Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. More...

#include <opencv2/xobjdetect.hpp>

Collaboration diagram for cv::HOGDescriptor:

Public Types
enum	{ DEFAULT_NLEVELS = 64 }

enum	DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL , DESCR_FORMAT_ROW_BY_ROW }

enum	HistogramNormType { L2Hys = 0 }

Public Member Functions
	HOGDescriptor (const HOGDescriptor &d)

	HOGDescriptor (const String &filename)

	HOGDescriptor (Size _winSize=Size(64, 128), Size _blockSize=Size(16, 16), Size _blockStride=Size(8, 8), Size _cellSize=Size(8, 8), int _nbins=9, int _derivAperture=1, double _winSigma=-1, HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys, double _L2HysThreshold=0.2, bool _gammaCorrection=true, int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
	Creates the HOG descriptor and detector with default parameters.

virtual	~HOGDescriptor ()
	Default destructor.

bool	checkDetectorSize () const
	Checks if detector size equal to descriptor size.

virtual void	compute (InputArray img, std::vector< float > &descriptors, Size winStride=Size(), Size padding=Size(), const std::vector< Point > &locations=std::vector< Point >()) const
	Computes HOG descriptors of given image.

virtual void	computeGradient (InputArray img, InputOutputArray grad, InputOutputArray angleOfs, Size paddingTL=Size(), Size paddingBR=Size()) const
	Computes gradients and quantized gradient orientations.

virtual void	copyTo (HOGDescriptor &c) const
	clones the HOGDescriptor

virtual void	detect (InputArray img, std::vector< Point > &foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const std::vector< Point > &searchLocations=std::vector< Point >()) const
	Performs object detection without a multi-scale window.

virtual void	detect (InputArray img, std::vector< Point > &foundLocations, std::vector< double > &weights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const std::vector< Point > &searchLocations=std::vector< Point >()) const
	Performs object detection without a multi-scale window.

virtual void	detectMultiScale (InputArray img, std::vector< Rect > &foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), double scale=1.05, double groupThreshold=2.0, bool useMeanshiftGrouping=false) const
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

virtual void	detectMultiScale (InputArray img, std::vector< Rect > &foundLocations, std::vector< double > &foundWeights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), double scale=1.05, double groupThreshold=2.0, bool useMeanshiftGrouping=false) const
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

virtual void	detectMultiScaleROI (InputArray img, std::vector< cv::Rect > &foundLocations, std::vector< DetectionROI > &locations, double hitThreshold=0, int groupThreshold=0) const
	evaluate specified ROI and return confidence value for each location in multiple scales

virtual void	detectROI (InputArray img, const std::vector< cv::Point > &locations, std::vector< cv::Point > &foundLocations, std::vector< double > &confidences, double hitThreshold=0, cv::Size winStride=Size(), cv::Size padding=Size()) const
	evaluate specified ROI and return confidence value for each location

size_t	getDescriptorSize () const
	Returns the number of coefficients required for the classification.

double	getWinSigma () const
	Returns winSigma value.

void	groupRectangles (std::vector< cv::Rect > &rectList, std::vector< double > &weights, int groupThreshold, double eps) const
	Groups the object candidate rectangles.

virtual bool	load (const String &filename, const String &objname=String())
	loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file

virtual bool	read (FileNode &fn)
	Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.

virtual void	save (const String &filename, const String &objname=String()) const
	saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

virtual void	setSVMDetector (InputArray svmdetector)
	Sets coefficients for the linear SVM classifier.

virtual void	write (FileStorage &fs, const String &objname) const
	Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.

Static Public Member Functions
static std::vector< float >	getDaimlerPeopleDetector ()
	Returns coefficients of the classifier trained for people detection (for 48x96 windows).

static std::vector< float >	getDefaultPeopleDetector ()
	Returns coefficients of the classifier trained for people detection (for 64x128 windows).

Public Attributes
Size	blockSize
	Block size in pixels. Align to cell size. Default value is Size(16,16).

Size	blockStride
	Block stride. It must be a multiple of cell size. Default value is Size(8,8).

Size	cellSize
	Cell size. Default value is Size(8,8).

int	derivAperture
	not documented

float	free_coef
	not documented

bool	gammaCorrection
	Flag to specify whether the gamma correction preprocessing is required or not.

HOGDescriptor::HistogramNormType	histogramNormType
	histogramNormType

double	L2HysThreshold
	L2-Hys normalization method shrinkage.

int	nbins
	Number of bins used in the calculation of histogram of gradients. Default value is 9.

int	nlevels
	Maximum number of detection window increases. Default value is 64.

