opencv/modules/objdetect/src/cascadedetect.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
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// derived from this software without specific prior written permission.
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//M*/
#include "precomp.hpp"
#include <cstdio>
namespace cv
{
// class for grouping object candidates, detected by Cascade Classifier, HOG etc.
// instance of the class is to be passed to cv::partition (see cxoperations.hpp)
class CV_EXPORTS SimilarRects
{
public:
SimilarRects(double _eps) : eps(_eps) {}
inline bool operator()(const Rect& r1, const Rect& r2) const
{
double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5;
return std::abs(r1.x - r2.x) <= delta &&
std::abs(r1.y - r2.y) <= delta &&
std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
}
double eps;
};
static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights)
{
if( groupThreshold <= 0 || rectList.empty() )
{
if( weights )
{
size_t i, sz = rectList.size();
weights->resize(sz);
for( i = 0; i < sz; i++ )
(*weights)[i] = 1;
}
return;
}
vector<int> labels;
int nclasses = partition(rectList, labels, SimilarRects(eps));
vector<Rect> rrects(nclasses);
vector<int> rweights(nclasses, 0);
int i, j, nlabels = (int)labels.size();
for( i = 0; i < nlabels; i++ )
{
int cls = labels[i];
rrects[cls].x += rectList[i].x;
rrects[cls].y += rectList[i].y;
rrects[cls].width += rectList[i].width;
rrects[cls].height += rectList[i].height;
rweights[cls]++;
}
for( i = 0; i < nclasses; i++ )
{
Rect r = rrects[i];
float s = 1.f/rweights[i];
rrects[i] = Rect(saturate_cast<int>(r.x*s),
saturate_cast<int>(r.y*s),
saturate_cast<int>(r.width*s),
saturate_cast<int>(r.height*s));
}
rectList.clear();
if( weights )
weights->clear();
for( i = 0; i < nclasses; i++ )
{
Rect r1 = rrects[i];
int n1 = rweights[i];
if( n1 <= groupThreshold )
continue;
// filter out small face rectangles inside large rectangles
for( j = 0; j < nclasses; j++ )
{
int n2 = rweights[j];
if( j == i || n2 <= groupThreshold )
continue;
Rect r2 = rrects[j];
int dx = saturate_cast<int>( r2.width * eps );
int dy = saturate_cast<int>( r2.height * eps );
if( i != j &&
r1.x >= r2.x - dx &&
r1.y >= r2.y - dy &&
r1.x + r1.width <= r2.x + r2.width + dx &&
r1.y + r1.height <= r2.y + r2.height + dy &&
(n2 > std::max(3, n1) || n1 < 3) )
break;
}
if( j == nclasses )
{
rectList.push_back(r1);
if( weights )
weights->push_back(n1);
}
}
}
void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps)
{
groupRectangles(rectList, groupThreshold, eps, 0);
}
void groupRectangles(vector<Rect>& rectList, vector<int>& weights, int groupThreshold, double eps)
{
groupRectangles(rectList, groupThreshold, eps, &weights);
}
#define CC_CASCADE_PARAMS "cascadeParams"
#define CC_STAGE_TYPE "stageType"
#define CC_FEATURE_TYPE "featureType"
#define CC_HEIGHT "height"
#define CC_WIDTH "width"
#define CC_STAGE_NUM "stageNum"
#define CC_STAGES "stages"
#define CC_STAGE_PARAMS "stageParams"
#define CC_BOOST "BOOST"
#define CC_MAX_DEPTH "maxDepth"
#define CC_WEAK_COUNT "maxWeakCount"
#define CC_STAGE_THRESHOLD "stageThreshold"
#define CC_WEAK_CLASSIFIERS "weakClassifiers"
#define CC_INTERNAL_NODES "internalNodes"
