OpenCV  4.1.0-dev
Open Source Computer Vision
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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:
cv::saliency::Objectness cv::saliency::Saliency cv::Algorithm

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,. More...
 
int getW () const
 
void read ()
 
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. More...
 
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. More...
 
void setW (int val)
 
void write () const
 
- Public Member Functions inherited from cv::saliency::Saliency
virtual ~Saliency ()
 Destructor. More...
 
bool computeSaliency (InputArray image, OutputArray saliencyMap)
 Compute the saliency. More...
 
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
 
virtual ~Algorithm ()
 
virtual void clear ()
 Clears the algorithm state. More...
 
virtual bool empty () const
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
 
virtual String getDefaultName () const
 
virtual void read (const FileNode &fn)
 Reads algorithm parameters from a file storage. More...
 
virtual void save (const String &filename) const
 
virtual void write (FileStorage &fs) const
 Stores algorithm parameters in a file storage. More...
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
 

Static Public Member Functions

static Ptr< ObjectnessBINGcreate ()
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp > load (const String &filename, const String &objname=String())
 Loads algorithm from the file. More...
 
template<typename _Tp >
static Ptr< _Tp > loadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String. More...
 
template<typename _Tp >
static Ptr< _Tp > read (const FileNode &fn)
 Reads algorithm from the file node. More...
 

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. More...
 
- Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const
 

Additional Inherited Members

- 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 from [37]

Constructor & Destructor Documentation

§ ObjectnessBING()

cv::saliency::ObjectnessBING::ObjectnessBING ( )

§ ~ObjectnessBING()

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

Member Function Documentation

§ computeSaliency()

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

§ computeSaliencyImpl()

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

Implements cv::saliency::Objectness.

§ create()

static Ptr<ObjectnessBING> cv::saliency::ObjectnessBING::create ( )
inlinestatic
Python:
retval=cv.saliency.ObjectnessBING_create()

§ getBase()

double cv::saliency::ObjectnessBING::getBase ( ) const
inline
Python:
retval=cv.saliency_ObjectnessBING.getBase()

§ getNSS()

int cv::saliency::ObjectnessBING::getNSS ( ) const
inline
Python:
retval=cv.saliency_ObjectnessBING.getNSS()

§ getobjectnessValues()

std::vector<float> 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.

§ getW()

int cv::saliency::ObjectnessBING::getW ( ) const
inline
Python:
retval=cv.saliency_ObjectnessBING.getW()

§ read()

void cv::saliency::ObjectnessBING::read ( )
Python:
None=cv.saliency_ObjectnessBING.read()

§ setBase()

void cv::saliency::ObjectnessBING::setBase ( double  val)
inline
Python:
None=cv.saliency_ObjectnessBING.setBase(val)

§ setBBResDir()

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
resultsDirresults' folder path

§ setNSS()

void cv::saliency::ObjectnessBING::setNSS ( int  val)
inline
Python:
None=cv.saliency_ObjectnessBING.setNSS(val)

§ setTrainingPath()

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
trainingPathtrained model path

§ setW()

void cv::saliency::ObjectnessBING::setW ( int  val)
inline
Python:
None=cv.saliency_ObjectnessBING.setW(val)

§ write()

void cv::saliency::ObjectnessBING::write ( ) const
Python:
None=cv.saliency_ObjectnessBING.write()

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