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
2026-04-30 15:07:49 +08:00
parent f4697396e4
commit 8f05a7c188
256 changed files with 60505 additions and 299 deletions
--- a/scripts/detect_objects_keyframes.py
+++ b/scripts/detect_objects_keyframes.py
@@ -0,0 +1,142 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Detect and Crop Envelopes/Objects in Keyframes
+"""
+
+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"
+
+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
+]
+
+
+# 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("🧠 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)
+
+    for img_name in FRAMES:
+        img_path = os.path.join(BASE_DIR, img_name)
+        if not os.path.exists(img_path):
+            continue
+
+        print(f"\n🔍 Scanning {img_name}...")
+        image = Image.open(img_path).convert("RGB")
+        img_cv = cv2.imread(img_path)
+
+        prompt = "<OPEN_VOCABULARY_DETECTION>"
+        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]
+
+        try:
+            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", [])
+            labels = results.get("bboxes_labels", [])
+
+            print(f"   📦 Raw Output: {results}")
+
+            if bboxes:
+                print(f"   ✅ Found {len(bboxes)} objects!")
+                for i, (box, label) in enumerate(zip(bboxes, labels)):
+                    x1, y1, x2, y2 = map(int, box)
+                    print(
+                        f"      📍 Object {i}: '{label}' at ({x1},{y1}) -> ({x2},{y2})"
+                    )
+
+                    # Draw and Crop
+                    cv2.rectangle(img_cv, (x1, y1), (x2, y2), (0, 255, 0), 3)
+                    cv2.putText(
+                        img_cv,
+                        label,
+                        (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        0.8,
+                        (0, 255, 0),
+                        2,
+                    )
+
+                    crop = img_cv[y1:y2, x1:x2]
+                    crop_path = os.path.join(
+                        BASE_DIR, f"crop_obj_{img_name.replace('.jpg', '')}_{i}.jpg"
+                    )
+                    cv2.imwrite(crop_path, crop)
+            else:
+                print("   ❌ No objects detected.")
+
+        except Exception as e:
+            print(f"   ⚠️ Error: {e}")
+
+except Exception as e:
+    print(f"❌ Error: {e}")