8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Quick stamp search on 20 critical frames using OWL-ViT
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"""
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import os
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import cv2
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import json
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import glob
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from PIL import Image
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import torch
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from transformers import OwlViTProcessor, OwlViTForObjectDetection
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BASE_DIR = "output/384b0ff44aaaa1f1/critical_scenes"
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RESULTS_DIR = "output/384b0ff44aaaa1f1/critical_results"
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os.makedirs(RESULTS_DIR, exist_ok=True)
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print("🔬 Loading OWL-ViT...")
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processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
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model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
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model.eval()
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SEARCH_TERMS = [
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"postage stamp",
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"stamp on envelope",
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"envelope",
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"hand holding paper",
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"document",
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]
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frames = sorted(glob.glob(os.path.join(BASE_DIR, "frame_*.jpg")))
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print(f"📸 Scanning {len(frames)} critical frames...")
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all_detections = []
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for frame_path in frames:
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frame_name = os.path.basename(frame_path)
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sec = frame_name.replace("frame_", "").replace("s.jpg", "")
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image = Image.open(frame_path).convert("RGB")
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for term in SEARCH_TERMS:
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inputs = processor(text=[[term]], images=image, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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target_sizes = torch.Tensor([image.size[::-1]])
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results = processor.post_process_object_detection(
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outputs=outputs, target_sizes=target_sizes, threshold=0.05
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)
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for score, label, box in zip(
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results[0]["scores"], results[0]["labels"], results[0]["boxes"]
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):
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s = float(score)
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if s > 0.08:
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det = {
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"frame": frame_name,
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"sec": sec,
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"term": term,
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"score": s,
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"bbox": box.tolist(),
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}
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all_detections.append(det)
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print(f" 📍 {sec}s | {term} | {s:.2f} | bbox={box.tolist()}")
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# Save crop
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x1, y1, x2, y2 = map(int, box.tolist())
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img = cv2.imread(frame_path)
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crop = img[y1:y2, x1:x2]
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if crop.size > 0:
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crop_name = f"stamp_{sec}s_{term.replace(' ', '_')}.jpg"
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cv2.imwrite(os.path.join(RESULTS_DIR, crop_name), crop)
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# Annotate
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cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 3)
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cv2.putText(
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img,
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f"{term} {s:.2f}",
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(x1, y1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.7,
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(0, 255, 0),
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2,
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)
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ann_name = f"annotated_{sec}s.jpg"
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cv2.imwrite(os.path.join(RESULTS_DIR, ann_name), img)
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with open(os.path.join(RESULTS_DIR, "results.json"), "w") as f:
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json.dump(all_detections, f, indent=2)
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print(f"\n🏁 Found {len(all_detections)} detections. Check {RESULTS_DIR}")
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