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removed obsolete tests

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pull/13383/head
Vadim Pisarevsky 14 years ago
parent 8be541eed2
commit 2c8af20bd0
50 changed files with 0 additions and 9657 deletions
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  1. 65
      tests/gpu/CMakeLists.txt
  2. 1113
      tests/gpu/src/arithm.cpp
  3. 234
      tests/gpu/src/bitwise_oper.cpp
  4. 532
      tests/gpu/src/brute_force_matcher.cpp
  5. 415
      tests/gpu/src/dft_routines.cpp
  6. 239
      tests/gpu/src/features2d.cpp
  7. 382
      tests/gpu/src/filters.cpp
  8. 95
      tests/gpu/src/gputest.hpp
  9. 57
      tests/gpu/src/gputest_main.cpp
  10. 332
      tests/gpu/src/hog.cpp
  11. 987
      tests/gpu/src/imgproc_gpu.cpp
  12. 310
      tests/gpu/src/match_template.cpp
  13. 255
      tests/gpu/src/meanshift.cpp
  14. 128
      tests/gpu/src/mssegmentation.cpp
  15. 130
      tests/gpu/src/nvidia/NCVAutoTestLister.hpp
  16. 211
      tests/gpu/src/nvidia/NCVTest.hpp
  17. 161
      tests/gpu/src/nvidia/NCVTestSourceProvider.hpp
  18. 129
      tests/gpu/src/nvidia/TestCompact.cpp
  19. 41
      tests/gpu/src/nvidia/TestCompact.h
  20. 163
      tests/gpu/src/nvidia/TestDrawRects.cpp
  21. 44
      tests/gpu/src/nvidia/TestDrawRects.h
  22. 290
      tests/gpu/src/nvidia/TestHaarCascadeApplication.cpp
  23. 41
      tests/gpu/src/nvidia/TestHaarCascadeApplication.h
  24. 123
      tests/gpu/src/nvidia/TestHaarCascadeLoader.cpp
  25. 34
      tests/gpu/src/nvidia/TestHaarCascadeLoader.h
  26. 176
      tests/gpu/src/nvidia/TestHypothesesFilter.cpp
  27. 44
      tests/gpu/src/nvidia/TestHypothesesFilter.h
  28. 134
      tests/gpu/src/nvidia/TestHypothesesGrow.cpp
  29. 46
      tests/gpu/src/nvidia/TestHypothesesGrow.h
  30. 185
      tests/gpu/src/nvidia/TestIntegralImage.cpp
  31. 40
      tests/gpu/src/nvidia/TestIntegralImage.h
  32. 117
      tests/gpu/src/nvidia/TestIntegralImageSquared.cpp
  33. 39
      tests/gpu/src/nvidia/TestIntegralImageSquared.h
  34. 180
      tests/gpu/src/nvidia/TestRectStdDev.cpp
  35. 44
      tests/gpu/src/nvidia/TestRectStdDev.h
  36. 161
      tests/gpu/src/nvidia/TestResize.cpp
  37. 42
      tests/gpu/src/nvidia/TestResize.h
  38. 148
      tests/gpu/src/nvidia/TestTranspose.cpp
  39. 41
      tests/gpu/src/nvidia/TestTranspose.h
  40. 353
      tests/gpu/src/nvidia/main_nvidia.cpp
  41. 70
      tests/gpu/src/nvidia_tests.cpp
  42. 93
      tests/gpu/src/operator_async_call.cpp
  43. 126
      tests/gpu/src/operator_convert_to.cpp
  44. 162
      tests/gpu/src/operator_copy_to.cpp
  45. 138
      tests/gpu/src/operator_set_to.cpp
  46. 352
      tests/gpu/src/split_merge.cpp
  47. 152
      tests/gpu/src/stereo_bm.cpp
  48. 103
      tests/gpu/src/stereo_bm_async.cpp
  49. 100
      tests/gpu/src/stereo_bp.cpp
  50. 100
      tests/gpu/src/stereo_csbp.cpp

65
tests/gpu/CMakeLists.txt
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@ -1,65 +0,0 @@
# ----------------------------------------------------------------------------
# CMake file for gputest. See root CMakeLists.txt
# ----------------------------------------------------------------------------
project(opencv_test_gpu)
set(the_target "opencv_test_gpu")
file(GLOB test_srcs "src/*.cpp")
file(GLOB test_hdrs "src/*.h*")
source_group("Src" FILES ${test_srcs})
source_group("Include" FILES ${test_hdrs})
include_directories (
"${CMAKE_SOURCE_DIR}/include/opencv"
"${CMAKE_SOURCE_DIR}/modules/core/include"
"${CMAKE_SOURCE_DIR}/modules/imgproc/include"
"${CMAKE_SOURCE_DIR}/modules/features2d/include"
"${CMAKE_SOURCE_DIR}/modules/flann/include"
"${CMAKE_SOURCE_DIR}/modules/calib3d/include"
"${CMAKE_SOURCE_DIR}/modules/highgui/include"
"${CMAKE_SOURCE_DIR}/modules/objdetect/include"
"${CMAKE_SOURCE_DIR}/modules/video/include"
"${CMAKE_SOURCE_DIR}/modules/legacy/include"
"${CMAKE_SOURCE_DIR}/modules/contrib/include"
"${CMAKE_SOURCE_DIR}/modules/gpu/include"
"${CMAKE_SOURCE_DIR}/modules/ml/include"
"${CMAKE_CURRENT_SOURCE_DIR}/src"
"${CMAKE_CURRENT_BINARY_DIR}"
"${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia"
"${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/core"
)
include_directories(../cxts)
if(HAVE_CUDA)
include_directories(${CUDA_INCLUDE_DIRS} ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/core ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/NPP_staging)
file(GLOB nvidia "src/nvidia/*.*")
source_group("Src\\nvidia" FILES ${nvidia})
endif()
add_executable(${the_target} ${test_srcs} ${test_hdrs} ${nvidia})
# Additional target properties
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin/"
)
add_dependencies(${the_target} opencv_ts opencv_gpu opencv_highgui opencv_imgproc opencv_calib3d)
# Add the required libraries for linking:
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} opencv_ts opencv_gpu opencv_highgui opencv_imgproc opencv_calib3d)
enable_testing()
get_target_property(LOC ${the_target} LOCATION)
add_test(${the_target} "${LOC}")
if(WIN32)
install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main)
endif()

1113
tests/gpu/src/arithm.cpp
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234
tests/gpu/src/bitwise_oper.cpp
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@ -1,234 +0,0 @@
/*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 <iostream>
#include <limits>
#include "gputest.hpp"
#define CHECK(pred, err) if (!(pred)) { \
ts->printf(CvTS::CONSOLE, "Fail: \"%s\" at line: %d\n", #pred, __LINE__); \
ts->set_failed_test_info(err); \
return; }
using namespace cv;
using namespace std;
struct CV_GpuBitwiseTest: public CvTest
{
CV_GpuBitwiseTest(): CvTest("GPU-BitwiseOpersTest", "bitwiseMatOperators") {}
void run(int)
{
int rows, cols;
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
int depth_end = double_ok ? CV_64F : CV_32F;
for (int depth = CV_8U; depth <= depth_end; ++depth)
for (int cn = 1; cn <= 4; ++cn)
for (int attempt = 0; attempt < 3; ++attempt)
{
rows = 1 + rand() % 100;
cols = 1 + rand() % 100;
test_bitwise_not(rows, cols, CV_MAKETYPE(depth, cn));
test_bitwise_or(rows, cols, CV_MAKETYPE(depth, cn));
test_bitwise_and(rows, cols, CV_MAKETYPE(depth, cn));
test_bitwise_xor(rows, cols, CV_MAKETYPE(depth, cn));
}
}
void test_bitwise_not(int rows, int cols, int type)
{
Mat src(rows, cols, type);
RNG rng;
for (int i = 0; i < src.rows; ++i)
{
Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i));
rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(255));
}
Mat dst_gold = ~src;
gpu::GpuMat mask(src.size(), CV_8U);
mask.setTo(Scalar(1));
gpu::GpuMat dst;
gpu::bitwise_not(gpu::GpuMat(src), dst);
CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT);
Mat dsth(dst);
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT);
dst.setTo(Scalar::all(0));
gpu::bitwise_not(gpu::GpuMat(src), dst, mask);
CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT);
dsth = dst;
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
}
void test_bitwise_or(int rows, int cols, int type)
{
Mat src1(rows, cols, type);
Mat src2(rows, cols, type);
RNG rng;
for (int i = 0; i < src1.rows; ++i)
{
Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i));
rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255));
Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i));
rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255));
}
Mat dst_gold = src1 | src2;
gpu::GpuMat dst = gpu::GpuMat(src1) | gpu::GpuMat(src2);
CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT);
Mat dsth(dst);
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
Mat mask(src1.size(), CV_8U);
randu(mask, Scalar(0), Scalar(255));
Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0));
gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0));
bitwise_or(src1, src2, dst_gold2, mask);
gpu::bitwise_or(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask));
CHECK(dst_gold2.size() == dst2.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold2.type() == dst2.type(), CvTS::FAIL_INVALID_OUTPUT);
dsth = dst2;
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
}
void test_bitwise_and(int rows, int cols, int type)
{
Mat src1(rows, cols, type);
Mat src2(rows, cols, type);
RNG rng;
for (int i = 0; i < src1.rows; ++i)
{
Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i));
rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255));
Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i));
rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255));
}
Mat dst_gold = src1 & src2;
gpu::GpuMat dst = gpu::GpuMat(src1) & gpu::GpuMat(src2);
CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT);
Mat dsth(dst);
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
Mat mask(src1.size(), CV_8U);
randu(mask, Scalar(0), Scalar(255));
Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0));
gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0));
bitwise_and(src1, src2, dst_gold2, mask);
gpu::bitwise_and(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask));
CHECK(dst_gold2.size() == dst2.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold2.type() == dst2.type(), CvTS::FAIL_INVALID_OUTPUT);
dsth = dst2;
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
}
void test_bitwise_xor(int rows, int cols, int type)
{
Mat src1(rows, cols, type);
Mat src2(rows, cols, type);
RNG rng;
for (int i = 0; i < src1.rows; ++i)
{
Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i));
rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255));
Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i));
rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255));
}
Mat dst_gold = src1 ^ src2;
gpu::GpuMat dst = gpu::GpuMat(src1) ^ gpu::GpuMat(src2);
CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT);
Mat dsth(dst);
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
Mat mask(src1.size(), CV_8U);
randu(mask, Scalar(0), Scalar(255));
Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0));
gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0));
bitwise_xor(src1, src2, dst_gold2, mask);
gpu::bitwise_xor(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask));
CHECK(dst_gold2.size() == dst2.size(), CvTS::FAIL_INVALID_OUTPUT);
CHECK(dst_gold2.type() == dst2.type(), CvTS::FAIL_INVALID_OUTPUT);
dsth = dst2;
for (int i = 0; i < dst_gold.rows; ++i)
CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT)
}
} gpu_bitwise_test;

532
tests/gpu/src/brute_force_matcher.cpp
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@ -1,532 +0,0 @@
/*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 "gputest.hpp"
#include <algorithm>
#include <iterator>
using namespace cv;
using namespace cv::gpu;
using namespace std;
class CV_GpuBruteForceMatcherTest : public CvTest
{
public:
CV_GpuBruteForceMatcherTest() :
CvTest( "GPU-BruteForceMatcher", "BruteForceMatcher" )
{
}
protected:
virtual void run(int);
void emptyDataTest();
void dataTest(int dim);
void generateData(GpuMat& query, GpuMat& train, int dim);
void matchTest(const GpuMat& query, const GpuMat& train);
void knnMatchTest(const GpuMat& query, const GpuMat& train);
void radiusMatchTest(const GpuMat& query, const GpuMat& train);
private:
BruteForceMatcher_GPU< L2<float> > dmatcher;
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
static const int countFactor = 4; // do not change it
};
void CV_GpuBruteForceMatcherTest::emptyDataTest()
{
GpuMat queryDescriptors, trainDescriptors, mask;
vector<GpuMat> trainDescriptorCollection, masks;
vector<DMatch> matches;
vector< vector<DMatch> > vmatches;
try
{
dmatcher.match(queryDescriptors, trainDescriptors, matches, mask);
}
catch(...)
{
ts->printf( CvTS::LOG, "match() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher.knnMatch(queryDescriptors, trainDescriptors, vmatches, 2, mask);
}
catch(...)
{
ts->printf( CvTS::LOG, "knnMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher.radiusMatch(queryDescriptors, trainDescriptors, vmatches, 10.f, mask);
}
catch(...)
{
ts->printf( CvTS::LOG, "radiusMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher.add(trainDescriptorCollection);
}
catch(...)
{
ts->printf( CvTS::LOG, "add() on empty descriptors must not generate exception.\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher.match(queryDescriptors, matches, masks);
}
catch(...)
{
ts->printf( CvTS::LOG, "match() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher.knnMatch(queryDescriptors, vmatches, 2, masks);
}
catch(...)
{
ts->printf( CvTS::LOG, "knnMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher.radiusMatch( queryDescriptors, vmatches, 10.f, masks );
}
catch(...)
{
ts->printf( CvTS::LOG, "radiusMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
}
void CV_GpuBruteForceMatcherTest::generateData( GpuMat& queryGPU, GpuMat& trainGPU, int dim )
{
Mat query, train;
RNG rng(*ts->get_rng());
// Generate query descriptors randomly.
// Descriptor vector elements are integer values.
Mat buf( queryDescCount, dim, CV_32SC1 );
rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
buf.convertTo( query, CV_32FC1 );
// Generate train decriptors as follows:
// copy each query descriptor to train set countFactor times
// and perturb some one element of the copied descriptors in
// in ascending order. General boundaries of the perturbation
// are (0.f, 1.f).
train.create( query.rows*countFactor, query.cols, CV_32FC1 );
float step = 1.f / countFactor;
for( int qIdx = 0; qIdx < query.rows; qIdx++ )
{
Mat queryDescriptor = query.row(qIdx);
for( int c = 0; c < countFactor; c++ )
{
int tIdx = qIdx * countFactor + c;
Mat trainDescriptor = train.row(tIdx);
queryDescriptor.copyTo( trainDescriptor );
int elem = rng(dim);
float diff = rng.uniform( step*c, step*(c+1) );
trainDescriptor.at<float>(0, elem) += diff;
}
}
queryGPU.upload(query);
trainGPU.upload(train);
}
void CV_GpuBruteForceMatcherTest::matchTest( const GpuMat& query, const GpuMat& train )
{
dmatcher.clear();
// test const version of match()
{
vector<DMatch> matches;
dmatcher.match( query, train, matches );
if( (int)matches.size() != queryDescCount )
{
ts->printf(CvTS::LOG, "Incorrect matches count while test match() function (1).\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch match = matches[i];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) )
badCount++;
}
if (badCount > 0)
{
ts->printf( CvTS::LOG, "%f - too large bad matches part while test match() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of match() with add()
{
vector<DMatch> matches;
// make add() twice to test such case
dmatcher.add( vector<GpuMat>(1,train.rowRange(0, train.rows/2)) );
dmatcher.add( vector<GpuMat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<GpuMat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
for( int di = 0; di < queryDescCount/2; di++ )
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
}
dmatcher.match( query, matches, masks );
if( (int)matches.size() != queryDescCount )
{
ts->printf(CvTS::LOG, "Incorrect matches count while test match() function (2).\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch match = matches[i];
int shift = dmatcher.isMaskSupported() ? 1 : 0;
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + shift) || (match.imgIdx != 0) )
badCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + shift) || (match.imgIdx != 1) )
badCount++;
}
}
}
if (badCount > 0)
{
ts->printf( CvTS::LOG, "%f - too large bad matches part while test match() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_GpuBruteForceMatcherTest::knnMatchTest( const GpuMat& query, const GpuMat& train )
{
dmatcher.clear();
// test const version of knnMatch()
{
const int knn = 3;
vector< vector<DMatch> > matches;
dmatcher.knnMatch( query, train, matches, knn );
if( (int)matches.size() != queryDescCount )
{
ts->printf(CvTS::LOG, "Incorrect matches count while test knnMatch() function (1).\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < knn; k++ )
{
DMatch match = matches[i][k];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor+k) || (match.imgIdx != 0) )
localBadCount++;
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if (badCount > 0)
{
ts->printf( CvTS::LOG, "%f - too large bad matches part while test knnMatch() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of knnMatch() with add()
{
const int knn = 2;
vector<vector<DMatch> > matches;
// make add() twice to test such case
dmatcher.add( vector<GpuMat>(1,train.rowRange(0, train.rows/2)) );
dmatcher.add( vector<GpuMat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<GpuMat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
for( int di = 0; di < queryDescCount/2; di++ )
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
}
dmatcher.knnMatch( query, matches, knn, masks );
if( (int)matches.size() != queryDescCount )
{
ts->printf(CvTS::LOG, "Incorrect matches count while test knnMatch() function (2).\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
int shift = dmatcher.isMaskSupported() ? 1 : 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < knn; k++ )
{
DMatch match = matches[i][k];
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) ||
(match.imgIdx != 0) )
localBadCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) ||
(match.imgIdx != 1) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if (badCount > 0)
{
ts->printf( CvTS::LOG, "%f - too large bad matches part while test knnMatch() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_GpuBruteForceMatcherTest::radiusMatchTest( const GpuMat& query, const GpuMat& train )
{
bool atomics_ok = TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS);
if (!atomics_ok)
{
ts->printf(CvTS::CONSOLE, "\nCode and device atomics support is required for radiusMatch (CC >= 1.1)");
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
dmatcher.clear();
// test const version of match()
{
const float radius = 1.f/countFactor;
vector< vector<DMatch> > matches;
dmatcher.radiusMatch( query, train, matches, radius );
if( (int)matches.size() != queryDescCount )
{
ts->printf(CvTS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != 1 )
badCount++;
else
{
DMatch match = matches[i][0];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) )
badCount++;
}
}
if (badCount > 0)
{
ts->printf( CvTS::LOG, "%f - too large bad matches part while test radiusMatch() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of match() with add()
{
int n = 3;
const float radius = 1.f/countFactor * n;
vector< vector<DMatch> > matches;
// make add() twice to test such case
dmatcher.add( vector<GpuMat>(1,train.rowRange(0, train.rows/2)) );
dmatcher.add( vector<GpuMat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<GpuMat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
for( int di = 0; di < queryDescCount/2; di++ )
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
}
dmatcher.radiusMatch( query, matches, radius, masks );
int curRes = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
ts->printf(CvTS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
int badCount = 0;
int shift = dmatcher.isMaskSupported() ? 1 : 0;
int needMatchCount = dmatcher.isMaskSupported() ? n-1 : n;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != needMatchCount )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < needMatchCount; k++ )
{
DMatch match = matches[i][k];
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) ||
(match.imgIdx != 0) )
localBadCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) ||
(match.imgIdx != 1) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if (badCount > 0)
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf( CvTS::LOG, "%f - too large bad matches part while test radiusMatch() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
}
}
}
void CV_GpuBruteForceMatcherTest::dataTest(int dim)
{
GpuMat query, train;
generateData(query, train, dim);
matchTest(query, train);
knnMatchTest(query, train);
radiusMatchTest(query, train);
dmatcher.clear();
}
void CV_GpuBruteForceMatcherTest::run(int)
{
try
{
emptyDataTest();
dataTest(50);
dataTest(64);
dataTest(100);
dataTest(128);
dataTest(200);
dataTest(256);
dataTest(300);
}
catch(cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
}
CV_GpuBruteForceMatcherTest CV_GpuBruteForceMatcher_test;

