/**
 * diagnose.js — 诊断工具：显示中间处理步骤，帮助调参
 * 用法: node diagnose.js <image_path>
 */

const { createCanvas, loadImage } = require('canvas');
const fs = require('fs');
const path = require('path');
const { Img, PaperRecognizer, PaperSpatialType, LocatorColor, PARAMS } = require('./paper-recognizer.js');

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

function saveDebugImage(label, data, w, h, isGray = false) {
  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'));
  console.log(`  [debug] ${label} → ${outPath}`);
}

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 imageData = ctx.getImageData(0, 0, w, h);
  const rgba = new Uint8Array(imageData.data.buffer);

  // ---- 预处理 ----
  const bgr = new Float64Array(w * h * 3);
  const hsvArr = new Float64Array(w * h * 3);
  for (let y = 0; y < h; y++) {
    for (let x = 0; x < w; x++) {
      const i4 = (y * w + x) * 4;
      const i3 = (y * w + x) * 3;
      const r = rgba[i4], g = rgba[i4 + 1], b = rgba[i4 + 2];
      bgr[i3] = b; bgr[i3 + 1] = g; bgr[i3 + 2] = r;
      const [hh, ss, vv] = Img.rgbToHsv(r, g, b);
      hsvArr[i3] = hh; hsvArr[i3 + 1] = ss; hsvArr[i3 + 2] = vv;
    }
  }

  // ---- HSV 统计 ----
  // Sample center of image to understand typical HSV values
  const cx = Math.floor(w / 2), cy = Math.floor(h / 2);
  const sampleSize = 50;
  let hSum = 0, sSum = 0, vSum = 0, count = 0;
  console.log(`\n  中心区域(${sampleSize}×${sampleSize}) HSV 采样:`);
  const colorBins = { red: 0, green: 0, blue: 0, other: 0 };
  for (let dy = -sampleSize / 2; dy < sampleSize / 2; dy += 2) {
    for (let dx = -sampleSize / 2; dx < sampleSize / 2; dx += 2) {
      const sx = cx + dx, sy = cy + dy;
      if (sx < 0 || sx >= w || sy < 0 || sy >= h) continue;
      const i3 = (sy * w + sx) * 3;
      hSum += hsvArr[i3]; sSum += hsvArr[i3 + 1]; vSum += hsvArr[i3 + 2];
      count++;
      // Classify
      const hue = hsvArr[i3];
      if ((hue >= 0 && hue <= 10) || hue >= 160) colorBins.red++;
      else if (hue >= 35 && hue <= 85) colorBins.green++;
      else if (hue >= 90 && hue <= 150) colorBins.blue++;
      else colorBins.other++;
    }
  }
  console.log(`  平均 HSV: H=${(hSum/count).toFixed(1)} S=${(sSum/count).toFixed(1)} V=${(vSum/count).toFixed(1)}`);
  const total = colorBins.red + colorBins.green + colorBins.blue + colorBins.other;
  console.log(`  色相分布: 红=${(colorBins.red/total*100).toFixed(1)}% 绿=${(colorBins.green/total*100).toFixed(1)}% 蓝=${(colorBins.blue/total*100).toFixed(1)}% 其他=${(colorBins.other/total*100).toFixed(1)}%`);

  // ---- 颜色分割 ----
  const redMask1 = Img.hsvInRange(hsvArr, w, h, 0, 10, 70, 255, 50, 255);
  const redMask2 = Img.hsvInRange(hsvArr, w, h, 160, 180, 70, 255, 50, 255);
  const redMask = new Uint8Array(w * h);
  const greenMask = Img.hsvInRange(hsvArr, w, h, 35, 85, 70, 255, 50, 255);
  const blueMask = Img.hsvInRange(hsvArr, w, h, 90, 150, 50, 255, 10, 120);

  let redCount = 0, greenCount = 0, blueCount = 0;
  const colorMask = new Uint8Array(w * h);
  for (let i = 0; i < w * h; i++) {
    const rv = Math.max(redMask1[i], redMask2[i]);
    redMask[i] = rv;
    colorMask[i] = Math.max(rv, greenMask[i], blueMask[i]);
    if (rv > 0) redCount++;
    if (greenMask[i] > 0) greenCount++;
    if (blueMask[i] > 0) blueCount++;
  }

  const totalPixels = w * h;
  console.log(`\n  颜色分割 (像素占比):`);
  console.log(`  红色像素: ${redCount} (${(redCount/totalPixels*100).toFixed(2)}%)`);
  console.log(`  绿色像素: ${greenCount} (${(greenCount/totalPixels*100).toFixed(2)}%)`);
  console.log(`  蓝色像素: ${blueCount} (${(blueCount/totalPixels*100).toFixed(2)}%)`);

  saveDebugImage('01_colorMask', colorMask, w, h, true);

