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/crop_real_stamps.py

@@ -0,0 +1,112 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Crop the newly detected stamps from the specific search.
+"""
+
+import os
+import cv2
+
+UUID = "384b0ff44aaaa1f1"
+OUTPUT_DIR = f"output/{UUID}/florence2_results"
+
+# Coordinates from the specific search result
+# These are placeholders - I need to re-run to get the exact boxes if they weren't printed.
+# Since I saw the logs, I know it found them.
+# But I need the exact coordinates. Let's run a detection script that crops them immediately.
+import torch
+import types
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+
+def patch_model(model):
+    inner_model = model.language_model
+    original_prepare = inner_model.prepare_inputs_for_generation
+
+    def patched_prepare(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        **kwargs,
+    ):
+        is_valid_cache = False
+        if past_key_values is not None:
+            if isinstance(past_key_values, (list, tuple)) and len(past_key_values) > 0:
+                first_layer = past_key_values[0]
+                if first_layer is not None and (
+                    not isinstance(first_layer, (list, tuple)) or len(first_layer) > 0
+                ):
+                    is_valid_cache = True
+
+        if not is_valid_cache:
+            return {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "past_key_values": None,
+                "use_cache": True,
+            }
+        else:
+            return original_prepare(
+                input_ids,
+                past_key_values=past_key_values,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                **kwargs,
+            )
+
+    inner_model.prepare_inputs_for_generation = types.MethodType(
+        patched_prepare, inner_model
+    )
+
+
+IMG_PATH = os.path.join(OUTPUT_DIR, "raw_6846.jpg")
+img_cv = cv2.imread(IMG_PATH)
+image = Image.open(IMG_PATH).convert("RGB")
+
+print("🧠 Reloading model to get coordinates...")
+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, attn_implementation="eager"
+    )
+    patch_model(model)
+
+    prompt = "<OPEN_VOCABULARY_DETECTION>"
+    term = "postage stamp"
+
+    inputs = processor(text=prompt, images=image, 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=prompt, image_size=(image.width, image.height)
+    )
+
+    results = parsed_answer.get("<OPEN_VOCABULARY_DETECTION>", {})
+    bboxes = results.get("bboxes", [])
+
+    if bboxes:
+        print(f"✅ Found {len(bboxes)} stamp(s)!")
+        for i, box in enumerate(bboxes):
+            x1, y1, x2, y2 = map(int, box)
+            print(f"   📍 Box {i + 1}: {box}")
+
+            # Crop
+            crop = img_cv[y1:y2, x1:x2]
+            out_name = f"stamp_crop_{i + 1}.jpg"
+            out_path = os.path.join(OUTPUT_DIR, out_name)
+            cv2.imwrite(out_path, crop)
+            print(f"   💾 Saved to {out_path}")
+    else:
+        print("❌ No stamps found.")
+
+except Exception as e:
+    print(f"❌ Error: {e}")