#define CC_LEAF_VALUES "leafValues"
#define CC_FEATURES "features"
#define CC_FEATURE_PARAMS "featureParams"
#define CC_MAX_CAT_COUNT "maxCatCount"
#define CC_HAAR "HAAR"
#define CC_RECTS "rects"
#define CC_TILTED "tilted"
#define CC_LBP "LBP"
#define CC_RECT "rect"
#define CV_SUM_PTRS( p0, p1, p2, p3, sum, rect, step ) \
/* (x, y) */ \
(p0) = sum + (rect).x + (step) * (rect).y, \
/* (x + w, y) */ \
(p1) = sum + (rect).x + (rect).width + (step) * (rect).y, \
/* (x + w, y) */ \
(p2) = sum + (rect).x + (step) * ((rect).y + (rect).height), \
/* (x + w, y + h) */ \
(p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)
#define CV_TILTED_PTRS( p0, p1, p2, p3, tilted, rect, step ) \
/* (x, y) */ \
(p0) = tilted + (rect).x + (step) * (rect).y, \
/* (x - h, y + h) */ \
(p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
/* (x + w, y + w) */ \
(p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width), \
/* (x + w - h, y + w + h) */ \
(p3) = tilted + (rect).x + (rect).width - (rect).height \
+ (step) * ((rect).y + (rect).width + (rect).height)
#define CALC_SUM_(p0, p1, p2, p3, offset) \
((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])
#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
FeatureEvaluator::~FeatureEvaluator() {}
bool FeatureEvaluator::read(const FileNode&) {return true;}
Ptr<FeatureEvaluator> FeatureEvaluator::clone() const { return Ptr<FeatureEvaluator>(); }
int FeatureEvaluator::getFeatureType() const {return -1;}
bool FeatureEvaluator::setImage(const Mat&, Size) {return true;}
bool FeatureEvaluator::setWindow(Point) { return true; }
double FeatureEvaluator::calcOrd(int) const { return 0.; }
int FeatureEvaluator::calcCat(int) const { return 0; }
//---------------------------------------------- HaarEvaluator ---------------------------------------
class HaarEvaluator : public FeatureEvaluator
{
public:
struct Feature
{
Feature();
float calc( int offset ) const;
void updatePtrs( const Mat& sum );
bool read( const FileNode& node );
bool tilted;
enum { RECT_NUM = 3 };
struct
{
Rect r;
float weight;
} rect[RECT_NUM];
const int* p[RECT_NUM][4];
};
HaarEvaluator();
virtual ~HaarEvaluator();
virtual bool read( const FileNode& node );
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const { return FeatureEvaluator::HAAR; }
virtual bool setImage(const Mat&, Size origWinSize);
virtual bool setWindow(Point pt);
double operator()(int featureIdx) const
{ return featuresPtr[featureIdx].calc(offset) * varianceNormFactor; }
virtual double calcOrd(int featureIdx) const
{ return (*this)(featureIdx); }
private:
Size origWinSize;
Ptr<vector<Feature> > features;
Feature* featuresPtr; // optimization
bool hasTiltedFeatures;
Mat sum0, sqsum0, tilted0;
Mat sum, sqsum, tilted;
Rect normrect;
const int *p[4];
const double *pq[4];
int offset;
double varianceNormFactor;
};
inline HaarEvaluator::Feature :: Feature()
{
tilted = false;
rect[0].r = rect[1].r = rect[2].r = Rect();
rect[0].weight = rect[1].weight = rect[2].weight = 0;
p[0][0] = p[0][1] = p[0][2] = p[0][3] =
p[1][0] = p[1][1] = p[1][2] = p[1][3] =
p[2][0] = p[2][1] = p[2][2] = p[2][3] = 0;
}
inline float HaarEvaluator::Feature :: calc( int offset ) const