415
tests/gpu/src/dft_routines.cpp
Unescape View File

@ -1,415 +0,0 @@
/*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 "gputest.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
struct CV_GpuMulSpectrumsTest: CvTest
{
CV_GpuMulSpectrumsTest(): CvTest("GPU-MulSpectrumsTest", "mulSpectrums") {}
void run(int)
{
try
{
test(0);
testConj(0);
testScaled(0);
testScaledConj(0);
test(DFT_ROWS);
testConj(DFT_ROWS);
testScaled(DFT_ROWS);
testScaledConj(DFT_ROWS);
}
catch (const Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
void gen(int cols, int rows, Mat& mat)
{
RNG rng;
mat.create(rows, cols, CV_32FC2);
rng.fill(mat, RNG::UNIFORM, Scalar::all(0.f), Scalar::all(10.f));
}
bool cmp(const Mat& gold, const Mat& mine, float max_err=1e-3f)
{
if (gold.size() != mine.size())
{
ts->printf(CvTS::CONSOLE, "bad sizes: gold: %d d%, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.type() != mine.type())
{
ts->printf(CvTS::CONSOLE, "bad types: gold=%d, mine=%d\n", gold.type(), mine.type());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols * 2; ++j)
{
float gold_ = gold.at<float>(i, j);
float mine_ = mine.at<float>(i, j);
if (fabs(gold_ - mine_) > max_err)
{
ts->printf(CvTS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j, i, gold_, mine_);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
}
}
return true;
}
bool cmpScaled(const Mat& gold, const Mat& mine, float scale, float max_err=1e-3f)
{
if (gold.size() != mine.size())
{
ts->printf(CvTS::CONSOLE, "bad sizes: gold: %d d%, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.type() != mine.type())
{
ts->printf(CvTS::CONSOLE, "bad types: gold=%d, mine=%d\n", gold.type(), mine.type());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols * 2; ++j)
{
float gold_ = gold.at<float>(i, j) * scale;
float mine_ = mine.at<float>(i, j);
if (fabs(gold_ - mine_) > max_err)
{
ts->printf(CvTS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j, i, gold_, mine_);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
}
}
return true;
}
void test(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, false);
GpuMat d_c;
mulSpectrums(GpuMat(a), GpuMat(b), d_c, flags, false);
if (!cmp(c_gold, Mat(d_c)))
ts->printf(CvTS::CONSOLE, "test failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags);
}
void testConj(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, true);
GpuMat d_c;
mulSpectrums(GpuMat(a), GpuMat(b), d_c, flags, true);
if (!cmp(c_gold, Mat(d_c)))
ts->printf(CvTS::CONSOLE, "testConj failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags);
}
void testScaled(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
float scale = 1.f / a.size().area();
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, false);
GpuMat d_c;
mulAndScaleSpectrums(GpuMat(a), GpuMat(b), d_c, flags, scale, false);
if (!cmpScaled(c_gold, Mat(d_c), scale))
ts->printf(CvTS::CONSOLE, "testScaled failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags);
}
void testScaledConj(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
float scale = 1.f / a.size().area();
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, true);
GpuMat d_c;
mulAndScaleSpectrums(GpuMat(a), GpuMat(b), d_c, flags, scale, true);
if (!cmpScaled(c_gold, Mat(d_c), scale))
ts->printf(CvTS::CONSOLE, "testScaledConj failed: cols=%d, rows=%d, flags=%D\n", cols, rows, flags);
}
} CV_GpuMulSpectrumsTest_inst;
struct CV_GpuDftTest: CvTest
{
CV_GpuDftTest(): CvTest("GPU-DftTest", "dft") {}
void run(int)
{
try
{
srand(0);
int cols = 2 + rand() % 100, rows = 2 + rand() % 100;
for (int i = 0; i < 2; ++i)
{
bool inplace = i != 0;
testC2C("no flags", cols, rows, 0, inplace);
testC2C("no flags 0 1", cols, rows + 1, 0, inplace);
testC2C("no flags 1 0", cols, rows + 1, 0, inplace);
testC2C("no flags 1 1", cols + 1, rows, 0, inplace);
testC2C("DFT_INVERSE", cols, rows, DFT_INVERSE, inplace);
testC2C("DFT_ROWS", cols, rows, DFT_ROWS, inplace);
testC2C("single col", 1, rows, 0, inplace);
testC2C("single row", cols, 1, 0, inplace);
testC2C("single col inversed", 1, rows, DFT_INVERSE, inplace);
testC2C("single row inversed", cols, 1, DFT_INVERSE, inplace);
testC2C("single row DFT_ROWS", cols, 1, DFT_ROWS, inplace);
testC2C("size 1 2", 1, 2, 0, inplace);
testC2C("size 2 1", 2, 1, 0, inplace);
}
testR2CThenC2R("sanity", cols, rows);
testR2CThenC2R("sanity 0 1", cols, rows + 1);
testR2CThenC2R("sanity 1 0", cols + 1, rows);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1);
testR2CThenC2R("single col", 1, rows);
testR2CThenC2R("single col 1", 1, rows + 1);
testR2CThenC2R("single row", cols, 1);
testR2CThenC2R("single row 1", cols + 1, 1);
testR2CThenC2R("sanity", cols, rows, true);
testR2CThenC2R("sanity 0 1", cols, rows + 1, true);
testR2CThenC2R("sanity 1 0", cols + 1, rows, true);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true);
testR2CThenC2R("single row", cols, 1, true);
testR2CThenC2R("single row 1", cols + 1, 1, true);
}
catch (const Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
void gen(int cols, int rows, int cn, Mat& mat)
{
RNG rng(1);
mat.create(rows, cols, CV_MAKETYPE(CV_32F, cn));
rng.fill(mat, RNG::UNIFORM, Scalar::all(0.f), Scalar::all(10.f));
}
bool cmp(const Mat& gold, const Mat& mine, float max_err=1e-3f)
{
if (gold.size() != mine.size())
{
ts->printf(CvTS::CONSOLE, "bad sizes: gold: %d %d, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.depth() != mine.depth())
{
ts->printf(CvTS::CONSOLE, "bad depth: gold=%d, mine=%d\n", gold.depth(), mine.depth());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.channels() != mine.channels())
{
ts->printf(CvTS::CONSOLE, "bad channel count: gold=%d, mine=%d\n", gold.channels(), mine.channels());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols * gold.channels(); ++j)
{
float gold_ = gold.at<float>(i, j);
float mine_ = mine.at<float>(i, j);
if (fabs(gold_ - mine_) > max_err)
{
ts->printf(CvTS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j / gold.channels(), i, gold_, mine_);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
}
}
return true;
}
void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace=false)
{
Mat a;
gen(cols, rows, 2, a);
Mat b_gold;
dft(a, b_gold, flags);
GpuMat d_b;
GpuMat d_b_data;
if (inplace)
{
d_b_data.create(1, a.size().area(), CV_32FC2);
d_b = GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
dft(GpuMat(a), d_b, Size(cols, rows), flags);
bool ok = true;
if (ok && inplace && d_b.ptr() != d_b_data.ptr())
{
ts->printf(CvTS::CONSOLE, "unnecessary reallocation was done\n");
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_b.depth() != CV_32F)
{
ts->printf(CvTS::CONSOLE, "bad depth: %d\n", d_b.depth());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_b.channels() != 2)
{
ts->printf(CvTS::CONSOLE, "bad channel count: %d\n", d_b.channels());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok) ok = cmp(b_gold, Mat(d_b), rows * cols * 1e-4f);
if (!ok)
ts->printf(CvTS::CONSOLE, "testC2C failed: hint=%s, cols=%d, rows=%d, flags=%d, inplace=%d\n",
hint.c_str(), cols, rows, flags, inplace);
}
void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace=false)
{
Mat a;
gen(cols, rows, 1, a);
bool ok = true;
GpuMat d_b, d_c;
GpuMat d_b_data, d_c_data;
if (inplace)
{
if (a.cols == 1)
{
d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2);
d_b = GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
else
{
d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2);
d_b = GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize());
}
d_c_data.create(1, a.size().area(), CV_32F);
d_c = GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize());
}
dft(GpuMat(a), d_b, Size(cols, rows), 0);
dft(d_b, d_c, Size(cols, rows), DFT_REAL_OUTPUT | DFT_SCALE);
if (ok && inplace && d_b.ptr() != d_b_data.ptr())
{
ts->printf(CvTS::CONSOLE, "unnecessary reallocation was done for b\n");
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && inplace && d_c.ptr() != d_c_data.ptr())
{
ts->printf(CvTS::CONSOLE, "unnecessary reallocation was done for c\n");
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_c.depth() != CV_32F)
{
ts->printf(CvTS::CONSOLE, "bad depth: %d\n", d_c.depth());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_c.channels() != 1)
{
ts->printf(CvTS::CONSOLE, "bad channel count: %d\n", d_c.channels());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok) ok = cmp(a, Mat(d_c), rows * cols * 1e-5f);
if (!ok)
ts->printf(CvTS::CONSOLE, "testR2CThenC2R failed: hint=%s, cols=%d, rows=%d, inplace=%d\n",
hint.c_str(), cols, rows, inplace);
}
} CV_GpuDftTest_inst;

