refactor: drop legacy image-processor and directus from pipeline

packages/image-processor/models/tiny_face_detector_model-shard1

@@ -1 +0,0 @@
-404: Not Found

packages/image-processor/models/tiny_face_detector_model-weights_manifest.json

@@ -1,30 +0,0 @@
-[
-  {
-      "weights":
-      [
-          {"name":"conv0/filters","shape":[3,3,3,16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009007044399485869,"min":-1.2069439495311063}},
-          {"name":"conv0/bias","shape":[16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005263455241334205,"min":-0.9211046672334858}},
-          {"name":"conv1/depthwise_filter","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004001977630690033,"min":-0.5042491814669441}},
-          {"name":"conv1/pointwise_filter","shape":[1,1,16,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013836609615999109,"min":-1.411334180831909}},
-          {"name":"conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0015159862590771096,"min":-0.30926119685173037}},
-          {"name":"conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002666276225856706,"min":-0.317286870876948}},
-          {"name":"conv2/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015265831292844286,"min":-1.6792414422128714}},
-          {"name":"conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0020280554598453,"min":-0.37113414915168985}},
-          {"name":"conv3/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006100742489683862,"min":-0.8907084034938438}},
-          {"name":"conv3/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.016276211832083907,"min":-2.0508026908425725}},
-          {"name":"conv3/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003394414279975143,"min":-0.7637432129944072}},
-          {"name":"conv4/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006716050119961009,"min":-0.8059260143953211}},
-          {"name":"conv4/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.021875603993733724,"min":-2.8875797271728514}},
-          {"name":"conv4/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0041141652009066415,"min":-0.8187188749804216}},
-          {"name":"conv5/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008423839597141042,"min":-0.9013508368940915}},
-          {"name":"conv5/pointwise_filter","shape":[1,1,256,512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.030007277283014035,"min":-3.8709387695088107}},
-          {"name":"conv5/bias","shape":[512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008402082966823203,"min":-1.4871686851277068}},
-          {"name":"conv8/filters","shape":[1,1,512,25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.028336129469030042,"min":-4.675461362389957}},
-          {"name":"conv8/bias","shape":[25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002268134028303857,"min":-0.41053225912299807}}
-      ],
-      "paths":
-      [
-          "tiny_face_detector_model.bin"
-      ]
-  }
-]

packages/image-processor/package.json

@@ -1,34 +0,0 @@
-{
-  "name": "@mintel/image-processor",
-  "version": "1.9.0",
-  "private": true,
-  "type": "module",
-  "main": "./dist/index.js",
-  "module": "./dist/index.js",
-  "types": "./dist/index.d.ts",
-  "exports": {
-    ".": {
-      "types": "./dist/index.d.ts",
-      "import": "./dist/index.js"
-    }
-  },
-  "scripts": {
-    "build": "tsup",
-    "dev": "tsup --watch",
-    "lint": "eslint src"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs": "^4.22.0",
-    "@tensorflow/tfjs-backend-wasm": "^4.22.0",
-    "@vladmandic/face-api": "^1.7.15",
-    "canvas": "^3.2.1",
-    "sharp": "^0.33.2"
-  },
-  "devDependencies": {
-    "@mintel/eslint-config": "workspace:*",
-    "@mintel/tsconfig": "workspace:*",
-    "@types/node": "^20.0.0",
-    "tsup": "^8.3.5",
-    "typescript": "^5.0.0"
-  }
-}

packages/image-processor/src/index.ts

@@ -1 +0,0 @@
-export * from './processor.js';

