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/find_blue_stamp_opencv.py

@@ -0,0 +1,101 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Find BLUE stamps (Inverted Jenny) or Envelopes
+Filter: Blue Color + Small/Medium Size
+"""
+
+import cv2
+import numpy as np
+import os
+
+UUID = "384b0ff44aaaa1f1"
+BASE_DIR = f"output/{UUID}/florence2_results"
+
+# Keyframes to check
+FRAMES = [
+    "scan_6751.jpg",  # 112:31
+    "scan_6755.jpg",  # 112:35
+    "scan_6756.jpg",  # 112:36 (Dialogue: "Give me the stamp")
+    "scan_6759.jpg",  # 112:39
+]
+
+print("🔍 Analyzing Keyframes for BLUE Stamps...")
+
+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)
+    h, w, _ = img.shape
+
+    # Convert to HSV
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # Blue Mask (Hue ~100-130)
+    # Adjusting for various shades of blue
+    mask1 = cv2.inRange(hsv, np.array([90, 50, 50]), np.array([130, 255, 255]))
+
+    # Optional: Also look for White/Paper (Envelope)
+    # mask2 = cv2.inRange(hsv, np.array([0, 0, 200]), np.array([180, 30, 255]))
+    # mask = mask1 + mask2
+    mask = mask1
+
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    print(f"\n🎞️ Scanning {frame_name} for Blue objects...")
+
+    found_count = 0
+    for cnt in contours:
+        area = cv2.contourArea(cnt)
+
+        # FILTER 1: Area Size
+        # We want something stamp-sized or envelope-sized
+        # Stamp: ~100 - 5000 px
+        # Envelope: ~1000 - 20000 px
+        if area < 100 or area > 20000:
+            continue
+
+        # FILTER 2: Aspect Ratio (Rectangular)
+        x, y, w_box, h_box = cv2.boundingRect(cnt)
+        aspect_ratio = float(w_box) / h_box
+
+        # Check if it looks like a rectangle (0.5 to 2.0 ratio roughly)
+        # But stamps can be small. Let's just check area mostly.
+
+        # Filter out very long thin lines (likely text)
+        if w_box < 5 or h_box < 5:
+            continue
+
+        # Filter out very large backgrounds
+        if w_box > 300 and h_box > 300:
+            continue
+
+        print(
+            f"   ✅ Found Blue Candidate: Area={int(area)}, Size={w_box}x{h_box}, Pos=({x},{y})"
+        )
+
+        # Draw
+        cv2.rectangle(img, (x, y), (x + w_box, y + h_box), (0, 255, 0), 2)
+        cv2.putText(
+            img,
+            f"BLUE? ({int(area)})",
+            (x, y - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (0, 255, 0),
+            1,
+        )
+
+        # Crop
+        crop = img[y : y + h_box, x : x + w_box]
+        crop_path = os.path.join(BASE_DIR, f"crop_blue_{frame_name}_{x}_{y}.jpg")
+        cv2.imwrite(crop_path, crop)
+
+        found_count += 1
+
+    if found_count > 0:
+        res_path = os.path.join(BASE_DIR, f"result_blue_{frame_name}")
+        cv2.imwrite(res_path, img)
+    else:
+        print("   ❌ No blue candidates found.")