RabbitMq的Ubuntu安装以及Python实现

一、RabbitMq简介

RabbitMq 是实现了高级消息队列协议（AMQP）的开源消息代理中间件。消息队列是一种应用程序对应用程序的通行方式，应用程序通过写消息，将消息传递于队列，由另一应用程序读取 完成通信。而作为中间件的 RabbitMq 无疑是目前最流行的消息队列之一。

RabbitMq 应用场景广泛：

• 系统的高可用：日常生活当中各种商城秒杀，高流量，高并发的场景。当服务器接收到如此大量请求处理业务时，有宕机的风险。某些业务可能极其复杂，但这部分不是高时效性，不需要立即反馈给用户，我们可以将这部分处理请求抛给队列，让程序后置去处理，减轻服务器在高并发场景下的压力。
• 分布式系统，集成系统，子系统之间的对接，以及架构设计中常常需要考虑消息队列的应用。

消息队列介绍及主流的解决方案：https://blog.csdn.net/fst438060684/article/details/86555114#t25

二、Ubuntu安装

2.1 安装支持库以及RabbitMq本体

查看ubuntu当前版本命令（笔者这里是 Ubuntu 20.04 LTS）：

cat /etc/issue

rabbitMq需要erlang语言的支持，在安装rabbitMq之前需要安装erlang，执行命令：

sudo apt-get install erlang-nox

安装rabbitMq命令：

# 更新apt
sudo apt-get update

# 安装server
sudo apt-get install rabbitmq-server

# 启动management（也就是监视网页）
sudo rabbitmq-plugins enable rabbitmq_management

2.2 RabbitMq的基础指令

# 安装完成后在rabbitMQ中添加用户
sudo rabbitmqctl add_user username password

# 将用户设置为管理员（只有管理员才能远程登录）
sudo rabbitmqctl set_user_tags username administrator

# 同时为用户设置读写等权限
sudo rabbitmqctl set_permissions -p / username ".*" ".*" ".*"

启动、关闭、重启：

# 启动：
sudo rabbitmq-server start
# 关闭： 
sudo rabbitmq-server stop
# 重启： 
sudo rabbitmq-server restart

在关闭的过程中也许会出现错误：

node with name "rabbit" already running on "mybox"

解决方案：

sudo rabbitmqctl status
sudo rabbitmqctl stop
# 再次查看status确认已经关闭服务
sudo rabbitmqctl status

要再次启动，建议的方法是：

sudo invoke-rc.d rabbitmq-server start

三、Python实现

rabbitmq 的发布与订阅要借助交换机（Exchange）的原理实现：

Exchange一共有三种工作模式：fanout, direct, topicd

3.1 模式一：fanout（群体发送）

这种模式下，传递到 exchange 的消息将会转发到所有与其绑定的queue上。

• 不需要指定 routing_key ，即使指定了也是无效。
• 需要提前将 exchange 和 queue绑定，一个exchange可以绑定多个queue，一个queue可以绑定多个exchange。
• 需要先启动订阅者，此模式下的队列是 consumer 随机生成的，发布者仅仅发布消息到 exchange ，由 exchange 转发消息至 queue。

Publish：

import pika
import json

credentials = pika.PlainCredentials('shampoo', '123456')  # mq用户名和密码
# 虚拟队列需要指定参数 virtual_host，如果是默认的可以不填。
connection = pika.BlockingConnection(pika.ConnectionParameters(host = '10.1.62.170',port = 5672,virtual_host = '/',credentials = credentials))
channel=connection.channel()
# 声明exchange，由exchange指定消息在哪个队列传递，如不存在，则创建。durable = True 代表exchange持久化存储，False 非持久化存储
channel.exchange_declare(exchange = 'python-test',durable = True, exchange_type='fanout')for i in range(10):
    message=json.dumps({'OrderId':"1000%s"%i})
# 向队列插入数值 routing_key是队列名。delivery_mode = 2 声明消息在队列中持久化，delivery_mod = 1 消息非持久化。routing_key 不需要配置
    channel.basic_publish(exchange = 'python-test',routing_key = '',body = message,
                          properties=pika.BasicProperties(delivery_mode = 2))
    print(message)
connection.close()

