//! Face Detection Module //! //! This module provides face detection functionality for the authentication pipeline. //! It includes types for representing detected faces and traits for implementing //! different detection backends. //! //! # Overview //! //! Face detection is the first ML step in the authentication pipeline. It locates //! faces in camera frames and provides bounding boxes for subsequent processing. //! //! # Coordinate System //! //! All coordinates are normalized to the range [0, 1] for resolution independence: //! - (0, 0) is the top-left corner //! - (1, 1) is the bottom-right corner //! - Use [`FaceDetection::to_pixels`] to convert to pixel coordinates //! //! # Detection Backends //! //! The module supports multiple detection backends via the [`FaceDetect`] trait: //! //! - [`SimpleFaceDetector`] - Basic detection for testing (no ML model required) //! - ONNX-based detectors (planned) - BlazeFace, MTCNN, RetinaFace //! //! # Example //! //! ```rust //! use linux_hello_daemon::{FaceDetection, FaceDetect, SimpleFaceDetector}; //! //! // Create a detector //! let detector = SimpleFaceDetector::new(0.5); //! //! // Detect faces in a grayscale image //! let image = vec![128u8; 640 * 480]; //! let detections = detector.detect(&image, 640, 480).unwrap(); //! //! for face in &detections { //! let (x, y, w, h) = face.to_pixels(640, 480); //! println!("Face at ({}, {}) size {}x{}, confidence: {:.2}", //! x, y, w, h, face.confidence); //! } //! ``` use linux_hello_common::Result; /// A detected face with bounding box and confidence score. /// /// Coordinates are normalized to [0, 1] for resolution independence. /// Use [`to_pixels`](Self::to_pixels) to convert to actual pixel coordinates. /// /// # Example /// /// ```rust /// use linux_hello_daemon::FaceDetection; /// /// let detection = FaceDetection { /// x: 0.25, // Face starts at 25% from left /// y: 0.1, // Face starts at 10% from top /// width: 0.5, // Face is 50% of image width /// height: 0.8, // Face is 80% of image height /// confidence: 0.95, /// }; /// /// // Convert to 640x480 pixel coordinates /// let (px, py, pw, ph) = detection.to_pixels(640, 480); /// assert_eq!((px, py, pw, ph), (160, 48, 320, 384)); /// ``` #[derive(Debug, Clone)] #[allow(dead_code)] // Public API, fields used by detection methods pub struct FaceDetection { /// X coordinate of top-left corner (0.0-1.0 normalized). pub x: f32, /// Y coordinate of top-left corner (0.0-1.0 normalized). pub y: f32, /// Width of bounding box (0.0-1.0 normalized). pub width: f32, /// Height of bounding box (0.0-1.0 normalized). pub height: f32, /// Detection confidence score (0.0-1.0). /// Higher values indicate more confident detections. pub confidence: f32, } impl FaceDetection { /// Convert normalized coordinates to pixel coordinates. /// /// # Arguments /// /// * `img_width` - Width of the image in pixels /// * `img_height` - Height of the image in pixels /// /// # Returns /// /// A tuple of (x, y, width, height) in pixel coordinates. /// /// # Example /// /// ```rust /// use linux_hello_daemon::FaceDetection; /// /// let det = FaceDetection { /// x: 0.5, y: 0.5, width: 0.25, height: 0.25, confidence: 0.9 /// }; /// let (x, y, w, h) = det.to_pixels(100, 100); /// assert_eq!