New bunch of documentation fixes
This commit is contained in:
Andrey Kamaev
@@ -137,7 +137,7 @@ CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
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CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
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CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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double scale_factor CV_DEFAULT(1.1),
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int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
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CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
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@@ -160,7 +160,7 @@ CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
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// Structure describes the position of the filter in the feature pyramid
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// l - level in the feature pyramid
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// (x, y) - coordinate in level l
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typedef struct
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typedef struct CvLSVMFilterPosition
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{
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int x;
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int y;
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@@ -174,14 +174,14 @@ typedef struct
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// penaltyFunction - vector describes penalty function (d_i in the paper)
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// pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
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// FILTER DESCRIPTION
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// Rectangular map (sizeX x sizeY),
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// Rectangular map (sizeX x sizeY),
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// every cell stores feature vector (dimension = p)
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// H - matrix of feature vectors
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// to set and get feature vectors (i,j)
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// to set and get feature vectors (i,j)
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// used formula H[(j * sizeX + i) * p + k], where
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// k - component of feature vector in cell (i, j)
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// END OF FILTER DESCRIPTION
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typedef struct{
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typedef struct CvLSVMFilterObject{
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CvLSVMFilterPosition V;
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float fineFunction[4];
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int sizeX;
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@@ -192,7 +192,7 @@ typedef struct{
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// data type: STRUCT CvLatentSvmDetector
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// structure contains internal representation of trained Latent SVM detector
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// num_filters - total number of filters (root plus part) in model
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// num_filters - total number of filters (root plus part) in model
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// num_components - number of components in model
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// num_part_filters - array containing number of part filters for each component
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// filters - root and part filters for all model components
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@@ -210,9 +210,9 @@ typedef struct CvLatentSvmDetector
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CvLatentSvmDetector;
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// data type: STRUCT CvObjectDetection
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// structure contains the bounding box and confidence level for detected object
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// structure contains the bounding box and confidence level for detected object
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// rect - bounding box for a detected object
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// score - confidence level
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// score - confidence level
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typedef struct CvObjectDetection
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{
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CvRect rect;
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@@ -247,28 +247,28 @@ CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
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CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
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/*
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// find rectangular regions in the given image that are likely
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// find rectangular regions in the given image that are likely
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// to contain objects and corresponding confidence levels
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//
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// API
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// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
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// CvLatentSvmDetector* detector,
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// CvMemStorage* storage,
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// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
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// CvLatentSvmDetector* detector,
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// CvMemStorage* storage,
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// float overlap_threshold = 0.5f,
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// int numThreads = -1);
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// INPUT
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// image - image to detect objects in
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// detector - Latent SVM detector in internal representation
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// storage - memory storage to store the resultant sequence
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// storage - memory storage to store the resultant sequence
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// of the object candidate rectangles
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// overlap_threshold - threshold for the non-maximum suppression algorithm
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// overlap_threshold - threshold for the non-maximum suppression algorithm
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= 0.5f [here will be the reference to original paper]
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// OUTPUT
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// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
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*/
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CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
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CvLatentSvmDetector* detector,
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CvMemStorage* storage,
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CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
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CvLatentSvmDetector* detector,
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CvMemStorage* storage,
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float overlap_threshold CV_DEFAULT(0.5f),
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int numThreads CV_DEFAULT(-1));
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@@ -285,7 +285,7 @@ CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
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namespace cv
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{
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///////////////////////////// Object Detection ////////////////////////////
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/*
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@@ -330,22 +330,22 @@ private:
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CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, int groupThreshold, double eps=0.2);
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CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
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CV_EXPORTS void groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights );
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CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
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CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
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vector<double>& levelWeights, int groupThreshold, double eps=0.2);
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CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
