Metadata-Version: 2.1
Name: FaceAnalyzer
Version: 0.0.1
Summary: A python library for face detection and features extraction based on mediapipe library
Home-page: https://github.com/ParisNeo/FaceAnalyzer
Author: Saifeddine ALOUI
Author-email: aloui.saifeddine@gmail.com
License: UNKNOWN
Description: # FaceAnalyzer
        A python library for face detection and features extraction based on mediapipe library
        
        ## Introduction
        FaceAnalyzer is a library based on mediapipe library and is provided under MIT Licence. It provides an object orientation tool to play around with faces.
        It can be used to :
        1 - Extract faces from an image
        2 - Measure the face position and orientation
        3 - Measure eyes openings
        4 - Detect blinks
        5 - Extract the face from an image (useful for face learning applications)
        6 - Compute face triangulation (builds triangular surfaces that can be used to build 3D models of the face)
        7 - Copy a face from an image to another.
        
        ## Requirements
        This library requires :
        1 - mediapipe (used for facial landmarks extraction)
        2 - opencv used for drawing and image morphing
        3 - scipy used for efficient delaulay triangulation
        4 - numpy, as any thing that uses math
        
        
        ## How to install
        Just install from pipy
        ```bash
        pip install FaceAnalyzer
        ```
        
        ## How to use
        
        ```python
        # Import the two main classes FaceAnalyzer and Face 
        from FaceAnalyzer import FaceAnalyzer, Face
        
        fa = FaceAnalyzer()
        # ... Recover an image in RGB format as numpy array (you can use pillow opencv but if you use opencv make sure you change the color space from BGR to RGB)
        # Now process the image
        fa.process(image)
        
        # Now you can find faces in fa.faces which is a list of instances of object Face
        if fa.nb_faces>0:
            print(f"{fa.nb_faces} Faces found")
            # We can get the landmarks in numpy format NX3 where N is the number of the landmarks and 3 is x,y,z coordinates 
            print(fa.faces[0].npLandmarks)
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: dev
