generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge pull request #299 from branch 'bitwangyaoyao_ocl' into 2.4

Browse Source
This commit is contained in:
Andrey Kamaev 2013-01-23 14:49:36 +04:00
commit 311d799344
10 changed files with 2448 additions and 1236 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

204
cmake/OpenCVDetectOpenCL.cmake
View File

@ -1,78 +1,138 @@
if(APPLE)
set(OPENCL_FOUND YES)
set(OPENCL_LIBRARIES "-framework OpenCL")
set(OPENCL_FOUND YES)
set(OPENCL_LIBRARIES "-framework OpenCL")
else()
#find_package(OpenCL QUIET)
if(WITH_OPENCLAMDFFT)
find_path(CLAMDFFT_INCLUDE_DIR
NAMES clAmdFft.h)
find_library(CLAMDFFT_LIBRARIES
NAMES clAmdFft.Runtime)
find_package(OpenCL QUIET)
if(WITH_OPENCLAMDFFT)
set(CLAMDFFT_SEARCH_PATH $ENV{CLAMDFFT_PATH})
if(NOT CLAMDFFT_SEARCH_PATH)
if(WIN32)
set( CLAMDFFT_SEARCH_PATH "C:\\Program Files (x86)\\AMD\\clAmdFft" )
endif()
endif()
if(WITH_OPENCLAMDBLAS)
find_path(CLAMDBLAS_INCLUDE_DIR
NAMES clAmdBlas.h)
find_library(CLAMDBLAS_LIBRARIES
NAMES clAmdBlas)
endif()
# Try AMD/ATI Stream SDK
if (NOT OPENCL_FOUND)
set(ENV_AMDSTREAMSDKROOT $ENV{AMDAPPSDKROOT})
set(ENV_OPENCLROOT $ENV{OPENCLROOT})
set(ENV_CUDA_PATH $ENV{CUDA_PATH})
if(ENV_AMDSTREAMSDKROOT)
set(OPENCL_INCLUDE_SEARCH_PATH ${ENV_AMDSTREAMSDKROOT}/include)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_AMDSTREAMSDKROOT}/lib/x86)
else()
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_AMDSTREAMSDKROOT}/lib/x86_64)
endif()
elseif(ENV_CUDA_PATH AND WIN32)
set(OPENCL_INCLUDE_SEARCH_PATH ${ENV_CUDA_PATH}/include)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_CUDA_PATH}/lib/Win32)
else()
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_CUDA_PATH}/lib/x64)
endif()
elseif(ENV_OPENCLROOT AND UNIX)
set(OPENCL_INCLUDE_SEARCH_PATH ${ENV_OPENCLROOT}/inc)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} /usr/lib)
else()
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} /usr/lib64)
endif()
endif()
if(OPENCL_INCLUDE_SEARCH_PATH)
find_path(OPENCL_INCLUDE_DIR
NAMES CL/cl.h OpenCL/cl.h
PATHS ${OPENCL_INCLUDE_SEARCH_PATH}
NO_DEFAULT_PATH)
else()
find_path(OPENCL_INCLUDE_DIR
NAMES CL/cl.h OpenCL/cl.h)
endif()
if(OPENCL_LIB_SEARCH_PATH)
find_library(OPENCL_LIBRARY NAMES OpenCL PATHS ${OPENCL_LIB_SEARCH_PATH} NO_DEFAULT_PATH)
else()
find_library(OPENCL_LIBRARY NAMES OpenCL)
endif()
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(
OPENCL
DEFAULT_MSG
OPENCL_LIBRARY OPENCL_INCLUDE_DIR
)
if(OPENCL_FOUND)
set(OPENCL_LIBRARIES ${OPENCL_LIBRARY})
set(HAVE_OPENCL 1)
else()
set(OPENCL_LIBRARIES)
endif()
set( CLAMDFFT_INCLUDE_SEARCH_PATH ${CLAMDFFT_SEARCH_PATH}/include )
if(UNIX)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(CLAMDFFT_LIB_SEARCH_PATH /usr/lib)
else()
set(CLAMDFFT_LIB_SEARCH_PATH /usr/lib64)
endif()
else()
set(HAVE_OPENCL 1)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(CLAMDFFT_LIB_SEARCH_PATH ${CLAMDFFT_SEARCH_PATH}\\lib32\\import)
else()
set(CLAMDFFT_LIB_SEARCH_PATH ${CLAMDFFT_SEARCH_PATH}\\lib64\\import)
endif()
endif()
find_path(CLAMDFFT_INCLUDE_DIR
NAMES clAmdFft.h
PATHS ${CLAMDFFT_INCLUDE_SEARCH_PATH}
PATH_SUFFIXES clAmdFft
NO_DEFAULT_PATH)
find_library(CLAMDFFT_LIBRARY
NAMES clAmdFft.Runtime
PATHS ${CLAMDFFT_LIB_SEARCH_PATH}
NO_DEFAULT_PATH)
if(CLAMDFFT_LIBRARY)
set(CLAMDFFT_LIBRARIES ${CLAMDFFT_LIBRARY})
else()
set(CLAMDFFT_LIBRARIES "")
endif()
endif()
if(WITH_OPENCLAMDBLAS)
set(CLAMDBLAS_SEARCH_PATH $ENV{CLAMDBLAS_PATH})
if(NOT CLAMDBLAS_SEARCH_PATH)
if(WIN32)
set( CLAMDBLAS_SEARCH_PATH "C:\\Program Files (x86)\\AMD\\clAmdBlas" )
endif()
endif()
set( CLAMDBLAS_INCLUDE_SEARCH_PATH ${CLAMDBLAS_SEARCH_PATH}/include )
if(UNIX)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(CLAMDBLAS_LIB_SEARCH_PATH /usr/lib)
else()
set(CLAMDBLAS_LIB_SEARCH_PATH /usr/lib64)
endif()
else()
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(CLAMDBLAS_LIB_SEARCH_PATH ${CLAMDBLAS_SEARCH_PATH}\\lib32\\import)
else()
set(CLAMDBLAS_LIB_SEARCH_PATH ${CLAMDBLAS_SEARCH_PATH}\\lib64\\import)
endif()
endif()
find_path(CLAMDBLAS_INCLUDE_DIR
NAMES clAmdBlas.h
PATHS ${CLAMDBLAS_INCLUDE_SEARCH_PATH}
PATH_SUFFIXES clAmdBlas
NO_DEFAULT_PATH)
find_library(CLAMDBLAS_LIBRARY
NAMES clAmdBlas
PATHS ${CLAMDBLAS_LIB_SEARCH_PATH}
NO_DEFAULT_PATH)
if(CLAMDBLAS_LIBRARY)
set(CLAMDBLAS_LIBRARIES ${CLAMDBLAS_LIBRARY})
else()
set(CLAMDBLAS_LIBRARIES "")
endif()
endif()
# Try AMD/ATI Stream SDK
if (NOT OPENCL_FOUND)
set(ENV_AMDSTREAMSDKROOT $ENV{AMDAPPSDKROOT})
set(ENV_OPENCLROOT $ENV{OPENCLROOT})
set(ENV_CUDA_PATH $ENV{CUDA_PATH})
if(ENV_AMDSTREAMSDKROOT)
set(OPENCL_INCLUDE_SEARCH_PATH ${ENV_AMDSTREAMSDKROOT}/include)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_AMDSTREAMSDKROOT}/lib/x86)
else()
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_AMDSTREAMSDKROOT}/lib/x86_64)
endif()
elseif(ENV_CUDA_PATH AND WIN32)
set(OPENCL_INCLUDE_SEARCH_PATH ${ENV_CUDA_PATH}/include)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_CUDA_PATH}/lib/Win32)
else()
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} ${ENV_CUDA_PATH}/lib/x64)
endif()
elseif(ENV_OPENCLROOT AND UNIX)
set(OPENCL_INCLUDE_SEARCH_PATH ${ENV_OPENCLROOT}/inc)
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} /usr/lib)
else()
set(OPENCL_LIB_SEARCH_PATH ${OPENCL_LIB_SEARCH_PATH} /usr/lib64)
endif()
endif()
if(OPENCL_INCLUDE_SEARCH_PATH)
find_path(OPENCL_INCLUDE_DIR
NAMES CL/cl.h OpenCL/cl.h
PATHS ${OPENCL_INCLUDE_SEARCH_PATH}
NO_DEFAULT_PATH)
else()
find_path(OPENCL_INCLUDE_DIR
NAMES CL/cl.h OpenCL/cl.h)
endif()
if(OPENCL_LIB_SEARCH_PATH)
find_library(OPENCL_LIBRARY NAMES OpenCL PATHS ${OPENCL_LIB_SEARCH_PATH} NO_DEFAULT_PATH)
else()
find_library(OPENCL_LIBRARY NAMES OpenCL)
endif()
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(
OPENCL
DEFAULT_MSG
OPENCL_LIBRARY OPENCL_INCLUDE_DIR
)
if(OPENCL_FOUND)
set(OPENCL_LIBRARIES ${OPENCL_LIBRARY})
set(HAVE_OPENCL 1)
else()
set(OPENCL_LIBRARIES)
endif()
else()
set(HAVE_OPENCL 1)
endif()
endif()

4
modules/ocl/include/opencv2/ocl/ocl.hpp
View File

@ -41,8 +41,8 @@
//
//M*/
#ifndef __OPENCV_GPU_HPP__
#define __OPENCV_GPU_HPP__
#ifndef __OPENCV_OCL_HPP__
#define __OPENCV_OCL_HPP__
#include <memory>
#include <vector>