Consume：

import pika

credentials = pika.PlainCredentials('shampoo', '123456')
connection = pika.BlockingConnection(pika.ConnectionParameters(host = '10.1.62.170',port = 5672,virtual_host = '/',credentials = credentials))
channel = connection.channel()
# 创建临时队列,队列名传空字符，consumer关闭后，队列自动删除
result = channel.queue_declare('',exclusive=True)
# 声明exchange，由exchange指定消息在哪个队列传递，如不存在，则创建。durable = True 代表exchange持久化存储，False 非持久化存储
channel.exchange_declare(exchange = 'python-test',durable = True, exchange_type='fanout')
# 绑定exchange和队列  exchange 使我们能够确切地指定消息应该到哪个队列去
channel.queue_bind(exchange = 'python-test',queue = result.method.queue)
# 定义一个回调函数来处理消息队列中的消息，这里是打印出来
def callback(ch, method, properties, body):
    ch.basic_ack(delivery_tag = method.delivery_tag)
    print(body.decode())

channel.basic_consume(result.method.queue,callback,# 设置成 False，在调用callback函数时，未收到确认标识，消息会重回队列。True，无论调用callback成功与否，消息都被消费掉
                      auto_ack = False)
channel.start_consuming()

3.2 模式二：direct（routing_key 定向发送）

这种工作模式的原理是 消息发送至 exchange，exchange 根据路由键（routing_key）转发到相对应的 queue 上。

• 可以使用默认 exchange =’ ’ ，也可以自定义 exchange
• 这种模式下不需要将 exchange 和 任何进行绑定，当然绑定也是可以的。可以将 exchange 和 queue ，routing_key 和 queue 进行绑定
• 传递或接受消息时 需要 指定 routing_key需要先启动 订阅者，此模式下的队列是 consumer 随机生成的，发布者 仅仅发布消息到 exchange ，由 exchange 转发消息至 queue。

Publish：

import pika
import json

credentials = pika.PlainCredentials('shampoo', '123456')  # mq用户名和密码
# 虚拟队列需要指定参数 virtual_host，如果是默认的可以不填。
connection = pika.BlockingConnection(pika.ConnectionParameters(host = '10.1.62.170',port = 5672,virtual_host = '/',credentials = credentials))
channel=connection.channel()
# 声明exchange，由exchange指定消息在哪个队列传递，如不存在，则创建。durable = True 代表exchange持久化存储，False 非持久化存储
channel.exchange_declare(exchange = 'python-test',durable = True, exchange_type='direct')

for i in range(10):
    message=json.dumps({'OrderId':"1000%s"%i})
# 指定 routing_key。delivery_mode = 2 声明消息在队列中持久化，delivery_mod = 1 消息非持久化
    channel.basic_publish(exchange = 'python-test',routing_key = 'OrderId',body = message,
                          properties=pika.BasicProperties(delivery_mode = 2))
    print(message)
connection.close()

Consume：

import pika

credentials = pika.PlainCredentials('shampoo', '123456')
connection = pika.BlockingConnection(pika.ConnectionParameters(host = '10.1.62.170',port = 5672,virtual_host = '/',credentials = credentials))
channel = connection.channel()
# 创建临时队列，队列名传空字符，consumer关闭后，队列自动删除
result = channel.queue_declare('',exclusive=True)
# 声明exchange，由exchange指定消息在哪个队列传递，如不存在，则创建。durable = True 代表exchange持久化存储，False 非持久化存储
channel.exchange_declare(exchange = 'python-test',durable = True, exchange_type='direct')
# 绑定exchange和队列  exchange 使我们能够确切地指定消息应该到哪个队列去
channel.queue_bind(exchange = 'python-test',queue = result.method.queue,routing_key='OrderId')
# 定义一个回调函数来处理消息队列中的消息，这里是打印出来
def callback(ch, method, properties, body):
    ch.basic_ack(delivery_tag = method.delivery_tag)
    print(body.decode())


#channel.basic_qos(prefetch_count=1)
# 告诉rabbitmq，用callback来接受消息
channel.basic_consume(result.method.queue,callback,
# 设置成 False，在调用callback函数时，未收到确认标识，消息会重回队列。True，无论调用callback成功与否，消息都被消费掉
                      auto_ack = False)
channel.start_consuming()

3.3 模式三：topicd（routing_key 正则匹配）

这种模式和第二种模式差不多，exchange 也是通过 路由键 routing_key 来转发消息到指定的 queue 。 不同点是 routing_key 使用正则表达式支持模糊匹配，但匹配规则又与常规的正则表达式不同，比如“#”是匹配全部，“*”是匹配一个词。

