The class implements Histogram of Oriented Gradients ([63]) object detector. More...

#include <opencv2/cudaobjdetect.hpp>

Collaboration diagram for cv::cuda::HOG:

Public Member Functions
virtual void	compute (InputArray img, OutputArray descriptors, Stream &stream=Stream::Null())=0
	Returns block descriptors computed for the whole image.

void	detect (InputArray img, std::vector< Point > &found_locations, std::vector< double > &confidences)

virtual void	detect (InputArray img, std::vector< Point > &found_locations, std::vector< double > *confidences=NULL)=0
	Performs object detection without a multi-scale window.

void	detectMultiScale (InputArray img, std::vector< Rect > &found_locations, std::vector< double > &confidences)

virtual void	detectMultiScale (InputArray img, std::vector< Rect > &found_locations, std::vector< double > *confidences=NULL)=0
	Performs object detection with a multi-scale window.

void	detectMultiScaleWithoutConf (InputArray img, std::vector< Rect > &found_locations)
	Performs object detection with a multi-scale window.

void	detectWithoutConf (InputArray img, std::vector< Point > &found_locations)
	Performs object detection without a multi-scale window.

virtual size_t	getBlockHistogramSize () const =0
	Returns the block histogram size.

virtual Mat	getDefaultPeopleDetector () const =0
	Returns coefficients of the classifier trained for people detection.

virtual HOGDescriptor::DescriptorStorageFormat	getDescriptorFormat () const =0

virtual size_t	getDescriptorSize () const =0
	Returns the number of coefficients required for the classification.

virtual bool	getGammaCorrection () const =0

virtual int	getGroupThreshold () const =0

virtual double	getHitThreshold () const =0

virtual double	getL2HysThreshold () const =0

virtual int	getNumLevels () const =0

virtual double	getScaleFactor () const =0

virtual double	getWinSigma () const =0

virtual Size	getWinStride () const =0

virtual void	setDescriptorFormat (HOGDescriptor::DescriptorStorageFormat descr_format)=0

virtual void	setGammaCorrection (bool gamma_correction)=0
	Flag to specify whether the gamma correction preprocessing is required or not.

virtual void	setGroupThreshold (int group_threshold)=0

virtual void	setHitThreshold (double hit_threshold)=0

virtual void	setL2HysThreshold (double threshold_L2hys)=0
	L2-Hys normalization method shrinkage.

virtual void	setNumLevels (int nlevels)=0
	Maximum number of detection window increases.

virtual void	setScaleFactor (double scale0)=0
	Coefficient of the detection window increase.

virtual void	setSVMDetector (InputArray detector)=0
	Sets coefficients for the linear SVM classifier.

virtual void	setWinSigma (double win_sigma)=0
	Gaussian smoothing window parameter.

virtual void	setWinStride (Size win_stride)=0
	Window stride. It must be a multiple of block stride.

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

Static Public Member Functions
static Ptr< HOG >	create (Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9)
	Creates the HOG descriptor and detector.

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.

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

The class implements Histogram of Oriented Gradients ([63]) object detector.

Note

An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/cpp/peopledetect.cpp
A CUDA example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/gpu/hog.cpp
(Python) An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/python/peopledetect.py

Member Function Documentation

◆ compute()

virtual void cv::cuda::HOG::compute	(	InputArray	img,
		OutputArray	descriptors,
		Stream &	stream = Stream::Null() )

pure virtual

Returns block descriptors computed for the whole image.

Parameters

img	Source image. See cuda::HOGDescriptor::detect for type limitations.
descriptors	2D array of descriptors.
stream	CUDA stream.

◆ create()

static Ptr< HOG > cv::cuda::HOG::create	(	Size	win_size = Size(64, 128),
		Size	block_size = Size(16, 16),
		Size	block_stride = Size(8, 8),
		Size	cell_size = Size(8, 8),
		int	nbins = 9 )

static

Creates the HOG descriptor and detector.

Parameters

win_size	Detection window size. Align to block size and block stride.
block_size	Block size in pixels. Align to cell size. Only (16,16) is supported for now.
block_stride	Block stride. It must be a multiple of cell size.
cell_size	Cell size. Only (8, 8) is supported for now.
nbins	Number of bins. Only 9 bins per cell are supported for now.

◆ detect() [1/2]

void cv::cuda::HOG::detect	(	InputArray	img,
		std::vector< Point > &	found_locations,
		std::vector< double > &	confidences )

inline

◆ detect() [2/2]

virtual void cv::cuda::HOG::detect	(	InputArray	img,
		std::vector< Point > &	found_locations,
		std::vector< double > *	confidences = NULL )

pure virtual

Performs object detection without a multi-scale window.

Parameters

img	Source image. CV_8UC1 and CV_8UC4 types are supported for now.
found_locations	Left-top corner points of detected objects boundaries.
confidences	Optional output array for confidences.

◆ detectMultiScale() [1/2]

void cv::cuda::HOG::detectMultiScale	(	InputArray	img,
		std::vector< Rect > &	found_locations,
		std::vector< double > &	confidences )

inline

◆ detectMultiScale() [2/2]

virtual void cv::cuda::HOG::detectMultiScale	(	InputArray	img,
		std::vector< Rect > &	found_locations,
		std::vector< double > *	confidences = NULL )

pure virtual

Performs object detection with a multi-scale window.

