# 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_tracker

def 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 threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='0',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        show_vid=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
        save_trajectories=False,  # save trajectories for each track
        save_vid=True,  # save confidences in --save-txt labels
        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
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs' / 'track',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=2,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        hide_class=False,  # hide IDs
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        vid_stride=1,  # video frame-rate stride
        retina_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 model
        exp_name = yolo_weights.stem
    elif type(yolo_weights) is list and len(yolo_weights) == 1:  # single models after --yolo_weights
        exp_name = Path(yolo_weights[0]).stem
    else:  # multiple models after --yolo_weights
        exp_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.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    if 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 = 1

    tracker_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 tracking
    seen, 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)
            continue
        with dt[0]:
            im = torch.from_numpy(im).to(device)
            im = im.half() if half else im.float()  # uint8 to fp16/32
            im /= 255.0  # 0 - 255 to 0.0 - 1.0
            if len(im.shape) == 3:
                im = im[None]  # expand for batch dim

        with 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 image
            seen += 1
            if webcam:  # nr_sources >= 1
                p, im0, _ = path[i], im0s[i].copy(), dataset.count
                p = Path(p)  # to Path
                s += f'{i}: '
                txt_file_name = p.name
                save_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 file
                if source.endswith(VID_FORMATS):
                    txt_file_name = p.stem
                    save_path = str(save_dir / p.name)  # im.jpg, vid.mp4, ...
                # folder with imgs
                else:
                    txt_file_name = p.parent.name  # get folder name containing current img
                    save_path = str(save_dir / p.parent.name)  # im.jpg, vid.mp4, ...
            curr_frames[i] = im0
            s += '%gx%g ' % im.shape[2:]  # print string

            annotator = 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 compensation
                    tracker_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 size
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
                with 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 image
                            c = int(cls)  # integer class
                            id = int(id)  # integer id
                            label = 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:
                                        pass
            else:   
                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:[email protected]: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  # expand
    opt.tracking_config = ROOT / 'trackers' / opt.tracking_method / 'configs' / (opt.tracking_method + '.yaml')
    print_args(vars(opt))
    return opt

def 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");