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
2026-05-11 07:03:22 +08:00
parent ef894a44ad
commit 39ba5ddf76
147 changed files with 19843 additions and 3053 deletions
@@ -0,0 +1,142 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Grounding DINO Base vs Large comparison test.
+Both use Swin-B backbone; Large trained on 7 datasets vs Base's 3.
+"""
+import json, os, sys, time, cv2, torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+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/gdino_comparison"
+LARGE_PATH = "/Users/accusys/momentry_core_0.1/models/gun/grounding-dino-large-hf"
+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", "rifle", "weapon"]
+
+cap = cv2.VideoCapture(VIDEO)
+fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+
+def get_frame(t_sec):
+    cap.set(cv2.CAP_PROP_POS_FRAMES, int(t_sec * fps))
+    ret, frame = cap.read()
+    return frame if ret else None
+
+models = {
+    "base": {"path": "IDEA-Research/grounding-dino-base", "label": "Base (3 datasets)"},
+    "large": {"path": LARGE_PATH, "label": "Large (7 datasets)"},
+}
+
+all_results = {}
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+print(f"Device: {device}")
+
+for model_name, model_info in models.items():
+    print(f"\n{'='*60}")
+    print(f"Loading {model_info['label']} ({model_name})...")
+    print(f"{'='*60}")
+    
+    t_load = time.time()
+    processor = AutoProcessor.from_pretrained(model_info["path"])
+    model = AutoModelForZeroShotObjectDetection.from_pretrained(model_info["path"]).to(device)
+    load_time = time.time() - t_load
+    print(f"  Loaded in {load_time:.1f}s")
+    
+    model_dets = {}
+    t0 = time.time()
+    
+    for t_sec, label in TIMEPOINTS:
+        frame = get_frame(t_sec)
+        if frame is None: continue
+        img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        
+        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=0.05, 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),
+                    "label": prompt,
+                })
+            model_dets[f"{label}_prompt-{prompt}"] = det_list
+    
+    elapsed = time.time() - t0
+    all_results[model_name] = {"elapsed": round(elapsed, 1), "detections": model_dets}
+    print(f"  Inference: {elapsed:.1f}s")
+    
+    del model
+    torch.mps.empty_cache()
+
+cap.release()
+
+# ========== Summary ==========
+print(f"\n{'='*60}")
+print("COMPARISON SUMMARY")
+print(f"{'='*60}")
+
+for model_name in ["base", "large"]:
+    d = all_results[model_name]
+    dets = d["detections"]
+    hits = sum(1 for v in dets.values() if v)
+    total = sum(len(v) for v in dets.values())
+    print(f"\n{model_name.upper()} ({d['elapsed']}s): {hits}/32 prompt-timepoint hits, {total} total detections")
+    
+    for t_sec, label in TIMEPOINTS:
+        candidates = []
+        for p in PROMPTS:
+            key = f"{label}_prompt-{p}"
+            key_rev = f"{label}_prompt-{p}."
+            for k in [key, key_rev]:
+                if k in dets and dets[k]:
+                    for dd in dets[k]:
+                        candidates.append((p, dd["score"]))
+        if candidates:
+            best = max(candidates, key=lambda x: x[1])
+            print(f"  {t_sec}s ({(t_sec//60)}:{t_sec%60:02d}): best={best[1]:.3f} (prompt='{best[0]}')")
+        else:
+            print(f"  {t_sec}s: no detections")
+
+# Per-timepoint comparison
+print(f"\n{'='*60}")
+print("PER-TIMEPOINT COMPARISON")
+print(f"{'='*60}")
+for t_sec, label in TIMEPOINTS:
+    base_best = None
+    large_best = None
+    for p in PROMPTS:
+        for mn in ["base", "large"]:
+            dets = all_results[mn]["detections"]
+            for k in [f"{label}_prompt-{p}", f"{label}_prompt-{p}."]:
+                if k in dets and dets[k]:
+                    scores = [dd["score"] for dd in dets[k]]
+                    best = max(scores)
+                    if mn == "base" and (base_best is None or best > base_best[1]):
+                        base_best = (p, best)
+                    if mn == "large" and (large_best is None or best > large_best[1]):
+                        large_best = (p, best)
+    
+    b_str = f"base={base_best[1]:.3f} ({base_best[0]})" if base_best else "base=no det"
+    l_str = f"large={large_best[1]:.3f} ({large_best[0]})" if large_best else "large=no det"
+    
+    delta = ""
+    if base_best and large_best:
+        d = large_best[1] - base_best[1]
+        delta = f" ({'+'if d>0 else ''}{d:.3f})"
+    
+    print(f"  {t_sec}s: {b_str:30s} | {l_str:30s}{delta}")
+
+# Save
+json.dump(all_results, open(os.path.join(OUTPUT_DIR, "comparison_results.json"), "w"), indent=2)
+print(f"\nSaved to {OUTPUT_DIR}/")