diff --git a/fastAPI_tarot.py b/fastAPI_tarot.py
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+import os
+import uuid
+import time
+import requests
+import numpy as np
+from typing import Optional
+from contextlib import asynccontextmanager
+
+import torch
+import matplotlib
+matplotlib.use('Agg') 
+import matplotlib.pyplot as plt
+
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Request, Depends, status
+from fastapi.security import APIKeyHeader
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import JSONResponse
+from PIL import Image
+
+from sam3.model_builder import build_sam3_image_model
+from sam3.model.sam3_image_processor import Sam3Processor
+from sam3.visualization_utils import plot_results
+
+# ------------------- 配置与路径 -------------------
+STATIC_DIR = "static"
+RESULT_IMAGE_DIR = os.path.join(STATIC_DIR, "results")
+os.makedirs(RESULT_IMAGE_DIR, exist_ok=True)
+
+# ------------------- API Key 核心配置 (已加固) -------------------
+VALID_API_KEY = "123quant-speed"
+API_KEY_HEADER_NAME = "X-API-Key"
+
+# 定义 Header 认证
+api_key_header = APIKeyHeader(name=API_KEY_HEADER_NAME, auto_error=False)
+
+async def verify_api_key(api_key: Optional[str] = Depends(api_key_header)):
+    """
+    强制验证 API Key
+    """
+    # 1. 检查是否有 Key
+    if not api_key:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing API Key. Please provide it in the header."
+        )
+    # 2. 检查 Key 是否正确
+    if api_key != VALID_API_KEY:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Invalid API Key."
+        )
+    # 3. 验证通过
+    return True
+
+# ------------------- 生命周期管理 -------------------
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print("="*40)
+    print("✅ API Key 保护已激活")
+    print(f"✅ 有效 Key: {VALID_API_KEY}")
+    print("="*40)
+    
+    print("正在加载 SAM3 模型到 GPU...")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if not torch.cuda.is_available():
+        print("警告: 未检测到 GPU，将使用 CPU，速度会较慢。")
+
+    model = build_sam3_image_model()
+    model = model.to(device)
+    model.eval()
+
+    processor = Sam3Processor(model)
+
+    app.state.model = model
+    app.state.processor = processor
+    app.state.device = device
+    
+    print(f"模型加载完成，设备: {device}")
+    
+    yield 
+    
+    print("正在清理资源...")
+
+# ------------------- FastAPI 初始化 -------------------
+app = FastAPI(
+    lifespan=lifespan, 
+    title="SAM3 Segmentation API",
+    description="## 🔒 受 API Key 保护\n请点击右上角 **Authorize** 并输入: `123quant-speed`",
+)
+
+# 手动添加 OpenAPI 安全配置，让 Docs 里的锁头生效
+app.openapi_schema = None
+def custom_openapi():
+    if app.openapi_schema:
+        return app.openapi_schema
+    from fastapi.openapi.utils import get_openapi
+    openapi_schema = get_openapi(
+        title=app.title,
+        version=app.version,
+        description=app.description,
+        routes=app.routes,
+    )
+    # 定义安全方案
+    openapi_schema["components"]["securitySchemes"] = {
+        "APIKeyHeader": {
+            "type": "apiKey",
+            "in": "header",
+            "name": API_KEY_HEADER_NAME,
+        }
+    }
+    # 为所有路径应用安全要求
+    for path in openapi_schema["paths"]:
+        for method in openapi_schema["paths"][path]:
+            openapi_schema["paths"][path][method]["security"] = [{"APIKeyHeader": []}]
+    app.openapi_schema = openapi_schema
+    return app.openapi_schema
+
+app.openapi = custom_openapi
+
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+# ------------------- 辅助函数 -------------------
+def load_image_from_url(url: str) -> Image.Image:
+    try:
+        headers = {'User-Agent': 'Mozilla/5.0'}
+        response = requests.get(url, headers=headers, stream=True, timeout=10)
+        response.raise_for_status()
+        image = Image.open(response.raw).convert("RGB")
+        return image
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"无法下载图片: {str(e)}")
+
+def crop_and_save_objects(image: Image.Image, masks, boxes, output_dir: str = RESULT_IMAGE_DIR) -> list[str]:
+    """
+    根据 mask 和 box 裁剪出独立的对象图片 (保留透明背景)
+    """
+    saved_files = []
+    # Convert image to numpy array
+    img_arr = np.array(image) # RGB (H, W, 3)
+    
+    for i, (mask, box) in enumerate(zip(masks, boxes)):
+        # Handle tensor/numpy conversions
+        if isinstance(mask, torch.Tensor):
+            mask_np = mask.cpu().numpy().squeeze()
+        else:
+            mask_np = mask.squeeze()
+            
+        if isinstance(box, torch.Tensor):
+            box_np = box.cpu().numpy()
+        else:
+            box_np = box
+            
+        # Get coordinates
+        x1, y1, x2, y2 = map(int, box_np)
+        
+        # Ensure coordinates are within bounds
+        x1 = max(0, x1)
+        y1 = max(0, y1)
+        x2 = min(image.width, x2)
+        y2 = min(image.height, y2)
+        
+        # Check valid crop
+        if x2 <= x1 or y2 <= y1:
+            continue
+            
+        # Create Alpha channel from mask (0 or 255)
+        # mask_np is boolean or float 0..1. If boolean, *255 -> 0/255.