{
float ret = rect[0].weight * CALC_SUM(p[0], offset) + rect[1].weight * CALC_SUM(p[1], offset);
if( rect[2].weight != 0.0f )
ret += rect[2].weight * CALC_SUM(p[2], offset);
return ret;
}
inline void HaarEvaluator::Feature :: updatePtrs( const Mat& sum )
{
const int* ptr = (const int*)sum.data;
size_t step = sum.step/sizeof(ptr[0]);
if (tilted)
{
CV_TILTED_PTRS( p[0][0], p[0][1], p[0][2], p[0][3], ptr, rect[0].r, step );
CV_TILTED_PTRS( p[1][0], p[1][1], p[1][2], p[1][3], ptr, rect[1].r, step );
if (rect[2].weight)
CV_TILTED_PTRS( p[2][0], p[2][1], p[2][2], p[2][3], ptr, rect[2].r, step );
}
else
{
CV_SUM_PTRS( p[0][0], p[0][1], p[0][2], p[0][3], ptr, rect[0].r, step );
CV_SUM_PTRS( p[1][0], p[1][1], p[1][2], p[1][3], ptr, rect[1].r, step );
if (rect[2].weight)
CV_SUM_PTRS( p[2][0], p[2][1], p[2][2], p[2][3], ptr, rect[2].r, step );
}
}
bool HaarEvaluator::Feature :: read( const FileNode& node )
{
FileNode rnode = node[CC_RECTS];
FileNodeIterator it = rnode.begin(), it_end = rnode.end();
int ri;
for( ri = 0; ri < RECT_NUM; ri++ )
{
rect[ri].r = Rect();
rect[ri].weight = 0.f;
}
for(ri = 0; it != it_end; ++it, ri++)
{
FileNodeIterator it2 = (*it).begin();
it2 >> rect[ri].r.x >> rect[ri].r.y >>
rect[ri].r.width >> rect[ri].r.height >> rect[ri].weight;
}
tilted = (int)node[CC_TILTED] != 0;
return true;
}
HaarEvaluator::HaarEvaluator()
{
features = new vector<Feature>();
}
HaarEvaluator::~HaarEvaluator()
{
}
bool HaarEvaluator::read(const FileNode& node)
{
features->resize(node.size());
featuresPtr = &(*features)[0];
FileNodeIterator it = node.begin(), it_end = node.end();
hasTiltedFeatures = false;
for(int i = 0; it != it_end; ++it, i++)
{
if(!featuresPtr[i].read(*it))
return false;
if( featuresPtr[i].tilted )
hasTiltedFeatures = true;
}
return true;
}
Ptr<FeatureEvaluator> HaarEvaluator::clone() const
{
HaarEvaluator* ret = new HaarEvaluator;
ret->origWinSize = origWinSize;
ret->features = features;
ret->featuresPtr = &(*ret->features)[0];
ret->hasTiltedFeatures = hasTiltedFeatures;
ret->sum0 = sum0, ret->sqsum0 = sqsum0, ret->tilted0 = tilted0;
ret->sum = sum, ret->sqsum = sqsum, ret->tilted = tilted;
ret->normrect = normrect;
memcpy( ret->p, p, 4*sizeof(p[0]) );
memcpy( ret->pq, pq, 4*sizeof(pq[0]) );
ret->offset = offset;
ret->varianceNormFactor = varianceNormFactor;
return ret;
}
bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize )
{
int rn = image.rows+1, cn = image.cols+1;
origWinSize = _origWinSize;
normrect = Rect(1, 1, origWinSize.width-2, origWinSize.height-2);
if (image.cols < origWinSize.width || image.rows < origWinSize.height)
return false;
if( sum0.rows < rn || sum0.cols < cn )
{
sum0.create(rn, cn, CV_32S);
sqsum0.create(rn, cn, CV_64F);
if (hasTiltedFeatures)
tilted0.create( rn, cn, CV_32S);
}
sum = Mat(rn, cn, CV_32S, sum0.data);
sqsum = Mat(rn, cn, CV_32S, sqsum0.data);
if( hasTiltedFeatures )
{
tilted = Mat(rn, cn, CV_32S, tilted0.data);
integral(image, sum, sqsum, tilted);
}
else
integral(image, sum, sqsum);
const int* sdata = (const int*)sum.data;
const double* sqdata = (const double*)sqsum.data;
size_t sumStep = sum.step/sizeof(sdata[0]);
size_t sqsumStep = sqsum.step/sizeof(sqdata[0]);