799
modules/ocl/src/filtering.cpp
View File

File diff suppressed because it is too large Load Diff

283
modules/ocl/src/haar.cpp
View File

@ -63,13 +63,13 @@ using namespace std;
namespace cv
{
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char *haarobjectdetect;
extern const char *haarobjectdetectbackup;
extern const char *haarobjectdetect_scaled2;
}
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char *haarobjectdetect;
extern const char *haarobjectdetectbackup;
extern const char *haarobjectdetect_scaled2;
}
}
/* these settings affect the quality of detection: change with care */
@ -883,13 +883,6 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
// bool roughSearch = (flags & CV_HAAR_DO_ROUGH_SEARCH) != 0;
//the Intel HD Graphics is unsupported
if (gimg.clCxt->impl->devName.find("Intel(R) HD Graphics") != string::npos)
{
cout << " Intel HD GPU device unsupported " << endl;
return NULL;
}
//double t = 0;
if( maxSize.height == 0 || maxSize.width == 0 )
{
@ -937,7 +930,7 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
if( gimg.cols < minSize.width || gimg.rows < minSize.height )
CV_Error(CV_StsError, "Image too small");
if( flags & CV_HAAR_SCALE_IMAGE )
if( (flags & CV_HAAR_SCALE_IMAGE) && gimg.clCxt->impl->devName.find("Intel(R) HD Graphics") == string::npos )
{
CvSize winSize0 = cascade->orig_window_size;
//float scalefactor = 1.1f;
@ -2170,41 +2163,41 @@ CvType haar_type( CV_TYPE_NAME_HAAR, gpuIsHaarClassifier,
namespace cv
{
HaarClassifierCascade::HaarClassifierCascade() {}
HaarClassifierCascade::HaarClassifierCascade(const String &filename)
{
load(filename);
}
HaarClassifierCascade::HaarClassifierCascade() {}
HaarClassifierCascade::HaarClassifierCascade(const String &filename)
{
load(filename);
}
bool HaarClassifierCascade::load(const String &filename)
{
cascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade *)cvLoad(filename.c_str(), 0, 0, 0));
return (CvHaarClassifierCascade *)cascade != 0;
}
bool HaarClassifierCascade::load(const String &filename)
{
cascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade *)cvLoad(filename.c_str(), 0, 0, 0));
return (CvHaarClassifierCascade *)cascade != 0;
}
void HaarClassifierCascade::detectMultiScale( const Mat &image,
Vector<Rect> &objects, double scaleFactor,
int minNeighbors, int flags,
Size minSize )
{
MemStorage storage(cvCreateMemStorage(0));
CvMat _image = image;
CvSeq *_objects = gpuHaarDetectObjects( &_image, cascade, storage, scaleFactor,
minNeighbors, flags, minSize );
Seq<Rect>(_objects).copyTo(objects);
}
void HaarClassifierCascade::detectMultiScale( const Mat &image,
Vector<Rect> &objects, double scaleFactor,
int minNeighbors, int flags,
Size minSize )
{
MemStorage storage(cvCreateMemStorage(0));
CvMat _image = image;
CvSeq *_objects = gpuHaarDetectObjects( &_image, cascade, storage, scaleFactor,
minNeighbors, flags, minSize );
Seq<Rect>(_objects).copyTo(objects);
}
int HaarClassifierCascade::runAt(Point pt, int startStage, int) const
{
return gpuRunHaarClassifierCascade(cascade, pt, startStage);
}
int HaarClassifierCascade::runAt(Point pt, int startStage, int) const
{
return gpuRunHaarClassifierCascade(cascade, pt, startStage);
}
void HaarClassifierCascade::setImages( const Mat &sum, const Mat &sqsum,
const Mat &tilted, double scale )
{
CvMat _sum = sum, _sqsum = sqsum, _tilted = tilted;
gpuSetImagesForHaarClassifierCascade( cascade, &_sum, &_sqsum, &_tilted, scale );
}
void HaarClassifierCascade::setImages( const Mat &sum, const Mat &sqsum,
const Mat &tilted, double scale )
{
CvMat _sum = sum, _sqsum = sqsum, _tilted = tilted;
gpuSetImagesForHaarClassifierCascade( cascade, &_sum, &_sqsum, &_tilted, scale );
}
}
#endif
@ -2579,116 +2572,116 @@ CvPoint pt, int start_stage */)
namespace cv
{
namespace ocl
namespace ocl
{
struct gpuHaarDetectObjects_ScaleImage_Invoker
{
gpuHaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade *_cascade,
int _stripSize, double _factor,
const Mat &_sum1, const Mat &_sqsum1, Mat *_norm1,
Mat *_mask1, Rect _equRect, ConcurrentRectVector &_vec )
{
cascade = _cascade;
stripSize = _stripSize;
factor = _factor;
sum1 = _sum1;
sqsum1 = _sqsum1;
norm1 = _norm1;
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
}
struct gpuHaarDetectObjects_ScaleImage_Invoker
void operator()( const BlockedRange &range ) const
{
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width * factor), cvRound(winSize0.height * factor));
int y1 = range.begin() * stripSize, y2 = min(range.end() * stripSize, sum1.rows - 1 - winSize0.height);
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
if( gpuRunHaarClassifierCascade( /*cascade, cvPoint(x, y), 0*/ ) > 0 )
vec->push_back(Rect(cvRound(x * factor), cvRound(y * factor),
winSize.width, winSize.height));
}
}
const CvHaarClassifierCascade *cascade;
int stripSize;
double factor;
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
ConcurrentRectVector *vec;
};
struct gpuHaarDetectObjects_ScaleCascade_Invoker
{
gpuHaarDetectObjects_ScaleCascade_Invoker( const CvHaarClassifierCascade *_cascade,
Size _winsize, const Range &_xrange, double _ystep,
size_t _sumstep, const int **_p, const int **_pq,
ConcurrentRectVector &_vec )
{
cascade = _cascade;
winsize = _winsize;
xrange = _xrange;
ystep = _ystep;
sumstep = _sumstep;
p = _p;
pq = _pq;
vec = &_vec;
}
void operator()( const BlockedRange &range ) const
{
int iy, startY = range.begin(), endY = range.end();
const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3];
const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3];
bool doCannyPruning = p0 != 0;
int sstep = (int)(sumstep / sizeof(p0[0]));
for( iy = startY; iy < endY; iy++ )
{
gpuHaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade *_cascade,
int _stripSize, double _factor,
const Mat &_sum1, const Mat &_sqsum1, Mat *_norm1,
Mat *_mask1, Rect _equRect, ConcurrentRectVector &_vec )
int ix, y = cvRound(iy * ystep), ixstep = 1;
for( ix = xrange.start; ix < xrange.end; ix += ixstep )
{
cascade = _cascade;
stripSize = _stripSize;
factor = _factor;
sum1 = _sum1;
sqsum1 = _sqsum1;
norm1 = _norm1;
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
}
int x = cvRound(ix * ystep); // it should really be ystep, not ixstep
void operator()( const BlockedRange &range ) const
{
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width * factor), cvRound(winSize0.height * factor));
int y1 = range.begin() * stripSize, y2 = min(range.end() * stripSize, sum1.rows - 1 - winSize0.height);
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
if( gpuRunHaarClassifierCascade( /*cascade, cvPoint(x, y), 0*/ ) > 0 )
vec->push_back(Rect(cvRound(x * factor), cvRound(y * factor),
winSize.width, winSize.height));
}
}
const CvHaarClassifierCascade *cascade;
int stripSize;
double factor;
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
ConcurrentRectVector *vec;
};
struct gpuHaarDetectObjects_ScaleCascade_Invoker
{
gpuHaarDetectObjects_ScaleCascade_Invoker( const CvHaarClassifierCascade *_cascade,
Size _winsize, const Range &_xrange, double _ystep,
size_t _sumstep, const int **_p, const int **_pq,
ConcurrentRectVector &_vec )
{
cascade = _cascade;
winsize = _winsize;
xrange = _xrange;
ystep = _ystep;
sumstep = _sumstep;
p = _p;
pq = _pq;
vec = &_vec;
}
void operator()( const BlockedRange &range ) const
{
int iy, startY = range.begin(), endY = range.end();
const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3];
const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3];
bool doCannyPruning = p0 != 0;
int sstep = (int)(sumstep / sizeof(p0[0]));
for( iy = startY; iy < endY; iy++ )
if( doCannyPruning )
{
int ix, y = cvRound(iy * ystep), ixstep = 1;
for( ix = xrange.start; ix < xrange.end; ix += ixstep )
int offset = y * sstep + x;
int s = p0[offset] - p1[offset] - p2[offset] + p3[offset];
int sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset];
if( s < 100 || sq < 20 )
{
int x = cvRound(ix * ystep); // it should really be ystep, not ixstep
if( doCannyPruning )
{
int offset = y * sstep + x;
int s = p0[offset] - p1[offset] - p2[offset] + p3[offset];
int sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset];
if( s < 100 || sq < 20 )
{
ixstep = 2;
continue;
}
}
int result = gpuRunHaarClassifierCascade(/* cascade, cvPoint(x, y), 0 */);
if( result > 0 )
vec->push_back(Rect(x, y, winsize.width, winsize.height));
ixstep = result != 0 ? 1 : 2;
ixstep = 2;
continue;
}
}
int result = gpuRunHaarClassifierCascade(/* cascade, cvPoint(x, y), 0 */);
if( result > 0 )
vec->push_back(Rect(x, y, winsize.width, winsize.height));
ixstep = result != 0 ? 1 : 2;
}
const CvHaarClassifierCascade *cascade;
double ystep;
size_t sumstep;
Size winsize;
Range xrange;
const int **p;
const int **pq;
ConcurrentRectVector *vec;
};
}
}
const CvHaarClassifierCascade *cascade;
double ystep;
size_t sumstep;
Size winsize;
Range xrange;
const int **p;
const int **pq;
ConcurrentRectVector *vec;
};
}
}
/*

