# RetinaFace Face Detection 

*author: David Wang*



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## Description

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 repository is an adaptation from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface).


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## 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:*

```python
from towhee 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()
```

<img src="https://towhee.io/face-detection/retinaface/raw/branch/main/result2.jpg" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method:

***face_detection.retinaface()***


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## 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.