A python library for face detection and features extraction based on mediapipe library
Project 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
pip install FaceAnalyzer
How to use
# 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)
# Get head position and orientation compared to the reference pose (here the first frame will define the orientation 0,0,0)
pos, ori = fa.faces[0].get_head_posture(orientation_style=1)
Make sure you look at the examples folder in the repository for more details.
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