20
modules/ocl/src/kernels/arithm_mul.cl
View File

@ -16,6 +16,7 @@
//
// @Authors
// Jia Haipeng, jiahaipeng95@gmail.com
// Dachuan Zhao, dachuan@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
@ -260,3 +261,22 @@ __kernel void arithm_mul_D6 (__global double *src1, int src1_step, int src1_offs
}
}
#endif
__kernel void arithm_muls_D5 (__global float *src1, int src1_step, int src1_offset,
__global float *dst, int dst_step, int dst_offset,
int rows, int cols, int dst_step1, float scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, (x << 2) + src1_offset);
int dst_index = mad24(y, dst_step, (x << 2) + dst_offset);
float data1 = *((__global float *)((__global char *)src1 + src1_index));
float tmp = data1 * scalar;
*((__global float *)((__global char *)dst + dst_index)) = tmp;
}
}

460
modules/ocl/src/kernels/haarobjectdetect_scaled2.cl
View File

@ -44,75 +44,75 @@
//M*/
// Enter your kernel in this window
#pragma OPENCL EXTENSION cl_amd_printf:enable
//#pragma OPENCL EXTENSION cl_amd_printf:enable
#define CV_HAAR_FEATURE_MAX 3
typedef int sumtype;
typedef float sqsumtype;
typedef struct __attribute__((aligned (128))) GpuHidHaarFeature
typedef struct __attribute__((aligned(128))) GpuHidHaarFeature
{
struct __attribute__((aligned (32)))
{
int p0 __attribute__((aligned (4)));
int p1 __attribute__((aligned (4)));
int p2 __attribute__((aligned (4)));
int p3 __attribute__((aligned (4)));
float weight __attribute__((aligned (4)));
}
rect[CV_HAAR_FEATURE_MAX] __attribute__((aligned (32)));
struct __attribute__((aligned(32)))
{
int p0 __attribute__((aligned(4)));
int p1 __attribute__((aligned(4)));
int p2 __attribute__((aligned(4)));
int p3 __attribute__((aligned(4)));
float weight __attribute__((aligned(4)));
}
rect[CV_HAAR_FEATURE_MAX] __attribute__((aligned(32)));
}
GpuHidHaarFeature;
typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode
typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned (64)));
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned(64)));
float weight[CV_HAAR_FEATURE_MAX] /*__attribute__((aligned (16)))*/;
float threshold /*__attribute__((aligned (4)))*/;
float alpha[2] __attribute__((aligned (8)));
int left __attribute__((aligned (4)));
int right __attribute__((aligned (4)));
float alpha[2] __attribute__((aligned(8)));
int left __attribute__((aligned(4)));
int right __attribute__((aligned(4)));
}
GpuHidHaarTreeNode;
typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
{
int count __attribute__((aligned (4)));
GpuHidHaarTreeNode* node __attribute__((aligned (8)));
float* alpha __attribute__((aligned (8)));
int count __attribute__((aligned(4)));
GpuHidHaarTreeNode *node __attribute__((aligned(8)));
float *alpha __attribute__((aligned(8)));
}
GpuHidHaarClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
{
int count __attribute__((aligned (4)));
float threshold __attribute__((aligned (4)));
int two_rects __attribute__((aligned (4)));
int reserved0 __attribute__((aligned (8)));
int reserved1 __attribute__((aligned (8)));
int reserved2 __attribute__((aligned (8)));
int reserved3 __attribute__((aligned (8)));
int count __attribute__((aligned(4)));
float threshold __attribute__((aligned(4)));
int two_rects __attribute__((aligned(4)));
int reserved0 __attribute__((aligned(8)));
int reserved1 __attribute__((aligned(8)));
int reserved2 __attribute__((aligned(8)));
int reserved3 __attribute__((aligned(8)));
}
GpuHidHaarStageClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
{
int count __attribute__((aligned (4)));
int is_stump_based __attribute__((aligned (4)));
int has_tilted_features __attribute__((aligned (4)));
int is_tree __attribute__((aligned (4)));
int pq0 __attribute__((aligned (4)));
int pq1 __attribute__((aligned (4)));
int pq2 __attribute__((aligned (4)));
int pq3 __attribute__((aligned (4)));
int p0 __attribute__((aligned (4)));
int p1 __attribute__((aligned (4)));
int p2 __attribute__((aligned (4)));
int p3 __attribute__((aligned (4)));
float inv_window_area __attribute__((aligned (4)));
}GpuHidHaarClassifierCascade;
int count __attribute__((aligned(4)));
int is_stump_based __attribute__((aligned(4)));
int has_tilted_features __attribute__((aligned(4)));
int is_tree __attribute__((aligned(4)));
int pq0 __attribute__((aligned(4)));
int pq1 __attribute__((aligned(4)));
int pq2 __attribute__((aligned(4)));
int pq3 __attribute__((aligned(4)));
int p0 __attribute__((aligned(4)));
int p1 __attribute__((aligned(4)));
int p2 __attribute__((aligned(4)));
int p3 __attribute__((aligned(4)));
float inv_window_area __attribute__((aligned(4)));
} GpuHidHaarClassifierCascade;
__kernel void gpuRunHaarClassifierCascade_scaled2(
global GpuHidHaarStageClassifier * stagecascadeptr,
global int4 * info,
global GpuHidHaarTreeNode * nodeptr,
global const int * restrict sum,
global const float * restrict sqsum,
global int4 * candidate,
global GpuHidHaarStageClassifier *stagecascadeptr,
global int4 *info,
global GpuHidHaarTreeNode *nodeptr,
global const int *restrict sum,
global const float *restrict sqsum,
global int4 *candidate,
const int step,
const int loopcount,
const int start_stage,
@ -120,215 +120,167 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
const int end_stage,
const int startnode,
const int splitnode,
global int4 * p,
//const int4 * pq,
global float * correction,
const int nodecount)
global int4 *p,
//const int4 * pq,
global float *correction,
const int nodecount)
{
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
int grpnumx = get_num_groups(0);
int grpidx=get_group_id(0);
int lclidx = get_local_id(0);
int lclidy = get_local_id(1);
int lcl_sz = mul24(grpszx,grpszy);
int lcl_id = mad24(lclidy,grpszx,lclidx);
__local int lclshare[1024];
__local int* glboutindex=lclshare+0;
__local int* lclcount=glboutindex+1;
__local int* lcloutindex=lclcount+1;
__local float* partialsum=(__local float*)(lcloutindex+(lcl_sz<<1));
glboutindex[0]=0;
int outputoff = mul24(grpidx,256);
candidate[outputoff+(lcl_id<<2)] = (int4)0;
candidate[outputoff+(lcl_id<<2)+1] = (int4)0;
candidate[outputoff+(lcl_id<<2)+2] = (int4)0;
candidate[outputoff+(lcl_id<<2)+3] = (int4)0;
for(int scalei = 0; scalei <loopcount; scalei++)
{
int4 scaleinfo1;
scaleinfo1 = info[scalei];
int width = (scaleinfo1.x & 0xffff0000) >> 16;
int height = scaleinfo1.x & 0xffff;
int grpnumperline =(scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
float correction_t=correction[scalei];
int ystep=(int)(max(2.0f,factor)+0.5f);
for(int grploop=get_group_id(0);grploop<totalgrp;grploop+=grpnumx){
int4 cascadeinfo=p[scalei];
int grpidy = grploop / grpnumperline;
int grpidx = grploop - mul24(grpidy, grpnumperline);
int ix = mad24(grpidx,grpszx,lclidx);
int iy = mad24(grpidy,grpszy,lclidy);
int x=ix*ystep;
int y=iy*ystep;
lcloutindex[lcl_id]=0;
lclcount[0]=0;
int result=1,nodecounter;
float mean,variance_norm_factor;
//if((ix < width) && (iy < height))
{
const int p_offset = mad24(y, step, x);
cascadeinfo.x +=p_offset;
cascadeinfo.z +=p_offset;
mean = (sum[mad24(cascadeinfo.y,step,cascadeinfo.x)] - sum[mad24(cascadeinfo.y,step,cascadeinfo.z)] -
sum[mad24(cascadeinfo.w,step,cascadeinfo.x)] + sum[mad24(cascadeinfo.w,step,cascadeinfo.z)])
*correction_t;
variance_norm_factor =sqsum[mad24(cascadeinfo.y,step, cascadeinfo.x)] - sqsum[mad24(cascadeinfo.y, step, cascadeinfo.z)] -
sqsum[mad24(cascadeinfo.w, step, cascadeinfo.x)] + sqsum[mad24(cascadeinfo.w, step, cascadeinfo.z)];
variance_norm_factor = variance_norm_factor * correction_t - mean * mean;
variance_norm_factor = variance_norm_factor >=0.f ? sqrt(variance_norm_factor) : 1.f;
result = 1;
nodecounter = startnode+nodecount*scalei;
for(int stageloop = start_stage; stageloop < split_stage&&result; stageloop++ )
{
float stage_sum = 0.f;
int4 stageinfo = *(global int4*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
for(int nodeloop = 0; nodeloop < stageinfo.x; nodeloop++ )
{
__global GpuHidHaarTreeNode* currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
float2 alpha2 = *(__global float2*)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x +=p_offset;
info1.z +=p_offset;
info2.x +=p_offset;
info2.z +=p_offset;
float classsum = (sum[mad24(info1.y,step,info1.x)] - sum[mad24(info1.y,step,info1.z)] -
sum[mad24(info1.w,step,info1.x)] + sum[mad24(info1.w,step,info1.z)]) * w.x;
classsum += (sum[mad24(info2.y,step,info2.x)] - sum[mad24(info2.y,step,info2.z)] -
sum[mad24(info2.w,step,info2.x)] + sum[mad24(info2.w,step,info2.z)]) * w.y;
info3.x +=p_offset;
info3.z +=p_offset;
classsum += (sum[mad24(info3.y,step,info3.x)] - sum[mad24(info3.y,step,info3.z)] -
sum[mad24(info3.w,step,info3.x)] + sum[mad24(info3.w,step,info3.z)]) * w.z;
stage_sum += classsum >= nodethreshold ? alpha2.y : alpha2.x;
nodecounter++;
}
result=(stage_sum>=stagethreshold);
}
if(result&&(ix<width)&&(iy<height))
{
int queueindex=atomic_inc(lclcount);
lcloutindex[queueindex<<1]=(y<<16)|x;
lcloutindex[(queueindex<<1)+1]=as_int(variance_norm_factor);
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount=lclcount[0];
nodecounter=splitnode+nodecount*scalei;