CV_SUM_PTRS( p[0], p[1], p[2], p[3], sdata, normrect, sumStep );
CV_SUM_PTRS( pq[0], pq[1], pq[2], pq[3], sqdata, normrect, sqsumStep );
size_t fi, nfeatures = features->size();
for( fi = 0; fi < nfeatures; fi++ )
featuresPtr[fi].updatePtrs( !featuresPtr[fi].tilted ? sum : tilted );
return true;
}
bool HaarEvaluator::setWindow( Point pt )
{
if( pt.x < 0 || pt.y < 0 ||
pt.x + origWinSize.width >= sum.cols-2 ||
pt.y + origWinSize.height >= sum.rows-2 )
return false;
size_t pOffset = pt.y * (sum.step/sizeof(int)) + pt.x;
size_t pqOffset = pt.y * (sqsum.step/sizeof(double)) + pt.x;
int valsum = CALC_SUM(p, pOffset);
double valsqsum = CALC_SUM(pq, pqOffset);
double nf = (double)normrect.area() * valsqsum - (double)valsum * valsum;
if( nf > 0. )
nf = sqrt(nf);
else
nf = 1.;
varianceNormFactor = 1./nf;
offset = (int)pOffset;
return true;
}
//---------------------------------------------- LBPEvaluator -------------------------------------
class LBPEvaluator : public FeatureEvaluator
{
public:
struct Feature
{
Feature();
Feature( int x, int y, int _block_w, int _block_h ) :
rect(x, y, _block_w, _block_h) {}
int calc( int offset ) const;
void updatePtrs( const Mat& sum );
bool read(const FileNode& node );
Rect rect; // weight and height for block
const int* p[16]; // fast
};
LBPEvaluator();
virtual ~LBPEvaluator();
virtual bool read( const FileNode& node );
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const { return FeatureEvaluator::LBP; }
virtual bool setImage(const Mat& image, Size _origWinSize);
virtual bool setWindow(Point pt);
int operator()(int featureIdx) const
{ return featuresPtr[featureIdx].calc(offset); }
virtual int calcCat(int featureIdx) const
{ return (*this)(featureIdx); }
private:
Size origWinSize;
Ptr<vector<Feature> > features;
Feature* featuresPtr; // optimization
Mat sum0, sum;
Rect normrect;
int offset;
};
inline LBPEvaluator::Feature :: Feature()
{
rect = Rect();
for( int i = 0; i < 16; i++ )
p[i] = 0;
}
inline int LBPEvaluator::Feature :: calc( int offset ) const
{
int cval = CALC_SUM_( p[5], p[6], p[9], p[10], offset );
return (CALC_SUM_( p[0], p[1], p[4], p[5], offset ) >= cval ? 128 : 0) | // 0
(CALC_SUM_( p[1], p[2], p[5], p[6], offset ) >= cval ? 64 : 0) | // 1
(CALC_SUM_( p[2], p[3], p[6], p[7], offset ) >= cval ? 32 : 0) | // 2
(CALC_SUM_( p[6], p[7], p[10], p[11], offset ) >= cval ? 16 : 0) | // 5
(CALC_SUM_( p[10], p[11], p[14], p[15], offset ) >= cval ? 8 : 0)| // 8
(CALC_SUM_( p[9], p[10], p[13], p[14], offset ) >= cval ? 4 : 0)| // 7
(CALC_SUM_( p[8], p[9], p[12], p[13], offset ) >= cval ? 2 : 0)| // 6
(CALC_SUM_( p[4], p[5], p[8], p[9], offset ) >= cval ? 1 : 0);
}
inline void LBPEvaluator::Feature :: updatePtrs( const Mat& sum )
{
const int* ptr = (const int*)sum.data;
size_t step = sum.step/sizeof(ptr[0]);
Rect tr = rect;
CV_SUM_PTRS( p[0], p[1], p[4], p[5], ptr, tr, step );
tr.x += 2*rect.width;
CV_SUM_PTRS( p[2], p[3], p[6], p[7], ptr, tr, step );
tr.y += 2*rect.height;
CV_SUM_PTRS( p[10], p[11], p[14], p[15], ptr, tr, step );
tr.x -= 2*rect.width;
CV_SUM_PTRS( p[8], p[9], p[12], p[13], ptr, tr, step );
}
bool LBPEvaluator::Feature :: read(const FileNode& node )
{
FileNode rnode = node[CC_RECT];
