{"author":"Adrian Bulat","author_email":"adrian@adrianbulat.com","bugtrack_url":null,"classifiers":["Development Status :: 5 - Production/Stable","License :: OSI Approved :: BSD License","Natural Language :: English","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.7","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9"],"description":"# Face Recognition\n\nDetect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates.\n\nBuild using [FAN](https://www.adrianbulat.com)'s state-of-the-art deep learning based face alignment method. \n\n

\n\n**Note:** The lua version is available [here](https://github.com/1adrianb/2D-and-3D-face-alignment).\n\nFor numerical evaluations it is highly recommended to use the lua version which uses indentical models with the ones evaluated in the paper. More models will be added soon.\n\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Test Face alignmnet](https://github.com/1adrianb/face-alignment/workflows/Test%20Face%20alignmnet/badge.svg)](https://github.com/1adrianb/face-alignment/actions?query=workflow%3A%22Test+Face+alignmnet%22) [![Anaconda-Server Badge](https://anaconda.org/1adrianb/face_alignment/badges/version.svg)](https://anaconda.org/1adrianb/face_alignment)\n[![PyPI version](https://badge.fury.io/py/face-alignment.svg)](https://pypi.org/project/face-alignment/)\n\n## Features\n\n#### Detect 2D facial landmarks in pictures\n\n

\n 3D-FAN-Full example \n

\n\n```python\nimport face_alignment\nfrom skimage import io\n\nfa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False)\n\ninput = io.imread('../test/assets/aflw-test.jpg')\npreds = fa.get_landmarks(input)\n```\n\n#### Detect 3D facial landmarks in pictures\n\n

\n 3D-FAN-Full example \n

\n\n```python\nimport face_alignment\nfrom skimage import io\n\nfa = face_alignment.FaceAlignment(face_alignment.LandmarksType.THREE_D, flip_input=False)\n\ninput = io.imread('../test/assets/aflw-test.jpg')\npreds = fa.get_landmarks(input)\n```\n\n#### Process an entire directory in one go\n\n```python\nimport face_alignment\nfrom skimage import io\n\nfa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False)\n\npreds = fa.get_landmarks_from_directory('../test/assets/')\n```\n\n#### Detect the landmarks using a specific face detector.\n\nBy default the package will use the SFD face detector. However the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes.\n\n```python\nimport face_alignment\n\n# sfd for SFD, dlib for Dlib and folder for existing bounding boxes.\nfa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, face_detector='sfd')\n```\n\n#### Running on CPU/GPU\nIn order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device flag:\n\n```python\nimport torch\nimport face_alignment\n\n# cuda for CUDA, mps for Apple M1/2 GPUs.\nfa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, device='cpu')\n\n# running using lower precision\nfa = fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, dtype=torch.bfloat16, device='cuda')\n```\n\nPlease also see the ``examples`` folder\n\n#### Supported face detectors\n\n```python\n\n# dlib (fast, may miss faces)\nmodel = FaceAlignment(landmarks_type= LandmarksType.TWO_D, face_detector='dlib')\n\n# SFD (likely best results, but slowest)\nmodel = FaceAlignment(landmarks_type= LandmarksType.TWO_D, face_detector='sfd')\n\n# Blazeface (front camera model)\nmodel = FaceAlignment(landmarks_type= LandmarksType.TWO_D, face_detector='blazeface')\n\n# Blazeface (back camera model)\nmodel = FaceAlignment(landmarks_type= LandmarksType.TWO_D, face_detector='blazeface', face_detector_kwargs={'back_model': True})\n\n```\n\n## Installation\n\n### Requirements\n\n* Python 3.5+ (it may work with other versions too). Last version with support for python 2.7 was v1.1.1\n* Linux, Windows or macOS\n* pytorch (>=1.5)\n\nWhile not required, for optimal performance(especially for the detector) it is **highly** recommended to run the code using a CUDA enabled GPU.\n\n### Binaries\n\nThe easiest way to install it is using either pip or conda:\n\n| **Using pip** | **Using conda** |\n|------------------------------|--------------------------------------------|\n| `pip install face-alignment` | `conda install -c 1adrianb face_alignment` |\n| | |\n\nAlternatively, bellow, you can find instruction to build it from source.\n\n### From source\n\n Install pytorch and pytorch dependencies. Please check the [pytorch readme](https://github.com/pytorch/pytorch) for this.\n\n#### Get the Face Alignment source code\n```bash\ngit clone https://github.com/1adrianb/face-alignment\n```\n#### Install the Face Alignment lib\n```bash\npip install -r requirements.txt\npython setup.py install\n```\n\n### Docker image\n\nA Dockerfile is provided to build images with cuda support and cudnn. For more instructions about running and building a docker image check the orginal Docker documentation.\n```\ndocker build -t face-alignment .\n```\n\n## How does it work?\n\nWhile here the work is presented as a black-box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my [webpage](https://www.adrianbulat.com).\n\n## Contributions\n\nAll contributions are welcomed. If you encounter any issue (including examples of images where it fails) feel free to open an issue. If you plan to add a new features please open an issue to discuss this prior to making a pull request.\n\n## Citation\n\n```\n@inproceedings{bulat2017far,\n title={How far are we from solving the 2D \\& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)},\n author={Bulat, Adrian and Tzimiropoulos, Georgios},\n booktitle={International Conference on Computer Vision},\n year={2017}\n}\n```\n\nFor citing dlib, pytorch or any other packages used here please check the original page of their respective authors.\n\n## Acknowledgements\n\n* To the [pytorch](http://pytorch.org/) team for providing such an awesome deeplearning framework\n* To [my supervisor](http://www.cs.nott.ac.uk/~pszyt/) for his patience and suggestions.\n* To all other python developers that made available the rest of the packages used in this repository.\n","description_content_type":"text/markdown","docs_url":null,"download_url":"","downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":"https://github.com/1adrianb/face-alignment","keywords":"","license":"BSD","license_expression":null,"license_files":null,"maintainer":"","maintainer_email":"","name":"face-alignment","package_url":"https://pypi.org/project/face-alignment/","platform":null,"project_url":"https://pypi.org/project/face-alignment/","project_urls":{"Homepage":"https://github.com/1adrianb/face-alignment"},"provides_extra":null,"release_url":"https://pypi.org/project/face-alignment/1.4.1/","requires_dist":["torch","numpy","scipy (>=0.17)","scikit-image","opencv-python","tqdm","numba","enum34 ; python_version < \"3.4\""],"requires_python":">=3","summary":"Detector 2D or 3D face landmarks from Python","version":"1.4.1","yanked":false,"yanked_reason":null}

Dependency	Optional
enum34	false
numba	false
numpy	false
opencv-python	false
scikit-image	false
scipy	false
torch	false
tqdm	false

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