RetinaFace Face Detection

author: David Wang

Description

This operator detects faces in the images by using RetinaFace Detector. It will return the bounding box positions and the confidence scores of detected faces. This repository is an adaptation from biubug6/Pytorch_Retinaface.

Code Example

Load an image from path './turing.png' and use the pre-trained RetinaFace model to generate face bounding boxes and confidence scores.

Write a pipeline with explicit inputs/outputs name specifications:

from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', ('bbox','score'), ops.face_detection.retinaface())
        .map(('img', 'bbox'),'crop', ops.image_crop())
        .output('img', 'crop', 'bbox', 'score')
)

DataCollection(p('turing.png')).show()

Factory Constructor

Create the operator via the following factory method:

face_detection.retinaface()

Interface

A face detection operator takes an image as input. It generates the bounding box positions and confidence scores in ndarray.

Parameters:

​ img: towhee.types.Image (a sub-class of numpy.ndarray)

​ the image to detect faces from.

​ supported types: numpy.ndarray

Returns:

List[(int, int, int, int)]

​ The position of the bounding boxes for the faces detected.

List[float]

​ The confidence scores for the face detected in the bounding boxes.