opencv/modules/gpu/src/cascadeclassifier.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other GpuMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
#include "precomp.hpp"
#include <vector>
#include <iostream>
using namespace cv;
using namespace cv::gpu;
using namespace std;
struct Stage
{
int first;
int ntrees;
float threshold;
Stage(int f = 0, int n = 0, float t = 0.f) : first(f), ntrees(n), threshold(t) {}
};
struct DTreeNode
{
int featureIdx;
//float threshold; // for ordered features only
int left;
int right;
DTreeNode(int f = 0, int l = 0, int r = 0) : featureIdx(f), left(l), right(r) {}
};
#if !defined (HAVE_CUDA)
// ============ old fashioned haar cascade ==============================================//
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string&) { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { throw_nogpu(); }
bool cv::gpu::CascadeClassifier_GPU::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU::load(const string&) { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const { throw_nogpu(); return Size(); }
int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
// ============ LBP cascade ==============================================//
cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP() { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP() { throw_nogpu(); }
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bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string&) { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { throw_nogpu(); return Size(); }
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired) { throw_nogpu();}
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
#else
cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP()
{
}
cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()
{
}
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void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired)
{
integral.create(desired.width + 1, desired.height + 1, CV_32FC1);
}
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bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
{
return stage_mat.empty();
}
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
{
FileStorage fs(classifierAsXml, FileStorage::READ);
if (!fs.isOpened())
return false;
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return read(fs.getFirstTopLevelNode());
}
#define GPU_CC_STAGE_TYPE "stageType"
#define GPU_CC_FEATURE_TYPE "featureType"
#define GPU_CC_BOOST "BOOST"
#define GPU_CC_LBP "LBP"
#define GPU_CC_MAX_CAT_COUNT "maxCatCount"
#define GPU_CC_HEIGHT "height"
#define GPU_CC_WIDTH "width"
#define GPU_CC_STAGE_PARAMS "stageParams"
#define GPU_CC_MAX_DEPTH "maxDepth"
#define GPU_CC_FEATURE_PARAMS "featureParams"
#define GPU_CC_STAGES "stages"
#define GPU_CC_STAGE_THRESHOLD "stageThreshold"
#define GPU_THRESHOLD_EPS 1e-5f
#define GPU_CC_WEAK_CLASSIFIERS "weakClassifiers"
#define GPU_CC_INTERNAL_NODES "internalNodes"
#define GPU_CC_LEAF_VALUES "leafValues"
bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
{
std::string stageTypeStr = (string)root[GPU_CC_STAGE_TYPE];
CV_Assert(stageTypeStr == GPU_CC_BOOST);
string featureTypeStr = (string)root[GPU_CC_FEATURE_TYPE];
CV_Assert(featureTypeStr == GPU_CC_LBP);
NxM.width = (int)root[GPU_CC_WIDTH];
NxM.height = (int)root[GPU_CC_HEIGHT];
CV_Assert( NxM.height > 0 && NxM.width > 0 );
isStumps = ((int)(root[GPU_CC_STAGE_PARAMS][GPU_CC_MAX_DEPTH]) == 1) ? true : false;
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CV_Assert(isStumps);
// features
FileNode fn = root[GPU_CC_FEATURE_PARAMS];
if (fn.empty())
return false;
ncategories = fn[GPU_CC_MAX_CAT_COUNT];
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subsetSize = (ncategories + 31) / 32, nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 );
fn = root[GPU_CC_STAGES];
if (fn.empty())
return false;
std::vector<Stage> stages;
stages.reserve(fn.size());
std::vector<int> cl_trees;
std::vector<DTreeNode> 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[GPU_CC_STAGE_THRESHOLD] - GPU_THRESHOLD_EPS;
fns = fns[GPU_CC_WEAK_CLASSIFIERS];
if (fns.empty())
return false;
st.ntrees = (int)fns.size();
st.first = (int)cl_trees.size();
stages.push_back(st);
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[GPU_CC_INTERNAL_NODES];
FileNode leafValues = fnw[GPU_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);
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; )
{
DTreeNode node((int)*(iIt++), (int)*(iIt++), (int)*(iIt++));
cl_nodes.push_back(node);
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);
}
}
// copy data structures on gpu
stage_mat = cv::gpu::GpuMat(1, (int)stages.size() * sizeof(Stage), CV_8UC1);
