//! Dump RAW files' mosaics for training the learned denoise (FR-DEV-3g).
//!
//! The training repo must read photosites the way the app reads them —
//! same black and white levels, same active area, same CFA phase — or a
//! network trained on one phase runs on another and paints moiré everywhere
//! (denoise.md §4.4). So it reads this, not LibRaw.
//!
//! Reads `input<TAB>output-prefix` lines on stdin and writes, per line,
//! `prefix.npy` (the whole readout, masked border included, `u16`, row-major)
//! and `prefix.json` (what `decode` and `metadata` say about it). The border
//! is kept because its optically black photosites are a dark frame for free:
//! read noise and row noise at that ISO.
//!
//! ```sh
//! printf 'IMG_0001.CR2\tout/IMG_0001\n' |
//!     cargo run --release -p dr-decode --example mosaic_dump
//! ```

use std::io::{BufRead, Write};

fn main() {
    let mut failed = 0;
    for line in std::io::stdin().lock().lines() {
        let line = line.expect("stdin");
        let Some((input, prefix)) = line.split_once('\t') else {
            continue;
        };
        match dump(input, prefix) {
            Ok(()) => println!("ok\t{input}"),
            Err(e) => {
                failed += 1;
                println!("fail\t{input}\t{e}");
            }
        }
        std::io::stdout().flush().ok();
    }
    std::process::exit(if failed > 0 { 1 } else { 0 });
}

fn dump(input: &str, prefix: &str) -> Result<(), String> {
    let bytes = std::fs::read(input).map_err(|e| e.to_string())?;
    let raw = dr_decode::decode(&bytes).map_err(|e| e.to_string())?;
    if raw.samples_per_pixel != 1 {
        return Err("linear DNG: no photosites".into());
    }
    let meta = dr_decode::metadata(&bytes).map_err(|e| e.to_string())?;

    let mut npy = Vec::with_capacity(raw.data.len() * 2 + 128);
    let mut header = format!(
        "{{'descr': '<u2', 'fortran_order': False, 'shape': ({}, {}), }}",
        raw.height, raw.width
    );
    // The header, its magic and length are padded to a multiple of 64.
    while (10 + header.len() + 1) % 64 != 0 {
        header.push(' ');
    }
    header.push('\n');
    npy.extend_from_slice(b"\x93NUMPY\x01\x00");
    npy.extend_from_slice(&(header.len() as u16).to_le_bytes());
    npy.extend_from_slice(header.as_bytes());
    for v in &raw.data {
        npy.extend_from_slice(&v.to_le_bytes());
    }
    std::fs::write(format!("{prefix}.npy"), npy).map_err(|e| e.to_string())?;

    let opt = |v: Option<f32>| v.map_or("null".to_string(), |v| v.to_string());
    let matrix = raw
        .color_matrix
        .map_or("null".to_string(), |m| format!("{m:?}"));
    let json = format!(
        concat!(
            "{{\"source\": {:?}, \"make\": {:?}, \"model\": {:?}, ",
            "\"width\": {}, \"height\": {}, ",
            "\"crop\": [{}, {}, {}, {}], \"cfa\": {:?}, ",
            "\"black\": {:?}, \"white\": {}, \"wb\": {:?}, \"cam_to_srgb\": {}, ",
            "\"iso\": {}, \"shutter\": {}, \"aperture\": {}, \"captured_at\": {}}}\n"
        ),
        input,
        raw.make,
        raw.model,
        raw.width,
        raw.height,
        raw.crop.x,
        raw.crop.y,
        raw.crop.width,
        raw.crop.height,
        format!("{:?}", raw.cfa_pattern),
        raw.black_level,
        raw.white_level,
        raw.wb_coeffs,
        matrix,
        meta.iso.map_or("null".to_string(), |v| v.to_string()),
        opt(meta.shutter),
        opt(meta.aperture),
        meta.captured_at
            .map_or("null".to_string(), |v| v.to_string()),
    );
    std::fs::write(format!("{prefix}.json"), json).map_err(|e| e.to_string())
}