for(int stageloop=split_stage;stageloop<end_stage&&queuecount>0;stageloop++)
{
lclcount[0]=0;
barrier(CLK_LOCAL_MEM_FENCE);
int2 stageinfo=*(global int2*)(stagecascadeptr+stageloop);
float stagethreshold=as_float(stageinfo.y);
int perfscale=queuecount>4?3:2;
int queuecount_loop=(queuecount+(1<<perfscale)-1)>>perfscale;
int lcl_compute_win=lcl_sz>>perfscale;
int lcl_compute_win_id=(lcl_id>>(6-perfscale));
int lcl_loops=(stageinfo.x+lcl_compute_win-1)>>(6-perfscale);
int lcl_compute_id=lcl_id-(lcl_compute_win_id<<(6-perfscale));
for(int queueloop=0;queueloop<queuecount_loop&&lcl_compute_win_id<queuecount;queueloop++)
{
float stage_sum=0.f;
int temp_coord=lcloutindex[lcl_compute_win_id<<1];
float variance_norm_factor=as_float(lcloutindex[(lcl_compute_win_id<<1)+1]);
int queue_offset=mad24(((temp_coord&(int)0xffff0000)>>16),step,temp_coord&0xffff);
int tempnodecounter=lcl_compute_id;
float part_sum=0.f;
for(int lcl_loop=0;lcl_loop<lcl_loops&&tempnodecounter<stageinfo.x;lcl_loop++)
{
__global GpuHidHaarTreeNode* currentnodeptr = (nodeptr + nodecounter + tempnodecounter);
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
float2 alpha2 = *(__global float2*)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x +=queue_offset;
info1.z +=queue_offset;
info2.x +=queue_offset;
info2.z +=queue_offset;
float classsum = (sum[mad24(info1.y,step,info1.x)] - sum[mad24(info1.y,step,info1.z)] -
sum[mad24(info1.w,step,info1.x)] + sum[mad24(info1.w,step,info1.z)]) * w.x;
classsum += (sum[mad24(info2.y,step,info2.x)] - sum[mad24(info2.y,step,info2.z)] -
sum[mad24(info2.w,step,info2.x)] + sum[mad24(info2.w,step,info2.z)]) * w.y;
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
int grpnumx = get_num_groups(0);
int grpidx = get_group_id(0);
int lclidx = get_local_id(0);
int lclidy = get_local_id(1);
int lcl_sz = mul24(grpszx, grpszy);
int lcl_id = mad24(lclidy, grpszx, lclidx);
__local int lclshare[1024];
__local int *glboutindex = lclshare + 0;
__local int *lclcount = glboutindex + 1;
__local int *lcloutindex = lclcount + 1;
__local float *partialsum = (__local float *)(lcloutindex + (lcl_sz << 1));
glboutindex[0] = 0;
int outputoff = mul24(grpidx, 256);
candidate[outputoff + (lcl_id << 2)] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 1] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 2] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 3] = (int4)0;
info3.x +=queue_offset;
info3.z +=queue_offset;
classsum += (sum[mad24(info3.y,step,info3.x)] - sum[mad24(info3.y,step,info3.z)] -
sum[mad24(info3.w,step,info3.x)] + sum[mad24(info3.w,step,info3.z)]) * w.z;
part_sum += classsum >= nodethreshold ? alpha2.y : alpha2.x;
tempnodecounter+=lcl_compute_win;
}
partialsum[lcl_id]=part_sum;
barrier(CLK_LOCAL_MEM_FENCE);
for(int i=0;i<lcl_compute_win&&(lcl_compute_id==0);i++)
{
stage_sum+=partialsum[lcl_id+i];
}
if(stage_sum>=stagethreshold&&(lcl_compute_id==0))
{
int queueindex=atomic_inc(lclcount);
lcloutindex[queueindex<<1]=temp_coord;
lcloutindex[(queueindex<<1)+1]=as_int(variance_norm_factor);
}
lcl_compute_win_id+=(1<<perfscale);
barrier(CLK_LOCAL_MEM_FENCE);
}
queuecount=lclcount[0];
nodecounter+=stageinfo.x;
}
if(lcl_id<queuecount)
{
int temp=lcloutindex[lcl_id<<1];
int x=temp&0xffff;
int y=(temp&(int)0xffff0000)>>16;
temp=glboutindex[0];
int4 candidate_result;
candidate_result.zw=(int2)convert_int_rtn(factor*20.f);
candidate_result.x=x;
candidate_result.y=y;
atomic_inc(glboutindex);
candidate[outputoff+temp+lcl_id]=candidate_result;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
for (int scalei = 0; scalei < loopcount; scalei++)
{
int4 scaleinfo1;
scaleinfo1 = info[scalei];
int width = (scaleinfo1.x & 0xffff0000) >> 16;
int height = scaleinfo1.x & 0xffff;
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
float correction_t = correction[scalei];
int ystep = (int)(max(2.0f, factor) + 0.5f);
for (int grploop = get_group_id(0); grploop < totalgrp; grploop += grpnumx)
{
int4 cascadeinfo = p[scalei];
int grpidy = grploop / grpnumperline;
int grpidx = grploop - mul24(grpidy, grpnumperline);
int ix = mad24(grpidx, grpszx, lclidx);
int iy = mad24(grpidy, grpszy, lclidy);
int x = ix * ystep;
int y = iy * ystep;
lcloutindex[lcl_id] = 0;
lclcount[0] = 0;
int result = 1, nodecounter;
float mean, variance_norm_factor;
//if((ix < width) && (iy < height))
{
const int p_offset = mad24(y, step, x);
cascadeinfo.x += p_offset;
cascadeinfo.z += p_offset;
mean = (sum[mad24(cascadeinfo.y, step, cascadeinfo.x)] - sum[mad24(cascadeinfo.y, step, cascadeinfo.z)] -
sum[mad24(cascadeinfo.w, step, cascadeinfo.x)] + sum[mad24(cascadeinfo.w, step, cascadeinfo.z)])
* correction_t;
variance_norm_factor = sqsum[mad24(cascadeinfo.y, step, cascadeinfo.x)] - sqsum[mad24(cascadeinfo.y, step, cascadeinfo.z)] -
sqsum[mad24(cascadeinfo.w, step, cascadeinfo.x)] + sqsum[mad24(cascadeinfo.w, step, cascadeinfo.z)];
variance_norm_factor = variance_norm_factor * correction_t - mean * mean;
variance_norm_factor = variance_norm_factor >= 0.f ? sqrt(variance_norm_factor) : 1.f;
result = 1;
nodecounter = startnode + nodecount * scalei;
for (int stageloop = start_stage; stageloop < end_stage && result; stageloop++)
{
float stage_sum = 0.f;
int4 stageinfo = *(global int4 *)(stagecascadeptr + stageloop);
float stagethreshold = as_float(stageinfo.y);
for (int nodeloop = 0; nodeloop < stageinfo.x; nodeloop++)
{
__global GpuHidHaarTreeNode *currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4 *)(&(currentnodeptr->weight[0]));
float2 alpha2 = *(__global float2 *)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x += p_offset;
info1.z += p_offset;
info2.x += p_offset;
info2.z += p_offset;
float classsum = (sum[mad24(info1.y, step, info1.x)] - sum[mad24(info1.y, step, info1.z)] -
sum[mad24(info1.w, step, info1.x)] + sum[mad24(info1.w, step, info1.z)]) * w.x;
classsum += (sum[mad24(info2.y, step, info2.x)] - sum[mad24(info2.y, step, info2.z)] -
sum[mad24(info2.w, step, info2.x)] + sum[mad24(info2.w, step, info2.z)]) * w.y;
info3.x += p_offset;
info3.z += p_offset;
classsum += (sum[mad24(info3.y, step, info3.x)] - sum[mad24(info3.y, step, info3.z)] -
sum[mad24(info3.w, step, info3.x)] + sum[mad24(info3.w, step, info3.z)]) * w.z;
stage_sum += classsum >= nodethreshold ? alpha2.y : alpha2.x;
nodecounter++;
}
result = (stage_sum >= stagethreshold);
}
if (result && (ix < width) && (iy < height))
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex << 1] = (y << 16) | x;
lcloutindex[(queueindex << 1) + 1] = as_int(variance_norm_factor);
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
nodecounter = splitnode + nodecount * scalei;
if (lcl_id < queuecount)
{
int temp = lcloutindex[lcl_id << 1];
int x = temp & 0xffff;
int y = (temp & (int)0xffff0000) >> 16;
temp = glboutindex[0];
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rtn(factor * 20.f);
candidate_result.x = x;
candidate_result.y = y;
atomic_inc(glboutindex);
candidate[outputoff + temp + lcl_id] = candidate_result;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
}
}
}
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode * orinode, global GpuHidHaarTreeNode * newnode,float scale,float weight_scale,int nodenum)
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, int nodenum)
{
int counter=get_global_id(0);
int tr_x[3],tr_y[3],tr_h[3],tr_w[3],i=0;
GpuHidHaarTreeNode t1 = *(orinode + counter);
#pragma unroll
for(i=0;i<3;i++){
tr_x[i]=(int)(t1.p[i][0]*scale+0.5f);
tr_y[i]=(int)(t1.p[i][1]*scale+0.5f);
tr_w[i]=(int)(t1.p[i][2]*scale+0.5f);
tr_h[i]=(int)(t1.p[i][3]*scale+0.5f);
}
t1.weight[0]=t1.p[2][0]?-(t1.weight[1]*tr_h[1]*tr_w[1]+t1.weight[2]*tr_h[2]*tr_w[2])/(tr_h[0]*tr_w[0]):-t1.weight[1]*tr_h[1]*tr_w[1]/(tr_h[0]*tr_w[0]);
counter+=nodenum;
#pragma unroll
for(i=0;i<3;i++)
{
newnode[counter].p[i][0]=tr_x[i];
newnode[counter].p[i][1]=tr_y[i];
newnode[counter].p[i][2]=tr_x[i]+tr_w[i];
newnode[counter].p[i][3]=tr_y[i]+tr_h[i];
newnode[counter].weight[i]=t1.weight[i]*weight_scale;
}
newnode[counter].left=t1.left;
newnode[counter].right=t1.right;
newnode[counter].threshold=t1.threshold;
newnode[counter].alpha[0]=t1.alpha[0];
newnode[counter].alpha[1]=t1.alpha[1];
int counter = get_global_id(0);
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
GpuHidHaarTreeNode t1 = *(orinode + counter);
#pragma unroll
for (i = 0; i < 3; i++)
{
tr_x[i] = (int)(t1.p[i][0] * scale + 0.5f);
tr_y[i] = (int)(t1.p[i][1] * scale + 0.5f);
tr_w[i] = (int)(t1.p[i][2] * scale + 0.5f);
tr_h[i] = (int)(t1.p[i][3] * scale + 0.5f);
}
t1.weight[0] = t1.p[2][0] ? -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]) : -t1.weight[1] * tr_h[1] * tr_w[1] / (tr_h[0] * tr_w[0]);
counter += nodenum;
#pragma unroll
for (i = 0; i < 3; i++)
{
newnode[counter].p[i][0] = tr_x[i];
newnode[counter].p[i][1] = tr_y[i];
newnode[counter].p[i][2] = tr_x[i] + tr_w[i];
newnode[counter].p[i][3] = tr_y[i] + tr_h[i];
newnode[counter].weight[i] = t1.weight[i] * weight_scale;
}
newnode[counter].left = t1.left;
newnode[counter].right = t1.right;
newnode[counter].threshold = t1.threshold;
newnode[counter].alpha[0] = t1.alpha[0];
newnode[counter].alpha[1] = t1.alpha[1];
}

