RetinaFace Face Detection

Authors: David Wang

Desription

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 repo is an adaptaion from biubug6/Pytorch_Retinaface.

Code Example

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

Write the pipeline in simplified style:

import towhee

towhee.glob('turing.png') \
  .image_decode.cv2() \
  .face_detection.retinaface() \
  .show()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.glob['path']('turing.png') \
  .image_decode.cv2['path', 'img']() \
  .face_detection.retinaface['img', ('bbox','score')]() \
  .select('img', 'bbox', 'score') \
  .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 back to ndarray.

Parameters:

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

​ the image to detect faces.

​ supported types: numpy.ndarray

Returns:

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

​ The detected face bounding boxes.

List[float]

​ The detected face bounding boxes confident scores.