+        alpha = (mask_np * 255).astype(np.uint8)
+        
+        # Combine RGB and Alpha
+        rgba = np.dstack((img_arr, alpha))
+        
+        # Convert back to PIL for cropping
+        pil_rgba = Image.fromarray(rgba)
+        
+        # Crop to bounding box
+        cropped = pil_rgba.crop((x1, y1, x2, y2))
+        
+        # Save
+        filename = f"tarot_{uuid.uuid4().hex}_{i}.png" # Use png for transparency
+        save_path = os.path.join(output_dir, filename)
+        cropped.save(save_path)
+        saved_files.append(filename)
+        
+    return saved_files
+
+def generate_and_save_result(image: Image.Image, inference_state, output_dir: str = RESULT_IMAGE_DIR) -> str:
+    filename = f"seg_{uuid.uuid4().hex}.jpg"
+    save_path = os.path.join(output_dir, filename)
+    plot_results(image, inference_state)
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    return filename
+
+# ------------------- API 接口 (强制依赖验证) -------------------
+@app.post("/segment", dependencies=[Depends(verify_api_key)])
+async def segment(
+    request: Request,
+    prompt: str = Form(...),
+    file: Optional[UploadFile] = File(None),
+    image_url: Optional[str] = Form(None)
+):
+    if not file and not image_url:
+        raise HTTPException(status_code=400, detail="必须提供 file (图片文件) 或 image_url (图片链接)")
+
+    try:
+        if file:
+            image = Image.open(file.file).convert("RGB")
+        elif image_url:
+            image = load_image_from_url(image_url)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"图片解析失败: {str(e)}")
+
+    processor = request.app.state.processor
+
+    try:
+        inference_state = processor.set_image(image)
+        output = processor.set_text_prompt(state=inference_state, prompt=prompt)
+        masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"模型推理错误: {str(e)}")
+
+    try:
+        filename = generate_and_save_result(image, inference_state)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"绘图保存错误: {str(e)}")
+
+    file_url = request.url_for("static", path=f"results/{filename}")
+
+    return JSONResponse(content={
+        "status": "success",
+        "result_image_url": str(file_url),
+        "detected_count": len(masks),
+        "scores": scores.tolist() if torch.is_tensor(scores) else scores
+    })
+
+@app.post("/segment_tarot", dependencies=[Depends(verify_api_key)])
+async def segment_tarot(
+    request: Request,
+    file: Optional[UploadFile] = File(None),
+    image_url: Optional[str] = Form(None),
+    expected_count: int = Form(3)
+):
+    """
+    塔罗牌分割专用接口
+    1. 检测是否包含指定数量的塔罗牌 (默认为 3)
+    2. 如果是，分别抠出这些牌并返回
+    """
+    if not file and not image_url:
+        raise HTTPException(status_code=400, detail="必须提供 file (图片文件) 或 image_url (图片链接)")
+
+    try:
+        if file:
+            image = Image.open(file.file).convert("RGB")
+        elif image_url:
+            image = load_image_from_url(image_url)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"图片解析失败: {str(e)}")
+
+    processor = request.app.state.processor
+
+    try:
+        inference_state = processor.set_image(image)
+        # 固定 Prompt 检测塔罗牌
+        output = processor.set_text_prompt(state=inference_state, prompt="tarot card")
+        masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"模型推理错误: {str(e)}")
+
+    # 核心逻辑：判断数量
+    detected_count = len(masks)
+    
+    # 创建本次请求的独立文件夹 (时间戳_UUID前8位)
+    request_id = f"{int(time.time())}_{uuid.uuid4().hex[:8]}"
+    output_dir = os.path.join(RESULT_IMAGE_DIR, request_id)
+    os.makedirs(output_dir, exist_ok=True)
+
+    if detected_count != expected_count:
+        # 保存一张图用于调试/反馈
+        try:
+            filename = generate_and_save_result(image, inference_state, output_dir=output_dir)
+            file_url = request.url_for("static", path=f"results/{request_id}/{filename}")
+        except:
+            file_url = None
+        
+        return JSONResponse(
+            status_code=400, 
+            content={
+                "status": "failed",
+                "message": f"检测到 {detected_count} 个目标，需要严格的 {expected_count} 张塔罗牌。请调整拍摄角度或背景。",
+                "detected_count": detected_count,
+                "debug_image_url": str(file_url) if file_url else None
+            }
+        )
+
+    # 数量正确，执行抠图
+    try:
+        filenames = crop_and_save_objects(image, masks, boxes, output_dir=output_dir)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"抠图处理错误: {str(e)}")
+
+    # 生成 URL 列表
+    card_urls = [str(request.url_for("static", path=f"results/{request_id}/{fname}")) for fname in filenames]
+    
+    # 生成整体效果图
+    try:
+        main_filename = generate_and_save_result(image, inference_state, output_dir=output_dir)
+        main_file_url = str(request.url_for("static", path=f"results/{request_id}/{main_filename}"))
+    except:
+        main_file_url = None
+
+    return JSONResponse(content={
+        "status": "success",
+        "message": f"成功识别并分割 {expected_count} 张塔罗牌",
+        "tarot_cards": card_urls,
+        "full_visualization": main_file_url,
+        "scores": scores.tolist() if torch.is_tensor(scores) else scores
+    })
+
+if __name__ == "__main__":
+    import uvicorn
+    # 注意：如果你的文件名不是 fastAPI_tarot.py，请修改下面第一个参数
+    uvicorn.run(
+        "fastAPI_tarot:app", 
+        host="127.0.0.1",
+        port=55600,
+        proxy_headers=True,
+        forwarded_allow_ips="*",
+        reload=False # 生产环境建议关闭 reload，确保代码完全重载
+    )
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