845
modules/ocl/src/kernels/pyrlk.cl
View File

File diff suppressed because it is too large Load Diff

764
modules/ocl/src/kernels/pyrlk_no_image.cl Normal file
View File

@ -0,0 +1,764 @@
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Sen Liu, sen@multicorewareinc.com
//
// 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 oclMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#define BUFFER 256
void reduce3(float val1, float val2, float val3, __local float *smem1, __local float *smem2, __local float *smem3, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
smem3[tid] = val3;
barrier(CLK_LOCAL_MEM_FENCE);
#if BUFFER > 128
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
smem3[tid] = val3 += smem3[tid + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
#if BUFFER > 64
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
smem2[tid] = val2 += smem2[tid + 64];
smem3[tid] = val3 += smem3[tid + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (tid < 32)
{
smem1[tid] = val1 += smem1[tid + 32];
smem2[tid] = val2 += smem2[tid + 32];
smem3[tid] = val3 += smem3[tid + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
smem1[tid] = val1 += smem1[tid + 16];
smem2[tid] = val2 += smem2[tid + 16];
smem3[tid] = val3 += smem3[tid + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
volatile __local float *vmem1 = smem1;
volatile __local float *vmem2 = smem2;
volatile __local float *vmem3 = smem3;
vmem1[tid] = val1 += vmem1[tid + 8];
vmem2[tid] = val2 += vmem2[tid + 8];
vmem3[tid] = val3 += vmem3[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem2[tid] = val2 += vmem2[tid + 4];
vmem3[tid] = val3 += vmem3[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem2[tid] = val2 += vmem2[tid + 2];
vmem3[tid] = val3 += vmem3[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
vmem2[tid] = val2 += vmem2[tid + 1];
vmem3[tid] = val3 += vmem3[tid + 1];
}
}
void reduce2(float val1, float val2, __local float *smem1, __local float *smem2, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
barrier(CLK_LOCAL_MEM_FENCE);
#if BUFFER > 128
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
#if BUFFER > 64
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
smem2[tid] = val2 += smem2[tid + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (tid < 32)
{
smem1[tid] = val1 += smem1[tid + 32];
smem2[tid] = val2 += smem2[tid + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
smem1[tid] = val1 += smem1[tid + 16];
smem2[tid] = val2 += smem2[tid + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
volatile __local float *vmem1 = smem1;
volatile __local float *vmem2 = smem2;
vmem1[tid] = val1 += vmem1[tid + 8];
vmem2[tid] = val2 += vmem2[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem2[tid] = val2 += vmem2[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem2[tid] = val2 += vmem2[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
vmem2[tid] = val2 += vmem2[tid + 1];
}
}
void reduce1(float val1, __local float *smem1, int tid)
{
smem1[tid] = val1;
barrier(CLK_LOCAL_MEM_FENCE);
#if BUFFER > 128
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
#if BUFFER > 64
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (tid < 32)
{
smem1[tid] = val1 += smem1[tid + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
volatile __local float *vmem1 = smem1;
vmem1[tid] = val1 += vmem1[tid + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
volatile __local float *vmem1 = smem1;
vmem1[tid] = val1 += vmem1[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
}
}
#define SCALE (1.0f / (1 << 20))
#define THRESHOLD 0.01f
#define DIMENSION 21
float readImage2Df_C1(__global const float *image, const float x, const float y, const int rows, const int cols, const int elemCntPerRow)
{
float2 coor = (float2)(x, y);
int i0 = clamp((int)floor(coor.x), 0, cols - 1);
int j0 = clamp((int)floor(coor.y), 0, rows - 1);
int i1 = clamp((int)floor(coor.x) + 1, 0, cols - 1);
int j1 = clamp((int)floor(coor.y) + 1, 0, rows - 1);
float a = coor.x - floor(coor.x);
float b = coor.y - floor(coor.y);
return (1 - a) * (1 - b) * image[mad24(j0, elemCntPerRow, i0)]
+ a * (1 - b) * image[mad24(j0, elemCntPerRow, i1)]
+ (1 - a) * b * image[mad24(j1, elemCntPerRow, i0)]
+ a * b * image[mad24(j1, elemCntPerRow, i1)];
}
__kernel void lkSparse_C1_D5(__global const float *I, __global const float *J,
__global const float2 *prevPts, int prevPtsStep, __global float2 *nextPts, int nextPtsStep, __global uchar *status, __global float *err,
const int level, const int rows, const int cols, const int elemCntPerRow,
int PATCH_X, int PATCH_Y, int cn, int c_winSize_x, int c_winSize_y, int c_iters, char calcErr)
{
__local float smem1[BUFFER];
__local float smem2[BUFFER];
__local float smem3[BUFFER];
float2 c_halfWin = (float2)((c_winSize_x - 1) >> 1, (c_winSize_y - 1) >> 1);
const int tid = mad24(get_local_id(1), get_local_size(0), get_local_id(0));
float2 prevPt = prevPts[get_group_id(0)] * (1.0f / (1 << level));
if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
{
if (tid == 0 && level == 0)
{
status[get_group_id(0)] = 0;
}
return;
}
prevPt -= c_halfWin;
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
float I_patch[1][3];
float dIdx_patch[1][3];
float dIdy_patch[1][3];
for (int yBase = get_local_id(1), i = 0; yBase < c_winSize_y; yBase += get_local_size(1), ++i)
{
for (int xBase = get_local_id(0), j = 0; xBase < c_winSize_x; xBase += get_local_size(0), ++j)
{
float x = (prevPt.x + xBase);
float y = (prevPt.y + yBase);
I_patch[i][j] = readImage2Df_C1(I, x, y, rows, cols, elemCntPerRow);
float dIdx = 3.0f * readImage2Df_C1(I, x + 1, y - 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C1(I, x + 1, y, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C1(I, x + 1, y + 1, rows, cols, elemCntPerRow) -
(3.0f * readImage2Df_C1(I, x - 1, y - 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C1(I, x - 1, y, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C1(I, x - 1, y + 1, rows, cols, elemCntPerRow));
float dIdy = 3.0f * readImage2Df_C1(I, x - 1, y + 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C1(I, x, y + 1, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C1(I, x + 1, y + 1, rows, cols, elemCntPerRow) -
(3.0f * readImage2Df_C1(I, x - 1, y - 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C1(I, x, y - 1, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C1(I, x + 1, y - 1, rows, cols, elemCntPerRow));
dIdx_patch[i][j] = dIdx;
dIdy_patch[i][j] = dIdy;
A11 += dIdx * dIdx;
A12 += dIdx * dIdy;
A22 += dIdy * dIdy;
}
}
reduce3(A11, A12, A22, smem1, smem2, smem3, tid);
barrier(CLK_LOCAL_MEM_FENCE);
A11 = smem1[0];
A12 = smem2[0];
A22 = smem3[0];
float D = A11 * A22 - A12 * A12;
if (D < 1.192092896e-07f)
{
if (tid == 0 && level == 0)
{
status[get_group_id(0)] = 0;
}
return;
}
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
float2 nextPt = nextPts[get_group_id(0)];
nextPt = nextPt * 2.0f - c_halfWin;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_halfWin.x || nextPt.x >= cols || nextPt.y < -c_halfWin.y || nextPt.y >= rows)
{
if (tid == 0 && level == 0)
{
status[get_group_id(0)] = 0;
}
return;
}
float b1 = 0;
float b2 = 0;
for (int y = get_local_id(1), i = 0; y < c_winSize_y; y += get_local_size(1), ++i)
{
for (int x = get_local_id(0), j = 0; x < c_winSize_x; x += get_local_size(0), ++j)
{
float diff = (readImage2Df_C1(J, nextPt.x + x, nextPt.y + y, rows, cols, elemCntPerRow) - I_patch[i][j]) * 32.0f;
b1 += diff * dIdx_patch[i][j];
b2 += diff * dIdy_patch[i][j];
}
}
reduce2(b1, b2, smem1, smem2, tid);
barrier(CLK_LOCAL_MEM_FENCE);
b1 = smem1[0];
b2 = smem2[0];
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt += delta;
//if (fabs(delta.x) < THRESHOLD && fabs(delta.y) < THRESHOLD)
// break;
}
float errval = 0.0f;
if (calcErr)
{
for (int y = get_local_id(1), i = 0; y < c_winSize_y; y += get_local_size(1), ++i)
{
for (int x = get_local_id(0), j = 0; x < c_winSize_x; x += get_local_size(0), ++j)
{
float diff = readImage2Df_C1(J, nextPt.x + x, nextPt.y + y, rows, cols, elemCntPerRow) - I_patch[i][j];
errval += fabs(diff);
}
}
reduce1(errval, smem1, tid);
}
if (tid == 0)
{
nextPt += c_halfWin;
nextPts[get_group_id(0)] = nextPt;
if (calcErr)
{
err[get_group_id(0)] = smem1[0] / (c_winSize_x * c_winSize_y);
}
}
}
float4 readImage2Df_C4(__global const float4 *image, const float x, const float y, const int rows, const int cols, const int elemCntPerRow)
{
float2 coor = (float2)(x, y);
int i0 = clamp((int)floor(coor.x), 0, cols - 1);
int j0 = clamp((int)floor(coor.y), 0, rows - 1);
int i1 = clamp((int)floor(coor.x) + 1, 0, cols - 1);
int j1 = clamp((int)floor(coor.y) + 1, 0, rows - 1);
float a = coor.x - floor(coor.x);
float b = coor.y - floor(coor.y);
return (1 - a) * (1 - b) * image[mad24(j0, elemCntPerRow, i0)]
+ a * (1 - b) * image[mad24(j0, elemCntPerRow, i1)]
+ (1 - a) * b * image[mad24(j1, elemCntPerRow, i0)]
+ a * b * image[mad24(j1, elemCntPerRow, i1)];
}
