org.opencv.objdetect

Class HOGDescriptor

java.lang.Object

org.opencv.objdetect.HOGDescriptor

public class HOGDescriptor
extends Object

Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs CITE: Dalal2005 . useful links: https://hal.inria.fr/inria-00548512/document/ https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor http://www.learnopencv.com/histogram-of-oriented-gradients http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_NLEVELS
static int	DESCR_FORMAT_COL_BY_COL
static int	DESCR_FORMAT_ROW_BY_ROW
static int	L2Hys
protected long	nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
	HOGDescriptor() Creates the HOG descriptor and detector with default parameters.
protected	HOGDescriptor(long addr)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels)
	HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels, boolean _signedGradient)
	HOGDescriptor(String filename) Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.

Method Summary

Modifier and Type	Method and Description
static HOGDescriptor	__fromPtr__(long addr)
boolean	checkDetectorSize() Checks if detector size equal to descriptor size.
void	compute(Mat img, MatOfFloat descriptors) Computes HOG descriptors of given image.
void	compute(Mat img, MatOfFloat descriptors, Size winStride) Computes HOG descriptors of given image.
void	compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding) Computes HOG descriptors of given image.
void	compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding, MatOfPoint locations) Computes HOG descriptors of given image.
void	computeGradient(Mat img, Mat grad, Mat angleOfs) Computes gradients and quantized gradient orientations.
void	computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL) Computes gradients and quantized gradient orientations.
void	computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL, Size paddingBR) Computes gradients and quantized gradient orientations.
void	detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights) Performs object detection without a multi-scale window.
void	detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold) Performs object detection without a multi-scale window.
void	detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride) Performs object detection without a multi-scale window.
void	detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding) Performs object detection without a multi-scale window.
void	detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding, MatOfPoint searchLocations) Performs object detection without a multi-scale window.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights) Detects objects of different sizes in the input image.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold) Detects objects of different sizes in the input image.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride) Detects objects of different sizes in the input image.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding) Detects objects of different sizes in the input image.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale) Detects objects of different sizes in the input image.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double groupThreshold) Detects objects of different sizes in the input image.
void	detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double groupThreshold, boolean useMeanshiftGrouping) Detects objects of different sizes in the input image.
protected void	finalize()
Size	get_blockSize()
Size	get_blockStride()
Size	get_cellSize()
int	get_derivAperture()
boolean	get_gammaCorrection()
int	get_histogramNormType()
double	get_L2HysThreshold()
int	get_nbins()
int	get_nlevels()
boolean	get_signedGradient()
MatOfFloat	get_svmDetector()
double	get_winSigma()
Size	get_winSize()
static MatOfFloat	getDaimlerPeopleDetector() Returns coefficients of the classifier trained for people detection (for 48x96 windows).
static MatOfFloat	getDefaultPeopleDetector() Returns coefficients of the classifier trained for people detection (for 64x128 windows).
long	getDescriptorSize() Returns the number of coefficients required for the classification.
long	getNativeObjAddr()
double	getWinSigma() Returns winSigma value
boolean	load(String filename) loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
boolean	load(String filename, String objname) loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
void	save(String filename) saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
void	save(String filename, String objname) saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
void	setSVMDetector(Mat svmdetector) Sets coefficients for the linear SVM classifier.

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

nativeObj

protected final long nativeObj

DEFAULT_NLEVELS

public static final int DEFAULT_NLEVELS

See Also:

Constant Field Values

DESCR_FORMAT_COL_BY_COL

public static final int DESCR_FORMAT_COL_BY_COL

See Also:

Constant Field Values

DESCR_FORMAT_ROW_BY_ROW

public static final int DESCR_FORMAT_ROW_BY_ROW

See Also:

Constant Field Values

L2Hys

public static final int L2Hys

See Also:

Constant Field Values

Constructor Detail

HOGDescriptor

protected HOGDescriptor(long addr)

HOGDescriptor

public HOGDescriptor()

Creates the HOG descriptor and detector with default parameters. aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold,
                     boolean _gammaCorrection,
                     int _nlevels,
                     boolean _signedGradient)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

_winSigma - sets winSigma with given value.

