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/deep_analysis_112_36.py

@@ -0,0 +1,161 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Deep Analysis of 112:36 Frame
+1. Detailed Captioning
+2. Search for "Envelope" and "Hand holding object"
+"""
+
+import os
+import cv2
+import torch
+import types
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+UUID = "384b0ff44aaaa1f1"
+BASE_DIR = f"output/{UUID}/florence2_results"
+IMG_NAME = "scan_6756.jpg"  # 112:36
+IMG_PATH = os.path.join(BASE_DIR, IMG_NAME)
+
+
+# Patch for compatibility
+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
+    )
+
+
+print(f"📷 Loading image: {IMG_PATH}")
+if not os.path.exists(IMG_PATH):
+    print("❌ Image not found.")
+    exit()
+
+image = Image.open(IMG_PATH).convert("RGB")
+
+print("🧠 Loading Florence-2 model...")
+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)
+
+    # 1. Detailed Caption
+    print("\n📝 Generating Detailed Caption...")
+    prompt = "<DETAILED_CAPTION>"
+    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=True)[0]
+    print(f"🗣️ Caption: {generated_text}")
+
+    # 2. Object Detection for specific items
+    search_terms = ["envelope", "letter", "hand holding paper", "stamp", "small paper"]
+    img_cv = cv2.imread(IMG_PATH)
+
+    for term in search_terms:
+        print(f"\n🔍 Detecting '{term}'...")
+        prompt_ovd = "<OPEN_VOCABULARY_DETECTION>"
+        # Note: OVD usually takes text input differently or relies on generation.
+        # For Florence-2, OVD often requires text_input in processor or prompt format.
+        # We will try the standard way first.
+
+        inputs = processor(text=prompt_ovd, 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]
+
+        try:
+            parsed_answer = processor.post_process_generation(
+                generated_text, task=prompt_ovd, image_size=(image.width, image.height)
+            )
+            results = parsed_answer.get("<OPEN_VOCABULARY_DETECTION>", {})
+            bboxes = results.get("bboxes", [])
+            labels = results.get("bboxes_labels", [])
+
+            if bboxes:
+                print(f"   ✅ Found '{term}': {labels}")
+                for i, (box, label) in enumerate(zip(bboxes, labels)):
+                    if term.lower() in label.lower() or (
+                        term == "envelope" and "paper" in label.lower()
+                    ):
+                        x1, y1, x2, y2 = map(int, box)
+                        print(f"      📍 Box: ({x1},{y1}) -> ({x2},{y2})")
+
+                        # Crop
+                        crop = img_cv[y1:y2, x1:x2]
+                        crop_path = os.path.join(
+                            BASE_DIR, f"crop_deep_{term.replace(' ', '_')}_{i}.jpg"
+                        )
+                        cv2.imwrite(crop_path, crop)
+
+                        # Draw
+                        cv2.rectangle(img_cv, (x1, y1), (x2, y2), (0, 255, 0), 3)
+                        cv2.putText(
+                            img_cv,
+                            label,
+                            (x1, y1 - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX,
+                            1,
+                            (0, 255, 0),
+                            2,
+                        )
+            else:
+                print(f"   ❌ Not found.")
+        except Exception as e:
+            print(f"   ⚠️ Error: {e}")
+
+    res_path = os.path.join(BASE_DIR, "deep_analysis_result.jpg")
+    cv2.imwrite(res_path, img_cv)
+    print(f"\n🎨 Result saved to {res_path}")
+
+except Exception as e:
+    print(f"❌ Error: {e}")