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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Breaking Changes in v1.2.0

• The detect function now accepts opencv::core::Mat instead of image::RgbImage
  • This change significantly improves performance by:
    • Eliminating unnecessary image conversions
    • Using OpenCV's optimized memory management
    • Leveraging hardware-accelerated image processing operations
    • Reducing memory allocations and copies
    • Providing direct compatibility with OpenCV's extensive computer vision ecosystem
• Added builder pattern for easier model configuration
• Added support for relative output coordinates
• Updated default parameters:
  • Confidence threshold changed from 0.5 to 0.25
  • IoU threshold changed from 0.5 to 0.4

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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.
• Builder Pattern: Fluent interface for easy model configuration.
• Customizable Parameters:
  • Input size
  • Confidence threshold
  • IoU threshold
  • Relative output coordinates
• 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 = "1.2.0", features = ["async"] } # Enable async feature if needed

To enable synchronous mode only, omit the async feature:

[dependencies]
rusty_scrfd = "1.2.0"

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Usage

Using the Builder Pattern (Recommended)

use rusty_scrfd::builder::SCRFDBuilder;
use ort::session::SessionBuilder;
use std::collections::HashMap;
use opencv::imgcodecs::imread;
use opencv::imgcodecs::IMREAD_COLOR;

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 using the builder pattern
    let mut scrfd = SCRFDBuilder::new(session)
        .set_input_size((640, 640))
        .set_conf_thres(0.25)
        .set_iou_thres(0.4)
        .set_relative_output(true)
        .build()?;

    // Load an image using OpenCV
    let image = imread("path/to/image.jpg", IMREAD_COLOR)?;

    // 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(())
}

Direct Initialization

use rusty_scrfd::SCRFD;
use ort::session::SessionBuilder;
use std::collections::HashMap;
use opencv::imgcodecs::imread;
use opencv::imgcodecs::IMREAD_COLOR;

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),  // input size
        0.25,        // confidence threshold
        0.4,         // IoU threshold
        true         // relative output
    )?;

    // Load an image using OpenCV
    let image = imread("path/to/image.jpg", IMREAD_COLOR)?;

    // 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 = "1.2.0", features = ["async"] }

use rusty_scrfd::builder::SCRFDBuilder;
use ort::session::SessionBuilder;
use std::collections::HashMap;
use opencv::imgcodecs::imread;
use opencv::imgcodecs::IMREAD_COLOR;

#[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 using the builder pattern
    let scrfd = SCRFDBuilder::new(session)
        .set_input_size((640, 640))
        .set_conf_thres(0.25)
        .set_iou_thres(0.4)
        .set_relative_output(true)
        .build_async()?;

    // Load an image using OpenCV
    let image = imread("path/to/image.jpg", IMREAD_COLOR)?;

    // 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

Builder Pattern

The SCRFDBuilder provides a fluent interface for configuring SCRFD models:

let model = SCRFDBuilder::new(session)
    .set_input_size((640, 640))    // Set input dimensions
    .set_conf_thres(0.25)          // Set confidence threshold
    .set_iou_thres(0.4)            // Set IoU threshold
    .set_relative_output(true)      // Enable relative output
    .build()?;                      // Build synchronous model

For async models:

let model = SCRFDBuilder::new(session)
    .set_input_size((640, 640))
    .set_conf_thres(0.25)
    .set_iou_thres(0.4)
    .set_relative_output(true)
    .build_async()?;               // Build asynchronous model

Default Parameters

• Input size: (640, 640)
• Confidence threshold: 0.25
• IoU threshold: 0.4
• Relative output: true

Detect Function

The detect function now accepts OpenCV's Mat type instead of image::RgbImage:

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

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