Rust Faces - Face Detection Models with Rust Interface

This project aims to provide a Rust interface for multiple state-of-the-art face detection models.

Features

• Integration of multiple face detection models;
• Rust interface: The core implementation is written in Rust, leveraging its safety, performance, and concurrency features for preprocessing, non-maxima suppression, and other non-neural-network computationally intensive tasks.
• ONNX Runtime inference provided by ort.
• Language bindings: The project will provide bindings to Python (W.I.P.), C (⚙️), C++ (⚙️), Java (⚙️), and C# (⚙️), enabling more developers with face technologies.
• Easy integration

Supported Face Detection Models

The project aims to include a selection of popular and high-performing face detection models, such as:

• [ ] RetinaFace
• [ ] BlazeFace - W.I.P.
• [ ] MTCNN (Multi-Task Cascaded Convolutional Networks)
• [ ] EfficientDet

Please note that the availability of specific models may vary depending on the licensing terms and open-source availability of the respective models.

Usage

As we are in early development

Install the crate:

shell $ cargo add rust-faces --features viz

```rust use rust_faces::{ viz, DetectionParams, FaceDetection, FaceDetectorBuilder, ToArray3, ToRgb8, };

pub fn main() { let face_detector = FaceDetectorBuilder::new(FaceDetection::BlazeFace640) .download() .build() .expect("Fail to load the face detector.");

let image = image::open("tests/data/images/faces.jpg")
    .expect("Can't open test image.")
    .into_rgb8()
    .into_array3();
let faces = face_detector.detect(image.view().into_dyn()).unwrap();

let mut image = image.to_rgb8();
viz::draw_faces(&mut image, faces);
std::fs::create_dir_all("tests/output").expect("Can't create test output dir.");
image
    .save("tests/output/should_have_smooth_design.jpg")
    .expect("Can't save test image.");

} ```

Python usage

Requirements

• Rust
• Onnx Runtime - Download one of the releases

shell $ pip install git+https://github.com/rust-faces/python

shell $ export LD_LIBRARY_PATH=<path to onnx runtime lib directory>

For Python, use the provided Python wrapper:

```python import numpy as np from PIL import Image import matplotlib.pyplot as plt from matplotlib.patches import Rectangle

import pyrustfaces as prf

detector = prf.build_detector(prf.FaceDetection.BlazeFace640) image = np.array(Image.open(args.image)) rects, _confidences, _landmarks = detector.detect(image) plt.imshow(image)

for rect in rects: plt.gca().add_patch( Rectangle( (rect[0], rect[1]), rect[2], rect[3], edgecolor="red", facecolor="none", lw=2, ) ) plt.show() ```

Contributions

Contributions to the project are welcome! If you have suggestions, bug reports, or would like to add support for additional face detection models or programming languages, please feel free to submit a pull request or open an issue.

License

This project is licensed under the MIT License.