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/test_florence2_stamps.py

@@ -0,0 +1,83 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Test Florence-2 for "Stamps" Detection
+Florence-2 is superior to OWL-ViT for small objects and detailed description.
+"""
+
+import os
+import json
+import cv2
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+UUID = "384b0ff44aaaa1f1"
+VIDEO_PATH = f"output/{UUID}/{UUID}.mp4"
+OUTPUT_DIR = f"output/{UUID}/florence2_results"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# Frame where "stamp" is heavily discussed
+TIMESTAMP = 6846.0
+
+print(f"📽️ Extracting frame at {TIMESTAMP}s...")
+cap = cv2.VideoCapture(VIDEO_PATH)
+cap.set(cv2.CAP_PROP_POS_MSEC, TIMESTAMP * 1000)
+ret, frame = cap.read()
+cap.release()
+
+if not ret:
+    print("❌ Failed to read frame.")
+    exit()
+
+# Save raw frame
+raw_path = os.path.join(OUTPUT_DIR, f"raw_{int(TIMESTAMP)}.jpg")
+cv2.imwrite(raw_path, frame)
+print(f"💾 Raw frame saved.")
+
+image_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+print("🧠 Loading Florence-2 model (this may take a moment)...")
+try:
+    processor = AutoProcessor.from_pretrained(
+        "microsoft/Florence-2-base", trust_remote_code=True
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        "microsoft/Florence-2-base", trust_remote_code=True
+    )
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    exit()
+
+# Test 1: Open Vocabulary Detection
+print("🔍 Testing Open Vocabulary Detection for 'stamp'...")
+prompt = "stamp"
+inputs = processor(text=prompt, images=image_pil, return_tensors="pt")
+
+generated_ids = model.generate(
+    input_ids=inputs["input_ids"],
+    pixel_values=inputs["pixel_values"],
+    max_new_tokens=1024,
+    num_beams=3,
+)
+
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+parsed_answer = processor.post_process_generation(
+    generated_text,
+    task="<OPEN_VOCABULARY_DETECTION>",
+    image_size=(image_pil.width, image_pil.height),
+)
+
+print(f"📝 Florence-2 Result: {parsed_answer}")
+
+# Test 2: Detailed Caption (To see if it notices the stamp in context)
+print("📝 Testing Detailed Caption...")
+inputs = processor(text="<DETAILED_CAPTION>", images=image_pil, return_tensors="pt")
+generated_ids = model.generate(
+    input_ids=inputs["input_ids"],
+    pixel_values=inputs["pixel_values"],
+    max_new_tokens=1024,
+)
+caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(f"📝 Caption: {caption}")
+
+print("🏁 Done.")