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CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
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double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
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class CV_EXPORTS FeatureEvaluator
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{
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public:
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public:
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enum { HAAR = 0, LBP = 1, HOG = 2 };
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virtual ~FeatureEvaluator();
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virtual bool read(const FileNode& node);
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virtual Ptr<FeatureEvaluator> clone() const;
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virtual int getFeatureType() const;
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virtual bool setImage(const Mat& img, Size origWinSize);
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virtual bool setWindow(Point p);
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@@ -371,7 +371,7 @@ public:
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CV_WRAP CascadeClassifier();
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CV_WRAP CascadeClassifier( const string& filename );
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virtual ~CascadeClassifier();
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CV_WRAP virtual bool empty() const;
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CV_WRAP bool load( const string& filename );
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virtual bool read( const FileNode& node );
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@@ -475,7 +475,7 @@ public:
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class CV_EXPORTS MaskGenerator
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{
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public:
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virtual ~MaskGenerator() {}
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virtual ~MaskGenerator() {}
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virtual cv::Mat generateMask(const cv::Mat& src)=0;
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virtual void initializeMask(const cv::Mat& /*src*/) {};
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};
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@@ -488,7 +488,7 @@ protected:
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Ptr<MaskGenerator> maskGenerator;
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};
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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struct CV_EXPORTS_W HOGDescriptor
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@@ -496,13 +496,13 @@ struct CV_EXPORTS_W HOGDescriptor
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public:
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enum { L2Hys=0 };
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enum { DEFAULT_NLEVELS=64 };
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CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
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cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
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histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
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histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
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nlevels(HOGDescriptor::DEFAULT_NLEVELS)
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{}
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CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
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Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
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int _histogramNormType=HOGDescriptor::L2Hys,
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@@ -513,28 +513,28 @@ public:
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histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
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gammaCorrection(_gammaCorrection), nlevels(_nlevels)
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{}
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CV_WRAP HOGDescriptor(const String& filename)
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{
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load(filename);
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}
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HOGDescriptor(const HOGDescriptor& d)
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{
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d.copyTo(*this);
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}
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virtual ~HOGDescriptor() {}
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CV_WRAP size_t getDescriptorSize() const;
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CV_WRAP bool checkDetectorSize() const;
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CV_WRAP double getWinSigma() const;
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CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
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virtual bool read(FileNode& fn);
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virtual void write(FileStorage& fs, const String& objname) const;
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CV_WRAP virtual bool load(const String& filename, const String& objname=String());
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CV_WRAP virtual void save(const String& filename, const String& objname=String()) const;
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virtual void copyTo(HOGDescriptor& c) const;
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@@ -544,9 +544,9 @@ public:
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Size winStride=Size(), Size padding=Size(),
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const vector<Point>& locations=vector<Point>()) const;
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//with found weights output
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CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
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CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
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CV_OUT vector<double>& weights,
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double hitThreshold=0, Size winStride=Size(),
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double hitThreshold=0, Size winStride=Size(),
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Size padding=Size(),
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const vector<Point>& searchLocations=vector<Point>()) const;
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//without found weights output
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@@ -555,22 +555,22 @@ public:
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Size padding=Size(),
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const vector<Point>& searchLocations=vector<Point>()) const;
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//with result weights output
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CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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CV_OUT vector<double>& foundWeights, double hitThreshold=0,
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Size winStride=Size(), Size padding=Size(), double scale=1.05,
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CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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CV_OUT vector<double>& foundWeights, double hitThreshold=0,
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Size winStride=Size(), Size padding=Size(), double scale=1.05,
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double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
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//without found weights output
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virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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double hitThreshold=0, Size winStride=Size(),
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Size padding=Size(), double scale=1.05,
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Size padding=Size(), double scale=1.05,
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double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
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CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
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Size paddingTL=Size(), Size paddingBR=Size()) const;
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CV_WRAP static vector<float> getDefaultPeopleDetector();
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CV_WRAP static vector<float> getDaimlerPeopleDetector();
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CV_PROP Size winSize;
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CV_PROP Size blockSize;
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CV_PROP Size blockStride;
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