Parameters

img	Source image. See cuda::HOGDescriptor::detect for type limitations.
found_locations	Detected objects boundaries.
confidences	Optional output array for confidences.

◆ detectMultiScaleWithoutConf()

void cv::cuda::HOG::detectMultiScaleWithoutConf	(	InputArray	img,
		std::vector< Rect > &	found_locations )

inline

Performs object detection with a multi-scale window.

Parameters

img	Source image. See cuda::HOGDescriptor::detect for type limitations.
found_locations	Detected objects boundaries.

◆ detectWithoutConf()

void cv::cuda::HOG::detectWithoutConf	(	InputArray	img,
		std::vector< Point > &	found_locations )

inline

Performs object detection without a multi-scale window.

Parameters

img	Source image. CV_8UC1 and CV_8UC4 types are supported for now.
found_locations	Left-top corner points of detected objects boundaries.

◆ getBlockHistogramSize()

virtual size_t cv::cuda::HOG::getBlockHistogramSize ( ) const

pure virtual

Returns the block histogram size.

◆ getDefaultPeopleDetector()

virtual Mat cv::cuda::HOG::getDefaultPeopleDetector ( ) const

pure virtual

Returns coefficients of the classifier trained for people detection.

◆ getDescriptorFormat()

virtual HOGDescriptor::DescriptorStorageFormat cv::cuda::HOG::getDescriptorFormat ( ) const

pure virtual

◆ getDescriptorSize()

virtual size_t cv::cuda::HOG::getDescriptorSize ( ) const

pure virtual

Returns the number of coefficients required for the classification.

◆ getGammaCorrection()

virtual bool cv::cuda::HOG::getGammaCorrection ( ) const

pure virtual

◆ getGroupThreshold()

virtual int cv::cuda::HOG::getGroupThreshold ( ) const

pure virtual

◆ getHitThreshold()

virtual double cv::cuda::HOG::getHitThreshold ( ) const

pure virtual

◆ getL2HysThreshold()

virtual double cv::cuda::HOG::getL2HysThreshold ( ) const

pure virtual

◆ getNumLevels()

virtual int cv::cuda::HOG::getNumLevels ( ) const

pure virtual

◆ getScaleFactor()

virtual double cv::cuda::HOG::getScaleFactor ( ) const

pure virtual

◆ getWinSigma()

virtual double cv::cuda::HOG::getWinSigma ( ) const

pure virtual

◆ getWinStride()

virtual Size cv::cuda::HOG::getWinStride ( ) const

pure virtual

◆ setDescriptorFormat()

virtual void cv::cuda::HOG::setDescriptorFormat ( HOGDescriptor::DescriptorStorageFormat descr_format )

pure virtual

Descriptor storage format:

DESCR_FORMAT_ROW_BY_ROW - Row-major order.
DESCR_FORMAT_COL_BY_COL - Column-major order.

◆ setGammaCorrection()

virtual void cv::cuda::HOG::setGammaCorrection ( bool gamma_correction )

pure virtual

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

◆ setGroupThreshold()

virtual void cv::cuda::HOG::setGroupThreshold ( int group_threshold )

pure virtual

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

◆ setHitThreshold()

virtual void cv::cuda::HOG::setHitThreshold ( double hit_threshold )

pure virtual

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.

◆ setL2HysThreshold()

virtual void cv::cuda::HOG::setL2HysThreshold ( double threshold_L2hys )

pure virtual

L2-Hys normalization method shrinkage.

◆ setNumLevels()

virtual void cv::cuda::HOG::setNumLevels ( int nlevels )

pure virtual

Maximum number of detection window increases.

◆ setScaleFactor()

virtual void cv::cuda::HOG::setScaleFactor ( double scale0 )

pure virtual

Coefficient of the detection window increase.

◆ setSVMDetector()

virtual void cv::cuda::HOG::setSVMDetector ( InputArray detector )

pure virtual

Sets coefficients for the linear SVM classifier.

◆ setWinSigma()

virtual void cv::cuda::HOG::setWinSigma ( double win_sigma )

pure virtual

Gaussian smoothing window parameter.

◆ setWinStride()

virtual void cv::cuda::HOG::setWinStride ( Size win_stride )

pure virtual

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

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

opencv2/cudaobjdetect.hpp

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ compute()

◆ create()

◆ detect() [1/2]

◆ detect() [2/2]

◆ detectMultiScale() [1/2]

◆ detectMultiScale() [2/2]

◆ detectMultiScaleWithoutConf()

◆ detectWithoutConf()

◆ getBlockHistogramSize()

◆ getDefaultPeopleDetector()

◆ getDescriptorFormat()

◆ getDescriptorSize()

◆ getGammaCorrection()

◆ getGroupThreshold()

◆ getHitThreshold()

◆ getL2HysThreshold()

◆ getNumLevels()

◆ getScaleFactor()

◆ getWinSigma()

◆ getWinStride()

◆ setDescriptorFormat()

◆ setGammaCorrection()

◆ setGroupThreshold()

◆ setHitThreshold()

◆ setL2HysThreshold()

◆ setNumLevels()

◆ setScaleFactor()

◆ setSVMDetector()

◆ setWinSigma()

◆ setWinStride()