stage_mat.upload(cv::Mat(1, stages.size() * sizeof(Stage), CV_8UC1, &(stages[0]) ));
trees_mat = cv::gpu::GpuMat(1, (int)cl_trees.size(), CV_32SC1);
stage_mat.upload(cv::Mat(cl_trees));
nodes_mat = cv::gpu::GpuMat(1, (int)cl_nodes.size() * sizeof(DTreeNode), CV_8UC1);
stage_mat.upload(cv::Mat(1, cl_nodes.size() * sizeof(DTreeNode), CV_8UC1, &(cl_nodes[0]) ));
leaves_mat = cv::gpu::GpuMat(1, (int)cl_leaves.size(), CV_32FC1);
stage_mat.upload(cv::Mat(cl_leaves));
subsets_mat = cv::gpu::GpuMat(1, (int)subsets.size(), CV_32SC1);
stage_mat.upload(cv::Mat(subsets));
return true;
}
#undef GPU_CC_STAGE_TYPE
#undef GPU_CC_BOOST
#undef GPU_CC_FEATURE_TYPE
#undef GPU_CC_LBP
#undef GPU_CC_MAX_CAT_COUNT
#undef GPU_CC_HEIGHT
#undef GPU_CC_WIDTH
#undef GPU_CC_STAGE_PARAMS
#undef GPU_CC_MAX_DEPTH
#undef GPU_CC_FEATURE_PARAMS
#undef GPU_CC_STAGES
#undef GPU_CC_STAGE_THRESHOLD
#undef GPU_THRESHOLD_EPS
#undef GPU_CC_WEAK_CLASSIFIERS
#undef GPU_CC_INTERNAL_NODES
#undef GPU_CC_LEAF_VALUES
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Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const
{
return NxM;
}
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namespace cv { namespace gpu { namespace device
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{
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namespace lbp
{
void CascadeClassify(DevMem2Db image, DevMem2Db objects, double scaleFactor = 1.2, int minNeighbors = 4, cudaStream_t stream = 0);
}
}}}
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, GpuMat& scaledImageBuffer, GpuMat& objects, double scaleFactor, int minNeighbors /*, Size minSize=Size()*/)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
CV_Assert(empty());
const int defaultObjSearchNum = 100;
if( !objects.empty() && objects.depth() == CV_32S)
objects.reshape(4, 1);
else
objects.create(1 , defaultObjSearchNum, CV_32SC4);
scaledImageBuffer.create(image.size(), image.type());
// TODO: specify max objects size
for( double factor = 1; ; factor *= scaleFactor )
{
cv::Size windowSize(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
cv::Size scaledImageSize(cvRound( image.cols / factor ), cvRound( image.rows / factor ));
cv::Size processingRectSize( scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
// nothing to do
if (processingRectSize.width <= 0 || processingRectSize.height <= 0 )
break;
// TODO: min max object sizes cheching
cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, INTER_NEAREST);
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//prepare image for evaluation
integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32FC1);
cv::gpu::integral(scaledImageBuffer, integral);
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int step = (factor <= 2.) + 1;
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int stripCount = 1, stripSize = processingRectSize.height;
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int y1 = 0;
int y2 = processingRectSize.height;
for (int y = y1; y < y2; y += step)
for (int x = 0; x < processingRectSize.width; x+=step)
{
//ToDO: classify
int result = 0;
}
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}
// TODO: reject levels
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return 0;
}
// ============ old fashioned haar cascade ==============================================//
struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
{
CascadeClassifierImpl(const string& filename) : lastAllocatedFrameSize(-1, -1)
{
ncvSetDebugOutputHandler(NCVDebugOutputHandler);
ncvSafeCall( load(filename) );
}
NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, NcvSize32u 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;
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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();
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,
ncvMinSize,
minNeighbors,
scaleStep, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
return NCV_SUCCESS;
}
NcvSize32u getClassifierSize() const { return haar.ClassifierSize; }
cv::Size getClassifierCvSize() const { return cv::Size(haar.ClassifierSize.width, haar.ClassifierSize.height); }
private:
static void NCVDebugOutputHandler(const std::string &msg) { CV_Error(CV_GpuApiCallError, msg.c_str()); }
NCVStatus load(const string& classifierFile)
{
int devId = cv::gpu::getDevice();
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
// Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
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gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, static_cast<int>(devProp.textureAlignment));
