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# 实时检测跟踪模块，并将检测跟踪结果保存到数据库中
# -*- coding: utf-8 -*-
import argparse
import os
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
import sys
import platform
import platform
import numpy as np
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from shapely.geometry import Polygon
import numpy as np
import matplotlib.pyplot as plt
import pymysql
import time
from datetime import datetime  
import json
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # yolov5 strongsort root directory
WEIGHTS = ROOT / 'weights'
if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
if str(ROOT / 'yolov5') not in sys.path:sys.path.append(str(ROOT / 'yolov5'))  # add yolov5 ROOT to PATH
if str(ROOT / 'trackers' / 'strongsort') not in sys.path:sys.path.append(str(ROOT / 'trackers' / 'strongsort'))  # add strong_sort ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
from yolov5.models.common import DetectMultiBackend
from yolov5.utils.dataloaders import VID_FORMATS, LoadImages, LoadStreams
from yolov5.utils.general import (LOGGER, Profile, check_img_size, non_max_suppression, scale_boxes, check_requirements, cv2,check_imshow, xyxy2xywh, increment_path, strip_optimizer, colorstr, print_args, check_file)
from yolov5.utils.torch_utils import select_device, time_sync
from yolov5.utils.plots import Annotator, colors, save_one_box
from trackers.multi_tracker_zoo import create_trackerdef get_connection():"""创建并返回一个新的数据库连接。"""# 数据库连接信息host = 'localhost'user = 'root'password = '123456'database = 'video_streaming_database'return pymysql.connect(host=host, user=user, password=password, database=database)def ensure_connection(connection):"""确保连接有效。如果连接无效，则重新建立连接。"""if connection is None or not connection.open:print("Connection is invalid or closed. Reconnecting...")return get_connection()return connection@torch.no_grad()
def run(source='0',yolo_weights=WEIGHTS / 'yolov5m.pt',  # model.pt path(s),reid_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt',  # model.pt path,tracking_method='strongsort',tracking_config=None,imgsz=(640, 640),  # inference size (height, width)cam_ip = '192.168.31.97',conf_thres=0.25,  # confidence thresholdiou_thres=0.45,  # NMS IOU thresholdmax_det=1000,  # maximum detections per imagedevice='0',  # cuda device, i.e. 0 or 0,1,2,3 or cpushow_vid=False,  # show resultssave_txt=False,  # save results to *.txtsave_conf=False,  # save confidences in --save-txt labelssave_crop=False,  # save cropped prediction boxessave_trajectories=False,  # save trajectories for each tracksave_vid=True,  # save confidences in --save-txt labelsnosave=False,  # do not save images/videosclasses=None,  # filter by class: --class 0, or --class 0 2 3agnostic_nms=False,  # class-agnostic NMSaugment=False,  # augmented inferencevisualize=False,  # visualize featuresupdate=False,  # update all modelsproject=ROOT / 'runs' / 'track',  # save results to project/namename='exp',  # save results to project/nameexist_ok=False,  # existing project/name ok, do not incrementline_thickness=2,  # bounding box thickness (pixels)hide_labels=False,  # hide labelshide_conf=False,  # hide confidenceshide_class=False,  # hide IDshalf=False,  # use FP16 half-precision inferencednn=False,  # use OpenCV DNN for ONNX inferencevid_stride=1,  # video frame-rate strideretina_masks=False,
):source = str(source)is_file = Path(source).suffix[1:] in (VID_FORMATS)is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)if is_url and is_file:source = check_file(source)  if not isinstance(yolo_weights, list):  # single yolo modelexp_name = yolo_weights.stemelif type(yolo_weights) is list and len(yolo_weights) == 1:  # single models after --yolo_weightsexp_name = Path(yolo_weights[0]).stemelse:  # multiple models after --yolo_weightsexp_name = 'ensemble'# 结果保存路径project = os.path.join(os.path.dirname(source), (source.split("\\")[-1][:-4])) + "_det"save_dir = increment_path(Path(project), exist_ok=exist_ok)  # increment run(save_dir / 'tracks' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir# 载入模型device = select_device(device)model = DetectMultiBackend(yolo_weights, device=device, dnn=dnn, data=None, fp16=half)stride, names, pt = model.stride, model.names, model.ptimgsz = check_img_size(imgsz, s=stride)  # check image sizeif webcam:show_vid = check_imshow()dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)nr_sources = len(dataset)else:dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)nr_sources = 1tracker_list = []for i in range(nr_sources):tracker = create_tracker(tracking_method, tracking_config, reid_weights, device, half)tracker_list.append(tracker, )if