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/search_blue_stamp.py

@@ -0,0 +1,100 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Find Inverted Jenny Stamp (Blue Rectangle in Hands)
+"""
+
+import cv2
+import numpy as np
+import os
+
+UUID = "384b0ff44aaaa1f1"
+BASE_DIR = f"output/{UUID}/florence2_results"
+
+# Frames to check
+FRAMES = [
+    "scan_6756.jpg",  # 112:36
+    "scan_6763.jpg",  # 112:43
+    "scan_6790.jpg",  # 113:10
+    "scan_6813.jpg",  # 113:33
+    "scan_6832.jpg",  # 113:52
+]
+
+print("🔍 Searching for SMALL BLUE RECTANGLES in hands (Inverted Jenny)...")
+
+for frame_name in FRAMES:
+    img_path = os.path.join(BASE_DIR, frame_name)
+    if not os.path.exists(img_path):
+        continue
+
+    img = cv2.imread(img_path)
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # 1. Hand Detection (Skin Tone) - Broad range
+    skin_mask = cv2.inRange(hsv, np.array([0, 20, 40]), np.array([25, 150, 255]))
+
+    # Clean up mask
+    kernel = np.ones((5, 5), np.uint8)
+    skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_CLOSE, kernel)
+    skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_OPEN, kernel)
+
+    # 2. Blue Detection (Stamp Background)
+    # Inverted Jenny is Blue and Red. We look for the Blue part.
+    blue_mask = cv2.inRange(hsv, np.array([90, 40, 40]), np.array([130, 255, 255]))
+
+    # 3. Intersection: Blue INSIDE/NEAR Hands
+    # We dilate the skin mask to include things held IN the hand
+    skin_dilated = cv2.dilate(skin_mask, kernel, iterations=3)
+
+    # Find blue things touching hands
+    stamp_candidate_mask = cv2.bitwise_and(blue_mask, skin_dilated)
+
+    # 4. Find contours in the intersection
+    contours, _ = cv2.findContours(
+        stamp_candidate_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    print(f"\n🎞️ Scanning {frame_name}...")
+    found_count = 0
+
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        area = cv2.contourArea(cnt)
+
+        # Stamp size: Small rectangle (bigger than a dot, smaller than a face)
+        # Area: 200 - 5000 pixels
+        if 200 < area < 5000:
+            aspect_ratio = float(w) / h
+
+            # Check if it looks like a stamp (rectangular, aspect ratio 0.8 - 1.5 roughly)
+            if 0.6 < aspect_ratio < 1.8:
+                found_count += 1
+                print(f"   ✅ Candidate: Area={int(area)}, Pos=({x},{y}), Size={w}x{h}")
+
+                # Crop with padding
+                pad = 5
+                crop = img[
+                    max(0, y - pad) : min(img.shape[0], y + h + pad),
+                    max(0, x - pad) : min(img.shape[1], x + w + pad),
+                ]
+                crop_path = os.path.join(
+                    BASE_DIR, f"blue_stamp_{frame_name}_{x}_{y}.jpg"
+                )
+                cv2.imwrite(crop_path, crop)
+
+                # Draw on main image
+                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)
+                cv2.putText(
+                    img,
+                    f"BLUE STAMP?",
+                    (x, y - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6,
+                    (0, 255, 0),
+                    2,
+                )
+
+    if found_count == 0:
+        print("   ❌ No blue stamp candidates found in hands.")
+    else:
+        res_path = os.path.join(BASE_DIR, f"result_blue_{frame_name}")
+        cv2.imwrite(res_path, img)