# RetinaFace Face Detection 

*Authors: David Wang*



## Desription

This operator detects faces in the images by using RetinaFace Detector[1]. It will return the bounding box positions and the confidence scores of detected faces. This repo is a adapataion from [2].

## Code Example

Load an image from path './dog.jpg'
and use the pretrained RetinaFace to generate face bounding boxes. 

 *Write the pipeline in simplified style*:

```python
from towhee import ops

bboxes = dc.glob('nmb46.jpg') \
  .image_decode.cv2() \
  .face_detection.retinaface() \
  .to_list() 
```
*Write a same pipeline with explicit inputs/outputs name specifications:*

```python
from towhee import dc

dc.glob['path']('./dog.jpg') \
  .image_decode.cv2['path', 'img']() \
  .image_embedding.timm['img', 'vec'](model_name='resnet50') \
  .face_detection.retinaface() \
  .to_list()
```
## Factory Constructor

Create the operator via the following factory method.

***ops.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:**

​	***image***: *numpy.ndarray.*

​	the image to detect faces.

​	supported types: numpy.ndarray

**Returns:**

*numpy.ndarray*

​	The detected face bounding boxes.

*numpy.ndarray*

​	The detected face bounding boxes confident scores.

## Reference

[1]. https://arxiv.org/abs/1905.00641  
[2]. https://github.com/biubug6/Pytorch_Retinaface