/**
 * diagnose2.js — 使用 PaperRecognizer 内部函数诊断
 */
const { createCanvas, loadImage } = require('canvas');
const fs = require('fs');
const path = require('path');
const { Img, PaperRecognizer } = require('./paper-recognizer.js');

const OUTPUT_DIR = '/home/ubuntu/nanocode/web-opencv/miniappB/samples/';

function saveDebugImage(label, data, w, h, isGray) {
  const canvas = createCanvas(w, h);
  const ctx = canvas.getContext('2d');
  const imgData = ctx.createImageData(w, h);
  for (let i = 0; i < w * h; i++) {
    const val = data[i];
    imgData.data[i * 4] = isGray ? val : val;
    imgData.data[i * 4 + 1] = isGray ? val : val;
    imgData.data[i * 4 + 2] = isGray ? val : val;
    imgData.data[i * 4 + 3] = 255;
  }
  ctx.putImageData(imgData, 0, 0);
  const outPath = path.join(OUTPUT_DIR, label + '.png');
  fs.writeFileSync(outPath, canvas.toBuffer('image/png'));
}

// Patch bgrImageToHsv for diagnose
Img.bgrImageToHsv_ = Img.bgrImageToHsv || function(bgr, w, h) {
  const out = new Float64Array(w * h * 3);
  for (let i = 0; i < w * h; i++) {
    const j = i * 3;
    const [hh, ss, vv] = Img.bgrToHsv(bgr[j], bgr[j+1], bgr[j+2]);
    out[j] = hh; out[j+1] = ss; out[j+2] = vv;
  }
  return out;
};

async function diagnose(imagePath) {
  const fname = path.basename(imagePath);
  console.log(`诊断: ${fname}`);

  const img = await loadImage(imagePath);
  const w = img.width, h = img.height;
  console.log(`尺寸: ${w}×${h}`);

  const canvas = createCanvas(w, h);
  const ctx = canvas.getContext('2d');
  ctx.drawImage(img, 0, 0);
  const imgData = ctx.getImageData(0, 0, w, h);
  const rgba = new Uint8Array(imgData.data.buffer);

  // Convert RGBA → BGR (matching paper-recognizer preprocess)
  const bgr = new Float64Array(w * h * 3);
  const gray = new Float64Array(w * h);
  for (let i = 0; i < w * h; i++) {
    const i4 = i * 4, i3 = i * 3;
    const r = rgba[i4], g = rgba[i4+1], b = rgba[i4+2];
    bgr[i3] = b; bgr[i3+1] = g; bgr[i3+2] = r;
    gray[i] = 0.299 * r + 0.587 * g + 0.114 * b;
  }

  // HSV via paper-recognizer's bgrImageToHsv
  const hsv = Img.bgrImageToHsv(bgr, w, h);

  // ---- HSV sampling from locator-like regions ----
  // Find bright-ish, saturated regions near the center strip
  console.log(`\n--- HSV 全图统计 ---`);
  const stats = { h: [], s: [], v: [] };
  for (let i = 0; i < w * h; i++) {
    const j = i * 3;
    stats.h.push(hsv[j]); stats.s.push(hsv[j+1]); stats.v.push(hsv[j+2]);
  }
  stats.h.sort((a,b)=>a-b); stats.s.sort((a,b)=>a-b); stats.v.sort((a,b)=>a-b);
  const m = stats.h.length;
  const p5 = Math.floor(m*0.05), p25 = Math.floor(m*0.25), p50 = Math.floor(m*0.5), p75 = Math.floor(m*0.75), p95 = Math.floor(m*0.95);
  console.log(`  H: p5=${stats.h[p5].toFixed(0)} p50=${stats.h[p50].toFixed(0)} p95=${stats.h[p95].toFixed(0)}`);
  console.log(`  S: p5=${stats.s[p5].toFixed(0)} p50=${stats.s[p50].toFixed(0)} p95=${stats.s[p95].toFixed(0)}`);
  console.log(`  V: p5=${stats.v[p5].toFixed(0)} p50=${stats.v[p50].toFixed(0)} p95=${stats.v[p95].toFixed(0)}`);

  // ---- Color segmentation (matching actual code) ----
  console.log(`\n--- 颜色分割 ---`);
  
  // Blue mask
  const blueMask = Img.hsvInRange(hsv, w, h, 180, 260, 30, 255, 30, 220);
  // Green mask  
  const greenMask = Img.hsvInRange(hsv, w, h, 80, 170, 30, 255, 30, 220);
  // Red mask (added - paper-recognizer missing this!)
  const redMask1 = Img.hsvInRange(hsv, w, h, 0, 15, 30, 255, 30, 220);
  const redMask2 = Img.hsvInRange(hsv, w, h, 340, 360, 30, 255, 30, 220);
  
  const redMask = new Uint8Array(w * h);
  for (let i = 0; i < w * h; i++) redMask[i] = Math.max(redMask1[i], redMask2[i]);

  const colorMask = new Uint8Array(w * h);
  for (let i = 0; i < w * h; i++) {
    colorMask[i] = Math.max(blueMask[i], greenMask[i], redMask[i]);
  }

