COLAB_GUIDE.md

# Google Colab 雲端高精轉換說明與程式碼

本指南提供在 Google Colab (免費 GPU) 上運行 **Qwen2.5-VL 大模型視覺定位** 與 **LaMa GPU 影像修復** 的完整程式碼。

您只需在 Google Colab 建立一個新筆記本，將硬體加速器設為 **T4 GPU**，並將下方的程式碼貼入儲存格中執行即可。

---

## 1. Colab 儲存格一：安裝依賴庫 (GPU 版)

```bash
!pip install -q transformers diffusers accelerate bitsandbytes sentencepiece pdf2image opencv-python numpy pillow
!apt-get install -y -qq poppler-utils
```

---

## 2. Colab 儲存格二：雲端核心處理腳本

```python
import os
import json
import uuid
import shutil
import zipfile
import torch
import cv2
import numpy as np
from PIL import Image
from pdf2image import convert_from_path
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfig

# 1. 初始化 Qwen2.5-VL-7B-Instruct 4-bit 量化版 (節省 VRAM 避免 OOM)
print("載入 Qwen2.5-VL 大模型中...")
model_id = "Qwen/Qwen2.5-VL-7B-Instruct"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

# 2. 載入 LaMa GPU Inpainting 模型 (利用 Diffusers 庫)
from diffusers import AutoPipelineForInpainting
print("載入 LaMa Inpainting GPU 模型中...")
pipe = AutoPipelineForInpainting.from_pretrained(
    "diffusers/stable-diffusion-xl-1.0-inpainting-base", 
    torch_dtype=torch.float16, 
    variant="fp16"
).to("cuda")

def process_pdf_to_zip(pdf_path, output_zip_path):
    temp_dir = "./colab_temp"
    os.makedirs(temp_dir, exist_ok=True)
    
    # PDF 轉圖片
    print("正在將 PDF 轉換為高清圖片...")
    images = convert_from_path(pdf_path, dpi=150)
    
    metadata = {
        "project_name": "NotebookLM_Slide_Conversion",
        "total_slides": len(images),
        "slides": []
    }
    
    for idx, img in enumerate(images):
        slide_id = f"slide_{idx}"
        img_name = f"{slide_id}_orig.png"
        clean_img_name = f"{slide_id}_clean.png"
        
        img_path = os.path.join(temp_dir, img_name)
        img.save(img_path, "PNG")
        
        w_px, h_px = img.size
        print(f"處理第 {idx+1}/{len(images)} 頁 (尺寸: {w_px}x{h_px})...")
        
        # 呼叫 Qwen2.5-VL 進行 Visual Grounding (視覺定位)
        # 這裡會給大模型下達 Prompt 提取文字內容與相對於圖片的 0-1000 座標
        prompt = (
            "你是一個精準的簡報版面分析專家。請分析這張簡報圖片，找出所有的文字區塊。請精確定位每個文字區塊的 Bounding Box，並輸出其代表的文字內容。請嚴格以下列 JSON 格式輸出，不要包含任何額外的 Markdown 標籤或說明文字：\n"
            "{\n"
            "  \"width\": 圖片總寬度,\n"
            "  \"height\": 圖片總高度,\n"
            "  \"blocks\": [\n"
            "    {\"text\": \"文字內容1\", \"box_2d\": [ymin, xmin, ymax, xmax], \"type\": \"title/content\"}\n"
            "  ]\n"
            "}\n"
            "注意：座標 [ymin, xmin, ymax, xmax] 必須是相對於圖片寬高的 0-1000 正規化數值。"
        )
        
        # 使用 transformers 處理圖像與 prompt
        inputs = processor(text=[prompt], images=[img], padding=True, return_tensors="pt").to("cuda")
        generated_ids = model.generate(**inputs, max_new_tokens=1024)
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        
        # 解析 JSON 結果
        try:
            # 去除 markdown 標記
            cleaned_text = generated_text.strip()
            if cleaned_text.startswith("```json"):
                cleaned_text = cleaned_text[7:]
            if cleaned_text.endswith("```"):
                cleaned_text = cleaned_text[:-3]
            ocr_data = json.loads(cleaned_text.strip())
        except Exception as e:
            print(f"解析 JSON 失敗，嘗試後備機制: {e}")
            ocr_data = {"width": w_px, "height": h_px, "blocks": []}
            
        # 建立去字遮罩 (Mask)
        mask = np.zeros((h_px, w_px), dtype=np.uint8)
        blocks_list = []
        
        for block in ocr_data.get("blocks", []):
            ymin, xmin, ymax, xmax = block["box_2d"]
            
            # 將 0-1000 歸一化座標轉回實際像素
            py_min = int(ymin * h_px / 1000)
            px_min = int(xmin * w_px / 1000)
            py_max = int(ymax * h_px / 1000)
            px_max = int(xmax * w_px / 1000)
            
            # 依文字高度自適應膨脹
            box_h = py_max - py_min
            dilation = max(2, int(round(box_h * 0.08)))
            
            px_min = max(0, px_min - dilation)
            py_min = max(0, py_min - dilation)
            px_max = min(w_px, px_max + dilation)
            py_max = min(h_px, py_max + dilation)
            
            cv2.rectangle(mask, (px_min, py_min), (px_max, py_max), 255, -1)
            
            blocks_list.append({
                "text": block["text"],
                "box_2d": [ymin, xmin, ymax, xmax],
                "type": block.get("type", "content")
            })
            
        # 執行 GPU Inpainting 去字修補
        # 轉換為 PIL Image 用於 diffusers
        pil_mask = Image.fromarray(mask)
        # 用穩定擴散 (SDXL/LaMa) 修復背景
        clean_img = pipe(prompt="clean slide background", image=img, mask_image=pil_mask).images[0]
        
        clean_img_path = os.path.join(temp_dir, clean_img_name)
        clean_img.save(clean_img_path, "PNG")
        
        metadata["slides"].append({
            "slide_index": idx,
            "bg_image_name": clean_img_name,
            "width": w_px,
            "height": h_px,
            "blocks": blocks_list
        })
        
    # 寫入 metadata.json
    with open(os.path.join(temp_dir, "metadata.json"), "w", encoding="utf-8") as f:
        json.dump(metadata, f, ensure_ascii=False, indent=2)
        
    # 打包為 ZIP
    with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_file:
        for root, dirs, files in os.walk(temp_dir):
            for file in files:
                if file.endswith("clean.png") or file == "metadata.json":
                    file_path = os.path.join(root, file)
                    zip_file.write(file_path, os.path.basename(file_path))
                    
    # 清理暫存資料夾
    shutil.rmtree(temp_dir, ignore_errors=True)
    print(f"轉換打包成功！壓縮包已儲存至: {output_zip_path}")

# 執行範例：
# process_pdf_to_zip("input.pdf", "Pack.zip")
```