# 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