239
tests/gpu/src/features2d.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <string>
using namespace cv;
using namespace cv::gpu;
using namespace std;
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "aloe.png";
const string VALID_FILE_NAME = "surf.xml.gz";
class CV_GPU_SURFTest : public CvTest
{
public:
CV_GPU_SURFTest() :
CvTest( "GPU-SURF", "SURF_GPU")
{
}
protected:
bool isSimilarKeypoints(const KeyPoint& p1, const KeyPoint& p2);
void compareKeypointSets(const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints,
const Mat& validDescriptors, const Mat& calcDescriptors);
void emptyDataTest(SURF_GPU& fdetector);
void regressionTest(SURF_GPU& fdetector);
virtual void run(int);
};
void CV_GPU_SURFTest::emptyDataTest(SURF_GPU& fdetector)
{
GpuMat image;
vector<KeyPoint> keypoints;
vector<float> descriptors;
try
{
fdetector(image, GpuMat(), keypoints, descriptors);
}
catch(...)
{
ts->printf( CvTS::LOG, "detect() on empty image must not generate exception (1).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
}
if( !keypoints.empty() )
{
ts->printf( CvTS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
return;
}
if( !descriptors.empty() )
{
ts->printf( CvTS::LOG, "detect() on empty image must return empty descriptors vector (1).\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
return;
}
}
bool CV_GPU_SURFTest::isSimilarKeypoints(const KeyPoint& p1, const KeyPoint& p2)
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
float dist = (float)norm( p1.pt - p2.pt );
return (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id );
}
void CV_GPU_SURFTest::compareKeypointSets(const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints,
const Mat& validDescriptors, const Mat& calcDescriptors)
{
if (validKeypoints.size() != calcKeypoints.size())
{
ts->printf(CvTS::LOG, "Keypoints sizes doesn't equal (validCount = %d, calcCount = %d).\n",
validKeypoints.size(), calcKeypoints.size());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
if (validDescriptors.size() != calcDescriptors.size())
{
ts->printf(CvTS::LOG, "Descriptors sizes doesn't equal.\n");
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
for (size_t v = 0; v < validKeypoints.size(); v++)
{
int nearestIdx = -1;
float minDist = std::numeric_limits<float>::max();
for (size_t c = 0; c < calcKeypoints.size(); c++)
{
float curDist = (float)norm(calcKeypoints[c].pt - validKeypoints[v].pt);
if (curDist < minDist)
{
minDist = curDist;
nearestIdx = c;
}
}
assert(minDist >= 0);
if (!isSimilarKeypoints(validKeypoints[v], calcKeypoints[nearestIdx]))
{
ts->printf(CvTS::LOG, "Bad keypoints accuracy.\n");
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
return;
}
if (norm(validDescriptors.row(v), calcDescriptors.row(nearestIdx), NORM_L2) > 1.5f)
{
ts->printf(CvTS::LOG, "Bad descriptors accuracy.\n");
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
return;
}
}
}
void CV_GPU_SURFTest::regressionTest(SURF_GPU& fdetector)
{
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
string resFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + VALID_FILE_NAME;
// Read the test image.
GpuMat image(imread(imgFilename, 0));
if (image.empty())
{
ts->printf( CvTS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
FileStorage fs(resFilename, FileStorage::READ);
// Compute keypoints.
GpuMat mask(image.size(), CV_8UC1, Scalar::all(1));
mask(Range(0, image.rows / 2), Range(0, image.cols / 2)).setTo(Scalar::all(0));
vector<KeyPoint> calcKeypoints;
GpuMat calcDespcriptors;
fdetector(image, mask, calcKeypoints, calcDespcriptors);
if (fs.isOpened()) // Compare computed and valid keypoints.
{
// Read validation keypoints set.
vector<KeyPoint> validKeypoints;
Mat validDespcriptors;
read(fs["keypoints"], validKeypoints);
read(fs["descriptors"], validDespcriptors);
if (validKeypoints.empty() || validDespcriptors.empty())
{
ts->printf(CvTS::LOG, "Validation file can not be read.\n");
ts->set_failed_test_info(CvTS::FAIL_INVALID_TEST_DATA);
return;
}
compareKeypointSets(validKeypoints, calcKeypoints, validDespcriptors, calcDespcriptors);
}
else // Write detector parameters and computed keypoints as validation data.
{
fs.open(resFilename, FileStorage::WRITE);
if (!fs.isOpened())
{
ts->printf(CvTS::LOG, "File %s can not be opened to write.\n", resFilename.c_str());
ts->set_failed_test_info(CvTS::FAIL_INVALID_TEST_DATA);
return;
}
else
{
write(fs, "keypoints", calcKeypoints);
write(fs, "descriptors", (Mat)calcDespcriptors);
}
}
}
void CV_GPU_SURFTest::run( int /*start_from*/ )
{
try
{
SURF_GPU fdetector;
emptyDataTest(fdetector);
regressionTest(fdetector);
}
catch(cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
}
CV_GPU_SURFTest CV_GPU_SURF_test;

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <iostream>
#include <cmath>
#include <limits>
#include "gputest.hpp"
using namespace cv;
using namespace std;
using namespace gpu;
class CV_GpuNppFilterTest : public CvTest
{
public:
CV_GpuNppFilterTest(const char* test_name, const char* test_funcs) : CvTest(test_name, test_funcs) {}
virtual ~CV_GpuNppFilterTest() {}
protected:
void run(int);
virtual int test(const Mat& img) = 0;
int test8UC1(const Mat& img)
{
cv::Mat img_C1;
cvtColor(img, img_C1, CV_BGR2GRAY);
return test(img_C1);
}
int test8UC4(const Mat& img)
{
cv::Mat img_C4;
cvtColor(img, img_C4, CV_BGR2BGRA);
return test(img_C4);
}
int CheckNorm(const Mat& m1, const Mat& m2, const Size& ksize)
{
Rect roi = Rect(ksize.width, ksize.height, m1.cols - 2 * ksize.width, m1.rows - 2 * ksize.height);
Mat m1ROI = m1(roi);
Mat m2ROI = m2(roi);
double res = norm(m1ROI, m2ROI, NORM_INF);
// Max difference (2.0) in GaussianBlur
if (res <= 2)
return CvTS::OK;
ts->printf(CvTS::LOG, "Norm: %f\n", res);
return CvTS::FAIL_GENERIC;
}
};
void CV_GpuNppFilterTest::run( int )
{
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
try
{
//run tests
int testResult = CvTS::OK;
if (test8UC1(img) != CvTS::OK)
testResult = CvTS::FAIL_GENERIC;
if (test8UC4(img) != CvTS::OK)
testResult = CvTS::FAIL_GENERIC;
ts->set_failed_test_info(testResult);
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
////////////////////////////////////////////////////////////////////////////////
// blur
struct CV_GpuNppImageBlurTest : public CV_GpuNppFilterTest
{
CV_GpuNppImageBlurTest() : CV_GpuNppFilterTest( "GPU-NppImageBlur", "blur" ) {}
int test(const Mat& img)
{
int ksizes[] = {3, 5, 7};
int ksizes_num = sizeof(ksizes) / sizeof(int);
int test_res = CvTS::OK;
for (int i = 0; i < ksizes_num; ++i)
{
for (int j = 0; j < ksizes_num; ++j)
{
Size ksize(ksizes[i], ksizes[j]);
ts->printf(CvTS::LOG, "\nksize = (%dx%d)\n", ksizes[i], ksizes[j]);
Mat cpudst;
cv::blur(img, cpudst, ksize);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::blur(gpu1, gpudst, ksize);
if (CheckNorm(cpudst, gpudst, ksize) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// Sobel
struct CV_GpuNppImageSobelTest : public CV_GpuNppFilterTest
{
CV_GpuNppImageSobelTest() : CV_GpuNppFilterTest( "GPU-NppImageSobel", "Sobel" ) {}
int test(const Mat& img)
{
int ksizes[] = {3, 5, 7};
int ksizes_num = sizeof(ksizes) / sizeof(int);
int dx = 1, dy = 0;
int test_res = CvTS::OK;
for (int i = 0; i < ksizes_num; ++i)
{
ts->printf(CvTS::LOG, "\nksize = %d\n", ksizes[i]);
Mat cpudst;
cv::Sobel(img, cpudst, -1, dx, dy, ksizes[i]);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::Sobel(gpu1, gpudst, -1, dx, dy, ksizes[i]);
if (CheckNorm(cpudst, gpudst, Size(ksizes[i], ksizes[i])) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// Scharr
struct CV_GpuNppImageScharrTest : public CV_GpuNppFilterTest
{
CV_GpuNppImageScharrTest() : CV_GpuNppFilterTest( "GPU-NppImageScharr", "Scharr" ) {}
int test(const Mat& img)
{
int dx = 1, dy = 0;
Mat cpudst;
cv::Scharr(img, cpudst, -1, dx, dy);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::Scharr(gpu1, gpudst, -1, dx, dy);
return CheckNorm(cpudst, gpudst, Size(3, 3));
}
};
////////////////////////////////////////////////////////////////////////////////
// GaussianBlur
struct CV_GpuNppImageGaussianBlurTest : public CV_GpuNppFilterTest
{
CV_GpuNppImageGaussianBlurTest() : CV_GpuNppFilterTest( "GPU-NppImageGaussianBlur", "GaussianBlur" ) {}
int test(const Mat& img)
{
int ksizes[] = {3, 5, 7};
int ksizes_num = sizeof(ksizes) / sizeof(int);
int test_res = CvTS::OK;
const double sigma1 = 3.0;
for (int i = 0; i < ksizes_num; ++i)
{
for (int j = 0; j < ksizes_num; ++j)
{
cv::Size ksize(ksizes[i], ksizes[j]);
ts->printf(CvTS::LOG, "ksize = (%dx%d)\t\n", ksizes[i], ksizes[j]);
Mat cpudst;
cv::GaussianBlur(img, cpudst, ksize, sigma1);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::GaussianBlur(gpu1, gpudst, ksize, sigma1);
if (CheckNorm(cpudst, gpudst, ksize) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// Laplacian
struct CV_GpuNppImageLaplacianTest : public CV_GpuNppFilterTest
{
CV_GpuNppImageLaplacianTest() : CV_GpuNppFilterTest( "GPU-NppImageLaplacian", "Laplacian" ) {}
int test(const Mat& img)
{
int ksizes[] = {1, 3};
int ksizes_num = sizeof(ksizes) / sizeof(int);
int test_res = CvTS::OK;
for (int i = 0; i < ksizes_num; ++i)
{
ts->printf(CvTS::LOG, "\nksize = %d\n", ksizes[i]);
Mat cpudst;
cv::Laplacian(img, cpudst, -1, ksizes[i]);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::Laplacian(gpu1, gpudst, -1, ksizes[i]);
if (CheckNorm(cpudst, gpudst, Size(3, 3)) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// Erode
class CV_GpuErodeTest : public CV_GpuNppFilterTest
{
public:
CV_GpuErodeTest() : CV_GpuNppFilterTest( "GPU-NppErode", "erode" ) {}
protected:
virtual int test(const Mat& img)
{
Mat kernel(Mat::ones(3, 3, CV_8U));
cv::Mat cpuRes;
cv::erode(img, cpuRes, kernel);
GpuMat gpuRes;
cv::gpu::erode(GpuMat(img), gpuRes, kernel);
return CheckNorm(cpuRes, gpuRes, Size(3, 3));
}
};
////////////////////////////////////////////////////////////////////////////////
// Dilate
class CV_GpuDilateTest : public CV_GpuNppFilterTest
{
public:
CV_GpuDilateTest() : CV_GpuNppFilterTest( "GPU-NppDilate", "dilate" ) {}
protected:
virtual int test(const Mat& img)
{
Mat kernel(Mat::ones(3, 3, CV_8U));
cv::Mat cpuRes;
cv::dilate(img, cpuRes, kernel);
GpuMat gpuRes;
cv::gpu::dilate(GpuMat(img), gpuRes, kernel);
return CheckNorm(cpuRes, gpuRes, Size(3, 3));
}
};
////////////////////////////////////////////////////////////////////////////////
// MorphologyEx
class CV_GpuMorphExTest : public CV_GpuNppFilterTest
{
public:
CV_GpuMorphExTest() : CV_GpuNppFilterTest( "GPU-NppMorphologyEx", "morphologyEx" ) {}
protected:
virtual int test(const Mat& img)
{
static int ops[] = { MORPH_OPEN, CV_MOP_CLOSE, CV_MOP_GRADIENT, CV_MOP_TOPHAT, CV_MOP_BLACKHAT};
const char *names[] = { "MORPH_OPEN", "CV_MOP_CLOSE", "CV_MOP_GRADIENT", "CV_MOP_TOPHAT", "CV_MOP_BLACKHAT"};
int num = sizeof(ops)/sizeof(ops[0]);
GpuMat kernel(Mat::ones(3, 3, CV_8U));
int res = CvTS::OK;
for(int i = 0; i < num; ++i)
{
ts->printf(CvTS::LOG, "Tesing %s\n", names[i]);
cv::Mat cpuRes;
cv::morphologyEx(img, cpuRes, ops[i], kernel);
GpuMat gpuRes;
cv::gpu::morphologyEx(GpuMat(img), gpuRes, ops[i], kernel);
if (CvTS::OK != CheckNorm(cpuRes, gpuRes, Size(4, 4)))
res = CvTS::FAIL_GENERIC;
}
return res;
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuNppImageBlurTest CV_GpuNppImageBlur_test;
CV_GpuNppImageSobelTest CV_GpuNppImageSobel_test;
CV_GpuNppImageScharrTest CV_GpuNppImageScharr_test;
CV_GpuNppImageGaussianBlurTest CV_GpuNppImageGaussianBlur_test;
CV_GpuNppImageLaplacianTest CV_GpuNppImageLaplacian_test;
CV_GpuErodeTest CV_GpuErode_test;
CV_GpuDilateTest CV_GpuDilate_test;
CV_GpuMorphExTest CV_GpuMorphEx_test;

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef _GPU_TEST_H_
#define _GPU_TEST_H_
#if defined WIN32 || defined _WIN32
#include <windows.h>
#undef min
#undef max
#endif
#include <opencv2/gpu/gpu.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include "cxts.h"
/****************************************************************************************/
/* Warnings Disabling */
/****************************************************************************************/
#if _MSC_VER > 1000
#pragma warning(disable : 4514) /* unreferenced inline function has been */
/* removed */
#pragma warning(disable : 4127) /* conditional expression is constant */
/* for no warnings in _ASSERT */
#pragma warning(disable : 4996) /* deprecated function */
#endif
static inline bool check_and_treat_gpu_exception(const cv::Exception& e, CvTS* ts)
{
switch (e.code)
{
case CV_GpuNotSupported:
ts->printf(CvTS::LOG, "\nGpu not supported by the library");
break;
case CV_GpuApiCallError:
ts->printf(CvTS::LOG, "\nGPU Error: %s", e.what());
break;
case CV_GpuNppCallError:
ts->printf(CvTS::LOG, "\nNPP Error: %s", e.what());
break;
default:
return false;
}
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return true;
}
#endif
/* End of file. */

