1e-3);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, SolvePnPRansac, ALL_DEVICES);
////////////////////////////////////////////////////////////////////////////////
// reprojectImageTo3D
PARAM_TEST_CASE(ReprojectImageTo3D, cv::gpu::DeviceInfo, cv::Size, MatDepth, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ReprojectImageTo3D, Accuracy)
{
cv::Mat disp = randomMat(size, depth, 5.0, 30.0);
cv::Mat Q = randomMat(cv::Size(4, 4), CV_32FC1, 0.1, 1.0);
cv::gpu::GpuMat dst;
cv::gpu::reprojectImageTo3D(loadMat(disp, useRoi), dst, Q, 3);
cv::Mat dst_gold;
cv::reprojectImageTo3D(disp, dst_gold, Q, false);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, ReprojectImageTo3D, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatDepth(CV_8U), MatDepth(CV_16S)),
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA
modules/gpu/test/test_copy_make_border.cpp
+104 -104
View File
@@ -1,104 +1,104 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
IMPLEMENT_PARAM_CLASS(Border, int)
PARAM_TEST_CASE(CopyMakeBorder, cv::gpu::DeviceInfo, cv::Size, MatType, Border, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int border;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
border = GET_PARAM(3);
borderType = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(CopyMakeBorder, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Scalar val = randomScalar(0, 255);
cv::gpu::GpuMat dst = createMat(cv::Size(size.width + 2 * border, size.height + 2 * border), type, useRoi);
cv::gpu::copyMakeBorder(loadMat(src, useRoi), dst, border, border, border, border, borderType, val);
cv::Mat dst_gold;
cv::copyMakeBorder(src, dst_gold, border, border, border, border, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CopyMakeBorder, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1),
MatType(CV_8UC3),
MatType(CV_8UC4),
MatType(CV_16UC1),
MatType(CV_16UC3),
MatType(CV_16UC4),
MatType(CV_32FC1),
MatType(CV_32FC3),
MatType(CV_32FC4)),
testing::Values(Border(1), Border(10), Border(50)),
ALL_BORDER_TYPES,
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
IMPLEMENT_PARAM_CLASS(Border, int)
PARAM_TEST_CASE(CopyMakeBorder, cv::gpu::DeviceInfo, cv::Size, MatType, Border, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int border;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
border = GET_PARAM(3);
borderType = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(CopyMakeBorder, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Scalar val = randomScalar(0, 255);
cv::gpu::GpuMat dst = createMat(cv::Size(size.width + 2 * border, size.height + 2 * border), type, useRoi);
cv::gpu::copyMakeBorder(loadMat(src, useRoi), dst, border, border, border, border, borderType, val);
cv::Mat dst_gold;
cv::copyMakeBorder(src, dst_gold, border, border, border, border, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CopyMakeBorder, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1),
MatType(CV_8UC3),
MatType(CV_8UC4),
MatType(CV_16UC1),
MatType(CV_16UC3),
MatType(CV_16UC4),
MatType(CV_32FC1),
MatType(CV_32FC3),
MatType(CV_32FC4)),
testing::Values(Border(1), Border(10), Border(50)),
ALL_BORDER_TYPES,
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA
modules/gpu/test/test_core.cpp
+3291 -3291
View File
File diff suppressed because it is too large Load Diff
modules/gpu/test/test_denoising.cpp
+3 -3
View File
@@ -72,7 +72,7 @@ PARAM_TEST_CASE(BilateralFilter, cv::gpu::DeviceInfo, cv::Size, MatType)
TEST_P(BilateralFilter, Accuracy)
{
cv::Mat src = randomMat(size, type);
src.convertTo(src, type);
cv::gpu::GpuMat dst;
@@ -111,7 +111,7 @@ TEST_P(BruteForceNonLocalMeans, Regression)
cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
ASSERT_FALSE(bgr.empty());
cv::Mat gray;
cv::cvtColor(bgr, gray, CV_BGR2GRAY);
@@ -154,7 +154,7 @@ TEST_P(FastNonLocalMeans, Regression)
{
using cv::gpu::GpuMat;
cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
ASSERT_FALSE(bgr.empty());
cv::Mat gray;
modules/gpu/test/test_features2d.cpp
+998 -998
View File
File diff suppressed because it is too large Load Diff
modules/gpu/test/test_filters.cpp
+558 -558
View File
File diff suppressed because it is too large Load Diff
modules/gpu/test/test_gpumat.cpp
+329 -329
View File
@@ -1,329 +1,329 @@
/*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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
// 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 {
////////////////////////////////////////////////////////////////////////////////
// SetTo
PARAM_TEST_CASE(SetTo, 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());
}
};
TEST_P(SetTo, Zero)
{
cv::Scalar zero = cv::Scalar::all(0);
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(zero);
EXPECT_MAT_NEAR(cv::Mat::zeros(size, type), mat, 0.0);
}
TEST_P(SetTo, SameVal)
{
cv::Scalar val = cv::Scalar::all(randomDouble(0.0, 255.0));
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
}
}
TEST_P(SetTo, DifferentVal)
{
cv::Scalar val = randomScalar(0.0, 255.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
}
}
TEST_P(SetTo, Masked)
{
cv::Scalar val = randomScalar(0.0, 255.0);
cv::Mat mat_gold = randomMat(size, type);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val, loadMat(mask));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat mat = loadMat(mat_gold, useRoi);
mat.setTo(val, loadMat(mask, useRoi));
mat_gold.setTo(val, mask);
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, SetTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_TYPES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// CopyTo
PARAM_TEST_CASE(CopyTo, 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());
}
};
TEST_P(CopyTo, WithOutMask)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
d_src.copyTo(dst);
EXPECT_MAT_NEAR(src, dst, 0.0);
}
TEST_P(CopyTo, Masked)
{
cv::Mat src = randomMat(size, type);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat d_src = loadMat(src);
cv::gpu::GpuMat dst;
d_src.copyTo(dst, loadMat(mask, useRoi));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = loadMat(cv::Mat::zeros(size, type), useRoi);
d_src.copyTo(dst, loadMat(mask, useRoi));
cv::Mat dst_gold = cv::Mat::zeros(size, type);
src.copyTo(dst_gold, mask);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, CopyTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_TYPES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// ConvertTo
PARAM_TEST_CASE(ConvertTo, cv::gpu::DeviceInfo, cv::Size, MatDepth, MatDepth, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth1;
int depth2;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth1 = GET_PARAM(2);
depth2 = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ConvertTo, WithOutScaling)
{
cv::Mat src = randomMat(size, depth1);
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat d_src = loadMat(src);
cv::gpu::GpuMat dst;
d_src.convertTo(dst, depth2);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = createMat(size, depth2, useRoi);
d_src.convertTo(dst, depth2);
cv::Mat dst_gold;
src.convertTo(dst_gold, depth2);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
TEST_P(ConvertTo, WithScaling)
{
cv::Mat src = randomMat(size, depth1);
double a = randomDouble(0.0, 1.0);
double b = randomDouble(-10.0, 10.0);
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat d_src = loadMat(src);
cv::gpu::GpuMat dst;
d_src.convertTo(dst, depth2, a, b);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = createMat(size, depth2, useRoi);
d_src.convertTo(dst, depth2, a, b);
cv::Mat dst_gold;
src.convertTo(dst_gold, depth2, a, b);
EXPECT_MAT_NEAR(dst_gold, dst, depth2 < CV_32F ? 1.0 : 1e-4);
}
}
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, ConvertTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
ALL_DEPTH,
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA
/*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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
// 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 {
////////////////////////////////////////////////////////////////////////////////
// SetTo
PARAM_TEST_CASE(SetTo, 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());
}
};
TEST_P(SetTo, Zero)
{
cv::Scalar zero = cv::Scalar::all(0);
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(zero);
EXPECT_MAT_NEAR(cv::Mat::zeros(size, type), mat, 0.0);
}
TEST_P(SetTo, SameVal)
{
cv::Scalar val = cv::Scalar::all(randomDouble(0.0, 255.0));
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
}
}
TEST_P(SetTo, DifferentVal)
{
cv::Scalar val = randomScalar(0.0, 255.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
}
}
TEST_P(SetTo, Masked)
{
cv::Scalar val = randomScalar(0.0, 255.0);
cv::Mat mat_gold = randomMat(size, type);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val, loadMat(mask));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat mat = loadMat(mat_gold, useRoi);
mat.setTo(val, loadMat(mask, useRoi));
mat_gold.setTo(val, mask);
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, SetTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_TYPES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// CopyTo
PARAM_TEST_CASE(CopyTo, 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());
}
};
TEST_P(CopyTo, WithOutMask)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
d_src.copyTo(dst);
EXPECT_MAT_NEAR(src, dst, 0.0);
}
TEST_P(CopyTo, Masked)
{
cv::Mat src = randomMat(size, type);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat d_src = loadMat(src);
cv::gpu::GpuMat dst;
d_src.copyTo(dst, loadMat(mask, useRoi));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = loadMat(cv::Mat::zeros(size, type), useRoi);
d_src.copyTo(dst, loadMat(mask, useRoi));
cv::Mat dst_gold = cv::Mat::zeros(size, type);
src.copyTo(dst_gold, mask);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, CopyTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_TYPES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// ConvertTo
PARAM_TEST_CASE(ConvertTo, cv::gpu::DeviceInfo, cv::Size, MatDepth, MatDepth, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth1;
int depth2;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth1 = GET_PARAM(2);
depth2 = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ConvertTo, WithOutScaling)
{
cv::Mat src = randomMat(size, depth1);
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat d_src = loadMat(src);
cv::gpu::GpuMat dst;
d_src.convertTo(dst, depth2);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = createMat(size, depth2, useRoi);
d_src.convertTo(dst, depth2);
cv::Mat dst_gold;
src.convertTo(dst_gold, depth2);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
TEST_P(ConvertTo, WithScaling)
{
cv::Mat src = randomMat(size, depth1);
double a = randomDouble(0.0, 1.0);
double b = randomDouble(-10.0, 10.0);
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat d_src = loadMat(src);
cv::gpu::GpuMat dst;
d_src.convertTo(dst, depth2, a, b);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
cv::gpu::GpuMat dst = createMat(size, depth2, useRoi);
d_src.convertTo(dst, depth2, a, b);
cv::Mat dst_gold;
src.convertTo(dst_gold, depth2, a, b);
EXPECT_MAT_NEAR(dst_gold, dst, depth2 < CV_32F ? 1.0 : 1e-4);
}
}
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, ConvertTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
ALL_DEPTH,
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA
modules/gpu/test/test_imgproc.cpp
+1131 -1131
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File diff suppressed because it is too large Load Diff
modules/gpu/test/test_nvidia.cpp
+153 -153
View File
@@ -1,153 +1,153 @@
/*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 "test_precomp.hpp"
#if defined HAVE_CUDA
OutputLevel nvidiaTestOutputLevel = OutputLevelNone;
#endif
#if defined HAVE_CUDA && !defined(CUDA_DISABLER)
using namespace cvtest;
using namespace testing;
struct NVidiaTest : TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
std::string _path;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
_path = TS::ptr()->get_data_path().c_str();
_path = _path + "haarcascade/";
}
};
struct NPPST : NVidiaTest {};
struct NCV : NVidiaTest {};
//TEST_P(NPPST, Integral)
//{
// bool res = nvidia_NPPST_Integral_Image(path, nvidiaTestOutputLevel);
// ASSERT_TRUE(res);
//}
TEST_P(NPPST, SquaredIntegral)
{
bool res = nvidia_NPPST_Squared_Integral_Image(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, RectStdDev)
{
bool res = nvidia_NPPST_RectStdDev(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, Resize)
{
bool res = nvidia_NPPST_Resize(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, VectorOperations)
{