cpuCascadeAllocator = new 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 = new NCVVectorAlloc<HaarStage64>(*cpuCascadeAllocator, haarNumStages);
h_haarNodes = new NCVVectorAlloc<HaarClassifierNode128>(*cpuCascadeAllocator, haarNumNodes);
h_haarFeatures = 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 = new NCVVectorAlloc<HaarStage64>(*gpuCascadeAllocator, haarNumStages);
d_haarNodes = new NCVVectorAlloc<HaarClassifierNode128>(*gpuCascadeAllocator, haarNumNodes);
d_haarFeatures = 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
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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);
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gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
cpuAllocator = new 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);
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;
};
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() : findLargestObject(false), visualizeInPlace(false), impl(0) {}
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename) : findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { release(); }
bool cv::gpu::CascadeClassifier_GPU::empty() const { return impl == 0; }
void cv::gpu::CascadeClassifier_GPU::release() { if (impl) { delete impl; impl = 0; } }
bool cv::gpu::CascadeClassifier_GPU::load(const string& filename)
{
release();
impl = new CascadeClassifierImpl(filename);
return !this->empty();
}
Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const
{
return this->empty() ? Size() : impl->getClassifierCvSize();
}
int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor, int minNeighbors, Size minSize)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
CV_Assert( !this->empty());
const int defaultObjSearchNum = 100;
if (objectsBuf.empty())
{
objectsBuf.create(1, defaultObjSearchNum, DataType<Rect>::type);
}
NcvSize32u ncvMinSize = impl->getClassifierSize();
if (ncvMinSize.width < (unsigned)minSize.width && ncvMinSize.height < (unsigned)minSize.height)
{
ncvMinSize.width = minSize.width;
ncvMinSize.height = minSize.height;
}
unsigned int numDetections;
ncvSafeCall( impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections) );
return numDetections;
}
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struct RectConvert
{
Rect operator()(const NcvRect32u& nr) const { return Rect(nr.x, nr.y, nr.width, nr.height); }
NcvRect32u operator()(const Rect& nr) const
{
NcvRect32u rect;
rect.x = nr.x;
rect.y = nr.y;
rect.width = nr.width;
rect.height = nr.height;
return rect;
}
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};
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void groupRectangles(std::vector<NcvRect32u> &hypotheses, int groupThreshold, double eps, std::vector<Ncv32u> *weights)
{
vector<Rect> rects(hypotheses.size());
std::transform(hypotheses.begin(), hypotheses.end(), rects.begin(), RectConvert());
if (weights)
{
vector<int> weights_int;
weights_int.assign(weights->begin(), weights->end());
cv::groupRectangles(rects, weights_int, groupThreshold, eps);
}
else
{
cv::groupRectangles(rects, groupThreshold, eps);
}
std::transform(rects.begin(), rects.end(), hypotheses.begin(), RectConvert());
hypotheses.resize(rects.size());
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}
NCVStatus loadFromXML(const std::string &filename,
HaarClassifierCascadeDescriptor &haar,
std::vector<HaarStage64> &haarStages,
std::vector<HaarClassifierNode128> &haarClassifierNodes,
std::vector<HaarFeature64> &haarFeatures)
{
NCVStatus ncvStat;
haar.NumStages = 0;
haar.NumClassifierRootNodes = 0;
haar.NumClassifierTotalNodes = 0;
haar.NumFeatures = 0;
haar.ClassifierSize.width = 0;
haar.ClassifierSize.height = 0;
haar.bHasStumpsOnly = true;
haar.bNeedsTiltedII = false;
Ncv32u curMaxTreeDepth;
std::vector<char> xmlFileCont;
std::vector<HaarClassifierNode128> h_TmpClassifierNotRootNodes;
haarStages.resize(0);
haarClassifierNodes.resize(0);
haarFeatures.resize(0);
Ptr<CvHaarClassifierCascade> oldCascade = (CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0);
if (oldCascade.empty())
{
return NCV_HAAR_XML_LOADING_EXCEPTION;
}
haar.ClassifierSize.width = oldCascade->orig_window_size.width;
haar.ClassifierSize.height = oldCascade->orig_window_size.height;
int stagesCound = oldCascade->count;
for(int s = 0; s < stagesCound; ++s) // by stages
{
HaarStage64 curStage;
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curStage.setStartClassifierRootNodeOffset(static_cast<Ncv32u>(haarClassifierNodes.size()));
curStage.setStageThreshold(oldCascade->stage_classifier[s].threshold);
int treesCount = oldCascade->stage_classifier[s].count;
