cv::FaceRecognizerSF Class Reference

DNN-based face recognizer. More...

#include <opencv2/objdetect/face.hpp>

Collaboration diagram for cv::FaceRecognizerSF:

Public Types
enum	DisType {   FR_COSINE =0 ,   FR_NORM_L2 =1 }
	Definition of distance used for calculating the distance between two face features. More...

Public Member Functions
virtual	~FaceRecognizerSF ()
virtual void	alignCrop (InputArray src_img, InputArray face_box, OutputArray aligned_img) const =0
	Aligns detected face with the source input image and crops it.
virtual void	feature (InputArray aligned_img, OutputArray face_feature)=0
	Extracts face feature from aligned image.
virtual double	match (InputArray face_feature1, InputArray face_feature2, int dis_type=FaceRecognizerSF::FR_COSINE) const =0
	Calculates the distance between two face features.

Static Public Member Functions
static Ptr< FaceRecognizerSF >	create (const String &framework, const std::vector< uchar > &bufferModel, const std::vector< uchar > &bufferConfig, int backend_id=0, int target_id=0)
	Creates an instance of this class from a buffer containing the model weights and configuration.
static Ptr< FaceRecognizerSF >	create (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config, int backend_id=0, int target_id=0)
	Creates an instance of this class with given parameters.

Detailed Description

DNN-based face recognizer.

model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface

Member Enumeration Documentation

DisType

enum cv::FaceRecognizerSF::DisType

Definition of distance used for calculating the distance between two face features.

Enumerator
FR_COSINE
FR_NORM_L2

Constructor & Destructor Documentation

~FaceRecognizerSF()

virtual cv::FaceRecognizerSF::~FaceRecognizerSF ( )

inlinevirtual

Member Function Documentation

alignCrop()

virtual void cv::FaceRecognizerSF::alignCrop ( InputArray  src_img, InputArray  face_box, OutputArray  aligned_img  ) const

pure virtual

Python:
	cv.FaceRecognizerSF.alignCrop(	src_img, face_box[, aligned_img]	) ->	aligned_img

Aligns detected face with the source input image and crops it.

Parameters

src_img	input image
face_box	the detected face result from the input image
aligned_img	output aligned image

create() [1/2]

static Ptr< FaceRecognizerSF > cv::FaceRecognizerSF::create ( const String &  framework, const std::vector< uchar > &  bufferModel, const std::vector< uchar > &  bufferConfig, int  backend_id = 0, int  target_id = 0  )

static

Python:
	cv.FaceRecognizerSF.create(	model, config[, backend_id[, target_id]]	) ->	retval
	cv.FaceRecognizerSF.create(	framework, bufferModel, bufferConfig[, backend_id[, target_id]]	) ->	retval
	cv.FaceRecognizerSF_create(	model, config[, backend_id[, target_id]]	) ->	retval
	cv.FaceRecognizerSF_create(	framework, bufferModel, bufferConfig[, backend_id[, target_id]]	) ->	retval

Creates an instance of this class from a buffer containing the model weights and configuration.

Parameters

framework	Name of the framework (ONNX, etc.)
bufferModel	A buffer containing the binary model weights.
bufferConfig	A buffer containing the network configuration.
backend_id	The id of the backend.
target_id	The id of the target device.

Returns

A pointer to the created instance of FaceRecognizerSF.

create() [2/2]

static Ptr< FaceRecognizerSF > cv::FaceRecognizerSF::create ( CV_WRAP_FILE_PATH const String &  model, CV_WRAP_FILE_PATH const String &  config, int  backend_id = 0, int  target_id = 0  )

static

Python:
	cv.FaceRecognizerSF.create(	model, config[, backend_id[, target_id]]	) ->	retval
	cv.FaceRecognizerSF.create(	framework, bufferModel, bufferConfig[, backend_id[, target_id]]	) ->	retval
	cv.FaceRecognizerSF_create(	model, config[, backend_id[, target_id]]	) ->	retval
	cv.FaceRecognizerSF_create(	framework, bufferModel, bufferConfig[, backend_id[, target_id]]	) ->	retval

Creates an instance of this class with given parameters.

Parameters

model	the path of the onnx model used for face recognition
config	the path to the config file for compability, which is not requested for ONNX models
backend_id	the id of backend
target_id	the id of target device

feature()

virtual void cv::FaceRecognizerSF::feature ( InputArray  aligned_img, OutputArray  face_feature  )

pure virtual

Python:
	cv.FaceRecognizerSF.feature(	aligned_img[, face_feature]	) ->	face_feature

Extracts face feature from aligned image.

Parameters

aligned_img	input aligned image
face_feature	output face feature

match()

virtual double cv::FaceRecognizerSF::match ( InputArray  face_feature1, InputArray  face_feature2, int  dis_type = FaceRecognizerSF::FR_COSINE  ) const

pure virtual

Python:
	cv.FaceRecognizerSF.match(	face_feature1, face_feature2[, dis_type]	) ->	retval

Calculates the distance between two face features.

Parameters

face_feature1	the first input feature
face_feature2	the second input feature of the same size and the same type as face_feature1
dis_type	defines how to calculate the distance between two face features with optional values "FR_COSINE" or "FR_NORM_L2"

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

• opencv2/objdetect/face.hpp