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move CUDA object detection algorithms to separate module

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This commit is contained in:
Vladislav Vinogradov
2015-01-14 12:55:44 +03:00
parent b2a5e66fc5
commit f126f371b2
22 changed files with 624 additions and 270 deletions
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modules/cudaobjdetect/src/cascadeclassifier.cpp Normal 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.
//
//
// 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.
//
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// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
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//
//M*/
#include "precomp.hpp"
#include "opencv2/objdetect/objdetect_c.h"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA() { throw_no_cuda(); }
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA(const String&) { throw_no_cuda(); }
cv::cuda::CascadeClassifier_CUDA::~CascadeClassifier_CUDA() { throw_no_cuda(); }
bool cv::cuda::CascadeClassifier_CUDA::empty() const { throw_no_cuda(); return true; }
bool cv::cuda::CascadeClassifier_CUDA::load(const String&) { throw_no_cuda(); return true; }
Size cv::cuda::CascadeClassifier_CUDA::getClassifierSize() const { throw_no_cuda(); return Size();}
void cv::cuda::CascadeClassifier_CUDA::release() { throw_no_cuda(); }
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale( const GpuMat&, GpuMat&, double, int, Size) {throw_no_cuda(); return -1;}
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale( const GpuMat&, GpuMat&, Size, Size, double, int) {throw_no_cuda(); return -1;}
#else
struct cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
{
public:
CascadeClassifierImpl(){}
virtual ~CascadeClassifierImpl(){}
virtual unsigned int process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize, cv::Size maxObjectSize) = 0;
virtual cv::Size getClassifierCvSize() const = 0;
virtual bool read(const String& classifierAsXml) = 0;
};
#ifndef HAVE_OPENCV_CUDALEGACY
struct cv::cuda::CascadeClassifier_CUDA::HaarCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
{
public:
HaarCascade()
{
throw_no_cuda();
}
unsigned int process(const GpuMat&, GpuMat&, float, int, bool, bool, cv::Size, cv::Size)
{
throw_no_cuda();
return 0;
}
cv::Size getClassifierCvSize() const
{
throw_no_cuda();
return cv::Size();
}
bool read(const String&)
{
throw_no_cuda();
return false;
}
};
#else
struct cv::cuda::CascadeClassifier_CUDA::HaarCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
{
public:
HaarCascade() : lastAllocatedFrameSize(-1, -1)
{
ncvSetDebugOutputHandler(NCVDebugOutputHandler);
}
bool read(const String& filename)
{
ncvSafeCall( load(filename) );
return true;
}
NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize,
/*out*/unsigned int& numDetections)
{
calculateMemReqsAndAllocate(src.size());
NCVMemPtr src_beg;
src_beg.ptr = (void*)src.ptr<Ncv8u>();
src_beg.memtype = NCVMemoryTypeDevice;
NCVMemSegment src_seg;
src_seg.begin = src_beg;
src_seg.size = src.step * src.rows;
NCVMatrixReuse<Ncv8u> d_src(src_seg, static_cast<int>(devProp.textureAlignment), src.cols, src.rows, static_cast<int>(src.step), true);
ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
CV_Assert(objects.rows == 1);
NCVMemPtr objects_beg;
objects_beg.ptr = (void*)objects.ptr<NcvRect32u>();
objects_beg.memtype = NCVMemoryTypeDevice;
NCVMemSegment objects_seg;
objects_seg.begin = objects_beg;
objects_seg.size = objects.step * objects.rows;
NCVVectorReuse<NcvRect32u> d_rects(objects_seg, objects.cols);
ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
NcvSize32u roi;
roi.width = d_src.width();
roi.height = d_src.height();
NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height);
Ncv32u flags = 0;
flags |= findLargestObject? NCVPipeObjDet_FindLargestObject : 0;
flags |= visualizeInPlace ? NCVPipeObjDet_VisualizeInPlace : 0;
ncvStat = ncvDetectObjectsMultiScale_device(
d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures,
winMinSize,
minNeighbors,
scaleStep, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
return NCV_SUCCESS;
}
unsigned int process(const GpuMat& image, GpuMat& objectsBuf, float scaleFactor, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size minSize, cv::Size /*maxObjectSize*/)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
const int defaultObjSearchNum = 100;
if (objectsBuf.empty())
{
objectsBuf.create(1, defaultObjSearchNum, DataType<Rect>::type);
}
cv::Size ncvMinSize = this->getClassifierCvSize();
if (ncvMinSize.width < minSize.width && ncvMinSize.height < minSize.height)
{
ncvMinSize.width = minSize.width;
ncvMinSize.height = minSize.height;
}
unsigned int numDetections;
ncvSafeCall(this->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections));
return numDetections;
}
cv::Size getClassifierCvSize() const { return cv::Size(haar.ClassifierSize.width, haar.ClassifierSize.height); }
private:
static void NCVDebugOutputHandler(const String &msg) { CV_Error(cv::Error::GpuApiCallError, msg.c_str()); }
NCVStatus load(const String& classifierFile)
{
int devId = cv::cuda::getDevice();
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
// Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
gpuCascadeAllocator = makePtr<NCVMemNativeAllocator>(NCVMemoryTypeDevice, static_cast<int>(devProp.textureAlignment));
cpuCascadeAllocator = makePtr<NCVMemNativeAllocator>(NCVMemoryTypeHostPinned, static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuCascadeAllocator->isInitialized(), "Error creating cascade GPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCascadeAllocator->isInitialized(), "Error creating cascade CPU allocator", NCV_CUDA_ERROR);
Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
ncvStat = ncvHaarGetClassifierSize(classifierFile, haarNumStages, haarNumNodes, haarNumFeatures);
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error reading classifier size (check the file)", NCV_FILE_ERROR);
h_haarStages.reset (new NCVVectorAlloc<HaarStage64>(*cpuCascadeAllocator, haarNumStages));
h_haarNodes.reset (new NCVVectorAlloc<HaarClassifierNode128>(*cpuCascadeAllocator, haarNumNodes));
h_haarFeatures.reset(new NCVVectorAlloc<HaarFeature64>(*cpuCascadeAllocator, haarNumFeatures));
ncvAssertPrintReturn(h_haarStages->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(h_haarNodes->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(h_haarFeatures->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
ncvStat = ncvHaarLoadFromFile_host(classifierFile, haar, *h_haarStages, *h_haarNodes, *h_haarFeatures);