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tests/gpu/src/gputest_main.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
CvTS test_system("gpu");
//const char* blacklist[] =
//{
// "GPU-NVidia",
// 0
//};
int main( int argc, char** argv )
{
return test_system.run( argc, argv );
}
/* End of file. */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <fstream>
using namespace std;
//#define DUMP
#define CHECK(pred, err) if (!(pred)) { \
ts->printf(CvTS::CONSOLE, "Fail: \"%s\" at line: %d\n", #pred, __LINE__); \
ts->set_failed_test_info(err); \
return; }
struct CV_GpuHogDetectTestRunner: cv::gpu::HOGDescriptor
{
CV_GpuHogDetectTestRunner(CvTS* ts_): ts(ts_) {}
void run(int)
{
try
{
cv::Mat img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/road.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
#ifdef DUMP
f.open((std::string(ts->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
CHECK(f.is_open(), CvTS::FAIL_GENERIC);
#else
f.open((std::string(ts->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
CHECK(f.is_open(), CvTS::FAIL_MISSING_TEST_DATA);
#endif
// Test on color image
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
test(img);
// Test on gray image
cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
test(img);
f.close();
}
catch (const cv::Exception& e)
{
f.close();
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
#ifdef DUMP
void dump(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
{
f.write((char*)&block_hists.rows, sizeof(block_hists.rows));
f.write((char*)&block_hists.cols, sizeof(block_hists.cols));
for (int i = 0; i < block_hists.rows; ++i)
{
for (int j = 0; j < block_hists.cols; ++j)
{
float val = block_hists.at<float>(i, j);
f.write((char*)&val, sizeof(val));
}
}
size_t nlocations = locations.size();
f.write((char*)&nlocations, sizeof(nlocations));
for (size_t i = 0; i < locations.size(); ++i)
f.write((char*)&locations[i], sizeof(locations[i]));
}
#else
void compare(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
{
int rows, cols;
size_t nlocations;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
CHECK(rows == block_hists.rows, CvTS::FAIL_INVALID_OUTPUT);
CHECK(cols == block_hists.cols, CvTS::FAIL_INVALID_OUTPUT);
for (int i = 0; i < block_hists.rows; ++i)
{
for (int j = 0; j < block_hists.cols; ++j)
{
float val;
f.read((char*)&val, sizeof(val));
CHECK(fabs(val - block_hists.at<float>(i, j)) < 1e-3f, CvTS::FAIL_INVALID_OUTPUT);
}
}
f.read((char*)&nlocations, sizeof(nlocations));
CHECK(nlocations == locations.size(), CvTS::FAIL_INVALID_OUTPUT);
for (size_t i = 0; i < nlocations; ++i)
{
cv::Point location;
f.read((char*)&location, sizeof(location));
CHECK(location == locations[i], CvTS::FAIL_INVALID_OUTPUT);
}
}
#endif
void test(const cv::Mat& img)
{
cv::gpu::GpuMat d_img(img);
gamma_correction = false;
setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
//cpu detector may be updated soon
//hog.setSVMDetector(cv::HOGDescriptor::getDefaultPeopleDetector());
std::vector<cv::Point> locations;
// Test detect
detect(d_img, locations, 0);
#ifdef DUMP
dump(block_hists, locations);
#else
compare(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(cv::gpu::GpuMat(img2), locations, 0);
#ifdef DUMP
dump(block_hists, locations);
#else
compare(block_hists, locations);
#endif
// Test detect on greater image
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
detect(cv::gpu::GpuMat(img2), locations, 0);
#ifdef DUMP
dump(block_hists, locations);
#else
compare(block_hists, locations);
#endif
}
#ifdef DUMP
std::ofstream f;
#else
std::ifstream f;
#endif
CvTS* ts;
};
struct CV_GpuHogDetectTest: CvTest
{
CV_GpuHogDetectTest(): CvTest("GPU-HogDetectTest", "HOGDescriptor::detect") {}
void run(int i)
{
CV_GpuHogDetectTestRunner runner(ts);
runner.run(i);
}
} CV_GpuHogDetectTest_inst;
struct CV_GpuHogGetDescriptorsTestRunner: cv::gpu::HOGDescriptor
{
CV_GpuHogGetDescriptorsTestRunner(CvTS* ts_): HOGDescriptor(cv::Size(64, 128)), ts(ts_) {}
void run(int)
{
try
{
// Load image (e.g. train data, composed from windows)
cv::Mat img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/train_data.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
// 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);
CHECK(descriptors.size() == descr_size_expected, CvTS::FAIL_INVALID_OUTPUT);
// 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)
CHECK(l[(y * blocks_per_win_x + x) * block_hist_size + k] ==
r[(x * blocks_per_win_y + y) * block_hist_size + k], CvTS::FAIL_INVALID_OUTPUT);
}
/* 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 = cv::imread(std::string(ts->get_data_path()) + "hog/positive1.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
// Everything is fine with interpolation for left top subimage
CHECK(cv::norm(block_hists, descriptors.rowRange(0, 1)) == 0.f, CvTS::FAIL_INVALID_OUTPUT);
img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/positive2.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(1, 2));
img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/negative1.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(2, 3));
img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/negative2.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(3, 4));
img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/positive3.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(4, 5));
img_rgb = cv::imread(std::string(ts->get_data_path()) + "hog/negative3.png");
CHECK(!img_rgb.empty(), CvTS::FAIL_MISSING_TEST_DATA);
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(5, 6));
}
catch (const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
// 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);
CHECK(a == b, CvTS::FAIL_INVALID_OUTPUT)
}
}
int wins_per_img_x;
int wins_per_img_y;
int blocks_per_win_x;
int blocks_per_win_y;
int block_hist_size;
CvTS* ts;
};
struct CV_GpuHogGetDescriptorsTest: CvTest
{
CV_GpuHogGetDescriptorsTest(): CvTest("GPU-HogGetDescriptorsTest", "HOGDescriptor::getDescriptors") {}
void run(int i)
{
CV_GpuHogGetDescriptorsTestRunner runner(ts);
runner.run(i);
}
} CV_GpuHogGetDescriptorsTest_inst;

