face-detection-tflite 0.6.0
A Python port of Google MediaPipe Face Detection modules
License: MIT
Requires Python: >=3.8
Keywords: AI, face-detection, tensorflow, tflite, face-landmarks, iris-detection, face-mesh

# Face Detection For Python

This package implements parts of Google®'s [**MediaPipe**](https://mediapipe.dev/#!) models in pure Python (with a little help from Numpy and PIL) without `Protobuf` graphs and with minimal dependencies (just [**TF Lite**](https://www.tensorflow.org/lite/api_docs) and [**Pillow**](https://python-pillow.org/)).

## Models and Examples

The package provides the following models:

* Face Detection

![Face detection example](https://raw.githubusercontent.com/patlevin/face-detection-tflite/main/docs/group_photo.jpg)

* Face Landmark Detection

![Face landmark example](https://raw.githubusercontent.com/patlevin/face-detection-tflite/main/docs/portrait_fl.jpg)

* Iris Landmark Detection

![Iris landmark example](https://raw.githubusercontent.com/patlevin/face-detection-tflite/main/docs/eyes.jpg)

* Iris recoloring example

![Iris recoloring example](https://raw.githubusercontent.com/patlevin/face-detection-tflite/main/docs/recolored.jpg)

## 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:

```python
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](https://en.wikipedia.org/wiki/Precision_and_recall),
whereas the sparse model features a higher [Precision](https://en.wikipedia.org/wiki/Precision_and_recall).

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](https://pypi.org/project/face-detection-tflite/0.1.0/)
and can be installed via:

```sh
pip install -U face-detection-tflite
```

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

```sh
pip install .
```

from a shell or command prompt.