test(photos): dimension-overlay PNG stations + results.overlayPng (#63)

Failing TDD for M2 dimension-overlay copy of the calibration inventory PNG: width stations every 12 in from EXIF bottom (6, 18, … ≤ L−6), axis-right length graphic, and Calculate storing overlayPng without touching batchApply[cal].
2026-09-15 21:29:59 +00:00
parent 3936c575b2
commit c674931f5c
2 changed files with 471 additions and 0 deletions
@@ -0,0 +1,286 @@
+import assert from 'node:assert/strict';
+import { readFileSync } from 'node:fs';
+import { dirname, join } from 'node:path';
+import { fileURLToPath } from 'node:url';
+import { test, beforeEach, afterEach } from 'node:test';
+import { get } from 'svelte/store';
+import type { Draft, Mask, MaskParams, WorkerIn, WorkerOut } from './capture.types';
+import { createCaptureSession, type DraftStore } from './session';
+
+const here = dirname(fileURLToPath(import.meta.url));
+
+function tinyJpeg(label: string): File {
+  const bytes = new Uint8Array([0xff, 0xd8, 0xff, 0xe0, label.charCodeAt(0) & 0xff]);
+  return new File([bytes], `${label}.jpg`, { type: 'image/jpeg' });
+}
+
+function memStore(): DraftStore & { peek: () => Draft | null } {
+  let mem: Draft | null = null;
+  return {
+    loadDraft: async () => mem,
+    saveDraft: async (d) => {
+      mem = d;
+    },
+    clearDraft: async () => {
+      mem = null;
+    },
+    peek: () => mem,
+  };
+}
+
+let store: ReturnType<typeof memStore>;
+
+beforeEach(() => {
+  store = memStore();
+});
+
+type MaskBehavior = {
+  w: number;
+  h: number;
+  slabPx: number;
+  slabBounds?: { x: number; y: number; w: number; h: number };
+};
+
+class FakeMaskWorker {
+  onmessage: ((ev: MessageEvent<WorkerOut>) => void) | null = null;
+  static inits: Blob[] = [];
+  static maskCalls: { sheet: string; sensitivity: number; blob: Blob | null }[] = [];
+  static lastInitBlob: Blob | null = null;
+  static behaviorFor: (blob: Blob) => MaskBehavior = () => ({
+    w: 1000,
+    h: 900,
+    slabPx: 1000 * 900,
+    slabBounds: { x: 0, y: 0, w: 1000, h: 900 },
+  });
+
+  constructor(_url: URL | string, _opts?: WorkerOptions) {}
+
+  postMessage(msg: WorkerIn) {
+    if (msg.kind === 'init') {
+      FakeMaskWorker.inits.push(msg.blob);
+      FakeMaskWorker.lastInitBlob = msg.blob;
+      const id = msg.id;
+      const b = FakeMaskWorker.behaviorFor(msg.blob);
+      queueMicrotask(() => {
+        this.onmessage?.({ data: { id, kind: 'decoded', w: b.w, h: b.h } } as MessageEvent<WorkerOut>);
+      });
+      return;
+    }
+    if (msg.kind === 'mask') {
+      const id = msg.id;
+      const blob = FakeMaskWorker.lastInitBlob;
+      FakeMaskWorker.maskCalls.push({
+        sheet: msg.sheet,
+        sensitivity: msg.sensitivity,
+        blob,
+      });
+      const b = blob ? FakeMaskWorker.behaviorFor(blob) : { w: 40, h: 20, slabPx: 0 };
+      const bitset = new Uint8Array(Math.max(1, b.w * b.h)).fill(b.slabPx > 0 ? 1 : 0);
+      const bbox = {
+        corners: [
+          [b.w, 0],
+          [b.w, b.h],
+          [0, b.h],
+          [0, 0],
+        ] as [number, number][],
+        longSidePx: b.w,
+        angleDeg: 0,
+      };
+      const slabBounds = b.slabBounds ?? { x: 0, y: 0, w: b.w, h: b.h };
+      queueMicrotask(() => {
+        this.onmessage?.({
+          data: {