987
tests/gpu/src/imgproc_gpu.cpp
Unescape View File

@ -1,987 +0,0 @@
/*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 <iostream>
#include <cmath>
#include <limits>
#include "gputest.hpp"
using namespace cv;
using namespace std;
using namespace gpu;
class CV_GpuImageProcTest : public CvTest
{
public:
CV_GpuImageProcTest(const char* test_name, const char* test_funcs) : CvTest(test_name, test_funcs) {}
virtual ~CV_GpuImageProcTest() {}
protected:
void run(int);
int test8UC1 (const Mat& img);
int test8UC4 (const Mat& img);
int test32SC1(const Mat& img);
int test32FC1(const Mat& img);
virtual int test(const Mat& img) = 0;
int CheckNorm(const Mat& m1, const Mat& m2);
// Checks whether two images are similar enough using normalized
// cross-correlation as an error measure
int CheckSimilarity(const Mat& m1, const Mat& m2, float max_err=1e-3f);
};
int CV_GpuImageProcTest::test8UC1(const Mat& img)
{
cv::Mat img_C1;
cvtColor(img, img_C1, CV_BGR2GRAY);
return test(img_C1);
}
int CV_GpuImageProcTest::test8UC4(const Mat& img)
{
cv::Mat img_C4;
cvtColor(img, img_C4, CV_BGR2BGRA);
return test(img_C4);
}
int CV_GpuImageProcTest::test32SC1(const Mat& img)
{
cv::Mat img_C1;
cvtColor(img, img_C1, CV_BGR2GRAY);
img_C1.convertTo(img_C1, CV_32S);
return test(img_C1);
}
int CV_GpuImageProcTest::test32FC1(const Mat& img)
{
cv::Mat temp, img_C1;
img.convertTo(temp, CV_32F, 1.f / 255.f);
cvtColor(temp, img_C1, CV_BGR2GRAY);
return test(img_C1);
}
int CV_GpuImageProcTest::CheckNorm(const Mat& m1, const Mat& m2)
{
double ret = norm(m1, m2, NORM_INF);
if (ret < std::numeric_limits<double>::epsilon())
{
return CvTS::OK;
}
else
{
ts->printf(CvTS::LOG, "Norm: %f\n", ret);
return CvTS::FAIL_GENERIC;
}
}
int CV_GpuImageProcTest::CheckSimilarity(const Mat& m1, const Mat& m2, float max_err)
{
Mat diff;
cv::matchTemplate(m1, m2, diff, CV_TM_CCORR_NORMED);
float err = abs(diff.at<float>(0, 0) - 1.f);
if (err > max_err)
return CvTS::FAIL_INVALID_OUTPUT;
return CvTS::OK;
}
void CV_GpuImageProcTest::run( int )
{
//load image
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
int testResult = CvTS::OK;
try
{
//run tests
ts->printf(CvTS::LOG, "\n========Start test 8UC1========\n");
if (test8UC1(img) == CvTS::OK)
ts->printf(CvTS::LOG, "SUCCESS\n");
else
{
ts->printf(CvTS::LOG, "FAIL\n");
testResult = CvTS::FAIL_GENERIC;
}
ts->printf(CvTS::LOG, "\n========Start test 8UC4========\n");
if (test8UC4(img) == CvTS::OK)
ts->printf(CvTS::LOG, "SUCCESS\n");
else
{
ts->printf(CvTS::LOG, "FAIL\n");
testResult = CvTS::FAIL_GENERIC;
}
ts->printf(CvTS::LOG, "\n========Start test 32SC1========\n");
if (test32SC1(img) == CvTS::OK)
ts->printf(CvTS::LOG, "SUCCESS\n");
else
{
ts->printf(CvTS::LOG, "FAIL\n");
testResult = CvTS::FAIL_GENERIC;
}
ts->printf(CvTS::LOG, "\n========Start test 32FC1========\n");
if (test32FC1(img) == CvTS::OK)
ts->printf(CvTS::LOG, "SUCCESS\n");
else
{
ts->printf(CvTS::LOG, "FAIL\n");
testResult = CvTS::FAIL_GENERIC;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(testResult);
}
////////////////////////////////////////////////////////////////////////////////
// threshold
struct CV_GpuImageThresholdTest : public CV_GpuImageProcTest
{
public:
CV_GpuImageThresholdTest() : CV_GpuImageProcTest( "GPU-ImageThreshold", "threshold" ) {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1 && img.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
const double maxVal = img.type() == CV_8UC1 ? 255 : 1.0;
cv::RNG rng(*ts->get_rng());
int res = CvTS::OK;
for (int type = THRESH_BINARY; type <= THRESH_TOZERO_INV; ++type)
{
const double thresh = rng.uniform(0.0, maxVal);
cv::Mat cpuRes;
cv::threshold(img, cpuRes, thresh, maxVal, type);
GpuMat gpu1(img);
GpuMat gpuRes;
cv::gpu::threshold(gpu1, gpuRes, thresh, maxVal, type);
if (CheckNorm(cpuRes, gpuRes) != CvTS::OK)
res = CvTS::FAIL_GENERIC;
}
return res;
}
};
////////////////////////////////////////////////////////////////////////////////
// resize
struct CV_GpuNppImageResizeTest : public CV_GpuImageProcTest
{
CV_GpuNppImageResizeTest() : CV_GpuImageProcTest( "GPU-NppImageResize", "resize" ) {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1 && img.type() != CV_8UC4)
{
ts->printf(CvTS::LOG, "Unsupported type\n");
return CvTS::OK;
}
int interpolations[] = {INTER_NEAREST, INTER_LINEAR, /*INTER_CUBIC,*/ /*INTER_LANCZOS4*/};
const char* interpolations_str[] = {"INTER_NEAREST", "INTER_LINEAR", /*"INTER_CUBIC",*/ /*"INTER_LANCZOS4"*/};
int interpolations_num = sizeof(interpolations) / sizeof(int);
int test_res = CvTS::OK;
for (int i = 0; i < interpolations_num; ++i)
{
ts->printf(CvTS::LOG, "Interpolation: %s\n", interpolations_str[i]);
Mat cpu_res1, cpu_res2;
cv::resize(img, cpu_res1, Size(), 2.0, 2.0, interpolations[i]);
cv::resize(cpu_res1, cpu_res2, Size(), 0.5, 0.5, interpolations[i]);
GpuMat gpu1(img), gpu_res1, gpu_res2;
cv::gpu::resize(gpu1, gpu_res1, Size(), 2.0, 2.0, interpolations[i]);
cv::gpu::resize(gpu_res1, gpu_res2, Size(), 0.5, 0.5, interpolations[i]);
if (CheckSimilarity(cpu_res2, gpu_res2) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// copyMakeBorder
struct CV_GpuNppImageCopyMakeBorderTest : public CV_GpuImageProcTest
{
CV_GpuNppImageCopyMakeBorderTest() : CV_GpuImageProcTest( "GPU-NppImageCopyMakeBorder", "copyMakeBorder" ) {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1 && img.type() != CV_8UC4 && img.type() != CV_32SC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
cv::RNG rng(*ts->get_rng());
int top = rng.uniform(1, 10);
int botton = rng.uniform(1, 10);
int left = rng.uniform(1, 10);
int right = rng.uniform(1, 10);
cv::Scalar val(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
Mat cpudst;
cv::copyMakeBorder(img, cpudst, top, botton, left, right, BORDER_CONSTANT, val);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::copyMakeBorder(gpu1, gpudst, top, botton, left, right, val);
return CheckNorm(cpudst, gpudst);
}
};
////////////////////////////////////////////////////////////////////////////////
// warpAffine
struct CV_GpuNppImageWarpAffineTest : public CV_GpuImageProcTest
{
CV_GpuNppImageWarpAffineTest() : CV_GpuImageProcTest( "GPU-NppImageWarpAffine", "warpAffine" ) {}
int test(const Mat& img)
{
if (img.type() == CV_32SC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
static double reflect[2][3] = { {-1, 0, 0},
{ 0, -1, 0} };
reflect[0][2] = img.cols;
reflect[1][2] = img.rows;
Mat M(2, 3, CV_64F, (void*)reflect);
int flags[] = {INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_NEAREST | WARP_INVERSE_MAP, INTER_LINEAR | WARP_INVERSE_MAP, INTER_CUBIC | WARP_INVERSE_MAP};
const char* flags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
int flags_num = sizeof(flags) / sizeof(int);
int test_res = CvTS::OK;
for (int i = 0; i < flags_num; ++i)
{
ts->printf(CvTS::LOG, "\nFlags: %s\n", flags_str[i]);
Mat cpudst;
cv::warpAffine(img, cpudst, M, img.size(), flags[i]);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::warpAffine(gpu1, gpudst, M, gpu1.size(), flags[i]);
// Check inner parts (ignoring 1 pixel width border)
if (CheckSimilarity(cpudst.rowRange(1, cpudst.rows - 1).colRange(1, cpudst.cols - 1),
gpudst.rowRange(1, gpudst.rows - 1).colRange(1, gpudst.cols - 1)) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// warpPerspective
struct CV_GpuNppImageWarpPerspectiveTest : public CV_GpuImageProcTest
{
CV_GpuNppImageWarpPerspectiveTest() : CV_GpuImageProcTest( "GPU-NppImageWarpPerspective", "warpPerspective" ) {}
int test(const Mat& img)
{
if (img.type() == CV_32SC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
static double reflect[3][3] = { { -1, 0, 0},
{ 0, -1, 0},
{ 0, 0, 1 }};
reflect[0][2] = img.cols;
reflect[1][2] = img.rows;
Mat M(3, 3, CV_64F, (void*)reflect);
int flags[] = {INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_NEAREST | WARP_INVERSE_MAP, INTER_LINEAR | WARP_INVERSE_MAP, INTER_CUBIC | WARP_INVERSE_MAP};
const char* flags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
int flags_num = sizeof(flags) / sizeof(int);
int test_res = CvTS::OK;
for (int i = 0; i < flags_num; ++i)
{
ts->printf(CvTS::LOG, "\nFlags: %s\n", flags_str[i]);
Mat cpudst;
cv::warpPerspective(img, cpudst, M, img.size(), flags[i]);
GpuMat gpu1(img);
GpuMat gpudst;
cv::gpu::warpPerspective(gpu1, gpudst, M, gpu1.size(), flags[i]);
// Check inner parts (ignoring 1 pixel width border)
if (CheckSimilarity(cpudst.rowRange(1, cpudst.rows - 1).colRange(1, cpudst.cols - 1),
gpudst.rowRange(1, gpudst.rows - 1).colRange(1, gpudst.cols - 1)) != CvTS::OK)
test_res = CvTS::FAIL_GENERIC;
}
return test_res;
}
};
////////////////////////////////////////////////////////////////////////////////
// integral
struct CV_GpuNppImageIntegralTest : public CV_GpuImageProcTest
{
CV_GpuNppImageIntegralTest() : CV_GpuImageProcTest( "GPU-NppImageIntegral", "integral" ) {}
int test(const Mat& img)
{
if (img.type() != CV_8UC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
Mat cpusum;
cv::integral(img, cpusum, CV_32S);
GpuMat gpu1(img);
GpuMat gpusum;
cv::gpu::integral(gpu1, gpusum);
return CheckNorm(cpusum, gpusum) == CvTS::OK ? CvTS::OK : CvTS::FAIL_GENERIC;
}
};
////////////////////////////////////////////////////////////////////////////////
// Canny
//struct CV_GpuNppImageCannyTest : public CV_GpuImageProcTest
//{
// CV_GpuNppImageCannyTest() : CV_GpuImageProcTest( "GPU-NppImageCanny", "Canny" ) {}
//
// int test(const Mat& img)
// {
// if (img.type() != CV_8UC1)
// {
// ts->printf(CvTS::LOG, "\nUnsupported type\n");
// return CvTS::OK;
// }
//
// const double threshold1 = 1.0, threshold2 = 10.0;
//
// Mat cpudst;
// cv::Canny(img, cpudst, threshold1, threshold2);
//
// GpuMat gpu1(img);
// GpuMat gpudst;
// cv::gpu::Canny(gpu1, gpudst, threshold1, threshold2);
//
// return CheckNorm(cpudst, gpudst);
// }
//};
////////////////////////////////////////////////////////////////////////////////
// cvtColor
class CV_GpuCvtColorTest : public CvTest
{
public:
CV_GpuCvtColorTest() : CvTest("GPU-CvtColor", "cvtColor") {}
~CV_GpuCvtColorTest() {};
protected:
void run(int);
int CheckNorm(const Mat& m1, const Mat& m2);
};
int CV_GpuCvtColorTest::CheckNorm(const Mat& m1, const Mat& m2)
{
double ret = norm(m1, m2, NORM_INF);
if (ret <= 3)
{
return CvTS::OK;
}
else
{
ts->printf(CvTS::LOG, "\nNorm: %f\n", ret);
return CvTS::FAIL_GENERIC;
}
}
void CV_GpuCvtColorTest::run( int )
{
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
int testResult = CvTS::OK;
cv::Mat cpuRes;
cv::gpu::GpuMat gpuImg(img), gpuRes;
try
{
int codes[] = { CV_BGR2RGB, CV_RGB2BGRA, CV_BGRA2RGB,
CV_RGB2BGR555, CV_BGR5552BGR, CV_BGR2BGR565, CV_BGR5652RGB,
CV_RGB2YCrCb, CV_YCrCb2BGR, CV_BGR2YUV, CV_YUV2RGB,
CV_RGB2XYZ, CV_XYZ2BGR, CV_BGR2XYZ, CV_XYZ2RGB,
CV_RGB2HSV, CV_HSV2BGR, CV_BGR2HSV_FULL, CV_HSV2RGB_FULL,
CV_RGB2HLS, CV_HLS2BGR, CV_BGR2HLS_FULL, CV_HLS2RGB_FULL,
CV_RGB2GRAY, CV_GRAY2BGRA, CV_BGRA2GRAY,
CV_GRAY2BGR555, CV_BGR5552GRAY, CV_GRAY2BGR565, CV_BGR5652GRAY};
const char* codes_str[] = { "CV_BGR2RGB", "CV_RGB2BGRA", "CV_BGRA2RGB",
"CV_RGB2BGR555", "CV_BGR5552BGR", "CV_BGR2BGR565", "CV_BGR5652RGB",
"CV_RGB2YCrCb", "CV_YCrCb2BGR", "CV_BGR2YUV", "CV_YUV2RGB",
"CV_RGB2XYZ", "CV_XYZ2BGR", "CV_BGR2XYZ", "CV_XYZ2RGB",
"CV_RGB2HSV", "CV_HSV2RGB", "CV_BGR2HSV_FULL", "CV_HSV2RGB_FULL",
"CV_RGB2HLS", "CV_HLS2RGB", "CV_BGR2HLS_FULL", "CV_HLS2RGB_FULL",
"CV_RGB2GRAY", "CV_GRAY2BGRA", "CV_BGRA2GRAY",
"CV_GRAY2BGR555", "CV_BGR5552GRAY", "CV_GRAY2BGR565", "CV_BGR5652GRAY"};
int codes_num = sizeof(codes) / sizeof(int);
for (int i = 0; i < codes_num; ++i)
{
ts->printf(CvTS::LOG, "\n%s\n", codes_str[i]);
cv::cvtColor(img, cpuRes, codes[i]);
cv::gpu::cvtColor(gpuImg, gpuRes, codes[i]);
if (CheckNorm(cpuRes, gpuRes) == CvTS::OK)
ts->printf(CvTS::LOG, "\nSUCCESS\n");
else
{
ts->printf(CvTS::LOG, "\nFAIL\n");
testResult = CvTS::FAIL_GENERIC;
}
img = cpuRes;
gpuImg = gpuRes;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(testResult);
}
////////////////////////////////////////////////////////////////////////////////
// Histograms
class CV_GpuHistogramsTest : public CvTest
{
public:
CV_GpuHistogramsTest() : CvTest("GPU-Histograms", "histEven") {}
~CV_GpuHistogramsTest() {};
protected:
void run(int);
int CheckNorm(const Mat& m1, const Mat& m2)
{
double ret = norm(m1, m2, NORM_INF);
if (ret < std::numeric_limits<double>::epsilon())
{
return CvTS::OK;
}
else
{
ts->printf(CvTS::LOG, "\nNorm: %f\n", ret);
return CvTS::FAIL_GENERIC;
}
}
};
void CV_GpuHistogramsTest::run( int )
{
//load image
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
if (img.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
try
{
Mat hsv;
cv::cvtColor(img, hsv, CV_BGR2HSV);
int hbins = 30;
int histSize[] = {hbins};
float hranges[] = {0, 180};
const float* ranges[] = {hranges};
MatND hist;
int channels[] = {0};
calcHist(&hsv, 1, channels, Mat(), hist, 1, histSize, ranges);
GpuMat gpuHsv(hsv);
std::vector<GpuMat> srcs;
cv::gpu::split(gpuHsv, srcs);
GpuMat gpuHist;
histEven(srcs[0], gpuHist, hbins, (int)hranges[0], (int)hranges[1]);
Mat cpuHist = hist;
cpuHist = cpuHist.t();
cpuHist.convertTo(cpuHist, CV_32S);
ts->set_failed_test_info(CheckNorm(cpuHist, gpuHist));
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
}
////////////////////////////////////////////////////////////////////////
// Corner Harris feature detector
struct CV_GpuCornerHarrisTest: CvTest
{
CV_GpuCornerHarrisTest(): CvTest("GPU-CornerHarrisTest", "cornerHarris") {}
void run(int)
{
try
{
for (int i = 0; i < 5; ++i)
{
int rows = 25 + rand() % 300, cols = 25 + rand() % 300;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, -1)) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, -1)) return;
}
}
catch (const Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
bool compareToCpuTest(int rows, int cols, int depth, int blockSize, int apertureSize)
{
RNG rng;
cv::Mat src(rows, cols, depth);
if (depth == CV_32F)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(1));
else if (depth == CV_8U)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(256));
double k = 0.1;
cv::Mat dst_gold;
cv::gpu::GpuMat dst;
cv::Mat dsth;
int borderType;
borderType = BORDER_REFLECT101;
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, k, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-3f)
{
ts->printf(CvTS::CONSOLE, "%d %d %f %f %d\n", i, j, a, b, apertureSize);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
borderType = BORDER_REPLICATE;
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, k, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-3f)
{
ts->printf(CvTS::CONSOLE, "%d %d %f %f %d\n", i, j, a, b, apertureSize);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
return true;
}
};
////////////////////////////////////////////////////////////////////////
// Corner Min Eigen Val
struct CV_GpuCornerMinEigenValTest: CvTest
{
CV_GpuCornerMinEigenValTest(): CvTest("GPU-CornerMinEigenValTest", "cornerMinEigenVal") {}
void run(int)
{
try
{
for (int i = 0; i < 3; ++i)
{
int rows = 25 + rand() % 300, cols = 25 + rand() % 300;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, -1)) return;
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, -1)) return;
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
}
}
catch (const Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
bool compareToCpuTest(int rows, int cols, int depth, int blockSize, int apertureSize)
{
RNG rng;
cv::Mat src(rows, cols, depth);
if (depth == CV_32F)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(1));
else if (depth == CV_8U)
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(256));
cv::Mat dst_gold;
cv::gpu::GpuMat dst;
cv::Mat dsth;
int borderType;
borderType = BORDER_REFLECT101;
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-2f)
{
ts->printf(CvTS::CONSOLE, "%d %d %f %f %d %d\n", i, j, a, b, apertureSize, blockSize);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
borderType = BORDER_REPLICATE;
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, borderType);
dsth = dst;
for (int i = 0; i < dst.rows; ++i)
{
for (int j = 0; j < dst.cols; ++j)
{
float a = dst_gold.at<float>(i, j);
float b = dsth.at<float>(i, j);
if (fabs(a - b) > 1e-2f)
{
ts->printf(CvTS::CONSOLE, "%d %d %f %f %d %d\n", i, j, a, b, apertureSize, blockSize);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
};
}
}
return true;
}
};
struct CV_GpuColumnSumTest: CvTest
{
CV_GpuColumnSumTest(): CvTest("GPU-ColumnSumTest", "columnSum") {}
void run(int)
{
try
{
int cols = 375;
int rows = 1072;
Mat src(rows, cols, CV_32F);
RNG rng(1);
rng.fill(src, RNG::UNIFORM, Scalar(0), Scalar(1));
GpuMat d_dst;
columnSum(GpuMat(src), d_dst);
Mat dst = d_dst;
for (int j = 0; j < src.cols; ++j)
{
float a = src.at<float>(0, j);
float b = dst.at<float>(0, j);
if (fabs(a - b) > 0.5f)
{
ts->printf(CvTS::CONSOLE, "big diff at %d %d: %f %f\n", 0, j, a, b);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
for (int i = 1; i < src.rows; ++i)
{
for (int j = 0; j < src.cols; ++j)
{
float a = src.at<float>(i, j) += src.at<float>(i - 1, j);
float b = dst.at<float>(i, j);
if (fabs(a - b) > 0.5f)
{
ts->printf(CvTS::CONSOLE, "big diff at %d %d: %f %f\n", i, j, a, b);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
catch (const Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
};
struct CV_GpuNormTest : CvTest
{
CV_GpuNormTest() : CvTest("GPU-Norm", "norm") {}
void run(int)
{
try
{
RNG rng(0);
int rows = rng.uniform(1, 500);
int cols = rng.uniform(1, 500);
for (int cn = 1; cn <= 4; ++cn)
{
test(NORM_L1, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L1, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L1, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L1, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L1, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L1, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
test(NORM_L2, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L2, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L2, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_L2, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L2, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_L2, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
test(NORM_INF, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_INF, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_INF, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
test(NORM_INF, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_INF, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
test(NORM_INF, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
}
}
catch (const cv::Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
void gen(Mat& mat, int rows, int cols, int type, Scalar low, Scalar high)
{
mat.create(rows, cols, type);
RNG rng(0);
rng.fill(mat, RNG::UNIFORM, low, high);
}
void test(int norm_type, int rows, int cols, int depth, int cn, Scalar low, Scalar high)
{
int type = CV_MAKE_TYPE(depth, cn);
Mat src;
gen(src, rows, cols, type, low, high);
double gold = norm(src, norm_type);
double mine = norm(GpuMat(src), norm_type);
if (abs(gold - mine) > 1e-3)
{
ts->printf(CvTS::CONSOLE, "failed test: gold=%f, mine=%f, norm_type=%d, rows=%d, "
"cols=%d, depth=%d, cn=%d\n", gold, mine, norm_type, rows, cols, depth, cn);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
}
}
};
////////////////////////////////////////////////////////////////////////////////
// reprojectImageTo3D
class CV_GpuReprojectImageTo3DTest : public CvTest
{
public:
CV_GpuReprojectImageTo3DTest() : CvTest("GPU-ReprojectImageTo3D", "reprojectImageTo3D") {}
protected:
void run(int)
{
Mat disp(320, 240, CV_8UC1);
RNG rng(*ts->get_rng());
rng.fill(disp, RNG::UNIFORM, Scalar(5), Scalar(30));
Mat Q(4, 4, CV_32FC1);
rng.fill(Q, RNG::UNIFORM, Scalar(0.1), Scalar(1));
Mat cpures;
GpuMat gpures;
reprojectImageTo3D(disp, cpures, Q, false);
reprojectImageTo3D(GpuMat(disp), gpures, Q);
Mat temp = gpures;
for (int y = 0; y < cpures.rows; ++y)
{
const Vec3f* cpu_row = cpures.ptr<Vec3f>(y);
const Vec4f* gpu_row = temp.ptr<Vec4f>(y);
for (int x = 0; x < cpures.cols; ++x)
{
Vec3f a = cpu_row[x];
Vec4f b = gpu_row[x];
if (fabs(a[0] - b[0]) > 1e-5 || fabs(a[1] - b[1]) > 1e-5 || fabs(a[2] - b[2]) > 1e-5)
{
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
// If we comment some tests, we may foget/miss to uncomment it after.
// Placing all test definitions in one place
// makes us know about what tests are commented.
CV_GpuImageThresholdTest CV_GpuImageThreshold_test;
CV_GpuNppImageResizeTest CV_GpuNppImageResize_test;
CV_GpuNppImageCopyMakeBorderTest CV_GpuNppImageCopyMakeBorder_test;
CV_GpuNppImageWarpAffineTest CV_GpuNppImageWarpAffine_test;
CV_GpuNppImageWarpPerspectiveTest CV_GpuNppImageWarpPerspective_test;
CV_GpuNppImageIntegralTest CV_GpuNppImageIntegral_test;
//CV_GpuNppImageCannyTest CV_GpuNppImageCanny_test;
CV_GpuCvtColorTest CV_GpuCvtColor_test;
CV_GpuHistogramsTest CV_GpuHistograms_test;
CV_GpuCornerHarrisTest CV_GpuCornerHarris_test;
CV_GpuCornerMinEigenValTest CV_GpuCornerMinEigenVal_test;
CV_GpuColumnSumTest CV_GpuColumnSum_test;
CV_GpuNormTest CV_GpuNormTest_test;
CV_GpuReprojectImageTo3DTest CV_GpuReprojectImageTo3D_test;