bool res = nvidia_NPPST_Vector_Operations(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, Transpose)
{
bool res = nvidia_NPPST_Transpose(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, VectorOperations)
{
bool res = nvidia_NCV_Vector_Operations(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HaarCascadeLoader)
{
bool res = nvidia_NCV_Haar_Cascade_Loader(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HaarCascadeApplication)
{
bool res = nvidia_NCV_Haar_Cascade_Application(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HypothesesFiltration)
{
bool res = nvidia_NCV_Hypotheses_Filtration(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, Visualization)
{
// this functionality doesn't used in gpu module
bool res = nvidia_NCV_Visualization(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
INSTANTIATE_TEST_CASE_P(GPU_NVidia, NPPST, ALL_DEVICES);
INSTANTIATE_TEST_CASE_P(GPU_NVidia, NCV, ALL_DEVICES);
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#if defined HAVE_CUDA
OutputLevel nvidiaTestOutputLevel = OutputLevelNone;
#endif
#if defined HAVE_CUDA && !defined(CUDA_DISABLER)
using namespace cvtest;
using namespace testing;
struct NVidiaTest : TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
std::string _path;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
_path = TS::ptr()->get_data_path().c_str();
_path = _path + "haarcascade/";
}
};
struct NPPST : NVidiaTest {};
struct NCV : NVidiaTest {};
//TEST_P(NPPST, Integral)
//{
// bool res = nvidia_NPPST_Integral_Image(path, nvidiaTestOutputLevel);
// ASSERT_TRUE(res);
//}
TEST_P(NPPST, SquaredIntegral)
{
bool res = nvidia_NPPST_Squared_Integral_Image(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, RectStdDev)
{
bool res = nvidia_NPPST_RectStdDev(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, Resize)
{
bool res = nvidia_NPPST_Resize(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, VectorOperations)
{
bool res = nvidia_NPPST_Vector_Operations(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, Transpose)
{
bool res = nvidia_NPPST_Transpose(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, VectorOperations)
{
bool res = nvidia_NCV_Vector_Operations(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HaarCascadeLoader)
{
bool res = nvidia_NCV_Haar_Cascade_Loader(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HaarCascadeApplication)
{
bool res = nvidia_NCV_Haar_Cascade_Application(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HypothesesFiltration)
{
bool res = nvidia_NCV_Hypotheses_Filtration(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, Visualization)
{
// this functionality doesn't used in gpu module
bool res = nvidia_NCV_Visualization(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
INSTANTIATE_TEST_CASE_P(GPU_NVidia, NPPST, ALL_DEVICES);
INSTANTIATE_TEST_CASE_P(GPU_NVidia, NCV, ALL_DEVICES);
#endif // HAVE_CUDA
modules/gpu/test/test_objdetect.cpp
+427 -427
View File
@@ -1,427 +1,427 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
//#define DUMP
struct HOG : testing::TestWithParam<cv::gpu::DeviceInfo>, cv::gpu::HOGDescriptor
{
cv::gpu::DeviceInfo devInfo;
#ifdef DUMP
std::ofstream f;
#else
std::ifstream f;
#endif
int wins_per_img_x;
int wins_per_img_y;
int blocks_per_win_x;
int blocks_per_win_y;
int block_hist_size;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
#ifdef DUMP
void dump(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
{
f.write((char*)&blockHists.rows, sizeof(blockHists.rows));
f.write((char*)&blockHists.cols, sizeof(blockHists.cols));
for (int i = 0; i < blockHists.rows; ++i)
{
for (int j = 0; j < blockHists.cols; ++j)
{
float val = blockHists.at<float>(i, j);
f.write((char*)&val, sizeof(val));
}
}
int nlocations = locations.size();
f.write((char*)&nlocations, sizeof(nlocations));
for (int i = 0; i < locations.size(); ++i)
f.write((char*)&locations[i], sizeof(locations[i]));
}
#else
void compare(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
{
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
ASSERT_EQ(rows, blockHists.rows);
ASSERT_EQ(cols, blockHists.cols);
for (int i = 0; i < blockHists.rows; ++i)
{
for (int j = 0; j < blockHists.cols; ++j)
{
float val;
f.read((char*)&val, sizeof(val));
ASSERT_NEAR(val, blockHists.at<float>(i, j), 1e-3);
}
}
int nlocations;
f.read((char*)&nlocations, sizeof(nlocations));
ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
for (int i = 0; i < nlocations; ++i)
{
cv::Point location;
f.read((char*)&location, sizeof(location));
ASSERT_EQ(location, locations[i]);
}
}
#endif
void testDetect(const cv::Mat& img)
{
gamma_correction = false;
setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
std::vector<cv::Point> locations;
// Test detect
detect(loadMat(img), locations, 0);
#ifdef DUMP
dump(cv::Mat(block_hists), locations);
#else
compare(cv::Mat(block_hists), locations);
#endif
// Test detect on smaller image
cv::Mat img2;
cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
detect(loadMat(img2), locations, 0);
#ifdef DUMP
dump(cv::Mat(block_hists), locations);
#else
compare(cv::Mat(block_hists), locations);
#endif
// Test detect on greater image
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
detect(loadMat(img2), locations, 0);
#ifdef DUMP
dump(cv::Mat(block_hists), locations);
#else
compare(cv::Mat(block_hists), locations);
#endif
}
// Does not compare border value, as interpolation leads to delta
void compare_inner_parts(cv::Mat d1, cv::Mat d2)
{
for (int i = 1; i < blocks_per_win_y - 1; ++i)
for (int j = 1; j < blocks_per_win_x - 1; ++j)
for (int k = 0; k < block_hist_size; ++k)
{
float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
ASSERT_FLOAT_EQ(a, b);
}
}
};
// desabled while resize does not fixed
TEST_P(HOG, DISABLED_Detect)
{
cv::Mat img_rgb = readImage("hog/road.png");
ASSERT_FALSE(img_rgb.empty());
#ifdef DUMP
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
ASSERT_TRUE(f.is_open());
#else
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
ASSERT_TRUE(f.is_open());
#endif
// Test on color image
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
testDetect(img);
// Test on gray image
cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
testDetect(img);
f.close();
}
TEST_P(HOG, GetDescriptors)
{
// Load image (e.g. train data, composed from windows)
cv::Mat img_rgb = readImage("hog/train_data.png");
ASSERT_FALSE(img_rgb.empty());
// Convert to C4
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
cv::gpu::GpuMat d_img(img);
// Convert train images into feature vectors (train table)
cv::gpu::GpuMat descriptors, descriptors_by_cols;
getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
// Check size of the result train table
wins_per_img_x = 3;
wins_per_img_y = 2;
blocks_per_win_x = 7;
blocks_per_win_y = 15;
block_hist_size = 36;
cv::Size descr_size_expected = cv::Size(blocks_per_win_x * blocks_per_win_y * block_hist_size,
wins_per_img_x * wins_per_img_y);
ASSERT_EQ(descr_size_expected, descriptors.size());
// Check both formats of output descriptors are handled correctly
cv::Mat dr(descriptors);
cv::Mat dc(descriptors_by_cols);
for (int i = 0; i < wins_per_img_x * wins_per_img_y; ++i)
{
const float* l = dr.rowRange(i, i + 1).ptr<float>();
const float* r = dc.rowRange(i, i + 1).ptr<float>();
for (int y = 0; y < blocks_per_win_y; ++y)
for (int x = 0; x < blocks_per_win_x; ++x)
for (int k = 0; k < block_hist_size; ++k)
ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
r[(x * blocks_per_win_y + y) * block_hist_size + k]);
}
/* Now we want to extract the same feature vectors, but from single images. NOTE: results will
be defferent, due to border values interpolation. Using of many small images is slower, however we
wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
works good, it can be checked in the gpu_hog sample */
img_rgb = readImage("hog/positive1.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
// Everything is fine with interpolation for left top subimage
ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
img_rgb = readImage("hog/positive2.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(1, 2)));
img_rgb = readImage("hog/negative1.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(2, 3)));
img_rgb = readImage("hog/negative2.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(3, 4)));
img_rgb = readImage("hog/positive3.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(4, 5)));
img_rgb = readImage("hog/negative3.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(5, 6)));
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, HOG, ALL_DEVICES);
//============== caltech hog tests =====================//
struct CalTech : public ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string> >
{
cv::gpu::DeviceInfo devInfo;
cv::Mat img;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
img = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
}
};
TEST_P(CalTech, HOG)
{
cv::gpu::GpuMat d_img(img);
cv::Mat markedImage(img.clone());
cv::gpu::HOGDescriptor d_hog;
d_hog.setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
d_hog.nlevels = d_hog.nlevels + 32;
std::vector<cv::Rect> found_locations;
d_hog.detectMultiScale(d_img, found_locations);
#if defined (LOG_CASCADE_STATISTIC)
for (int i = 0; i < (int)found_locations.size(); i++)
{
cv::Rect r = found_locations[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
cv::imshow("Res", markedImage); cv::waitKey();
#endif
}
INSTANTIATE_TEST_CASE_P(detect, CalTech, testing::Combine(ALL_DEVICES,
::testing::Values<std::string>("caltech/image_00000009_0.png", "caltech/image_00000032_0.png",
"caltech/image_00000165_0.png", "caltech/image_00000261_0.png", "caltech/image_00000469_0.png",
"caltech/image_00000527_0.png", "caltech/image_00000574_0.png")));
//////////////////////////////////////////////////////////////////////////////////////////
/// LBP classifier
PARAM_TEST_CASE(LBP_Read_classifier, cv::gpu::DeviceInfo, int)
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(LBP_Read_classifier, Accuracy)
{
cv::gpu::CascadeClassifier_GPU classifier;
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
ASSERT_TRUE(classifier.load(classifierXmlPath));
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_Read_classifier,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
PARAM_TEST_CASE(LBP_classify, cv::gpu::DeviceInfo, int)
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(LBP_classify, Accuracy)
{
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
std::string imagePath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/er.png";
cv::CascadeClassifier cpuClassifier(classifierXmlPath);
ASSERT_FALSE(cpuClassifier.empty());
cv::Mat image = cv::imread(imagePath);
image = image.colRange(0, image.cols/2);
cv::Mat grey;
cvtColor(image, grey, CV_BGR2GRAY);
ASSERT_FALSE(image.empty());
std::vector<cv::Rect> rects;
cpuClassifier.detectMultiScale(grey, rects);
cv::Mat markedImage = image.clone();
std::vector<cv::Rect>::iterator it = rects.begin();
for (; it != rects.end(); ++it)
cv::rectangle(markedImage, *it, CV_RGB(0, 0, 255));
cv::gpu::CascadeClassifier_GPU gpuClassifier;
ASSERT_TRUE(gpuClassifier.load(classifierXmlPath));
cv::gpu::GpuMat gpu_rects;
cv::gpu::GpuMat tested(grey);
int count = gpuClassifier.detectMultiScale(tested, gpu_rects);
#if defined (LOG_CASCADE_STATISTIC)
cv::Mat downloaded(gpu_rects);
const cv::Rect* faces = downloaded.ptr<cv::Rect>();
for (int i = 0; i < count; i++)
{
cv::Rect r = faces[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
#endif
#if defined (LOG_CASCADE_STATISTIC)
cv::imshow("Res", markedImage); cv::waitKey();
#endif
(void)count;
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_classify,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
} // namespace
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
//#define DUMP
struct HOG : testing::TestWithParam<cv::gpu::DeviceInfo>, cv::gpu::HOGDescriptor
{
cv::gpu::DeviceInfo devInfo;
#ifdef DUMP
std::ofstream f;
#else
std::ifstream f;
#endif
int wins_per_img_x;
int wins_per_img_y;
int blocks_per_win_x;
int blocks_per_win_y;
int block_hist_size;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
#ifdef DUMP
void dump(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
{
f.write((char*)&blockHists.rows, sizeof(blockHists.rows));
f.write((char*)&blockHists.cols, sizeof(blockHists.cols));
for (int i = 0; i < blockHists.rows; ++i)
{
for (int j = 0; j < blockHists.cols; ++j)
{
float val = blockHists.at<float>(i, j);
f.write((char*)&val, sizeof(val));
}
}
int nlocations = locations.size();
f.write((char*)&nlocations, sizeof(nlocations));
