from utils.websocket_server import room_manager

import time
import torch
from utils.yolov5.models.common import DetectMultiBackend
from utils.yolov5.utils.torch_utils import select_device
from utils.yolov5.utils.dataloaders import LoadImages, LoadStreams
from utils.yolov5.utils.general import check_img_size, non_max_suppression, cv2, scale_coords, xyxy2xywh
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(2)  # 等待2s，等待websocket进入

    for path, im, im0s, vid_cap, s in dataset:
        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