docling-pdf 0.40.1

//! Layout detection via the RT-DETR (`docling-layout-heron`) model exported to
//! ONNX, run with `ort`. A port of docling-ibm-models' `LayoutPredictor`:
//! resize the page image to 640×640 and rescale to `[0,1]` (the heron processor
//! has `do_normalize=false`), run the model, then RT-DETR
//! `post_process_object_detection` (sigmoid → top-k over query×class →
//! center-to-corners boxes scaled to the page).

use image::imageops::FilterType;
use image::RgbImage;
use ort::session::Session;
use ort::value::Tensor;

/// The 17 canonical layout classes, indexed by the model's class id
/// (`config.json` `id2label`).
pub const LABELS: [&str; 17] = [
    "caption",
    "footnote",
    "formula",
    "list_item",
    "page_footer",
    "page_header",
    "picture",
    "section_header",
    "table",
    "text",
    "title",
    "document_index",
    "code",
    "checkbox_selected",
    "checkbox_unselected",
    "form",
    "key_value_region",
];

/// One detected region, in page points (top-left origin).
#[derive(Debug, Clone)]
pub struct Region {
    pub label: &'static str,
    pub score: f32,
    pub l: f32,
    pub t: f32,
    pub r: f32,
    pub b: f32,
}

/// Base confidence threshold (docling-ibm-models `base_threshold`): the raw
/// RT-DETR floor before docling's `LayoutPostprocessor` applies its stricter
/// per-label thresholds ([`label_threshold`]).
const THRESHOLD: f32 = 0.3;
const SIDE: u32 = 640;

/// Per-label confidence threshold, ported from docling's
/// `LayoutPostprocessor.CONFIDENCE_THRESHOLDS`. The raw predictor keeps every
/// detection above the 0.3 base; the postprocessor then drops a cluster whose
/// score is below its label's threshold. Applying it here (equivalent, since
/// every per-label threshold is ≥ the 0.3 base) keeps low-confidence pictures /
/// tables / list-items out of the assembly, matching docling.
pub fn label_threshold(label: &str) -> f32 {
    match label {
        "section_header"
        | "title"
        | "code"
        | "checkbox_selected"
        | "checkbox_unselected"
        | "form"
        | "key_value_region"
        | "document_index" => 0.45,
        // caption, footnote, formula, list_item, page_footer, page_header,
        // picture, table, text — all 0.5 in docling.
        _ => 0.5,
    }
}

pub struct LayoutModel {
    session: Session,
    /// Set when a multi-page inference fails — e.g. a locally built pre-#73
    /// static graph (fixed batch=1) via `DOCLING_LAYOUT_ONNX` or a stale
    /// `layout_heron_int8.onnx`. Batched calls then fall back to per-page runs
    /// instead of failing the conversion.
    batch_unsupported: bool,
}

impl LayoutModel {
    /// Load the ONNX model from `DOCLING_LAYOUT_ONNX`. Without the override,
    /// prefers `models/layout_heron_int8.onnx` when present (the quantized
    /// default; `DOCLING_RS_FP32=1` opts out), else `models/layout_heron.onnx`.
    pub fn load() -> Result<Self, String> {
        Self::load_with(crate::intra_threads())
    }

    /// Like [`load`](Self::load) but with an explicit intra-op thread count. A
    /// parallel page-worker pool loads its helper models on a single thread each
    /// and gets its speed-up from running pages concurrently instead.
    pub fn load_with(intra: usize) -> Result<Self, String> {
        let path = crate::model_path(
            "DOCLING_LAYOUT_ONNX",
            "models/layout_heron.onnx",
            "models/layout_heron_int8.onnx",
        );
        let session = Session::builder()
            .map_err(|e| format!("layout: builder: {e}"))?
            // Let inference use the available cores (ort otherwise defaults low);
            // a large PDF runs this model once per page.
            .with_intra_threads(intra)
            .map_err(|e| format!("layout: intra_threads: {e}"))?
            .commit_from_file(&path)
            .map_err(|e| format!("layout: load {path}: {e}"))?;
        Ok(Self {
            session,
            batch_unsupported: false,
        })
    }

    /// Detect layout regions on a page image. `page_w`/`page_h` are the page size
    /// in points; returned boxes are in those coordinates.
    pub fn predict(
        &mut self,
        img: &RgbImage,
        page_w: f32,
        page_h: f32,
    ) -> Result<Vec<Region>, String> {
        Ok(self
            .predict_batch(&[(img, page_w, page_h)])?
            .pop()
            .expect("one result per input page"))
    }

