Face Detection For Python

This package implements parts of Google®'s MediaPipe models in pure Python (with a little help from Numpy and PIL) without Protobuf graphs and with minimal dependencies (just TF Lite and Pillow).

Models and Examples

The package provides the following models:

• Face Detection

• Face Landmark Detection

• Iris Landmark Detection

• Iris recoloring example

Motivation

The package doesn't use the graph approach implemented by MediaPipe and is therefore not as flexible. It is, however, somewhat easier to use and understand and more accessible to recreational programming and experimenting with the pretrained ML models than the rather complex MediaPipe framework.

Here's how face detection works and an image like shown above can be produced:

from fdlite import FaceDetection, FaceDetectionModel
from fdlite.render import Colors, detections_to_render_data, render_to_image 
from PIL import Image

image = Image.open('group.jpg')
detect_faces = FaceDetection(model_type=FaceDetectionModel.BACK_CAMERA)
faces = detect_faces(image)
if not len(faces):
    print('no faces detected :(')
else:
    render_data = detections_to_render_data(faces, bounds_color=Colors.GREEN)
    render_to_image(render_data, image).show()

While this example isn't that much simpler than the MediaPipe equivalent, some models (e.g. iris detection) aren't available in the Python API.

Note that the package ships with five models:

• FaceDetectionModel.FRONT_CAMERA - a smaller model optimised for selfies and close-up portraits; this is the default model used
• FaceDetectionModel.BACK_CAMERA - a larger model suitable for group images and wider shots with smaller faces
• FaceDetectionModel.SHORT - a model best suited for short range images, i.e. faces are within 2 metres from the camera
• FaceDetectionModel.FULL - a model best suited for mid range images, i.e. faces are within 5 metres from the camera
• FaceDetectionModel.FULL_SPARSE - a model best suited for mid range images, i.e. faces are within 5 metres from the camera

The FaceDetectionModel.FULL and FaceDetectionModel.FULL_SPARSE models are equivalent in terms of detection quality. They differ in that the full model is a dense model whereas the sparse model runs up to 30% faster on CPUs. On a GPU, both models exhibit similar runtime performance. In addition, the dense full model has slightly better Recall, whereas the sparse model features a higher Precision.

If you don't know whether the image is a close-up portrait or you get no detections with the default model, try using the BACK_CAMERA-model instead.

Installation

The latest release version is available in PyPI and can be installed via:

pip install -U face-detection-tflite

The package can be also installed from source by navigating to the folder containing setup.py and running

pip install .

from a shell or command prompt.