# MobileFaceNet Face Landmark Detector

*author: David Wang*


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## Description

[MobileFaceNets](https://arxiv.org/pdf/1804.07573) is a class of extremely efficient CNN models to extract 68 landmarks from a facial image. It use less than 1 million parameters and is specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. This repository is an adaptation from [cuijian/pytorch_face_landmark](https://github.com/cunjian/pytorch_face_landmark).


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## Code Example

Extract facial landmarks from './img1.jpg'. 

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

```python
from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', 'landmark', ops.face_landmark_detection.mobilefacenet())
        .output('img', 'landmark')
)

DataCollection(p('./img1.jpg')).show()
```

<img src="https://towhee.io/face-landmark-detection/mobilefacenet/raw/branch/main/result.png" alt="result1" style="height:20px;"/>


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## Factory Constructor

Create the operator via the following factory method:

***face_landmark_detection.mobilefacenet(pretrained = True)***

**Parameters:**

​  ***pretrained***

​  whether load the pre-trained weights.

​  supported types: `bool`, default is True, using pre-trained weights.


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## Interface

An image embedding operator takes an image as input. it extracts the embedding as ndarray.

**Parameters:**

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

​	The input image.


**Returns:** *numpy.ndarray*	

​	The extracted facial landmarks.