    /// Detect layout regions on several page images with **one** inference call
    /// (issue #73). The ONNX export has a dynamic batch dimension, so a worker
    /// can amortize the per-run framework overhead and keep its cores busier on
    /// multi-page documents. Results are per-image, index-aligned with `pages`,
    /// and identical to calling [`predict`](Self::predict) per page.
    pub fn predict_batch(
        &mut self,
        pages: &[(&RgbImage, f32, f32)],
    ) -> Result<Vec<Vec<Region>>, String> {
        if pages.len() > 1 && self.batch_unsupported {
            return self.predict_singly(pages);
        }
        match self.run_batch(pages) {
            Err(e) if pages.len() > 1 => {
                // A graph without the dynamic batch dim (pre-#73 export) fails
                // only for batch > 1 — remember and recover per page.
                eprintln!(
                    "docling-pdf: layout model rejected a {}-page batch ({e}); \
                     falling back to per-page inference — re-export with \
                     scripts/install/export_layout.py for batched layout",
                    pages.len()
                );
                self.batch_unsupported = true;
                self.predict_singly(pages)
            }
            other => other,
        }
    }

    fn predict_singly(
        &mut self,
        pages: &[(&RgbImage, f32, f32)],
    ) -> Result<Vec<Vec<Region>>, String> {
        pages
            .iter()
            .map(|p| Ok(self.run_batch(&[*p])?.pop().expect("one result")))
            .collect()
    }

    fn run_batch(&mut self, pages: &[(&RgbImage, f32, f32)]) -> Result<Vec<Vec<Region>>, String> {
        if pages.is_empty() {
            return Ok(Vec::new());
        }
        // Resize each page to 640×640 (RT-DETR ignores aspect ratio), rescale to
        // [0,1], lay out as NCHW.
        let n = (SIDE * SIDE) as usize;
        let batch = pages.len();
        let mut data = vec![0f32; batch * 3 * n];
        for (p, (img, _, _)) in pages.iter().enumerate() {
            let resized = image::imageops::resize(*img, SIDE, SIDE, FilterType::Triangle);
            let page_off = p * 3 * n;
            for (i, px) in resized.pixels().enumerate() {
                data[page_off + i] = px[0] as f32 / 255.0;
                data[page_off + n + i] = px[1] as f32 / 255.0;
                data[page_off + 2 * n + i] = px[2] as f32 / 255.0;
            }
        }
        let input = Tensor::from_array(([batch, 3, SIDE as usize, SIDE as usize], data))
            .map_err(|e| format!("layout: input tensor: {e}"))?;
        let outputs = self
            .session
            .run(ort::inputs!["pixel_values" => input])
            .map_err(|e| format!("layout: inference: {e}"))?;
        let (lshape, logits) = outputs["logits"]
            .try_extract_tensor::<f32>()
            .map_err(|e| format!("layout: extract logits: {e}"))?;
        let (_, boxes) = outputs["pred_boxes"]
            .try_extract_tensor::<f32>()
            .map_err(|e| format!("layout: extract boxes: {e}"))?;

        let num_queries = lshape[1] as usize;
        let num_classes = lshape[2] as usize;

        let mut all = Vec::with_capacity(batch);
        for (p, (_, page_w, page_h)) in pages.iter().enumerate() {
            let logits =
                &logits[p * num_queries * num_classes..(p + 1) * num_queries * num_classes];
            let boxes = &boxes[p * num_queries * 4..(p + 1) * num_queries * 4];

            // sigmoid over every (query, class); take the top `num_queries` scores.
            let mut scored: Vec<(f32, usize)> = (0..num_queries * num_classes)
                .map(|idx| (sigmoid(logits[idx]), idx))
                .collect();
            scored.sort_unstable_by(|a, b| b.0.total_cmp(&a.0));
            scored.truncate(num_queries);

            let mut regions = Vec::new();
            for (score, idx) in scored {
                if score <= THRESHOLD {
                    continue;
                }
                let label_id = idx % num_classes;
                let q = idx / num_classes;
                let cx = boxes[q * 4];
                let cy = boxes[q * 4 + 1];
                let w = boxes[q * 4 + 2];
                let h = boxes[q * 4 + 3];
                // center_to_corners, then scale normalized coords to page points.
                let l = (cx - w / 2.0) * page_w;
                let t = (cy - h / 2.0) * page_h;
                let r = (cx + w / 2.0) * page_w;
                let b = (cy + h / 2.0) * page_h;
                regions.push(Region {
                    label: LABELS.get(label_id).copied().unwrap_or("text"),
                    score,
                    l,
                    t,
                    r,
                    b,
                });
            }
            all.push(regions);
        }
        Ok(all)
    }
}

fn sigmoid(x: f32) -> f32 {
    1.0 / (1.0 + (-x).exp())
}