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Added ROC-curve calculating to the cascade detection algorithm

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This commit is contained in:
Alexey Kazakov
2011-04-22 10:03:05 +00:00
parent 06070dfcf6
commit e9aa6fa012
11 changed files with 160 additions and 617 deletions
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86
modules/objdetect/src/haar.cpp
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@@ -654,8 +654,8 @@ double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
int result = -1;
@@ -698,7 +698,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
while( ptr )
{
double stage_sum = 0;
stage_sum = 0.0;
for( j = 0; j < ptr->count; j++ )
{
@@ -724,7 +724,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
for( i = start_stage; i < cascade->count; i++ )
{
#ifndef CV_HAAR_USE_SSE
double stage_sum = 0;
stage_sum = 0.0;
#else
__m128d stage_sum = _mm_setzero_pd();
#endif
@@ -796,7 +796,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
{
for( i = start_stage; i < cascade->count; i++ )
{
double stage_sum = 0;
stage_sum = 0.0;
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
@@ -809,10 +809,16 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
return -i;
}
}
return 1;
}
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
{
double stage_sum;
return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
}
namespace cv
{
@@ -822,7 +828,9 @@ struct HaarDetectObjects_ScaleImage_Invoker
HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
int _stripSize, double _factor,
const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec )
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec,
std::vector<int>& _levels, std::vector<double>& _weights,
bool _outputLevels )
{
cascade = _cascade;
stripSize = _stripSize;
@@ -833,6 +841,8 @@ struct HaarDetectObjects_ScaleImage_Invoker
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
rejectLevels = _outputLevels ? &_levels : 0;
levelWeights = _outputLevels ? &_weights : 0;
}
void operator()( const BlockedRange& range ) const
@@ -902,9 +912,26 @@ struct HaarDetectObjects_ScaleImage_Invoker
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
if( cvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
double gypWeight;
int result = cvRunHaarClassifierCascadeSum( cascade, cvPoint(x,y), gypWeight, 0 );
if( rejectLevels )
{
if( result == 1 )
result = -1*cascade->count;
if( cascade->count + result < 4 )
{
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
rejectLevels->push_back(-result);
levelWeights->push_back(gypWeight);
}
}
else
{
if( result > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
}
}
}
@@ -914,6 +941,8 @@ struct HaarDetectObjects_ScaleImage_Invoker
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
ConcurrentRectVector* vec;
std::vector<int>* rejectLevels;
std::vector<double>* levelWeights;
};
@@ -983,10 +1012,11 @@ struct HaarDetectObjects_ScaleCascade_Invoker
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade,
CvMemStorage* storage, double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
cvHaarDetectObjectsForROC( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors, int flags,
CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
const double GROUP_EPS = 0.2;
CvMat stub, *img = (CvMat*)_img;
@@ -1119,7 +1149,7 @@ cvHaarDetectObjects( const CvArr* _img,
cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
(((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep,
factor, cv::Mat(&sum1), cv::Mat(&sqsum1), &_norm1, &_mask1,
cv::Rect(equRect), allCandidates));
cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels));
}
}
else
@@ -1250,7 +1280,16 @@ cvHaarDetectObjects( const CvArr* _img,
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
{
if( outputRejectLevels )
{
groupRectangles(rectList, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
}
}
else
rweights.resize(rectList.size(),0);
@@ -1275,7 +1314,7 @@ cvHaarDetectObjects( const CvArr* _img,
{
CvAvgComp c;
c.rect = rectList[i];
c.neighbors = rweights[i];
c.neighbors = !rweights.empty() ? rweights[i] : 0;
cvSeqPush( result_seq, &c );
}
}
@@ -1283,6 +1322,19 @@ cvHaarDetectObjects( const CvArr* _img,
return result_seq;
}
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
{
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
scaleFactor, minNeighbors, flags, minSize, maxSize, false );
}
static CvHaarClassifierCascade*
icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )