feat(heal): add server-side AI inpainting endpoint via ONNX Runtime

docker/backend/src/inpaintService.ts

@@ -0,0 +1,74 @@
+import * as ort from 'onnxruntime-node';
+import { existsSync } from 'node:fs';
+
+const MODEL_PATH = process.env.INPAINT_MODEL_PATH || '/models/migan_pipeline_v2.onnx';
+
+let sessionPromise: Promise<ort.InferenceSession | null> | null = null;
+
+export async function getInpaintSession(): Promise<ort.InferenceSession | null> {
+  if (!sessionPromise) {
+    sessionPromise = (async () => {
+      const candidates = [
+        MODEL_PATH,
+        '/home/locpham/RecipesCam/docker/frontend/public/models/migan_pipeline_v2.onnx',
+        '../frontend/public/models/migan_pipeline_v2.onnx',
+        './models/migan_pipeline_v2.onnx',
+      ];
+      let foundPath: string | null = null;
+      for (const p of candidates) {
+        if (existsSync(p)) {
+          foundPath = p;
+          break;
+        }
+      }
+      if (!foundPath) {
+        console.warn('[inpaintService] Model file not found in candidates:', candidates);
+        return null;
+      }
+
+      console.log('[inpaintService] Loading ONNX model from:', foundPath);
+      const t0 = Date.now();
+      const session = await ort.InferenceSession.create(foundPath, {
+        executionProviders: ['cpu'],
+        graphOptimizationLevel: 'all',
+      });
+      console.log('[inpaintService] Model loaded in ' + (Date.now() - t0) + 'ms');
+      return session;
+    })().catch((err) => {
+      console.error('[inpaintService] Failed to load ONNX model:', err);
+      sessionPromise = null;
+      return null;
+    });
+  }
+  return sessionPromise;
+}
+
+export async function runServerInpaint(
+  srcBytes: Uint8Array,
+  maskBytes: Uint8Array,
+  w: number,
+  h: number
+): Promise<Uint8Array | null> {
+  const session = await getInpaintSession();
+  if (!session) return null;
+
+  const in0 = new ort.Tensor('uint8', srcBytes, [1, 3, h, w]);
+  const in1 = new ort.Tensor('uint8', maskBytes, [1, 1, h, w]);
+
+  const out = await session.run({
+    [session.inputNames[0]]: in0,
+    [session.inputNames[1]]: in1,
+  });
+
+  const tensor = out[session.outputNames[0]];
+  const data = tensor.data as Uint8Array;
+  const patch = new Uint8Array(4 * w * h);
+  const plane = w * h;
+  for (let i = 0; i < plane; i++) {
+    patch[i * 4] = data[i];
+    patch[i * 4 + 1] = data[plane + i];
+    patch[i * 4 + 2] = data[2 * plane + i];
+    patch[i * 4 + 3] = 255;
+  }
+  return patch;
+}