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#include "precomp.hpp"
#include "opencv2/objdetect/objdetect_c.h"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const String&) { throw_no_cuda(); return Ptr<cuda::CascadeClassifier>(); }
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const FileStorage&) { throw_no_cuda(); return Ptr<cuda::CascadeClassifier>(); }
#else
//
// CascadeClassifierBase
//
namespace
{
class CascadeClassifierBase : public cuda::CascadeClassifier
{
public:
CascadeClassifierBase();
virtual void setMaxObjectSize(Size maxObjectSize) { maxObjectSize_ = maxObjectSize; }
virtual Size getMaxObjectSize() const { return maxObjectSize_; }
virtual void setMinObjectSize(Size minSize) { minObjectSize_ = minSize; }
virtual Size getMinObjectSize() const { return minObjectSize_; }
virtual void setScaleFactor(double scaleFactor) { scaleFactor_ = scaleFactor; }
virtual double getScaleFactor() const { return scaleFactor_; }
virtual void setMinNeighbors(int minNeighbors) { minNeighbors_ = minNeighbors; }
virtual int getMinNeighbors() const { return minNeighbors_; }
virtual void setFindLargestObject(bool findLargestObject) { findLargestObject_ = findLargestObject; }
virtual bool getFindLargestObject() { return findLargestObject_; }
virtual void setMaxNumObjects(int maxNumObjects) { maxNumObjects_ = maxNumObjects; }
virtual int getMaxNumObjects() const { return maxNumObjects_; }
protected:
Size maxObjectSize_;
Size minObjectSize_;
double scaleFactor_;
int minNeighbors_;
bool findLargestObject_;
int maxNumObjects_;
};
CascadeClassifierBase::CascadeClassifierBase() :
maxObjectSize_(),
minObjectSize_(),
scaleFactor_(1.2),
minNeighbors_(4),
findLargestObject_(false),
maxNumObjects_(100)
{
}
}
//
// HaarCascade
//
#ifdef HAVE_OPENCV_CUDALEGACY
namespace
{
class HaarCascade_Impl : public CascadeClassifierBase
{
public:
explicit HaarCascade_Impl(const String& filename);
virtual Size getClassifierSize() const;
virtual void detectMultiScale(InputArray image,
OutputArray objects,
Stream& stream);
virtual void convert(OutputArray gpu_objects,
std::vector<Rect>& objects);
private:
NCVStatus load(const String& classifierFile);
NCVStatus calculateMemReqsAndAllocate(const Size& frameSize);
NCVStatus process(const GpuMat& src, GpuMat& objects, cv::Size ncvMinSize, /*out*/ unsigned int& numDetections);
Size lastAllocatedFrameSize;
Ptr<NCVMemStackAllocator> gpuAllocator;
Ptr<NCVMemStackAllocator> cpuAllocator;
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;
};
static void NCVDebugOutputHandler(const String &msg)
{
CV_Error(Error::GpuApiCallError, msg.c_str());
}
HaarCascade_Impl::HaarCascade_Impl(const String& filename) :
lastAllocatedFrameSize(-1, -1)
{
ncvSetDebugOutputHandler(NCVDebugOutputHandler);
ncvSafeCall( load(filename) );
}
Size HaarCascade_Impl::getClassifierSize() const
{
return Size(haar.ClassifierSize.width, haar.ClassifierSize.height);
}
void HaarCascade_Impl::detectMultiScale(InputArray _image,
OutputArray _objects,
Stream& stream)
{
const GpuMat image = _image.getGpuMat();
CV_Assert( image.depth() == CV_8U);
CV_Assert( scaleFactor_ > 1 );
CV_Assert( !stream );
Size ncvMinSize = getClassifierSize();
if (ncvMinSize.width < minObjectSize_.width && ncvMinSize.height < minObjectSize_.height)
{
ncvMinSize.width = minObjectSize_.width;
ncvMinSize.height = minObjectSize_.height;
}
BufferPool pool(stream);
GpuMat objectsBuf = pool.getBuffer(1, maxNumObjects_, DataType<Rect>::type);
unsigned int numDetections;
ncvSafeCall( process(image, objectsBuf, ncvMinSize, numDetections) );
if (numDetections > 0)
{
objectsBuf.colRange(0, numDetections).copyTo(_objects);
}
else
{
_objects.release();
}
}
void HaarCascade_Impl::convert(OutputArray _gpu_objects, std::vector<Rect>& objects)
{
if (_gpu_objects.empty())
{
objects.clear();
return;
}
Mat gpu_objects;
if (_gpu_objects.kind() == _InputArray::CUDA_GPU_MAT)
{
_gpu_objects.getGpuMat().download(gpu_objects);
}
else
{
gpu_objects = _gpu_objects.getMat();
}
CV_Assert( gpu_objects.rows == 1 );
CV_Assert( gpu_objects.type() == DataType<Rect>::type );
Rect* ptr = gpu_objects.ptr<Rect>();
objects.assign(ptr, ptr + gpu_objects.cols);
}
