Face-Alignment Introduction
Face-alignment is a project that enables users to detect facial landmarks using Python, utilizing one of the most accurate face alignment networks in the world. This tool can detect facial points in both 2D and 3D coordinates, providing a comprehensive solution for tasks related to facial recognition and analysis.
Built on Advanced Technology
The face-alignment tool is built using FAN, an advanced deep learning-based face alignment method created by Adrian Bulat. This method is recognized for its precise capability to align faces and identify landmarks accurately.
Facial Landmark Detection Features
2D Facial Landmark Detection
With tools to process images and extract 2D facial landmarks, users can easily detect important facial points. Here’s a simple python code snippet to illustrate this feature:
import face_alignment
from skimage import io
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False)
input = io.imread('../test/assets/aflw-test.jpg')
preds = fa.get_landmarks(input)
This functionality makes it straightforward to analyze images and recognize specific facial features in two dimensions.
3D Facial Landmark Detection
For advanced applications, face-alignment can also process images to identify 3D facial landmarks. This feature is particularly useful for applications that need spatial and depth information about facial structures.
import face_alignment
from skimage import io
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.THREE_D, flip_input=False)
input = io.imread('../test/assets/aflw-test.jpg')
preds = fa.get_landmarks(input)
Bulk Image Processing
Another impressive feature of face-alignment is the ability to process an entire directory of images all at once, which streamlines operations for users with large datasets:
import face_alignment
from skimage import io
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False)
preds = fa.get_landmarks_from_directory('../test/assets/')
Custom Face Detector Support
The tool provides flexibility by allowing different face detectors. By default, it uses the SFD face detector, but users also have options to use other detectors like dlib or BlazeFace, or even use existing ground truth bounding boxes.
Versatility with CPU/GPU Execution
Face-alignment supports running on both CPUs and GPUs, allowing users to specify their preferred hardware. It’s also optimized to utilize lower precision for enhanced performance on compatible devices.
import torch
import face_alignment
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, device='cpu')
# for GPU
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, dtype=torch.bfloat16, device='cuda')
Installation Guide
Face-alignment requires Python 3.5+ and can be installed easily using pip or conda for convenience:
# Using pip
pip install face-alignment
# Using conda
conda install -c 1adrianb face_alignment
Additionally, the source code is available for those who prefer a more hands-on installation process.
Docker Support
For users who prefer Docker, a Dockerfile is provided, allowing builds with CUDA support for efficient deployment and execution in containerized environments.
Contribution and Community
The project encourages contributions from the community. Users can raise issues for any challenges they face, including cases of image processing failures. Contributions in the form of feature requests or improvements are welcomed following discussions via issue tracking.
Acknowledgements
Special thanks to the PyTorch team for their terrific deep learning framework, and to all contributors that provide support and suggestions improving this project. Collaboration has been key to developing and maintaining the capabilities of face-alignment.
Whether for research, application development, or exploration, face-alignment provides a robust package for anyone interested in facial landmark detection, offering wide-ranging functionality with precision and adaptability.