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1 : 0;\n return out;\n}\n\n// 1D squared Euclidean distance transform.\n// Input f[i] = 0 at feature pixels, INF elsewhere.\n// Output d[i] = min_j ( (i-j)^2 + f[j] )\nfunction edt1d(f: Float64Array): Float64Array {\n const n = f.length;\n const d = new Float64Array(n);\n const v = new Int32Array(n);\n const z = new Float64Array(n + 1);\n\n let k = 0;\n v[0] = 0;\n z[0] = -INF;\n z[1] = INF;\n\n const sq = (x: number) => x * x;\n\n for (let q = 1; q < n; q++) {\n let s = ((f[q] + sq(q)) - (f[v[k]] + sq(v[k]))) / (2 * (q - v[k]));\n while (s <= z[k]) {\n k--;\n s = ((f[q] + sq(q)) - (f[v[k]] + sq(v[k]))) / (2 * (q - v[k]));\n }\n k++;\n v[k] = q;\n z[k] = s;\n z[k + 1] = INF;\n }\n\n k = 0;\n for (let q = 0; q < n; q++) {\n while (z[k + 1] < q) k++;\n const dx = q - v[k];\n d[q] = sq(dx) + f[v[k]];\n }\n return d;\n}\n\n// Exact 2D Euclidean distance transform to nearest \"feature\" pixel (intersection==1).\n// Returns Float64Array of Euclidean distances (not squared), length = w*h.\nfunction distanceToIntersection(\n intersection: Uint8Array,\n width: number,\n height: number,\n): Float64Array {\n const n = width * height;\n\n // First pass: EDT along columns (y dimension)\n const colTmp = new Float64Array(n); // squared distances after col pass\n for (let x = 0; x < width; x++) {\n const f = new Float64Array(height);\n for (let y = 0; y < height; y++) {\n const idx = y * width + x;\n f[y] = intersection[idx] === 1 ? 0 : INF;\n }\n const dcol = edt1d(f);\n for (let y = 0; y < height; y++) {\n colTmp[y * width + x] = dcol[y];\n }\n }\n\n // Second pass: EDT along rows (x dimension)\n const out = new Float64Array(n); // Euclidean distances\n for (let y = 0; y < height; y++) {\n const f = new Float64Array(width);\n for (let x = 0; x < width; x++) {\n f[x] = colTmp[y * width + x];\n }\n const drow = edt1d(f); // still squared\n for (let x = 0; x < width; x++) {\n out[y * width + x] = Math.sqrt(drow[x]);\n }\n }\n\n return out;\n}\n\nfunction compare(\n img1Gray: Uint8Array,\n img2Gray: Uint8Array,\n width: number,\n height: number,\n threshold: number,\n): Stats {\n const img1 = toBinary(img1Gray);\n const img2 = toBinary(img2Gray);\n const n = width * height;\n\n const intersection = new Uint8Array(n);\n const xor = new Uint8Array(n);\n const img1Only = new Uint8Array(n);\n const img2Only = new Uint8Array(n);\n\n let intersectionPixels = 0;\n let xorPixels = 0;\n let img1OnlyPixels = 0;\n let img2OnlyPixels = 0;\n\n for (let i = 0; i < n; i++) {\n const a = img1[i];\n const b = img2[i];\n const inter = (a & b) as 0 | 1;\n const x = (a ^ b) as 0 | 1;\n const aOnly = (a & (b ^ 1)) as 0 | 1;\n const bOnly = (b & (a ^ 1)) as 0 | 1;\n\n intersection[i] = inter;\n xor[i] = x;\n img1Only[i] = aOnly;\n img2Only[i] = bOnly;\n\n intersectionPixels += inter;\n xorPixels += x;\n img1OnlyPixels += aOnly;\n img2OnlyPixels += bOnly;\n }\n\n // Distance to nearest intersection pixel\n const dist = distanceToIntersection(intersection, width, height);\n\n let maxDist = 0;\n let sumDist = 0;\n let pixelsAbove = 0;\n let img1Above = 0;\n let img2Above = 0;\n\n for (let i = 0; i < n; i++) {\n if (xor[i] === 1) {\n const d = dist[i];\n if (d > maxDist) maxDist = d;\n sumDist += d;\n if (d > threshold) {\n pixelsAbove++;\n if (img1Only[i] === 1) img1Above++;\n if (img2Only[i] === 1) img2Above++;\n }\n }\n }\n\n const meanDist = xorPixels > 0 ? sumDist / xorPixels : 0;\n const pctAbove = xorPixels > 0 ? (pixelsAbove / xorPixels) * 100 : 0;\n const img1PctAbove = img1OnlyPixels > 0\n ? (img1Above / img1OnlyPixels) * 100\n : 0;\n const img2PctAbove = img2OnlyPixels > 0\n ? (img2Above / img2OnlyPixels) * 100\n : 0;\n\n return {\n intersectionPixels,\n xorPixels,\n img1OnlyPixels,\n img2OnlyPixels,\n maxDistance: maxDist,\n meanDistance: meanDist,\n threshold,\n pixelsAboveThreshold: pixelsAbove,\n percentageAboveThreshold: pctAbove,\n img1PixelsAboveThreshold: img1Above,\n img1PercentageAboveThreshold: img1PctAbove,\n img2PixelsAboveThreshold: img2Above,\n img2PercentageAboveThreshold: img2PctAbove,\n distanceMapShape: `${height}x${width}`,\n };\n}\n\nexport default async function handler(req: Request): Promise {\n if (req.method !