# Mobilefacenet Face Landmark Detecter

*authors: David Wang*


## Desription

A class of extremely efficient CNN models to extract 68 landmarks from a facial image[1].


```python
from towhee import ops

model = ops.face_landmark_detection.mobilefacenet()
landmark = model(img)
```

## Factory Constructor

Create the operator via the following factory method

***ops.face_landmark_detection.mobilefacenet()***



## Interface

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

**Args:**

​  ***framework***

​  the framework of the model

​  supported types: `str`, default is 'pytorch'


​  ***pretrained***

​  whether load the pretrained weights..

​  supported types: `bool`, default is True, using pretrained weights


**Parameters:**

​	***image***: *towhee._types.Image*

​	The input image.


**Returns:**: *numpy.ndarray*

​	The extracted facial landmark.

## Code Example

extracted facial landmark from './img1.jpg'. 

*Write the pipeline in simplified style*:

```python
import towhee.DataCollection as dc

dc.glob('./img1.jpg')
  .face_landmark_detection.mobilefacenet()
  .to_list()
```

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

```python
import towhee.DataCollection as dc

dc.glob['path']('./img1.jpg')
  .image_decode.cv2['path', 'img']()
  .face_landmark_detection.mobilefacenet()
  .to_list()
```


## Reference

[1].https://arxiv.org/pdf/1804.07573.pdf