from face_encoder import FaceEncoder
# Load the module
FE = FaceEncoder('/path/to/main_folder_people/', img_size=160, recognition_threshold=0.3)
# Read images in people folder to create a database
encodes_db = FE.db_prepare(show_face=True, save_file=True, encoding_file_path='./data/encodes_db.pt')
"""--- Different ways to send an input to get results ---"""
# Image path as string
image = FE.recognizer('/path/to/image', encodes_db, pil_write=True)
# PIL.Image
image = FE.recognizer(Image.open('/path/to/image'), encodes_db, pil_write=True)
# numpy image
image = FE.recognizer(np.array(unknown_image), encodes_db, pil_write=True)For testing the module yourself, open the prepared jupyter notebook in colab via the following link
First: install requirements.txt
$ pip3 install -r requirements.txt
then in your local computer to start Django server run following commands in your terminal:
1- makemigrations
$ python3 manage.py makemigrations
2- migrate
$ python3 manage.py migrate
Optional
-
createsuperuser
$ python3 manage.py createsuperuser
3- runserver
$ python manage.py runserver 0.0.0.0:8000
$ docker-compose up --build
For the FaceRecognition module I use the following repo as my reference:
https://github.com/miladlink/torch_face