SCRFD - Rust Package for Face Detection

SCRFD is a Rust library for face detection, providing both synchronous and asynchronous support. It utilizes ONNX Runtime for high-performance inference and supports bounding box and keypoint detection.

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Features

• Face Detection: Detect bounding boxes and landmarks for faces in images.
• Asynchronous Support: Optional async functionality for non-blocking operations.
• Customizable Parameters:
  • Input size
  • Confidence threshold
  • IoU threshold
• Efficient Processing:
  • Anchor-based detection
  • Optimized caching for anchor centers

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Installation

Add the library to your Cargo.toml:

[dependencies]
rusty_scrfd = { version = "1.1.0", features = ["async"] } # Enable async feature if needed

To enable synchronous mode only, omit the async feature:

[dependencies]
rusty_scrfd = "1.1.0"

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Usage

Synchronous Example

use rusty_scrfd::SCRFD;
use image::open;
use ort::session::SessionBuilder;
use std::collections::HashMap;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load the ONNX model
    let model_path = "path/to/scrfd_model.onnx";
    let session = SessionBuilder::new().unwrap().with_model_from_file(model_path)?;

    // Initialize SCRFD
    let mut scrfd = SCRFD::new(session, (640, 640), 0.5, 0.4)?;

    // Load an image
    let image = open("path/to/image.jpg")?.into_rgb8();

    // Center cache to optimize anchor generation
    let mut center_cache = HashMap::new();

    // Detect faces
    let (bboxes, keypoints) = scrfd.detect(&image, 5, "max", &mut center_cache)?;

    println!("Bounding boxes: {:?}", bboxes);
    if let Some(kps) = keypoints {
        println!("Keypoints: {:?}", kps);
    }

    Ok(())
}

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Asynchronous Example

Enable the async feature in Cargo.toml:

[dependencies]
rusty_scrfd = { version = "1.1.0", features = ["async"] }

use rusty_scrfd::SCRFDAsync;
use image::open;
use ort::session::SessionBuilder;
use std::collections::HashMap;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load the ONNX model
    let model_path = "path/to/scrfd_model.onnx";
    let session = SessionBuilder::new().unwrap().with_model_from_file(model_path)?;

    // Initialize SCRFDAsync
    let scrfd = SCRFDAsync::new((640, 640), 0.5, 0.4, session)?;

    // Load an image
    let image = open("path/to/image.jpg")?.into_rgb8();

    // Center cache to optimize anchor generation
    let mut center_cache = HashMap::new();

    // Detect faces asynchronously
    let (bboxes, keypoints) = scrfd.detect(&image, 5, "max", &mut center_cache).await?;

    println!("Bounding boxes: {:?}", bboxes);
    if let Some(kps) = keypoints {
        println!("Keypoints: {:?}", kps);
    }

    Ok(())
}

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API Documentation

Structs

1. SCRFD (Synchronous)

• Constructor:

  pub fn new(
      session: Session,
      input_size: (i32, i32),
      conf_thres: f32,
      iou_thres: f32,
  ) -> Result<Self, Box<dyn Error>>;

  • session: ONNX Runtime session.
  • input_size: Tuple of input width and height.
  • conf_thres: Confidence threshold for face detection.
  • iou_thres: IoU threshold for non-maximum suppression.

• Methods:

  • detect:

    pub fn detect(
        &mut self,
        image: &RgbImage,
        max_num: usize,
        metric: &str,
        center_cache: &mut HashMap<(i32, i32, i32), Array2<f32>>,
    ) -> Result<(Array2<f32>, Option<Array3<f32>>), Box<dyn Error>>;

    • Detect faces in the input image.
    • Returns: Detected bounding boxes and optional keypoints.

2. SCRFDAsync (Asynchronous)

• Constructor:

  pub fn new(
      input_size: (i32, i32),
      conf_thres: f32,
      iou_thres: f32,
      session: Session,
  ) -> Result<Self, Box<dyn Error>>;

  • Same parameters as SCRFD.

• Methods:

  • detect:

    pub async fn detect(
        &self,
        image: &RgbImage,
        max_num: usize,
        metric: &str,
        center_cache: &mut HashMap<(i32, i32, i32), Array2<f32>>,
    ) -> Result<(Array2<f32>, Option<Array3<f32>>), Box<dyn Error>>;

    • Asynchronous version of detect.

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Helper Functions

Available in ScrfdHelpers:

• generate_anchor_centers: Efficiently generate anchor centers for feature maps.
• distance2bbox: Convert distances to bounding boxes.
• distance2kps: Convert distances to keypoints.
• nms: Perform non-maximum suppression to filter detections.

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Contributing

Contributions are welcome! Please open an issue or submit a pull request for improvements.

Running Tests

• For synchronous features:

  cargo test

• For asynchronous features:

  cargo test --features async

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License

This library is licensed under the MIT License. See the LICENSE file for details.

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References

• SCRFD Paper
• ONNX Runtime