NCVStatus HaarCascade_Impl::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 HaarCascade_Impl::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;
}
NCVStatus HaarCascade_Impl::process(const GpuMat& src, GpuMat& objects, 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;
ncvStat = ncvDetectObjectsMultiScale_device(
d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures,
winMinSize,
minNeighbors_,
scaleFactor_, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
return NCV_SUCCESS;
}
}
#endif
//
// LbpCascade
//
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);
}
}}}
namespace
{
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;
};
class LbpCascade_Impl : public CascadeClassifierBase
{
public:
explicit LbpCascade_Impl(const FileStorage& file);
virtual Size getClassifierSize() const { return NxM; }
virtual void detectMultiScale(InputArray image,
OutputArray objects,
Stream& stream);
virtual void convert(OutputArray gpu_objects,
std::vector<Rect>& objects);
private:
bool load(const FileNode &root);
void allocateBuffers(cv::Size frame);
private:
struct Stage
{
int first;
int ntrees;
float threshold;
};
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;
};
LbpCascade_Impl::LbpCascade_Impl(const FileStorage& file)
{
load(file.getFirstTopLevelNode());
}
void LbpCascade_Impl::detectMultiScale(InputArray _image,
OutputArray _objects,
Stream& stream)
{
const GpuMat image = _image.getGpuMat();
CV_Assert( image.depth() == CV_8U);
CV_Assert( scaleFactor_ > 1 );
CV_Assert( !stream );
const float grouping_eps = 0.2f;
BufferPool pool(stream);
GpuMat objects = pool.getBuffer(1, maxNumObjects_, DataType<Rect>::type);
// 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));
// generate integral for scale
cuda::resize(image, src, level.sFrame, 0, 0, cv::INTER_LINEAR);
cuda::integral(src, sint);
// 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 (minNeighbors_ <= 0 || objects.empty())
return;
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
device::lbp::connectedConmonents(candidates, classified, objects, minNeighbors_, grouping_eps, dclassified.ptr<unsigned int>());
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaDeviceSynchronize() );
if (classified > 0)
{
objects.colRange(0, classified).copyTo(_objects);
}
else
{
_objects.release();
}
}
void LbpCascade_Impl::convert(OutputArray _gpu_objects, std::vector<Rect>& objects)
{
if (_gpu_objects.empty())
{
objects.clear();
return;
}
Mat gpu_objects;
if (_gpu_objects.kind() == _InputArray::CUDA_GPU_MAT)
{
_gpu_objects.getGpuMat().download(gpu_objects);
}
else
{
gpu_objects = _gpu_objects.getMat();
}
CV_Assert( gpu_objects.rows == 1 );
CV_Assert( gpu_objects.type() == DataType<Rect>::type );
Rect* ptr = gpu_objects.ptr<Rect>();
objects.assign(ptr, ptr + gpu_objects.cols);
}
bool LbpCascade_Impl::load(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;
}
void LbpCascade_Impl::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);
}
}
}
//
// create
//
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const String& filename)
{
String fext = filename.substr(filename.find_last_of(".") + 1);
fext = fext.toLowerCase();
if (fext == "nvbin")
{
#ifndef HAVE_OPENCV_CUDALEGACY
CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade");
return Ptr<cuda::CascadeClassifier>();
#else
return makePtr<HaarCascade_Impl>(filename);
#endif
}
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
{
#ifndef HAVE_OPENCV_CUDALEGACY
CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade");
return Ptr<cuda::CascadeClassifier>();
#else
return makePtr<HaarCascade_Impl>(filename);
#endif
}
const char *CUDA_CC_LBP = "LBP";
String featureTypeStr = (String)fs.getFirstTopLevelNode()["featureType"];
if (featureTypeStr == CUDA_CC_LBP)
{
return makePtr<LbpCascade_Impl>(fs);
}
else
{
#ifndef HAVE_OPENCV_CUDALEGACY
CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade");
return Ptr<cuda::CascadeClassifier>();
#else
return makePtr<HaarCascade_Impl>(filename);
#endif
}
CV_Error(Error::StsUnsupportedFormat, "Unsupported format for CUDA CascadeClassifier");
return Ptr<cuda::CascadeClassifier>();
}
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const FileStorage& file)
{
return makePtr<LbpCascade_Impl>(file);
}
#endif