1. FastAPI 服务器
2. Celery 任务队列
3. RabbitMQ 作为消息代理
4. 定时任务处理

首先创建项目结构：

c:\Users\Administrator\Desktop\meitu\
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── celery_app.py
│   ├── tasks.py
│   └── config.py
├── requirements.txt
└── celery_worker.py

1. 首先创建 requirements.txt：

fastapi==0.104.1
uvicorn==0.24.0
celery==5.3.4
python-dotenv==1.0.0
requests==2.31.0

2. 创建配置文件：

from dotenv import load_dotenv
import osload_dotenv()# RabbitMQ配置
RABBITMQ_HOST = os.getenv("RABBITMQ_HOST", "localhost")
RABBITMQ_PORT = os.getenv("RABBITMQ_PORT", "5672")
RABBITMQ_USER = os.getenv("RABBITMQ_USER", "guest")
RABBITMQ_PASS = os.getenv("RABBITMQ_PASS", "guest")# Celery配置
CELERY_BROKER_URL = f"amqp://{RABBITMQ_USER}:{RABBITMQ_PASS}@{RABBITMQ_HOST}:{RABBITMQ_PORT}//"
CELERY_RESULT_BACKEND = "rpc://"# 定时任务配置
CELERY_BEAT_SCHEDULE = {'process-images-every-hour': {'task': 'app.tasks.process_images','schedule': 3600.0,  # 每小时执行一次},'daily-cleanup': {'task': 'app.tasks.cleanup_old_images','schedule': 86400.0,  # 每天执行一次}
}

3. 创建 Celery 应用：

from celery import Celery
from app.config import CELERY_BROKER_URL, CELERY_RESULT_BACKEND, CELERY_BEAT_SCHEDULEcelery_app = Celery('image_processing',broker=CELERY_BROKER_URL,backend=CELERY_RESULT_BACKEND,include=['app.tasks']
)# 配置定时任务
celery_app.conf.beat_schedule = CELERY_BEAT_SCHEDULE
celery_app.conf.timezone = 'Asia/Shanghai'

4. 创建任务文件：

from app.celery_app import celery_app
from app.watermark import ImageWatermarker
import os
from datetime import datetime, timedelta@celery_app.task
def add_watermark_task(image_path, text, position='center'):"""异步添加水印任务"""watermarker = ImageWatermarker()try:result_path = watermarker.add_watermark(image_path=image_path,text=text,position=position)return {"status": "success", "output_path": result_path}except Exception as e:return {"status": "error", "message": str(e)}@celery_app.task
def process_images():"""定时处理图片任务"""image_dir = "images/pending"if not os.path.exists(image_dir):return {"status": "error", "message": "Pending directory not found"}processed = 0for image in os.listdir(image_dir):if image.lower().endswith(('.png', '.jpg', '.jpeg')):add_watermark_task.delay(os.path.join(image_dir, image),"自动处理水印",'center')processed += 1return {"status": "success", "processed": processed}@celery_app.task
def cleanup_old_images():"""清理旧图片任务"""output_dir = "images/processed"if not os.path.exists(output_dir):return {"status": "error", "message": "Output directory not found"}threshold_date = datetime.now() - timedelta(days=7)cleaned = 0for image in os.listdir(output_dir):image_path = os.path.join(output_dir, image)if os.path.getctime(image_path) < threshold_date.timestamp():os.remove(image_path)cleaned += 1return {"status": "success", "cleaned": cleaned}

5. 创建 FastAPI 应用：

from fastapi import FastAPI, File, UploadFile, BackgroundTasks
from fastapi.responses import JSONResponse
import os
from app.tasks import add_watermark_task
from app.celery_app import celery_appapp = FastAPI(title="图片水印处理服务")@app.post("/upload/")
async def upload_image(file: UploadFile = File(...),text: str = "水印文本",position: str = "center"
):# 保存上传的文件file_path = f"images/uploads/{file.filename}"os.makedirs(os.path.dirname(file_path), exist_ok=True)with open(file_path, "wb") as buffer:content = await file.read()buffer.write(content)# 创建异步任务task = add_watermark_task.delay(file_path, text, position)return JSONResponse({"status": "success","message": "图片已上传并加入处理队列","task_id": task.id})@app.get("/task/{task_id}")
async def get_task_status(task_id: str):task = celery_app.AsyncResult(task_id)if task.ready():return {"status": "completed", "result": task.result}return {"status": "processing"}@app.get("/tasks/scheduled")
async def get_scheduled_tasks():return {"tasks": celery_app.conf.beat_schedule}

6. 创建 Celery worker 启动文件：

from app.celery_app import celery_appif __name__ == '__main__':celery_app.start()

使用说明：

1. 首先安装依赖：

pip install -r requirements.txt

2. 确保 RabbitMQ 服务已启动

3. 启动 FastAPI 服务器：

uvicorn app.main:app --reload

4. 启动 Celery worker：

celery -A celery_worker.celery_app worker --loglevel=info

5. 启动 Celery beat（定时任务）：

celery -A celery_worker.celery_app beat --loglevel=info

这个系统提供以下功能：

1. 通过 FastAPI 接口上传图片并异步处理水印
2. 使用 Celery 处理异步任务队列
3. 使用 RabbitMQ 作为消息代理
4. 支持定时任务：
   • 每小时自动处理待处理图片
   • 每天清理一周前的旧图片
5. 支持任务状态查询
6. 支持查看计划任务列表

API 端点：

• POST /upload/ - 上传图片并创建水印任务
• GET /task/{task_id} - 查询任务状态
• GET /tasks/scheduled - 查看计划任务列表