FileNodeIterator it = rnode.begin();
it >> rect.x >> rect.y >> rect.width >> rect.height;
return true;
}
LBPEvaluator::LBPEvaluator()
{
features = new vector<Feature>();
}
LBPEvaluator::~LBPEvaluator()
{
}
bool LBPEvaluator::read( const FileNode& node )
{
features->resize(node.size());
featuresPtr = &(*features)[0];
FileNodeIterator it = node.begin(), it_end = node.end();
for(int i = 0; it != it_end; ++it, i++)
{
if(!featuresPtr[i].read(*it))
return false;
}
return true;
}
Ptr<FeatureEvaluator> LBPEvaluator::clone() const
{
LBPEvaluator* ret = new LBPEvaluator;
ret->origWinSize = origWinSize;
ret->features = features;
ret->featuresPtr = &(*ret->features)[0];
ret->sum0 = sum0, ret->sum = sum;
ret->normrect = normrect;
ret->offset = offset;
return ret;
}
bool LBPEvaluator::setImage( const Mat& image, Size _origWinSize )
{
int rn = image.rows+1, cn = image.cols+1;
origWinSize = _origWinSize;
if( image.cols < origWinSize.width || image.rows < origWinSize.height )
return false;
if( sum0.rows < rn || sum0.cols < cn )
sum0.create(rn, cn, CV_32S);
sum = Mat(rn, cn, CV_32S, sum0.data);
integral(image, sum);
size_t fi, nfeatures = features->size();
for( fi = 0; fi < nfeatures; fi++ )
featuresPtr[fi].updatePtrs( sum );
return true;
}
bool LBPEvaluator::setWindow( Point pt )
{
if( pt.x < 0 || pt.y < 0 ||
pt.x + origWinSize.width >= sum.cols-2 ||
pt.y + origWinSize.height >= sum.rows-2 )
return false;
offset = pt.y * ((int)sum.step/sizeof(int)) + pt.x;
return true;
}
Ptr<FeatureEvaluator> FeatureEvaluator::create(int featureType)
{
return featureType == HAAR ? Ptr<FeatureEvaluator>(new HaarEvaluator) :
featureType == LBP ? Ptr<FeatureEvaluator>(new LBPEvaluator) : Ptr<FeatureEvaluator>();
}
//---------------------------------------- Classifier Cascade --------------------------------------------
CascadeClassifier::CascadeClassifier()
{
}
CascadeClassifier::CascadeClassifier(const string& filename)
{ load(filename); }
CascadeClassifier::~CascadeClassifier()
{
}
bool CascadeClassifier::empty() const
{
return oldCascade.empty() && stages.empty();
}
bool CascadeClassifier::load(const string& filename)
{
oldCascade.release();
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
if( read(fs.getFirstTopLevelNode()) )
return true;
fs.release();
oldCascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0));
return !oldCascade.empty();
}
template<class FEval>
inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_feval )
{
int si, nstages = (int)cascade.stages.size();
int nodeOfs = 0, leafOfs = 0;
FEval& feval = (FEval&)*_feval;
float* cascadeLeaves = &cascade.leaves[0];
CascadeClassifier::DTreeNode* cascadeNodes = &cascade.nodes[0];
CascadeClassifier::DTree* cascadeWeaks = &cascade.classifiers[0];
CascadeClassifier::Stage* cascadeStages = &cascade.stages[0];
for( si = 0; si < nstages; si++ )
{
CascadeClassifier::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifier::DTree& weak = cascadeWeaks[stage.first + wi];
int idx = 0, root = nodeOfs;
do
{
CascadeClassifier::DTreeNode& node = cascadeNodes[root + idx];
double val = feval(node.featureIdx);
idx = val < node.threshold ? node.left : node.right;
}
while( idx > 0 );
sum += cascadeLeaves[leafOfs - idx];
