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/paligemma_vs_gdino.py

@@ -0,0 +1,121 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Full comparison: Grounding DINO Base vs PaliGemma 3B mix-224
+Tests on 8 known timepoints with gun/stamp prompts.
+"""
+import json, os, sys, time, cv2, torch, re
+from PIL import Image
+
+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/paligemma_vs_gdino"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+TIMEPOINTS = [
+    (2646, "2646s"), (3188, "3188s"), (3697, "3697s"),
+    (5341, "5341s"), (5461, "5461s"), (6309, "6309s"),
+    (6377, "6377s"), (6479, "6479s"),
+]
+PROMPTS = ["gun", "pistol", "stamp", "envelope", "passport"]
+
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+print(f"Device: {device}")
+
+# Load all frames
+cap = cv2.VideoCapture(VIDEO)
+fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+frames = {}
+for t_sec, label in TIMEPOINTS:
+    cap.set(cv2.CAP_PROP_POS_FRAMES, int(t_sec * fps))
+    ret, frame = cap.read()
+    if ret: frames[label] = frame
+cap.release()
+print(f"Loaded {len(frames)} frames")
+
+all_results = {}
+
+# ===== Grounding DINO Base =====
+print("\n" + "="*60)
+print("Grounding DINO Base")
+print("="*60)
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+t0 = time.time()
+gd_proc = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
+gd_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to(device)
+gd_dets = {}
+for label, frame in frames.items():
+    img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    for pname in PROMPTS:
+        inputs = gd_proc(images=img, text=f"{pname}.", return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = gd_model(**inputs)
+        target = torch.tensor([img.size[::-1]])
+        dets = gd_proc.post_process_grounded_object_detection(outputs, threshold=0.1, target_sizes=target)[0]
+        scores = [round(s.item(), 3) for s in dets["scores"]] if len(dets["boxes"]) > 0 else []
+        gd_dets[f"{label}_{pname}"] = scores
+all_results["grounding-dino-base"] = {"elapsed": round(time.time()-t0, 1), "detections": gd_dets}
+print(f"  Done: {all_results['grounding-dino-base']['elapsed']}s")
+del gd_model; torch.mps.empty_cache()
+
+# ===== PaliGemma 3B mix-224 =====
+print("\n" + "="*60)
+print("PaliGemma 3B mix-224")
+print("="*60)
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+t0 = time.time()
+pg_proc = AutoProcessor.from_pretrained("google/paligemma-3b-mix-224")
+pg_model = PaliGemmaForConditionalGeneration.from_pretrained(
+    "google/paligemma-3b-mix-224", dtype=torch.bfloat16
+).to(device)
+print(f"  Model loaded: {sum(p.numel() for p in pg_model.parameters())/1e6:.0f}M params")
+pg_dets = {}
+for label, frame in frames.items():
+    img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    for pname in PROMPTS:
+        t_infer = time.time()
+        prompt = f"detect {pname}"
+        inputs = pg_proc(text=prompt, images=img, return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = pg_model.generate(**inputs, max_new_tokens=100)
+        result = pg_proc.decode(outputs[0], skip_special_tokens=True)
+        infer_time = time.time() - t_infer
+        
+        # Parse bboxes from output
+        locs = re.findall(r'<loc(\d+)>', result)
+        n_dets = len(locs) // 4
+        has_detection = n_dets > 0 or (pname in result.lower() and 'detect' not in result.lower())
+        
+        scores = []
+        if has_detection:
+            for _ in range(n_dets if n_dets > 0 else 1):
+                scores.append(1.0)
+        
+        pg_dets[f"{label}_{pname}"] = scores
+        if has_detection:
+            print(f"  {label} prompt={pname:10s}: {n_dets} det ({infer_time:.1f}s) result={result[:80]}")
+all_results["paligemma-3b-mix-224"] = {"elapsed": round(time.time()-t0, 1), "detections": pg_dets}
+del pg_model; torch.mps.empty_cache()
+
+# ===== Summary =====
+print("\n" + "="*70)
+print(f"{'Model':<28} {'Time':>8} {'Params':>8} {'Gun hits':>12} {'Pistol hits':>14} {'Stamp h':>10}")
+print("-"*80)
+for model_name in ["grounding-dino-base", "paligemma-3b-mix-224"]:
+    d = all_results[model_name]
+    dets = d["detections"]
+    summary = {}
+    for pname in PROMPTS:
+        hits = 0
+        for label, _, _ in TIMEPOINTS:
+            key = f"{label}_{pname}"
+            if key in dets and dets[key]:
+                hits += 1
+        summary[pname] = hits
+    
+    params = "232M" if "grounding" in model_name else "2923M"
+    gun_h = summary.get("gun", 0)
+    pistol_h = summary.get("pistol", 0)
+    stamp_h = summary.get("stamp", 0)
+    print(f"{model_name:<28} {d['elapsed']:>7.1f}s {params:>8} {gun_h:>6d}/8 {pistol_h:>6d}/8 {stamp_h:>6d}/8")
+
+json.dump(all_results, open(os.path.join(OUTPUT_DIR, "comparison.json"), "w"), indent=2)
+print(f"\nSaved to {OUTPUT_DIR}/")