batch-face 1.0.0

Batch Face Preprocessing for Modern Research

Batch Face for Modern Research

🚧Documentation under construction, check tests folder for more details. 🚧

This repo provides the out-of-box face detection and face alignment with batch input support and enables real-time application on CPU.

Features

1. Batch input support for faster data processing.
2. Smart API.
3. Ultrafast with inference runtime acceleration.
4. Automatically download pre-trained weights.
5. Minimal dependencies.

Requirements

• Linux, Windows or macOS
• Python 3.5+ (it may work with other versions too)
• opencv-python
• PyTorch (>=1.0)
• ONNX (optional)

While not required, for optimal performance it is highly recommended to run the code using a CUDA enabled GPU.

Install

The easiest way to install it is using pip:

pip install git+https://github.com/elliottzheng/batch-face.git@master

No extra setup needs, most of the pretrained weights will be downloaded automatically.

Usage

You can clone the repo and run tests like this

python -m tests.camera

Face Detection

Detect face and five landmarks on single image

import cv2
from batch_face import RetinaFace

detector = RetinaFace(gpu_id=0)
img = cv2.imread("examples/obama.jpg")
faces = detector(img, cv=True) # set cv to False for rgb input, the default value of cv is False
box, landmarks, score = faces[0]

Running on CPU/GPU

In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device id.

from batch_face import RetinaFace
# 0 means using GPU with id 0 for inference
# default -1: means using cpu for inference
detector = RetinaFace(gpu_id=0) 

	GPU(GTX 1080TI,batch size=1)	GPU(GTX 1080TI，batch size=750)	CPU(Intel(R) Core(TM) i7-7800X CPU @ 3.50GHz)
FPS	44.02405810720893	96.64058005582535	15.452635835550483
SPF	0.022714852809906007	0.010347620010375976	0.0647138786315918

Batch input for faster detection

Detector with CUDA process batch input faster than the same amount of single input.

import cv2
from batch_face import RetinaFace

detector = RetinaFace()
img= cv2.imread('examples/obama.jpg')[...,::-1]
all_faces = detector([img,img]) # return faces list of all images
box, landmarks, score = all_faces[0][0]

Note: All the input images must of the same size, for input images with different size, please use detector.pseudo_batch_detect.

Face Alignment

face alignment on single image

from batch_face import drawLandmark_multiple, LandmarkPredictor, RetinaFace

predictor = LandmarkPredictor(0)
detector = RetinaFace(0)

imgname = "examples/obama.jpg"
img = cv2.imread(imgname)

faces = detector(img, cv=True)

if len(faces) == 0:
    print("NO face is detected!")
    exit(-1)

# the first input for the predictor is a list of face boxes. [[x1,y1,x2,y2]]
results = predictor(faces, img, from_fd=True) # from_fd=True to convert results from our detection results to simple boxes

for face, landmarks in zip(faces, results):
    img = drawLandmark_multiple(img, face[0], landmarks)

References

• Face Detection Network and pretrained model are from biubug6/Pytorch_Retinaface
• Face Alignment Network and pretrained model are from cunjian/pytorch_face_landmark

@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}