== \"POST\") {\n return Response.json(\n {\n ok: false,\n message: \"POST JSON: { width, height, image1, image2, threshold? }\",\n },\n { status: 405 },\n );\n }\n\n try {\n const body = await req.json();\n const width = Number(body.width);\n const height = Number(body.height);\n const threshold = body.threshold == null ? 1.5 : Number(body.threshold);\n\n if (\n !Number.isFinite(width) || !Number.isFinite(height) || width <= 0 ||\n height <= 0\n ) {\n return Response.json({ ok: false, message: \"Invalid width/height\" }, {\n status: 400,\n });\n }\n\n const img1Gray = decodeImage(body.image1, width, height);\n const img2Gray = decodeImage(body.image2, width, height);\n\n const stats = compare(img1Gray, img2Gray, width, height, threshold);\n return Response.json({ ok: true, ...stats });\n } catch (e) {\n return Response.json(\n { ok: false, message: e instanceof Error ? 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Classification Pass\n for (let y = 0; y < height; y++) {\n for (let x = 0; x < width; x++) {\n const idx = (y * width + x) * 4;\n const r = data[idx];\n const g = data[idx + 1];\n const b = data[idx + 2];\n const brightness = (r + g + b) / 3;\n\n const isRed = r > thresholds.red.r - thresholds.red.tolerance &&\n g < thresholds.red.g + thresholds.red.tolerance &&\n b < thresholds.red.b + thresholds.red.tolerance;\n\n if (isRed) targetPoints.push([x, y]);\n if (brightness >= thresholds.white.brightness) {\n whitePoints.push([x, y]);\n isWhiteMask[y * width + x] = 1;\n }\n if (brightness <= thresholds.black.brightness) blackPoints.push([x, y]);\n }\n if (y % 50 === 0) onProgress((y / height) * 0.1);\n }\n\n if (targetPoints.length === 0) throw new Error(\"No red pixels found.\");\n if (whitePoints.length === 0 && blackPoints.length === 0) {\n throw new Error(\"No reference pixels (B/W) found.\");\n }\n\n const distances: number[] = [];\n let sumDist = 0;\n let maxDist = 0;\n let minDist = Infinity;\n\n // Collection for vector lines\n const vectorLines: string[] = [];\n const probePathBuckets: string[] = Array(10).fill(\"\");\n\n const isNearWhite = (px: number, py: number) => {\n const r = 2;\n for (let dy = -r; dy <= r; dy++) {\n for (let dx = -r; dx <= r; dx++) {\n const nx = px + dx;\n const ny = py + dy;\n if (nx >= 0 && nx < width && ny >= 0 && ny < height) {\n if (isWhiteMask[ny * width + nx]) return true;\n }\n }\n }\n return false;\n };\n\n // 2. Distance Computation & Geometry Construction\n // Sample vectors to maintain SVG performance (max ~8000 lines)\n const vectorStep = Math.max(1, Math.floor(targetPoints.length / 8000));\n\n for (let i = 0; i < targetPoints.length; i++) {\n const [tx, ty] = targetPoints[i];\n const onWhite = isNearWhite(tx, ty);\n const referenceSet = onWhite ? blackPoints : whitePoints;\n\n let minD = Infinity;\n let closestPt: [number, number] | null = null;\n\n for (let j = 0; j < referenceSet.length; j++) {\n const [rx, ry] = referenceSet[j];\n const dx = tx - rx;\n const dy = ty - ry;\n const dSq = dx * dx + dy * dy;\n if (dSq < minD * minD) {\n minD = Math.sqrt(dSq);\n closestPt = [rx, ry];\n }\n if (minD < 0.5) {\n minD = 0;\n closestPt = [rx, ry];\n break;\n }\n }\n\n distances.push(minD);\n sumDist += minD;\n if (minD > maxDist) maxDist = minD;\n if (minD < minDist) minDist = minD;\n\n // Build vector lines with marker reference\n if (closestPt && i % vectorStep === 0) {\n const [rx, ry] = closestPt;\n // Only draw arrowhead if distance is >= 2px\n const markerAttr = minD >= 2 ? ' marker-end=\"url(#arrowhead)\"' : \"\";\n vectorLines.push(\n ``,\n );\n }\n\n // Add to probe path bucket (all probes represented)\n const alphaLevel = Math.min(9, Math.floor((minD / maxVisualDistance) * 10));\n probePathBuckets[alphaLevel] += `M${tx},${ty}h0.1`;\n\n if (i % 2000 === 0) onProgress(0.1 + (i / targetPoints.length) * 0.8);\n }\n\n // 3. Assemble SVG String\n let probeElements = \"\";\n probePathBuckets.forEach((path, i) => {\n if (path) {\n const opacity = (i + 1) / 10;\n probeElements +=\n ``;\n }\n });\n\n const svgString = `\n \n `.trim();\n\n const base64Svg = btoa(unescape(encodeURIComponent(svgString)));\n const processedImageUrl = `data:image/svg+xml;base64,${base64Svg}`;\n\n onProgress(1.0);\n\n return {\n distances,\n averageDistance: sumDist / targetPoints.length,\n maxDistance: maxDist,\n minDistance: minDist === Infinity ? 