e75c4d6f07
- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
255 lines
8.3 KiB
Python
255 lines
8.3 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Fast Multi-Stage Stamp Search
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Stage 1: OpenCV fast container detection (skin/hands, rectangles/paper)
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Stage 2: OWL-ViT only on container crops for stamp detection
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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 time
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import numpy as np
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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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UUID = "384b0ff44aaaa1f1"
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VIDEO_PATH = f"output/{UUID}/{UUID}.mp4"
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OUTPUT_DIR = f"output/{UUID}/fast_stamp_search"
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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CROPS_DIR = os.path.join(OUTPUT_DIR, "crops")
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os.makedirs(CROPS_DIR, exist_ok=True)
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FRAME_INTERVAL = 5
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MIN_STAMP_SCORE = 0.06
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print("=" * 60)
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print("⚡ Fast Multi-Stage Stamp Search")
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print("=" * 60)
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cap = cv2.VideoCapture(VIDEO_PATH)
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_sec = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) / fps)
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print(f"📹 Video: {total_sec}s ({total_sec // 60} min), {fps:.1f} fps")
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# Load OWL-ViT once for stamp detection
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print("🔬 Loading OWL-ViT stamp detector...")
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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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STAMP_TERMS = ["postage stamp", "stamp on paper", "small stamp", "stamp"]
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def find_containers_fast(frame):
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"""Fast OpenCV-based container detection"""
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containers = []
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h, w = frame.shape[:2]
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# 1. Skin color detection (hands)
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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skin_mask = cv2.inRange(hsv, np.array([0, 20, 70]), np.array([20, 150, 255]))
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skin_mask += cv2.inRange(hsv, np.array([160, 20, 70]), np.array([179, 150, 255]))
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# Morphological cleanup
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
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skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_CLOSE, kernel)
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skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_OPEN, kernel)
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# Find hand contours
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contours, _ = cv2.findContours(
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skin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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for cnt in contours:
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area = cv2.contourArea(cnt)
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if 2000 < area < h * w * 0.4:
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x, y, w_cnt, h_cnt = cv2.boundingRect(cnt)
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margin = 40
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containers.append(
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{
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"type": "hand",
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"bbox": [
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max(0, x - margin),
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max(0, y - margin),
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min(w, x + w_cnt + margin),
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min(h, y + h_cnt + margin),
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],
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}
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)
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# 2. Bright rectangular regions (envelopes/paper)
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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_, bright = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
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contours, _ = cv2.findContours(bright, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for cnt in contours:
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area = cv2.contourArea(cnt)
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if 5000 < area < h * w * 0.5:
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x, y, w_cnt, h_cnt = cv2.boundingRect(cnt)
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aspect = w_cnt / h_cnt if h_cnt > 0 else 0
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if 0.3 < aspect < 3.0:
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margin = 30
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containers.append(
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{
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"type": "paper",
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"bbox": [
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max(0, x - margin),
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max(0, y - margin),
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min(w, x + w_cnt + margin),
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min(h, y + h_cnt + margin),
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],
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}
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)
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return containers
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all_results = []
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start_time = time.time()
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for sec in range(0, total_sec, FRAME_INTERVAL):
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cap.set(cv2.CAP_PROP_POS_MSEC, sec * 1000)
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ret, frame = cap.read()
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if not ret:
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continue
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elapsed = time.time() - start_time
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eta = (elapsed / (sec / FRAME_INTERVAL + 1)) * (
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total_sec / FRAME_INTERVAL - sec / FRAME_INTERVAL - 1
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)
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# Stage 1: Fast container detection
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containers = find_containers_fast(frame)
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if not containers:
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if sec % 60 == 0:
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print(
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f" [{sec // 60}min/{total_sec // 60}min] No containers | ETA: {eta:.0f}s"
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)
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continue
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print(
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f" [{sec}s] Found {len(containers)} containers ({[c['type'] for c in containers]})"
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)
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# Stage 2: OWL-ViT stamp detection on each container
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for container in containers:
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cx1, cy1, cx2, cy2 = container["bbox"]
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container_img = frame[cy1:cy2, cx1:cx2]
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if container_img.size == 0:
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continue
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ch, cw = container_img.shape[:2]
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# Scale up for better detection
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scale = max(2, 500 // max(ch, cw))
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if scale > 1:
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scaled = cv2.resize(
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container_img, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
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)
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else:
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scaled = container_img
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scaled_pil = Image.fromarray(cv2.cvtColor(scaled, cv2.COLOR_BGR2RGB))
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sh, sw = scaled.shape[:2]
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for term in STAMP_TERMS:
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try:
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inputs = processor(
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text=[[term]], images=scaled_pil, return_tensors="pt"
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)
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with torch.no_grad():
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outputs = model(**inputs)
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target_sizes = torch.Tensor([sh, sw])
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results = processor.post_process_object_detection(
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outputs=outputs,
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target_sizes=target_sizes,
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threshold=MIN_STAMP_SCORE,
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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 > MIN_STAMP_SCORE:
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sx1, sy1, sx2, sy2 = box.tolist()
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orig_w = (sx2 - sx1) / scale
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orig_h = (sy2 - sy1) / scale
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if not (15 < orig_w < 200 and 15 < orig_h < 200):
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continue
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ox1 = cx1 + int(sx1 / scale)
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oy1 = cy1 + int(sy1 / scale)
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ox2 = cx1 + int(sx2 / scale)
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oy2 = cy1 + int(sy2 / scale)
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crop = frame[oy1:oy2, ox1:ox2]
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if crop.size == 0:
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continue
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result = {
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"timestamp": sec,
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"container": container["type"],
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"stamp_term": term,
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"score": s,
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"bbox": [ox1, oy1, ox2, oy2],
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"size": [int(orig_w), int(orig_h)],
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}
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all_results.append(result)
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# Save
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crop_name = f"stamp_{sec}s_{term.replace(' ', '_')}_{s:.2f}.jpg"
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cv2.imwrite(os.path.join(CROPS_DIR, crop_name), crop)
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# Annotate full frame
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cv2.rectangle(frame, (ox1, oy1), (ox2, oy2), (0, 255, 0), 3)
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cv2.putText(
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frame,
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f"{term[:8]} {s:.2f}",
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(ox1, oy1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.6,
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(0, 255, 0),
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2,
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)
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print(
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f" 🎯 {sec}s | {term} | {s:.2f} | {int(orig_w)}x{int(orig_h)}px"
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)
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except Exception:
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pass
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# Save annotated frame if stamps found
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if any(r["timestamp"] == sec for r in all_results):
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ann_path = os.path.join(OUTPUT_DIR, f"annotated_{sec}s.jpg")
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cv2.imwrite(ann_path, frame)
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cap.release()
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# Deduplicate by timestamp
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seen = set()
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unique = []
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for r in all_results:
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ts = r["timestamp"]
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if ts not in seen:
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seen.add(ts)
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unique.append(r)
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unique.sort(key=lambda x: x["score"], reverse=True)
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print(f"\n{'=' * 60}")
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print(f"📊 Found {len(unique)} unique stamp candidates")
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for r in unique:
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print(
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f" 🎯 {r['timestamp']}s | {r['stamp_term']} | {r['score']:.2f} | via: {r['container']}"
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)
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with open(os.path.join(OUTPUT_DIR, "results.json"), "w") as f:
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json.dump(unique, f, indent=2)
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print(f"\n🏁 Done. Crops: {CROPS_DIR}")
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