FastAPI

fastAPI_main.py

@@ -25,48 +25,60 @@ STATIC_DIR = "static"
 RESULT_IMAGE_DIR = os.path.join(STATIC_DIR, "results")
 os.makedirs(RESULT_IMAGE_DIR, exist_ok=True)
 
+# ------------------- 核心修改：图片压缩函数 -------------------
+def compress_image(image: Image.Image, max_size: int = 1920, quality: int = 85) -> Image.Image:
+    """
+    如果图片边长超过 max_size，则按比例压缩。
+    :param image: PIL Image 对象
+    :param max_size: 图片最大边长 (宽或高)
+    :param quality: 仅用于保存时的参考，这里主要做尺寸压缩
+    :return: 压缩后的 PIL Image 对象
+    """
+    width, height = image.size
+    
+    # 如果图片本身就很小，直接返回
+    if width <= max_size and height <= max_size:
+        return image
+
+    # 计算缩放比例
+    if width > height:
+        new_width = max_size
+        new_height = int(height * (max_size / width))
+    else:
+        new_height = max_size
+        new_width = int(width * (max_size / height))
+    
+    # 使用 LANCZOS 滤镜进行高质量下采样
+    print(f"压缩图片: {width}x{height} -> {new_width}x{new_height}")
+    return image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
 # ------------------- 生命周期管理 -------------------
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    """
-    FastAPI 生命周期管理器：在服务启动时加载模型，关闭时清理资源
-    """
     print("正在加载 SAM3 模型到 GPU...")
-    
-    # 1. 检测设备
     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。")
 
-    # 2. 加载模型 (全局单例)
     model = build_sam3_image_model()
     model = model.to(device)
-    model.eval() # 切换到评估模式
-
-    # 3. 初始化 Processor
+    model.eval()
     processor = Sam3Processor(model)
 
-    # 4. 存入 app.state 供全局访问
     app.state.model = model
     app.state.processor = processor
     app.state.device = device
     
     print(f"模型加载完成，设备: {device}")
-    
-    yield  # 服务运行中...
-    
-    # 清理资源 (如果需要)
+    yield
     print("正在清理资源...")
 
 # ------------------- FastAPI 初始化 -------------------
 app = FastAPI(lifespan=lifespan, title="SAM3 Segmentation API")
-
-# 挂载静态文件目录，用于通过 URL 访问生成的图片
 app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
 
 # ------------------- 辅助函数 -------------------
 def load_image_from_url(url: str) -> Image.Image:
-    """从网络 URL 下载图片"""
     try:
         headers = {'User-Agent': 'Mozilla/5.0'}
         response = requests.get(url, headers=headers, stream=True, timeout=10)
@@ -77,18 +89,11 @@ def load_image_from_url(url: str) -> Image.Image:
         raise HTTPException(status_code=400, detail=f"无法下载图片: {str(e)}")
 
 def generate_and_save_result(image: Image.Image, inference_state) -> str:
-    """生成可视化结果图并保存，返回文件名"""
-    # 生成唯一文件名防止冲突
     filename = f"seg_{uuid.uuid4().hex}.jpg"
     save_path = os.path.join(RESULT_IMAGE_DIR, filename)
-
-    # 绘图 (复用你提供的逻辑)
     plot_results(image, inference_state)
-    
-    # 保存
     plt.savefig(save_path, dpi=150, bbox_inches='tight')
-    plt.close() # 务必关闭，防止内存泄漏
-
+    plt.close()
     return filename
 
 # ------------------- API 接口 -------------------
@@ -99,44 +104,41 @@ async def segment(
     file: Optional[UploadFile] = File(None),
     image_url: Optional[str] = Form(None)
 ):
-    """
-    接收图片 (文件上传 或 URL) 和 文本提示词，返回分割后的图片 URL。
-    """
-    
-    # 1. 校验输入
     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)")
+        raise HTTPException(status_code=400, detail="必须提供 file 或 image_url")
 
-    # 2. 获取图片对象
+    # 1. 获取图片对象
     try:
         if file:
             image = Image.open(file.file).convert("RGB")
         elif image_url:
             image = load_image_from_url(image_url)
+        
+        # ========== 关键修改位置 ==========
+        # 在送入模型前，强制压缩图片
+        image = compress_image(image, max_size=1920) 
+        # ===================================
+        
     except Exception as e:
         raise HTTPException(status_code=400, detail=f"图片解析失败: {str(e)}")
 
-    # 3. 获取模型
+    # 2. 获取模型
     processor = request.app.state.processor
 
-    # 4. 执行推理
+    # 3. 执行推理
     try:
-        # 这一步内部应该已经由 Sam3Processor 处理了 GPU 张量转移
         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)}")
 