nodeOfs += weak.nodeCount;
leafOfs += weak.nodeCount + 1;
}
if( sum < stage.threshold )
return -si;
}
return 1;
}
template<class FEval>
inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_feval )
{
int si, nstages = (int)cascade.stages.size();
int nodeOfs = 0, leafOfs = 0;
FEval& feval = (FEval&)*_feval;
size_t subsetSize = (cascade.ncategories + 31)/32;
int* cascadeSubsets = &cascade.subsets[0];
float* cascadeLeaves = &cascade.leaves[0];
CascadeClassifier::DTreeNode* cascadeNodes = &cascade.nodes[0];
CascadeClassifier::DTree* cascadeWeaks = &cascade.classifiers[0];
CascadeClassifier::Stage* cascadeStages = &cascade.stages[0];
for( si = 0; si < nstages; si++ )
{
CascadeClassifier::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifier::DTree& weak = cascadeWeaks[stage.first + wi];
int idx = 0, root = nodeOfs;
do
{
CascadeClassifier::DTreeNode& node = cascadeNodes[root + idx];
int c = feval(node.featureIdx);
const int* subset = &cascadeSubsets[(root + idx)*subsetSize];
idx = (subset[c>>5] & (1 << (c & 31))) ? node.left : node.right;
}
while( idx > 0 );
sum += cascadeLeaves[leafOfs - idx];
nodeOfs += weak.nodeCount;
leafOfs += weak.nodeCount + 1;
}
if( sum < stage.threshold )
return -si;
}
return 1;
}
template<class FEval>
inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_feval )
{
int si, nstages = (int)cascade.stages.size();
int nodeOfs = 0, leafOfs = 0;
FEval& feval = (FEval&)*_feval;
float* cascadeLeaves = &cascade.leaves[0];
CascadeClassifier::DTreeNode* cascadeNodes = &cascade.nodes[0];
CascadeClassifier::Stage* cascadeStages = &cascade.stages[0];
for( si = 0; si < nstages; si++ )
{
CascadeClassifier::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
for( wi = 0; wi < ntrees; wi++, nodeOfs++, leafOfs+= 2 )
{
CascadeClassifier::DTreeNode& node = cascadeNodes[nodeOfs];
double val = feval(node.featureIdx);
sum += cascadeLeaves[ val < node.threshold ? leafOfs : leafOfs+1 ];
}
if( sum < stage.threshold )
return -si;
}
return 1;
}
template<class FEval>
inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_feval )
{
int si, nstages = (int)cascade.stages.size();
int nodeOfs = 0, leafOfs = 0;
FEval& feval = (FEval&)*_feval;
size_t subsetSize = (cascade.ncategories + 31)/32;
int* cascadeSubsets = &cascade.subsets[0];
float* cascadeLeaves = &cascade.leaves[0];
CascadeClassifier::DTreeNode* cascadeNodes = &cascade.nodes[0];
CascadeClassifier::Stage* cascadeStages = &cascade.stages[0];
for( si = 0; si < nstages; si++ )
{
CascadeClassifier::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifier::DTreeNode& node = cascadeNodes[nodeOfs];
int c = feval(node.featureIdx);
const int* subset = &cascadeSubsets[nodeOfs*subsetSize];
sum += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
nodeOfs++;
leafOfs += 2;
}
if( sum < stage.threshold )
return -si;
}
return 1;
}
int CascadeClassifier::runAt( Ptr<FeatureEvaluator> &_feval, Point pt )
{
CV_Assert( oldCascade.empty() );
/*if( !oldCascade.empty() )
return cvRunHaarClassifierCascade(oldCascade, pt, 0);*/
assert(featureType == FeatureEvaluator::HAAR ||
featureType == FeatureEvaluator::LBP);
return !_feval->setWindow(pt) ? -1 :
is_stump_based ? ( featureType == FeatureEvaluator::HAAR ?