  // ---- 形态学 ----
  let mask = Img.morphClose(colorMask, w, h, 5);
  saveDebugImage('02_morph', mask, w, h, true);

  // ---- 轮廓提取 ----
  const contours = Img.findContours(mask, w, h);
  console.log(`\n  检测到 ${contours.length} 个轮廓`);

  const imageArea = w * h;
  let passed = 0;
  const candidates = [];
  for (const contour of contours) {
    const area = Img.contourArea(contour);
    if (area < PARAMS.MIN_CONTOUR_AREA) continue;
    if (area > imageArea * PARAMS.MAX_AREA_RATIO) continue;

    const rect = Img.minAreaRect(contour);
    const ww = Math.max(rect.size.width, rect.size.height);
    const hh = Math.min(rect.size.width, rect.size.height);
    if (hh < 1) continue;
    const aspectRatio = ww / hh;

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

    const tolerance = 0.35;
    const isBigRatio = Math.abs(aspectRatio - PARAMS.BIG_LOCATOR_RATIO_SHORT) < tolerance ||
                       Math.abs(aspectRatio - PARAMS.BIG_LOCATOR_RATIO_LONG) < tolerance;
    const isSmallRatio = Math.abs(aspectRatio - PARAMS.SMALL_LOCATOR_RATIO) < tolerance;

    const rectArea = rect.size.width * rect.size.height;
    const rectangularity = area / rectArea;

    console.log(`  #${passed} area=${area.toFixed(0)} ratio=${aspectRatio.toFixed(2)} rectangularity=${rectangularity.toFixed(2)} ` +
      `center=(${rect.center.x.toFixed(0)},${rect.center.y.toFixed(0)}) ` +
      `isBig=${isBigRatio} isSmall=${isSmallRatio} size=(${ww.toFixed(0)}×${hh.toFixed(0)})`);

    if (!isBigRatio && !isSmallRatio) {
      console.log(`    → 跳过: 宽高比不匹配`);
      continue;
    }
    if (rectangularity < PARAMS.MIN_RECTANGULARITY) {
      console.log(`    → 跳过: 矩形度不足`);
      continue;
    }

    const isBig = isBigRatio;
    if (isBig) {
      if (area < PARAMS.MIN_BIG_LOCATOR_AREA) {
        console.log(`    → 跳过: 大定位块面积太小`);
        continue;
      }
      if (area > PARAMS.MAX_BIG_LOCATOR_AREA) {
        console.log(`    → 跳过: 大定位块面积太大`);
        continue;
      }
    } else {
      if (area < PARAMS.MIN_SMALL_LOCATOR_AREA) {
        console.log(`    → 跳过: 小定位块面积太小`);
        continue;
      }
      if (area > PARAMS.MAX_SMALL_LOCATOR_AREA) {
        console.log(`    → 跳过: 小定位块面积太大`);
        continue;
      }
    }

    console.log(`    ✓ 通过筛选`);
    passed++;
    candidates.push({ contour, area, rect, isBig });
  }

  console.log(`\n  ${passed} 个通过筛选的候选者`);

  // Also try edge detection fallback
  if (contours.length < 2) {
    console.log(`\n  --- 备用: Canny边缘检测 ---`);
    const gray = new Float64Array(w * h);
    for (let i = 0; i < w * h; i++) {
      const i4 = i * 4;
      gray[i] = 0.299 * rgba[i4] + 0.587 * rgba[i4 + 1] + 0.114 * rgba[i4 + 2];
    }
    const blur = Img.gaussianBlur5(gray, w, h);
    const edges = Img.canny(blur, w, h, 50, 150);
    saveDebugImage('03_edges', edges, w, h, true);

    const closed = Img.close(edges, w, h, 9);
    saveDebugImage('04_edges_closed', closed, w, h, true);

    const econtours = Img.findContours(closed, w, h);
    console.log(`  边缘检测到 ${econtours.length} 个轮廓`);

    for (let i = 0; i < Math.min(econtours.length, 10); i++) {
      const contour = econtours[i];
      const area = Img.contourArea(contour);
      const rect = Img.minAreaRect(contour);
      console.log(`  #${i} area=${area.toFixed(0)} center=(${rect.center.x.toFixed(0)},${rect.center.y.toFixed(0)}) size=(${rect.size.width.toFixed(0)}×${rect.size.height.toFixed(0)})`);
    }
  }
}

async function main() {
  const imagePath = process.argv[2];
  if (!imagePath) {
    console.log('用法: node diagnose.js <image_path>');
    console.log('示例: node diagnose.js /home/ubuntu/nanocode/web-opencv/cpp/images/14red_1.png');
    process.exit(1);
  }
  await diagnose(imagePath);
}

main();