191 lines
6.2 KiB
Python
191 lines
6.2 KiB
Python
import os
|
||
import uuid
|
||
import requests
|
||
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 generate_and_save_result(image: Image.Image, inference_state) -> str:
|
||
filename = f"seg_{uuid.uuid4().hex}.jpg"
|
||
save_path = os.path.join(RESULT_IMAGE_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
|
||
})
|
||
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
# 注意：如果你的文件名不是 fastAPI_nocom.py，请修改下面第一个参数
|
||
uvicorn.run(
|
||
"fastAPI_nocom: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
|
||
) |