YOLOv5将检测结果发送到MQTT服务器（附代码）

最新推荐文章于 2025-06-05 16:00:15 发布

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#网络

MQTT基础知识

MQTT的报文格式：

前两个control header和packet lenth表示固定报文格式，后两个为剩余数据长度。

variable header：可变长度报头，主要服务于后面的payload

payload：有效数据载荷，就是要发送的数据内容，数据载荷会根据variable header长度的变化而变化。这里面就包含了我们后面会用到的topic和message，一次可发送256M的数据。

控制报文的类型

这里简单解释一下上面的参数：

Reserve：这个地方是保留使用的，我们不用管他，也用不到

Connect：客户端开始向服务器发送请求，说：“我要开始连接你啦”

CONNACK：服务器收到连接请求后发出的回执，说：“收到收到，你连吧”

PUBLISH：连接之后，双方可以发布消息了，注意是双向通讯

PUBACK：客户端和服务器任意一端收到消息之后，都会互相告知对方一声：“我收到你的消息啦”

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插播一个知识点：QoS >>>表示两人通讯的等级，等级越高，通讯越可靠。

QoS=0时，表示客户端或服务器任意一方发送的消息，接收者最多只收一次，收不到就算了，收不到就摆烂，不太可靠

QoS=1时，表示客户端或服务器任意一方发送的消息，接收者至少能收到一次，接收者会不停接收，直到收到消息为止，比较负责，但存在重复接收的问题

QoS=2时，通讯最复杂，简单来讲，信息不容易丢失和重复。

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后续参数都差不多，都是 “请求+答复” 的模式。。。。。。。。。

以上表里的内容可以参考下面这张图来看，这是MQTT传输数据的方式和过程：

补充一下客户端和服务器的知识：

代码部分

detect.py部分插入的代码

import argparse
import os.path
import time
from pathlib import Path
import json
import paho.mqtt.client as mqtt
import MQTT.publish
from MQTT import publish_test, publish
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

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
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized


def json_content(fn):
    def inner():
        result = fn()
        print(result)
        print("这是装饰器")
    return inner



# @json_content
def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    save_img = not opt.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 = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.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(opt.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 img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.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)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]


    # 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=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        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], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, 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
            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 opt.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 view_img:  # Add bbox to image
                        label = f'{names[int(cls)]} {conf:.2f}'
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)


                ###---------------添加代码：按照json输出-------------------------
                    save_json = True

                    if save_json:
                        file_name = save_path.split('/')
                        json_content_dict = {
                            "name": file_name[len(file_name) - 1],
                            "category": (names[int(cls)]),
                            "bbox": torch.tensor(xyxy).view(1, 4).view(-1).tolist(),
                            "score": conf.tolist()
                        }

                        print(json_content_dict)

                        publish_test.run(json_content_dict)  # 将检测到的坐标信息加载到mqtt的发布模块

            # 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)


    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}")



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





if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='/home/ma/project/yolov5-5.0_crack/runs/train/exp63/weights/best.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='/home/ma/project/yolov5-5.0_crack/fire_test/231826574-1-16.mp4', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=448, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display 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('--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')
    opt = parser.parse_args()
    print(opt)
    check_requirements(exclude=('pycocotools', 'thop'))

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect()

                strip_optimizer(opt.weights)
        else:
            publish_test.connect_mqtt()  # 先启动连接
            detect()

publish.py部分的代码，用于项MQTT服务器发布检测框信息

# python 3.6
import json
import random
import time
from paho.mqtt import client as mqtt_client

broker = '123.249.86.54'  #tcp://123.249.86.54:1883
port = 1883
topic = "robot"
# generate client ID with pub prefix randomly
client_id = f'python-mqtt-id-{random.randint(0, 1000)}'


def connect_mqtt():
    def on_connect(client, userdata, flags, rc):  # 连接回调函数，rc=0代表成功，其他为失败
        if rc == 0:
            print(f"已连接到服务器{broker}，rc={rc}")
        else:
            print(f"连接失败!，rc={rc}")



    client = mqtt_client.Client(client_id) # 创建客户端对象
    client.on_connect = on_connect  # 建立连接回调
    client.username_pw_set("building-robot-client", "Cw123456")  # 设置服务器连接帐号和密码
    client.connect(broker, port) # 连接到指定服务器和端口
    client.loop_start()     # 启动循环
    return client


def publish(client, msg):
    msg_count = 0  # 计数器
    while msg_count < 1:
        # time.sleep(1) # 设置间隔时间
        result = client.publish(topic, payload=json.dumps(msg))  # 将消息内容整体转化为json内容，并发送出去
        print(msg)
        # result: [0, 1]
        status = result[0]  # 0号元素代表状态信息，若为0表示成功发送，若为1表示发送失败
        if status == 0:
            print(f"正在将 `{msg}` 发送到 `{topic}主题`...")
            print("发送成功！！")
        else:
            print(f"发送到{topic}失败！！！")

        msg_count += 1


def run(msg):
    client = connect_mqtt()
    client.username_pw_set("building-robot-client", "Cw123456")  # 设置服务器连接帐号和密码
    client.loop_start()

    publish(client, msg)


if __name__ == '__main__':
    run("swd")

发送后的效果