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g: number; b: number; tolerance: number };\n black: { brightness: number };\n white: { brightness: number };\n}\n\nexport interface AnalysisResult {\n distances: number[];\n averageDistance: number;\n maxDistance: number;\n minDistance: number;\n targetPixelCount: number;\n processedImageUrl: string;\n imageWidth: number;\n imageHeight: number;\n}\n\nexport interface ProcessingState {\n status: \"idle\" | \"processing\" | \"completed\" | \"error\";\n progress: number;\n message?: string;\n}","deleted","label",[110],"350a0b1c-fac0-11f0-ab56-42dde27851f2","processImage.ts","e22b74c6-fabf-11f0-892a-42dde27851f2",12,["D",1769436387890],4,"export interface ColorThresholds {\n red: { r: number; g: number; b: number; tolerance: number };\n black: { brightness: number };\n white: { brightness: number };\n}\n\nexport interface AnalysisResult {\n distances: number[];\n averageDistance: number;\n maxDistance: number;\n minDistance: number;\n targetPixelCount: number;\n processedImageUrl: string;\n imageWidth: number;\n imageHeight: number;\n}\n\nexport interface ProcessingState {\n status: \"idle\" | \"processing\" | \"completed\" | \"error\";\n progress: number;\n message?: string;\n}\n\n/**\n * Contextual Distance Transform with Scalable SVG Output.\n * Generates an SVG string containing:\n * - A black background.\n * - Faint underlay of the original image (10% opacity).\n * - Red probe points (heatmap-style) as vector paths.\n * - High-precision white vectors (lines with SVG markers) connecting probes to references.\n */\n\nexport async function processImage(\n imageData: ImageData,\n thresholds: ColorThresholds,\n originalImageUri: string,\n onProgress: (p: number) => void,\n): Promise {\n const { width, height, data } = imageData;\n const targetPoints: [number, number][] = [];\n const whitePoints: [number, number][] = [];\n const blackPoints: [number, number][] = [];\n\n const isWhiteMask = new Uint8Array(width * height);\n const diagonal = Math.sqrt(width * width + height * height);\n const maxVisualDistance = diagonal * 0.03;\n\n // 1. 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}",[128],"122ea878-fac0-11f0-ab56-42dde27851f2","main.ts","d95a8364-fabf-11f0-a929-42dde27851f2",10,["D",1769436329408],3,"http","// @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 ? e.message : String(e) },\n { status: 500 },\n ),\n );\n }\n}",[136],"dbf60b50-0016-11f1-85b9-42dde27851f2","compare.ts","3f3608a2-0015-11f1-bda7-42dde27851f2",["D",1770023360464],"// Val Town HTTP val\n// POST JSON:\n//\n// Option 1 (URLs):\n// {\n// \"imageUrl1\": \"https://.../a.png\",\n// \"imageUrl2\": \"https://.../b.png\",\n// \"threshold\": 1.5\n// }\n//\n// Option 2 (raw grayscale buffers):\n// {\n// \"width\": 200,\n// \"height\": 200,\n// \"image1\": number[] | string, // length=width*height, values 0..255 OR base64/raw-bytes\n// \"image2\": number[] | string,\n// \"threshold\": 1.5\n// }\n//\n// Returns JSON stats (distance map not returned; only shape).\n\nimport { Image } from \"npm:imagescript@1.3.0\";\n\nconst INF = 1e15;\n\ntype Stats = {\n intersectionPixels: number;\n xorPixels: number;\n img1OnlyPixels: number;\n img2OnlyPixels: number;\n maxDistance: number;\n meanDistance: number;\n threshold: number;\n pixelsAboveThreshold: number;\n percentageAboveThreshold: number;\n img1PixelsAboveThreshold: number;\n img1PercentageAboveThreshold: number;\n img2PixelsAboveThreshold: number;\n img2PercentageAboveThreshold: number;\n distanceMapShape: string;\n};\n\nfunction normalizeBase64(s: string): string {\n // strip data URL prefix if present\n if (s.startsWith(\"data:\")) s = s.slice(s.indexOf(\",\") + 1);\n // base64url -> base64\n s = s.replace(/-/g, \"+\").replace(/_/g, \"/\");\n // pad\n const pad = s.length % 4;\n if (pad !== 0) s += \"=\".repeat(4 - pad);\n return s;\n}\n\nfunction decodeRawGray(\n input: number[] | string,\n width: number,\n height: number,\n): Uint8Array {\n const n = width * height;\n\n if (typeof input === \"string\") {\n const s = normalizeBase64(input);\n let bin: string;\n try {\n bin = atob(s);\n } catch {\n throw new Error(\n \"Failed to decode base64 (check base64url/padding/data-url)\",\n );\n }\n const bytes = Uint8Array.from(bin, (c) => c.charCodeAt(0));\n if (bytes.length !== n) {\n throw new Error(\n `decoded length ${bytes.length} ≠ width*height ${n} (you likely sent PNG bytes, not raw pixels)`,\n );\n }\n return bytes;\n }\n\n if (!Array.isArray(input) || input.length !