UMat	oclSvmDetector
	coefficients for the linear SVM classifier used when OpenCL is enabled

bool	signedGradient
	Indicates signed gradient will be used or not.

std::vector< float >	svmDetector
	coefficients for the linear SVM classifier.

double	winSigma
	Gaussian smoothing window parameter.

Size	winSize
	Detection window size. Align to block size and block stride. Default value is Size(64,128).

Detailed Description

Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.

the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs [63] .

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

Examples: samples/hog_tapi.cpp, and samples/peopledetect.cpp.

Member Enumeration Documentation

◆ anonymous enum

anonymous enum

Enumerator
DEFAULT_NLEVELS	Default nlevels value.

◆ DescriptorStorageFormat

enum cv::HOGDescriptor::DescriptorStorageFormat

Enumerator
DESCR_FORMAT_COL_BY_COL
DESCR_FORMAT_ROW_BY_ROW

◆ HistogramNormType

enum cv::HOGDescriptor::HistogramNormType

Enumerator
L2Hys	Default histogramNormType.

Constructor & Destructor Documentation

◆ HOGDescriptor() [1/3]

cv::HOGDescriptor::HOGDescriptor	(	Size	_winSize = Size(64, 128),
		Size	_blockSize = Size(16, 16),
		Size	_blockStride = Size(8, 8),
		Size	_cellSize = Size(8, 8),
		int	_nbins = 9,
		int	_derivAperture = 1,
		double	_winSigma = -1,
		HOGDescriptor::HistogramNormType	_histogramNormType = HOGDescriptor::L2Hys,
		double	_L2HysThreshold = 0.2,
		bool	_gammaCorrection = true,
		int	_nlevels = HOGDescriptor::DEFAULT_NLEVELS,
		bool	_signedGradient = false )

inline

Python:
	cv.HOGDescriptor(	[, _winSize[, _blockSize[, _blockStride[, _cellSize[, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels[, _signedGradient]]]]]]]]]]]]	) ->	<HOGDescriptor object>
	cv.HOGDescriptor(	filename	) ->	<HOGDescriptor object>

Creates the HOG descriptor and detector with default parameters.

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() [2/3]

cv::HOGDescriptor::HOGDescriptor ( const String & filename )

inline

Python:
	cv.HOGDescriptor(	[, _winSize[, _blockSize[, _blockStride[, _cellSize[, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels[, _signedGradient]]]]]]]]]]]]	) ->	<HOGDescriptor object>
	cv.HOGDescriptor(	filename	) ->	<HOGDescriptor object>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

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.

◆ HOGDescriptor() [3/3]

cv::HOGDescriptor::HOGDescriptor ( const HOGDescriptor & d )

inline

Python:
	cv.HOGDescriptor(	[, _winSize[, _blockSize[, _blockStride[, _cellSize[, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels[, _signedGradient]]]]]]]]]]]]	) ->	<HOGDescriptor object>
	cv.HOGDescriptor(	filename	) ->	<HOGDescriptor object>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

d	the HOGDescriptor which cloned to create a new one.

Here is the call graph for this function:

◆ ~HOGDescriptor()

virtual cv::HOGDescriptor::~HOGDescriptor ( )

inlinevirtual

Default destructor.

Member Function Documentation

◆ checkDetectorSize()

bool cv::HOGDescriptor::checkDetectorSize ( ) const

Python:
	cv.HOGDescriptor.checkDetectorSize(		) ->	retval

Checks if detector size equal to descriptor size.

◆ compute()

virtual void cv::HOGDescriptor::compute	(	InputArray	img,
		std::vector< float > &	descriptors,
		Size	winStride = Size(),
		Size	padding = Size(),
		const std::vector< Point > &	locations = std::vector< Point >() ) const

virtual

Python:
	cv.HOGDescriptor.compute(	img[, winStride[, padding[, locations]]]	) ->	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
winStride	Window stride. It must be a multiple of block stride.
padding	Padding
locations	Vector of Point

◆ computeGradient()

virtual void cv::HOGDescriptor::computeGradient	(	InputArray	img,
		InputOutputArray	grad,
		InputOutputArray	angleOfs,
		Size	paddingTL = Size(),
		Size	paddingBR = Size() ) const

virtual

Python:
	cv.HOGDescriptor.computeGradient(	img, grad, angleOfs[, paddingTL[, paddingBR]]	) ->	grad, 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
paddingTL	Padding from top-left
paddingBR	Padding from bottom-right

◆ copyTo()

virtual void cv::HOGDescriptor::copyTo ( HOGDescriptor & c ) const

virtual

clones the HOGDescriptor

Parameters

c	cloned HOGDescriptor

◆ detect() [1/2]

virtual void cv::HOGDescriptor::detect	(	InputArray	img,
		std::vector< Point > &	foundLocations,
		double	hitThreshold = 0,
		Size	winStride = Size(),
		Size	padding = Size(),
		const std::vector< Point > &	searchLocations = std::vector< Point >() ) const

virtual

Python:
	cv.HOGDescriptor.detect(	img[, hitThreshold[, winStride[, padding[, searchLocations]]]]	) ->	foundLocations, 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.
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 locations to search.