+            id,
+            kind: 'ready',
+            slabPx: b.slabPx,
+            bitset,
+            bbox,
+            slabBounds,
+          },
+        } as MessageEvent<WorkerOut>);
+      });
+      return;
+    }
+  }
+
+  terminate() {}
+}
+
+const RealWorker = globalThis.Worker;
+const RealCreateImageBitmap = globalThis.createImageBitmap;
+
+function installFakes() {
+  FakeMaskWorker.inits = [];
+  FakeMaskWorker.maskCalls = [];
+  FakeMaskWorker.lastInitBlob = null;
+  FakeMaskWorker.behaviorFor = () => ({
+    w: 1000,
+    h: 900,
+    slabPx: 1000 * 900,
+    slabBounds: { x: 0, y: 0, w: 1000, h: 900 },
+  });
+  (globalThis as unknown as { Worker: typeof FakeMaskWorker }).Worker = FakeMaskWorker;
+  (globalThis as unknown as { createImageBitmap: typeof createImageBitmap }).createImageBitmap =
+    async (blob: ImageBitmapSource) => {
+      const b = blob instanceof Blob ? FakeMaskWorker.behaviorFor(blob) : { w: 1000, h: 900 };
+      return { width: b.w, height: b.h, close() {} } as ImageBitmap;
+    };
+}
+
+function restoreGlobals() {
+  if (RealWorker === undefined) {
+    delete (globalThis as unknown as { Worker?: unknown }).Worker;
+  } else {
+    globalThis.Worker = RealWorker;
+  }
+  if (RealCreateImageBitmap === undefined) {
+    delete (globalThis as unknown as { createImageBitmap?: unknown }).createImageBitmap;
+  } else {
+    globalThis.createImageBitmap = RealCreateImageBitmap;
+  }
+}
+
+afterEach(() => {
+  restoreGlobals();
+});
+
+async function settleOverlay(
+  s: ReturnType<typeof createCaptureSession>,
+  ms = 2500,
+): Promise<Mask> {
+  const start = Date.now();
+  while (Date.now() - start < ms) {
+    const cap = get(s.capture);
+    if (cap.k === 'mask' && cap.overlay && cap.overlay.slabPx > 0) return cap.overlay;
+    await new Promise((r) => setTimeout(r, 5));
+  }
+  throw new Error('mask overlay never settled');
+}
+
+async function driveToMeasure(
+  s: ReturnType<typeof createCaptureSession>,
+  photos: File[],
+  calIndex: number,
+  maskParams: Partial<MaskParams> = { sheet: 'green', sensitivity: 50 },
+) {
+  for (const p of photos) await s.addToBatch(p);
+  s.continueBatch();
+  s.pickCalibration(calIndex);
+  if (typeof maskParams.sensitivity === 'number') s.setSensitivity(maskParams.sensitivity);
+  // setSheet runs an immediate pass (after sensitivity so the pass sees frozen params)
+  s.setSheet(maskParams.sheet ?? 'auto');
+  await settleOverlay(s);
+  s.approveMask();
+  s.approveAxis();
+  s.setLength(320); // 40 in
+  s.setThickness(16); // 2 in
+}
+
+test('test(photos): Calculate stores overlayPng on results, clean inventory untouched (#63)', async () => {
+  installFakes();
+  let encodeN = 0;
+  const s = createCaptureSession({
+    draft: store,
+    encodeInventory: async () => {
+      encodeN++;
+      return {
+        blob: new Blob([`clean-${encodeN}`], { type: 'image/png' }),
+        sourceBelowFloor: false,
+      };
+    },
+  } as Parameters<typeof createCaptureSession>[0]);
+
+  const a = tinyJpeg('a');
+  const b = tinyJpeg('b'); // cal
+  await driveToMeasure(s, [a, b], 1, { sheet: 'green' });
+  await s.calculate();
+
+  const cap = get(s.capture);
+  assert.equal(cap.k, 'measure');
+  if (cap.k !== 'measure') return;
+  assert.ok(cap.results, 'results present');