((x, y, w, h), (50, 50, 25, 25)); /// ``` #[allow(dead_code)] // Public API, used by auth service pub fn to_pixels(&self, img_width: u32, img_height: u32) -> (u32, u32, u32, u32) { let x = (self.x * img_width as f32) as u32; let y = (self.y * img_height as f32) as u32; let w = (self.width * img_width as f32) as u32; let h = (self.height * img_height as f32) as u32; (x, y, w, h) } } /// Trait for face detection backends. /// /// Implement this trait to add support for different face detection models /// or algorithms. All implementations should return normalized coordinates. /// /// # Implementing a Custom Detector /// /// ```rust,ignore /// use linux_hello_daemon::{FaceDetect, FaceDetection}; /// use linux_hello_common::Result; /// /// struct MyDetector { /// model: OnnxModel, /// } /// /// impl FaceDetect for MyDetector { /// fn detect(&self, image_data: &[u8], width: u32, height: u32) -> Result> { /// // Run model inference and return detections /// let detections = self.model.run(image_data, width, height)?; /// Ok(detections) /// } /// } /// ``` #[allow(dead_code)] // Public API trait pub trait FaceDetect { /// Detect faces in a grayscale image. /// /// # Arguments /// /// * `image_data` - Raw grayscale pixel data (one byte per pixel) /// * `width` - Image width in pixels /// * `height` - Image height in pixels /// /// # Returns /// /// A vector of detected faces with normalized coordinates and confidence scores. /// Returns an empty vector if no faces are detected. fn detect(&self, image_data: &[u8], width: u32, height: u32) -> Result>; } /// Simple face detection using basic image analysis. /// /// This is a placeholder implementation that assumes a centered face /// if the image has reasonable contrast. It is intended for testing only /// and should not be used in production. /// /// # Algorithm /// /// Returns a centered face detection if the image mean brightness /// is between 30 and 225 (indicating reasonable contrast). #[allow(dead_code)] // Public API, used by lib but not bin pub fn detect_face_simple(image_data: &[u8], _width: u32, _height: u32) -> Option { // Very simple centered face assumption for testing // In production, this would use proper CV techniques if image_data.is_empty() { return None; } // Calculate image statistics let sum: u64 = image_data.iter().map(|&x| x as u64).sum(); let mean = sum / image_data.len() as u64; // If image has reasonable contrast, assume face is centered if mean > 30 && mean < 225 { Some(FaceDetection { x: 0.25, y: 0.15, width: 0.5, height: 0.7, confidence: 0.5, // Low confidence for simple detection }) } else { None } } /// Placeholder face detector for testing (no ML model required) #[allow(dead_code)] // Public API for testing pub struct SimpleFaceDetector { confidence_threshold: f32, } impl SimpleFaceDetector { /// Create a new simple face detector /// /// Public API - used for testing and placeholder implementation #[allow(dead_code)] // Public API, used in tests pub fn new(confidence_threshold: f32) -> Self { Self { confidence_threshold, } } } impl FaceDetect for SimpleFaceDetector { fn detect(&self, image_data: &[u8], width: u32, height: u32) -> Result> { match detect_face_simple(image_data, width, height) { Some(det) if det.confidence >= self.confidence_threshold => Ok(vec![det]), Some(_) => Ok(vec![]), // Below threshold None => Ok(vec![]), } } } // TODO: Add OnnxFaceDetector once ort API stabilizes // The ort 2.0.0-rc API has breaking changes from 1.x // Will implement proper ONNX detection in Phase 2 #[cfg(test)] mod tests { use super::*; #[test] fn test_face_detection_pixels() { let detection = FaceDetection { x: 0.25, y: 0.1, width: 0.5, height: 0.8, confidence: 0.95, }; let (x, y, w, h) = detection.to_pixels(640, 480); assert_eq!(x, 160); assert_eq!(y, 48); assert_eq!(w, 320); assert_eq!(h, 384); } #[test] fn test_simple_detection() { // Create a simple gradient image let width = 100; let height = 100; let image: Vec = (0..width * height) .map(|i| ((i % 256) as u8).wrapping_add(50)) .collect(); let detection = detect_face_simple(&image, width as u32, height as u32); assert!(detection.is_some()); } #[test] fn test_simple_detector_trait() { let detector = SimpleFaceDetector::new(0.3); let image: Vec = vec![128; 100 * 100]; // Mid-gray image let detections = detector.detect(&image, 100, 100).unwrap(); assert_eq!(detections.len(), 1); } }