predictOrderedStump<HaarEvaluator>( *this, _feval ) :
predictCategoricalStump<LBPEvaluator>( *this, _feval ) ) :
( featureType == FeatureEvaluator::HAAR ?
predictOrdered<HaarEvaluator>( *this, _feval ) :
predictCategorical<LBPEvaluator>( *this, _feval ) );
}
bool CascadeClassifier::setImage( Ptr<FeatureEvaluator> &_feval, const Mat& image )
{
/*if( !oldCascade.empty() )
{
Mat sum(image.rows+1, image.cols+1, CV_32S);
Mat tilted(image.rows+1, image.cols+1, CV_32S);
Mat sqsum(image.rows+1, image.cols+1, CV_64F);
integral(image, sum, sqsum, tilted);
CvMat _sum = sum, _sqsum = sqsum, _tilted = tilted;
cvSetImagesForHaarClassifierCascade( oldCascade, &_sum, &_sqsum, &_tilted, 1. );
return true;
}*/
return empty() ? false : _feval->setImage(image, origWinSize );
}
struct CascadeClassifierInvoker
{
CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor, ConcurrentRectVector& _vec )
{
cc = &_cc;
sz1 = _sz1;
stripSize = _stripSize;
yStep = _yStep;
factor = _factor;
vec = &_vec;
}
void operator()(const BlockedRange& range) const
{
Ptr<FeatureEvaluator> feval = cc->feval->clone();
int y1 = range.begin()*stripSize, y2 = min(range.end()*stripSize, sz1.height);
Size winSize(cvRound(cc->origWinSize.width*factor), cvRound(cc->origWinSize.height*factor));
for( int y = y1; y < y2; y += yStep )
for( int x = 0; x < sz1.width; x += yStep )
{
int r = cc->runAt(feval, Point(x, y));
if( r > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
if( r == 0 )
x += yStep;
}
}
CascadeClassifier* cc;
Size sz1;
int stripSize, yStep;
double factor;
ConcurrentRectVector* vec;
};
struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects,
double scaleFactor, int minNeighbors,
int flags, Size minSize )
{
const double GROUP_EPS = 0.2;
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
if( empty() )
return;
if( !oldCascade.empty() )
{
MemStorage storage(cvCreateMemStorage(0));
CvMat _image = image;
CvSeq* _objects = cvHaarDetectObjects( &_image, oldCascade, storage, scaleFactor,
minNeighbors, flags, minSize );
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
objects.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
return;
}
objects.clear();
Mat img = image, imgbuf(image.rows+1, image.cols+1, CV_8U);
if( img.channels() > 1 )
{
Mat temp;
cvtColor(img, temp, CV_BGR2GRAY);
img = temp;
}
ConcurrentRectVector allCandidates;
for( double factor = 1; ; factor *= scaleFactor )
{
int stripCount, stripSize;
Size winSize( cvRound(origWinSize.width*factor), cvRound(origWinSize.height*factor) );
Size sz( cvRound( img.cols/factor ), cvRound( img.rows/factor ) );
Size sz1( sz.width - origWinSize.width, sz.height - origWinSize.height );
if( sz1.width <= 0 || sz1.height <= 0 )
break;
if( winSize.width < minSize.width || winSize.height < minSize.height )
continue;
int yStep = factor > 2. ? 1 : 2;
#ifdef HAVE_TBB
const int PTS_PER_THREAD = 1000;
stripCount = ((sz1.width/yStep)*(sz1.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
stripCount = std::min(std::max(stripCount, 1), 100);
stripSize = (((sz1.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
#else
stripCount = 1;
stripSize = sz1.height;
#endif
Mat img1( sz, CV_8U, imgbuf.data );
