153 lines
5.7 KiB
Python
153 lines
5.7 KiB
Python
import time
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import torch
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from app.util.yolov5.models.common import DetectMultiBackend
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from app.util.yolov5.utils.torch_utils import select_device
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from app.util.yolov5.utils.dataloaders import LoadStreams
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from app.util.yolov5.utils.general import check_img_size, non_max_suppression, cv2, scale_coords, xyxy2xywh
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from app.websocket.web_socket_server import room_manager
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from app.common.redis_cli import redis_conn
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from deep_sort.deep_sort import DeepSort
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palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
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# 初始化 DeepSORT 跟踪器
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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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async def run_deepsort_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: int, idx_to_class: dict):
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"""
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rtsp 视频流推理
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:param detect_id: 训练集的id
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:param weights_pt: 权重文件
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:param rtsp_url: 视频流地址
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:param data: yaml文件
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:param idx_to_class: yaml文件
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:return:
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"""
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room = 'deepsort_rtsp_' + str(detect_id)
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# 选择设备（CPU 或 GPU）
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device = select_device('cpu')
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is_gpu = redis_conn.get('is_gpu')
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# 判断是否存在cuda版本
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if is_gpu == 'True':
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device = select_device('cuda:0')
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# 加载模型
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model = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size((640, 640), s=stride) # check image size
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dataset = LoadStreams(rtsp_url, img_size=imgsz, stride=stride, auto=pt, vid_stride=1)
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bs = len(dataset)
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))
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time.sleep(3) # 等待3s，等待websocket进入
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for path, im, im0s, vid_cap, s in dataset:
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if room_manager.rooms.get(room):
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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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if model.xml and im.shape[0] > 1:
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ims = torch.chunk(im, im.shape[0], 0)
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# Inference
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if model.xml and im.shape[0] > 1:
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pred = None
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for image in ims:
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if pred is None:
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pred = model(image, augment=False, visualize=False).unsqueeze(0)
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else:
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pred = torch.cat((pred, model(image, augment=False, visualize=False).unsqueeze(0)),
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dim=0)
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pred = [pred, None]
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else:
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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, 0.45, 0.45, None, False, max_det=1000)
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image = im0s[0]
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pred[:, :4] = scale_coords(im.shape[2:], pred[:, :4], image.shape).round()
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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 = yolo_to_deepsort_format(pred)
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# select person class
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mask = cls_ids == 0
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bbox_xywh = bbox_xywh[mask]
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# bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
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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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# 调用Deep SORT更新方法
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outputs, _ = deepsort.update(bbox_xywh, cls_conf, cls_ids, image)
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# draw boxes for visualization
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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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image = draw_boxes(image, bbox_xyxy, names, identities)
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# 将帧编码为 JPEG
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ret, jpeg = cv2.imencode('.jpg', image)
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if ret:
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frame_data = jpeg.tobytes()
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await room_manager.send_stream_to_room(room, frame_data)
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else:
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print(room, '结束跟踪')
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break
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def draw_boxes(img, bbox, names=None, identities=None, offset=(0, 0)):
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for i, box in enumerate(bbox):
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x1, y1, x2, y2 = [int(i) for i in box]
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x1 += offset[0]
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x2 += offset[0]
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y1 += offset[1]
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y2 += offset[1]
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# box text and bar
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id = int(identities[i]) if identities is not None else 0
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color = compute_color_for_labels(id)
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label = '{:}{:d}'.format(names[i], id)
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t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
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cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
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cv2.putText(img, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
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return img
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def compute_color_for_labels(label):
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"""
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Simple function that adds fixed color depending on the class
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"""
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color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
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return tuple(color)
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def yolo_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: xywh, conf, cls
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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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