Dump RAW mosaics for training the learned denoise
denoise.md §4.4 requires the training repo to read photosites through dr-decode, not LibRaw, so black and white levels, the active area and the CFA phase match what the app will feed the network. mosaic_dump reads `input<TAB>prefix` lines and writes the whole readout as .npy plus a JSON of what decode and metadata report. The masked border is kept: its optically black photosites are a free dark frame for the noise profile.
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//! Dump RAW files' mosaics for training the learned denoise (FR-DEV-3g).
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//!
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//! The training repo must read photosites the way the app reads them —
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//! same black and white levels, same active area, same CFA phase — or a
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//! network trained on one phase runs on another and paints moiré everywhere
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//! (denoise.md §4.4). So it reads this, not LibRaw.
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//!
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//! Reads `input<TAB>output-prefix` lines on stdin and writes, per line,
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//! `prefix.npy` (the whole readout, masked border included, `u16`, row-major)
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//! and `prefix.json` (what `decode` and `metadata` say about it). The border
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//! is kept because its optically black photosites are a dark frame for free:
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//! read noise and row noise at that ISO.
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//!
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//! ```sh
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//! printf 'IMG_0001.CR2\tout/IMG_0001\n' |
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//! cargo run --release -p dr-decode --example mosaic_dump
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//! ```
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use std::io::{BufRead, Write};
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fn main() {
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let mut failed = 0;
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for line in std::io::stdin().lock().lines() {
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let line = line.expect("stdin");
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let Some((input, prefix)) = line.split_once('\t') else {
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continue;
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};
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match dump(input, prefix) {
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Ok(()) => println!("ok\t{input}"),
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Err(e) => {
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failed += 1;
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println!("fail\t{input}\t{e}");
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}
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}
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std::io::stdout().flush().ok();
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}
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std::process::exit(if failed > 0 { 1 } else { 0 });
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}
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fn dump(input: &str, prefix: &str) -> Result<(), String> {
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let bytes = std::fs::read(input).map_err(|e| e.to_string())?;
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let raw = dr_decode::decode(&bytes).map_err(|e| e.to_string())?;
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if raw.samples_per_pixel != 1 {
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return Err("linear DNG: no photosites".into());
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}
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let meta = dr_decode::metadata(&bytes).map_err(|e| e.to_string())?;
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let mut npy = Vec::with_capacity(raw.data.len() * 2 + 128);
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let mut header = format!(
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"{{'descr': '<u2', 'fortran_order': False, 'shape': ({}, {}), }}",
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raw.height, raw.width
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);
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// The header, its magic and length are padded to a multiple of 64.
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while (10 + header.len() + 1) % 64 != 0 {
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header.push(' ');
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}
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header.push('\n');
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npy.extend_from_slice(b"\x93NUMPY\x01\x00");
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npy.extend_from_slice(&(header.len() as u16).to_le_bytes());
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npy.extend_from_slice(header.as_bytes());
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for v in &raw.data {
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npy.extend_from_slice(&v.to_le_bytes());
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}
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std::fs::write(format!("{prefix}.npy"), npy).map_err(|e| e.to_string())?;
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let opt = |v: Option<f32>| v.map_or("null".to_string(), |v| v.to_string());
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let matrix = raw
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.color_matrix
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.map_or("null".to_string(), |m| format!("{m:?}"));
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let json = format!(
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concat!(
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"{{\"source\": {:?}, \"make\": {:?}, \"model\": {:?}, ",
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"\"width\": {}, \"height\": {}, ",
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"\"crop\": [{}, {}, {}, {}], \"cfa\": {:?}, ",
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"\"black\": {:?}, \"white\": {}, \"wb\": {:?}, \"cam_to_srgb\": {}, ",
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"\"iso\": {}, \"shutter\": {}, \"aperture\": {}, \"captured_at\": {}}}\n"
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),
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input,
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raw.make,
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raw.model,
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raw.width,
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raw.height,
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raw.crop.x,
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raw.crop.y,
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raw.crop.width,
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raw.crop.height,
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format!("{:?}", raw.cfa_pattern),
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raw.black_level,
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raw.white_level,
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raw.wb_coeffs,
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matrix,
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meta.iso.map_or("null".to_string(), |v| v.to_string()),
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opt(meta.shutter),
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opt(meta.aperture),
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meta.captured_at
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.map_or("null".to_string(), |v| v.to_string()),
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);
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std::fs::write(format!("{prefix}.json"), json).map_err(|e| e.to_string())
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}
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@@ -9,7 +9,7 @@ Denominators are parsed from [`requirements.md`](requirements.md) at run time, n
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| Metric | Value |
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|---|---|
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| Source files scanned | 486 |
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| Source files scanned | 487 |
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| TRACES tags found | 2033 |
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| Requirements defined | 193 |
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| Requirements deferred (post-v1) | 24 |
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