for(int t = 0; t < treesCount; ++t) // by trees
{
Ncv32u nodeId = 0;
CvHaarClassifier* tree = &oldCascade->stage_classifier[s].classifier[t];
int nodesCount = tree->count;
for(int n = 0; n < nodesCount; ++n) //by features
{
CvHaarFeature* feature = &tree->haar_feature[n];
HaarClassifierNode128 curNode;
curNode.setThreshold(tree->threshold[n]);
NcvBool bIsLeftNodeLeaf = false;
NcvBool bIsRightNodeLeaf = false;
HaarClassifierNodeDescriptor32 nodeLeft;
if ( tree->left[n] <= 0 )
{
Ncv32f leftVal = tree->alpha[-tree->left[n]];
ncvStat = nodeLeft.create(leftVal);
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
bIsLeftNodeLeaf = true;
}
else
{
Ncv32u leftNodeOffset = tree->left[n];
nodeLeft.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + leftNodeOffset - 1));
haar.bHasStumpsOnly = false;
}
curNode.setLeftNodeDesc(nodeLeft);
HaarClassifierNodeDescriptor32 nodeRight;
if ( tree->right[n] <= 0 )
{
Ncv32f rightVal = tree->alpha[-tree->right[n]];
ncvStat = nodeRight.create(rightVal);
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
bIsRightNodeLeaf = true;
}
else
{
Ncv32u rightNodeOffset = tree->right[n];
nodeRight.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + rightNodeOffset - 1));
haar.bHasStumpsOnly = false;
}
curNode.setRightNodeDesc(nodeRight);
Ncv32u tiltedVal = feature->tilted;
haar.bNeedsTiltedII = (tiltedVal != 0);
Ncv32u featureId = 0;
for(int l = 0; l < CV_HAAR_FEATURE_MAX; ++l) //by rects
{
Ncv32u rectX = feature->rect[l].r.x;
Ncv32u rectY = feature->rect[l].r.y;
Ncv32u rectWidth = feature->rect[l].r.width;
Ncv32u rectHeight = feature->rect[l].r.height;
Ncv32f rectWeight = feature->rect[l].weight;
if (rectWeight == 0/* && rectX == 0 &&rectY == 0 && rectWidth == 0 && rectHeight == 0*/)
break;
HaarFeature64 curFeature;
ncvStat = curFeature.setRect(rectX, rectY, rectWidth, rectHeight, haar.ClassifierSize.width, haar.ClassifierSize.height);
curFeature.setWeight(rectWeight);
ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
haarFeatures.push_back(curFeature);
featureId++;
}
HaarFeatureDescriptor32 tmpFeatureDesc;
ncvStat = tmpFeatureDesc.create(haar.bNeedsTiltedII, bIsLeftNodeLeaf, bIsRightNodeLeaf,
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featureId, static_cast<Ncv32u>(haarFeatures.size()) - featureId);
ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
curNode.setFeatureDesc(tmpFeatureDesc);
if (!nodeId)
{
//root node
haarClassifierNodes.push_back(curNode);
curMaxTreeDepth = 1;
}
else
{
//other node
h_TmpClassifierNotRootNodes.push_back(curNode);
curMaxTreeDepth++;
}
nodeId++;
}
}
curStage.setNumClassifierRootNodes(treesCount);
haarStages.push_back(curStage);
}
//fill in cascade stats
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haar.NumStages = static_cast<Ncv32u>(haarStages.size());
haar.NumClassifierRootNodes = static_cast<Ncv32u>(haarClassifierNodes.size());
haar.NumClassifierTotalNodes = static_cast<Ncv32u>(haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size());
haar.NumFeatures = static_cast<Ncv32u>(haarFeatures.size());
//merge root and leaf nodes in one classifiers array
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Ncv32u offsetRoot = static_cast<Ncv32u>(haarClassifierNodes.size());
for (Ncv32u i=0; i<haarClassifierNodes.size(); i++)
{
HaarFeatureDescriptor32 featureDesc = haarClassifierNodes[i].getFeatureDesc();
HaarClassifierNodeDescriptor32 nodeLeft = haarClassifierNodes[i].getLeftNodeDesc();
if (!featureDesc.isLeftNodeLeaf())
{
Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
nodeLeft.create(newOffset);
}
haarClassifierNodes[i].setLeftNodeDesc(nodeLeft);
HaarClassifierNodeDescriptor32 nodeRight = haarClassifierNodes[i].getRightNodeDesc();
if (!featureDesc.isRightNodeLeaf())
{
Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
nodeRight.create(newOffset);
}
haarClassifierNodes[i].setRightNodeDesc(nodeRight);
}
for (Ncv32u i=0; i<h_TmpClassifierNotRootNodes.size(); i++)
{
HaarFeatureDescriptor32 featureDesc = h_TmpClassifierNotRootNodes[i].getFeatureDesc();
HaarClassifierNodeDescriptor32 nodeLeft = h_TmpClassifierNotRootNodes[i].getLeftNodeDesc();
if (!featureDesc.isLeftNodeLeaf())
{
Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
nodeLeft.create(newOffset);
}
h_TmpClassifierNotRootNodes[i].setLeftNodeDesc(nodeLeft);
HaarClassifierNodeDescriptor32 nodeRight = h_TmpClassifierNotRootNodes[i].getRightNodeDesc();
if (!featureDesc.isRightNodeLeaf())
{
Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
nodeRight.create(newOffset);
}
h_TmpClassifierNotRootNodes[i].setRightNodeDesc(nodeRight);
haarClassifierNodes.push_back(h_TmpClassifierNotRootNodes[i]);
}
return NCV_SUCCESS;
}
#endif /* HAVE_CUDA */