Python FastAPI+Celery+RabbitMQ实现分布式图片水印处理系统
作者：老大白菜
这篇文章主要为大家详细介绍了Python FastAPI如何结合Celery以及RabbitMQ实现简单的分布式图片水印处理系统,感兴趣的小伙伴可以跟随小编一起学习一下
实现思路
- FastAPI 服务器
- Celery 任务队列
- RabbitMQ 作为消息代理
- 定时任务处理
完整步骤
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 os
load_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_SCHEDULE
celery_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 = 0
for 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 += 1
return {"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 = 0
for 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 += 1
return {"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_app
app = 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_app
if __name__ == '__main__':
celery_app.start()
使用说明
首先安装依赖：
pip install -r requirements.txt
确保 RabbitMQ 服务已启动
启动 FastAPI 服务器：
uvicorn app.main:app --reload
启动 Celery worker：
celery -A celery_worker.celery_app worker --loglevel=info
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：
- 通过 FastAPI 接口上传图片并异步处理水印
- 使用 Celery 处理异步任务队列
- 使用 RabbitMQ 作为消息代理
- 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
- 支持任务状态查询
- 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
以上就是Python FastAPI+Celery+RabbitMQ实现分布式图片水印处理系统的详细内容，更多关于Python图片水印的资料请关注脚本之家其它相关文章！