cv::FaceDetectorYN Class Reference

DNN-based face detector. More...

#include <opencv2/objdetect/face.hpp>

Collaboration diagram for cv::FaceDetectorYN:

Public Member Functions
virtual	~FaceDetectorYN ()
virtual int	detect (InputArray image, OutputArray faces)=0
	Detects faces in the input image. Following is an example output.
virtual Size	getInputSize ()=0
virtual float	getNMSThreshold ()=0
virtual float	getScoreThreshold ()=0
virtual int	getTopK ()=0
virtual void	setInputSize (const Size &input_size)=0
	Set the size for the network input, which overwrites the input size of creating model. Call this method when the size of input image does not match the input size when creating model.
virtual void	setNMSThreshold (float nms_threshold)=0
	Set the Non-maximum-suppression threshold to suppress bounding boxes that have IoU greater than the given value.
virtual void	setScoreThreshold (float score_threshold)=0
	Set the score threshold to filter out bounding boxes of score less than the given value.
virtual void	setTopK (int top_k)=0
	Set the number of bounding boxes preserved before NMS.

Static Public Member Functions
static Ptr< FaceDetectorYN >	create (const String &framework, const std::vector< uchar > &bufferModel, const std::vector< uchar > &bufferConfig, const Size &input_size, float score_threshold=0.9f, float nms_threshold=0.3f, int top_k=5000, int backend_id=0, int target_id=0)
static Ptr< FaceDetectorYN >	create (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config, const Size &input_size, float score_threshold=0.9f, float nms_threshold=0.3f, int top_k=5000, int backend_id=0, int target_id=0)
	Creates an instance of face detector class with given parameters.

Detailed Description

DNN-based face detector.

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

Constructor & Destructor Documentation

~FaceDetectorYN()

virtual cv::FaceDetectorYN::~FaceDetectorYN ( )

inlinevirtual

Member Function Documentation

create() [1/2]

static Ptr< FaceDetectorYN > cv::FaceDetectorYN::create ( const String & framework, const std::vector< uchar > & bufferModel, const std::vector< uchar > & bufferConfig, const Size & input_size, float score_threshold = 0.9f, float nms_threshold = 0.3f, int top_k = 5000, int backend_id = 0, int target_id = 0 )

static

Python:
	cv.FaceDetectorYN.create(	model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval
	cv.FaceDetectorYN.create(	framework, bufferModel, bufferConfig, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval
	cv.FaceDetectorYN_create(	model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval
	cv.FaceDetectorYN_create(	framework, bufferModel, bufferConfig, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval

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

Parameters

framework	Name of origin framework
bufferModel	A buffer with a content of binary file with weights
bufferConfig	A buffer with a content of text file contains network configuration
input_size	the size of the input image
score_threshold	the threshold to filter out bounding boxes of score smaller than the given value
nms_threshold	the threshold to suppress bounding boxes of IoU bigger than the given value
top_k	keep top K bboxes before NMS
backend_id	the id of backend
target_id	the id of target device

create() [2/2]

static Ptr< FaceDetectorYN > cv::FaceDetectorYN::create ( CV_WRAP_FILE_PATH const String & model, CV_WRAP_FILE_PATH const String & config, const Size & input_size, float score_threshold = 0.9f, float nms_threshold = 0.3f, int top_k = 5000, int backend_id = 0, int target_id = 0 )

static

Python:
	cv.FaceDetectorYN.create(	model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval
	cv.FaceDetectorYN.create(	framework, bufferModel, bufferConfig, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval
	cv.FaceDetectorYN_create(	model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval
	cv.FaceDetectorYN_create(	framework, bufferModel, bufferConfig, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]	) ->	retval

Creates an instance of face detector class with given parameters.

Parameters

model	the path to the requested model
config	the path to the config file for compability, which is not requested for ONNX models
input_size	the size of the input image
score_threshold	the threshold to filter out bounding boxes of score smaller than the given value
nms_threshold	the threshold to suppress bounding boxes of IoU bigger than the given value
top_k	keep top K bboxes before NMS
backend_id	the id of backend
target_id	the id of target device

detect()

virtual int cv::FaceDetectorYN::detect ( InputArray image, OutputArray faces )

pure virtual

Python:
	cv.FaceDetectorYN.detect(	image[, faces]	) ->	retval, faces

Detects faces in the input image. Following is an example output.

image

Parameters

image

an image to detect

faces

detection results stored in a 2D cv::Mat of shape [num_faces, 15] 0-1: x, y of bbox top left corner 2-3: width, height of bbox 4-5: x, y of right eye (blue point in the example image) 6-7: x, y of left eye (red point in the example image) 8-9: x, y of nose tip (green point in the example image) 10-11: x, y of right corner of mouth (pink point in the example image) 12-13: x, y of left corner of mouth (yellow point in the example image) 14: face score

getInputSize()

virtual Size cv::FaceDetectorYN::getInputSize ( )

pure virtual

Python:
	cv.FaceDetectorYN.getInputSize(		) ->	retval

getNMSThreshold()

virtual float cv::FaceDetectorYN::getNMSThreshold ( )

pure virtual

Python:
	cv.FaceDetectorYN.getNMSThreshold(		) ->	retval

getScoreThreshold()

virtual float cv::FaceDetectorYN::getScoreThreshold ( )

pure virtual

Python:
	cv.FaceDetectorYN.getScoreThreshold(		) ->	retval

getTopK()

virtual int cv::FaceDetectorYN::getTopK ( )

pure virtual

Python:
	cv.FaceDetectorYN.getTopK(		) ->	retval

setInputSize()

virtual void cv::FaceDetectorYN::setInputSize ( const Size & input_size )

pure virtual

Python:
	cv.FaceDetectorYN.setInputSize(	input_size	) ->	None

Set the size for the network input, which overwrites the input size of creating model. Call this method when the size of input image does not match the input size when creating model.

Parameters

input_size

the size of the input image

setNMSThreshold()

virtual void cv::FaceDetectorYN::setNMSThreshold ( float nms_threshold )

pure virtual

Python:
	cv.FaceDetectorYN.setNMSThreshold(	nms_threshold	) ->	None

Set the Non-maximum-suppression threshold to suppress bounding boxes that have IoU greater than the given value.

Parameters

nms_threshold

threshold for NMS operation

setScoreThreshold()

virtual void cv::FaceDetectorYN::setScoreThreshold ( float score_threshold )

pure virtual

Python:
	cv.FaceDetectorYN.setScoreThreshold(	score_threshold	) ->	None

Set the score threshold to filter out bounding boxes of score less than the given value.

Parameters

score_threshold

threshold for filtering out bounding boxes

setTopK()

virtual void cv::FaceDetectorYN::setTopK ( int top_k )

pure virtual

Python:
	cv.FaceDetectorYN.setTopK(	top_k	) ->	None

Set the number of bounding boxes preserved before NMS.

Parameters

top_k

the number of bounding boxes to preserve from top rank based on score

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

• opencv2/objdetect/face.hpp