hasattr(tracker_list[i], 'model'):if hasattr(tracker_list[i].model, 'warmup'):tracker_list[i].model.warmup()outputs = [None] * nr_sources# Run trackingseen, windows, dt = 0, [], (Profile(), Profile(), Profile(), Profile())curr_frames, prev_frames = [None] * nr_sources, [None] * nr_sources# 数据库连接信息host = 'localhost'user = 'root'password = '123456'database = 'video_streaming_database'connection = pymysql.connect(host=host, user=user, password=password, database=database)data = []for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset):start_time = time.time()im_Original = im0s# 隔帧操作，实际测试对跟踪计数影响很大if frame_idx % 2 != 0:im_Original_resieze = cv2.resize(im_Original, dsize=None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)cv2.imwrite(os.path.join(str(save_dir), cam_ip + "_" + str(frame_idx + 1).zfill(8) + ".jpg"), im_Original_resieze)continuewith dt[0]:im = torch.from_numpy(im).to(device)im = im.half() if half else im.float()  # uint8 to fp16/32im /= 255.0  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dimwith dt[1]:pred = model(im, augment=augment, visualize=visualize)with dt[2]:pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)# 处理检测结果 for i, det in enumerate(pred):  # detections per imageseen += 1if webcam:  # nr_sources >= 1p, im0, _ = path[i], im0s[i].copy(), dataset.countp = Path(p)  # to Paths += f'{i}: 'txt_file_name = p.namesave_path = str(save_dir / p.name)  # im.jpg, vid.mp4, ...else:p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)p = Path(p)  # to Pathf# video fileif source.endswith(VID_FORMATS):txt_file_name = p.stemsave_path = str(save_dir / p.name)  # im.jpg, vid.mp4, ...# folder with imgselse:txt_file_name = p.parent.name  # get folder name containing current imgsave_path = str(save_dir / p.parent.name)  # im.jpg, vid.mp4, ...curr_frames[i] = im0s += '%gx%g ' % im.shape[2:]  # print stringannotator = Annotator(im0, line_width=line_thickness, example=str(names))if hasattr(tracker_list[i], 'tracker') and hasattr(tracker_list[i].tracker, 'camera_update'):if prev_frames[i] is not None and curr_frames[i] is not None:  # camera motion compensationtracker_list[i].tracker.camera_update(prev_frames[i], curr_frames[i])if det is not None and len(det):det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()  # rescale boxes to im0 sizefor c in det[:, 5].unique():n = (det[:, 5] == c).sum()  # detections per classs += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to stringwith dt[3]:outputs[i] = tracker_list[i].update(det.cpu(), im0)# 处理跟踪结果 if len(outputs[i]) > 0:for j, (output) in enumerate(outputs[i]):bbox = output[0:4]id = output[4]cls = output[5]conf = output[6]bbox_x = int((output[0] + output[2]) / 2)bbox_y = int((output[1] + output[3]) / 2)bbox_w = int(output[2] - output[0])bbox_h = int(output[3] - output[1])if save_vid or save_crop or show_vid:  # Add bbox to imagec = int(cls)  # integer classid = int(id)  # integer idlabel = None if hide_labels else (f'{id} {names[c]}' if hide_conf else \(f'{id} {conf:.2f}' if hide_class else f'{id} {names[c]} {conf:.2f}'))color = colors(c, True)annotator.box_label(bbox, label, color=color)if save_trajectories and tracking_method == 'strongsort':q = output[7]tracker_list[i].trajectory(im0, q, color=color)if save_crop:bbox = np.array(bbox)                        if frame_idx % 12 == 0:save_one_box(bbox.astype(np.int16), im_Original, file = save_dir / f'{id}' /  (cam_ip + "_"+ str(frame_idx + 1).zfill(8) + "_" + str(id).zfill(4) + "_"+ str(int(bbox_x)).zfill(4) + "_"+ str(int(bbox_y)).zfill(4) + "_"+ str(int(bbox_w)).zfill(4) + "_"+ str(int(bbox_h)).zfill(4) + "_"+ str(int(float(conf) * 10000))+ f'.jpg'), BGR=True)# 将检测跟踪中间结果保存到数据库中connection = ensure_connection(connection)  # 确保连接有效# 获取当前日期和时刻  current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')try:with connection.cursor() as cursor:# 插入数据的SQL语句insert_sql = """INSERT INTO new_detection_tracking_results_1 (camera_ip, frame_number, tracking_id, crop_image_path, event_datetime)VALUES (%s, %s, %s, %s, %s);"""# 示例数据data = [(cam_ip, int(frame_idx+1), int(id), save_dir / f'{id}' /  (cam_ip + "_"+ str(frame_idx + 1).zfill(8) + "_" + str(id).zfill(4) + "_"+ str(int(bbox_x)).zfill(4) + "_"+ str(int(bbox_y)).zfill(4) + "_"+ str(int(bbox_w)).zfill(4) + "_"+ str(int(bbox_h)).zfill(4) + "_"+ str(int(float(conf) * 10000))+ f'.jpg'),current_time)]# 执行插入操作cursor.executemany(insert_sql, data)connection.commit()finally:passelse:   pass# # 将检测跟踪的原图，标注图，检测结果保存到数据库中im_Original_resieze = cv2.resize(im_Original, dsize=None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)cv2.imwrite(os.path.join(str(save_dir), cam_ip + "_" + str(frame_idx + 