◆ detect() [2/2]

virtual void cv::HOGDescriptor::detect	(	InputArray	img,
		std::vector< Point > &	foundLocations,
		std::vector< double > &	weights,
		double	hitThreshold = 0,
		Size	winStride = Size(),
		Size	padding = Size(),
		const std::vector< Point > &	searchLocations = std::vector< Point >() ) const

virtual

Python:
	cv.HOGDescriptor.detect(	img[, hitThreshold[, winStride[, padding[, searchLocations]]]]	) ->	foundLocations, 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.
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.

◆ detectMultiScale() [1/2]

virtual void cv::HOGDescriptor::detectMultiScale	(	InputArray	img,
		std::vector< Rect > &	foundLocations,
		double	hitThreshold = 0,
		Size	winStride = Size(),
		Size	padding = Size(),
		double	scale = 1.05,
		double	groupThreshold = 2.0,
		bool	useMeanshiftGrouping = false ) const

virtual

Python:
	cv.HOGDescriptor.detectMultiScale(	img[, hitThreshold[, winStride[, padding[, scale[, groupThreshold[, useMeanshiftGrouping]]]]]]	) ->	foundLocations, 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.
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() [2/2]

virtual void cv::HOGDescriptor::detectMultiScale	(	InputArray	img,
		std::vector< Rect > &	foundLocations,
		std::vector< double > &	foundWeights,
		double	hitThreshold = 0,
		Size	winStride = Size(),
		Size	padding = Size(),
		double	scale = 1.05,
		double	groupThreshold = 2.0,
		bool	useMeanshiftGrouping = false ) const

virtual

Python:
	cv.HOGDescriptor.detectMultiScale(	img[, hitThreshold[, winStride[, padding[, scale[, groupThreshold[, useMeanshiftGrouping]]]]]]	) ->	foundLocations, 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.
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

Examples: samples/peopledetect.cpp.

◆ detectMultiScaleROI()

virtual void cv::HOGDescriptor::detectMultiScaleROI	(	InputArray	img,
		std::vector< cv::Rect > &	foundLocations,
		std::vector< DetectionROI > &	locations,
		double	hitThreshold = 0,
		int	groupThreshold = 0 ) const

virtual

evaluate specified ROI and return confidence value for each location in multiple scales

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.
locations	Vector of DetectionROI
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.
groupThreshold	Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.

◆ detectROI()

virtual void cv::HOGDescriptor::detectROI	(	InputArray	img,
		const std::vector< cv::Point > &	locations,
		std::vector< cv::Point > &	foundLocations,
		std::vector< double > &	confidences,
		double	hitThreshold = 0,
		cv::Size	winStride = Size(),
		cv::Size	padding = Size() ) const

virtual

evaluate specified ROI and return confidence value for each location

Parameters

img	Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
locations	Vector of Point
foundLocations	Vector of Point where each Point is detected object's top-left point.
confidences	confidences
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	winStride
padding	padding

◆ getDaimlerPeopleDetector()

static std::vector< float > cv::HOGDescriptor::getDaimlerPeopleDetector ( )

static

Python:
	cv.HOGDescriptor.getDaimlerPeopleDetector(		) ->	retval
	cv.HOGDescriptor_getDaimlerPeopleDetector(		) ->	retval

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

◆ getDefaultPeopleDetector()

static std::vector< float > cv::HOGDescriptor::getDefaultPeopleDetector ( )

static

Python:
	cv.HOGDescriptor.getDefaultPeopleDetector(		) ->	retval
	cv.HOGDescriptor_getDefaultPeopleDetector(		) ->	retval

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

◆ getDescriptorSize()

size_t cv::HOGDescriptor::getDescriptorSize ( ) const

Python:
	cv.HOGDescriptor.getDescriptorSize(		) ->	retval

Returns the number of coefficients required for the classification.

◆ getWinSigma()

double cv::HOGDescriptor::getWinSigma ( ) const

Python:
	cv.HOGDescriptor.getWinSigma(		) ->	retval

Returns winSigma value.

◆ groupRectangles()

void cv::HOGDescriptor::groupRectangles	(	std::vector< cv::Rect > &	rectList,
		std::vector< double > &	weights,
		int	groupThreshold,
		double	eps ) const

Groups the object candidate rectangles.