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error loading classifier", NCV_FILE_ERROR);
d_haarStages.reset (new NCVVectorAlloc<HaarStage64>(*gpuCascadeAllocator, haarNumStages));
d_haarNodes.reset (new NCVVectorAlloc<HaarClassifierNode128>(*gpuCascadeAllocator, haarNumNodes));
d_haarFeatures.reset(new NCVVectorAlloc<HaarFeature64>(*gpuCascadeAllocator, haarNumFeatures));
ncvAssertPrintReturn(d_haarStages->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(d_haarNodes->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(d_haarFeatures->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
ncvStat = h_haarStages->copySolid(*d_haarStages, 0);
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
ncvStat = h_haarNodes->copySolid(*d_haarNodes, 0);
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
ncvStat = h_haarFeatures->copySolid(*d_haarFeatures, 0);
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
return NCV_SUCCESS;
}
NCVStatus calculateMemReqsAndAllocate(const Size& frameSize)
{
if (lastAllocatedFrameSize == frameSize)
{
return NCV_SUCCESS;
}
// Calculate memory requirements and create real allocators
NCVMemStackAllocator gpuCounter(static_cast<int>(devProp.textureAlignment));
NCVMemStackAllocator cpuCounter(static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", NCV_CUDA_ERROR);
NCVMatrixAlloc<Ncv8u> d_src(gpuCounter, frameSize.width, frameSize.height);
NCVMatrixAlloc<Ncv8u> h_src(cpuCounter, frameSize.width, frameSize.height);
ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVVectorAlloc<NcvRect32u> d_rects(gpuCounter, 100);
ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NcvSize32u roi;
roi.width = d_src.width();
roi.height = d_src.height();
Ncv32u numDetections;
ncvStat = ncvDetectObjectsMultiScale_device(d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures, haar.ClassifierSize, 4, 1.2f, 1, 0, gpuCounter, cpuCounter, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
gpuAllocator = makePtr<NCVMemStackAllocator>(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
cpuAllocator = makePtr<NCVMemStackAllocator>(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuAllocator->isInitialized(), "Error creating GPU memory allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuAllocator->isInitialized(), "Error creating CPU memory allocator", NCV_CUDA_ERROR);
lastAllocatedFrameSize = frameSize;
return NCV_SUCCESS;
}
cudaDeviceProp devProp;
NCVStatus ncvStat;
Ptr<NCVMemNativeAllocator> gpuCascadeAllocator;
Ptr<NCVMemNativeAllocator> cpuCascadeAllocator;
Ptr<NCVVectorAlloc<HaarStage64> > h_haarStages;
Ptr<NCVVectorAlloc<HaarClassifierNode128> > h_haarNodes;
Ptr<NCVVectorAlloc<HaarFeature64> > h_haarFeatures;
HaarClassifierCascadeDescriptor haar;
Ptr<NCVVectorAlloc<HaarStage64> > d_haarStages;
Ptr<NCVVectorAlloc<HaarClassifierNode128> > d_haarNodes;
Ptr<NCVVectorAlloc<HaarFeature64> > d_haarFeatures;
Size lastAllocatedFrameSize;
Ptr<NCVMemStackAllocator> gpuAllocator;
Ptr<NCVMemStackAllocator> cpuAllocator;
virtual ~HaarCascade(){}
};
#endif
cv::Size operator -(const cv::Size& a, const cv::Size& b)
{
return cv::Size(a.width - b.width, a.height - b.height);
}
cv::Size operator +(const cv::Size& a, const int& i)
{
return cv::Size(a.width + i, a.height + i);
}
cv::Size operator *(const cv::Size& a, const float& f)
{
return cv::Size(cvRound(a.width * f), cvRound(a.height * f));
}
cv::Size operator /(const cv::Size& a, const float& f)
{
return cv::Size(cvRound(a.width / f), cvRound(a.height / f));
}
bool operator <=(const cv::Size& a, const cv::Size& b)
{
return a.width <= b.width && a.height <= b.width;
}
struct PyrLavel
{
PyrLavel(int _order, float _scale, cv::Size frame, cv::Size window, cv::Size minObjectSize)
{
do
{
order = _order;
scale = pow(_scale, order);
sFrame = frame / scale;
workArea = sFrame - window + 1;
sWindow = window * scale;
_order++;
} while (sWindow <= minObjectSize);
}
bool isFeasible(cv::Size maxObj)
{
return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj;
}
PyrLavel next(float factor, cv::Size frame, cv::Size window, cv::Size minObjectSize)
{
return PyrLavel(order + 1, factor, frame, window, minObjectSize);
}
int order;
float scale;
cv::Size sFrame;
cv::Size workArea;
cv::Size sWindow;
};
namespace cv { namespace cuda { namespace device
{
namespace lbp
{
void classifyPyramid(int frameW,
int frameH,
int windowW,
int windowH,
float initalScale,
float factor,
int total,
const PtrStepSzb& mstages,
const int nstages,
const PtrStepSzi& mnodes,
const PtrStepSzf& mleaves,
const PtrStepSzi& msubsets,
const PtrStepSzb& mfeatures,
const int subsetSize,
PtrStepSz<int4> objects,
unsigned int* classified,
PtrStepSzi integral);
void connectedConmonents(PtrStepSz<int4> candidates, int ncandidates, PtrStepSz<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
}
}}}
struct cv::cuda::CascadeClassifier_CUDA::LbpCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
{
public:
struct Stage
{
int first;
int ntrees;
float threshold;
};
LbpCascade(){}
virtual ~LbpCascade(){}
virtual unsigned int process(const GpuMat& image, GpuMat& objects, float scaleFactor, int groupThreshold, bool /*findLargestObject*/,
bool /*visualizeInPlace*/, cv::Size minObjectSize, cv::Size maxObjectSize)
{
CV_Assert(scaleFactor > 1 && image.depth() == CV_8U);
// const int defaultObjSearchNum = 100;
const float grouping_eps = 0.2f;
if( !objects.empty() && objects.depth() == CV_32S)
objects.reshape(4, 1);
else
objects.create(1 , image.cols >> 4, CV_32SC4);
// used for debug
// candidates.setTo(cv::Scalar::all(0));
// objects.setTo(cv::Scalar::all(0));
if (maxObjectSize == cv::Size())
maxObjectSize = image.size();
allocateBuffers(image.size());
unsigned int classified = 0;
GpuMat dclassified(1, 1, CV_32S);
cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );
PyrLavel level(0, scaleFactor, image.size(), NxM, minObjectSize);
while (level.isFeasible(maxObjectSize))
{
int acc = level.sFrame.width + 1;
float iniScale = level.scale;
cv::Size area = level.workArea;
int step = 1 + (level.scale <= 2.f);
int total = 0, prev = 0;
while (acc <= integralFactor * (image.cols + 1) && level.isFeasible(maxObjectSize))
{
// create sutable matrix headers
GpuMat src = resuzeBuffer(cv::Rect(0, 0, level.sFrame.width, level.sFrame.height));
GpuMat sint = integral(cv::Rect(prev, 0, level.sFrame.width + 1, level.sFrame.height + 1));
GpuMat buff = integralBuffer;
// generate integral for scale
cuda::resize(image, src, level.sFrame, 0, 0, cv::INTER_LINEAR);
cuda::integral(src, sint, buff);
// calculate job
int totalWidth = level.workArea.width / step;
