Pure Python - Face detection & anti-spoofing. Support Web API & Command-line interface.
- Python 3.6↑: https://www.python.org/downloads/
- Pytorch 1.5.0↑: https://pytorch.org/get-started/previous-versions/
# Install Pytorch cuda if using NVIDIA GPU device. Default: CPU device
> pip3 install torch==1.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.htmlor
# CUDA 10.2
> pip3 install torch==1.5.0
# CUDA 10.1
> pip3 install torch==1.5.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 9.2
> pip3 install torch==1.5.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html- Python package requirements:
> pip3 install -r requirements.txt
- Docker v20.10.5↑: https://docs.docker.com/get-docker/
- Docker Compose v1.28.5↑: https://docs.docker.com/compose/install
Support Docker with environment setting:
# edit API config in `.example` or container env
> cp .example .env
> docker-compose build && docker-compose up -d
NOTE: all method work in RGB pixel format. (OpenCV pixel format is BGR -> convert before using)
- Python API
from library.util.image import imread
from library.face_detector import FaceDetector
face_detector = FaceDetector("data/pretrained/retina_face.pth.tar")
image = imread("images/fake_001.jpg") # image in RGB format
faces = face_detector(image)
>>> faces # [[box, score, land_mark]]
[(array([181, 5, 551, 441], dtype=int32),
0.99992156,
array([[249, 147],
[412, 145],
[306, 192],
[266, 313],
[404, 311]], dtype=int32))]- CLI
> python3 service.py detect data/test/*{.jpg,.png}
- Web API
> curl --location --request POST 'http://localhost:8000/detect' --form 'image=@"data/test/fake_001.jpg"'
- Python API
from library.util.image import imread
from library.face_detector import FaceDetector
from library.face_antspoofing import SpoofingDetector
face_detector = FaceDetector("data/pretrained/retina_face.pth.tar")
face_antispoofing = SpoofingDetector("data/pretrained/fasnet_v1se_v2.pth.tar")
image = imread("images/fake_001.jpg") # image in RGB format
faces = face_detector(image)
>>> face_antispoofing([box for box, _, _ in faces], image) # [(is_real, score)]
[(False, 0.5154606513679028)]
- CLI
> python3 service.py spoofing data/test/*{.jpg,.png}
- Web API
> curl --location --request POST 'http://localhost:8000/spoofing' --form 'image=@"data/test/fake_001.jpg"'
- class
library.face_detector.FaceDetector:__init__:model_path: (str) Path of pre-trained modeldetect_threshold(float): Threshold of confidence score of detector. Default: 0.975scale_size(int): Scale size input image. Recommend in [240, 1080]. Default: 480device: device model loaded in. Default: cpu
process: Detect faces in a imageimage(numpy.ndarray): image source return: List[Tuple[List[int], float, List[List[int]]]] - [(box, score, land_mark)]
- class
library.face_antspoofing.SpoofingDetector:__init__:model_path: (str) Path of pre-trained modeldevice: device model loaded in. Default: cpuface_size(tuple(int, int)): model face input size. Default: (80, 80)
predict: Predict faces is spoof or not.boxes: face's boxesimage(numpy.ndarray): image source return: Sequence[Tuple[bool, float]] - [(is_real, score)]
> python service.py --help
Usage: service.py [OPTIONS] COMMAND [ARGS]...
Options:
--detector-model TEXT Face detector model file path
--detector-threshold FLOAT Face detector model threshold
--detector-scale INTEGER Face detector model scale. >= 240
--spoofing-model TEXT Face anti-spoofing file path
--device TEXT Device to load model.
--version Show the version and exit.
--help Show this message and exit.
Commands:
api Run service as API
detect Detect face in images
spoofing Detect spoofing face in images
> python service.py detect --help
Usage: service.py detect [OPTIONS] IMAGES...
Detect face in images
Options:
-j, --json PATH Export result to json file
-q, --quiet Turn off STD output
-c, --count Counting image during process
-y, --overwrite Force write json file.
--help Show this message and exit.
-
Input: image's path (support file globs)
Example:python service.py detect ./*{.jpg,.png}- match with any file with extension isjpgandpng. -
Output option:
--json PATH: Export result to JSON file
{ "nums": "int", "boxes": "List[int]", "scores": "List[float]", "landmarks": "List[int]" }--quietTurn off STD output--countCounting image during process--overwriteForce write JSOn file.
> python service.py spoofing --help
Usage: service.py spoofing [OPTIONS] IMAGES...
Detect spoofing face in images
Options:
-j, --json PATH Export result to json file
-q, --quiet Turn off STD output
-c, --count Counting image during process
-y, --overwrite Force write json file.
--help Show this message and exit.
-
Input: image's path (support file globs)
Example:python service.py spoofing ./*{.jpg,.png}- match with any file with extension isjpgandpng. -
Output option:
--json PATH: Export result to json file
{ "nums": "int", "is_reals": "List[bool]", "scores": "List[float]", "boxes": "List[int]" }--quietTurn off STD output--countCounting image during process--overwriteForce write json file.
> python service.py api --help
Usage: service.py api [OPTIONS]
Run service as API
Options:
--host TEXT API host. Default: localhost
--port INTEGER API port. Default: 8000
--version TEXT API version.
--help Show this message and exit.Run with default uvicorn setting:
> python service.py api
INFO: Started server process [19802]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://localhost:8000 (Press CTRL+C to quit)Support Docker with environment setting:
# edit API config in `.example` or container env
> cp .example .env
> docker-compose build && docker-compose up -d- Method:
POST - URL:
/detect - Form-data params:
- images:
FileorURL of image
- images:
- Method:
POST - Path:
/spoofing - Form-data params:
- images:
FileorURL of image
- images:
Licensed under the Apache License, Version 2.0
- Author: Tin Dang
- Email: [email protected]
- Website: www.vietmoney.dev
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