feat: Phase 1 handover - schema migration, correction mechanism, API fixes

Schema changes: dev.chunks->dev.chunk, remove old_chunk_id/chunk_index
Correction: asr-1.json format, generate/apply scripts
API: 37/37 endpoints fixed and tested
Docs: HANDOVER_V2.0.md for M4

scripts/zero_shot_objects_test.py

@@ -0,0 +1,103 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Test Grounding DINO Large on stamps, envelopes, passports, letters.
+"""
+import json, os, time, cv2, torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+MODEL_PATH = "/Users/accusys/momentry_core_0.1/models/gun/grounding-dino-large-hf"
+VIDEO = "/Users/accusys/momentry/var/sftpgo/data/demo/Charade (1963) Cary Grant & Audrey Hepburn \uff5c Comedy Mystery Romance Thriller \uff5c Full Movie.mp4"
+OUTPUT_DIR = "/Users/accusys/momentry/output_dev/zero_shot_objects"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# Timepoints per object type
+TESTS = [
+    # (label, time_sec, prompts)
+    ("stamp_001", 429, ["stamp", "postage stamp"]),
+    ("stamp_002", 691, ["stamp", "envelope", "letter"]),
+    ("stamp_003", 5443, ["stamp", "envelope"]),
+    ("stamp_004", 5500, ["stamp"]),
+    ("stamp_005", 5506, ["stamp"]),
+    ("envelope_001", 5443, ["envelope"]),
+    ("envelope_002", 5467, ["envelope"]),
+    ("envelope_003", 5786, ["envelope"]),
+    ("passport_001", 762, ["passport", "identification"]),
+    ("passport_002", 3491, ["passport", "identification"]),
+    ("passport_003", 5054, ["passport"]),
+    ("letter_001", 691, ["letter", "envelope"]),
+    ("letter_002", 5434, ["letter", "envelope"]),
+    ("letter_003", 5783, ["letter", "stamp"]),
+]
+
+print(f"Loading Large model...")
+t0 = time.time()
+processor = AutoProcessor.from_pretrained(MODEL_PATH)
+model = AutoModelForZeroShotObjectDetection.from_pretrained(MODEL_PATH)
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+model.to(device)
+print(f"Loaded in {time.time()-t0:.1f}s, device={device}")
+
+cap = cv2.VideoCapture(VIDEO)
+fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+
+results = {}
+t_infer = time.time()
+
+for label, t_sec, prompts in TESTS:
+    cap.set(cv2.CAP_PROP_POS_FRAMES, int(t_sec * fps))
+    ret, frame = cap.read()
+    if frame is None: continue
+    
+    img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    key = f"{label}_{t_sec}s"
+    results[key] = {"time": t_sec, "time_str": f"{t_sec//60}:{t_sec%60:02d}", "prompts": {}}
+    
+    for prompt in prompts:
+        inputs = processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        target = torch.tensor([img.size[::-1]])
+        dets = processor.post_process_grounded_object_detection(
+            outputs, threshold=0.1, target_sizes=target
+        )[0]
+        
+        det_list = []
+        for i in range(len(dets["boxes"])):
+            det_list.append({
+                "bbox": [round(v, 1) for v in dets["boxes"][i].tolist()],
+                "score": round(dets["scores"][i].item(), 3),
+            })
+        results[key]["prompts"][prompt] = det_list
+        
+        # Save annotated frame
+        if det_list:
+            cv2_img = frame.copy()
+            for d in det_list:
+                x1, y1, x2, y2 = [int(v) for v in d["bbox"]]
+                cv2.rectangle(cv2_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                cv2.putText(cv2_img, f"{prompt} {d['score']:.2f}", (x1, y1-5),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+            cv2.imwrite(os.path.join(OUTPUT_DIR, f"{label}_{t_sec}s_{prompt}.jpg"), cv2_img,
+                       [cv2.IMWRITE_JPEG_QUALITY, 85])
+
+cap.release()
+elapsed = time.time() - t_infer
+
+# Summary
+print(f"\n{'='*60}")
+print(f"Results ({elapsed:.0f}s)")
+print(f"{'='*60}")
+for key, data in sorted(results.items()):
+    found = [p for p, dets in data["prompts"].items() if dets]
+    if found:
+        best = max(
+            ((p, d["score"]) for p, dets in data["prompts"].items() for d in dets),
+            key=lambda x: x[1]
+        )
+        print(f"  {data['time_str']} {key:20s} ✅ {best[1]:.3f} ({best[0]})")
+    else:
+        print(f"  {data['time_str']} {key:20s} ❌ none")
+
+json.dump(results, open(os.path.join(OUTPUT_DIR, "results.json"), "w"), indent=2)
+print(f"\nScreenshots saved to {OUTPUT_DIR}/")