total += totalWidth * (level.workArea.height / step);
// go to next pyramide level
level = level.next(scaleFactor, image.size(), NxM, minObjectSize);
area = level.workArea;
step = (1 + (level.scale <= 2.f));
prev = acc;
acc += level.sFrame.width + 1;
}
device::lbp::classifyPyramid(image.cols, image.rows, NxM.width - 1, NxM.height - 1, iniScale, scaleFactor, total, stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat,
leaves_mat, subsets_mat, features_mat, subsetSize, candidates, dclassified.ptr<unsigned int>(), integral);
}
if (groupThreshold <= 0 || objects.empty())
return 0;
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
device::lbp::connectedConmonents(candidates, classified, objects, groupThreshold, grouping_eps, dclassified.ptr<unsigned int>());
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaDeviceSynchronize() );
return classified;
}
virtual cv::Size getClassifierCvSize() const { return NxM; }
bool read(const String& classifierAsXml)
{
FileStorage fs(classifierAsXml, FileStorage::READ);
return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
}
private:
void allocateBuffers(cv::Size frame)
{
if (frame == cv::Size())
return;
if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows)
{
resuzeBuffer.create(frame, CV_8UC1);
integral.create(frame.height + 1, integralFactor * (frame.width + 1), CV_32SC1);
#ifdef HAVE_OPENCV_CUDALEGACY
NcvSize32u roiSize;
roiSize.width = frame.width;
roiSize.height = frame.height;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::cuda::getDevice()) );
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
integralBuffer.create(1, bufSize, CV_8UC1);
#endif
candidates.create(1 , frame.width >> 1, CV_32SC4);
}
}
bool read(const FileNode &root)
{
const char *CUDA_CC_STAGE_TYPE = "stageType";
const char *CUDA_CC_FEATURE_TYPE = "featureType";
const char *CUDA_CC_BOOST = "BOOST";
const char *CUDA_CC_LBP = "LBP";
const char *CUDA_CC_MAX_CAT_COUNT = "maxCatCount";
const char *CUDA_CC_HEIGHT = "height";
const char *CUDA_CC_WIDTH = "width";
const char *CUDA_CC_STAGE_PARAMS = "stageParams";
const char *CUDA_CC_MAX_DEPTH = "maxDepth";
const char *CUDA_CC_FEATURE_PARAMS = "featureParams";
const char *CUDA_CC_STAGES = "stages";
const char *CUDA_CC_STAGE_THRESHOLD = "stageThreshold";
const float CUDA_THRESHOLD_EPS = 1e-5f;
const char *CUDA_CC_WEAK_CLASSIFIERS = "weakClassifiers";
const char *CUDA_CC_INTERNAL_NODES = "internalNodes";
const char *CUDA_CC_LEAF_VALUES = "leafValues";
const char *CUDA_CC_FEATURES = "features";
const char *CUDA_CC_RECT = "rect";
String stageTypeStr = (String)root[CUDA_CC_STAGE_TYPE];
CV_Assert(stageTypeStr == CUDA_CC_BOOST);
String featureTypeStr = (String)root[CUDA_CC_FEATURE_TYPE];
CV_Assert(featureTypeStr == CUDA_CC_LBP);
NxM.width = (int)root[CUDA_CC_WIDTH];
NxM.height = (int)root[CUDA_CC_HEIGHT];
CV_Assert( NxM.height > 0 && NxM.width > 0 );
isStumps = ((int)(root[CUDA_CC_STAGE_PARAMS][CUDA_CC_MAX_DEPTH]) == 1) ? true : false;
CV_Assert(isStumps);
FileNode fn = root[CUDA_CC_FEATURE_PARAMS];
if (fn.empty())
return false;
ncategories = fn[CUDA_CC_MAX_CAT_COUNT];
subsetSize = (ncategories + 31) / 32;
nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 );
fn = root[CUDA_CC_STAGES];
if (fn.empty())
return false;
std::vector<Stage> stages;
stages.reserve(fn.size());
std::vector<int> cl_trees;
std::vector<int> cl_nodes;
std::vector<float> cl_leaves;
std::vector<int> subsets;
FileNodeIterator it = fn.begin(), it_end = fn.end();
for (size_t si = 0; it != it_end; si++, ++it )
{
FileNode fns = *it;
Stage st;
st.threshold = (float)fns[CUDA_CC_STAGE_THRESHOLD] - CUDA_THRESHOLD_EPS;
fns = fns[CUDA_CC_WEAK_CLASSIFIERS];
if (fns.empty())
return false;
st.ntrees = (int)fns.size();
st.first = (int)cl_trees.size();
stages.push_back(st);// (int, int, float)
cl_trees.reserve(stages[si].first + stages[si].ntrees);
// weak trees
FileNodeIterator it1 = fns.begin(), it1_end = fns.end();
for ( ; it1 != it1_end; ++it1 )
{
FileNode fnw = *it1;
FileNode internalNodes = fnw[CUDA_CC_INTERNAL_NODES];
FileNode leafValues = fnw[CUDA_CC_LEAF_VALUES];
if ( internalNodes.empty() || leafValues.empty() )
return false;
int nodeCount = (int)internalNodes.size()/nodeStep;
cl_trees.push_back(nodeCount);
cl_nodes.reserve((cl_nodes.size() + nodeCount) * 3);
cl_leaves.reserve(cl_leaves.size() + leafValues.size());
if( subsetSize > 0 )
subsets.reserve(subsets.size() + nodeCount * subsetSize);
// nodes
FileNodeIterator iIt = internalNodes.begin(), iEnd = internalNodes.end();
for( ; iIt != iEnd; )
{
cl_nodes.push_back((int)*(iIt++));
cl_nodes.push_back((int)*(iIt++));
cl_nodes.push_back((int)*(iIt++));
if( subsetSize > 0 )
for( int j = 0; j < subsetSize; j++, ++iIt )
subsets.push_back((int)*iIt);
}
// leaves
iIt = leafValues.begin(), iEnd = leafValues.end();
for( ; iIt != iEnd; ++iIt )
cl_leaves.push_back((float)*iIt);
}
}
fn = root[CUDA_CC_FEATURES];
if( fn.empty() )
return false;
std::vector<uchar> features;
features.reserve(fn.size() * 4);
FileNodeIterator f_it = fn.begin(), f_end = fn.end();
for (; f_it != f_end; ++f_it)
{
FileNode rect = (*f_it)[CUDA_CC_RECT];
FileNodeIterator r_it = rect.begin();
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
}
// copy data structures on gpu
stage_mat.upload(cv::Mat(1, (int) (stages.size() * sizeof(Stage)), CV_8UC1, (uchar*)&(stages[0]) ));
trees_mat.upload(cv::Mat(cl_trees).reshape(1,1));
nodes_mat.upload(cv::Mat(cl_nodes).reshape(1,1));
leaves_mat.upload(cv::Mat(cl_leaves).reshape(1,1));
subsets_mat.upload(cv::Mat(subsets).reshape(1,1));
features_mat.upload(cv::Mat(features).reshape(4,1));
return true;
}
enum stage { BOOST = 0 };
enum feature { LBP = 1, HAAR = 2 };
static const stage stageType = BOOST;
static const feature featureType = LBP;
cv::Size NxM;
bool isStumps;
int ncategories;
int subsetSize;
int nodeStep;
// gpu representation of classifier
GpuMat stage_mat;
GpuMat trees_mat;
GpuMat nodes_mat;
GpuMat leaves_mat;
GpuMat subsets_mat;
GpuMat features_mat;
GpuMat integral;
GpuMat integralBuffer;
GpuMat resuzeBuffer;
GpuMat candidates;
static const int integralFactor = 4;
};
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA()
: findLargestObject(false), visualizeInPlace(false), impl(0) {}
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA(const String& filename)
: findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
cv::cuda::CascadeClassifier_CUDA::~CascadeClassifier_CUDA() { release(); }
void cv::cuda::CascadeClassifier_CUDA::release() { if (impl) { delete impl; impl = 0; } }
bool cv::cuda::CascadeClassifier_CUDA::empty() const { return impl == 0; }