1).zfill(8) + ".jpg"), im_Original_resieze)# 保存检测跟踪结果到文件if outputs[0] == None:track_outputs = []else:track_outputs = [[float(x[0] / 2), float(x[1] / 2), float(x[2] / 2), float(x[3] / 2), int(x[4]), float(x[6]), ""]for x in outputs[0]]data_dict = {}for row in track_outputs:key = int(row[4])  value = row data_dict[key] = value json_output_path = os.path.join(str(save_dir), cam_ip + "_" + str(frame_idx + 1).zfill(8) + "_track.json")with open(json_output_path, 'w') as json_file:json.dump(data_dict, json_file, indent=4)# 记录结束时间  end_time = time.time()  # 计算并打印运行时间  print(f"第{frame_idx}帧，程序运行时间: {end_time - start_time}秒")if end_time - start_time >= 0.0833333333333333333333:print(f"第{frame_idx}帧，程序运行时间: {end_time - start_time}秒")if (end_time - start_time < 0.0833333333333333333333):time.sleep(0.0833333333333333333333-end_time+start_time)def parse_opt():parser = argparse.ArgumentParser()parser.add_argument('--yolo-weights', nargs='+', type=Path, default=R'/home/hitsz/yk_workspace/Yolov5_track/weights/train_citys_bdd_4S_crowdhuman_coco_labs_liucl_1215_no_freeze_no_freeze_yolov5m3/weights/v5m_861.pt', help='model.pt path(s)')parser.add_argument('--reid-weights', type=Path, default=R'weights\osnet_x1_0_msmt17.pt')parser.add_argument('--tracking-method', type=str, default='bytetrack', help='strongsort, ocsort, bytetrack')parser.add_argument('--tracking-config', type=Path, default=None)parser.add_argument('--source', type=str, default=R"02_output_0.mp4", help='file/dir/URL/glob, 0 for webcam')  # 下面为输入为摄像头视频流的参数设置# parser.add_argument('--source', type=str, default=R'rtsp://admin:1234qwer@192.168.1.64:554/Streaming/Channels/101', help='file/dir/URL/glob, 0 for webcam')  parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')parser.add_argument('--cam_ip', type=str, default='192.168.31.97')parser.add_argument('--conf-thres', type=float, default=0.45, help='confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.25, help='NMS IoU threshold')parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--show-vid', default=False, action='store_true' , help='display tracking video results')parser.add_argument('--save-txt', default=True, action='store_true', help='save results to *.txt')parser.add_argument('--save-conf', default=True, action='store_true', help='save confidences in --save-txt labels')parser.add_argument('--save-crop', default=True, action='store_true', help='save cropped prediction boxes')parser.add_argument('--save-trajectories', default=True, action='store_true', help='save trajectories for each track')parser.add_argument('--save-vid', default=True, action='store_true', help='save video tracking results')parser.add_argument('--nosave', default=False, action='store_true', help='do not save images/videos')parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features')parser.add_argument('--update', action='store_true', help='update all models')parser.add_argument('--project', default=R"/home/hitsz/yk_web/Yolov5_track/results/test_save_results1", help='save results to project/name')parser.add_argument('--name', default='test', 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=2, 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('--hide-class', default=False, action='store_true', help='hide IDs')parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution')opt = parser.parse_args()opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expandopt.tracking_config = ROOT / 'trackers' / opt.tracking_method / 'configs' / (opt.tracking_method + '.yaml')print_args(vars(opt))return optdef main(opt):check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))run(**vars(opt))if __name__ == "__main__":opt = parse_opt()main(opt)import { createApp, createElementBlock } from 'vue';
import App from './App.vue';
import "@/assets/less/index.less";
import router from "@/router";import ElementPlus from 'element-plus'
import 'element-plus/dist/index.css'
import * as ElementPlusIconsVue from '@element-plus/icons-vue'
import {createPinia} from 'pinia'
import "video.js/dist/video-js.css";import "@/api/mock.js";
import api from '@/api/api'
import {useALLDataStore} from "@/stores"function isRoute(to){const routes = router.getRoutes();// 检查是否有匹配的路由return routes.some(route => {// 处理动态路径匹配const regex = new RegExp(`^${route.path.replace(/:\w+/g, '\\w+')}$`);return regex.test(to.path);});
}const pinia = createPinia();
const app = createApp(App);app.config.globalProperties.$api = api;for (const [key, component] of Object.entries(ElementPlusIconsVue)) {app.component(key, component)}app.use(pinia)
const store = useALLDataStore();
app.use(ElementPlus)
store.addMenu(router,"refresh")
app.use(router).mount("#app");