resize( img, img1, sz, 0, 0, CV_INTER_LINEAR );
if( !feval->setImage( img1, origWinSize ) )
break;
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker(*this, sz1, stripSize, yStep, factor, allCandidates));
}
objects.resize(allCandidates.size());
std::copy(allCandidates.begin(), allCandidates.end(), objects.begin());
groupRectangles( objects, minNeighbors, GROUP_EPS );
}
bool CascadeClassifier::read(const FileNode& root)
{
// load stage params
string stageTypeStr = (string)root[CC_STAGE_TYPE];
if( stageTypeStr == CC_BOOST )
stageType = BOOST;
else
return false;
string featureTypeStr = (string)root[CC_FEATURE_TYPE];
if( featureTypeStr == CC_HAAR )
featureType = FeatureEvaluator::HAAR;
else if( featureTypeStr == CC_LBP )
featureType = FeatureEvaluator::LBP;
else
return false;
origWinSize.width = (int)root[CC_WIDTH];
origWinSize.height = (int)root[CC_HEIGHT];
CV_Assert( origWinSize.height > 0 && origWinSize.width > 0 );
is_stump_based = (int)(root[CC_STAGE_PARAMS][CC_MAX_DEPTH]) == 1 ? true : false;
// load feature params
FileNode fn = root[CC_FEATURE_PARAMS];
if( fn.empty() )
return false;
ncategories = fn[CC_MAX_CAT_COUNT];
int subsetSize = (ncategories + 31)/32,
nodeStep = 3 + ( ncategories>0 ? subsetSize : 1 );
// load stages
fn = root[CC_STAGES];
if( fn.empty() )
return false;
stages.reserve(fn.size());
classifiers.clear();
nodes.clear();
FileNodeIterator it = fn.begin(), it_end = fn.end();
for( int si = 0; it != it_end; si++, ++it )
{
FileNode fns = *it;
Stage stage;
stage.threshold = fns[CC_STAGE_THRESHOLD];
fns = fns[CC_WEAK_CLASSIFIERS];
if(fns.empty())
return false;
stage.ntrees = (int)fns.size();
stage.first = (int)classifiers.size();
stages.push_back(stage);
classifiers.reserve(stages[si].first + stages[si].ntrees);
FileNodeIterator it1 = fns.begin(), it1_end = fns.end();
for( ; it1 != it1_end; ++it1 ) // weak trees
{
FileNode fnw = *it1;
FileNode internalNodes = fnw[CC_INTERNAL_NODES];
FileNode leafValues = fnw[CC_LEAF_VALUES];
if( internalNodes.empty() || leafValues.empty() )
return false;
DTree tree;
tree.nodeCount = (int)internalNodes.size()/nodeStep;
classifiers.push_back(tree);
nodes.reserve(nodes.size() + tree.nodeCount);
leaves.reserve(leaves.size() + leafValues.size());
if( subsetSize > 0 )
subsets.reserve(subsets.size() + tree.nodeCount*subsetSize);
FileNodeIterator it2 = internalNodes.begin(), it2_end = internalNodes.end();
for( ; it2 != it2_end; ) // nodes
{
DTreeNode node;
node.left = (int)*it2; ++it2;
node.right = (int)*it2; ++it2;
node.featureIdx = (int)*it2; ++it2;
if( subsetSize > 0 )
{
for( int j = 0; j < subsetSize; j++, ++it2 )
subsets.push_back((int)*it2);
node.threshold = 0.f;
}
else
{
node.threshold = (float)*it2; ++it2;
}
nodes.push_back(node);
}
it2 = leafValues.begin(), it2_end = leafValues.end();
for( ; it2 != it2_end; ++it2 ) // leaves
leaves.push_back((float)*it2);
}
}
// load features
feval = FeatureEvaluator::create(featureType);
fn = root[CC_FEATURES];
if( fn.empty() )
return false;
return feval->read(fn);
}
template<> void Ptr<CvHaarClassifierCascade>::delete_obj()
{ cvReleaseHaarClassifierCascade(&obj); }
} // namespace cv
/* End of file. */