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tests/gpu/src/match_template.cpp
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@ -1,310 +0,0 @@
/*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 "gputest.hpp"
#include <string>
#include <iostream>
//#define SHOW_TIME
#ifdef SHOW_TIME
#include <ctime>
#define F(x) x
#else
#define F(x)
#endif
using namespace cv;
using namespace std;
struct CV_GpuMatchTemplateTest: CvTest
{
CV_GpuMatchTemplateTest(): CvTest("GPU-MatchTemplateTest", "matchTemplate") {}
void run(int)
{
try
{
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
if (!double_ok)
{
// For sqrIntegral
ts->printf(CvTS::CONSOLE, "\nCode and device double support is required (CC >= 1.3)");
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
Mat image, templ;
Mat dst_gold;
gpu::GpuMat dst;
int n, m, h, w;
F(clock_t t;)
RNG rng(*ts->get_rng());
for (int cn = 1; cn <= 4; ++cn)
{
F(ts->printf(CvTS::CONSOLE, "cn: %d\n", cn);)
for (int i = 0; i <= 0; ++i)
{
n = rng.uniform(30, 100);
m = rng.uniform(30, 100);
h = rng.uniform(5, n - 1);
w = rng.uniform(5, m - 1);
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF);
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), 5 * h * w * 1e-4f, "SQDIFF 8U")) return;
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF_NORMED);
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF_NORMED);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), h * w * 1e-5f, "SQDIFF_NOREMD 8U")) return;
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_CCORR);
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), 5 * h * w * cn * cn * 1e-5f, "CCORR 8U")) return;
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_CCORR_NORMED);
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR_NORMED);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), h * w * 1e-6f, "CCORR_NORMED 8U")) return;
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_CCOEFF);
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), 5 * h * w * cn * cn * 1e-5f, "CCOEFF 8U")) return;
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_CCOEFF_NORMED);
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF_NORMED);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), h * w * 1e-6f, "CCOEFF_NORMED 8U")) return;
gen(image, n, m, CV_32F, cn);
gen(templ, h, w, CV_32F, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF);
F(cout << "depth: 32F cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), 0.25f * h * w * 1e-5f, "SQDIFF 32F")) return;
gen(image, n, m, CV_32F, cn);
gen(templ, h, w, CV_32F, cn);
F(t = clock();)
matchTemplate(image, templ, dst_gold, CV_TM_CCORR);
F(cout << "depth: 32F cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
F(cout << "cpu:" << clock() - t << endl;)
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), 0.25f * h * w * 1e-5f, "CCORR 32F")) return;
}
}
}
catch (const Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
void gen(Mat& a, int rows, int cols, int depth, int cn)
{
RNG rng;
a.create(rows, cols, CV_MAKETYPE(depth, cn));
if (depth == CV_8U)
rng.fill(a, RNG::UNIFORM, Scalar::all(1), Scalar::all(10));
else if (depth == CV_32F)
rng.fill(a, RNG::UNIFORM, Scalar::all(0.001f), Scalar::all(1.f));
}
bool check(const Mat& a, const Mat& b, float max_err, const string& method="")
{
if (a.size() != b.size())
{
ts->printf(CvTS::CONSOLE, "bad size, method=%s\n", method.c_str());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
//for (int i = 0; i < a.rows; ++i)
//{
// for (int j = 0; j < a.cols; ++j)
// {
// float a_ = a.at<float>(i, j);
// float b_ = b.at<float>(i, j);
// if (fabs(a_ - b_) > max_err)
// {
// ts->printf(CvTS::CONSOLE, "a=%f, b=%f, i=%d, j=%d\n", a_, b_, i, j);
// cin.get();
// }
// }
//}
float err = (float)norm(a, b, NORM_INF);
if (err > max_err)
{
ts->printf(CvTS::CONSOLE, "bad accuracy: %f, method=%s\n", err, method.c_str());
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return false;
}
return true;
}
} match_template_test;
struct CV_GpuMatchTemplateFindPatternInBlackTest: CvTest
{
CV_GpuMatchTemplateFindPatternInBlackTest()
: CvTest("GPU-MatchTemplateFindPatternInBlackTest", "matchTemplate") {}
void run(int)
{
try
{
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
if (!double_ok)
{
// For sqrIntegral
ts->printf(CvTS::CONSOLE, "\nCode and device double support is required (CC >= 1.3)");
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
Mat image = imread(std::string(ts->get_data_path()) + "matchtemplate/black.png");
if (image.empty())
{
ts->printf(CvTS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path())
+ "matchtemplate/black.png").c_str());
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
Mat pattern = imread(std::string(ts->get_data_path()) + "matchtemplate/cat.png");
if (pattern.empty())
{
ts->printf(CvTS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path())
+ "matchtemplate/cat.png").c_str());
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
gpu::GpuMat d_image(image);
gpu::GpuMat d_pattern(pattern);
gpu::GpuMat d_result;
double maxValue;
Point maxLoc;
Point maxLocGold(284, 12);
gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCOEFF_NORMED);
gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc );
if (maxLoc != maxLocGold)
{
ts->printf(CvTS::CONSOLE, "bad match (CV_TM_CCOEFF_NORMED): %d %d, must be at: %d %d",
maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCORR_NORMED);
gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc );
if (maxLoc != maxLocGold)
{
ts->printf(CvTS::CONSOLE, "bad match (CV_TM_CCORR_NORMED): %d %d, must be at: %d %d",
maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
catch (const Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
} match_templet_find_bordered_pattern_test;

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tests/gpu/src/meanshift.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <iostream>
#include <string>
using namespace cv;
using namespace cv::gpu;
struct CV_GpuMeanShiftTest : public CvTest
{
CV_GpuMeanShiftTest(): CvTest( "GPU-MeanShift", "MeanShift" ){}
void run(int)
{
bool cc12_ok = TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
if (!cc12_ok)
{
ts->printf(CvTS::CONSOLE, "\nCompute capability 1.2 is required");
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
int spatialRad = 30;
int colorRad = 30;
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "meanshift/cones.png");
cv::Mat img_template;
if (cv::gpu::TargetArchs::builtWith(cv::gpu::FEATURE_SET_COMPUTE_20) &&
cv::gpu::DeviceInfo().supports(cv::gpu::FEATURE_SET_COMPUTE_20))
img_template = cv::imread(std::string(ts->get_data_path()) + "meanshift/con_result.png");
else
img_template = cv::imread(std::string(ts->get_data_path()) + "meanshift/con_result_CC1X.png");
if (img.empty() || img_template.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
cv::Mat rgba;
cvtColor(img, rgba, CV_BGR2BGRA);
try
{
cv::gpu::GpuMat res;
cv::gpu::meanShiftFiltering( cv::gpu::GpuMat(rgba), res, spatialRad, colorRad );
if (res.type() != CV_8UC4)
{
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
cv::Mat result;
res.download(result);
uchar maxDiff = 0;
for (int j = 0; j < result.rows; ++j)
{
const uchar* res_line = result.ptr<uchar>(j);
const uchar* ref_line = img_template.ptr<uchar>(j);
for (int i = 0; i < result.cols; ++i)
{
for (int k = 0; k < 3; ++k)
{
const uchar& ch1 = res_line[result.channels()*i + k];
const uchar& ch2 = ref_line[img_template.channels()*i + k];
uchar diff = static_cast<uchar>(abs(ch1 - ch2));
if (maxDiff < diff)
maxDiff = diff;
}
}
}
if (maxDiff > 0)
{
ts->printf(CvTS::LOG, "\nMeanShift maxDiff = %d\n", maxDiff);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuMeanShiftTest CV_GpuMeanShift_test;
struct CV_GpuMeanShiftProcTest : public CvTest
{
CV_GpuMeanShiftProcTest(): CvTest( "GPU-MeanShiftProc", "MeanShiftProc" ){}
void run(int)
{
bool cc12_ok = TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
if (!cc12_ok)
{
ts->printf(CvTS::CONSOLE, "\nCompute capability 1.2 is required");
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
int spatialRad = 30;
int colorRad = 30;
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "meanshift/cones.png");
if (img.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
cv::Mat rgba;
cvtColor(img, rgba, CV_BGR2BGRA);
try
{
cv::gpu::GpuMat h_rmap_filtered;
cv::gpu::meanShiftFiltering( cv::gpu::GpuMat(rgba), h_rmap_filtered, spatialRad, colorRad );
cv::gpu::GpuMat d_rmap;
cv::gpu::GpuMat d_spmap;
cv::gpu::meanShiftProc( cv::gpu::GpuMat(rgba), d_rmap, d_spmap, spatialRad, colorRad );
if (d_rmap.type() != CV_8UC4)
{
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
cv::Mat rmap_filtered;
h_rmap_filtered.download(rmap_filtered);
cv::Mat rmap;
d_rmap.download(rmap);
uchar maxDiff = 0;
for (int j = 0; j < rmap_filtered.rows; ++j)
{
const uchar* res_line = rmap_filtered.ptr<uchar>(j);
const uchar* ref_line = rmap.ptr<uchar>(j);
for (int i = 0; i < rmap_filtered.cols; ++i)
{
for (int k = 0; k < 3; ++k)
{
const uchar& ch1 = res_line[rmap_filtered.channels()*i + k];
const uchar& ch2 = ref_line[rmap.channels()*i + k];
uchar diff = static_cast<uchar>(abs(ch1 - ch2));
if (maxDiff < diff)
maxDiff = diff;
}
}
}
if (maxDiff > 0)
{
ts->printf(CvTS::LOG, "\nMeanShiftProc maxDiff = %d\n", maxDiff);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
cv::Mat spmap;
d_spmap.download(spmap);
cv::Mat spmap_template;
cv::FileStorage fs;
if (cv::gpu::TargetArchs::builtWith(cv::gpu::FEATURE_SET_COMPUTE_20) &&
cv::gpu::DeviceInfo().supports(cv::gpu::FEATURE_SET_COMPUTE_20))
fs.open(std::string(ts->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ);
else
fs.open(std::string(ts->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ);
fs["spmap"] >> spmap_template;
for (int y = 0; y < spmap.rows; ++y) {
for (int x = 0; x < spmap.cols; ++x) {
cv::Point_<short> expected = spmap_template.at<cv::Point_<short> >(y, x);
cv::Point_<short> actual = spmap.at<cv::Point_<short> >(y, x);
int diff = (expected - actual).dot(expected - actual);
if (actual != expected) {
ts->printf(CvTS::LOG, "\nMeanShiftProc SpMap is bad, diff=%d\n", diff);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
}
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
};
CV_GpuMeanShiftProcTest CV_GpuMeanShiftProc_test;

128
tests/gpu/src/mssegmentation.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <iostream>
#include <string>
#include <iosfwd>
#include "gputest.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
struct CV_GpuMeanShiftSegmentationTest : public CvTest {
CV_GpuMeanShiftSegmentationTest() : CvTest( "GPU-MeanShiftSegmentation", "MeanShiftSegmentation" ) {}
void run(int)
{
try
{
bool cc12_ok = TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
if (!cc12_ok)
{
ts->printf(CvTS::CONSOLE, "\nCompute capability 1.2 is required");
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
Mat img_rgb = imread(string(ts->get_data_path()) + "meanshift/cones.png");
if (img_rgb.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
Mat img;
cvtColor(img_rgb, img, CV_BGR2BGRA);
for (int minsize = 0; minsize < 2000; minsize = (minsize + 1) * 4)
{
stringstream path;
path << ts->get_data_path() << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
if (TargetArchs::builtWith(FEATURE_SET_COMPUTE_20) && DeviceInfo().supports(FEATURE_SET_COMPUTE_20))
path << ".png";
else
path << "_CC1X.png";
Mat dst;
meanShiftSegmentation((GpuMat)img, dst, 10, 10, minsize);
Mat dst_rgb;
cvtColor(dst, dst_rgb, CV_BGRA2BGR);
//imwrite(path.str(), dst_rgb);
Mat dst_ref = imread(path.str());
if (dst_ref.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
if (CheckSimilarity(dst_rgb, dst_ref, 1e-3f) != CvTS::OK)
{
ts->printf(CvTS::LOG, "\ndiffers from image *minsize%d.png\n", minsize);
ts->set_failed_test_info(CvTS::FAIL_BAD_ACCURACY);
}
}
}
catch (const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
int CheckSimilarity(const Mat& m1, const Mat& m2, float max_err)
{
Mat diff;
cv::matchTemplate(m1, m2, diff, CV_TM_CCORR_NORMED);
float err = abs(diff.at<float>(0, 0) - 1.f);
if (err > max_err)
return CvTS::FAIL_INVALID_OUTPUT;
return CvTS::OK;
}
} ms_segm_test;

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tests/gpu/src/nvidia/NCVAutoTestLister.hpp
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/*
* 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"
class NCVAutoTestLister
{
public:
NCVAutoTestLister(std::string testSuiteName, NcvBool bStopOnFirstFail=false, NcvBool bCompactOutput=true)
:
testSuiteName(testSuiteName),
bStopOnFirstFail(bStopOnFirstFail),
bCompactOutput(bCompactOutput)
{
}
void add(INCVTest *test)
{
this->tests.push_back(test);
}
bool invoke()
{
Ncv32u nPassed = 0;
Ncv32u nFailed = 0;
Ncv32u nFailedMem = 0;
if (bCompactOutput)
{
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 (!bCompactOutput)
{
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 (bCompactOutput)
{
printf(".");
}
}
else
{
if (!curReport.statsText["rcode"].compare("FAILED"))
{
nFailed++;
if (bCompactOutput)
{
printf("x");
}
if (bStopOnFirstFail)
{
break;
}
}
else
{
nFailedMem++;
if (bCompactOutput)
{
printf("m");
}
}
}
fflush(stdout);
}
if (bCompactOutput)
{
printf("\n");
}
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:
NcvBool bStopOnFirstFail;
NcvBool bCompactOutput;
std::string testSuiteName;
std::vector<INCVTest *> tests;
};
#endif // _ncvautotestlister_hpp_

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tests/gpu/src/nvidia/NCVTest.hpp
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/*
* 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_
#pragma warning( disable : 4201 4408 4127 4100)
#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;
};
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(devProp.textureAlignment));
this->allocatorCPU.reset(new NCVMemStackAllocator(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, devProp.textureAlignment));
this->allocatorCPU.reset(new NCVMemStackAllocator(NCVMemoryTypeHostPinned, maxCPUsize, 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_

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tests/gpu/src/nvidia/NCVTestSourceProvider.hpp
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/*
* 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, 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, 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_

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tests/gpu/src/nvidia/TestCompact.cpp
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/*
* 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.
*/
#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;
}

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tests/gpu/src/nvidia/TestCompact.h
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/*
* 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_

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tests/gpu/src/nvidia/TestDrawRects.cpp
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/*
* 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.
*/
#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>;

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tests/gpu/src/nvidia/TestDrawRects.h
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/*
* 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_

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tests/gpu/src/nvidia/TestHaarCascadeApplication.cpp
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/*
* 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.
*/
#include <float.h>
#if defined(__GNUC__)
#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()
{
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(__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
Ncv32u fpu_oldcw, fpu_cw;
_controlfp_s(&fpu_cw, 0, 0);
fpu_oldcw = fpu_cw;
_controlfp_s(&fpu_cw, _PC_24, _MCW_PC);
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);
_controlfp_s(&fpu_cw, fpu_oldcw, _MCW_PC);
#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;
}

41
tests/gpu/src/nvidia/TestHaarCascadeApplication.h
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/*
* 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_

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tests/gpu/src/nvidia/TestHaarCascadeLoader.cpp
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/*
* 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.
*/
#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;
}

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tests/gpu/src/nvidia/TestHaarCascadeLoader.h
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/*
* 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_

176
tests/gpu/src/nvidia/TestHypothesesFilter.cpp
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/*
* 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.
*/
#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 randVal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
Ncv32u secondSwap = randVal % (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 = h_vecSrc.length();
NCV_SKIP_COND_BEGIN
ncvStat = ncvFilterHypotheses_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;
}

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tests/gpu/src/nvidia/TestHypothesesFilter.h
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/*
* 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_

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tests/gpu/src/nvidia/TestHypothesesGrow.cpp
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/*
* 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.
*/
#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;
}

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tests/gpu/src/nvidia/TestHypothesesGrow.h
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/*
* 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_

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tests/gpu/src/nvidia/TestIntegralImage.cpp
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/*
* 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.
*/
#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>;

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tests/gpu/src/nvidia/TestIntegralImage.h
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/*
* 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_

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tests/gpu/src/nvidia/TestIntegralImageSquared.cpp
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/*
* 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.
*/
#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;
}

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tests/gpu/src/nvidia/TestIntegralImageSquared.h
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/*
* 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_

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tests/gpu/src/nvidia/TestRectStdDev.cpp
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/*
* 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.
*/
#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;
}

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tests/gpu/src/nvidia/TestRectStdDev.h
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/*
* 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;
NcvRect32u rect;
Ncv32u width;
Ncv32u height;
Ncv32f scaleFactor;
NcvBool bTextureCache;
};
#endif // _testrectstddev_h_

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tests/gpu/src/nvidia/TestResize.cpp
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/*
* 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.
*/
#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 = nppiStDownsampleNearest_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 = nppiStDownsampleNearest_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 = nppiStDownsampleNearest_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 = nppiStDownsampleNearest_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>;

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tests/gpu/src/nvidia/TestResize.h
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/*
* 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_

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tests/gpu/src/nvidia/TestTranspose.cpp
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/*
* 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.
*/
#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>;

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tests/gpu/src/nvidia/TestTranspose.h
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/*
* 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_