-    # 5. 生成可视化并保存
+    # 4. 生成可视化并保存
     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)}")
 
-    # 6. 构建返回 URL
-    # request.url_for 会自动根据当前域名生成正确的访问链接
     file_url = request.url_for("static", path=f"results/{filename}")
 
     return JSONResponse(content={
@@ -148,12 +150,10 @@ async def segment(
 
 if __name__ == "__main__":
     import uvicorn
-    
-    # 使用 Python 函数参数的方式传递配置
     uvicorn.run(
-        "fastAPI_main:app",  # 注意：这里要改成你的文件名:app对象名
+        "fastAPI_main:app", 
         host="127.0.0.1",
         port=55600,
-        proxy_headers=True,                # 对应 --proxy-headers
-        forwarded_allow_ips="*"            # 对应 --forwarded-allow-ips="*"
+        proxy_headers=True,
+        forwarded_allow_ips="*"
     )

fastAPI_nocom.py

@@ -0,0 +1,159 @@
+import os
+import uuid
+import requests
+from typing import Optional
+from contextlib import asynccontextmanager
+
+import torch
+import matplotlib
+# 关键：设置非交互式后端，避免服务器环境下报错
+matplotlib.use('Agg') 
+import matplotlib.pyplot as plt
+
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import JSONResponse
+from PIL import Image
+
+# SAM3 相关导入 (请确保你的环境中已正确安装 sam3)
+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)
+
+# ------------------- 生命周期管理 -------------------
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """
+    FastAPI 生命周期管理器：在服务启动时加载模型，关闭时清理资源
+    """
+    print("正在加载 SAM3 模型到 GPU...")
+    
+    # 1. 检测设备
+    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。")
+
+    # 2. 加载模型 (全局单例)
+    model = build_sam3_image_model()
+    model = model.to(device)
+    model.eval() # 切换到评估模式
+
+    # 3. 初始化 Processor
+    processor = Sam3Processor(model)
+
+    # 4. 存入 app.state 供全局访问
+    app.state.model = model
+    app.state.processor = processor
+    app.state.device = device
+    
+    print(f"模型加载完成，设备: {device}")
+    
+    yield  # 服务运行中...
+    
+    # 清理资源 (如果需要)
+    print("正在清理资源...")
+
+# ------------------- FastAPI 初始化 -------------------
+app = FastAPI(lifespan=lifespan, title="SAM3 Segmentation API")
+
+# 挂载静态文件目录，用于通过 URL 访问生成的图片
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+# ------------------- 辅助函数 -------------------
+def load_image_from_url(url: str) -> Image.Image:
+    """从网络 URL 下载图片"""
+    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 generate_and_save_result(image: Image.Image, inference_state) -> str:
+    """生成可视化结果图并保存，返回文件名"""
+    # 生成唯一文件名防止冲突
+    filename = f"seg_{uuid.uuid4().hex}.jpg"
+    save_path = os.path.join(RESULT_IMAGE_DIR, filename)
+
+    # 绘图 (复用你提供的逻辑)
+    plot_results(image, inference_state)
+    
+    # 保存
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close() # 务必关闭，防止内存泄漏
+
+    return filename
+
+# ------------------- API 接口 -------------------
+@app.post("/segment")
+async def segment(
+    request: Request,
+    prompt: str = Form(...),
+    file: Optional[UploadFile] = File(None),
+    image_url: Optional[str] = Form(None)
+):
+    """
+    接收图片 (文件上传 或 URL) 和 文本提示词，返回分割后的图片 URL。
+    """
+    
+    # 1. 校验输入
+    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)")
+
+    # 2. 获取图片对象
+    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)}")
+
+    # 3. 获取模型
+    processor = request.app.state.processor
+
+    # 4. 执行推理
+    try:
+        # 这一步内部应该已经由 Sam3Processor 处理了 GPU 张量转移
+        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)}")
+
+    # 5. 生成可视化并保存
+    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)}")
+
+    # 6. 构建返回 URL
+    # request.url_for 会自动根据当前域名生成正确的访问链接
+    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
+    })
+
+if __name__ == "__main__":
+    import uvicorn
+    
+    # 使用 Python 函数参数的方式传递配置
+    uvicorn.run(
+        "fastAPI_main:app",  # 注意：这里要改成你的文件名:app对象名
+        host="127.0.0.1",
+        port=55600,
+        proxy_headers=True,                # 对应 --proxy-headers
+        forwarded_allow_ips="*"            # 对应 --forwarded-allow-ips="*"
+    )