OpenCV  4.1.0 Open Source Computer Vision
cv::saliency::ObjectnessBING Class Reference

Objectness algorithms based on [3] [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014. More...

#include <opencv2/saliency/saliencySpecializedClasses.hpp>

Inheritance diagram for cv::saliency::ObjectnessBING:

Public Member Functions

ObjectnessBING ()

virtual ~ObjectnessBING ()

bool computeSaliency (InputArray image, OutputArray saliencyMap)

double getBase () const

int getNSS () const

std::vector< float > getobjectnessValues ()
Return the list of the rectangles' objectness value,.

int getW () const

void setBase (double val)

void setBBResDir (const String &resultsDir)
This is a utility function that allows to set an arbitrary path in which the algorithm will save the optional results.

void setNSS (int val)

void setTrainingPath (const String &trainingPath)
This is a utility function that allows to set the correct path from which the algorithm will load the trained model.

void setW (int val)

void write () const

Static Public Member Functions

static Ptr< ObjectnessBINGcreate ()

Protected Member Functions

bool computeSaliencyImpl (InputArray image, OutputArray objectnessBoundingBox) CV_OVERRIDE
Performs all the operations and calls all internal functions necessary for the accomplishment of the Binarized normed gradients algorithm.

Protected Attributes inherited from cv::saliency::Saliency
String className

Detailed Description

Objectness algorithms based on [3] [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.

the Binarized normed gradients algorithm fromBING

Constructor & Destructor Documentation

 cv::saliency::ObjectnessBING::ObjectnessBING ( )
 virtual cv::saliency::ObjectnessBING::~ObjectnessBING ( )
virtual

Member Function Documentation

 bool cv::saliency::ObjectnessBING::computeSaliency ( InputArray image, OutputArray saliencyMap )
inline
Python:
retval, saliencyMap=cv.saliency_ObjectnessBING.computeSaliency(image[, saliencyMap])
 bool cv::saliency::ObjectnessBING::computeSaliencyImpl ( InputArray image, OutputArray objectnessBoundingBox )
protectedvirtual

Performs all the operations and calls all internal functions necessary for the accomplishment of the Binarized normed gradients algorithm.

Parameters
 image input image. According to the needs of this specialized algorithm, the param image is a single Mat objectnessBoundingBox objectness Bounding Box vector. According to the result given by this specialized algorithm, the objectnessBoundingBox is a vector. Each bounding box is represented by a Vec4i for (minX, minY, maxX, maxY).

Implements cv::saliency::Objectness.

 static Ptr cv::saliency::ObjectnessBING::create ( )
inlinestatic
Python:
retval=cv.saliency.ObjectnessBING_create()
 double cv::saliency::ObjectnessBING::getBase ( ) const
inline
Python:
retval=cv.saliency_ObjectnessBING.getBase()
 int cv::saliency::ObjectnessBING::getNSS ( ) const
inline
Python:
retval=cv.saliency_ObjectnessBING.getNSS()
 std::vector cv::saliency::ObjectnessBING::getobjectnessValues ( )
Python:
retval=cv.saliency_ObjectnessBING.getobjectnessValues()

Return the list of the rectangles' objectness value,.

in the same order as the vector<Vec4i> objectnessBoundingBox returned by the algorithm (in computeSaliencyImpl function). The bigger value these scores are, it is more likely to be an object window.

 int cv::saliency::ObjectnessBING::getW ( ) const
inline
Python:
retval=cv.saliency_ObjectnessBING.getW()
Python:
 void cv::saliency::ObjectnessBING::setBase ( double val )
inline
Python:
None=cv.saliency_ObjectnessBING.setBase(val)
 void cv::saliency::ObjectnessBING::setBBResDir ( const String & resultsDir )
Python:
None=cv.saliency_ObjectnessBING.setBBResDir(resultsDir)

This is a utility function that allows to set an arbitrary path in which the algorithm will save the optional results.

(ie writing on file the total number and the list of rectangles returned by objectess, one for each row).

Parameters
 resultsDir results' folder path
 void cv::saliency::ObjectnessBING::setNSS ( int val )
inline
Python:
None=cv.saliency_ObjectnessBING.setNSS(val)
 void cv::saliency::ObjectnessBING::setTrainingPath ( const String & trainingPath )
Python:
None=cv.saliency_ObjectnessBING.setTrainingPath(trainingPath)

This is a utility function that allows to set the correct path from which the algorithm will load the trained model.

Parameters
 trainingPath trained model path
 void cv::saliency::ObjectnessBING::setW ( int val )
inline
Python:
None=cv.saliency_ObjectnessBING.setW(val)
 void cv::saliency::ObjectnessBING::write ( ) const
Python:
None=cv.saliency_ObjectnessBING.write()

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