_histogramNormType - sets histogramNormType with given value.

_L2HysThreshold - sets L2HysThreshold with given value.

_gammaCorrection - sets gammaCorrection with given value.

_nlevels - sets nlevels with given value.

_signedGradient - sets signedGradient with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold,
                     boolean _gammaCorrection,
                     int _nlevels)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

_winSigma - sets winSigma with given value.

_histogramNormType - sets histogramNormType with given value.

_L2HysThreshold - sets L2HysThreshold with given value.

_gammaCorrection - sets gammaCorrection with given value.

_nlevels - sets nlevels with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold,
                     boolean _gammaCorrection)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

_winSigma - sets winSigma with given value.

_histogramNormType - sets histogramNormType with given value.

_L2HysThreshold - sets L2HysThreshold with given value.

_gammaCorrection - sets gammaCorrection with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

_winSigma - sets winSigma with given value.

_histogramNormType - sets histogramNormType with given value.

_L2HysThreshold - sets L2HysThreshold with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

_winSigma - sets winSigma with given value.

_histogramNormType - sets histogramNormType with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

_winSigma - sets winSigma with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

_derivAperture - sets derivAperture with given value.

HOGDescriptor

public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins)

Parameters:

_winSize - sets winSize with given value.

_blockSize - sets blockSize with given value.

_blockStride - sets blockStride with given value.

_cellSize - sets cellSize with given value.

_nbins - sets nbins with given value.

HOGDescriptor

public HOGDescriptor(String filename)

Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.

Parameters:

filename - The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.

Method Detail

getNativeObjAddr

public long getNativeObjAddr()

__fromPtr__

public static HOGDescriptor __fromPtr__(long addr)

getDescriptorSize

public long getDescriptorSize()

Returns the number of coefficients required for the classification.

Returns:

automatically generated

checkDetectorSize

public boolean checkDetectorSize()

Checks if detector size equal to descriptor size.

Returns:

automatically generated

getWinSigma

public double getWinSigma()

Returns winSigma value

Returns:

automatically generated

setSVMDetector

public void setSVMDetector(Mat svmdetector)

Sets coefficients for the linear SVM classifier.

Parameters:

svmdetector - coefficients for the linear SVM classifier.

load

public boolean load(String filename,
                    String objname)

loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file

Parameters:

filename - Name of the file to read.

objname - The optional name of the node to read (if empty, the first top-level node will be used).

Returns:

automatically generated

load

public boolean load(String filename)

loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file

Parameters:

filename - Name of the file to read.

Returns:

automatically generated

save

public void save(String filename,
                 String objname)

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

Parameters:

filename - File name

objname - Object name

save

public void save(String filename)

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

Parameters:

filename - File name

compute

public void compute(Mat img,
                    MatOfFloat descriptors,
                    Size winStride,
                    Size padding,
                    MatOfPoint locations)

Computes HOG descriptors of given image.

Parameters:

img - Matrix of the type CV_8U containing an image where HOG features will be calculated.

descriptors - Matrix of the type CV_32F

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

locations - Vector of Point

compute

public void compute(Mat img,
                    MatOfFloat descriptors,
                    Size winStride,
                    Size padding)

Computes HOG descriptors of given image.

Parameters:

img - Matrix of the type CV_8U containing an image where HOG features will be calculated.

descriptors - Matrix of the type CV_32F

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

compute

public void compute(Mat img,
                    MatOfFloat descriptors,
                    Size winStride)

Computes HOG descriptors of given image.

Parameters:

img - Matrix of the type CV_8U containing an image where HOG features will be calculated.

descriptors - Matrix of the type CV_32F

winStride - Window stride. It must be a multiple of block stride.

compute

public void compute(Mat img,
                    MatOfFloat descriptors)

Computes HOG descriptors of given image.