__kernel void lkSparse_C4_D5(__global const float *I, __global const float *J,
__global const float2 *prevPts, int prevPtsStep, __global float2 *nextPts, int nextPtsStep, __global uchar *status, __global float *err,
const int level, const int rows, const int cols, const int elemCntPerRow,
int PATCH_X, int PATCH_Y, int cn, int c_winSize_x, int c_winSize_y, int c_iters, char calcErr)
{
__local float smem1[BUFFER];
__local float smem2[BUFFER];
__local float smem3[BUFFER];
float2 c_halfWin = (float2)((c_winSize_x - 1) >> 1, (c_winSize_y - 1) >> 1);
const int tid = mad24(get_local_id(1), get_local_size(0), get_local_id(0));
float2 prevPt = prevPts[get_group_id(0)] * (1.0f / (1 << level));
if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
{
if (tid == 0 && level == 0)
{
status[get_group_id(0)] = 0;
}
return;
}
prevPt -= c_halfWin;
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
float4 I_patch[1][3];
float4 dIdx_patch[1][3];
float4 dIdy_patch[1][3];
__global float4 *ptrI = (__global float4 *)I;
for (int yBase = get_local_id(1), i = 0; yBase < c_winSize_y; yBase += get_local_size(1), ++i)
{
for (int xBase = get_local_id(0), j = 0; xBase < c_winSize_x; xBase += get_local_size(0), ++j)
{
float x = (prevPt.x + xBase);
float y = (prevPt.y + yBase);
I_patch[i][j] = readImage2Df_C4(ptrI, x, y, rows, cols, elemCntPerRow);
float4 dIdx = 3.0f * readImage2Df_C4(ptrI, x + 1, y - 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C4(ptrI, x + 1, y, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C4(ptrI, x + 1, y + 1, rows, cols, elemCntPerRow) -
(3.0f * readImage2Df_C4(ptrI, x - 1, y - 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C4(ptrI, x - 1, y, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C4(ptrI, x - 1, y + 1, rows, cols, elemCntPerRow));
float4 dIdy = 3.0f * readImage2Df_C4(ptrI, x - 1, y + 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C4(ptrI, x, y + 1, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C4(ptrI, x + 1, y + 1, rows, cols, elemCntPerRow) -
(3.0f * readImage2Df_C4(ptrI, x - 1, y - 1, rows, cols, elemCntPerRow) + 10.0f * readImage2Df_C4(ptrI, x, y - 1, rows, cols, elemCntPerRow) + 3.0f * readImage2Df_C4(ptrI, x + 1, y - 1, rows, cols, elemCntPerRow));
dIdx_patch[i][j] = dIdx;
dIdy_patch[i][j] = dIdy;
A11 += (dIdx * dIdx).x + (dIdx * dIdx).y + (dIdx * dIdx).z;
A12 += (dIdx * dIdy).x + (dIdx * dIdy).y + (dIdx * dIdy).z;
A22 += (dIdy * dIdy).x + (dIdy * dIdy).y + (dIdy * dIdy).z;
}
}
reduce3(A11, A12, A22, smem1, smem2, smem3, tid);
barrier(CLK_LOCAL_MEM_FENCE);
A11 = smem1[0];
A12 = smem2[0];
A22 = smem3[0];
float D = A11 * A22 - A12 * A12;
//pD[get_group_id(0)] = D;
if (D < 1.192092896e-07f)
{
if (tid == 0 && level == 0)
{
status[get_group_id(0)] = 0;
}
return;
}
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
float2 nextPt = nextPts[get_group_id(0)];
nextPt = nextPt * 2.0f - c_halfWin;
__global float4 *ptrJ = (__global float4 *)J;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_halfWin.x || nextPt.x >= cols || nextPt.y < -c_halfWin.y || nextPt.y >= rows)
{
if (tid == 0 && level == 0)
{
status[get_group_id(0)] = 0;
}
return;
}
float b1 = 0;
float b2 = 0;
for (int y = get_local_id(1), i = 0; y < c_winSize_y; y += get_local_size(1), ++i)
{
for (int x = get_local_id(0), j = 0; x < c_winSize_x; x += get_local_size(0), ++j)
{
float4 diff = (readImage2Df_C4(ptrJ, nextPt.x + x, nextPt.y + y, rows, cols, elemCntPerRow) - I_patch[i][j]) * 32.0f;
b1 += (diff * dIdx_patch[i][j]).x + (diff * dIdx_patch[i][j]).y + (diff * dIdx_patch[i][j]).z;
b2 += (diff * dIdy_patch[i][j]).x + (diff * dIdy_patch[i][j]).y + (diff * dIdy_patch[i][j]).z;
}
}
reduce2(b1, b2, smem1, smem2, tid);
barrier(CLK_LOCAL_MEM_FENCE);
b1 = smem1[0];
b2 = smem2[0];
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt += delta;
//if (fabs(delta.x) < THRESHOLD && fabs(delta.y) < THRESHOLD)
// break;
}
float errval = 0.0f;
if (calcErr)
{
for (int y = get_local_id(1), i = 0; y < c_winSize_y; y += get_local_size(1), ++i)
{
for (int x = get_local_id(0), j = 0; x < c_winSize_x; x += get_local_size(0), ++j)
{
float4 diff = readImage2Df_C4(ptrJ, nextPt.x + x, nextPt.y + y, rows, cols, elemCntPerRow) - I_patch[i][j];
errval += fabs(diff.x) + fabs(diff.y) + fabs(diff.z);
}
}
reduce1(errval, smem1, tid);
}
if (tid == 0)
{
nextPt += c_halfWin;
nextPts[get_group_id(0)] = nextPt;
if (calcErr)
{
err[get_group_id(0)] = smem1[0] / (3 * c_winSize_x * c_winSize_y);
}
}
}
int readImage2Di_C1(__global const int *image, float2 coor, int2 size, const int elemCntPerRow)
{
int i = clamp((int)floor(coor.x), 0, size.x - 1);
int j = clamp((int)floor(coor.y), 0, size.y - 1);
return image[mad24(j, elemCntPerRow, i)];
}
__kernel void lkDense_C1_D0(__global const int *I, __global const int *J, __global float *u, int uStep, __global float *v, int vStep, __global const float *prevU, int prevUStep, __global const float *prevV, int prevVStep,
const int rows, const int cols, /*__global float* err, int errStep, int cn,*/
const int elemCntPerRow, int c_winSize_x, int c_winSize_y, int c_iters, char calcErr)
{
int c_halfWin_x = (c_winSize_x - 1) / 2;
int c_halfWin_y = (c_winSize_y - 1) / 2;
const int patchWidth = get_local_size(0) + 2 * c_halfWin_x;
const int patchHeight = get_local_size(1) + 2 * c_halfWin_y;
__local int smem[8192];
__local int *I_patch = smem;
__local int *dIdx_patch = I_patch + patchWidth * patchHeight;
__local int *dIdy_patch = dIdx_patch + patchWidth * patchHeight;
const int xBase = get_group_id(0) * get_local_size(0);
const int yBase = get_group_id(1) * get_local_size(1);
int2 size = (int2)(cols, rows);
for (int i = get_local_id(1); i < patchHeight; i += get_local_size(1))
{
for (int j = get_local_id(0); j < patchWidth; j += get_local_size(0))
{
float x = xBase - c_halfWin_x + j + 0.5f;
float y = yBase - c_halfWin_y + i + 0.5f;
I_patch[i * patchWidth + j] = readImage2Di_C1(I, (float2)(x, y), size, elemCntPerRow);
// Sharr Deriv
dIdx_patch[i * patchWidth + j] = 3 * readImage2Di_C1(I, (float2)(x + 1, y - 1), size, elemCntPerRow) + 10 * readImage2Di_C1(I, (float2)(x + 1, y), size, elemCntPerRow) + 3 * readImage2Di_C1(I, (float2)(x + 1, y + 1), size, elemCntPerRow) -
(3 * readImage2Di_C1(I, (float2)(x - 1, y - 1), size, elemCntPerRow) + 10 * readImage2Di_C1(I, (float2)(x - 1, y), size, elemCntPerRow) + 3 * readImage2Di_C1(I, (float2)(x - 1, y + 1), size, elemCntPerRow));
dIdy_patch[i * patchWidth + j] = 3 * readImage2Di_C1(I, (float2)(x - 1, y + 1), size, elemCntPerRow) + 10 * readImage2Di_C1(I, (float2)(x, y + 1), size, elemCntPerRow) + 3 * readImage2Di_C1(I, (float2)(x + 1, y + 1), size, elemCntPerRow) -
(3 * readImage2Di_C1(I, (float2)(x - 1, y - 1), size, elemCntPerRow) + 10 * readImage2Di_C1(I, (float2)(x, y - 1), size, elemCntPerRow) + 3 * readImage2Di_C1(I, (float2)(x + 1, y - 1), size, elemCntPerRow));
}
}
barrier(CLK_LOCAL_MEM_FENCE);
// extract the patch from the first image, compute covariation matrix of derivatives
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x >= cols || y >= rows)
{
return;
}
int A11i = 0;
int A12i = 0;
int A22i = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int dIdx = dIdx_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
int dIdy = dIdy_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
A11i += dIdx * dIdx;
A12i += dIdx * dIdy;
A22i += dIdy * dIdy;
}
}
float A11 = A11i;
float A12 = A12i;
float A22 = A22i;
float D = A11 * A22 - A12 * A12;
//if (calcErr && GET_MIN_EIGENVALS)
// (err + y * errStep)[x] = minEig;
if (D < 1.192092896e-07f)
{
//if (calcErr)
// err(y, x) = 3.402823466e+38f;
return;
}
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
float2 nextPt;
nextPt.x = x + prevU[y / 2 * prevUStep / 4 + x / 2] * 2.0f;
nextPt.y = y + prevV[y / 2 * prevVStep / 4 + x / 2] * 2.0f;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < 0 || nextPt.x >= cols || nextPt.y < 0 || nextPt.y >= rows)
{
//if (calcErr)
// err(y, x) = 3.402823466e+38f;
return;
}
int b1 = 0;
int b2 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int iI = I_patch[(get_local_id(1) + i) * patchWidth + get_local_id(0) + j];
int iJ = readImage2Di_C1(J, (float2)(nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f), size, elemCntPerRow);
int diff = (iJ - iI) * 32;
int dIdx = dIdx_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
int dIdy = dIdy_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
b1 += diff * dIdx;
b2 += diff * dIdy;
}
}
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt.x += delta.x;
nextPt.y += delta.y;
if (fabs(delta.x) < 0.01f && fabs(delta.y) < 0.01f)
{
break;
}
}
u[y * uStep / 4 + x] = nextPt.x - x;
v[y * vStep / 4 + x] = nextPt.y - y;
if (calcErr)
{
int errval = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int iI = I_patch[(get_local_id(1) + i) * patchWidth + get_local_id(0) + j];
int iJ = readImage2Di_C1(J, (float2)(nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f), size, elemCntPerRow);
errval += abs(iJ - iI);
}
}
//err[y * errStep / 4 + x] = static_cast<float>(errval) / (c_winSize_x * c_winSize_y);
}
}

