132 lines
4.9 KiB
Python
132 lines
4.9 KiB
Python
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from utils.websocket_server import room_manager
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import time
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import torch
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from deep_sort.deep_sort import DeepSort
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from deep_sort.utils.draw import draw_boxes
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def yolov5_to_deepsort_format(pred):
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"""
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将YOLOv5的预测结果转换为Deep SORT所需的格式
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:param pred: YOLOv5的预测结果
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:return: 转换后的bbox_xywh, confs, class_ids
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"""
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# pred[:, :4] = xyxy2xywh(pred[:, :4])
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# xywh = pred[:, :4].cpu().numpy()
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# conf = pred[:, 4].cpu().numpy()
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# cls = pred[:, 5].cpu().numpy()
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# return xywh, conf, cls
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async def run_deepsort(
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detect_id: int,
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weights_pt: str,
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data: str,
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idx_to_class: {},
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sort_type: str = 'video',
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video_path: str = None,
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rtsp_url: str = None
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):
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"""
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deep_sort追踪，先经过yolov5对目标进行识别，
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再调用deepsort对目标进行追踪
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"""
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# room = 'deep_sort_' + str(detect_id)
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#
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# room_count = 'deep_sort_count_' + str(detect_id)
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#
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# # 选择设备（CPU 或 GPU）
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# device = select_device('cuda:0')
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#
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# model = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)
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#
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# deepsort = DeepSort(
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# model_path="deep_sort/deep/checkpoint/ckpt.t7", # ReID 模型路径
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# max_dist=0.2, # 外观特征匹配阈值（越小越严格）
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# max_iou_distance=0.7, # 最大IoU距离阈值
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# max_age=70, # 目标最大存活帧数（未匹配时保留的帧数）
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# n_init=3 # 初始确认帧数（连续匹配到n_init次后确认跟踪）
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# )
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# stride, names, pt = model.stride, model.names, model.pt
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# img_sz = check_img_size((640, 640), s=stride) # check image size
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# if sort_type == 'video':
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# dataset = LoadImages(video_path, img_size=img_sz, stride=stride, auto=pt, vid_stride=1)
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# else:
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# dataset = LoadStreams(rtsp_url, img_size=img_sz, stride=stride, auto=pt, vid_stride=1)
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# bs = len(dataset)
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#
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# count_result = {}
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# for key in idx_to_class.keys():
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# count_result[key] = set()
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#
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# model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *img_sz))
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#
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# time.sleep(3) # 等待3s，等待websocket进入
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#
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# start_time = time.time()
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#
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# for path, im, im0s, vid_cap, s in dataset:
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# # 检查是否已经超过10分钟（600秒）
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# elapsed_time = time.time() - start_time
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# if elapsed_time > 600: # 600 seconds = 10 minutes
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# print(room, "已达到最大执行时间，结束推理。")
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# break
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# if room_manager.rooms.get(room):
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# im0 = im0s[0]
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# im = torch.from_numpy(im).to(model.device)
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# im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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# im /= 255 # 0 - 255 to 0.0 - 1.0
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# if len(im.shape) == 3:
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# im = im[None] # expand for batch dim
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# pred = model(im, augment=False, visualize=False)
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# # NMS
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# pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000)[0]
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#
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# pred[:, :4] = scale_coords(im.shape[2:], pred[:, :4], im0.shape).round()
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#
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# # 使用YOLOv5进行检测后得到的pred
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# bbox_xywh, cls_conf, cls_ids = yolov5_to_deepsort_format(pred)
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#
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# mask = cls_ids == 0
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#
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# bbox_xywh = bbox_xywh[mask]
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# bbox_xywh[:, 2:] *= 1.2
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# cls_conf = cls_conf[mask]
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# cls_ids = cls_ids[mask]
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#
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# # 调用Deep SORT更新方法
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# outputs, _ = deepsort.update(bbox_xywh, cls_conf, cls_ids, im0)
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#
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# if len(outputs) > 0:
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# bbox_xyxy = outputs[:, :4]
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# identities = outputs[:, -1]
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# cls = outputs[:, -2]
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# names = [idx_to_class[str(label)] for label in cls]
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# # 开始画框
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# ori_img = draw_boxes(im0, bbox_xyxy, names, identities, None)
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#
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# # 获取所有被确认过的追踪目标
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# active_tracks = [
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# track for track in deepsort.tracker.tracks
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# if track.is_confirmed()
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# ]
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#
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# for tark in active_tracks:
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# class_id = str(tark.cls)
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# count_result[class_id].add(tark.track_id)
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# # 对应每个label进行计数
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# result = {}
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# for key in count_result.keys():
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# result[idx_to_class[key]] = len(count_result[key])
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# # 将帧编码为 JPEG
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# ret, jpeg = cv2.imencode('.jpg', ori_img)
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# if ret:
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# jpeg_bytes = jpeg.tobytes()
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# await room_manager.send_stream_to_room(room, jpeg_bytes)
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# await room_manager.send_to_room(room_count, str(result))
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# else:
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# print(room, '结束追踪')
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# break
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