feat: update Python processors and add utility scripts

- Update ASR, face, OCR, pose processors
- Add release pre-flight check script
- Add synonym generation, chunk processing scripts
- Add face recognition, stamp search utilities

scripts/scan_full_video_stamps.py

@@ -0,0 +1,76 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Scan full video frames for stamp-like regions (Blue+Red rectangles)
+"""
+
+import cv2
+import numpy as np
+import os
+import glob
+
+UUID = "384b0ff44aaaa1f1"
+BASE_DIR = f"output/{UUID}/full_video_scans"
+OUTPUT_DIR = f"output/{UUID}/stamp_candidates_full"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+print("🔍 Scanning full video frames for stamps...")
+
+frames = sorted(glob.glob(os.path.join(BASE_DIR, "frame_*.jpg")))
+print(f"Found {len(frames)} frames to scan.")
+
+total_candidates = 0
+
+for frame_path in frames:
+    img = cv2.imread(frame_path)
+    if img is None:
+        continue
+
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # Detect Blue regions
+    blue_mask = cv2.inRange(hsv, np.array([90, 40, 40]), np.array([130, 255, 255]))
+
+    # Detect Red regions
+    red_mask1 = cv2.inRange(hsv, np.array([0, 40, 40]), np.array([10, 255, 255]))
+    red_mask2 = cv2.inRange(hsv, np.array([170, 40, 40]), np.array([179, 255, 255]))
+    red_mask = red_mask1 + red_mask2
+
+    # Find contours in blue areas
+    contours, _ = cv2.findContours(
+        blue_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    frame_candidates = 0
+
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area < 200 or area > 30000:
+            continue
+
+        x, y, w, h = cv2.boundingRect(contour)
+        aspect_ratio = w / h if h > 0 else 0
+
+        # Stamps are roughly rectangular
+        if aspect_ratio < 0.5 or aspect_ratio > 2.0:
+            continue
+
+        # Check red content inside
+        roi_red = red_mask[y : y + h, x : x + w]
+        red_pixels = cv2.countNonZero(roi_red)
+        red_ratio = red_pixels / (w * h) if w * h > 0 else 0
+
+        if red_ratio > 0.08:
+            frame_candidates += 1
+            total_candidates += 1
+
+            # Save crop
+            crop = img[y : y + h, x : x + w]
+            basename = os.path.basename(frame_path).replace(".jpg", "")
+            crop_name = f"stamp_{basename}_{x}_{y}_red{int(red_ratio * 100)}.jpg"
+            cv2.imwrite(os.path.join(OUTPUT_DIR, crop_name), crop)
+
+    if frame_candidates > 0:
+        print(f"  📍 {os.path.basename(frame_path)}: {frame_candidates} candidates")
+
+print(f"\n🏁 Done. Found {total_candidates} total candidates in {OUTPUT_DIR}")