== n) {\n throw new Error(\n `image array length mismatch: got ${input?.length}, expected ${n}`,\n );\n }\n\n const out = new Uint8Array(n);\n for (let i = 0; i < n; i++) out[i] = input[i] & 0xff;\n return out;\n}\n\n// Fetch URL image and convert to grayscale pixel buffer (0..255)\nasync function fetchUrlAsGray(\n url: string,\n): Promise<{ gray: Uint8Array; width: number; height: number }> {\n const res = await fetch(url);\n if (!res.ok) throw new Error(`Failed to fetch ${url}: ${res.status}`);\n const bytes = new Uint8Array(await res.arrayBuffer());\n\n const img = await Image.decode(bytes); // supports PNG/JPEG/etc\n const { width, height } = img;\n\n const gray = new Uint8Array(width * height);\n const rgba = img.bitmap; // RGBA bytes\n\n for (let i = 0, p = 0; i < rgba.length; i += 4, p++) {\n const r = rgba[i], g = rgba[i + 1], b = rgba[i + 2];\n // luma\n gray[p] = Math.round(0.299 * r + 0.587 * g + 0.114 * b);\n }\n\n return { gray, width, height };\n}\n\n// grayscale -> binary 0/1 with threshold 127\nfunction toBinary(gray: Uint8Array): Uint8Array {\n const out = new Uint8Array(gray.length);\n for (let i = 0; i < gray.length; i++) out[i] = gray[i] > 127 ? 1 : 0;\n return out;\n}\n\n// 1D squared EDT (Felzenszwalb/Huttenlocher)\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 distance to nearest intersection pixel (Euclidean)\nfunction distanceToIntersection(\n intersection: Uint8Array,\n width: number,\n height: number,\n): Float64Array {\n const n = width * height;\n\n // pass 1: columns\n const colTmp = new Float64Array(n); // squared\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++) colTmp[y * width + x] = dcol[y];\n }\n\n // pass 2: rows\n const out = new Float64Array(n); // euclidean\n for (let y = 0; y < height; y++) {\n const f = new Float64Array(width);\n for (let x = 0; x < width; x++) f[x] = colTmp[y * width + x];\n const drow = edt1d(f); // squared\n for (let x = 0; x < width; x++) out[y * width + x] = Math.sqrt(drow[x]);\n }\n\n return out;\n}\n\nfunction computeStats(\n gray1: Uint8Array,\n gray2: Uint8Array,\n width: number,\n height: number,\n threshold: number,\n): Stats {\n const img1 = toBinary(gray1);\n const img2 = toBinary(gray2);\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\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 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 usage:\n \"POST { imageUrl1, imageUrl2, threshold? } OR { width, height, image1, image2, threshold? }\",\n },\n { status: 405 },\n );\n }\n\n try {\n const body = await req.json();\n const threshold = body.threshold == null ? 1.5 : Number(body.threshold);\n if (!Number.isFinite(threshold) || threshold < 0) {\n throw new Error(\"Invalid threshold\");\n }\n\n let width: number, height: number;\n let gray1: Uint8Array, gray2: Uint8Array;\n\n if (body.imageUrl1 && body.imageUrl2) {\n const a = await fetchUrlAsGray(String(body.imageUrl1));\n const b = await fetchUrlAsGray(String(body.imageUrl2));\n if (a.width !== b.width || a.height !== b.height) {\n throw new Error(\n `Image sizes differ: ${a.width}x${a.height} vs ${b.width}x${b.height}`,\n );\n }\n width = a.width;\n height = a.height;\n gray1 = a.gray;\n gray2 = b.gray;\n } else {\n width = Number(body.width);\n height = Number(body.height);\n if (\n !Number.isFinite(width) || !Number.isFinite(height) || width <= 0 ||\n height <= 0\n ) {\n throw new Error(\"Invalid width/height\");\n }\n gray1 = decodeRawGray(body.image1, width, height);\n gray2 = decodeRawGray(body.image2, width, height);\n }\n\n const stats = computeStats(gray1, gray2, width, height, threshold);\n return Response.json({ ok: true, width, height, ...stats });\n } catch (e) {\n return Response.json(\n { ok: false, message: e instanceof Error ? e.message : String(e) },\n { status: 400 },\n );\n }\n}",[145],"pulls","branches",1,"forks","envVars",{"_158":39,"_160":39},"connections",{"_158":39,"_159":39},"count","unfilledCount","emptyCount",{"_18":144,"_20":145,"_111":146,"_113":-5,"_114":65,"_115":63,"_117":100,"_22":175,"_62":132,"_120":141,"_122":148,"_124":26,"_125":-5,"_166":176,"_82":177},{"_18":135,"_20":136,"_111":137,"_113":-5,"_114":65,"_115":138,"_117":100,"_22":172,"_62":140,"_120":141,"_122":142,"_124":26,"_125":-5,"_166":173,"_82":174},{"_18":127,"_20":128,"_111":129,"_113":-5,"_114":65,"_115":130,"_117":100,"_22":169,"_62":132,"_120":121,"_122":133,"_124":26,"_125":-5,"_166":170,"_82":171},{"_18":109,"_20":110,"_111":112,"_113":-5,"_114":65,"_115":116,"_117":100,"_22":165,"_62":119,"_120":121,"_122":123,"_124":26,"_125":-5,"_166":167,"_82":168},["D",1769436290502],"children",[],[110],["D",1769436387890],[],[128],["D",1769436329408],[],[136],["D",1770023360464],[],[145],["D",1769436233995],{"_16":17,"_80":76},{"_16":17,"_74":-5,"_75":76,"_32":33,"_77":78,"_79":26},{"_62":63,"_20":64,"_18":65,"_66":19,"_67":-5,"_68":-5,"_69":-5,"_70":-5,"_71":-5,"_72":21,"_73":17},"ctx",{"_55":-5,"_102":215,"_216":217,"_218":219,"_220":26,"_221":26,"_222":26,"_223":100,"_224":-5,"_225":-7,"_226":-7,"_227":39,"_228":26,"_229":26,"_230":-5,"_231":26,"_232":26,"_233":26,"_234":-5},"ENV",{"_186":187,"_188":189,"_190":64,"_191":192,"_193":194,"_195":196,"_197":198,"_199":200,"_201":202,"_203":204,"_205":206,"_207":208,"_209":210,"_211":212,"_213":214},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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