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Signed-off-by: wxywb <xy.wang@zilliz.com>
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wxywb 3 years ago
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      README.md

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README.md

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# MobileFaceNet Face Landmark Detecter
*authors: David Wang*
*author: David Wang*
## Desription
<br />
## Description
[MobileFaceNets](https://arxiv.org/pdf/1804.07573) is a class of extremely efficient CNN models to extract 68 landmarks from a facial image, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. This repo is an adaptation from [cuijian/pytorch_face_landmark](https://github.com/cunjian/pytorch_face_landmark).
<br />
## Code Example
Extract facial landmarks from './img1.jpg'.
*Write the pipeline in simplified style*:
*Write the pipeline in simplified style:*
```python
import towhee
@ -31,12 +39,17 @@ import towhee
towhee.glob['path']('./img1.jpg') \
.image_decode.cv2['path', 'img']() \
.face_landmark_detection.mobilefacenet['img', 'landmark']() \
.select('img','landmark') \
.select['img','landmark']() \
.show()
```
<img src="https://towhee.io/face-landmark-detection/mobilefacenet/raw/branch/main/result.png" alt="result1" style="height:20px;"/>
<br />
## Factory Constructor
Create the operator via the following factory method
@ -51,13 +64,18 @@ Create the operator via the following factory method
​ supported types: `bool`, default is True, using pretrained weights.
<br />
## Interface
An image embedding operator takes an image as input. it extracts the embedding back to ndarray.
**Parameters:**
***img***: *towhee.types.Image (a sub-class of numpy.ndarray)*
***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)*
​ The input image.

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