Size cv::cuda::CascadeClassifier_CUDA::getClassifierSize() const
{
return this->empty() ? Size() : impl->getClassifierCvSize();
}
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor, int minNeighbors, Size minSize)
{
CV_Assert( !this->empty());
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, cv::Size());
}
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize, double scaleFactor, int minNeighbors)
{
CV_Assert( !this->empty());
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, maxObjectSize);
}
bool cv::cuda::CascadeClassifier_CUDA::load(const String& filename)
{
release();
String fext = filename.substr(filename.find_last_of(".") + 1);
fext = fext.toLowerCase();
if (fext == "nvbin")
{
impl = new HaarCascade();
return impl->read(filename);
}
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
{
impl = new HaarCascade();
return impl->read(filename);
}
const char *CUDA_CC_LBP = "LBP";
String featureTypeStr = (String)fs.getFirstTopLevelNode()["featureType"];
if (featureTypeStr == CUDA_CC_LBP)
impl = new LbpCascade();
else
impl = new HaarCascade();
impl->read(filename);
return !this->empty();
}
#endif

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modules/cudaobjdetect/src/cuda/hog.cu Normal 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/reduce.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/core/cuda/warp_shuffle.hpp"
namespace cv { namespace cuda { namespace device
{
// Other values are not supported
#define CELL_WIDTH 8
#define CELL_HEIGHT 8
#define CELLS_PER_BLOCK_X 2
#define CELLS_PER_BLOCK_Y 2
namespace hog
{
__constant__ int cnbins;
__constant__ int cblock_stride_x;
__constant__ int cblock_stride_y;
__constant__ int cnblocks_win_x;
__constant__ int cnblocks_win_y;
__constant__ int cblock_hist_size;
__constant__ int cblock_hist_size_2up;
__constant__ int cdescr_size;
__constant__ int cdescr_width;
/* Returns the nearest upper power of two, works only for
the typical GPU thread count (pert block) values */
int power_2up(unsigned int n)
{
if (n < 1) return 1;
else if (n < 2) return 2;
else if (n < 4) return 4;
else if (n < 8) return 8;
else if (n < 16) return 16;
else if (n < 32) return 32;
else if (n < 64) return 64;
else if (n < 128) return 128;
else if (n < 256) return 256;
else if (n < 512) return 512;
else if (n < 1024) return 1024;
return -1; // Input is too big
}
void set_up_constants(int nbins, int block_stride_x, int block_stride_y,
int nblocks_win_x, int nblocks_win_y)
{
cudaSafeCall( cudaMemcpyToSymbol(cnbins, &nbins, sizeof(nbins)) );
cudaSafeCall( cudaMemcpyToSymbol(cblock_stride_x, &block_stride_x, sizeof(block_stride_x)) );
cudaSafeCall( cudaMemcpyToSymbol(cblock_stride_y, &block_stride_y, sizeof(block_stride_y)) );
cudaSafeCall( cudaMemcpyToSymbol(cnblocks_win_x, &nblocks_win_x, sizeof(nblocks_win_x)) );
cudaSafeCall( cudaMemcpyToSymbol(cnblocks_win_y, &nblocks_win_y, sizeof(nblocks_win_y)) );
int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y;
cudaSafeCall( cudaMemcpyToSymbol(cblock_hist_size, &block_hist_size, sizeof(block_hist_size)) );
int block_hist_size_2up = power_2up(block_hist_size);
cudaSafeCall( cudaMemcpyToSymbol(cblock_hist_size_2up, &block_hist_size_2up, sizeof(block_hist_size_2up)) );
int descr_width = nblocks_win_x * block_hist_size;
cudaSafeCall( cudaMemcpyToSymbol(cdescr_width, &descr_width, sizeof(descr_width)) );
int descr_size = descr_width * nblocks_win_y;
cudaSafeCall( cudaMemcpyToSymbol(cdescr_size, &descr_size, sizeof(descr_size)) );
}
//----------------------------------------------------------------------------
// Histogram computation
template <int nblocks> // Number of histogram blocks processed by single GPU thread block
__global__ void compute_hists_kernel_many_blocks(const int img_block_width, const PtrStepf grad,
const PtrStepb qangle, float scale, float* block_hists)
{
const int block_x = threadIdx.z;
const int cell_x = threadIdx.x / 16;
const int cell_y = threadIdx.y;
const int cell_thread_x = threadIdx.x & 0xF;
if (blockIdx.x * blockDim.z + block_x >= img_block_width)
return;
extern __shared__ float smem[];
float* hists = smem;
float* final_hist = smem + cnbins * 48 * nblocks;
const int offset_x = (blockIdx.x * blockDim.z + block_x) * cblock_stride_x +
4 * cell_x + cell_thread_x;
const int offset_y = blockIdx.y * cblock_stride_y + 4 * cell_y;
const float* grad_ptr = grad.ptr(offset_y) + offset_x * 2;
const unsigned char* qangle_ptr = qangle.ptr(offset_y) + offset_x * 2;
// 12 means that 12 pixels affect on block's cell (in one row)
if (cell_thread_x < 12)
{
float* hist = hists + 12 * (cell_y * blockDim.z * CELLS_PER_BLOCK_Y +
cell_x + block_x * CELLS_PER_BLOCK_X) +
cell_thread_x;
for (int bin_id = 0; bin_id < cnbins; ++bin_id)
hist[bin_id * 48 * nblocks] = 0.f;
const int dist_x = -4 + (int)cell_thread_x - 4 * cell_x;
const int dist_y_begin = -4 - 4 * (int)threadIdx.y;
for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y)
{
float2 vote = *(const float2*)grad_ptr;
uchar2 bin = *(const uchar2*)qangle_ptr;
grad_ptr += grad.step/sizeof(float);
qangle_ptr += qangle.step;
int dist_center_y = dist_y - 4 * (1 - 2 * cell_y);
int dist_center_x = dist_x - 4 * (1 - 2 * cell_x);
float gaussian = ::expf(-(dist_center_y * dist_center_y +
dist_center_x * dist_center_x) * scale);
float interp_weight = (8.f - ::fabs(dist_y + 0.5f)) *
(8.f - ::fabs(dist_x + 0.5f)) / 64.f;
hist[bin.x * 48 * nblocks] += gaussian * interp_weight * vote.x;
hist[bin.y * 48 * nblocks] += gaussian * interp_weight * vote.y;
}
volatile float* hist_ = hist;
for (int bin_id = 0; bin_id < cnbins; ++bin_id, hist_ += 48 * nblocks)
{
if (cell_thread_x < 6) hist_[0] += hist_[6];
if (cell_thread_x < 3) hist_[0] += hist_[3];
if (cell_thread_x == 0)
final_hist[((cell_x + block_x * 2) * 2 + cell_y) * cnbins + bin_id]
= hist_[0] + hist_[1] + hist_[2];
}
}
__syncthreads();
float* block_hist = block_hists + (blockIdx.y * img_block_width +
blockIdx.x * blockDim.z + block_x) *
cblock_hist_size;
int tid = (cell_y * CELLS_PER_BLOCK_Y + cell_x) * 16 + cell_thread_x;
if (tid < cblock_hist_size)
block_hist[tid] = final_hist[block_x * cblock_hist_size + tid];
}
void compute_hists(int nbins, int block_stride_x, int block_stride_y,
int height, int width, const PtrStepSzf& grad,
const PtrStepSzb& qangle, float sigma, float* block_hists)
{
const int nblocks = 1;
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
block_stride_x;
int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) /
block_stride_y;
dim3 grid(divUp(img_block_width, nblocks), img_block_height);
dim3 threads(32, 2, nblocks);