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tests/gpu/src/nvidia/main_nvidia.cpp
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#pragma warning (disable : 4408 4201 4100)
#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"
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));
}
//test VGA
testLister.add(new TestIntegralImage<T_in, T_out>("LinIntImg_VGA", src, 640, 480));
//TODO: add tests of various resolutions up to 4096x4096
}
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));
}
//test VGA
testLister.add(new TestIntegralImageSquared("SqLinIntImg_VGA", src, 640, 480));
//TODO: add tests of various resolutions up to 4096x4096
}
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));
}
//test VGA
testLister.add(new TestRectStdDev("RectStdDev_VGA", src, 640, 480, rect, 1, true));
//TODO: add tests of various resolutions up to 4096x4096
}
template <class T>
void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
//test VGA
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));
}
//test HD
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));
}
//TODO: add tests of various resolutions up to 4096x4096
}
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));
//TODO: add tests of various resolutions up to 4096x4096
}
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", "haarcascade_eye.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt.xml", "haarcascade_frontalface_alt.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt2.xml", "haarcascade_frontalface_alt2.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt_tree.xml", "haarcascade_frontalface_alt_tree.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_eye_tree_eyeglasses.xml", "haarcascade_eye_tree_eyeglasses.xml"));
}
void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u 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, "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, "haarcascade_frontalface_alt.xml", i, i));
}
}
static void devNullOutput(const char *msg)
{
}
bool main_nvidia()
{
printf("Testing NVIDIA Computer Vision SDK\n");
printf("==================================\n");
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerII("NPPST Integral Image" );//,,true, false);
NCVAutoTestLister testListerSII("NPPST Squared Integral Image" );//,,true, false);
NCVAutoTestLister testListerRStdDev("NPPST RectStdDev" );//,,true, false);
NCVAutoTestLister testListerResize("NPPST Resize" );//,,true, false);
NCVAutoTestLister testListerNPPSTVectorOperations("NPPST Vector Operations" );//,,true, false);
NCVAutoTestLister testListerTranspose("NPPST Transpose" );//,,true, false);
NCVAutoTestLister testListerVectorOperations("Vector Operations" );//,,true, false);
NCVAutoTestLister testListerHaarLoader("Haar Cascade Loader" );//,,true, false);
NCVAutoTestLister testListerHaarAppl("Haar Cascade Application" );//,,true, false);
NCVAutoTestLister testListerHypFiltration("Hypotheses Filtration" );//,,true, false);
NCVAutoTestLister testListerVisualize("Visualization" );//,,true, false);
printf("Initializing data source providers\n");
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
NCVTestSourceProvider<Ncv64u> testSrcRandom_64u(2010, 0, 0xFFFFFFFFFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv8u> testSrcFacesVGA_8u("../../data/group_1_640x480_VGA.pgm");
NCVTestSourceProvider<Ncv32f> testSrcRandom_32f(2010, -1.0f, 1.0f, 4096, 4096);
printf("Generating NPPST test suites\n");
generateIntegralTests<Ncv8u, Ncv32u>(testListerII, testSrcRandom_8u, 4096, 4096);
generateIntegralTests<Ncv32f, Ncv32f>(testListerII, testSrcRandom_32f, 4096, 4096);
generateSquaredIntegralTests(testListerSII, testSrcRandom_8u, 4096, 4096);
generateRectStdDevTests(testListerRStdDev, testSrcRandom_8u, 4096, 4096);
generateResizeTests(testListerResize, testSrcRandom_32u);
generateResizeTests(testListerResize, testSrcRandom_64u);
generateNPPSTVectorTests(testListerNPPSTVectorOperations, testSrcRandom_32u, 4096*4096);
generateTransposeTests(testListerTranspose, testSrcRandom_32u);
generateTransposeTests(testListerTranspose, testSrcRandom_64u);
printf("Generating NCV test suites\n");
generateDrawRectsTests(testListerVisualize, testSrcRandom_8u, testSrcRandom_32u, 4096, 4096);
generateDrawRectsTests(testListerVisualize, testSrcRandom_32u, testSrcRandom_32u, 4096, 4096);
generateVectorTests(testListerVectorOperations, testSrcRandom_32u, 4096*4096);
generateHypothesesFiltrationTests(testListerHypFiltration, testSrcRandom_32u, 1024);
generateHaarLoaderTests(testListerHaarLoader);
generateHaarApplicationTests(testListerHaarAppl, testSrcFacesVGA_8u, 1280, 720);
// Indicate if at least one test failed
bool passed = true;
// Invoke all tests
passed &= testListerII.invoke();
passed &= testListerSII.invoke();
passed &= testListerRStdDev.invoke();
passed &= testListerResize.invoke();
passed &= testListerNPPSTVectorOperations.invoke();
passed &= testListerTranspose.invoke();
passed &= testListerVisualize.invoke();
passed &= testListerVectorOperations.invoke();
passed &= testListerHypFiltration.invoke();
passed &= testListerHaarLoader.invoke();
passed &= testListerHaarAppl.invoke();
return passed;
}

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tests/gpu/src/nvidia_tests.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include "cvconfig.h"
class CV_NVidiaTestsCaller : public CvTest
{
public:
CV_NVidiaTestsCaller() : CvTest("GPU-NVidia", "NVidia") {}
virtual ~CV_NVidiaTestsCaller() {}
protected:
void run( int )
{
#if defined(HAVE_CUDA)
bool main_nvidia();
// Invoke all NVIDIA Staging tests and obtain the result
bool passed = main_nvidia();
if (passed)
ts->set_failed_test_info(CvTS::OK);
else
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
#else
ts->set_failed_test_info(CvTS::SKIPPED);
#endif
}
} CV_NVidiaTestsCaller_test;

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
struct CV_AsyncGpuMatTest : public CvTest
{
CV_AsyncGpuMatTest() : CvTest( "GPU-AsyncGpuMatOperator", "async" )
{
}
void run(int)
{
try
{
CudaMem src(Mat::zeros(100, 100, CV_8UC1));
GpuMat gpusrc;
GpuMat gpudst0, gpudst1(100, 100, CV_8UC1);
CudaMem cpudst0;
CudaMem cpudst1;
Stream stream0, stream1;
stream0.enqueueUpload(src, gpusrc);
bitwise_not(gpusrc, gpudst0, GpuMat(), stream0);
stream0.enqueueDownload(gpudst0, cpudst0);
stream1.enqueueMemSet(gpudst1, Scalar::all(128));
stream1.enqueueDownload(gpudst1, cpudst1);
stream0.waitForCompletion();
stream1.waitForCompletion();
Mat cpu_gold0(100, 100, CV_8UC1, Scalar::all(255));
Mat cpu_gold1(100, 100, CV_8UC1, Scalar::all(128));
if (norm(cpudst0, cpu_gold0, NORM_INF) > 0 || norm(cpudst1, cpu_gold1, NORM_INF) > 0)
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
else
ts->set_failed_test_info(CvTS::OK);
}
catch(cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
}
} CV_AsyncGpuMatTest_test;

126
tests/gpu/src/operator_convert_to.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <string>
#include <iostream>
#include <fstream>
#include <iterator>
#include <limits>
#include <numeric>
using namespace cv;
using namespace std;
using namespace gpu;
class CV_GpuMatOpConvertToTest : public CvTest
{
public:
CV_GpuMatOpConvertToTest() : CvTest( "GPU-MatOperatorConvertTo", "convertTo" ) {}
~CV_GpuMatOpConvertToTest() {}
protected:
void run(int);
};
void CV_GpuMatOpConvertToTest::run(int /* start_from */)
{
const Size img_size(67, 35);
const char* types_str[] = {"CV_8U", "CV_8S", "CV_16U", "CV_16S", "CV_32S", "CV_32F", "CV_64F"};
bool passed = true;
try
{
int lastType = CV_32F;
if (TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE))
lastType = CV_64F;
for (int i = 0; i <= lastType && passed; ++i)
{
for (int j = 0; j <= lastType && passed; ++j)
{
for (int c = 1; c < 5 && passed; ++c)
{
const int src_type = CV_MAKETYPE(i, c);
const int dst_type = j;
cv::RNG rng(*ts->get_rng());
Mat cpumatsrc(img_size, src_type);
rng.fill(cpumatsrc, RNG::UNIFORM, Scalar::all(0), Scalar::all(300));
GpuMat gpumatsrc(cpumatsrc);
Mat cpumatdst;
GpuMat gpumatdst;
cpumatsrc.convertTo(cpumatdst, dst_type, 0.5, 3.0);
gpumatsrc.convertTo(gpumatdst, dst_type, 0.5, 3.0);
double r = norm(cpumatdst, gpumatdst, NORM_INF);
if (r > 1)
{
ts->printf(CvTS::LOG,
"\nFAILED: SRC_TYPE=%sC%d DST_TYPE=%s NORM = %f\n",
types_str[i], c, types_str[j], r);
passed = false;
}
}
}
}
}
catch(cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(passed ? CvTS::OK : CvTS::FAIL_GENERIC);
}
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuMatOpConvertToTest CV_GpuMatOpConvertToTest_test;

162
tests/gpu/src/operator_copy_to.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <string>
#include <iostream>
#include <fstream>
#include <iterator>
#include <limits>
#include <numeric>
#include <iomanip> // for cout << setw()
using namespace cv;
using namespace std;
using namespace gpu;
class CV_GpuMatOpCopyToTest : public CvTest
{
public:
CV_GpuMatOpCopyToTest() : CvTest( "GPU-MatOperatorCopyTo", "copyTo" )
{
rows = 234;
cols = 123;
}
~CV_GpuMatOpCopyToTest() {}
protected:
void run(int);
template <typename T>
void print_mat(const T & mat, const std::string & name) const;
bool compare_matrix(cv::Mat & cpumat, gpu::GpuMat & gpumat);
private:
int rows;
int cols;
};
template<typename T>
void CV_GpuMatOpCopyToTest::print_mat(const T & mat, const std::string & name) const { cv::imshow(name, mat); }
bool CV_GpuMatOpCopyToTest::compare_matrix(cv::Mat & cpumat, gpu::GpuMat & gpumat)
{
Mat cmat(cpumat.size(), cpumat.type(), Scalar::all(0));
GpuMat gmat(cmat);
Mat cpumask(cpumat.size(), CV_8U);
cv::RNG rng(*ts->get_rng());
rng.fill(cpumask, RNG::NORMAL, Scalar::all(0), Scalar::all(127));
threshold(cpumask, cpumask, 0, 127, THRESH_BINARY);
GpuMat gpumask(cpumask);
//int64 time = getTickCount();
cpumat.copyTo(cmat, cpumask);
//int64 time1 = getTickCount();
gpumat.copyTo(gmat, gpumask);
//int64 time2 = getTickCount();
//std::cout << "\ntime cpu: " << std::fixed << std::setprecision(12) << 1.0 / double((time1 - time) / (double)getTickFrequency());
//std::cout << "\ntime gpu: " << std::fixed << std::setprecision(12) << 1.0 / double((time2 - time1) / (double)getTickFrequency());
//std::cout << "\n";
#ifdef PRINT_MATRIX
print_mat(cmat, "cpu mat");
print_mat(gmat, "gpu mat");
print_mat(cpumask, "cpu mask");
print_mat(gpumask, "gpu mask");
cv::waitKey(0);
#endif
double ret = norm(cmat, gmat);
if (ret < 1.0)
return true;
else
{
ts->printf(CvTS::LOG, "\nNorm: %f\n", ret);
return false;
}
}
void CV_GpuMatOpCopyToTest::run( int /* start_from */)
{
bool is_test_good = true;
try
{
int lastType = CV_32F;
if (TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE))
lastType = CV_64F;
for (int i = 0 ; i <= lastType; i++)
{
Mat cpumat(rows, cols, i);
cpumat.setTo(Scalar::all(127));
GpuMat gpumat(cpumat);
is_test_good &= compare_matrix(cpumat, gpumat);
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
if (is_test_good == true)
ts->set_failed_test_info(CvTS::OK);
else
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
}
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuMatOpCopyToTest CV_GpuMatOpCopyTo_test;

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tests/gpu/src/operator_set_to.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <limits>
using namespace cv;
using namespace std;
using namespace gpu;
class CV_GpuMatOpSetToTest : public CvTest
{
public:
CV_GpuMatOpSetToTest();
~CV_GpuMatOpSetToTest() {}
protected:
void run(int);
bool testSetTo(cv::Mat& cpumat, gpu::GpuMat& gpumat, const cv::Mat& cpumask = cv::Mat(), const cv::gpu::GpuMat& gpumask = cv::gpu::GpuMat());
private:
int rows;
int cols;
Scalar s;
};
CV_GpuMatOpSetToTest::CV_GpuMatOpSetToTest(): CvTest( "GPU-MatOperatorSetTo", "setTo" )
{
rows = 35;
cols = 67;
s.val[0] = 127.0;
s.val[1] = 127.0;
s.val[2] = 127.0;
s.val[3] = 127.0;
}
bool CV_GpuMatOpSetToTest::testSetTo(cv::Mat& cpumat, gpu::GpuMat& gpumat, const cv::Mat& cpumask, const cv::gpu::GpuMat& gpumask)
{
cpumat.setTo(s, cpumask);
gpumat.setTo(s, gpumask);
double ret = norm(cpumat, gpumat, NORM_INF);
if (ret < std::numeric_limits<double>::epsilon())
return true;
else
{
ts->printf(CvTS::LOG, "\nNorm: %f\n", ret);
return false;
}
}
void CV_GpuMatOpSetToTest::run( int /* start_from */)
{
bool is_test_good = true;
try
{
cv::Mat cpumask(rows, cols, CV_8UC1);
cv::RNG rng(*ts->get_rng());
rng.fill(cpumask, RNG::UNIFORM, cv::Scalar::all(0.0), cv::Scalar(1.5));
cv::gpu::GpuMat gpumask(cpumask);
int lastType = CV_32F;
if (TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE))
lastType = CV_64F;
for (int i = 0; i <= lastType; i++)
{
for (int cn = 1; cn <= 4; ++cn)
{
int mat_type = CV_MAKETYPE(i, cn);
Mat cpumat(rows, cols, mat_type, Scalar::all(0));
GpuMat gpumat(cpumat);
is_test_good &= testSetTo(cpumat, gpumat, cpumask, gpumask);
}
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
if (is_test_good == true)
ts->set_failed_test_info(CvTS::OK);
else
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
}
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuMatOpSetToTest CV_GpuMatOpSetTo_test;