for (int i = 0; i < locations.size(); ++i)
f.write((char*)&locations[i], sizeof(locations[i]));
}
#else
void compare(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
{
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
ASSERT_EQ(rows, blockHists.rows);
ASSERT_EQ(cols, blockHists.cols);
for (int i = 0; i < blockHists.rows; ++i)
{
for (int j = 0; j < blockHists.cols; ++j)
{
float val;
f.read((char*)&val, sizeof(val));
ASSERT_NEAR(val, blockHists.at<float>(i, j), 1e-3);
}
}
int nlocations;
f.read((char*)&nlocations, sizeof(nlocations));
ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
for (int i = 0; i < nlocations; ++i)
{
cv::Point location;
f.read((char*)&location, sizeof(location));
ASSERT_EQ(location, locations[i]);
}
}
#endif
void testDetect(const cv::Mat& img)
{
gamma_correction = false;
setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
std::vector<cv::Point> locations;
// Test detect
detect(loadMat(img), locations, 0);
#ifdef DUMP
dump(cv::Mat(block_hists), locations);
#else
compare(cv::Mat(block_hists), locations);
#endif
// Test detect on smaller image
cv::Mat img2;
cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
detect(loadMat(img2), locations, 0);
#ifdef DUMP
dump(cv::Mat(block_hists), locations);
#else
compare(cv::Mat(block_hists), locations);
#endif
// Test detect on greater image
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
detect(loadMat(img2), locations, 0);
#ifdef DUMP
dump(cv::Mat(block_hists), locations);
#else
compare(cv::Mat(block_hists), locations);
#endif
}
// Does not compare border value, as interpolation leads to delta
void compare_inner_parts(cv::Mat d1, cv::Mat d2)
{
for (int i = 1; i < blocks_per_win_y - 1; ++i)
for (int j = 1; j < blocks_per_win_x - 1; ++j)
for (int k = 0; k < block_hist_size; ++k)
{
float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
ASSERT_FLOAT_EQ(a, b);
}
}
};
// desabled while resize does not fixed
TEST_P(HOG, DISABLED_Detect)
{
cv::Mat img_rgb = readImage("hog/road.png");
ASSERT_FALSE(img_rgb.empty());
#ifdef DUMP
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
ASSERT_TRUE(f.is_open());
#else
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
ASSERT_TRUE(f.is_open());
#endif
// Test on color image
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
testDetect(img);
// Test on gray image
cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
testDetect(img);
f.close();
}
TEST_P(HOG, GetDescriptors)
{
// Load image (e.g. train data, composed from windows)
cv::Mat img_rgb = readImage("hog/train_data.png");
ASSERT_FALSE(img_rgb.empty());
// Convert to C4
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
cv::gpu::GpuMat d_img(img);
// Convert train images into feature vectors (train table)
cv::gpu::GpuMat descriptors, descriptors_by_cols;
getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
// Check size of the result train table
wins_per_img_x = 3;
wins_per_img_y = 2;
blocks_per_win_x = 7;
blocks_per_win_y = 15;
block_hist_size = 36;
cv::Size descr_size_expected = cv::Size(blocks_per_win_x * blocks_per_win_y * block_hist_size,
wins_per_img_x * wins_per_img_y);
ASSERT_EQ(descr_size_expected, descriptors.size());
// Check both formats of output descriptors are handled correctly
cv::Mat dr(descriptors);
cv::Mat dc(descriptors_by_cols);
for (int i = 0; i < wins_per_img_x * wins_per_img_y; ++i)
{
const float* l = dr.rowRange(i, i + 1).ptr<float>();
const float* r = dc.rowRange(i, i + 1).ptr<float>();
for (int y = 0; y < blocks_per_win_y; ++y)
for (int x = 0; x < blocks_per_win_x; ++x)
for (int k = 0; k < block_hist_size; ++k)
ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
r[(x * blocks_per_win_y + y) * block_hist_size + k]);
}
/* Now we want to extract the same feature vectors, but from single images. NOTE: results will
be defferent, due to border values interpolation. Using of many small images is slower, however we
wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
works good, it can be checked in the gpu_hog sample */
img_rgb = readImage("hog/positive1.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
// Everything is fine with interpolation for left top subimage
ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
img_rgb = readImage("hog/positive2.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(1, 2)));
img_rgb = readImage("hog/negative1.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(2, 3)));
img_rgb = readImage("hog/negative2.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(3, 4)));
img_rgb = readImage("hog/positive3.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(4, 5)));
img_rgb = readImage("hog/negative3.png");
ASSERT_TRUE(!img_rgb.empty());
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(5, 6)));
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, HOG, ALL_DEVICES);
//============== caltech hog tests =====================//
struct CalTech : public ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string> >
{
cv::gpu::DeviceInfo devInfo;
cv::Mat img;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
img = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
}
};
TEST_P(CalTech, HOG)
{
cv::gpu::GpuMat d_img(img);
cv::Mat markedImage(img.clone());
cv::gpu::HOGDescriptor d_hog;
d_hog.setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
d_hog.nlevels = d_hog.nlevels + 32;
std::vector<cv::Rect> found_locations;
d_hog.detectMultiScale(d_img, found_locations);
#if defined (LOG_CASCADE_STATISTIC)
for (int i = 0; i < (int)found_locations.size(); i++)
{
cv::Rect r = found_locations[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
cv::imshow("Res", markedImage); cv::waitKey();
#endif
}
INSTANTIATE_TEST_CASE_P(detect, CalTech, testing::Combine(ALL_DEVICES,
::testing::Values<std::string>("caltech/image_00000009_0.png", "caltech/image_00000032_0.png",
"caltech/image_00000165_0.png", "caltech/image_00000261_0.png", "caltech/image_00000469_0.png",
"caltech/image_00000527_0.png", "caltech/image_00000574_0.png")));
//////////////////////////////////////////////////////////////////////////////////////////
/// LBP classifier
PARAM_TEST_CASE(LBP_Read_classifier, cv::gpu::DeviceInfo, int)
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(LBP_Read_classifier, Accuracy)
{
cv::gpu::CascadeClassifier_GPU classifier;
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
ASSERT_TRUE(classifier.load(classifierXmlPath));
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_Read_classifier,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
PARAM_TEST_CASE(LBP_classify, cv::gpu::DeviceInfo, int)
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(LBP_classify, Accuracy)
{
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
std::string imagePath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/er.png";
cv::CascadeClassifier cpuClassifier(classifierXmlPath);
ASSERT_FALSE(cpuClassifier.empty());
cv::Mat image = cv::imread(imagePath);
image = image.colRange(0, image.cols/2);
cv::Mat grey;
cvtColor(image, grey, CV_BGR2GRAY);
ASSERT_FALSE(image.empty());
std::vector<cv::Rect> rects;
cpuClassifier.detectMultiScale(grey, rects);
cv::Mat markedImage = image.clone();
std::vector<cv::Rect>::iterator it = rects.begin();
for (; it != rects.end(); ++it)
cv::rectangle(markedImage, *it, CV_RGB(0, 0, 255));
cv::gpu::CascadeClassifier_GPU gpuClassifier;
ASSERT_TRUE(gpuClassifier.load(classifierXmlPath));
cv::gpu::GpuMat gpu_rects;
cv::gpu::GpuMat tested(grey);
int count = gpuClassifier.detectMultiScale(tested, gpu_rects);
#if defined (LOG_CASCADE_STATISTIC)
cv::Mat downloaded(gpu_rects);
const cv::Rect* faces = downloaded.ptr<cv::Rect>();
for (int i = 0; i < count; i++)
{
cv::Rect r = faces[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
#endif
#if defined (LOG_CASCADE_STATISTIC)
cv::imshow("Res", markedImage); cv::waitKey();
#endif
(void)count;
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_classify,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
} // namespace
#endif // HAVE_CUDA
modules/gpu/test/test_precomp.cpp
+42 -42
View File
@@ -1,42 +1,42 @@
/*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 "test_precomp.hpp"
/*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 "test_precomp.hpp"
modules/gpu/test/test_precomp.hpp
+84 -84
View File
@@ -1,84 +1,84 @@
/*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*/
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# pragma GCC diagnostic ignored "-Wmissing-prototypes" //OSX
#endif
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include <cmath>
#include <cstdio>
#include <iostream>
#include <fstream>
#include <functional>
#include <sstream>
#include <string>
#include <limits>
#include <algorithm>
#include <iterator>
#include <stdexcept>
#include "cvconfig.h"
#ifdef HAVE_CUDA
#include <cuda.h>
#include <cuda_runtime.h>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/ts/ts.hpp"
#include "opencv2/ts/ts_perf.hpp"
#include "opencv2/gpu/gpu.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "utility.hpp"
#include "interpolation.hpp"
#include "main_test_nvidia.h"
#endif
#endif
/*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*/
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# pragma GCC diagnostic ignored "-Wmissing-prototypes" //OSX
#endif
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include <cmath>
#include <cstdio>
#include <iostream>
#include <fstream>
#include <functional>
#include <sstream>
#include <string>
#include <limits>
#include <algorithm>
#include <iterator>
#include <stdexcept>
#include "cvconfig.h"
#ifdef HAVE_CUDA
#include <cuda.h>
#include <cuda_runtime.h>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/ts/ts.hpp"
#include "opencv2/ts/ts_perf.hpp"
#include "opencv2/gpu/gpu.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "utility.hpp"
#include "interpolation.hpp"
#include "main_test_nvidia.h"
#endif
#endif
modules/gpu/test/test_remap.cpp
+177 -177
View File
@@ -1,177 +1,177 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void remapImpl(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
cv::Size dsize = xmap.size();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ymap.at<float>(y, x), xmap.at<float>(y, x), c, borderType, borderVal);
}
}
}
void remapGold(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
remapImpl<unsigned char, NearestInterpolator>,
remapImpl<signed char, NearestInterpolator>,
remapImpl<unsigned short, NearestInterpolator>,
remapImpl<short, NearestInterpolator>,
remapImpl<int, NearestInterpolator>,
remapImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
remapImpl<unsigned char, LinearInterpolator>,
remapImpl<signed char, LinearInterpolator>,
remapImpl<unsigned short, LinearInterpolator>,
remapImpl<short, LinearInterpolator>,
remapImpl<int, LinearInterpolator>,
remapImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
remapImpl<unsigned char, CubicInterpolator>,
remapImpl<signed char, CubicInterpolator>,
remapImpl<unsigned short, CubicInterpolator>,
remapImpl<short, CubicInterpolator>,
remapImpl<int, CubicInterpolator>,
remapImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
funcs[interpolation][src.depth()](src, xmap, ymap, dst, borderType, borderVal);
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(Remap, cv::gpu::DeviceInfo, cv::Size, MatType, Interpolation, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int interpolation;
int borderType;
bool useRoi;
cv::Mat xmap;
cv::Mat ymap;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
interpolation = GET_PARAM(3);
borderType = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
// rotation matrix
const double aplha = CV_PI / 4;
static double M[2][3] = { {std::cos(aplha), -std::sin(aplha), size.width / 2.0},
{std::sin(aplha), std::cos(aplha), 0.0}};
xmap.create(size, CV_32FC1);
ymap.create(size, CV_32FC1);
for (int y = 0; y < size.height; ++y)
{
for (int x = 0; x < size.width; ++x)
{
xmap.at<float>(y, x) = static_cast<float>(M[0][0] * x + M[0][1] * y + M[0][2]);
ymap.at<float>(y, x) = static_cast<float>(M[1][0] * x + M[1][1] * y + M[1][2]);
}
}
}
};
TEST_P(Remap, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(xmap.size(), type, useRoi);
cv::gpu::remap(loadMat(src, useRoi), dst, loadMat(xmap, useRoi), loadMat(ymap, useRoi), interpolation, borderType, val);
cv::Mat dst_gold;
remapGold(src, xmap, ymap, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Remap, 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)),
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void remapImpl(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