  let bluePx = 0, greenPx = 0, redPx = 0;
  for (let i = 0; i < w * h; i++) {
    if (blueMask[i] > 0) bluePx++;
    if (greenMask[i] > 0) greenPx++;
    if (redMask[i] > 0) redPx++;
  }
  const total = w * h;
  console.log(`  蓝: ${bluePx} (${(bluePx/total*100).toFixed(2)}%)`);
  console.log(`  绿: ${greenPx} (${(greenPx/total*100).toFixed(2)}%)`);
  console.log(`  红: ${redPx} (${(redPx/total*100).toFixed(2)}%)`);
  console.log(`  总: ${Math.max(bluePx, greenPx, redPx)} (${(Math.max(bluePx, greenPx, redPx)/total*100).toFixed(2)}%)`);

  saveDebugImage(`diag_${fname.replace(/\.\w+$/,'')}_01_color`, colorMask, w, h, true);

  // Morphology close
  const closed = Img.morphClose(colorMask, w, h, 5);
  const closed2 = Img.morphClose(closed, w, h, 5);
  saveDebugImage(`diag_${fname.replace(/\.\w+$/,'')}_02_closed`, closed2, w, h, true);

  // Find contours
  const contours = Img.findContours(closed2, w, h);
  console.log(`\n  轮廓数: ${contours.length}`);

  // Run through filtering logic (matching actual code)
  let candidateNum = 0;
  for (const ct of contours) {
    const area = Img.contourArea(ct);
    if (area < 100) continue;

    const brect = Img.boundingRect(ct);
    const bbArea = brect.w * brect.h;
    if (area / bbArea < 0.5) continue;

    const rotatedRect = Img.minAreaRect(ct);
    const rectArea = rotatedRect.size.w * rotatedRect.size.h;
    if (area / rectArea < 0.5) continue;

    const aspectRatio = Math.max(rotatedRect.size.w, rotatedRect.size.h) /
                        Math.max(Math.min(rotatedRect.size.w, rotatedRect.size.h), 1);

    if (aspectRatio >= 0.9 && aspectRatio <= 1.1) continue;
    if (aspectRatio > 4.0) continue;

    const tol = 0.35;
    const isBig = Math.abs(aspectRatio - 2.0) < tol || Math.abs(aspectRatio - 3.0) < tol;
    const isSmall = Math.abs(aspectRatio - 1.667) < tol;

    // Color determination
    const cx = Math.round(rotatedRect.center.x), cy = Math.round(rotatedRect.center.y);
    const i3 = (Math.min(h-1, Math.max(0, cy)) * w + Math.min(w-1, Math.max(0, cx))) * 3;
    const hh = hsv[i3], ss = hsv[i3+1], vv = hsv[i3+2];

    let colorLabel = '?';
    if (hh >= 180 && hh <= 260) colorLabel = 'BLUE';
    else if (hh >= 80 && hh <= 170) colorLabel = 'GREEN';
    else if (hh <= 15 || hh >= 340) colorLabel = 'RED';
    else colorLabel = `H=${hh.toFixed(0)}`;

    console.log(`  #${candidateNum} area=${area.toFixed(0)} ar=${aspectRatio.toFixed(2)} rect=${(area/rectArea).toFixed(2)} ` +
      `center=(${cx},${cy}) size=(${rotatedRect.size.w.toFixed(0)},${rotatedRect.size.h.toFixed(0)}) ` +
      `color=${colorLabel} isBig=${isBig} isSmall=${isSmall} ` +
      `hsv=(${hh.toFixed(0)},${ss.toFixed(0)},${vv.toFixed(0)})`);

    candidateNum++;
  }

  if (candidateNum === 0) {
    console.log(`  无候选轮廓! 尝试更宽松的参数...`);
    // Try lower thresholds
    const testMasks = [
      ['蓝 S20', Img.hsvInRange(hsv, w, h, 180, 260, 20, 255, 20, 255)],
      ['绿 S20', Img.hsvInRange(hsv, w, h, 80, 170, 20, 255, 20, 255)],
      ['红 S20', (() => {
        const m1 = Img.hsvInRange(hsv, w, h, 0, 15, 20, 255, 20, 255);
        const m2 = Img.hsvInRange(hsv, w, h, 340, 360, 20, 255, 20, 255);
        const m = new Uint8Array(w*h);
        for (let i=0;i<w*h;i++) m[i]=Math.max(m1[i],m2[i]);
        return m;
      })()]
    ];
    for (const [label, mask] of testMasks) {
      let cnt = 0;
      for (let i=0;i<w*h;i++) if(mask[i]>0) cnt++;
      console.log(`  ${label}: ${cnt} pixels (${(cnt/total*100).toFixed(2)}%)`);
    }
  }
}

async function main() {
  const args = process.argv.slice(2);
  const defaultImages = [
    '/home/ubuntu/nanocode/web-opencv/cpp/images/11green_1.png',
    '/home/ubuntu/nanocode/web-opencv/cpp/images/14red_1.png',
    '/home/ubuntu/nanocode/web-opencv/cpp/images/14blue_1.png',
  ];
  const files = args.length > 0 ? args : defaultImages;
  
  for (const f of files) {
    await diagnose(f);
  }
}

main();