Metadata-Version: 2.1
Name: face_alignment
Version: 1.2.0
Summary: Detector 2D or 3D face landmarks from Python
Home-page: https://github.com/1adrianb/face-alignment
Author: Adrian Bulat
Author-email: adrian@adrianbulat.com
License: BSD
Description: # Face Recognition
        
        Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates.
        
        Build using [FAN](https://www.adrianbulat.com)'s state-of-the-art deep learning based face alignment method. 
        
        <p align="center"><img src="docs/images/face-alignment-adrian.gif" /></p>
        
        **Note:** The lua version is available [here](https://github.com/1adrianb/2D-and-3D-face-alignment).
        
        For 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.
        
        [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)  [![Build Status](https://travis-ci.com/1adrianb/face-alignment.svg?branch=master)](https://travis-ci.com/1adrianb/face-alignment) [![Anaconda-Server Badge](https://anaconda.org/1adrianb/face_alignment/badges/version.svg)](https://anaconda.org/1adrianb/face_alignment)
        [![PyPI](https://img.shields.io/pypi/v/nine.svg?style=flat-square)](https://pypi.org/project/face-alignment/)
        
        ## Features
        
        #### Detect 2D facial landmarks in pictures
        
        <p align='center'>
        <img src='docs/images/2dlandmarks.png' title='3D-FAN-Full example' style='max-width:600px'></img>
        </p>
        
        ```python
        import face_alignment
        from skimage import io
        
        fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)
        
        input = io.imread('../test/assets/aflw-test.jpg')
        preds = fa.get_landmarks(input)
        ```
        
        #### Detect 3D facial landmarks in pictures
        
        <p align='center'>
        <img src='https://www.adrianbulat.com/images/image-z-examples.png' title='3D-FAN-Full example' style='max-width:600px'></img>
        </p>
        
        ```python
        import face_alignment
        from skimage import io
        
        fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, flip_input=False)
        
        input = io.imread('../test/assets/aflw-test.jpg')
        preds = fa.get_landmarks(input)
        ```
        
        #### Process an entire directory in one go
        
        ```python
        import face_alignment
        from skimage import io
        
        fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)
        
        preds = fa.get_landmarks_from_directory('../test/assets/')
        ```
        
        #### Detect the landmarks using a specific face detector.
        
        By default the package will use the SFD face detector. However the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes.
        
        ```python
        import face_alignment
        
        # sfd for SFD, dlib for Dlib and folder for existing bounding boxes.
        fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, face_detector='sfd')
        ```
        
        #### Running on CPU/GPU
        In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device flag:
        
        ```python
        import face_alignment
        
        # cuda for CUDA
        fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu')
        ```
        
        Please also see the ``examples`` folder
        
        ## Installation
        
        ### Requirements
        
        * Python 3.5+ (it may work with other versions too). Last version with support for python 2.7 was v1.1.1
        * Linux, Windows or macOS
        * pytorch (>=1.2)
        
        While not required, for optimal performance(especially for the detector) it is **highly** recommended to run the code using a CUDA enabled GPU.
        
        ### Binaries
        
        The easiest way to install it is using either pip or conda:
        
        | **Using pip**                | **Using conda**                            |
        |------------------------------|--------------------------------------------|
        | `pip install face-alignment` | `conda install -c 1adrianb face_alignment` |
        |                              |                                            |
        
        Alternatively, bellow, you can find instruction to build it from source.
        
        ### From source
        
         Install pytorch and pytorch dependencies. Please check the [pytorch readme](https://github.com/pytorch/pytorch) for this.
        
        #### Get the Face Alignment source code
        ```bash
        git clone https://github.com/1adrianb/face-alignment
        ```
        #### Install the Face Alignment lib
        ```bash
        pip install -r requirements.txt
        python setup.py install
        ```
        
        ### Docker image
        
        A 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.
        ```
        docker build -t face-alignment .
        ```
        
        ## How does it work?
        
        While 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).
        
        ## Contributions
        
        All 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.
        
        ## Citation
        
        ```
        @inproceedings{bulat2017far,
          title={How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)},
          author={Bulat, Adrian and Tzimiropoulos, Georgios},
          booktitle={International Conference on Computer Vision},
          year={2017}
        }
        ```
        
        For citing dlib, pytorch or any other packages used here please check the original page of their respective authors.
        
        ## Acknowledgements
        
        * To the [pytorch](http://pytorch.org/) team for providing such an awesome deeplearning framework
        * To [my supervisor](http://www.cs.nott.ac.uk/~pszyt/) for his patience and suggestions.
        * To all other python developers that made available the rest of the packages used in this repository.
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3
Description-Content-Type: text/markdown