packages/image-processor/src/processor.ts

@@ -1,217 +0,0 @@
-import sharp from "sharp";
-import { Canvas, Image, ImageData } from "canvas";
-// Use the ESM no-bundle build to avoid the default Node entrypoint
-// which hardcodes require('@tensorflow/tfjs-node') and crashes in Docker.
-// This build uses pure @tensorflow/tfjs (JS-only, no native C++ bindings).
-import * as faceapi from "@vladmandic/face-api/dist/face-api.esm-nobundle.js";
-import * as tf from "@tensorflow/tfjs";
-import path from "path";
-import { fileURLToPath } from "url";
-
-// Polyfill required by face-api for Node.js
-faceapi.env.monkeyPatch({ Canvas, Image, ImageData } as any);
-
-const __filename = fileURLToPath(import.meta.url);
-const __dirname = path.dirname(__filename);
-const MODEL_URL = path.join(__dirname, "../models");
-
-// State flag to ensure we only load weights once
-let modelsLoaded = false;
-
-async function loadModelsOnce() {
-  if (modelsLoaded) return;
-  // Initialize pure JS CPU backend (no native bindings needed)
-  await tf.setBackend("cpu");
-  await tf.ready();
-
-  // Load the microscopic TinyFaceDetector (~190KB)
-  await faceapi.nets.tinyFaceDetector.loadFromDisk(MODEL_URL);
-  modelsLoaded = true;
-}
-
-export interface ProcessImageOptions {
-  width: number;
-  height: number;
-  format?: "webp" | "jpeg" | "png" | "avif";
-  quality?: number;
-}
-
-/**
- * Maps a URL based on the IMGPROXY_URL_MAPPING environment variable.
- * Format: "match1:replace1,match2:replace2"
- */
-export function mapUrl(url: string, mappingString?: string): string {
-  if (!mappingString) return url;
-
-  const mappings = mappingString.split(",").map((m) => {
-    if (m.includes("|")) {
-      return m.split("|");
-    }
-
-    // Legacy support for simple "host:target" or cases where one side might have a protocol
-    // We try to find the split point that isn't part of a protocol "://"
-    const colonIndices = [];
-    for (let i = 0; i < m.length; i++) {
-      if (m[i] === ":") {
-        // Check if this colon is part of "://"
-        if (!(m[i + 1] === "/" && m[i + 2] === "/")) {
-          colonIndices.push(i);
-        }
-      }
-    }
-
-    if (colonIndices.length === 0) return [m];
-
-    // In legacy mode with colons, we take the LAST non-protocol colon as the separator
-    // This handles "http://host:port" or "host:http://target" better
-    const lastColon = colonIndices[colonIndices.length - 1];
-    return [m.substring(0, lastColon), m.substring(lastColon + 1)];
-  });
-
-  let mappedUrl = url;
-
-  for (const [match, replace] of mappings) {
-    if (match && replace && url.includes(match)) {
-      mappedUrl = url.replace(match, replace);
-    }
-  }
-
-  return mappedUrl;
-}
-
-/**
- * Parses legacy imgproxy options string.
- * Example: rs:fill:300:400/q:80
- */
-export function parseImgproxyOptions(
-  optionsStr: string,
-): Partial<ProcessImageOptions> {
-  const parts = optionsStr.split("/");
-  const options: Partial<ProcessImageOptions> = {};
-
-  for (const part of parts) {
-    if (part.startsWith("rs:")) {
-      const [, , w, h] = part.split(":");
-      if (w) options.width = parseInt(w, 10);
-      if (h) options.height = parseInt(h, 10);
-    } else if (part.startsWith("q:")) {
-      const q = part.split(":")[1];
-      if (q) options.quality = parseInt(q, 10);
-    } else if (part.startsWith("ext:")) {
-      const ext = part.split(":")[1] as any;
-      if (["webp", "jpeg", "png", "avif"].includes(ext)) {
-        options.format = ext;
-      }
-    }
-  }
-
-  return options;
-}
-
-export async function processImageWithSmartCrop(
-  inputBuffer: Buffer,
-  options: ProcessImageOptions,
-): Promise<Buffer> {
-  const sharpImage = sharp(inputBuffer);
-  const metadata = await sharpImage.metadata();
-
-  if (!metadata.width || !metadata.height) {
-    throw new Error("Could not read image metadata");
-  }
-
-  // Load ML models (noop if already loaded)
-  await loadModelsOnce();
-
-  // Convert sharp image to a Node-compatible canvas Image for face-api
-  const jpegBuffer = await sharpImage.jpeg().toBuffer();