cv::Size dsize = xmap.size();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ymap.at<float>(y, x), xmap.at<float>(y, x), c, borderType, borderVal);
}
}
}
void remapGold(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
remapImpl<unsigned char, NearestInterpolator>,
remapImpl<signed char, NearestInterpolator>,
remapImpl<unsigned short, NearestInterpolator>,
remapImpl<short, NearestInterpolator>,
remapImpl<int, NearestInterpolator>,
remapImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
remapImpl<unsigned char, LinearInterpolator>,
remapImpl<signed char, LinearInterpolator>,
remapImpl<unsigned short, LinearInterpolator>,
remapImpl<short, LinearInterpolator>,
remapImpl<int, LinearInterpolator>,
remapImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
remapImpl<unsigned char, CubicInterpolator>,
remapImpl<signed char, CubicInterpolator>,
remapImpl<unsigned short, CubicInterpolator>,
remapImpl<short, CubicInterpolator>,
remapImpl<int, CubicInterpolator>,
remapImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
funcs[interpolation][src.depth()](src, xmap, ymap, dst, borderType, borderVal);
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(Remap, cv::gpu::DeviceInfo, cv::Size, MatType, Interpolation, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int interpolation;
int borderType;
bool useRoi;
cv::Mat xmap;
cv::Mat ymap;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
interpolation = GET_PARAM(3);
borderType = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
// rotation matrix
const double aplha = CV_PI / 4;
static double M[2][3] = { {std::cos(aplha), -std::sin(aplha), size.width / 2.0},
{std::sin(aplha), std::cos(aplha), 0.0}};
xmap.create(size, CV_32FC1);
ymap.create(size, CV_32FC1);
for (int y = 0; y < size.height; ++y)
{
for (int x = 0; x < size.width; ++x)
{
xmap.at<float>(y, x) = static_cast<float>(M[0][0] * x + M[0][1] * y + M[0][2]);
ymap.at<float>(y, x) = static_cast<float>(M[1][0] * x + M[1][1] * y + M[1][2]);
}
}
}
};
TEST_P(Remap, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(xmap.size(), type, useRoi);
cv::gpu::remap(loadMat(src, useRoi), dst, loadMat(xmap, useRoi), loadMat(ymap, useRoi), interpolation, borderType, val);
cv::Mat dst_gold;
remapGold(src, xmap, ymap, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Remap, 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)),
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
#endif // HAVE_CUDA
modules/gpu/test/test_resize.cpp
+247 -247
View File
@@ -1,247 +1,247 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator>
void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
{
const int cn = src.channels();
cv::Size dsize(cv::saturate_cast<int>(src.cols * fx), cv::saturate_cast<int>(src.rows * fy));
dst.create(dsize, src.type());
float ifx = static_cast<float>(1.0 / fx);
float ify = static_cast<float>(1.0 / fy);
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, y * ify, x * ifx, c, cv::BORDER_REPLICATE);
}
}
}
void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation)
{
typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy);
static const func_t nearest_funcs[] =
{
resizeImpl<unsigned char, NearestInterpolator>,
resizeImpl<signed char, NearestInterpolator>,
resizeImpl<unsigned short, NearestInterpolator>,
resizeImpl<short, NearestInterpolator>,
resizeImpl<int, NearestInterpolator>,
resizeImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
resizeImpl<unsigned char, LinearInterpolator>,
resizeImpl<signed char, LinearInterpolator>,
resizeImpl<unsigned short, LinearInterpolator>,
resizeImpl<short, LinearInterpolator>,
resizeImpl<int, LinearInterpolator>,
resizeImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
resizeImpl<unsigned char, CubicInterpolator>,
resizeImpl<signed char, CubicInterpolator>,
resizeImpl<unsigned short, CubicInterpolator>,
resizeImpl<short, CubicInterpolator>,
resizeImpl<int, CubicInterpolator>,
resizeImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
funcs[interpolation][src.depth()](src, dst, fx, fy);
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(Resize, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
double coeff;
int interpolation;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
coeff = GET_PARAM(3);
interpolation = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(Resize, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_gold;
resizeGold(src, dst_gold, coeff, coeff, interpolation);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Resize, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
testing::Values(0.3, 0.5, 1.5, 2.0),
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
WHOLE_SUBMAT));
/////////////////
PARAM_TEST_CASE(ResizeSameAsHost, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
double coeff;
int interpolation;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
coeff = GET_PARAM(3);
interpolation = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
// downscaling only: used for classifiers
TEST_P(ResizeSameAsHost, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_gold;
cv::resize(src, dst_gold, cv::Size(), coeff, coeff, interpolation);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeSameAsHost, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
testing::Values(0.3, 0.5),
testing::Values(Interpolation(cv::INTER_AREA), Interpolation(cv::INTER_NEAREST)), //, Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(ResizeNPP, cv::gpu::DeviceInfo, MatType, double, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
double coeff;
int interpolation;
int type;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
coeff = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ResizeNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
ASSERT_FALSE(src.empty());
cv::gpu::GpuMat dst;
cv::gpu::resize(loadMat(src), dst, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_gold;
resizeGold(src, dst_gold, coeff, coeff, interpolation);
EXPECT_MAT_SIMILAR(dst_gold, dst, 1e-1);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
testing::Values(0.3, 0.5, 1.5, 2.0),
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR))));
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator>
void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
{
const int cn = src.channels();
cv::Size dsize(cv::saturate_cast<int>(src.cols * fx), cv::saturate_cast<int>(src.rows * fy));
dst.create(dsize, src.type());
float ifx = static_cast<float>(1.0 / fx);
float ify = static_cast<float>(1.0 / fy);
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, y * ify, x * ifx, c, cv::BORDER_REPLICATE);
}
}
}
void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation)
{
typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy);
static const func_t nearest_funcs[] =
{
resizeImpl<unsigned char, NearestInterpolator>,
resizeImpl<signed char, NearestInterpolator>,
resizeImpl<unsigned short, NearestInterpolator>,
resizeImpl<short, NearestInterpolator>,
resizeImpl<int, NearestInterpolator>,
resizeImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
resizeImpl<unsigned char, LinearInterpolator>,
resizeImpl<signed char, LinearInterpolator>,
resizeImpl<unsigned short, LinearInterpolator>,
resizeImpl<short, LinearInterpolator>,
resizeImpl<int, LinearInterpolator>,
resizeImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
resizeImpl<unsigned char, CubicInterpolator>,
resizeImpl<signed char, CubicInterpolator>,
resizeImpl<unsigned short, CubicInterpolator>,
resizeImpl<short, CubicInterpolator>,
resizeImpl<int, CubicInterpolator>,
resizeImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
funcs[interpolation][src.depth()](src, dst, fx, fy);
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(Resize, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
double coeff;
int interpolation;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
coeff = GET_PARAM(3);
interpolation = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(Resize, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_gold;
resizeGold(src, dst_gold, coeff, coeff, interpolation);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Resize, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
testing::Values(0.3, 0.5, 1.5, 2.0),
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
WHOLE_SUBMAT));
/////////////////
PARAM_TEST_CASE(ResizeSameAsHost, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
double coeff;
int interpolation;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
coeff = GET_PARAM(3);
interpolation = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
// downscaling only: used for classifiers
TEST_P(ResizeSameAsHost, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_gold;
cv::resize(src, dst_gold, cv::Size(), coeff, coeff, interpolation);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeSameAsHost, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
testing::Values(0.3, 0.5),
testing::Values(Interpolation(cv::INTER_AREA), Interpolation(cv::INTER_NEAREST)), //, Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(ResizeNPP, cv::gpu::DeviceInfo, MatType, double, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
double coeff;
int interpolation;
int type;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
coeff = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(ResizeNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
ASSERT_FALSE(src.empty());
cv::gpu::GpuMat dst;
cv::gpu::resize(loadMat(src), dst, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_gold;
resizeGold(src, dst_gold, coeff, coeff, interpolation);
EXPECT_MAT_SIMILAR(dst_gold, dst, 1e-1);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
testing::Values(0.3, 0.5, 1.5, 2.0),
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR))));
#endif // HAVE_CUDA
modules/gpu/test/test_threshold.cpp
+91 -91
View File
@@ -1,91 +1,91 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV)
#define ALL_THRESH_OPS testing::Values(ThreshOp(cv::THRESH_BINARY), ThreshOp(cv::THRESH_BINARY_INV), ThreshOp(cv::THRESH_TRUNC), ThreshOp(cv::THRESH_TOZERO), ThreshOp(cv::THRESH_TOZERO_INV))
PARAM_TEST_CASE(Threshold, cv::gpu::DeviceInfo, cv::Size, MatType, ThreshOp, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int threshOp;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
threshOp = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(Threshold, Accuracy)
{
cv::Mat src = randomMat(size, type);
double maxVal = randomDouble(20.0, 127.0);
double thresh = randomDouble(0.0, maxVal);
cv::gpu::GpuMat dst = createMat(src.size(), src.type(), useRoi);
cv::gpu::threshold(loadMat(src, useRoi), dst, thresh, maxVal, threshOp);
cv::Mat dst_gold;
cv::threshold(src, dst_gold, thresh, maxVal, threshOp);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Threshold, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_16SC1), MatType(CV_32FC1)),
ALL_THRESH_OPS,
WHOLE_SUBMAT));
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV)
#define ALL_THRESH_OPS testing::Values(ThreshOp(cv::THRESH_BINARY), ThreshOp(cv::THRESH_BINARY_INV), ThreshOp(cv::THRESH_TRUNC), ThreshOp(cv::THRESH_TOZERO), ThreshOp(cv::THRESH_TOZERO_INV))
PARAM_TEST_CASE(Threshold, cv::gpu::DeviceInfo, cv::Size, MatType, ThreshOp, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int threshOp;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
threshOp = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(Threshold, Accuracy)
{
cv::Mat src = randomMat(size, type);
double maxVal = randomDouble(20.0, 127.0);
double thresh = randomDouble(0.0, maxVal);
cv::gpu::GpuMat dst = createMat(src.size(), src.type(), useRoi);
cv::gpu::threshold(loadMat(src, useRoi), dst, thresh, maxVal, threshOp);
cv::Mat dst_gold;
cv::threshold(src, dst_gold, thresh, maxVal, threshOp);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Threshold, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_16SC1), MatType(CV_32FC1)),
ALL_THRESH_OPS,
WHOLE_SUBMAT));
#endif // HAVE_CUDA
modules/gpu/test/test_warp_affine.cpp
+273 -273
View File
@@ -1,273 +1,273 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace
{
cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
{
cv::Mat M(2, 3, CV_64FC1);
M.at<double>(0, 0) = std::cos(angle); M.at<double>(0, 1) = -std::sin(angle); M.at<double>(0, 2) = srcSize.width / 2;
M.at<double>(1, 0) = std::sin(angle); M.at<double>(1, 1) = std::cos(angle); M.at<double>(1, 2) = 0.0;
return M;
}
}
///////////////////////////////////////////////////////////////////
// Test buildWarpAffineMaps
PARAM_TEST_CASE(BuildWarpAffineMaps, cv::gpu::DeviceInfo, cv::Size, Inverse)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
