39ba5ddf76
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
85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Test Grounding DINO Large with COMBINED prompts — one inference per frame.
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"""
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import json, os, time, cv2, torch
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from PIL import Image
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from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
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MODEL_PATH = "/Users/accusys/momentry_core_0.1/models/gun/grounding-dino-large-hf"
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VIDEO = "/Users/accusys/momentry/var/sftpgo/data/demo/Charade (1963) Cary Grant & Audrey Hepburn \uff5c Comedy Mystery Romance Thriller \uff5c Full Movie.mp4"
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OUTPUT_DIR = "/Users/accusys/momentry/output_dev/zero_shot_objects"
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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TIMEPOINTS = [
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(429, "stamp"), (691, "stamp_letter"), (762, "passport"),
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(3491, "passport"), (5054, "passport"),
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(5434, "letter"), (5443, "stamp_envelope"),
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(5467, "envelope"), (5500, "stamp"), (5506, "stamp"),
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(5783, "letter"), (5786, "envelope"),
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]
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COMBINED_PROMPT = "stamp. postage stamp. envelope. passport. identification. letter."
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print("Loading Large model...")
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t0 = time.time()
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processor = AutoProcessor.from_pretrained(MODEL_PATH)
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model = AutoModelForZeroShotObjectDetection.from_pretrained(MODEL_PATH)
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device = "mps" if torch.backends.mps.is_available() else "cpu"
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model.to(device)
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print(f"Loaded in {time.time()-t0:.1f}s")
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cap = cv2.VideoCapture(VIDEO)
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fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
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print(f"\nTesting {len(TIMEPOINTS)} timepoints with combined prompt...")
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t_infer = time.time()
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for t_sec, label in TIMEPOINTS:
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cap.set(cv2.CAP_PROP_POS_FRAMES, int(t_sec * fps))
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ret, frame = cap.read()
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if frame is None: continue
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img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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# ONE inference with ALL prompts
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inputs = processor(images=img, text=COMBINED_PROMPT, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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target = torch.tensor([img.size[::-1]])
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dets = processor.post_process_grounded_object_detection(
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outputs, threshold=0.1, target_sizes=target
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)[0]
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det_list = []
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for i in range(len(dets["boxes"])):
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det_list.append({
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"bbox": [round(v, 1) for v in dets["boxes"][i].tolist()],
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"score": round(dets["scores"][i].item(), 3),
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"label": str(dets["labels"][i]) if "labels" in dets else "object",
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})
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# Classify which expected objects were found
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found = set()
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for d in det_list:
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lbl = d["label"].lower()
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for obj in ["stamp", "envelope", "passport", "letter"]:
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if obj in lbl:
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found.add(obj)
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found_str = ", ".join(sorted(found)) if found else "none"
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print(f" {t_sec//60}:{t_sec%60:02d} {label:20s} | {len(det_list)} dets | found: [{found_str}]")
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# Save annotated frame
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for d in det_list:
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x1, y1, x2, y2 = [int(v) for v in d["bbox"]]
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(frame, f"{d['label']} {d['score']:.2f}", (x1, y1-5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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cv2.imwrite(os.path.join(OUTPUT_DIR, f"combined_{t_sec}s.jpg"), frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
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cap.release()
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print(f"\nDone in {time.time()-t_infer:.0f}s")
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print(f"Screenshots: {OUTPUT_DIR}/")
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