-  const img = new Image();
-  img.src = jpegBuffer;
-  const canvas = new Canvas(img.width, img.height);
-  const ctx = canvas.getContext("2d");
-  ctx.drawImage(img, 0, 0, img.width, img.height);
-
-  // Detect faces locally using the tiny model
-  // Requires explicit any cast since the types expect HTML elements in browser contexts
-  const detections = await faceapi.detectAllFaces(
-    canvas as any,
-    new faceapi.TinyFaceDetectorOptions(),
-  );
-
-  let cropPosition: "center" | "attention" | number = "attention"; // Fallback to sharp's attention if no faces
-
-  if (detections.length > 0) {
-    // We have faces! Calculate the bounding box that contains all of them
-    let minX = metadata.width;
-    let minY = metadata.height;
-    let maxX = 0;
-    let maxY = 0;
-
-    for (const det of detections) {
-      const box = det.box;
-      if (box.x < minX) minX = Math.max(0, box.x);
-      if (box.y < minY) minY = Math.max(0, box.y);
-      if (box.x + box.width > maxX)
-        maxX = Math.min(metadata.width, box.x + box.width);
-      if (box.y + box.height > maxY)
-        maxY = Math.min(metadata.height, box.y + box.height);
-    }
-
-    const centerX = Math.floor(minX + (maxX - minX) / 2);
-    const centerY = Math.floor(minY + (maxY - minY) / 2);
-
-    const targetRatio = options.width / options.height;
-    const currentRatio = metadata.width / metadata.height;
-
-    let cropWidth = metadata.width;
-    let cropHeight = metadata.height;
-
-    // Determine the maximal crop window that maintains aspect ratio
-    if (currentRatio > targetRatio) {
-      cropWidth = Math.floor(metadata.height * targetRatio);
-    } else {
-      cropHeight = Math.floor(metadata.width / targetRatio);
-    }
-
-    // Center the crop window over the center of the faces
-    let cropX = Math.floor(centerX - cropWidth / 2);
-    let cropY = Math.floor(centerY - cropHeight / 2);
-
-    // Keep crop window inside image bounds
-    if (cropX < 0) cropX = 0;
-    if (cropY < 0) cropY = 0;
-    if (cropX + cropWidth > metadata.width) cropX = metadata.width - cropWidth;
-    if (cropY + cropHeight > metadata.height)
-      cropY = metadata.height - cropHeight;
-
-    // Pre-crop the image to isolate the faces before resizing
-    sharpImage.extract({
-      left: cropX,
-      top: cropY,
-      width: cropWidth,
-      height: cropHeight,
-    });
-
-    // As we manually calculated the exact focal box, we can now just center it
-    cropPosition = "center";
-  }
-
-  let finalImage = sharpImage.resize(options.width, options.height, {
-    fit: "cover",
-    position: cropPosition,
-  });
-
-  const format = options.format || "webp";
-  const quality = options.quality || 80;
-
-  if (format === "webp") {
-    finalImage = finalImage.webp({ quality });
-  } else if (format === "jpeg") {
-    finalImage = finalImage.jpeg({ quality });
-  } else if (format === "png") {
-    finalImage = finalImage.png({ quality });
-  } else if (format === "avif") {
-    finalImage = finalImage.avif({ quality });
-  }
-
-  return finalImage.toBuffer();
-}

packages/image-processor/tsconfig.json

@@ -1,19 +0,0 @@
-{
-    "extends": "@mintel/tsconfig/base.json",
-    "compilerOptions": {
-        "outDir": "dist",
-        "rootDir": "src",
-        "allowJs": true,
-        "esModuleInterop": true,
-        "module": "NodeNext",
-        "moduleResolution": "NodeNext"
-    },
-    "include": [
-        "src/**/*"
-    ],
-    "exclude": [
-        "node_modules",
-        "dist",
-        "**/*.test.ts"
-    ]
-}

packages/image-processor/tsup.config.ts

@@ -1,17 +0,0 @@
-import { defineConfig } from "tsup";
-
-export default defineConfig({
-  entry: ["src/index.ts"],
-  format: ["esm"],
-  dts: true,
-  clean: true,
-  // Bundle face-api and tensorflow inline (they're pure JS).
-  // Keep sharp and canvas external (they have native C++ bindings).
-  noExternal: ["@vladmandic/face-api", "@tensorflow/tfjs"],
-  external: [
-    "sharp",
-    "canvas",
-    "@tensorflow/tfjs-backend-wasm",
-    "@tensorflow/tfjs-backend-wasm/dist/index.js",
-  ],
-});