+  assert.ok(cap.results!.inventoryPng, 'clean cal PNG in results.inventoryPng');
+  assert.equal(cap.results!.inventoryPng.type, 'image/png');
+
+  const overlay = (cap.results as { overlayPng?: Blob }).overlayPng;
+  assert.ok(overlay, 'results.overlayPng must exist after Calculate (#63)');
+  assert.equal(overlay!.type, 'image/png');
+  assert.notEqual(overlay, cap.results!.inventoryPng, 'overlay is a second copy, not the clean PNG');
+
+  const apply = (cap as { batchApply?: unknown[] }).batchApply;
+  assert.ok(Array.isArray(apply));
+  assert.equal(apply![1], null, 'batchApply[cal] stays null');
+  assert.ok(apply![0] && (apply![0] as { status: string }).status === 'ok', 'siblings unchanged');
+
+  s.dispose();
+});
+
+test('test(photos): single-photo batch still yields clean + overlayPng (#63)', async () => {
+  installFakes();
+  const s = createCaptureSession({
+    draft: store,
+    encodeInventory: async () => ({
+      blob: new Blob(['only-clean'], { type: 'image/png' }),
+      sourceBelowFloor: false,
+    }),
+  } as Parameters<typeof createCaptureSession>[0]);
+
+  await driveToMeasure(s, [tinyJpeg('only')], 0, { sheet: 'auto' });
+  await s.calculate();
+
+  const cap = get(s.capture);
+  assert.equal(cap.k, 'measure');
+  if (cap.k !== 'measure') return;
+  assert.ok(cap.results!.inventoryPng);
+  assert.ok((cap.results as { overlayPng?: Blob }).overlayPng, 'overlay even for batch of one');
+  assert.deepEqual((cap as { batchApply?: unknown[] }).batchApply, [null]);
+  s.dispose();
+});
+
+test('test(photos): draft persists overlayPng alongside inventoryPng (#63)', async () => {
+  installFakes();
+  const s = createCaptureSession({
+    draft: store,
+    encodeInventory: async () => ({
+      blob: new Blob(['draft-clean'], { type: 'image/png' }),
+      sourceBelowFloor: false,
+    }),
+  } as Parameters<typeof createCaptureSession>[0]);
+
+  await driveToMeasure(s, [tinyJpeg('a'), tinyJpeg('b')], 0, { sheet: 'black' });
+  await s.calculate();
+  await new Promise((r) => setTimeout(r, 30));
+
+  const d = store.peek();
+  assert.ok(d);
+  assert.equal(d!.stage, 'measure');
+  assert.ok(d!.inventoryPng, 'draft keeps clean inventory');
+  assert.ok(
+    (d as { overlayPng?: Blob | null }).overlayPng,
+    'draft.overlayPng must be persisted for #64 lineup',
+  );
+  assert.equal(d!.batchApply![0], null, 'cal slot still null on draft');
+  s.dispose();
+});
+
+test('test(photos): dishonest proxies absent — no raw-photo overlay / no cal-slot stash (#63)', async () => {
+  const sessionSrc = readFileSync(join(here, 'session.ts'), 'utf8');
+  const overlaySrcPath = join(here, '..', 'png', 'overlay.ts');
+  let overlaySrc = '';
+  try {
+    overlaySrc = readFileSync(overlaySrcPath, 'utf8');
+  } catch {
+    overlaySrc = '';
+  }
+  assert.ok(
+    /overlayPng|encodeOverlay|dimensionOverlay|buildOverlayPlan/i.test(sessionSrc) ||
+      overlaySrc.length > 0,
+    'session or png/overlay must implement dimension overlay (#63)',
+  );
+  const calcBody = sessionSrc.match(/async calculate\(\)[\s\S]*?(?=\n    [a-zA-Z]+\(|\n  \})/);
+  assert.ok(calcBody, 'calculate() must exist');
+  assert.ok(
+    !/batchApply\s*\[\s*calIndex\s*\]\s*=\s*\{[^}]*overlay/i.test(calcBody![0]),
+    'must not put overlay into batchApply[cal]',
+  );
+});