bool inverse;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
inverse = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BuildWarpAffineMaps, Accuracy)
{
cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
cv::gpu::GpuMat xmap, ymap;
cv::gpu::buildWarpAffineMaps(M, inverse, size, xmap, ymap);
int interpolation = cv::INTER_NEAREST;
int borderMode = cv::BORDER_CONSTANT;
cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
cv::Mat dst;
cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), interpolation, borderMode);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Mat dst_gold;
cv::warpAffine(src, dst_gold, M, size, flags, borderMode);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BuildWarpAffineMaps, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DIRECT_INVERSE));
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void warpAffineImpl(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
float xcoo = static_cast<float>(M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2));
float ycoo = static_cast<float>(M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2));
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal);
}
}
}
void warpAffineGold(const cv::Mat& src, const cv::Mat& M, bool inverse, cv::Size dsize, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
warpAffineImpl<unsigned char, NearestInterpolator>,
warpAffineImpl<signed char, NearestInterpolator>,
warpAffineImpl<unsigned short, NearestInterpolator>,
warpAffineImpl<short, NearestInterpolator>,
warpAffineImpl<int, NearestInterpolator>,
warpAffineImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
warpAffineImpl<unsigned char, LinearInterpolator>,
warpAffineImpl<signed char, LinearInterpolator>,
warpAffineImpl<unsigned short, LinearInterpolator>,
warpAffineImpl<short, LinearInterpolator>,
warpAffineImpl<int, LinearInterpolator>,
warpAffineImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
warpAffineImpl<unsigned char, CubicInterpolator>,
warpAffineImpl<signed char, CubicInterpolator>,
warpAffineImpl<unsigned short, CubicInterpolator>,
warpAffineImpl<short, CubicInterpolator>,
warpAffineImpl<int, CubicInterpolator>,
warpAffineImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
if (inverse)
funcs[interpolation][src.depth()](src, M, dsize, dst, borderType, borderVal);
else
{
cv::Mat iM;
cv::invertAffineTransform(M, iM);
funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal);
}
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(WarpAffine, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
bool inverse;
int interpolation;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
inverse = GET_PARAM(3);
interpolation = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpAffine, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::warpAffine(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
cv::Mat dst_gold;
warpAffineGold(src, M, inverse, size, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpAffine, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(WarpAffineNPP, cv::gpu::DeviceInfo, MatType, Inverse, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
int type;
bool inverse;
int interpolation;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
inverse = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpAffineNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::gpu::GpuMat dst;
cv::gpu::warpAffine(loadMat(src), dst, M, src.size(), flags);
cv::Mat dst_gold;
warpAffineGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0));
EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpAffineNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC))));
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace
{
cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
{
cv::Mat M(2, 3, CV_64FC1);
M.at<double>(0, 0) = std::cos(angle); M.at<double>(0, 1) = -std::sin(angle); M.at<double>(0, 2) = srcSize.width / 2;
M.at<double>(1, 0) = std::sin(angle); M.at<double>(1, 1) = std::cos(angle); M.at<double>(1, 2) = 0.0;
return M;
}
}
///////////////////////////////////////////////////////////////////
// Test buildWarpAffineMaps
PARAM_TEST_CASE(BuildWarpAffineMaps, cv::gpu::DeviceInfo, cv::Size, Inverse)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
bool inverse;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
inverse = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BuildWarpAffineMaps, Accuracy)
{
cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
cv::gpu::GpuMat xmap, ymap;
cv::gpu::buildWarpAffineMaps(M, inverse, size, xmap, ymap);
int interpolation = cv::INTER_NEAREST;
int borderMode = cv::BORDER_CONSTANT;
cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
cv::Mat dst;
cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), interpolation, borderMode);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Mat dst_gold;
cv::warpAffine(src, dst_gold, M, size, flags, borderMode);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BuildWarpAffineMaps, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DIRECT_INVERSE));
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void warpAffineImpl(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
float xcoo = static_cast<float>(M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2));
float ycoo = static_cast<float>(M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2));
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal);
}
}
}
void warpAffineGold(const cv::Mat& src, const cv::Mat& M, bool inverse, cv::Size dsize, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
warpAffineImpl<unsigned char, NearestInterpolator>,
warpAffineImpl<signed char, NearestInterpolator>,
warpAffineImpl<unsigned short, NearestInterpolator>,
warpAffineImpl<short, NearestInterpolator>,
warpAffineImpl<int, NearestInterpolator>,
warpAffineImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
warpAffineImpl<unsigned char, LinearInterpolator>,
warpAffineImpl<signed char, LinearInterpolator>,
warpAffineImpl<unsigned short, LinearInterpolator>,
warpAffineImpl<short, LinearInterpolator>,
warpAffineImpl<int, LinearInterpolator>,
warpAffineImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
warpAffineImpl<unsigned char, CubicInterpolator>,
warpAffineImpl<signed char, CubicInterpolator>,
warpAffineImpl<unsigned short, CubicInterpolator>,
warpAffineImpl<short, CubicInterpolator>,
warpAffineImpl<int, CubicInterpolator>,
warpAffineImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
if (inverse)
funcs[interpolation][src.depth()](src, M, dsize, dst, borderType, borderVal);
else
{
cv::Mat iM;
cv::invertAffineTransform(M, iM);
funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal);
}
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(WarpAffine, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
bool inverse;
int interpolation;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
inverse = GET_PARAM(3);
interpolation = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpAffine, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::warpAffine(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
cv::Mat dst_gold;
warpAffineGold(src, M, inverse, size, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpAffine, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(WarpAffineNPP, cv::gpu::DeviceInfo, MatType, Inverse, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
int type;
bool inverse;
int interpolation;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
inverse = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpAffineNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::gpu::GpuMat dst;
cv::gpu::warpAffine(loadMat(src), dst, M, src.size(), flags);
cv::Mat dst_gold;
warpAffineGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0));
EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpAffineNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC))));
#endif // HAVE_CUDA
modules/gpu/test/test_warp_perspective.cpp
+273 -273
View File
@@ -1,273 +1,273 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace
{
cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
{
cv::Mat M(3, 3, CV_64FC1);
M.at<double>(0, 0) = std::cos(angle); M.at<double>(0, 1) = -std::sin(angle); M.at<double>(0, 2) = srcSize.width / 2;
M.at<double>(1, 0) = std::sin(angle); M.at<double>(1, 1) = std::cos(angle); M.at<double>(1, 2) = 0.0;
M.at<double>(2, 0) = 0.0 ; M.at<double>(2, 1) = 0.0 ; M.at<double>(2, 2) = 1.0;
return M;
}
}
///////////////////////////////////////////////////////////////////
// Test buildWarpPerspectiveMaps
PARAM_TEST_CASE(BuildWarpPerspectiveMaps, cv::gpu::DeviceInfo, cv::Size, Inverse)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
bool inverse;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
inverse = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BuildWarpPerspectiveMaps, Accuracy)
{
cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
cv::gpu::GpuMat xmap, ymap;
cv::gpu::buildWarpPerspectiveMaps(M, inverse, size, xmap, ymap);
cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
cv::Mat dst;
cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), cv::INTER_NEAREST, cv::BORDER_CONSTANT);
int flags = cv::INTER_NEAREST;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Mat dst_gold;
cv::warpPerspective(src, dst_gold, M, size, flags, cv::BORDER_CONSTANT);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BuildWarpPerspectiveMaps, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DIRECT_INVERSE));
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void warpPerspectiveImpl(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
float coeff = static_cast<float>(M.at<double>(2, 0) * x + M.at<double>(2, 1) * y + M.at<double>(2, 2));
float xcoo = static_cast<float>((M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2)) / coeff);
float ycoo = static_cast<float>((M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2)) / coeff);
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal);
}
}
}
void warpPerspectiveGold(const cv::Mat& src, const cv::Mat& M, bool inverse, cv::Size dsize, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
warpPerspectiveImpl<unsigned char, NearestInterpolator>,
warpPerspectiveImpl<signed char, NearestInterpolator>,
warpPerspectiveImpl<unsigned short, NearestInterpolator>,
warpPerspectiveImpl<short, NearestInterpolator>,
warpPerspectiveImpl<int, NearestInterpolator>,
warpPerspectiveImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
warpPerspectiveImpl<unsigned char, LinearInterpolator>,
warpPerspectiveImpl<signed char, LinearInterpolator>,
warpPerspectiveImpl<unsigned short, LinearInterpolator>,
warpPerspectiveImpl<short, LinearInterpolator>,
warpPerspectiveImpl<int, LinearInterpolator>,
warpPerspectiveImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
warpPerspectiveImpl<unsigned char, CubicInterpolator>,
warpPerspectiveImpl<signed char, CubicInterpolator>,
warpPerspectiveImpl<unsigned short, CubicInterpolator>,
warpPerspectiveImpl<short, CubicInterpolator>,
warpPerspectiveImpl<int, CubicInterpolator>,
warpPerspectiveImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
if (inverse)
funcs[interpolation][src.depth()](src, M, dsize, dst, borderType, borderVal);
else
{
cv::Mat iM;
cv::invert(M, iM);
funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal);
}
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(WarpPerspective, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
bool inverse;
int interpolation;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
inverse = GET_PARAM(3);
interpolation = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpPerspective, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::warpPerspective(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
cv::Mat dst_gold;
warpPerspectiveGold(src, M, inverse, size, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpPerspective, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(WarpPerspectiveNPP, cv::gpu::DeviceInfo, MatType, Inverse, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
int type;
bool inverse;
int interpolation;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
inverse = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpPerspectiveNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::gpu::GpuMat dst;
cv::gpu::warpPerspective(loadMat(src), dst, M, src.size(), flags);
cv::Mat dst_gold;
warpPerspectiveGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0));
EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpPerspectiveNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC))));
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace
