2cfcfdd1af
Phase 2.6.1: co_occurrence_edges migration - build_co_occurrence_edges_from_qdrant() - Qdrant embeddings → frame grouping → YOLO objects - Result: 6679 edges (vs 6701 PostgreSQL) Phase 2.6.2: face_face_edges migration - build_face_face_edges_from_qdrant() - Qdrant embeddings → frame grouping → face pairs - mutual_gaze detection preserved - Result: 6 edges (exact match) Phase 2.6.3: speaker_face_edges migration - build_speaker_face_edges_from_qdrant() - Qdrant embeddings → trace_id frame ranges - SPEAKS_AS edge creation Architecture: - All edges use Qdrant payload (no face_detections queries) - PostgreSQL fallback for empty Qdrant - Estimated 3.6x performance improvement Testing: - Playground (3003): ✓ All Phase 2.6 logs verified - Edge counts: ✓ Close match with PostgreSQL - Fallback: ✓ Working Docs: - docs_v1.0/DESIGN/TKG_PHASE2_6_EDGES_MIGRATION.md - docs_v1.0/M4_workspace/2026-06-21_phase2_6_test.md
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Search for "vase" in the video using OWL-ViT on a subset of frames.
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"""
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import os
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import cv2
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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/full_video_scans"
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RESULTS_DIR = "output/384b0ff44aaaa1f1/vase_search_results"
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os.makedirs(RESULTS_DIR, exist_ok=True)
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print("🔍 Searching for vases...")
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# Load model
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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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SEARCH_TERMS = ["vase", "flower vase", "urn", "pottery", "glass jar"]
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frames = sorted(glob.glob(os.path.join(BASE_DIR, "frame_*.jpg")))
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print(f"📸 Scanning {len(frames)} frames...")
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found_count = 0
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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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h, w = image.height, image.width
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target_sizes = torch.Tensor([[h, w]])
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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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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: # Threshold for visualization
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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"vase_{sec}s_{term.replace(' ', '_')}_{s:.2f}.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, 0, 255), 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, 0, 255),
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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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print(f" 📍 {sec}s | {term} | {s:.2f}")
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found_count += 1
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print(f"\n🏁 Done. Found {found_count} candidates.")
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