0 : minDist,\n targetPixelCount: targetPoints.length,\n processedImageUrl,\n imageWidth: width,\n imageHeight: height,\n };\n}",[141],"122ea878-fac0-11f0-ab56-42dde27851f2","main.ts","d95a8364-fabf-11f0-a929-42dde27851f2",10,["D",1769436329408],3,"// @ts-ignore - Val Town is Deno runtime\nimport { processImage } from \"./processImage.ts\"; // put your function in another val/module\nimport type { ColorThresholds } from \"./types.ts\";\n\n// Reasonable defaults (override via POST body)\nconst DEFAULT_THRESHOLDS: ColorThresholds = {\n red: { r: 255, g: 0, b: 0, tolerance: 40 },\n white: { brightness: 240 },\n black: { brightness: 20 },\n};\n\nfunction withCors(resp: Response) {\n const h = new Headers(resp.headers);\n h.set(\"Access-Control-Allow-Origin\", \"*\");\n h.set(\"Access-Control-Allow-Methods\", \"GET,POST,OPTIONS\");\n h.set(\"Access-Control-Allow-Headers\", \"Content-Type\");\n return new Response(resp.body, { status: resp.status, headers: h });\n}\n\nasync function fetchImageAsDataUrl(\n url: string,\n): Promise<{ blob: Blob; dataUrl: string }> {\n const r = await fetch(url);\n if (!r.ok) {\n throw new Error(`Failed to fetch image: ${r.status} ${r.statusText}`);\n }\n\n const contentType = r.headers.get(\"content-type\") ||\n \"application/octet-stream\";\n const bytes = new Uint8Array(await r.arrayBuffer());\n const blob = new Blob([bytes], { type: contentType });\n\n // Create a data URL for underlay in your SVG\n // (keeps SVG self-contained even if the original URL is private/expiring)\n const b64 = btoa(String.fromCharCode(...bytes));\n const dataUrl = `data:${contentType};base64,${b64}`;\n return { blob, dataUrl };\n}\n\nasync function decodeToImageData(blob: Blob): Promise {\n // Deno supports canvas primitives; we use createImageBitmap + OffscreenCanvas.\n // Docs: Canvas/ImageBitmapSource, etc. :contentReference[oaicite:2]{index=2}\n const bmp = await createImageBitmap(blob);\n const canvas = new OffscreenCanvas(bmp.width, bmp.height);\n const ctx = canvas.getContext(\"2d\");\n if (!ctx) throw new Error(\"No 2D canvas context available\");\n\n ctx.drawImage(bmp, 0, 0);\n return ctx.getImageData(0, 0, bmp.width, bmp.height);\n}\n\nexport default async function handler(req: Request): Promise {\n if (req.method === \"OPTIONS\") {\n return withCors(new Response(null, { status: 204 }));\n }\n\n try {\n let url: string | null = null;\n let thresholds: ColorThresholds = DEFAULT_THRESHOLDS;\n\n if (req.method === \"GET\") {\n const u = new URL(req.url);\n url = u.searchParams.get(\"url\");\n } else if (req.method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n url = body?.url ?? null;\n thresholds = body?.thresholds ?? thresholds;\n } else {\n return withCors(\n Response.json({ error: \"Use GET or POST.\" }, { status: 405 }),\n );\n }\n\n if (!url) {\n return withCors(\n Response.json(\n {\n error: \"Missing image url\",\n usage: {\n get: \"GET ?url=https://example.com/image.png\",\n post: {\n url: \"https://example.com/image.png\",\n thresholds: DEFAULT_THRESHOLDS,\n },\n },\n },\n { status: 400 },\n ),\n );\n }\n\n // Fetch + decode\n const { blob, dataUrl: originalImageUri } = await fetchImageAsDataUrl(url);\n const imageData = await decodeToImageData(blob);\n\n // Progress is optional; you can also stream/log this instead\n let lastProgress = 0;\n const onProgress = (p: number) => {\n // keep it cheap\n if (p - lastProgress >= 0.05 || p === 1) lastProgress = p;\n };\n\n const result = await processImage(\n imageData,\n thresholds,\n originalImageUri,\n onProgress,\n );\n\n return withCors(Response.json(result));\n } catch (e) {\n return withCors(\n Response.json(\n { error: e instanceof Error ? 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Diff · v16