cudaSafeCall(cudaFuncSetCacheConfig(compute_hists_kernel_many_blocks<nblocks>,
cudaFuncCachePreferL1));
// Precompute gaussian spatial window parameter
float scale = 1.f / (2.f * sigma * sigma);
int hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12 * nblocks) * sizeof(float);
int final_hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * nblocks) * sizeof(float);
int smem = hists_size + final_hists_size;
compute_hists_kernel_many_blocks<nblocks><<<grid, threads, smem>>>(
img_block_width, grad, qangle, scale, block_hists);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
//-------------------------------------------------------------
// Normalization of histograms via L2Hys_norm
//
template<int size>
__device__ float reduce_smem(float* smem, float val)
{
unsigned int tid = threadIdx.x;
float sum = val;
reduce<size>(smem, sum, tid, plus<float>());
if (size == 32)
{
#if __CUDA_ARCH__ >= 300
return shfl(sum, 0);
#else
return smem[0];
#endif
}
else
{
#if __CUDA_ARCH__ >= 300
if (threadIdx.x == 0)
smem[0] = sum;
#endif
__syncthreads();
return smem[0];
}
}
template <int nthreads, // Number of threads which process one block historgam
int nblocks> // Number of block hisograms processed by one GPU thread block
__global__ void normalize_hists_kernel_many_blocks(const int block_hist_size,
const int img_block_width,
float* block_hists, float threshold)
{
if (blockIdx.x * blockDim.z + threadIdx.z >= img_block_width)
return;
float* hist = block_hists + (blockIdx.y * img_block_width +
blockIdx.x * blockDim.z + threadIdx.z) *
block_hist_size + threadIdx.x;
__shared__ float sh_squares[nthreads * nblocks];
float* squares = sh_squares + threadIdx.z * nthreads;
float elem = 0.f;
if (threadIdx.x < block_hist_size)
elem = hist[0];
float sum = reduce_smem<nthreads>(squares, elem * elem);
float scale = 1.0f / (::sqrtf(sum) + 0.1f * block_hist_size);
elem = ::min(elem * scale, threshold);
sum = reduce_smem<nthreads>(squares, elem * elem);
scale = 1.0f / (::sqrtf(sum) + 1e-3f);
if (threadIdx.x < block_hist_size)
hist[0] = elem * scale;
}
void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
int height, int width, float* block_hists, float threshold)
{
const int nblocks = 1;
int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y;
int nthreads = power_2up(block_hist_size);
dim3 threads(nthreads, 1, nblocks);
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) / block_stride_y;
dim3 grid(divUp(img_block_width, nblocks), img_block_height);
if (nthreads == 32)
normalize_hists_kernel_many_blocks<32, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
else if (nthreads == 64)
normalize_hists_kernel_many_blocks<64, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
else if (nthreads == 128)
normalize_hists_kernel_many_blocks<64, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
else if (nthreads == 256)
normalize_hists_kernel_many_blocks<256, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
else if (nthreads == 512)
normalize_hists_kernel_many_blocks<512, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
else
CV_Error(cv::Error::StsBadArg, "normalize_hists: histogram's size is too big, try to decrease number of bins");
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
//---------------------------------------------------------------------
// Linear SVM based classification
//
// return confidence values not just positive location
template <int nthreads, // Number of threads per one histogram block
int nblocks> // Number of histogram block processed by single GPU thread block
__global__ void compute_confidence_hists_kernel_many_blocks(const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, const float* coefs,
float free_coef, float threshold, float* confidences)
{
const int win_x = threadIdx.z;
if (blockIdx.x * blockDim.z + win_x >= img_win_width)
return;
const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
blockIdx.x * win_block_stride_x * blockDim.z + win_x) *
cblock_hist_size;
float product = 0.f;
for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
product += coefs[i] * hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
__shared__ float products[nthreads * nblocks];
const int tid = threadIdx.z * nthreads + threadIdx.x;
reduce<nthreads>(products, product, tid, plus<float>());
if (threadIdx.x == 0)
confidences[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x] = product + free_coef;
}
void compute_confidence_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
float* coefs, float free_coef, float threshold, float *confidences)
{
const int nthreads = 256;
const int nblocks = 1;
int win_block_stride_x = win_stride_x / block_stride_x;
int win_block_stride_y = win_stride_y / block_stride_y;
int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
dim3 threads(nthreads, 1, nblocks);
dim3 grid(divUp(img_win_width, nblocks), img_win_height);
cudaSafeCall(cudaFuncSetCacheConfig(compute_confidence_hists_kernel_many_blocks<nthreads, nblocks>,
cudaFuncCachePreferL1));
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
block_stride_x;
compute_confidence_hists_kernel_many_blocks<nthreads, nblocks><<<grid, threads>>>(
img_win_width, img_block_width, win_block_stride_x, win_block_stride_y,
block_hists, coefs, free_coef, threshold, confidences);
cudaSafeCall(cudaThreadSynchronize());
}
template <int nthreads, // Number of threads per one histogram block
int nblocks> // Number of histogram block processed by single GPU thread block
__global__ void classify_hists_kernel_many_blocks(const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, const float* coefs,
float free_coef, float threshold, unsigned char* labels)
{
const int win_x = threadIdx.z;
if (blockIdx.x * blockDim.z + win_x >= img_win_width)
return;
const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
blockIdx.x * win_block_stride_x * blockDim.z + win_x) *
cblock_hist_size;
float product = 0.f;
for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
product += coefs[i] * hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
__shared__ float products[nthreads * nblocks];
const int tid = threadIdx.z * nthreads + threadIdx.x;
reduce<nthreads>(products, product, tid, plus<float>());
if (threadIdx.x == 0)
labels[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x] = (product + free_coef >= threshold);
}
void classify_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
float* coefs, float free_coef, float threshold, unsigned char* labels)
{
const int nthreads = 256;
const int nblocks = 1;
int win_block_stride_x = win_stride_x / block_stride_x;
int win_block_stride_y = win_stride_y / block_stride_y;