Parameters:

img - Matrix of the type CV_8U containing an image where HOG features will be calculated.

descriptors - Matrix of the type CV_32F

detect

public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold,
                   Size winStride,
                   Size padding,
                   MatOfPoint searchLocations)

Performs object detection without a multi-scale window.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.

weights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

searchLocations - Vector of Point includes set of requested locations to be evaluated.

detect

public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold,
                   Size winStride,
                   Size padding)

Performs object detection without a multi-scale window.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.

weights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

detect

public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold,
                   Size winStride)

Performs object detection without a multi-scale window.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.

weights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

detect

public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold)

Performs object detection without a multi-scale window.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.

weights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

detect

public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights)

Performs object detection without a multi-scale window.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.

weights - Vector that will contain confidence values for each detected object. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding,
                             double scale,
                             double groupThreshold,
                             boolean useMeanshiftGrouping)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

scale - Coefficient of the detection window increase.

groupThreshold - Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.

useMeanshiftGrouping - indicates grouping algorithm

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding,
                             double scale,
                             double groupThreshold)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

scale - Coefficient of the detection window increase.

groupThreshold - Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding,
                             double scale)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

padding - Padding

scale - Coefficient of the detection window increase. by many rectangles. 0 means not to perform grouping.

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride.

padding - Padding by many rectangles. 0 means not to perform grouping.

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

winStride - Window stride. It must be a multiple of block stride. by many rectangles. 0 means not to perform grouping.

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object.

hitThreshold - Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. by many rectangles. 0 means not to perform grouping.

detectMultiScale

public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

foundLocations - Vector of rectangles where each rectangle contains the detected object.

foundWeights - Vector that will contain confidence values for each detected object. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. by many rectangles. 0 means not to perform grouping.

computeGradient

public void computeGradient(Mat img,
                            Mat grad,
                            Mat angleOfs,
                            Size paddingTL,
                            Size paddingBR)

Computes gradients and quantized gradient orientations.

Parameters:

img - Matrix contains the image to be computed

grad - Matrix of type CV_32FC2 contains computed gradients

angleOfs - Matrix of type CV_8UC2 contains quantized gradient orientations

paddingTL - Padding from top-left

paddingBR - Padding from bottom-right

computeGradient

public void computeGradient(Mat img,
                            Mat grad,
                            Mat angleOfs,
                            Size paddingTL)

Computes gradients and quantized gradient orientations.

Parameters:

img - Matrix contains the image to be computed

grad - Matrix of type CV_32FC2 contains computed gradients

angleOfs - Matrix of type CV_8UC2 contains quantized gradient orientations

paddingTL - Padding from top-left

computeGradient

public void computeGradient(Mat img,
                            Mat grad,
                            Mat angleOfs)

Computes gradients and quantized gradient orientations.

Parameters:

img - Matrix contains the image to be computed

grad - Matrix of type CV_32FC2 contains computed gradients

angleOfs - Matrix of type CV_8UC2 contains quantized gradient orientations

getDefaultPeopleDetector

public static MatOfFloat getDefaultPeopleDetector()

Returns coefficients of the classifier trained for people detection (for 64x128 windows).

Returns:

automatically generated

getDaimlerPeopleDetector

public static MatOfFloat getDaimlerPeopleDetector()

Returns coefficients of the classifier trained for people detection (for 48x96 windows).

Returns:

automatically generated

get_winSize

public Size get_winSize()

get_blockSize

public Size get_blockSize()

get_blockStride

public Size get_blockStride()

get_cellSize

public Size get_cellSize()

get_nbins

public int get_nbins()

get_derivAperture

public int get_derivAperture()

get_winSigma

public double get_winSigma()

get_histogramNormType

public int get_histogramNormType()

get_L2HysThreshold

public double get_L2HysThreshold()

get_gammaCorrection

public boolean get_gammaCorrection()

get_svmDetector

public MatOfFloat get_svmDetector()

get_nlevels

public int get_nlevels()

get_signedGradient

public boolean get_signedGradient()

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class Object

Throws:

Throwable