DarkRoom/core/dr-face/examples/faces.rs

//! Detect, align and embed the faces in a JPEG.
//!
//! The thing worth looking at is whether the landmarks land on a real
//! photograph — the same reason `dr-segment` has `examples/detect.rs`.
//!
//!   cargo run -p dr-face --features inference --example faces -- \
//!       DET.onnx EMB.onnx photo.jpg [photo.jpg ...]
//!
//! The models must have had their input dims frozen first; see
//! `tools/fix-face-model-shapes.sh` and docs/faces.md §12 M1.

use std::time::Instant;

use dr_face::{align, DetectOptions, Detector, Embedder, ModelId};

fn main() {
    env_logger::init();

    let args: Vec<String> = std::env::args().skip(1).collect();
    if args.len() < 3 {
        eprintln!("usage: faces DET.onnx EMB.onnx IMAGE.jpg [IMAGE.jpg ...]");
        std::process::exit(2);
    }

    let t = Instant::now();
    let mut detector = Detector::from_path(&args[0]).expect("load detector");
    let mut embedder =
        Embedder::from_path(&args[1], ModelId::new("w600k_mbf")).expect("load embedder");
    println!(
        "loaded both models in {:?}  (strides {:?})",
        t.elapsed(),
        detector.strides()
    );

    let opts = DetectOptions::default();
    let mut all = Vec::new();

    for path in &args[2..] {
        let (rgb, w, h) = match load_jpeg(path) {
            Ok(v) => v,
            Err(e) => {
                println!("{path}: {e}");
                continue;
            }
        };

        let t = Instant::now();
        let dets = detector.detect(&rgb, w, h, &opts).expect("detect");
        let detect_ms = t.elapsed().as_secs_f64() * 1e3;

        println!(
            "\n{path}  ({w}×{h})  {} face(s) in {detect_ms:.0} ms",
            dets.len()
        );

        for (i, d) in dets.iter().enumerate() {
            let Some(aligned) = align::warp(&rgb, w, h, &d.landmarks) else {
                println!("  [{i}] degenerate landmarks, skipped");
                continue;
            };
            let t = Instant::now();
            let emb = embedder.embed(&aligned).expect("embed");
            let embed_ms = t.elapsed().as_secs_f64() * 1e3;

            println!(
                "  [{i}] conf {:.3}  box {:.0},{:.0} {:.0}×{:.0}  crop_px {:.0}  quality {:.1}  embed {embed_ms:.0} ms",
                d.confidence,
                d.bbox.0,
                d.bbox.1,
                d.width(),
                d.height(),
                aligned.source_px(),
                emb.quality,
            );
            all.push((path.clone(), i, emb.embedding));
        }
    }

    // Every pair, so the numbers can be eyeballed against the expectation that
    // faces from one identity's folder score high and everything else low.
    if all.len() > 1 {
        println!("\ncosine similarity");
        for i in 0..all.len() {
            for j in i + 1..all.len() {
                let cos = all[i].2.cosine(&all[j].2).expect("same model");
                println!(
                    "  {:.4}   {}#{}  vs  {}#{}",
                    cos,
                    short(&all[i].0),
                    all[i].1,
                    short(&all[j].0),
                    all[j].1
                );
            }
        }
    }
}

fn short(path: &str) -> String {
    let p = std::path::Path::new(path);
    let file = p.file_name().unwrap_or_default().to_string_lossy();
    match p.parent().and_then(|d| d.file_name()) {
        Some(dir) => format!("{}/{file}", dir.to_string_lossy()),
        None => file.into_owned(),
    }
}

/// Decode to the tightly packed `f32` RGB `0.0..=1.0` the crate expects.
fn load_jpeg(path: &str) -> Result<(Vec<f32>, usize, usize), String> {
    let bytes = std::fs::read(path).map_err(|e| e.to_string())?;
    let mut dec = zune_jpeg::JpegDecoder::new(&bytes);
    let px = dec.decode().map_err(|e| e.to_string())?;
    let info = dec.info().ok_or("no jpeg header")?;
    let (w, h) = (info.width as usize, info.height as usize);

    let rgb: Vec<f32> = match px.len() / (w * h) {
        3 => px.iter().map(|&v| v as f32 / 255.0).collect(),
        1 => px
            .iter()
            .flat_map(|&v| {
                let g = v as f32 / 255.0;
                [g, g, g]
            })
            .collect(),
        n => return Err(format!("{n} components per pixel, expected 1 or 3")),
    };
    Ok((rgb, w, h))
}