MobileFaceNet Face Landmark Detector

author: David Wang

Description

MobileFaceNets 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.

Code Example

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

Write a pipeline with explicit inputs/outputs name specifications:

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()

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.

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.