# RetinaFace Face Detection
*Authors: David Wang*
## Desription
This operator detects faces in the images by using [RetinaFace ](https://arxiv.org/abs/1905.00641 ) Detector. It will return the bounding box positions and the confidence scores of detected faces. This repo is an adaptaion from [biubug6/Pytorch_Retinaface ](https://github.com/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* :
```python
import towhee
towhee.glob('turing.png') \
.image_decode.cv2() \
.face_detection.retinaface() \
.show()
```
< img src = "https://towhee.io/face-detection/retinaface/raw/branch/main/result1.png" alt = "result1" style = "height:20px;" / >
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
towhee.glob['path']('turing.png') \
.image_decode.cv2['path', 'img']() \
.face_detection.retinaface['img', ('bbox','score')]() \
.select('img', 'bbox', 'score') \
.show()
```
< img src = "https://towhee.io/face-detection/retinaface/raw/branch/main/result2.png" alt = "result2" style = "height:60px;" / >
## 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***: *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.
