OpenCV  4.9.0-dev
Open Source Computer Vision
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Public Member Functions | List of all members
cv::GeneralizedHoughBallard Class Referenceabstract

finds arbitrary template in the grayscale image using Generalized Hough Transform More...

#include <opencv2/imgproc.hpp>

Inheritance diagram for cv::GeneralizedHoughBallard:
Collaboration diagram for cv::GeneralizedHoughBallard:

Public Member Functions

virtual int getLevels () const =0
 
virtual int getVotesThreshold () const =0
 
virtual void setLevels (int levels)=0
 R-Table levels.
 
virtual void setVotesThreshold (int votesThreshold)=0
 The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.
 
- Public Member Functions inherited from cv::GeneralizedHough
virtual void detect (InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes=noArray())=0
 
virtual void detect (InputArray image, OutputArray positions, OutputArray votes=noArray())=0
 find template on image
 
virtual int getCannyHighThresh () const =0
 
virtual int getCannyLowThresh () const =0
 
virtual double getDp () const =0
 
virtual int getMaxBufferSize () const =0
 
virtual double getMinDist () const =0
 
virtual void setCannyHighThresh (int cannyHighThresh)=0
 Canny high threshold.
 
virtual void setCannyLowThresh (int cannyLowThresh)=0
 Canny low threshold.
 
virtual void setDp (double dp)=0
 Inverse ratio of the accumulator resolution to the image resolution.
 
virtual void setMaxBufferSize (int maxBufferSize)=0
 Maximal size of inner buffers.
 
virtual void setMinDist (double minDist)=0
 Minimum distance between the centers of the detected objects.
 
virtual void setTemplate (InputArray edges, InputArray dx, InputArray dy, Point templCenter=Point(-1, -1))=0
 
virtual void setTemplate (InputArray templ, Point templCenter=Point(-1, -1))=0
 set template to search
 
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
 
virtual ~Algorithm ()
 
virtual void clear ()
 Clears the algorithm state.
 
virtual bool empty () const
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
 
virtual String getDefaultName () const
 
virtual void read (const FileNode &fn)
 Reads algorithm parameters from a file storage.
 
virtual void save (const String &filename) const
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 
virtual void write (FileStorage &fs) const
 Stores algorithm parameters in a file storage.
 
void write (FileStorage &fs, const String &name) const
 

Additional Inherited Members

- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tpload (const String &filename, const String &objname=String())
 Loads algorithm from the file.
 
template<typename _Tp >
static Ptr< _TploadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String.
 
template<typename _Tp >
static Ptr< _Tpread (const FileNode &fn)
 Reads algorithm from the file node.
 
- Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const
 

Detailed Description

finds arbitrary template in the grayscale image using Generalized Hough Transform

Detects position only without translation and rotation [15] .

Member Function Documentation

◆ getLevels()

virtual int cv::GeneralizedHoughBallard::getLevels ( ) const
pure virtual
Python:
cv.GeneralizedHoughBallard.getLevels() -> retval

◆ getVotesThreshold()

virtual int cv::GeneralizedHoughBallard::getVotesThreshold ( ) const
pure virtual
Python:
cv.GeneralizedHoughBallard.getVotesThreshold() -> retval

◆ setLevels()

virtual void cv::GeneralizedHoughBallard::setLevels ( int  levels)
pure virtual
Python:
cv.GeneralizedHoughBallard.setLevels(levels) -> None

R-Table levels.

◆ setVotesThreshold()

virtual void cv::GeneralizedHoughBallard::setVotesThreshold ( int  votesThreshold)
pure virtual
Python:
cv.GeneralizedHoughBallard.setVotesThreshold(votesThreshold) -> None

The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.


The documentation for this class was generated from the following file: