City-Lifeline-Cockpit/.scratch/gis-investigation-subagent/match-crops.cjs

const fs = require('fs');
const Jimp = require('jimp');

const [referencePath, mosaicPath, outputPrefix, scalesText] = process.argv.slice(2);
const scales = scalesText.split(',').map(Number);
const clamp = (v, min, max) => Math.max(min, Math.min(max, v));
const gray = (image) => {
  const { data, width, height } = image.bitmap;
  const result = new Float32Array(width * height);
  for (let p = 0, j = 0; p < result.length; p++, j += 4)
    result[p] = .299 * data[j] + .587 * data[j + 1] + .114 * data[j + 2];
  return result;
};
const rank = (scores, hit) => {
  if (scores.length >= 20 && hit.score < scores[scores.length - 1].score) return;
  if (scores.some(v => Math.abs(v.x - hit.x) < 8 && Math.abs(v.y - hit.y) < 8 && Math.abs(v.scale - hit.scale) < .04)) return;
  scores.push(hit);
  scores.sort((a, b) => b.score - a.score);
  if (scores.length > 20) scores.pop();
};
(async () => {
  const ref = await Jimp.read(fs.readFileSync(referencePath));
  const source = await Jimp.read(mosaicPath);
  const tw = ref.bitmap.width, th = ref.bitmap.height;
  const step = tw < 120 ? 4 : 7;
  const pts = [];
  const refPixels = gray(ref);
  for (let y = 1; y < th - 1; y += step)
    for (let x = 1; x < tw - 1; x += step) pts.push({ x, y, val: refPixels[y * tw + x] });
  const tm = pts.reduce((sum, p) => sum + p.val, 0) / pts.length;
  let tss = 0;
  for (const p of pts) { p.val -= tm; tss += p.val * p.val; }
  const scores = [];
  for (const scale of scales) {
    const resized = source.clone().resize(Math.round(source.bitmap.width * scale), Math.round(source.bitmap.height * scale));
    const sw = resized.bitmap.width, sh = resized.bitmap.height;
    const pix = gray(resized);
    let best = { score: -2 };
    const dx = sw > 2000 ? 5 : sw > 1200 ? 3 : 2;
    const dy = dx;
    for (let y = 0; y <= sh - th; y += dy) {
      for (let x = 0; x <= sw - tw; x += dx) {
        let sum = 0, squares = 0, dot = 0;
        for (const p of pts) {
          const v = pix[(y + p.y) * sw + x + p.x];
          sum += v; squares += v * v; dot += v * p.val;
        }
        const denom = Math.sqrt(tss * (squares - sum * sum / pts.length));
        const score = denom > 0 ? dot / denom : -1;
        if (score > best.score) best = { score, x, y, scale, sourceX: x / scale, sourceY: y / scale };
      }
    }
    console.log(JSON.stringify({ scale, ...best }));
    rank(scores, best);
  }
  console.log('BEST ' + JSON.stringify(scores));
  const top = scores[0];
  const crop = source.clone().crop(
    clamp(Math.round(top.sourceX), 0, source.bitmap.width - 1),
    clamp(Math.round(top.sourceY), 0, source.bitmap.height - 1),
    clamp(Math.round(tw / top.scale), 1, source.bitmap.width - Math.round(top.sourceX)),
    clamp(Math.round(th / top.scale), 1, source.bitmap.height - Math.round(top.sourceY))
  );
  await crop.writeAsync(outputPrefix + '-best.jpg');
  await ref.clone().resize(tw * 4, th * 4, Jimp.RESIZE_NEAREST_NEIGHBOR).writeAsync(outputPrefix + '-reference-upscaled.png');
})().catch(e => { console.error(e); process.exitCode = 1; });