//! ArcFace / MobileFaceNet inference (docs/faces.md §6).
//!
//! Takes an aligned crop and returns 512 L2-normalised floats. The alignment is
//! not optional and cannot be skipped by accident: [`Embedder::embed`] takes an
//! [`Aligned112`], which only [`crate::align::warp`] can construct.
//!
//! # The graph must have a fixed batch
//!
//! `w600k_mbf.onnx` declares its batch dimension as the literal `dim_param`
//! `"None"`, and tract fails to analyse the first Conv because of it. Pinned to
//! 1 by `tools/fix-face-model-shapes.sh`, it loads and runs.

use ndarray::Array4;

use crate::align::{Aligned112, ALIGNED_EDGE};
use crate::embedding::{normalise, Embedding, ModelId, EMBEDDING_DIM};
use crate::{install_backend, FaceError};

/// A loaded ArcFace graph.
pub struct Embedder {
    session: ort::session::Session,
    model: ModelId,
}

impl Embedder {
    pub fn from_path(path: impl AsRef<std::path::Path>, model: ModelId) -> Result<Self, FaceError> {
        let bytes = std::fs::read(path).map_err(FaceError::ModelRead)?;
        Self::from_bytes(&bytes, model)
    }

    pub fn from_bytes(bytes: &[u8], model: ModelId) -> Result<Self, FaceError> {
        install_backend();

        let session = ort::session::Session::builder()
            .map_err(FaceError::Inference)?
            .commit_from_memory(bytes)
            .map_err(FaceError::Inference)?;

        // One output, `[1, 512]`. Checked because an ArcFace variant with a
        // different embedding width would otherwise be read as a truncated
        // one, and 512 is baked into the catalog's BLOB width.
        let out = session.outputs().first().ok_or(FaceError::WrongModel {
            expected: "ArcFace",
            detail: "model has no outputs".into(),
        })?;
        let last = out.dtype().tensor_shape().and_then(|d| d.last().copied());
        if last != Some(EMBEDDING_DIM as i64) {
            return Err(FaceError::WrongModel {
                expected: "ArcFace",
                detail: format!(
                    "output '{}' is {:?}-wide, expected {EMBEDDING_DIM}",
                    out.name(),
                    last
                ),
            });
        }

        Ok(Self { session, model })
    }

    pub fn model(&self) -> &ModelId {
        &self.model
    }

    /// Embed one aligned face.
    pub fn embed(&mut self, face: &Aligned112) -> Result<Embedding, FaceError> {
        // `(x·255 − 127.5) / 128` — see the `/128` note in `detect::Letterbox`.
        let px = face.pixels();
        let mut input = Array4::<f32>::zeros((1, 3, ALIGNED_EDGE, ALIGNED_EDGE));
        for y in 0..ALIGNED_EDGE {
            for x in 0..ALIGNED_EDGE {
                for c in 0..3 {
                    let v = px[(y * ALIGNED_EDGE + x) * 3 + c];
                    input[[0, c, y, x]] = (v * 255.0 - 127.5) / 128.0;
                }
            }
        }

        let outputs = self
            .session
            .run(ort::inputs![
                ort::value::Tensor::from_array(input).map_err(FaceError::Inference)?
            ])
            .map_err(FaceError::Inference)?;

        let (_, data) = outputs[0]
            .try_extract_tensor::<f32>()
            .map_err(FaceError::Inference)?;
        if data.len() < EMBEDDING_DIM {
            return Err(FaceError::WrongModel {
                expected: "ArcFace",
                detail: format!("got {} values, expected {EMBEDDING_DIM}", data.len()),
            });
        }

        let mut v = Box::new([0.0_f32; EMBEDDING_DIM]);
        v.copy_from_slice(&data[..EMBEDDING_DIM]);
        normalise(&mut v);

        Ok(Embedding {
            model: self.model.clone(),
            v,
        })
    }
}