Parameters

rectList	Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
weights	Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
groupThreshold	Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
eps	Relative difference between sides of the rectangles to merge them into a group.

◆ load()

virtual bool cv::HOGDescriptor::load	(	const String &	filename,
		const String &	objname = String() )

virtual

Python:
	cv.HOGDescriptor.load(	filename[, objname]	) ->	retval

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).

◆ read()

virtual bool cv::HOGDescriptor::read ( FileNode & fn )

virtual

Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.

Parameters

fn File node

◆ save()

virtual void cv::HOGDescriptor::save	(	const String &	filename,
		const String &	objname = String() ) const

virtual

Python:
	cv.HOGDescriptor.save(	filename[, objname]	) ->	None

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

Parameters

filename	File name
objname	Object name

◆ setSVMDetector()

virtual void cv::HOGDescriptor::setSVMDetector ( InputArray svmdetector )

virtual

Python:
	cv.HOGDescriptor.setSVMDetector(	svmdetector	) ->	None

Sets coefficients for the linear SVM classifier.

Parameters

svmdetector coefficients for the linear SVM classifier.

Examples: samples/peopledetect.cpp.

◆ write()

virtual void cv::HOGDescriptor::write	(	FileStorage &	fs,
		const String &	objname ) const

virtual

Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.

Parameters

fs	File storage
objname	Object name

Member Data Documentation

◆ blockSize

Size cv::HOGDescriptor::blockSize

Block size in pixels. Align to cell size. Default value is Size(16,16).

◆ blockStride

Size cv::HOGDescriptor::blockStride

Block stride. It must be a multiple of cell size. Default value is Size(8,8).

◆ cellSize

Size cv::HOGDescriptor::cellSize

Cell size. Default value is Size(8,8).

◆ derivAperture

int cv::HOGDescriptor::derivAperture

not documented

◆ free_coef

float cv::HOGDescriptor::free_coef

not documented

◆ gammaCorrection

bool cv::HOGDescriptor::gammaCorrection

Flag to specify whether the gamma correction preprocessing is required or not.

◆ histogramNormType

HOGDescriptor::HistogramNormType cv::HOGDescriptor::histogramNormType

histogramNormType

◆ L2HysThreshold

double cv::HOGDescriptor::L2HysThreshold

L2-Hys normalization method shrinkage.

◆ nbins

int cv::HOGDescriptor::nbins

Number of bins used in the calculation of histogram of gradients. Default value is 9.

◆ nlevels

int cv::HOGDescriptor::nlevels

Maximum number of detection window increases. Default value is 64.

◆ oclSvmDetector

UMat cv::HOGDescriptor::oclSvmDetector

coefficients for the linear SVM classifier used when OpenCL is enabled

◆ signedGradient

bool cv::HOGDescriptor::signedGradient

Indicates signed gradient will be used or not.

◆ svmDetector

std::vector<float> cv::HOGDescriptor::svmDetector

coefficients for the linear SVM classifier.

◆ winSigma

double cv::HOGDescriptor::winSigma

Gaussian smoothing window parameter.

◆ winSize

Size cv::HOGDescriptor::winSize

Detection window size. Align to block size and block stride. Default value is Size(64,128).

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

opencv2/xobjdetect.hpp

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Detailed Description

Member Enumeration Documentation

◆ anonymous enum

◆ DescriptorStorageFormat

◆ HistogramNormType

Constructor & Destructor Documentation

◆ HOGDescriptor() [1/3]

◆ HOGDescriptor() [2/3]

◆ HOGDescriptor() [3/3]

◆ ~HOGDescriptor()

Member Function Documentation

◆ checkDetectorSize()

◆ compute()

◆ computeGradient()

◆ copyTo()

◆ detect() [1/2]

◆ detect() [2/2]

◆ detectMultiScale() [1/2]

◆ detectMultiScale() [2/2]

◆ detectMultiScaleROI()

◆ detectROI()

◆ getDaimlerPeopleDetector()

◆ getDefaultPeopleDetector()

◆ getDescriptorSize()

◆ getWinSigma()

◆ groupRectangles()

◆ load()

◆ read()

◆ save()

◆ setSVMDetector()

◆ write()

Member Data Documentation

◆ blockSize

◆ blockStride

◆ cellSize

◆ derivAperture

◆ free_coef

◆ gammaCorrection

◆ histogramNormType

◆ L2HysThreshold

◆ nbins

◆ nlevels

◆ oclSvmDetector

◆ signedGradient

◆ svmDetector

◆ winSigma

◆ winSize