int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
dim3 threads(nthreads, 1, nblocks);
dim3 grid(divUp(img_win_width, nblocks), img_win_height);
cudaSafeCall(cudaFuncSetCacheConfig(classify_hists_kernel_many_blocks<nthreads, nblocks>, cudaFuncCachePreferL1));
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
classify_hists_kernel_many_blocks<nthreads, nblocks><<<grid, threads>>>(
img_win_width, img_block_width, win_block_stride_x, win_block_stride_y,
block_hists, coefs, free_coef, threshold, labels);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
//----------------------------------------------------------------------------
// Extract descriptors
template <int nthreads>
__global__ void extract_descrs_by_rows_kernel(const int img_block_width, const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, PtrStepf descriptors)
{
// Get left top corner of the window in src
const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
blockIdx.x * win_block_stride_x) * cblock_hist_size;
// Get left top corner of the window in dst
float* descriptor = descriptors.ptr(blockIdx.y * gridDim.x + blockIdx.x);
// Copy elements from src to dst
for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
descriptor[i] = hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
}
void extract_descrs_by_rows(int win_height, int win_width, int block_stride_y, int block_stride_x, int win_stride_y, int win_stride_x,
int height, int width, float* block_hists, PtrStepSzf descriptors)
{
const int nthreads = 256;
int win_block_stride_x = win_stride_x / block_stride_x;
int win_block_stride_y = win_stride_y / block_stride_y;
int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
dim3 threads(nthreads, 1);
dim3 grid(img_win_width, img_win_height);
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
extract_descrs_by_rows_kernel<nthreads><<<grid, threads>>>(
img_block_width, win_block_stride_x, win_block_stride_y, block_hists, descriptors);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int nthreads>
__global__ void extract_descrs_by_cols_kernel(const int img_block_width, const int win_block_stride_x,
const int win_block_stride_y, const float* block_hists,
PtrStepf descriptors)
{
// Get left top corner of the window in src
const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
blockIdx.x * win_block_stride_x) * cblock_hist_size;
// Get left top corner of the window in dst
float* descriptor = descriptors.ptr(blockIdx.y * gridDim.x + blockIdx.x);
// Copy elements from src to dst
for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
{
int block_idx = i / cblock_hist_size;
int idx_in_block = i - block_idx * cblock_hist_size;
int y = block_idx / cnblocks_win_x;
int x = block_idx - y * cnblocks_win_x;
descriptor[(x * cnblocks_win_y + y) * cblock_hist_size + idx_in_block]
= hist[(y * img_block_width + x) * cblock_hist_size + idx_in_block];
}
}
void extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
PtrStepSzf descriptors)
{
const int nthreads = 256;
int win_block_stride_x = win_stride_x / block_stride_x;
int win_block_stride_y = win_stride_y / block_stride_y;
int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
dim3 threads(nthreads, 1);
dim3 grid(img_win_width, img_win_height);
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
extract_descrs_by_cols_kernel<nthreads><<<grid, threads>>>(
img_block_width, win_block_stride_x, win_block_stride_y, block_hists, descriptors);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
//----------------------------------------------------------------------------
// Gradients computation
template <int nthreads, int correct_gamma>
__global__ void compute_gradients_8UC4_kernel(int height, int width, const PtrStepb img,
float angle_scale, PtrStepf grad, PtrStepb qangle)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const uchar4* row = (const uchar4*)img.ptr(blockIdx.y);
__shared__ float sh_row[(nthreads + 2) * 3];
uchar4 val;
if (x < width)
val = row[x];
else
val = row[width - 2];
sh_row[threadIdx.x + 1] = val.x;
sh_row[threadIdx.x + 1 + (nthreads + 2)] = val.y;
sh_row[threadIdx.x + 1 + 2 * (nthreads + 2)] = val.z;
if (threadIdx.x == 0)
{
val = row[::max(x - 1, 1)];
sh_row[0] = val.x;
sh_row[(nthreads + 2)] = val.y;
sh_row[2 * (nthreads + 2)] = val.z;
}
if (threadIdx.x == blockDim.x - 1)
{
val = row[::min(x + 1, width - 2)];
sh_row[blockDim.x + 1] = val.x;
sh_row[blockDim.x + 1 + (nthreads + 2)] = val.y;
sh_row[blockDim.x + 1 + 2 * (nthreads + 2)] = val.z;
}
__syncthreads();
if (x < width)
{
float3 a, b;
b.x = sh_row[threadIdx.x + 2];
b.y = sh_row[threadIdx.x + 2 + (nthreads + 2)];
b.z = sh_row[threadIdx.x + 2 + 2 * (nthreads + 2)];
a.x = sh_row[threadIdx.x];
a.y = sh_row[threadIdx.x + (nthreads + 2)];
a.z = sh_row[threadIdx.x + 2 * (nthreads + 2)];
float3 dx;
if (correct_gamma)
dx = make_float3(::sqrtf(b.x) - ::sqrtf(a.x), ::sqrtf(b.y) - ::sqrtf(a.y), ::sqrtf(b.z) - ::sqrtf(a.z));
else
dx = make_float3(b.x - a.x, b.y - a.y, b.z - a.z);
float3 dy = make_float3(0.f, 0.f, 0.f);
if (blockIdx.y > 0 && blockIdx.y < height - 1)
{
val = ((const uchar4*)img.ptr(blockIdx.y - 1))[x];
a = make_float3(val.x, val.y, val.z);
val = ((const uchar4*)img.ptr(blockIdx.y + 1))[x];
b = make_float3(val.x, val.y, val.z);
if (correct_gamma)
dy = make_float3(::sqrtf(b.x) - ::sqrtf(a.x), ::sqrtf(b.y) - ::sqrtf(a.y), ::sqrtf(b.z) - ::sqrtf(a.z));
else
dy = make_float3(b.x - a.x, b.y - a.y, b.z - a.z);
}
float best_dx = dx.x;
float best_dy = dy.x;
float mag0 = dx.x * dx.x + dy.x * dy.x;
float mag1 = dx.y * dx.y + dy.y * dy.y;
if (mag0 < mag1)
{
best_dx = dx.y;
best_dy = dy.y;
mag0 = mag1;
}
mag1 = dx.z * dx.z + dy.z * dy.z;
if (mag0 < mag1)
{
best_dx = dx.z;
best_dy = dy.z;
mag0 = mag1;
}
mag0 = ::sqrtf(mag0);
float ang = (::atan2f(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)::floorf(ang);
ang -= hidx;
hidx = (hidx + cnbins) % cnbins;
((uchar2*)qangle.ptr(blockIdx.y))[x] = make_uchar2(hidx, (hidx + 1) % cnbins);
((float2*)grad.ptr(blockIdx.y))[x] = make_float2(mag0 * (1.f - ang), mag0 * ang);
}
}
void compute_gradients_8UC4(int nbins, int height, int width, const PtrStepSzb& img,
float angle_scale, PtrStepSzf grad, PtrStepSzb qangle, bool correct_gamma)
{
(void)nbins;
const int nthreads = 256;
dim3 bdim(nthreads, 1);
dim3 gdim(divUp(width, bdim.x), divUp(height, bdim.y));
if (correct_gamma)
compute_gradients_8UC4_kernel<nthreads, 1><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle);
else
compute_gradients_8UC4_kernel<nthreads, 0><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int nthreads, int correct_gamma>
