from utils.websocket_server import room_manager

import time
import torch
from deep_sort.deep_sort import DeepSort
from deep_sort.utils.draw import draw_boxes


def yolov5_to_deepsort_format(pred):
    """
    将YOLOv5的预测结果转换为Deep SORT所需的格式
    :param pred: YOLOv5的预测结果
    :return: 转换后的bbox_xywh, confs, class_ids
    """
    # pred[:, :4] = xyxy2xywh(pred[:, :4])
    # xywh = pred[:, :4].cpu().numpy()
    # conf = pred[:, 4].cpu().numpy()
    # cls = pred[:, 5].cpu().numpy()
    # return xywh, conf, cls


async def run_deepsort(
        detect_id: int,
        weights_pt: str,
        data: str,
        idx_to_class: {},
        sort_type: str = 'video',
        video_path: str = None,
        rtsp_url: str = None
):
    """
    deep_sort追踪，先经过yolov5对目标进行识别，
    再调用deepsort对目标进行追踪
    """
    # room = 'deep_sort_' + str(detect_id)
    #
    # room_count = 'deep_sort_count_' + str(detect_id)
    #
    # # 选择设备（CPU 或 GPU）
    # device = select_device('cuda:0')
    #
    # model = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)
    #
    # deepsort = DeepSort(
    #     model_path="deep_sort/deep/checkpoint/ckpt.t7",  # ReID 模型路径
    #     max_dist=0.2,  # 外观特征匹配阈值（越小越严格）
    #     max_iou_distance=0.7,  # 最大IoU距离阈值
    #     max_age=70,  # 目标最大存活帧数（未匹配时保留的帧数）
    #     n_init=3  # 初始确认帧数（连续匹配到n_init次后确认跟踪）
    # )
    # stride, names, pt = model.stride, model.names, model.pt
    # img_sz = check_img_size((640, 640), s=stride)  # check image size
    # if sort_type == 'video':
    #     dataset = LoadImages(video_path, img_size=img_sz, stride=stride, auto=pt, vid_stride=1)
    # else:
    #     dataset = LoadStreams(rtsp_url, img_size=img_sz, stride=stride, auto=pt, vid_stride=1)
    # bs = len(dataset)
    #
    # count_result = {}
    # for key in idx_to_class.keys():
    #     count_result[key] = set()
    #
    # model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *img_sz))
    #
    # time.sleep(3)  # 等待3s，等待websocket进入
    #
    # start_time = time.time()
    #
    # for path, im, im0s, vid_cap, s in dataset:
    #     # 检查是否已经超过10分钟（600秒）
    #     elapsed_time = time.time() - start_time
    #     if elapsed_time > 600:  # 600 seconds = 10 minutes
    #         print(room, "已达到最大执行时间，结束推理。")
    #         break
    #     if room_manager.rooms.get(room):
    #         im0 = im0s[0]
    #         im = torch.from_numpy(im).to(model.device)
    #         im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
    #         im /= 255  # 0 - 255 to 0.0 - 1.0
    #         if len(im.shape) == 3:
    #             im = im[None]  # expand for batch dim
    #         pred = model(im, augment=False, visualize=False)
    #         # NMS
    #         pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000)[0]
    #
    #         pred[:, :4] = scale_coords(im.shape[2:], pred[:, :4], im0.shape).round()
    #
    #         # 使用YOLOv5进行检测后得到的pred
    #         bbox_xywh, cls_conf, cls_ids = yolov5_to_deepsort_format(pred)
    #
    #         mask = cls_ids == 0
    #
    #         bbox_xywh = bbox_xywh[mask]
    #         bbox_xywh[:, 2:] *= 1.2
    #         cls_conf = cls_conf[mask]
    #         cls_ids = cls_ids[mask]
    #
    #         # 调用Deep SORT更新方法
    #         outputs, _ = deepsort.update(bbox_xywh, cls_conf, cls_ids, im0)
    #
    #         if len(outputs) > 0:
    #             bbox_xyxy = outputs[:, :4]
    #             identities = outputs[:, -1]
    #             cls = outputs[:, -2]
    #             names = [idx_to_class[str(label)] for label in cls]
    #             # 开始画框
    #             ori_img = draw_boxes(im0, bbox_xyxy, names, identities, None)
    #
    #             # 获取所有被确认过的追踪目标
    #             active_tracks = [
    #                 track for track in deepsort.tracker.tracks
    #                 if track.is_confirmed()
    #             ]
    #
    #             for tark in active_tracks:
    #                 class_id = str(tark.cls)
    #                 count_result[class_id].add(tark.track_id)
    #             # 对应每个label进行计数
    #             result = {}
    #             for key in count_result.keys():
    #                 result[idx_to_class[key]] = len(count_result[key])
    #             # 将帧编码为 JPEG
    #             ret, jpeg = cv2.imencode('.jpg', ori_img)
    #             if ret:
    #                 jpeg_bytes = jpeg.tobytes()
    #                 await room_manager.send_stream_to_room(room, jpeg_bytes)
    #                 await room_manager.send_to_room(room_count, str(result))
    #     else:
    #         print(room, '结束追踪')
    #         break