295
modules/ocl/src/pyrlk.cpp
View File

@ -48,23 +48,24 @@ using namespace cv::ocl;
#if !defined (HAVE_OPENCL)
void cv::ocl::PyrLKOpticalFlow::sparse(const oclMat &, const oclMat &, const oclMat &, oclMat &, oclMat &, oclMat *) { }
void cv::ocl::PyrLKOpticalFlow::sparse(const oclMat &, const oclMat &, const oclMat &, oclMat &, oclMat &, oclMat &) { }
void cv::ocl::PyrLKOpticalFlow::dense(const oclMat &, const oclMat &, oclMat &, oclMat &, oclMat *) { }
#else /* !defined (HAVE_OPENCL) */
namespace cv
{
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char *pyrlk;
extern const char *operator_setTo;
extern const char *operator_convertTo;
extern const char *operator_copyToM;
extern const char *arithm_mul;
extern const char *pyr_down;
}
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char *pyrlk;
extern const char *pyrlk_no_image;
extern const char *operator_setTo;
extern const char *operator_convertTo;
extern const char *operator_copyToM;
extern const char *arithm_mul;
extern const char *pyr_down;
}
}
struct dim3
@ -84,26 +85,26 @@ struct int2
namespace
{
void calcPatchSize(cv::Size winSize, int cn, dim3 &block, dim3 &patch, bool isDeviceArch11)
void calcPatchSize(cv::Size winSize, int cn, dim3 &block, dim3 &patch, bool isDeviceArch11)
{
winSize.width *= cn;
if (winSize.width > 32 && winSize.width > 2 * winSize.height)
{
winSize.width *= cn;
if (winSize.width > 32 && winSize.width > 2 * winSize.height)
{
block.x = isDeviceArch11 ? 16 : 32;
block.y = 8;
}
else
{
block.x = 16;
block.y = isDeviceArch11 ? 8 : 16;
}
patch.x = (winSize.width + block.x - 1) / block.x;
patch.y = (winSize.height + block.y - 1) / block.y;
block.z = patch.z = 1;
block.x = isDeviceArch11 ? 16 : 32;
block.y = 8;
}
else
{
block.x = 16;
block.y = isDeviceArch11 ? 8 : 16;
}
patch.x = (winSize.width + block.x - 1) / block.x;
patch.y = (winSize.height + block.y - 1) / block.y;
block.z = patch.z = 1;
}
}
inline int divUp(int total, int grain)
@ -530,7 +531,7 @@ void arithmetic_run(const oclMat &src1, oclMat &dst, string kernelName, const ch
void multiply_cus(const oclMat &src1, oclMat &dst, float scalar)
{
arithmetic_run(src1, dst, "arithm_muls", &pyrlk, (void *)(&scalar));
arithmetic_run(src1, dst, "arithm_muls", &arithm_mul, (void *)(&scalar));
}
void pyrdown_run_cus(const oclMat &src, const oclMat &dst)
@ -581,26 +582,26 @@ void pyrDown_cus(const oclMat &src, oclMat &dst)
//
//void callF(const oclMat& src, oclMat& dst, MultiplyScalar op, int mask)
//{
// Mat srcTemp;
// Mat dstTemp;
// src.download(srcTemp);
// dst.download(dstTemp);
// Mat srcTemp;
// Mat dstTemp;
// src.download(srcTemp);
// dst.download(dstTemp);
//
// int i;
// int j;
// int k;
// for(i = 0; i < srcTemp.rows; i++)
// {
// for(j = 0; j < srcTemp.cols; j++)
// {
// for(k = 0; k < srcTemp.channels(); k++)
// {
// ((float*)dstTemp.data)[srcTemp.channels() * (i * srcTemp.rows + j) + k] = (float)op(((float*)srcTemp.data)[srcTemp.channels() * (i * srcTemp.rows + j) + k]);
// }
// }
// }
// int i;
// int j;
// int k;
// for(i = 0; i < srcTemp.rows; i++)
// {
// for(j = 0; j < srcTemp.cols; j++)
// {
// for(k = 0; k < srcTemp.channels(); k++)
// {
// ((float*)dstTemp.data)[srcTemp.channels() * (i * srcTemp.rows + j) + k] = (float)op(((float*)srcTemp.data)[srcTemp.channels() * (i * srcTemp.rows + j) + k]);
// }
// }
// }
//
// dst = dstTemp;
// dst = dstTemp;
//}
//
//static inline bool isAligned(const unsigned char* ptr, size_t size)
@ -622,54 +623,54 @@ void pyrDown_cus(const oclMat &src, oclMat &dst)
// return;
// }
//
// Mat srcTemp;
// Mat dstTemp;
// src.download(srcTemp);
// dst.download(dstTemp);
// Mat srcTemp;
// Mat dstTemp;
// src.download(srcTemp);
// dst.download(dstTemp);
//
// int x_shifted;
// int x_shifted;
//
// int i;
// int j;
// for(i = 0; i < srcTemp.rows; i++)
// {
// const double* srcRow = (const double*)srcTemp.data + i * srcTemp.rows;
// int i;
// int j;
// for(i = 0; i < srcTemp.rows; i++)
// {
// const double* srcRow = (const double*)srcTemp.data + i * srcTemp.rows;
// double* dstRow = (double*)dstTemp.data + i * dstTemp.rows;;
//
// for(j = 0; j < srcTemp.cols; j++)
// {
// x_shifted = j * 4;
// for(j = 0; j < srcTemp.cols; j++)
// {
// x_shifted = j * 4;
//
// if(x_shifted + 4 - 1 < srcTemp.cols)
// {
// dstRow[x_shifted ] = op(srcRow[x_shifted ]);
// dstRow[x_shifted + 1] = op(srcRow[x_shifted + 1]);
// dstRow[x_shifted + 2] = op(srcRow[x_shifted + 2]);
// dstRow[x_shifted + 3] = op(srcRow[x_shifted + 3]);
// }
// else
// {
// for (int real_x = x_shifted; real_x < srcTemp.cols; ++real_x)
// {
// ((float*)dstTemp.data)[i * srcTemp.rows + real_x] = op(((float*)srcTemp.data)[i * srcTemp.rows + real_x]);
// }
// }
// }
// }
// if(x_shifted + 4 - 1 < srcTemp.cols)
// {
// dstRow[x_shifted ] = op(srcRow[x_shifted ]);
// dstRow[x_shifted + 1] = op(srcRow[x_shifted + 1]);
// dstRow[x_shifted + 2] = op(srcRow[x_shifted + 2]);
// dstRow[x_shifted + 3] = op(srcRow[x_shifted + 3]);
// }
// else
// {
// for (int real_x = x_shifted; real_x < srcTemp.cols; ++real_x)
// {
// ((float*)dstTemp.data)[i * srcTemp.rows + real_x] = op(((float*)srcTemp.data)[i * srcTemp.rows + real_x]);
// }
// }
// }
// }
//}
//
//void multiply(const oclMat& src1, double val, oclMat& dst, double scale = 1.0f);
//void multiply(const oclMat& src1, double val, oclMat& dst, double scale)
//{
// MultiplyScalar op(val, scale);
// //if(src1.channels() == 1 && dst.channels() == 1)
// //{
// // callT(src1, dst, op, 0);
// //}
// //else
// //{
// callF(src1, dst, op, 0);
// //}
// //if(src1.channels() == 1 && dst.channels() == 1)
// //{
// // callT(src1, dst, op, 0);
// //}
// //else
// //{
// callF(src1, dst, op, 0);
// //}
//}
cl_mem bindTexture(const oclMat &mat, int depth, int channels)
@ -735,46 +736,69 @@ void releaseTexture(cl_mem texture)
}
void lkSparse_run(oclMat &I, oclMat &J,
const oclMat &prevPts, oclMat &nextPts, oclMat &status, oclMat *err, bool GET_MIN_EIGENVALS, int ptcount,
const oclMat &prevPts, oclMat &nextPts, oclMat &status, oclMat& err, bool /*GET_MIN_EIGENVALS*/, int ptcount,
int level, /*dim3 block, */dim3 patch, Size winSize, int iters)
{
Context *clCxt = I.clCxt;
char platform[256] = {0};
cl_platform_id pid;
clGetDeviceInfo(*clCxt->impl->devices, CL_DEVICE_PLATFORM, sizeof(pid), &pid, NULL);
clGetPlatformInfo(pid, CL_PLATFORM_NAME, 256, platform, NULL);
std::string namestr = platform;
bool isImageSupported = true;
if(namestr.find("NVIDIA")!=string::npos || namestr.find("Intel")!=string::npos)
isImageSupported = false;
int elemCntPerRow = I.step / I.elemSize();
string kernelName = "lkSparse";
size_t localThreads[3] = { 8, 32, 1 };
size_t globalThreads[3] = { 8 * ptcount, 32, 1};
size_t localThreads[3] = { 8, isImageSupported?8:32, 1 };