__global__ void compute_gradients_8UC1_kernel(int height, int width, const PtrStepb img,
float angle_scale, PtrStepf grad, PtrStepb qangle)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned char* row = (const unsigned char*)img.ptr(blockIdx.y);
__shared__ float sh_row[nthreads + 2];
if (x < width)
sh_row[threadIdx.x + 1] = row[x];
else
sh_row[threadIdx.x + 1] = row[width - 2];
if (threadIdx.x == 0)
sh_row[0] = row[::max(x - 1, 1)];
if (threadIdx.x == blockDim.x - 1)
sh_row[blockDim.x + 1] = row[::min(x + 1, width - 2)];
__syncthreads();
if (x < width)
{
float dx;
if (correct_gamma)
dx = ::sqrtf(sh_row[threadIdx.x + 2]) - ::sqrtf(sh_row[threadIdx.x]);
else
dx = sh_row[threadIdx.x + 2] - sh_row[threadIdx.x];
float dy = 0.f;
if (blockIdx.y > 0 && blockIdx.y < height - 1)
{
float a = ((const unsigned char*)img.ptr(blockIdx.y + 1))[x];
float b = ((const unsigned char*)img.ptr(blockIdx.y - 1))[x];
if (correct_gamma)
dy = ::sqrtf(a) - ::sqrtf(b);
else
dy = a - b;
}
float mag = ::sqrtf(dx * dx + dy * dy);
float ang = (::atan2f(dy, dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)::floorf(ang);
ang -= hidx;
hidx = (hidx + cnbins) % cnbins;
((uchar2*)qangle.ptr(blockIdx.y))[x] = make_uchar2(hidx, (hidx + 1) % cnbins);
((float2*) grad.ptr(blockIdx.y))[x] = make_float2(mag * (1.f - ang), mag * ang);
}
}
void compute_gradients_8UC1(int nbins, int height, int width, const PtrStepSzb& img,
float angle_scale, PtrStepSzf grad, PtrStepSzb qangle, bool correct_gamma)
{
(void)nbins;
const int nthreads = 256;
dim3 bdim(nthreads, 1);
dim3 gdim(divUp(width, bdim.x), divUp(height, bdim.y));
if (correct_gamma)
compute_gradients_8UC1_kernel<nthreads, 1><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle);
else
compute_gradients_8UC1_kernel<nthreads, 0><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
//-------------------------------------------------------------------
// Resize
texture<uchar4, 2, cudaReadModeNormalizedFloat> resize8UC4_tex;
texture<uchar, 2, cudaReadModeNormalizedFloat> resize8UC1_tex;
__global__ void resize_for_hog_kernel(float sx, float sy, PtrStepSz<uchar> dst, int colOfs)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < dst.cols && y < dst.rows)
dst.ptr(y)[x] = tex2D(resize8UC1_tex, x * sx + colOfs, y * sy) * 255;
}
__global__ void resize_for_hog_kernel(float sx, float sy, PtrStepSz<uchar4> dst, int colOfs)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < dst.cols && y < dst.rows)
{
float4 val = tex2D(resize8UC4_tex, x * sx + colOfs, y * sy);
dst.ptr(y)[x] = make_uchar4(val.x * 255, val.y * 255, val.z * 255, val.w * 255);
}
}
template<class T, class TEX>
static void resize_for_hog(const PtrStepSzb& src, PtrStepSzb dst, TEX& tex)
{
tex.filterMode = cudaFilterModeLinear;
size_t texOfs = 0;
int colOfs = 0;
cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>();
cudaSafeCall( cudaBindTexture2D(&texOfs, tex, src.data, desc, src.cols, src.rows, src.step) );
if (texOfs != 0)
{
colOfs = static_cast<int>( texOfs/sizeof(T) );
cudaSafeCall( cudaUnbindTexture(tex) );
cudaSafeCall( cudaBindTexture2D(&texOfs, tex, src.data, desc, src.cols, src.rows, src.step) );
}
dim3 threads(32, 8);
dim3 grid(divUp(dst.cols, threads.x), divUp(dst.rows, threads.y));
float sx = static_cast<float>(src.cols) / dst.cols;
float sy = static_cast<float>(src.rows) / dst.rows;
resize_for_hog_kernel<<<grid, threads>>>(sx, sy, (PtrStepSz<T>)dst, colOfs);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
cudaSafeCall( cudaUnbindTexture(tex) );
}
void resize_8UC1(const PtrStepSzb& src, PtrStepSzb dst) { resize_for_hog<uchar> (src, dst, resize8UC1_tex); }
void resize_8UC4(const PtrStepSzb& src, PtrStepSzb dst) { resize_for_hog<uchar4>(src, dst, resize8UC4_tex); }
} // namespace hog
}}} // namespace cv { namespace cuda { namespace cudev
#endif /* CUDA_DISABLER */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if !defined CUDA_DISABLER
#include "lbp.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/saturate_cast.hpp"
namespace cv { namespace cuda { namespace device
{
namespace lbp
{
struct LBP
{
__host__ __device__ __forceinline__ LBP() {}
__device__ __forceinline__ int operator() (const int* integral, int ty, int fh, int fw, int& shift) const
{
int anchors[9];
anchors[0] = integral[ty];
anchors[1] = integral[ty + fw];
anchors[0] -= anchors[1];
anchors[2] = integral[ty + fw * 2];
anchors[1] -= anchors[2];
anchors[2] -= integral[ty + fw * 3];
ty += fh;
anchors[3] = integral[ty];
anchors[4] = integral[ty + fw];
anchors[3] -= anchors[4];
anchors[5] = integral[ty + fw * 2];
anchors[4] -= anchors[5];
anchors[5] -= integral[ty + fw * 3];
anchors[0] -= anchors[3];
anchors[1] -= anchors[4];
anchors[2] -= anchors[5];
// 0 - 2 contains s0 - s2
ty += fh;
anchors[6] = integral[ty];
anchors[7] = integral[ty + fw];
anchors[6] -= anchors[7];
anchors[8] = integral[ty + fw * 2];
anchors[7] -= anchors[8];
anchors[8] -= integral[ty + fw * 3];
anchors[3] -= anchors[6];
anchors[4] -= anchors[7];
anchors[5] -= anchors[8];
// 3 - 5 contains s3 - s5
anchors[0] -= anchors[4];
anchors[1] -= anchors[4];
anchors[2] -= anchors[4];
anchors[3] -= anchors[4];
anchors[5] -= anchors[4];
int response = (~(anchors[0] >> 31)) & 4;
response |= (~(anchors[1] >> 31)) & 2;;
response |= (~(anchors[2] >> 31)) & 1;
shift = (~(anchors[5] >> 31)) & 16;
shift |= (~(anchors[3] >> 31)) & 1;
ty += fh;
anchors[0] = integral[ty];
anchors[1] = integral[ty + fw];
anchors[0] -= anchors[1];
anchors[2] = integral[ty + fw * 2];
anchors[1] -= anchors[2];
anchors[2] -= integral[ty + fw * 3];
anchors[6] -= anchors[0];
anchors[7] -= anchors[1];
anchors[8] -= anchors[2];
// 0 -2 contains s6 - s8
anchors[6] -= anchors[4];
anchors[7] -= anchors[4];
anchors[8] -= anchors[4];
shift |= (~(anchors[6] >> 31)) & 2;
shift |= (~(anchors[7] >> 31)) & 4;
shift |= (~(anchors[8] >> 31)) & 8;
return response;
}
};
template<typename Pr>
__global__ void disjoin(int4* candidates, int4* objects, unsigned int n, int groupThreshold, float grouping_eps, unsigned int* nclasses)
{
unsigned int tid = threadIdx.x;
extern __shared__ int sbuff[];
int* labels = sbuff;
int* rrects = sbuff + n;
Pr predicate(grouping_eps);
partition(candidates, n, labels, predicate);
rrects[tid * 4 + 0] = 0;
rrects[tid * 4 + 1] = 0;
rrects[tid * 4 + 2] = 0;
rrects[tid * 4 + 3] = 0;
__syncthreads();
int cls = labels[tid];
Emulation::smem::atomicAdd((rrects + cls * 4 + 0), candidates[tid].x);
Emulation::smem::atomicAdd((rrects + cls * 4 + 1), candidates[tid].y);
Emulation::smem::atomicAdd((rrects + cls * 4 + 2), candidates[tid].z);
Emulation::smem::atomicAdd((rrects + cls * 4 + 3), candidates[tid].w);