{
cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
{
cv::Mat M(3, 3, CV_64FC1);
M.at<double>(0, 0) = std::cos(angle); M.at<double>(0, 1) = -std::sin(angle); M.at<double>(0, 2) = srcSize.width / 2;
M.at<double>(1, 0) = std::sin(angle); M.at<double>(1, 1) = std::cos(angle); M.at<double>(1, 2) = 0.0;
M.at<double>(2, 0) = 0.0 ; M.at<double>(2, 1) = 0.0 ; M.at<double>(2, 2) = 1.0;
return M;
}
}
///////////////////////////////////////////////////////////////////
// Test buildWarpPerspectiveMaps
PARAM_TEST_CASE(BuildWarpPerspectiveMaps, cv::gpu::DeviceInfo, cv::Size, Inverse)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
bool inverse;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
inverse = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BuildWarpPerspectiveMaps, Accuracy)
{
cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
cv::gpu::GpuMat xmap, ymap;
cv::gpu::buildWarpPerspectiveMaps(M, inverse, size, xmap, ymap);
cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
cv::Mat dst;
cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), cv::INTER_NEAREST, cv::BORDER_CONSTANT);
int flags = cv::INTER_NEAREST;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Mat dst_gold;
cv::warpPerspective(src, dst_gold, M, size, flags, cv::BORDER_CONSTANT);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BuildWarpPerspectiveMaps, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DIRECT_INVERSE));
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void warpPerspectiveImpl(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
float coeff = static_cast<float>(M.at<double>(2, 0) * x + M.at<double>(2, 1) * y + M.at<double>(2, 2));
float xcoo = static_cast<float>((M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2)) / coeff);
float ycoo = static_cast<float>((M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2)) / coeff);
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal);
}
}
}
void warpPerspectiveGold(const cv::Mat& src, const cv::Mat& M, bool inverse, cv::Size dsize, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
warpPerspectiveImpl<unsigned char, NearestInterpolator>,
warpPerspectiveImpl<signed char, NearestInterpolator>,
warpPerspectiveImpl<unsigned short, NearestInterpolator>,
warpPerspectiveImpl<short, NearestInterpolator>,
warpPerspectiveImpl<int, NearestInterpolator>,
warpPerspectiveImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
warpPerspectiveImpl<unsigned char, LinearInterpolator>,
warpPerspectiveImpl<signed char, LinearInterpolator>,
warpPerspectiveImpl<unsigned short, LinearInterpolator>,
warpPerspectiveImpl<short, LinearInterpolator>,
warpPerspectiveImpl<int, LinearInterpolator>,
warpPerspectiveImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
warpPerspectiveImpl<unsigned char, CubicInterpolator>,
warpPerspectiveImpl<signed char, CubicInterpolator>,
warpPerspectiveImpl<unsigned short, CubicInterpolator>,
warpPerspectiveImpl<short, CubicInterpolator>,
warpPerspectiveImpl<int, CubicInterpolator>,
warpPerspectiveImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
if (inverse)
funcs[interpolation][src.depth()](src, M, dsize, dst, borderType, borderVal);
else
{
cv::Mat iM;
cv::invert(M, iM);
funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal);
}
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(WarpPerspective, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
bool inverse;
int interpolation;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
inverse = GET_PARAM(3);
interpolation = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpPerspective, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::warpPerspective(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
cv::Mat dst_gold;
warpPerspectiveGold(src, M, inverse, size, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpPerspective, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(WarpPerspectiveNPP, cv::gpu::DeviceInfo, MatType, Inverse, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
int type;
bool inverse;
int interpolation;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
inverse = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpPerspectiveNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::gpu::GpuMat dst;
cv::gpu::warpPerspective(loadMat(src), dst, M, src.size(), flags);
cv::Mat dst_gold;
warpPerspectiveGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0));
EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpPerspectiveNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC))));
#endif // HAVE_CUDA
modules/gpu/test/utility.cpp
+424 -424
View File
@@ -1,424 +1,424 @@
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
using namespace testing;
using namespace testing::internal;
//////////////////////////////////////////////////////////////////////
// random generators
int randomInt(int minVal, int maxVal)
{
RNG& rng = TS::ptr()->get_rng();
return rng.uniform(minVal, maxVal);
}
double randomDouble(double minVal, double maxVal)
{
RNG& rng = TS::ptr()->get_rng();
return rng.uniform(minVal, maxVal);
}
Size randomSize(int minVal, int maxVal)
{
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
}
Scalar randomScalar(double minVal, double maxVal)
{
return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal));
}
Mat randomMat(Size size, int type, double minVal, double maxVal)
{
return randomMat(TS::ptr()->get_rng(), size, type, minVal, maxVal, false);
}
//////////////////////////////////////////////////////////////////////
// GpuMat create
cv::gpu::GpuMat createMat(cv::Size size, int type, bool useRoi)
{
Size size0 = size;
if (useRoi)
{
size0.width += randomInt(5, 15);
size0.height += randomInt(5, 15);
}
GpuMat d_m(size0, type);
if (size0 != size)
d_m = d_m(Rect((size0.width - size.width) / 2, (size0.height - size.height) / 2, size.width, size.height));
return d_m;
}
GpuMat loadMat(const Mat& m, bool useRoi)
{
GpuMat d_m = createMat(m.size(), m.type(), useRoi);
d_m.upload(m);
return d_m;
}
//////////////////////////////////////////////////////////////////////
// Image load
Mat readImage(const std::string& fileName, int flags)
{
return imread(TS::ptr()->get_data_path() + fileName, flags);
}
Mat readImageType(const std::string& fname, int type)
{
Mat src = readImage(fname, CV_MAT_CN(type) == 1 ? IMREAD_GRAYSCALE : IMREAD_COLOR);
if (CV_MAT_CN(type) == 4)
{
Mat temp;
cvtColor(src, temp, cv::COLOR_BGR2BGRA);
swap(src, temp);
}
src.convertTo(src, CV_MAT_DEPTH(type), CV_MAT_DEPTH(type) == CV_32F ? 1.0 / 255.0 : 1.0);
return src;
}
//////////////////////////////////////////////////////////////////////
// Image dumping
void dumpImage(const std::string& fileName, const cv::Mat& image)
{
cv::imwrite(TS::ptr()->get_data_path() + fileName, image);
}
//////////////////////////////////////////////////////////////////////
// Gpu devices
bool supportFeature(const DeviceInfo& info, FeatureSet feature)
{
return TargetArchs::builtWith(feature) && info.supports(feature);
}
DeviceManager& DeviceManager::instance()
{
static DeviceManager obj;
return obj;
}
void DeviceManager::load(int i)
{
devices_.clear();
devices_.reserve(1);
ostringstream msg;
if (i < 0 || i >= getCudaEnabledDeviceCount())
{
msg << "Incorrect device number - " << i;
throw runtime_error(msg.str());
}
DeviceInfo info(i);
if (!info.isCompatible())
{
msg << "Device " << i << " [" << info.name() << "] is NOT compatible with current GPU module build";
throw runtime_error(msg.str());
}
devices_.push_back(info);
}
void DeviceManager::loadAll()
{
int deviceCount = getCudaEnabledDeviceCount();
devices_.clear();
devices_.reserve(deviceCount);
for (int i = 0; i < deviceCount; ++i)
{
DeviceInfo info(i);
if (info.isCompatible())
{
devices_.push_back(info);
}
}
}
//////////////////////////////////////////////////////////////////////
// Additional assertion
Mat getMat(InputArray arr)
{
if (arr.kind() == _InputArray::GPU_MAT)
{
Mat m;
arr.getGpuMat().download(m);
return m;
}
return arr.getMat();
}
double checkNorm(InputArray m1, InputArray m2)
{
return norm(getMat(m1), getMat(m2), NORM_INF);
}
void minMaxLocGold(const Mat& src, double* minVal_, double* maxVal_, Point* minLoc_, Point* maxLoc_, const Mat& mask)
{
if (src.depth() != CV_8S)
{
minMaxLoc(src, minVal_, maxVal_, minLoc_, maxLoc_, mask);
return;
}
// OpenCV's minMaxLoc doesn't support CV_8S type
double minVal = numeric_limits<double>::max();
Point minLoc(-1, -1);
double maxVal = -numeric_limits<double>::max();
Point maxLoc(-1, -1);
for (int y = 0; y < src.rows; ++y)
{
const schar* src_row = src.ptr<signed char>(y);
const uchar* mask_row = mask.empty() ? 0 : mask.ptr<unsigned char>(y);
for (int x = 0; x < src.cols; ++x)
{
if (!mask_row || mask_row[x])
{
schar val = src_row[x];
if (val < minVal)
{
minVal = val;
minLoc = cv::Point(x, y);
}
if (val > maxVal)
{
maxVal = val;
maxLoc = cv::Point(x, y);
}
}
}
}
if (minVal_) *minVal_ = minVal;
if (maxVal_) *maxVal_ = maxVal;
if (minLoc_) *minLoc_ = minLoc;
if (maxLoc_) *maxLoc_ = maxLoc;
}
namespace
{
template <typename T, typename OutT> std::string printMatValImpl(const Mat& m, Point p)
{
const int cn = m.channels();
ostringstream ostr;
ostr << "(";
p.x /= cn;
ostr << static_cast<OutT>(m.at<T>(p.y, p.x * cn));
for (int c = 1; c < m.channels(); ++c)
{
ostr << ", " << static_cast<OutT>(m.at<T>(p.y, p.x * cn + c));
}
ostr << ")";
return ostr.str();
}
std::string printMatVal(const Mat& m, Point p)
{
typedef std::string (*func_t)(const Mat& m, Point p);
static const func_t funcs[] =
{
printMatValImpl<uchar, int>, printMatValImpl<schar, int>, printMatValImpl<ushort, int>, printMatValImpl<short, int>,
printMatValImpl<int, int>, printMatValImpl<float, float>, printMatValImpl<double, double>
};
return funcs[m.depth()](m, p);
}
}
testing::AssertionResult assertMatNear(const char* expr1, const char* expr2, const char* eps_expr, cv::InputArray m1_, cv::InputArray m2_, double eps)
{
Mat m1 = getMat(m1_);
Mat m2 = getMat(m2_);
if (m1.size() != m2.size())
{
return AssertionFailure() << "Matrices \"" << expr1 << "\" and \"" << expr2 << "\" have different sizes : \""
<< expr1 << "\" [" << PrintToString(m1.size()) << "] vs \""
<< expr2 << "\" [" << PrintToString(m2.size()) << "]";
}
if (m1.type() != m2.type())
{
return AssertionFailure() << "Matrices \"" << expr1 << "\" and \"" << expr2 << "\" have different types : \""
<< expr1 << "\" [" << PrintToString(MatType(m1.type())) << "] vs \""
<< expr2 << "\" [" << PrintToString(MatType(m2.type())) << "]";
}
Mat diff;
absdiff(m1.reshape(1), m2.reshape(1), diff);
double maxVal = 0.0;
Point maxLoc;
minMaxLocGold(diff, 0, &maxVal, 0, &maxLoc);
if (maxVal > eps)
{
return AssertionFailure() << "The max difference between matrices \"" << expr1 << "\" and \"" << expr2
<< "\" is " << maxVal << " at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ")"
<< ", which exceeds \"" << eps_expr << "\", where \""
<< expr1 << "\" at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ") evaluates to " << printMatVal(m1, maxLoc) << ", \""
<< expr2 << "\" at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ") evaluates to " << printMatVal(m2, maxLoc) << ", \""
<< eps_expr << "\" evaluates to " << eps;
}
return AssertionSuccess();
}
double checkSimilarity(InputArray m1, InputArray m2)
{
Mat diff;
matchTemplate(getMat(m1), getMat(m2), diff, CV_TM_CCORR_NORMED);
return std::abs(diff.at<float>(0, 0) - 1.f);
}
//////////////////////////////////////////////////////////////////////
// Helper structs for value-parameterized tests
vector<MatDepth> depths(int depth_start, int depth_end)
{
vector<MatDepth> v;
v.reserve((depth_end - depth_start + 1));
for (int depth = depth_start; depth <= depth_end; ++depth)
v.push_back(depth);
return v;
}
vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end)
{
vector<MatType> v;
v.reserve((depth_end - depth_start + 1) * (cn_end - cn_start + 1));
for (int depth = depth_start; depth <= depth_end; ++depth)
{
for (int cn = cn_start; cn <= cn_end; ++cn)
{
v.push_back(CV_MAKETYPE(depth, cn));
}
}
return v;
}
const vector<MatType>& all_types()
{
static vector<MatType> v = types(CV_8U, CV_64F, 1, 4);
return v;
}
void cv::gpu::PrintTo(const DeviceInfo& info, ostream* os)
{
(*os) << info.name();
}
void PrintTo(const UseRoi& useRoi, std::ostream* os)
{
if (useRoi)
(*os) << "sub matrix";
else
(*os) << "whole matrix";
}
void PrintTo(const Inverse& inverse, std::ostream* os)
{
if (inverse)
(*os) << "inverse";
else
(*os) << "direct";
}
void showDiff(InputArray gold_, InputArray actual_, double eps)
{
Mat gold = getMat(gold_);
Mat actual = getMat(actual_);
Mat diff;
absdiff(gold, actual, diff);