size_t globalThreads[3] = { 8 * ptcount, isImageSupported?8:32, 1};
int cn = I.oclchannels();
bool calcErr;
if (err)
char calcErr;
if (level == 0)
{
calcErr = true;
calcErr = 1;
}
else
{
calcErr = false;
calcErr = 0;
}
calcErr = true;
cl_mem ITex = bindTexture(I, I.depth(), cn);
cl_mem JTex = bindTexture(J, J.depth(), cn);
vector<pair<size_t , const void *> > args;
cl_mem ITex;
cl_mem JTex;
if (isImageSupported)
{
ITex = bindTexture(I, I.depth(), cn);
JTex = bindTexture(J, J.depth(), cn);
}
else
{
ITex = (cl_mem)I.data;
JTex = (cl_mem)J.data;
}
args.push_back( make_pair( sizeof(cl_mem), (void *)&ITex ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&JTex ));
//cl_mem clmD = clCreateBuffer(clCxt, CL_MEM_READ_WRITE, ptcount * sizeof(float), NULL, NULL);
args.push_back( make_pair( sizeof(cl_mem), (void *)&prevPts.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&prevPts.step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&nextPts.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&nextPts.step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&status.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&(err->data) ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&err.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&level ));
args.push_back( make_pair( sizeof(cl_int), (void *)&I.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&I.cols ));
if (!isImageSupported)
{
args.push_back( make_pair( sizeof(cl_int), (void *)&elemCntPerRow ) );
}
args.push_back( make_pair( sizeof(cl_int), (void *)&patch.x ));
args.push_back( make_pair( sizeof(cl_int), (void *)&patch.y ));
args.push_back( make_pair( sizeof(cl_int), (void *)&cn ));
@ -782,27 +806,29 @@ void lkSparse_run(oclMat &I, oclMat &J,
args.push_back( make_pair( sizeof(cl_int), (void *)&winSize.height ));
args.push_back( make_pair( sizeof(cl_int), (void *)&iters ));
args.push_back( make_pair( sizeof(cl_char), (void *)&calcErr ));
args.push_back( make_pair( sizeof(cl_char), (void *)&GET_MIN_EIGENVALS ));
//args.push_back( make_pair( sizeof(cl_char), (void *)&GET_MIN_EIGENVALS ));
openCLExecuteKernel2(clCxt, &pyrlk, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), CLFLUSH);
if (isImageSupported)
{
openCLExecuteKernel2(clCxt, &pyrlk, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), CLFLUSH);
releaseTexture(ITex);
releaseTexture(JTex);
releaseTexture(ITex);
releaseTexture(JTex);
}
else
{
//printf("Warning: The image2d_t is not supported by the device. Using alternative method!\n");
openCLExecuteKernel2(clCxt, &pyrlk_no_image, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), CLFLUSH);
}
}
void cv::ocl::PyrLKOpticalFlow::sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts, oclMat &status, oclMat *err)
{
if (prevImg.clCxt->impl->devName.find("Intel(R) HD Graphics") != string::npos)
{
cout << " Intel HD GPU device unsupported " << endl;
return;
}
if (prevPts.empty())
{
nextPts.release();
status.release();
if (err) err->release();
//if (err) err->release();
return;
}
@ -836,8 +862,15 @@ void cv::ocl::PyrLKOpticalFlow::sparse(const oclMat &prevImg, const oclMat &next
//status.setTo(Scalar::all(1));
setTo(status, Scalar::all(1));
//if (err)
// ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
bool errMat = false;
if (!err)
{
err = new oclMat(1, prevPts.cols, CV_32FC1);
errMat = true;
}
else
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
//ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, err);
// build the image pyramids.
@ -872,17 +905,22 @@ void cv::ocl::PyrLKOpticalFlow::sparse(const oclMat &prevImg, const oclMat &next
for (int level = maxLevel; level >= 0; level--)
{
lkSparse_run(prevPyr_[level], nextPyr_[level],
prevPts, nextPts, status, level == 0 && err ? err : 0, getMinEigenVals, prevPts.cols,
prevPts, nextPts, status, *err, getMinEigenVals, prevPts.cols,
level, /*block, */patch, winSize, iters);
}
clFinish(prevImg.clCxt->impl->clCmdQueue);
if(errMat)
delete err;
}
void lkDense_run(oclMat &I, oclMat &J, oclMat &u, oclMat &v,
oclMat &prevU, oclMat &prevV, oclMat *err, Size winSize, int iters)
{
Context *clCxt = I.clCxt;
bool isImageSupported = clCxt->impl->devName.find("Intel(R) HD Graphics") == string::npos;
int elemCntPerRow = I.step / I.elemSize();
string kernelName = "lkDense";
@ -901,8 +939,19 @@ void lkDense_run(oclMat &I, oclMat &J, oclMat &u, oclMat &v,
calcErr = false;
}
cl_mem ITex = bindTexture(I, I.depth(), cn);
cl_mem JTex = bindTexture(J, J.depth(), cn);
cl_mem ITex;
cl_mem JTex;
if (isImageSupported)
{
ITex = bindTexture(I, I.depth(), cn);
JTex = bindTexture(J, J.depth(), cn);
}
else
{
ITex = (cl_mem)I.data;
JTex = (cl_mem)J.data;
}
//int2 halfWin = {(winSize.width - 1) / 2, (winSize.height - 1) / 2};
//const int patchWidth = 16 + 2 * halfWin.x;
@ -926,15 +975,27 @@ void lkDense_run(oclMat &I, oclMat &J, oclMat &u, oclMat &v,
args.push_back( make_pair( sizeof(cl_int), (void *)&I.cols ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&(*err).data ));
//args.push_back( make_pair( sizeof(cl_int), (void *)&(*err).step ));
if (!isImageSupported)
{
args.push_back( make_pair( sizeof(cl_int), (void *)&elemCntPerRow ) );
}
args.push_back( make_pair( sizeof(cl_int), (void *)&winSize.width ));
args.push_back( make_pair( sizeof(cl_int), (void *)&winSize.height ));
args.push_back( make_pair( sizeof(cl_int), (void *)&iters ));
args.push_back( make_pair( sizeof(cl_char), (void *)&calcErr ));
openCLExecuteKernel2(clCxt, &pyrlk, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), CLFLUSH);
if (isImageSupported)
{
openCLExecuteKernel2(clCxt, &pyrlk, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), CLFLUSH);
releaseTexture(ITex);
releaseTexture(JTex);
releaseTexture(ITex);
releaseTexture(JTex);
}
else
{
//printf("Warning: The image2d_t is not supported by the device. Using alternative method!\n");
openCLExecuteKernel2(clCxt, &pyrlk_no_image, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), CLFLUSH);
}
}
void cv::ocl::PyrLKOpticalFlow::dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err)

10
modules/ocl/test/test_pyrlk.cpp
View File

@ -118,9 +118,9 @@ TEST_P(Sparse, Mat)
cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void *)&status[0]);
d_status.download(status_mat);
//std::vector<float> err(d_err.cols);
//cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
//d_err.download(err_mat);
std::vector<float> err(d_err.cols);
cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
d_err.download(err_mat);
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
@ -153,9 +153,9 @@ TEST_P(Sparse, Mat)
}
}
double bad_ratio = static_cast<double>(mistmatch) / (nextPts.size() * 2);
double bad_ratio = static_cast<double>(mistmatch) / (nextPts.size());
ASSERT_LE(bad_ratio, 0.05f);
ASSERT_LE(bad_ratio, 0.02f);
}