__syncthreads();
labels[tid] = 0;
__syncthreads();
Emulation::smem::atomicInc((unsigned int*)labels + cls, n);
__syncthreads();
*nclasses = 0;
int active = labels[tid];
if (active)
{
int* r1 = rrects + tid * 4;
float s = 1.f / active;
r1[0] = saturate_cast<int>(r1[0] * s);
r1[1] = saturate_cast<int>(r1[1] * s);
r1[2] = saturate_cast<int>(r1[2] * s);
r1[3] = saturate_cast<int>(r1[3] * s);
}
__syncthreads();
if (active && active >= groupThreshold)
{
int* r1 = rrects + tid * 4;
int4 r_out = make_int4(r1[0], r1[1], r1[2], r1[3]);
int aidx = Emulation::smem::atomicInc(nclasses, n);
objects[aidx] = r_out;
}
}
void connectedConmonents(PtrStepSz<int4> candidates, int ncandidates, PtrStepSz<int4> objects, int groupThreshold, float grouping_eps, unsigned int* nclasses)
{
if (!ncandidates) return;
int block = ncandidates;
int smem = block * ( sizeof(int) + sizeof(int4) );
disjoin<InSameComponint><<<1, block, smem>>>(candidates, objects, ncandidates, groupThreshold, grouping_eps, nclasses);
cudaSafeCall( cudaGetLastError() );
}
struct Cascade
{
__host__ __device__ __forceinline__ Cascade(const Stage* _stages, int _nstages, const ClNode* _nodes, const float* _leaves,
const int* _subsets, const uchar4* _features, int _subsetSize)
: stages(_stages), nstages(_nstages), nodes(_nodes), leaves(_leaves), subsets(_subsets), features(_features), subsetSize(_subsetSize){}
__device__ __forceinline__ bool operator() (int y, int x, int* integral, const int pitch) const
{
int current_node = 0;
int current_leave = 0;
for (int s = 0; s < nstages; ++s)
{
float sum = 0;
Stage stage = stages[s];
for (int t = 0; t < stage.ntrees; t++)
{
ClNode node = nodes[current_node];
uchar4 feature = features[node.featureIdx];
int shift;
int c = evaluator(integral, (y + feature.y) * pitch + x + feature.x, feature.w * pitch, feature.z, shift);
int idx = (subsets[ current_node * subsetSize + c] & ( 1 << shift)) ? current_leave : current_leave + 1;
sum += leaves[idx];
current_node += 1;
current_leave += 2;
}
if (sum < stage.threshold)
return false;
}
return true;
}
const Stage* stages;
const int nstages;
const ClNode* nodes;
const float* leaves;
const int* subsets;
const uchar4* features;
const int subsetSize;
const LBP evaluator;
};
// stepShift, scale, width_k, sum_prev => y = sum_prev + tid_k / width_k, x = tid_k - tid_k / width_k
__global__ void lbp_cascade(const Cascade cascade, int frameW, int frameH, int windowW, int windowH, float scale, const float factor,
const int total, int* integral, const int pitch, PtrStepSz<int4> objects, unsigned int* classified)
{
int ftid = blockIdx.x * blockDim.x + threadIdx.x;
if (ftid >= total) return;
int step = (scale <= 2.f);
int windowsForLine = (__float2int_rn( __fdividef(frameW, scale)) - windowW) >> step;
int stotal = windowsForLine * ( (__float2int_rn( __fdividef(frameH, scale)) - windowH) >> step);
int wshift = 0;
int scaleTid = ftid;
while (scaleTid >= stotal)
{
scaleTid -= stotal;
wshift += __float2int_rn(__fdividef(frameW, scale)) + 1;
scale *= factor;
step = (scale <= 2.f);
windowsForLine = ( ((__float2int_rn(__fdividef(frameW, scale)) - windowW) >> step));
stotal = windowsForLine * ( (__float2int_rn(__fdividef(frameH, scale)) - windowH) >> step);
}
int y = __fdividef(scaleTid, windowsForLine);
int x = scaleTid - y * windowsForLine;
x <<= step;
y <<= step;
if (cascade(y, x + wshift, integral, pitch))
{
if(x >= __float2int_rn(__fdividef(frameW, scale)) - windowW) return;
int4 rect;
rect.x = __float2int_rn(x * scale);
rect.y = __float2int_rn(y * scale);
rect.z = __float2int_rn(windowW * scale);
rect.w = __float2int_rn(windowH * scale);
int res = atomicInc(classified, (unsigned int)objects.cols);
objects(0, res) = rect;
}
}
void classifyPyramid(int frameW, int frameH, int windowW, int windowH, float initialScale, float factor, int workAmount,
const PtrStepSzb& mstages, const int nstages, const PtrStepSzi& mnodes, const PtrStepSzf& mleaves, const PtrStepSzi& msubsets, const PtrStepSzb& mfeatures,
const int subsetSize, PtrStepSz<int4> objects, unsigned int* classified, PtrStepSzi integral)
{
const int block = 128;
int grid = divUp(workAmount, block);
cudaFuncSetCacheConfig(lbp_cascade, cudaFuncCachePreferL1);
Cascade cascade((Stage*)mstages.ptr(), nstages, (ClNode*)mnodes.ptr(), mleaves.ptr(), msubsets.ptr(), (uchar4*)mfeatures.ptr(), subsetSize);
lbp_cascade<<<grid, block>>>(cascade, frameW, frameH, windowW, windowH, initialScale, factor, workAmount, integral.ptr(), (int)integral.step / sizeof(int), objects, classified);
}
}
}}}
#endif /* CUDA_DISABLER */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CUDA_DEVICE_LBP_HPP_
#define __OPENCV_CUDA_DEVICE_LBP_HPP_
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/emulation.hpp"
namespace cv { namespace cuda { namespace device {
namespace lbp {
struct Stage
{
int first;
int ntrees;
float threshold;
};
struct ClNode
{
int left;
int right;
int featureIdx;
};
struct InSameComponint
{
public:
__device__ __forceinline__ InSameComponint(float _eps) : eps(_eps) {}
__device__ __forceinline__ InSameComponint(const InSameComponint& other) : eps(other.eps) {}
__device__ __forceinline__ bool operator()(const int4& r1, const int4& r2) const
{
float delta = eps * (::min(r1.z, r2.z) + ::min(r1.w, r2.w)) * 0.5f;
return ::abs(r1.x - r2.x) <= delta && ::abs(r1.y - r2.y) <= delta
&& ::abs(r1.x + r1.z - r2.x - r2.z) <= delta && ::abs(r1.y + r1.w - r2.y - r2.w) <= delta;
}
float eps;
};
template<typename Pr>
__device__ __forceinline__ void partition(int4* vec, unsigned int n, int* labels, Pr predicate)
{
unsigned tid = threadIdx.x;
labels[tid] = tid;
__syncthreads();
for (unsigned int id = 0; id < n; id++)
{
if (tid != id && predicate(vec[tid], vec[id]))
{
int p = labels[tid];
int q = labels[id];
if (p < q)
{
Emulation::smem::atomicMin(labels + id, p);
}
else if (p > q)
{
Emulation::smem::atomicMin(labels + tid, q);
}
}
}
__syncthreads();
}
} // lbp
} } }// namespaces
#endif

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// 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,
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// License Agreement
// For Open Source Computer Vision Library
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
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#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include <limits>
#include "opencv2/cudaobjdetect.hpp"
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudawarping.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_CUDALEGACY
# include "opencv2/cudalegacy/private.hpp"
#endif
#endif /* __OPENCV_PRECOMP_H__ */