threshold(diff, diff, eps, 255.0, cv::THRESH_BINARY);
namedWindow("gold", WINDOW_NORMAL);
namedWindow("actual", WINDOW_NORMAL);
namedWindow("diff", WINDOW_NORMAL);
imshow("gold", gold);
imshow("actual", actual);
imshow("diff", diff);
waitKey();
}
#endif // HAVE_CUDA
/*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 "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
using namespace testing;
using namespace testing::internal;
//////////////////////////////////////////////////////////////////////
// random generators
int randomInt(int minVal, int maxVal)
{
RNG& rng = TS::ptr()->get_rng();
return rng.uniform(minVal, maxVal);
}
double randomDouble(double minVal, double maxVal)
{
RNG& rng = TS::ptr()->get_rng();
return rng.uniform(minVal, maxVal);
}
Size randomSize(int minVal, int maxVal)
{
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
}
Scalar randomScalar(double minVal, double maxVal)
{
return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal));
}
Mat randomMat(Size size, int type, double minVal, double maxVal)
{
return randomMat(TS::ptr()->get_rng(), size, type, minVal, maxVal, false);
}
//////////////////////////////////////////////////////////////////////
// GpuMat create
cv::gpu::GpuMat createMat(cv::Size size, int type, bool useRoi)
{
Size size0 = size;
if (useRoi)
{
size0.width += randomInt(5, 15);
size0.height += randomInt(5, 15);
}
GpuMat d_m(size0, type);
if (size0 != size)
d_m = d_m(Rect((size0.width - size.width) / 2, (size0.height - size.height) / 2, size.width, size.height));
return d_m;
}
GpuMat loadMat(const Mat& m, bool useRoi)
{
GpuMat d_m = createMat(m.size(), m.type(), useRoi);
d_m.upload(m);
return d_m;
}
//////////////////////////////////////////////////////////////////////
// Image load
Mat readImage(const std::string& fileName, int flags)
{
return imread(TS::ptr()->get_data_path() + fileName, flags);
}
Mat readImageType(const std::string& fname, int type)
{
Mat src = readImage(fname, CV_MAT_CN(type) == 1 ? IMREAD_GRAYSCALE : IMREAD_COLOR);
if (CV_MAT_CN(type) == 4)
{
Mat temp;
cvtColor(src, temp, cv::COLOR_BGR2BGRA);
swap(src, temp);
}
src.convertTo(src, CV_MAT_DEPTH(type), CV_MAT_DEPTH(type) == CV_32F ? 1.0 / 255.0 : 1.0);
return src;
}
//////////////////////////////////////////////////////////////////////
// Image dumping
void dumpImage(const std::string& fileName, const cv::Mat& image)
{
cv::imwrite(TS::ptr()->get_data_path() + fileName, image);
}
//////////////////////////////////////////////////////////////////////
// Gpu devices
bool supportFeature(const DeviceInfo& info, FeatureSet feature)
{
return TargetArchs::builtWith(feature) && info.supports(feature);
}
DeviceManager& DeviceManager::instance()
{
static DeviceManager obj;
return obj;
}
void DeviceManager::load(int i)
{
devices_.clear();
devices_.reserve(1);
ostringstream msg;
if (i < 0 || i >= getCudaEnabledDeviceCount())
{
msg << "Incorrect device number - " << i;
throw runtime_error(msg.str());
}
DeviceInfo info(i);
if (!info.isCompatible())
{
msg << "Device " << i << " [" << info.name() << "] is NOT compatible with current GPU module build";
throw runtime_error(msg.str());
}
devices_.push_back(info);
}
void DeviceManager::loadAll()
{
int deviceCount = getCudaEnabledDeviceCount();
devices_.clear();
devices_.reserve(deviceCount);
for (int i = 0; i < deviceCount; ++i)
{
DeviceInfo info(i);
if (info.isCompatible())
{
devices_.push_back(info);
}
}
}
//////////////////////////////////////////////////////////////////////
// Additional assertion
Mat getMat(InputArray arr)
{
if (arr.kind() == _InputArray::GPU_MAT)
{
Mat m;
arr.getGpuMat().download(m);
return m;
}
return arr.getMat();
}
double checkNorm(InputArray m1, InputArray m2)
{
return norm(getMat(m1), getMat(m2), NORM_INF);
}
void minMaxLocGold(const Mat& src, double* minVal_, double* maxVal_, Point* minLoc_, Point* maxLoc_, const Mat& mask)
{
if (src.depth() != CV_8S)
{
minMaxLoc(src, minVal_, maxVal_, minLoc_, maxLoc_, mask);
return;
}
// OpenCV's minMaxLoc doesn't support CV_8S type
double minVal = numeric_limits<double>::max();
Point minLoc(-1, -1);
double maxVal = -numeric_limits<double>::max();
Point maxLoc(-1, -1);
for (int y = 0; y < src.rows; ++y)
{
const schar* src_row = src.ptr<signed char>(y);
const uchar* mask_row = mask.empty() ? 0 : mask.ptr<unsigned char>(y);
for (int x = 0; x < src.cols; ++x)
{
if (!mask_row || mask_row[x])
{
schar val = src_row[x];
if (val < minVal)
{
minVal = val;
minLoc = cv::Point(x, y);
}
if (val > maxVal)
{
maxVal = val;
maxLoc = cv::Point(x, y);
}
}
}
}
if (minVal_) *minVal_ = minVal;
if (maxVal_) *maxVal_ = maxVal;
if (minLoc_) *minLoc_ = minLoc;
if (maxLoc_) *maxLoc_ = maxLoc;
}
namespace
{
template <typename T, typename OutT> std::string printMatValImpl(const Mat& m, Point p)
{
const int cn = m.channels();
ostringstream ostr;
ostr << "(";
p.x /= cn;
ostr << static_cast<OutT>(m.at<T>(p.y, p.x * cn));
for (int c = 1; c < m.channels(); ++c)
{
ostr << ", " << static_cast<OutT>(m.at<T>(p.y, p.x * cn + c));
}
ostr << ")";
return ostr.str();
}
std::string printMatVal(const Mat& m, Point p)
{
typedef std::string (*func_t)(const Mat& m, Point p);
static const func_t funcs[] =
{
printMatValImpl<uchar, int>, printMatValImpl<schar, int>, printMatValImpl<ushort, int>, printMatValImpl<short, int>,
printMatValImpl<int, int>, printMatValImpl<float, float>, printMatValImpl<double, double>
};
return funcs[m.depth()](m, p);
}
}
testing::AssertionResult assertMatNear(const char* expr1, const char* expr2, const char* eps_expr, cv::InputArray m1_, cv::InputArray m2_, double eps)
{
Mat m1 = getMat(m1_);
Mat m2 = getMat(m2_);
if (m1.size() != m2.size())
{
return AssertionFailure() << "Matrices \"" << expr1 << "\" and \"" << expr2 << "\" have different sizes : \""
<< expr1 << "\" [" << PrintToString(m1.size()) << "] vs \""
<< expr2 << "\" [" << PrintToString(m2.size()) << "]";
}
if (m1.type() != m2.type())
{
return AssertionFailure() << "Matrices \"" << expr1 << "\" and \"" << expr2 << "\" have different types : \""
<< expr1 << "\" [" << PrintToString(MatType(m1.type())) << "] vs \""
<< expr2 << "\" [" << PrintToString(MatType(m2.type())) << "]";
}
Mat diff;
absdiff(m1.reshape(1), m2.reshape(1), diff);
double maxVal = 0.0;
Point maxLoc;
minMaxLocGold(diff, 0, &maxVal, 0, &maxLoc);
if (maxVal > eps)
{
return AssertionFailure() << "The max difference between matrices \"" << expr1 << "\" and \"" << expr2
<< "\" is " << maxVal << " at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ")"
<< ", which exceeds \"" << eps_expr << "\", where \""
<< expr1 << "\" at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ") evaluates to " << printMatVal(m1, maxLoc) << ", \""
<< expr2 << "\" at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ") evaluates to " << printMatVal(m2, maxLoc) << ", \""
<< eps_expr << "\" evaluates to " << eps;
}
return AssertionSuccess();
}
double checkSimilarity(InputArray m1, InputArray m2)
{
Mat diff;
matchTemplate(getMat(m1), getMat(m2), diff, CV_TM_CCORR_NORMED);
return std::abs(diff.at<float>(0, 0) - 1.f);
}
//////////////////////////////////////////////////////////////////////
// Helper structs for value-parameterized tests
vector<MatDepth> depths(int depth_start, int depth_end)
{
vector<MatDepth> v;
v.reserve((depth_end - depth_start + 1));
for (int depth = depth_start; depth <= depth_end; ++depth)
v.push_back(depth);
return v;
}
vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end)
{
vector<MatType> v;
v.reserve((depth_end - depth_start + 1) * (cn_end - cn_start + 1));
for (int depth = depth_start; depth <= depth_end; ++depth)
{
for (int cn = cn_start; cn <= cn_end; ++cn)
{
v.push_back(CV_MAKETYPE(depth, cn));
}
}
return v;
}
const vector<MatType>& all_types()
{
static vector<MatType> v = types(CV_8U, CV_64F, 1, 4);
return v;
}
void cv::gpu::PrintTo(const DeviceInfo& info, ostream* os)
{
(*os) << info.name();
}
void PrintTo(const UseRoi& useRoi, std::ostream* os)
{
if (useRoi)
(*os) << "sub matrix";
else
(*os) << "whole matrix";
}
void PrintTo(const Inverse& inverse, std::ostream* os)
{
if (inverse)
(*os) << "inverse";
else
(*os) << "direct";
}
void showDiff(InputArray gold_, InputArray actual_, double eps)
{
Mat gold = getMat(gold_);
Mat actual = getMat(actual_);
Mat diff;
absdiff(gold, actual, diff);
threshold(diff, diff, eps, 255.0, cv::THRESH_BINARY);
namedWindow("gold", WINDOW_NORMAL);
namedWindow("actual", WINDOW_NORMAL);
namedWindow("diff", WINDOW_NORMAL);
imshow("gold", gold);
imshow("actual", actual);
imshow("diff", diff);
waitKey();
}
#endif // HAVE_CUDA
modules/gpu/test/utility.hpp
+296 -296
View File
@@ -1,296 +1,296 @@
/*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*/
#ifndef __OPENCV_TEST_UTILITY_HPP__
#define __OPENCV_TEST_UTILITY_HPP__
//////////////////////////////////////////////////////////////////////
// random generators
int randomInt(int minVal, int maxVal);
double randomDouble(double minVal, double maxVal);
cv::Size randomSize(int minVal, int maxVal);
cv::Scalar randomScalar(double minVal, double maxVal);
cv::Mat randomMat(cv::Size size, int type, double minVal = 0.0, double maxVal = 255.0);
//////////////////////////////////////////////////////////////////////
// GpuMat create
cv::gpu::GpuMat createMat(cv::Size size, int type, bool useRoi = false);
cv::gpu::GpuMat loadMat(const cv::Mat& m, bool useRoi = false);
//////////////////////////////////////////////////////////////////////
// Image load
//! read image from testdata folder
cv::Mat readImage(const std::string& fileName, int flags = cv::IMREAD_COLOR);
//! read image from testdata folder and convert it to specified type
cv::Mat readImageType(const std::string& fname, int type);
//////////////////////////////////////////////////////////////////////
// Image dumping
void dumpImage(const std::string& fileName, const cv::Mat& image);
//////////////////////////////////////////////////////////////////////
// Gpu devices
//! return true if device supports specified feature and gpu module was built with support the feature.
bool supportFeature(const cv::gpu::DeviceInfo& info, cv::gpu::FeatureSet feature);
class DeviceManager
{
public:
static DeviceManager& instance();
void load(int i);
void loadAll();
const std::vector<cv::gpu::DeviceInfo>& values() const { return devices_; }
private:
std::vector<cv::gpu::DeviceInfo> devices_;
};
#define ALL_DEVICES testing::ValuesIn(DeviceManager::instance().values())
//////////////////////////////////////////////////////////////////////
// Additional assertion
cv::Mat getMat(cv::InputArray arr);
double checkNorm(cv::InputArray m1, cv::InputArray m2);
void minMaxLocGold(const cv::Mat& src, double* minVal_, double* maxVal_ = 0, cv::Point* minLoc_ = 0, cv::Point* maxLoc_ = 0, const cv::Mat& mask = cv::Mat());
testing::AssertionResult assertMatNear(const char* expr1, const char* expr2, const char* eps_expr, cv::InputArray m1, cv::InputArray m2, double eps);
#define EXPECT_MAT_NEAR(m1, m2, eps) EXPECT_PRED_FORMAT3(assertMatNear, m1, m2, eps)
#define ASSERT_MAT_NEAR(m1, m2, eps) ASSERT_PRED_FORMAT3(assertMatNear, m1, m2, eps)
#define EXPECT_SCALAR_NEAR(s1, s2, eps) \
{ \
EXPECT_NEAR(s1[0], s2[0], eps); \
EXPECT_NEAR(s1[1], s2[1], eps); \
EXPECT_NEAR(s1[2], s2[2], eps); \
EXPECT_NEAR(s1[3], s2[3], eps); \
}
#define ASSERT_SCALAR_NEAR(s1, s2, eps) \
{ \
ASSERT_NEAR(s1[0], s2[0], eps); \
ASSERT_NEAR(s1[1], s2[1], eps); \
ASSERT_NEAR(s1[2], s2[2], eps); \
ASSERT_NEAR(s1[3], s2[3], eps); \
}
#define EXPECT_POINT2_NEAR(p1, p2, eps) \
{ \
EXPECT_NEAR(p1.x, p2.x, eps); \
EXPECT_NEAR(p1.y, p2.y, eps); \
}
#define ASSERT_POINT2_NEAR(p1, p2, eps) \
{ \
ASSERT_NEAR(p1.x, p2.x, eps); \
ASSERT_NEAR(p1.y, p2.y, eps); \
}
#define EXPECT_POINT3_NEAR(p1, p2, eps) \
{ \
EXPECT_NEAR(p1.x, p2.x, eps); \
EXPECT_NEAR(p1.y, p2.y, eps); \
EXPECT_NEAR(p1.z, p2.z, eps); \
}
#define ASSERT_POINT3_NEAR(p1, p2, eps) \
{ \
ASSERT_NEAR(p1.x, p2.x, eps); \
ASSERT_NEAR(p1.y, p2.y, eps); \
ASSERT_NEAR(p1.z, p2.z, eps); \
}
double checkSimilarity(cv::InputArray m1, cv::InputArray m2);
#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkSimilarity(mat1, mat2), eps); \
}
#define ASSERT_MAT_SIMILAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
ASSERT_LE(checkSimilarity(mat1, mat2), eps); \
}
//////////////////////////////////////////////////////////////////////
// Helper structs for value-parameterized tests
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
namespace cv { namespace gpu
{
void PrintTo(const DeviceInfo& info, std::ostream* os);
}}
#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113))
// Depth
using perf::MatDepth;
//! return vector with depths from specified range.