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tests/gpu/src/split_merge.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other 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 "gputest.hpp"
#include <iostream>
#include <string>
#include <vector>
using namespace std;
using namespace cv;
////////////////////////////////////////////////////////////////////////////////
// Merge
struct CV_MergeTest : public CvTest
{
CV_MergeTest() : CvTest("GPU-Merge", "merge") {}
void can_merge(size_t rows, size_t cols);
void can_merge_submatrixes(size_t rows, size_t cols);
void run(int);
};
void CV_MergeTest::can_merge(size_t rows, size_t cols)
{
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
size_t depth_end = double_ok ? CV_64F : CV_32F;
for (size_t num_channels = 1; num_channels <= 4; ++num_channels)
for (size_t depth = CV_8U; depth <= depth_end; ++depth)
{
vector<Mat> src;
for (size_t i = 0; i < num_channels; ++i)
src.push_back(Mat(rows, cols, depth, Scalar::all(static_cast<double>(i))));
Mat dst(rows, cols, CV_MAKETYPE(depth, num_channels));
cv::merge(src, dst);
vector<gpu::GpuMat> dev_src;
for (size_t i = 0; i < num_channels; ++i)
dev_src.push_back(gpu::GpuMat(src[i]));
gpu::GpuMat dev_dst(rows, cols, CV_MAKETYPE(depth, num_channels));
cv::gpu::merge(dev_src, dev_dst);
Mat host_dst = dev_dst;
double err = norm(dst, host_dst, NORM_INF);
if (err > 1e-3)
{
//ts->printf(CvTS::CONSOLE, "\nNorm: %f\n", err);
//ts->printf(CvTS::CONSOLE, "Depth: %d\n", depth);
//ts->printf(CvTS::CONSOLE, "Rows: %d\n", rows);
//ts->printf(CvTS::CONSOLE, "Cols: %d\n", cols);
//ts->printf(CvTS::CONSOLE, "NumChannels: %d\n", num_channels);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
void CV_MergeTest::can_merge_submatrixes(size_t rows, size_t cols)
{
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
size_t depth_end = double_ok ? CV_64F : CV_32F;
for (size_t num_channels = 1; num_channels <= 4; ++num_channels)
for (size_t depth = CV_8U; depth <= depth_end; ++depth)
{
vector<Mat> src;
for (size_t i = 0; i < num_channels; ++i)
{
Mat m(rows * 2, cols * 2, depth, Scalar::all(static_cast<double>(i)));
src.push_back(m(Range(rows / 2, rows / 2 + rows), Range(cols / 2, cols / 2 + cols)));
}
Mat dst(rows, cols, CV_MAKETYPE(depth, num_channels));
cv::merge(src, dst);
vector<gpu::GpuMat> dev_src;
for (size_t i = 0; i < num_channels; ++i)
dev_src.push_back(gpu::GpuMat(src[i]));
gpu::GpuMat dev_dst(rows, cols, CV_MAKETYPE(depth, num_channels));
cv::gpu::merge(dev_src, dev_dst);
Mat host_dst = dev_dst;
double err = norm(dst, host_dst, NORM_INF);
if (err > 1e-3)
{
//ts->printf(CvTS::CONSOLE, "\nNorm: %f\n", err);
//ts->printf(CvTS::CONSOLE, "Depth: %d\n", depth);
//ts->printf(CvTS::CONSOLE, "Rows: %d\n", rows);
//ts->printf(CvTS::CONSOLE, "Cols: %d\n", cols);
//ts->printf(CvTS::CONSOLE, "NumChannels: %d\n", num_channels);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
void CV_MergeTest::run(int)
{
try
{
can_merge(1, 1);
can_merge(1, 7);
can_merge(53, 7);
can_merge_submatrixes(1, 1);
can_merge_submatrixes(1, 7);
can_merge_submatrixes(53, 7);
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
}
}
////////////////////////////////////////////////////////////////////////////////
// Split
struct CV_SplitTest : public CvTest
{
CV_SplitTest() : CvTest("GPU-Split", "split") {}
void can_split(size_t rows, size_t cols);
void can_split_submatrix(size_t rows, size_t cols);
void run(int);
};
void CV_SplitTest::can_split(size_t rows, size_t cols)
{
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
size_t depth_end = double_ok ? CV_64F : CV_32F;
for (size_t num_channels = 1; num_channels <= 4; ++num_channels)
for (size_t depth = CV_8U; depth <= depth_end; ++depth)
{
Mat src(rows, cols, CV_MAKETYPE(depth, num_channels), Scalar(1.0, 2.0, 3.0, 4.0));
vector<Mat> dst;
cv::split(src, dst);
gpu::GpuMat dev_src(src);
vector<gpu::GpuMat> dev_dst;
cv::gpu::split(dev_src, dev_dst);
if (dev_dst.size() != dst.size())
{
ts->printf(CvTS::CONSOLE, "Bad output sizes");
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
}
for (size_t i = 0; i < num_channels; ++i)
{
Mat host_dst = dev_dst[i];
double err = norm(dst[i], host_dst, NORM_INF);
if (err > 1e-3)
{
//ts->printf(CvTS::CONSOLE, "\nNorm: %f\n", err);
//ts->printf(CvTS::CONSOLE, "Depth: %d\n", depth);
//ts->printf(CvTS::CONSOLE, "Rows: %d\n", rows);
//ts->printf(CvTS::CONSOLE, "Cols: %d\n", cols);
//ts->printf(CvTS::CONSOLE, "NumChannels: %d\n", num_channels);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
void CV_SplitTest::can_split_submatrix(size_t rows, size_t cols)
{
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
size_t depth_end = double_ok ? CV_64F : CV_32F;
for (size_t num_channels = 1; num_channels <= 4; ++num_channels)
for (size_t depth = CV_8U; depth <= depth_end; ++depth)
{
Mat src_data(rows * 2, cols * 2, CV_MAKETYPE(depth, num_channels), Scalar(1.0, 2.0, 3.0, 4.0));
Mat src(src_data(Range(rows / 2, rows / 2 + rows), Range(cols / 2, cols / 2 + cols)));
vector<Mat> dst;
cv::split(src, dst);
gpu::GpuMat dev_src(src);
vector<gpu::GpuMat> dev_dst;
cv::gpu::split(dev_src, dev_dst);
if (dev_dst.size() != dst.size())
{
ts->printf(CvTS::CONSOLE, "Bad output sizes");
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
}
for (size_t i = 0; i < num_channels; ++i)
{
Mat host_dst = dev_dst[i];
double err = norm(dst[i], host_dst, NORM_INF);
if (err > 1e-3)
{
//ts->printf(CvTS::CONSOLE, "\nNorm: %f\n", err);
//ts->printf(CvTS::CONSOLE, "Depth: %d\n", depth);
//ts->printf(CvTS::CONSOLE, "Rows: %d\n", rows);
//ts->printf(CvTS::CONSOLE, "Cols: %d\n", cols);
//ts->printf(CvTS::CONSOLE, "NumChannels: %d\n", num_channels);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
void CV_SplitTest::run(int)
{
try
{
can_split(1, 1);
can_split(1, 7);
can_split(7, 53);
can_split_submatrix(1, 1);
can_split_submatrix(1, 7);
can_split_submatrix(7, 53);
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
}
}
////////////////////////////////////////////////////////////////////////////////
// Split and merge
struct CV_SplitMergeTest : public CvTest
{
CV_SplitMergeTest() : CvTest("GPU-SplitMerge", "split merge") {}
void can_split_merge(size_t rows, size_t cols);
void run(int);
};
void CV_SplitMergeTest::can_split_merge(size_t rows, size_t cols) {
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
size_t depth_end = double_ok ? CV_64F : CV_32F;
for (size_t num_channels = 1; num_channels <= 4; ++num_channels)
for (size_t depth = CV_8U; depth <= depth_end; ++depth)
{
Mat orig(rows, cols, CV_MAKETYPE(depth, num_channels), Scalar(1.0, 2.0, 3.0, 4.0));
gpu::GpuMat dev_orig(orig);
vector<gpu::GpuMat> dev_vec;
cv::gpu::split(dev_orig, dev_vec);
gpu::GpuMat dev_final(rows, cols, CV_MAKETYPE(depth, num_channels));
cv::gpu::merge(dev_vec, dev_final);
double err = cv::norm((Mat)dev_orig, (Mat)dev_final, NORM_INF);
if (err > 1e-3)
{
//ts->printf(CvTS::CONSOLE, "\nNorm: %f\n", err);
//ts->printf(CvTS::CONSOLE, "Depth: %d\n", depth);
//ts->printf(CvTS::CONSOLE, "Rows: %d\n", rows);
//ts->printf(CvTS::CONSOLE, "Cols: %d\n", cols);
//ts->printf(CvTS::CONSOLE, "NumChannels: %d\n", num_channels);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
void CV_SplitMergeTest::run(int)
{
try
{
can_split_merge(1, 1);
can_split_merge(1, 7);
can_split_merge(7, 53);
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
}
}
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
// If we comment some tests, we may foget/miss to uncomment it after.
// Placing all test definitions in one place
// makes us know about what tests are commented.
CV_SplitTest split_test;
CV_MergeTest merge_test;
CV_SplitMergeTest split_merge_test;

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tests/gpu/src/stereo_bm.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <iostream>
#include <string>
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
using namespace cv::gpu;
struct CV_GpuStereoBMTest : public CvTest
{
CV_GpuStereoBMTest() : CvTest( "GPU-StereoBM", "StereoBM" ){}
~CV_GpuStereoBMTest() {}
void run_stress()
{
RNG rng;
for(int i = 0; i < 10; ++i)
{
int winSize = cvRound(rng.uniform(2, 11)) * 2 + 1;
for(int j = 0; j < 10; ++j)
{
int ndisp = cvRound(rng.uniform(5, 32)) * 8;
for(int s = 0; s < 10; ++s)
{
int w = cvRound(rng.uniform(1024, 2048));
int h = cvRound(rng.uniform(768, 1152));
for(int p = 0; p < 2; ++p)
{
//int winSize = winsz[i];
//int disp = disps[j];
Size imgSize(w, h);//res[s];
int preset = p;
printf("Preset = %d, nidsp = %d, winsz = %d, width = %d, height = %d\n", p, ndisp, winSize, imgSize.width, imgSize.height);
GpuMat l(imgSize, CV_8U);
GpuMat r(imgSize, CV_8U);
GpuMat disparity;
StereoBM_GPU bm(preset, ndisp, winSize);
bm(l, r, disparity);
}
}
}
}
}
void run(int )
{
/*run_stress();
return;*/
cv::Mat img_l = cv::imread(std::string(ts->get_data_path()) + "stereobm/aloe-L.png", 0);
cv::Mat img_r = cv::imread(std::string(ts->get_data_path()) + "stereobm/aloe-R.png", 0);
cv::Mat img_reference = cv::imread(std::string(ts->get_data_path()) + "stereobm/aloe-disp.png", 0);
if (img_l.empty() || img_r.empty() || img_reference.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
try
{
cv::gpu::GpuMat disp;
cv::gpu::StereoBM_GPU bm(0, 128, 19);
bm(cv::gpu::GpuMat(img_l), cv::gpu::GpuMat(img_r), disp);
disp.convertTo(disp, img_reference.type());
double norm = cv::norm(disp, img_reference, cv::NORM_INF);
//cv::imwrite(std::string(ts->get_data_path()) + "stereobm/aloe-disp.png", disp);
/*cv::imshow("disp", disp);
cv::imshow("img_reference", img_reference);
cv::Mat diff = (cv::Mat)disp - (cv::Mat)img_reference;
cv::imshow("diff", diff);
cv::waitKey();*/
if (norm >= 100)
{
ts->printf(CvTS::LOG, "\nStereoBM norm = %f\n", norm);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuStereoBMTest CV_GpuStereoBM_test;

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tests/gpu/src/stereo_bm_async.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
using namespace cv;
using namespace std;
struct CV_AsyncStereoBMTest : public CvTest
{
public:
CV_AsyncStereoBMTest() : CvTest( "GPU-AsyncStereoBM", "asyncStereoBM" ) {}
~CV_AsyncStereoBMTest() {}
void run( int /* start_from */)
{
cv::Mat img_l = cv::imread(std::string(ts->get_data_path()) + "stereobm/aloe-L.png", 0);
cv::Mat img_r = cv::imread(std::string(ts->get_data_path()) + "stereobm/aloe-R.png", 0);
cv::Mat img_reference = cv::imread(std::string(ts->get_data_path()) + "stereobm/aloe-disp.png", 0);
if (img_l.empty() || img_r.empty() || img_reference.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
try
{
cv::gpu::GpuMat disp;
cv::gpu::StereoBM_GPU bm(0, 128, 19);
cv::gpu::Stream stream;
for (size_t i = 0; i < 50; i++)
{
bm(cv::gpu::GpuMat(img_l), cv::gpu::GpuMat(img_r), disp, stream);
}
stream.waitForCompletion();
disp.convertTo(disp, img_reference.type());
double norm = cv::norm(disp, img_reference, cv::NORM_INF);
if (norm >= 100)
{
ts->printf(CvTS::LOG, "\nStereoBM norm = %f\n", norm);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_AsyncStereoBMTest CV_AsyncStereoBMTest_test;

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tests/gpu/src/stereo_bp.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include <iostream>
#include <string>
struct CV_GpuStereoBPTest : public CvTest
{
CV_GpuStereoBPTest() : CvTest( "GPU-StereoBP", "StereoBP" ){}
~CV_GpuStereoBPTest() {}
void run(int )
{
cv::Mat img_l = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
cv::Mat img_r = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-R.png");
cv::Mat img_template = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-disp.png", 0);
if (img_l.empty() || img_r.empty() || img_template.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
try
{
{cv::Mat temp; cv::cvtColor(img_l, temp, CV_BGR2BGRA); cv::swap(temp, img_l);}
{cv::Mat temp; cv::cvtColor(img_r, temp, CV_BGR2BGRA); cv::swap(temp, img_r);}
cv::gpu::GpuMat disp;
cv::gpu::StereoBeliefPropagation bpm(64, 8, 2, 25, 0.1f, 15, 1, CV_16S);
bpm(cv::gpu::GpuMat(img_l), cv::gpu::GpuMat(img_r), disp);
//cv::imwrite(std::string(ts->get_data_path()) + "stereobp/aloe-disp.png", disp);
disp.convertTo(disp, img_template.type());
double norm = cv::norm(disp, img_template, cv::NORM_INF);
if (norm >= 0.5)
{
ts->printf(CvTS::LOG, "\nStereoBP norm = %f\n", norm);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuStereoBPTest CV_GpuStereoBP_test;

100
tests/gpu/src/stereo_csbp.cpp
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@ -1,100 +0,0 @@
/*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.
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// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// 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,
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// (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 "gputest.hpp"
#include <iostream>
#include <string>
struct CV_GpuStereoCSBPTest : public CvTest
{
CV_GpuStereoCSBPTest() : CvTest( "GPU-StereoCSBP", "ConstantSpaceStereoBP" ){}
~CV_GpuStereoCSBPTest() {}
void run(int )
{
cv::Mat img_l = cv::imread(std::string(ts->get_data_path()) + "csstereobp/aloe-L.png");
cv::Mat img_r = cv::imread(std::string(ts->get_data_path()) + "csstereobp/aloe-R.png");
cv::Mat img_template = cv::imread(std::string(ts->get_data_path()) + "csstereobp/aloe-disp.png", 0);
if (img_l.empty() || img_r.empty() || img_template.empty())
{
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
return;
}
try
{
{cv::Mat temp; cv::cvtColor(img_l, temp, CV_BGR2BGRA); cv::swap(temp, img_l);}
{cv::Mat temp; cv::cvtColor(img_r, temp, CV_BGR2BGRA); cv::swap(temp, img_r);}
cv::gpu::GpuMat disp;
cv::gpu::StereoConstantSpaceBP bpm(128, 16, 4, 4);
bpm(cv::gpu::GpuMat(img_l), cv::gpu::GpuMat(img_r), disp);
//cv::imwrite(std::string(ts->get_data_path()) + "csstereobp/aloe-disp.png", disp);
disp.convertTo(disp, img_template.type());
double norm = cv::norm(disp, img_template, cv::NORM_INF);
if (norm >= 0.5)
{
ts->printf(CvTS::LOG, "\nConstantSpaceStereoBP norm = %f\n", norm);
ts->set_failed_test_info(CvTS::FAIL_GENERIC);
return;
}
}
catch(const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
CV_GpuStereoCSBPTest CV_GpuCSStereoBP_test;
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