主要目标是适应摄像头rtsp流的检测

如果是普通文件夹或者图片，run中的while True去掉即可。

web_client是根据需求创建的客户端，将检测到的数据打包发送给服务器

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.Usage:$ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
"""import argparse
import json
import os
import sys
import time
import moment
from pathlib import Pathimport cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnnFILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom models.experimental import attempt_load
from utils.datasets import LoadImages, LoadStreams
from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \strip_optimizer, xyxy2xywh
from utils.plots import Annotator, colors
from utils.torch_utils import load_classifier, select_device, time_syncfrom mytools import read_yaml_all, base64_encode_img
from message_base import MessageBase
from websocket_client import WebClientclass Detect:def __init__(self, config: dict, client: WebClient):self.config = configself.weights = self.config.get("weights")  # weights pathself.source = self.config.get("source")  # source self.imgsz = self.config.get("imgsz")  # imgszself.conf_thres = self.config.get("conf_thres")self.iou_thres = self.config.get("iou_thres")self.max_det = self.config.get("max_det")self.device = self.config.get("device")  # "cpu" or "0,1,2,3"self.view_img = self.config.get("view_img")  # show resultsself.save_txt = self.config.get("save_txt")  # save results to *.txtself.save_conf = self.config.get("save_conf")  # save confidences in --save-txt labelsself.save_crop = self.config.get("save_crop")  # save cropped prediction boxesself.nosave = self.config.get("nosave")  # do not save images/videosself.classes = self.config.get("classes")  # filter by class: --class 0, or --class 0 2 3self.agnostic_nms = self.config.get("agnostic_nms")  # class-agnostic NMSself.augment = self.config.get("augment")  # augmented inferenceself.visualize = self.config.get("visualize")  # visualize featuresself.update = self.config.get("update")  # update all modelsself.save_path = self.config.get("save_path")  # save results to project/nameself.line_thickness = self.config.get("line_thickness")  # bounding box thickness (pixels)self.hide_labels = self.config.get("hide_labels")  # hide labelsself.hide_conf = self.config.get("hide_conf")  # hide confidencesself.half = self.config.get("half")  # use FP16 half-precision inferenceself.dnn = self.config.get("dnn")  # use OpenCV DNN for ONNX inferenceself.func_device = self.config.get("func_device")  # 对应功能的设备名字self.save_img = not self.nosave and not self.source.endswith('.txt')  # save inference imagesself.webcam = self.source.isnumeric() or self.source.endswith('.txt') or self.source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))set_logging()self.device = select_device(self.device)self.half = self.device.type != 'cpu'  # half precision only supported on CUDAself.model = attempt_load(self.weights, map_location=self.device)self.imgsz = check_img_size(self.imgsz, s=int(self.model.stride.max()))self.stride = int(self.model.stride.max())self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names# 获取数据if self.webcam:self.view_img = check_imshow()cudnn.benchmark = True  # set True to speed up constant image size inferenceself.dataset = LoadStreams(self.source, img_size=self.imgsz, stride=self.stride, auto=True)self.bs = len(self.dataset)  # batch_sizeelse:self.dataset = LoadImages(self.source, img_size=self.imgsz, stride=self.stride, auto=True)self.bs = 1  # batch_sizeself.client = client  # 客户端self.last_time = moment.now()self.check_time_step = 5  # 每隔多少时间检测一次os.mkdir(self.save_path) if not os.path.exists(self.save_path) else Nonedef inference(self, img):img = torch.from_numpy(img).to(self.device)img = img.half() if self.half else img.float()  # uint8 to fp16/32img /= 255.0  # 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:img = img.unsqueeze(0)pred = self.model(img, augment=self.augment)[0]# NMSpred = non_max_suppression(pred, self.conf_thres, self.iou_thres,self.classes, self.agnostic_nms, max_det=self.max_det)return preddef process(self, im0s, img, pred, path):for i, det in enumerate(pred):  # per imageif self.webcam:  # batch_size >= 1p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), self.dataset.countelse:p, s, im0, frame = path, '', im0s.copy(), getattr(self.dataset, 'frame', 0)p = Path(p)  # to Pathtxt_path = str(self.save_path + "/" + 'labels' + "/" + p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')  # img.txts += '%gx%g ' % img.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhimc = im0.copy() if self.save_crop else im0  # for save_cropannotator = Annotator(im0, line_width=self.line_thickness, example=str(self.names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classs += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):c = int(cls)label = self.names[c]# if label == "person":if label:  # 根据对应标签做处理# annotator.box_label(xyxy, label, color=colors(c, True)) # 画框t = int(time.time())img_path = f"{self.save_path}/{self.func_device}_{label}_{t}.jpg"crop = save_one_box(xyxy, imc, img_path, BGR=True)x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])data = {"device": self.func_device,"value": {"label": label,"time": t,"locate": (x1, y1, x2, y2),"crop": base64_encode_img(crop)}}data = json.dumps(data)  # 打包数据try:self.client.send(data)  # 客户端发送数据passexcept Exception as err:print("发送失败:", err)self.client.connect()self.client.send(data)print("重连成功！")print(data)# if self.save_txt:  # Write to file#     xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(#         -1).tolist()  # normalized xywh#     line = (cls, *xywh, conf) if self.save_conf else (cls, *xywh)  # label format#     with open(txt_path + '.txt', 'a') as f:#         f.write(('%g ' * len(line)).rstrip() % line + '\n')# 画框# if self.save_img or self.save_crop or self.view_img:  # Add bbox to image#     c = int(cls)  # integer class#     label = None if self.hide_labels else (self.names[c] if self.hide_conf else#                                            f'{self.names[c]} {conf:.2f}')#     annotator.box_label(xyxy, label, color=colors(c, True))def run(self):self.client.connect()while True:for path, img, im0s, vid_cap in self.dataset:if self.last_time.__lt__(moment.now()):self.last_time = moment.now().add(seconds=self.check_time_step)try:pred = self.inference(img)self.process(im0s, img, pred, path)              except Exception as err:print(err)if self.save_txt or self.save_img:s = f"\n{len(list(self.save_path.glob('labels/*.txt')))} labels saved to {self.save_path / 'labels'}" if self.save_txt else ''print(f"Results saved to {colorstr('bold', self.save_path)}{s}")if self.update:strip_optimizer(self.weights)  # update model (to fix SourceChangeWarning)if __name__ == "__main__":message_base = MessageBase()wc = WebClient("192.168.6.28", 8000)configs = read_yaml_all("yolo_configs.yaml")config = read_yaml_all("configs.yaml")device_name = config.get("DEVICE_LIST")[0]device_source = config.get("RTSP_URLS").get(device_name)configs["source"] = device_sourceconfigs["func_device"] = device_nameprint(configs)detect = Detect(configs, wc)detect.run()