The project is a wrap over yolov5-face repo. Made simple portable interface for model import and inference. Model detects faces on images and returns bounding boxes and coordinates of 5 facial keypoints, which can be used for face alignment.

pip install -r requirements.txt

from face_detector import YoloDetector
import numpy as np
from PIL import Image

model = YoloDetector(target_size=720, device="cuda:0", min_face=90)
orgimg = np.array(Image.open('test_image.jpg'))
bboxes,points = model.predict(orgimg)

You can also pass several images packed in a list to get multi-image predictions:

bboxes,points = model.predict([image1,image2])

You can align faces, using align class method for predicted keypoints. May be useful in conjunction with facial recognition neural network to increase accuracy:

crops = model.align(orgimg, points[0])

If you want to use model class outside root folder, export it into you PYTHONPATH:

export PYTHONPATH="${PYTHONPATH}:/path/to/yoloface/project/"

or the same from python:

import sys
sys.path.append("/path/to/yoloface/project/")

You can use any model from yolov5-face repo. Default models are saved as entire torch module and are bound to the specific classes and the exact directory structure used when the model was saved by authors. To make model portable and run it via my interface you must save it as pytorch state_dict and put new weights in weights/ folder. Example below:

model = torch.load('weights/yolov5m-face.pt', map_location='cpu')['model']
torch.save(model.state_dict(),'path/to/project/weights/yolov5m_state_dict.pt')

Then when creating YoloDetector class object, pass new model name and corresponding yaml config from models/ folder as class arguments. Example below:

model = YoloFace(weights_name='yolov5m_state_dict.pt',config_name='yolov5m.yaml',target_size=720)

Thanks deepcam-cn for pretrained models.