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Merge pull request #891 from NCBee:2.4

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Andrey Pavlenko 2013-07-31 16:38:14 +04:00 committed by OpenCV Buildbot
commit 9b5d1596dc
3 changed files with 115 additions and 37 deletions
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54
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@ -192,12 +192,12 @@ typedef struct CvLSVMFilterObject{
// data type: STRUCT CvLatentSvmDetector
// structure contains internal representation of trained Latent SVM detector
// num_filters - total number of filters (root plus part) in model
// num_components - number of components in model
// num_part_filters - array containing number of part filters for each component
// filters - root and part filters for all model components
// b - biases for all model components
// score_threshold - confidence level threshold
// num_filters - total number of filters (root plus part) in model
// num_components - number of components in model
// num_part_filters - array containing number of part filters for each component
// filters - root and part filters for all model components
// b - biases for all model components
// score_threshold - confidence level threshold
typedef struct CvLatentSvmDetector
{
int num_filters;
@ -211,8 +211,8 @@ CvLatentSvmDetector;
// data type: STRUCT CvObjectDetection
// structure contains the bounding box and confidence level for detected object
// rect - bounding box for a detected object
// score - confidence level
// rect - bounding box for a detected object
// score - confidence level
typedef struct CvObjectDetection
{
CvRect rect;
@ -228,7 +228,7 @@ typedef struct CvObjectDetection
// API
// CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
// INPUT
// filename - path to the file containing the parameters of
// filename - path to the file containing the parameters of
- trained Latent SVM detector
// OUTPUT
// trained Latent SVM detector in internal representation
@ -241,7 +241,7 @@ CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
// API
// void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
// INPUT
// detector - CvLatentSvmDetector structure to be released
// detector - CvLatentSvmDetector structure to be released
// OUTPUT
*/
CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
@ -252,16 +252,16 @@ CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
//
// API
// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// float overlap_threshold = 0.5f,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// float overlap_threshold = 0.5f,
// int numThreads = -1);
// INPUT
// image - image to detect objects in
// detector - Latent SVM detector in internal representation
// storage - memory storage to store the resultant sequence
// of the object candidate rectangles
// overlap_threshold - threshold for the non-maximum suppression algorithm
// image - image to detect objects in
// detector - Latent SVM detector in internal representation
// storage - memory storage to store the resultant sequence
// of the object candidate rectangles
// overlap_threshold - threshold for the non-maximum suppression algorithm
= 0.5f [here will be the reference to original paper]
// OUTPUT
// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
@ -327,6 +327,23 @@ private:
vector<string> classNames;
};
// 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;
};
CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights );
@ -611,6 +628,7 @@ public:
// read/parse Dalal's alt model file
void readALTModel(std::string modelfile);
void groupRectangles(vector<cv::Rect>& rectList, vector<double>& weights, int groupThreshold, double eps) const;
};

18
modules/objdetect/src/cascadedetect.cpp
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@ -114,24 +114,6 @@ struct Logger
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;
};
void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights)
{
if( groupThreshold <= 0 || rectList.empty() )

80
modules/objdetect/src/hog.cpp
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@ -1060,7 +1060,7 @@ void HOGDescriptor::detectMultiScale(
}
else
{
groupRectangles(foundLocations, (int)finalThreshold, 0.2);
groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
}
}
@ -2634,4 +2634,82 @@ void HOGDescriptor::readALTModel(std::string modelfile)
fclose(modelfl);
}
void HOGDescriptor::groupRectangles(vector<cv::Rect>& rectList, vector<double>& weights, int groupThreshold, double eps) const
{
if( groupThreshold <= 0 || rectList.empty() )
{
return;
}
CV_Assert(rectList.size() == weights.size());
vector<int> labels;
int nclasses = partition(rectList, labels, SimilarRects(eps));
vector<cv::Rect_<double> > rrects(nclasses);
vector<int> numInClass(nclasses, 0);
vector<double> foundWeights(nclasses, DBL_MIN);
vector<double> totalFactorsPerClass(nclasses, 1);
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;
foundWeights[cls] = max(foundWeights[cls], weights[i]);
numInClass[cls]++;
}
for( i = 0; i < nclasses; i++ )
{
// find the average of all ROI in the cluster
cv::Rect_<double> r = rrects[i];
double s = 1.0/numInClass[i];
rrects[i] = cv::Rect_<double>(cv::saturate_cast<double>(r.x*s),
cv::saturate_cast<double>(r.y*s),
cv::saturate_cast<double>(r.width*s),
cv::saturate_cast<double>(r.height*s));
}
rectList.clear();
weights.clear();
for( i = 0; i < nclasses; i++ )
{
cv::Rect r1 = rrects[i];
int n1 = numInClass[i];
double w1 = foundWeights[i];
if( n1 <= groupThreshold )
continue;
// filter out small rectangles inside large rectangles
for( j = 0; j < nclasses; j++ )
{
int n2 = numInClass[j];
if( j == i || n2 <= groupThreshold )
continue;
cv::Rect r2 = rrects[j];
int dx = cv::saturate_cast<int>( r2.width * eps );
int dy = cv::saturate_cast<int>( r2.height * eps );
if( 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);
weights.push_back(w1);
}
}
}
}