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Added to objdetect module: meanshift grouping (groupRectangles_meanshift(...) for input rectangles); new pedestrian detection model, trained on the Daimler base (getDaimlerPeopleDetector(...) ).

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Some changes in the HOGDescriptor class interface(objdetect.hpp) (added useMeanShiftGrouping parameter to the detectMultiScale(...) function)
This commit is contained in:
Alexey Kazakov
2011-04-19 09:05:15 +00:00
parent 2057f2c452
commit e863c3d5f6
4 changed files with 924 additions and 25 deletions
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97
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@@ -275,6 +275,41 @@ namespace cv
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<double>& resultWeights, int groupThreshold = 2, double eps=0.2);
CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
class MeanshiftGrouping
{
public:
MeanshiftGrouping(const Point3d& densKer, const vector<Point3d>& posV,
const vector<double>& wV, double modeEps = 1e-4,
int maxIt = 20);
void getModes(vector<Point3d>& modesV, vector<double>& resWeightsV, const double eps);
protected:
vector<Point3d> positionsV;
vector<double> weightsV;
Point3d densityKernel;
int positionsCount;
vector<Point3d> meanshiftV;
vector<Point3d> distanceV;
int iterMax;
double modeEps;
Point3d getNewValue(const Point3d& inPt) const;
double getResultWeight(const Point3d& inPt) const;
Point3d moveToMode(Point3d aPt) const;
double getDistance(Point3d p1, Point3d p2) const;
};
class CV_EXPORTS FeatureEvaluator
{
@@ -312,7 +347,10 @@ public:
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
Size minSize=Size(),
Size maxSize=Size() );
Size maxSize=Size(),
bool outputRejectLevels = false,
vector<int>& rejectLevels = vector<int>(0));
bool isOldFormatCascade() const;
virtual Size getOriginalWindowSize() const;
@@ -321,7 +359,8 @@ public:
protected:
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
int stripSize, int yStep, double factor, vector<Rect>& candidates );
int stripSize, int yStep, double factor, vector<Rect>& candidates,
bool outputRejectLevels = false, vector<int>& rejectLevels = vector<int>(0) );
protected:
enum { BOOST = 0 };
@@ -387,6 +426,35 @@ protected:
Data data;
Ptr<FeatureEvaluator> featureEvaluator;
Ptr<CvHaarClassifierCascade> oldCascade;
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
public:
int getNumStages()
{
int numStages;
if( !isOldFormatCascade() )
{
numStages = data.stages.size();
}
else
{
numStages = this->oldCascade->count;
}
return numStages;
}
void setNumStages(int stageCount)
{
if( !isOldFormatCascade() )
{
if( stageCount )
data.stages.resize(stageCount);
}
else
if( stageCount )
this->oldCascade->count = stageCount;
}
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
};
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
@@ -438,23 +506,38 @@ public:
CV_WRAP virtual bool load(const String& filename, const String& objname=String());
CV_WRAP virtual void save(const String& filename, const String& objname=String()) const;
virtual void copyTo(HOGDescriptor& c) const;
CV_WRAP virtual void compute(const Mat& img,
CV_OUT vector<float>& descriptors,
Size winStride=Size(), Size padding=Size(),
const vector<Point>& locations=vector<Point>()) const;
//with found weights output
CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
vector<double>& weights,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(),
const vector<Point>& searchLocations=vector<Point>()) const;
//without found weights output
CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(),
const vector<Point>& searchLocations=vector<Point>()) const;
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(), double scale=1.05,
int groupThreshold=2) const;
//with result weights output
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
vector<double>& foundWeights, double hitThreshold=0,
Size winStride=Size(), Size padding=Size(), double scale=1.05,
double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
//without found weights output
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(), double scale=1.05,
double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
Size paddingTL=Size(), Size paddingBR=Size()) const;
static vector<float> getDefaultPeopleDetector();
static vector<float> getDaimlerPeopleDetector();
CV_PROP Size winSize;
CV_PROP Size blockSize;