std::vector<MatDepth> depths(int depth_start, int depth_end);
#define ALL_DEPTH testing::Values(MatDepth(CV_8U), MatDepth(CV_8S), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32S), MatDepth(CV_32F), MatDepth(CV_64F))
#define DEPTHS(depth_start, depth_end) testing::ValuesIn(depths(depth_start, depth_end))
#define DEPTH_PAIRS testing::Values(std::make_pair(MatDepth(CV_8U), MatDepth(CV_8U)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_16U)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_16S)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_16U), MatDepth(CV_16U)), \
std::make_pair(MatDepth(CV_16U), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_16U), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_16U), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_16S), MatDepth(CV_16S)), \
std::make_pair(MatDepth(CV_16S), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_16S), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_16S), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_32S), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_32S), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_32S), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_32F), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_32F), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_64F), MatDepth(CV_64F)))
// Type
using perf::MatType;
//! return vector with types from specified range.
std::vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end);
//! return vector with all types (depth: CV_8U-CV_64F, channels: 1-4).
const std::vector<MatType>& all_types();
#define ALL_TYPES testing::ValuesIn(all_types())
#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end))
// ROI
class UseRoi
{
public:
inline UseRoi(bool val = false) : val_(val) {}
inline operator bool() const { return val_; }
private:
bool val_;
};
void PrintTo(const UseRoi& useRoi, std::ostream* os);
#define WHOLE testing::Values(UseRoi(false))
#define SUBMAT testing::Values(UseRoi(true))
#define WHOLE_SUBMAT testing::Values(UseRoi(false), UseRoi(true))
// Direct/Inverse
class Inverse
{
public:
inline Inverse(bool val = false) : val_(val) {}
inline operator bool() const { return val_; }
private:
bool val_;
};
void PrintTo(const Inverse& useRoi, std::ostream* os);
#define DIRECT_INVERSE testing::Values(Inverse(false), Inverse(true))
// Param class
#define IMPLEMENT_PARAM_CLASS(name, type) \
class name \
{ \
public: \
name ( type arg = type ()) : val_(arg) {} \
operator type () const {return val_;} \
private: \
type val_; \
}; \
inline void PrintTo( name param, std::ostream* os) \
{ \
*os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \
}
IMPLEMENT_PARAM_CLASS(Channels, int)
#define ALL_CHANNELS testing::Values(Channels(1), Channels(2), Channels(3), Channels(4))
#define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4))
// Flags and enums
CV_ENUM(NormCode, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2, cv::NORM_TYPE_MASK, cv::NORM_RELATIVE, cv::NORM_MINMAX)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::INTER_AREA)
CV_ENUM(BorderType, cv::BORDER_REFLECT101, cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP)
#define ALL_BORDER_TYPES testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP))
CV_FLAGS(WarpFlags, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::WARP_INVERSE_MAP)
//////////////////////////////////////////////////////////////////////
// Other
void showDiff(cv::InputArray gold, cv::InputArray actual, double eps);
#endif // __OPENCV_TEST_UTILITY_HPP__
/*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*/
#ifndef __OPENCV_TEST_UTILITY_HPP__
#define __OPENCV_TEST_UTILITY_HPP__
//////////////////////////////////////////////////////////////////////
// random generators
int randomInt(int minVal, int maxVal);
double randomDouble(double minVal, double maxVal);
cv::Size randomSize(int minVal, int maxVal);
cv::Scalar randomScalar(double minVal, double maxVal);
cv::Mat randomMat(cv::Size size, int type, double minVal = 0.0, double maxVal = 255.0);
//////////////////////////////////////////////////////////////////////
// GpuMat create
cv::gpu::GpuMat createMat(cv::Size size, int type, bool useRoi = false);
cv::gpu::GpuMat loadMat(const cv::Mat& m, bool useRoi = false);
//////////////////////////////////////////////////////////////////////
// Image load
//! read image from testdata folder
cv::Mat readImage(const std::string& fileName, int flags = cv::IMREAD_COLOR);
//! read image from testdata folder and convert it to specified type
cv::Mat readImageType(const std::string& fname, int type);
//////////////////////////////////////////////////////////////////////
// Image dumping
void dumpImage(const std::string& fileName, const cv::Mat& image);
//////////////////////////////////////////////////////////////////////
// Gpu devices
//! return true if device supports specified feature and gpu module was built with support the feature.
bool supportFeature(const cv::gpu::DeviceInfo& info, cv::gpu::FeatureSet feature);
class DeviceManager
{
public:
static DeviceManager& instance();
void load(int i);
void loadAll();
const std::vector<cv::gpu::DeviceInfo>& values() const { return devices_; }
private:
std::vector<cv::gpu::DeviceInfo> devices_;
};
#define ALL_DEVICES testing::ValuesIn(DeviceManager::instance().values())
//////////////////////////////////////////////////////////////////////
// Additional assertion
cv::Mat getMat(cv::InputArray arr);
double checkNorm(cv::InputArray m1, cv::InputArray m2);
void minMaxLocGold(const cv::Mat& src, double* minVal_, double* maxVal_ = 0, cv::Point* minLoc_ = 0, cv::Point* maxLoc_ = 0, const cv::Mat& mask = cv::Mat());
testing::AssertionResult assertMatNear(const char* expr1, const char* expr2, const char* eps_expr, cv::InputArray m1, cv::InputArray m2, double eps);
#define EXPECT_MAT_NEAR(m1, m2, eps) EXPECT_PRED_FORMAT3(assertMatNear, m1, m2, eps)
#define ASSERT_MAT_NEAR(m1, m2, eps) ASSERT_PRED_FORMAT3(assertMatNear, m1, m2, eps)
#define EXPECT_SCALAR_NEAR(s1, s2, eps) \
{ \
EXPECT_NEAR(s1[0], s2[0], eps); \
EXPECT_NEAR(s1[1], s2[1], eps); \
EXPECT_NEAR(s1[2], s2[2], eps); \
EXPECT_NEAR(s1[3], s2[3], eps); \
}
#define ASSERT_SCALAR_NEAR(s1, s2, eps) \
{ \
ASSERT_NEAR(s1[0], s2[0], eps); \
ASSERT_NEAR(s1[1], s2[1], eps); \
ASSERT_NEAR(s1[2], s2[2], eps); \
ASSERT_NEAR(s1[3], s2[3], eps); \
}
#define EXPECT_POINT2_NEAR(p1, p2, eps) \
{ \
EXPECT_NEAR(p1.x, p2.x, eps); \
EXPECT_NEAR(p1.y, p2.y, eps); \
}
#define ASSERT_POINT2_NEAR(p1, p2, eps) \
{ \
ASSERT_NEAR(p1.x, p2.x, eps); \
ASSERT_NEAR(p1.y, p2.y, eps); \
}
#define EXPECT_POINT3_NEAR(p1, p2, eps) \
{ \
EXPECT_NEAR(p1.x, p2.x, eps); \
EXPECT_NEAR(p1.y, p2.y, eps); \
EXPECT_NEAR(p1.z, p2.z, eps); \
}
#define ASSERT_POINT3_NEAR(p1, p2, eps) \
{ \
ASSERT_NEAR(p1.x, p2.x, eps); \
ASSERT_NEAR(p1.y, p2.y, eps); \
ASSERT_NEAR(p1.z, p2.z, eps); \
}
double checkSimilarity(cv::InputArray m1, cv::InputArray m2);
#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkSimilarity(mat1, mat2), eps); \
}
#define ASSERT_MAT_SIMILAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
ASSERT_LE(checkSimilarity(mat1, mat2), eps); \
}
//////////////////////////////////////////////////////////////////////
// Helper structs for value-parameterized tests
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
namespace cv { namespace gpu
{
void PrintTo(const DeviceInfo& info, std::ostream* os);
}}
#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113))
// Depth
using perf::MatDepth;
//! return vector with depths from specified range.
std::vector<MatDepth> depths(int depth_start, int depth_end);
#define ALL_DEPTH testing::Values(MatDepth(CV_8U), MatDepth(CV_8S), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32S), MatDepth(CV_32F), MatDepth(CV_64F))
#define DEPTHS(depth_start, depth_end) testing::ValuesIn(depths(depth_start, depth_end))
#define DEPTH_PAIRS testing::Values(std::make_pair(MatDepth(CV_8U), MatDepth(CV_8U)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_16U)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_16S)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_8U), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_16U), MatDepth(CV_16U)), \
std::make_pair(MatDepth(CV_16U), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_16U), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_16U), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_16S), MatDepth(CV_16S)), \
std::make_pair(MatDepth(CV_16S), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_16S), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_16S), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_32S), MatDepth(CV_32S)), \
std::make_pair(MatDepth(CV_32S), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_32S), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_32F), MatDepth(CV_32F)), \
std::make_pair(MatDepth(CV_32F), MatDepth(CV_64F)), \
\
std::make_pair(MatDepth(CV_64F), MatDepth(CV_64F)))
// Type
using perf::MatType;
//! return vector with types from specified range.
std::vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end);
//! return vector with all types (depth: CV_8U-CV_64F, channels: 1-4).
const std::vector<MatType>& all_types();
#define ALL_TYPES testing::ValuesIn(all_types())
#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end))
// ROI
class UseRoi
{
public:
inline UseRoi(bool val = false) : val_(val) {}
inline operator bool() const { return val_; }
private:
bool val_;
};
void PrintTo(const UseRoi& useRoi, std::ostream* os);
#define WHOLE testing::Values(UseRoi(false))
#define SUBMAT testing::Values(UseRoi(true))
#define WHOLE_SUBMAT testing::Values(UseRoi(false), UseRoi(true))
// Direct/Inverse
class Inverse
{
public:
inline Inverse(bool val = false) : val_(val) {}
inline operator bool() const { return val_; }
private:
bool val_;
};
void PrintTo(const Inverse& useRoi, std::ostream* os);
#define DIRECT_INVERSE testing::Values(Inverse(false), Inverse(true))
// Param class
#define IMPLEMENT_PARAM_CLASS(name, type) \
class name \
{ \
public: \
name ( type arg = type ()) : val_(arg) {} \
operator type () const {return val_;} \
private: \
type val_; \
}; \
inline void PrintTo( name param, std::ostream* os) \
{ \
*os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \
}
IMPLEMENT_PARAM_CLASS(Channels, int)
#define ALL_CHANNELS testing::Values(Channels(1), Channels(2), Channels(3), Channels(4))
#define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4))
// Flags and enums
CV_ENUM(NormCode, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2, cv::NORM_TYPE_MASK, cv::NORM_RELATIVE, cv::NORM_MINMAX)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::INTER_AREA)
CV_ENUM(BorderType, cv::BORDER_REFLECT101, cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP)
#define ALL_BORDER_TYPES testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP))
CV_FLAGS(WarpFlags, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::WARP_INVERSE_MAP)
//////////////////////////////////////////////////////////////////////
// Other
void showDiff(cv::InputArray gold, cv::InputArray actual, double eps);
#endif // __OPENCV_TEST_UTILITY_HPP__