Mobilefacenet Face Landmark Detecter

authors: David Wang

Desription

A class of extremely efficient CNN models to extract 68 landmarks from a facial imageMobileFaceNets.

Code Example

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

Write the pipeline in simplified style:

from towhee import dc

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

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

from towhee import dc

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

Factory Constructor

Create the operator via the following factory method

ops.face_landmark_detection.mobilefacenet(pretrained = True)

Parameters:

​ pretrained

​ whether load the pretrained weights..

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

Interface

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

Args:

​ pretrained

​ whether load the pretrained weights..

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

Parameters:

​ image: np.ndarray

​ The input image.

Returns:: numpy.ndarray

​ The extracted facial landmark.