举例： routing_key =“#orderid#”，意思是将消息转发至所有 routing_key 包含 “orderid” 字符的队列中。代码和 模式二 direct 类似，就不贴出来了。

四、基于RabbitMq改写的YOLO_detect信息传输功能

yolo_detect_publish端

import argparse
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
from MyEncoder import MyEncoder
import pika
import json

@torch.no_grad()
def detect(weights='yolov5s.pt',  # model.pt path(s)
           source='data/images',  # file/dir/URL/glob, 0 for webcam
           imgsz=640,  # inference size (pixels)
           conf_thres=0.25,  # confidence threshold
           iou_thres=0.45,  # NMS IOU threshold
           max_det=1000,  # maximum detections per image
           device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
           view_img=False,  # show results
           save_txt=False,  # save results to *.txt
           save_conf=False,  # save confidences in --save-txt labels
           save_crop=False,  # save cropped prediction boxes
           nosave=False,  # do not save images/videos
           classes=None,  # filter by class: --class 0, or --class 0 2 3
           agnostic_nms=False,  # class-agnostic NMS
           augment=False,  # augmented inference
           update=False,  # update all models
           project='runs/detect',  # save results to project/name
           name='exp',  # save results to project/name
           exist_ok=False,  # existing project/name ok, do not increment
           line_thickness=3,  # bounding box thickness (pixels)
           hide_labels=False,  # hide labels
           hide_conf=False,  # hide confidences
           half=False,  # use FP16 half-precision inference
           ):
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check image size
    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet50', n=2)  # initialize
        modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        im0 = plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            credentials = pika.PlainCredentials('AI_Detect', 'qwert')
            connection = pika.BlockingConnection(pika.ConnectionParameters(host = 'localhost',port = 5672,virtual_host = '/',credentials = credentials))
            channel=connection.channel()
            channel.exchange_declare(exchange = 'python-test',durable = True, exchange_type='fanout')
            message=json.dumps(f'{s}Done. ({t2 - t1:.3f}s)',cls=MyEncoder,indent=4)
            channel.basic_publish(exchange = 'python-test',routing_key = '',body = message,
            properties=pika.BasicProperties(delivery_mode = 2))
            print(message)
            
            # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer.write(im0)

    connection.close()
    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")

    if update:
        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)

    print(f'Done. ({time.time() - t0:.3f}s)')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='data/images', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    opt = parser.parse_args()
    print(opt)
    check_requirements(exclude=('tensorboard', 'thop'))

    detect(**vars(opt))

yolo_detect_consume端

import pika

credentials = pika.PlainCredentials('AI_Detect', 'qwert')  # mq用户名和密码
# 虚拟队列需要指定参数 virtual_host，如果是默认的可以不填。
connection = pika.BlockingConnection(pika.ConnectionParameters(host = 'localhost',port = 5672,virtual_host = '/',credentials = credentials))
channel=connection.channel()
# 创建临时队列,队列名传空字符，consumer关闭后，队列自动删除
result = channel.queue_declare('',exclusive=True)
# 声明exchange，由exchange指定消息在哪个队列传递，如不存在，则创建。durable = True 代表exchange持久化存储，False 非持久化存储
channel.exchange_declare(exchange = 'python-test',durable = True, exchange_type='fanout')
# 绑定exchange和队列  exchange 使我们能够确切地指定消息应该到哪个队列去
channel.queue_bind(exchange = 'python-test',queue = result.method.queue)
# 定义一个回调函数来处理消息队列中的消息，这里是打印出来
def callback(ch, method, properties, body):
    ch.basic_ack(delivery_tag = method.delivery_tag)
    print(body.decode())

channel.basic_consume(result.method.queue,callback,# 设置成 False，在调用callback函数时，未收到确认标识，消息会重回队列。True，无论调用callback成功与否，消息都被消费掉
                      auto_ack = False)
channel.start_consuming()

五、报错及解决方案

4.1 TypeError: Object of type ‘type’ is not JSON serializable

解决：编写一个解码类 遇到byte就转为str

编写一个MyEncoder.py文件，放在目标根目录下

import json
class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, bytes):
            return str(obj, encoding='utf-8')
        return json.JSONEncoder.default(self, obj)

使用方法：

from MyEncoder import MyEncoder
json.dumps(data,cls=MyEncoder,indent=4) # 原方法json.dumps(data)改写