yolov5_deepsort目标跟踪算法

### YOLOv5与DeepSort结合的目标跟踪算法实现教程 #### 初始化环境和模型加载 为了构建YOLOv5与DeepSort相结合的对象跟踪系统，首先需要安装并配置必要的库和工具。这包括但不限于YOLOv5、DeepSort及其依赖项。 ```python import torch from models.experimental import attempt_load from utils.general import non_max_suppression, scale_coords from deep_sort.utils.parser import get_config from deep_sort.deep_sort import DeepSort ``` 这段代码展示了如何导入所需的模块和类来准备YOLOv5和DeepSort的集成[^1]。 #### 加载预训练权重及初始化追踪器实例 接着，在实际应用之前要确保已经下载了合适的YOLOv5权重文件，并通过下面的方式创建了一个`DeepSort`对象用于后续处理： ```python device = 'cuda' if torch.cuda.is_available() else 'cpu' model = attempt_load('yolov5s.pt', map_location=device) cfg_deepsort = get_config() cfg_deepsort.merge_from_file("deep_sort/configs/deep_sort.yaml") deepsort = DeepSort(cfg_deepsort.DEEPSORT.REID_CKPT, max_dist=cfg_deepsort.DEEPSORT.MAX_DIST, min_confidence=cfg_deepsort.DEEPSORT.MIN_CONFIDENCE, nms_max_overlap=cfg_deepsort.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deepsort.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg_deepsort.DEEPSORT.MAX_AGE, n_init=cfg_deepsort.DEEPSORT.N_INIT, nn_budget=cfg_deepsort.DEEPSORT.NN_BUDGET,) ``` 此部分实现了YOLOv5模型的加载以及基于配置文件设置了DeepSort的具体参数[^2]。 #### 执行检测与跟踪流程 当一切就绪之后就可以进入核心逻辑——即对每一帧图像先调用YOLOv5获取边界框预测结果；再利用这些信息作为输入传递给DeepSort完成身份识别工作。以下是简化版伪代码表示该过程： ```python def detect_and_track(frame): imgsz = 640 # 图像尺寸调整大小 stride = int(model.stride.max()) # 模型最大步幅 im0 = frame.copy() # Padded resize img = letterbox(im0, new_shape=imgsz, auto=True)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(device) img = img.float() / 255.0 # 归一化至[0, 1] if len(img.shape) == 3: img = img.unsqueeze(0) pred = model(img)[0] det = non_max_suppression(pred, conf_thres=0.4, iou_thres=0.5)[0] bbox_xywh = [] confidences = [] if det is not None and len(det): det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() for *xyxy, conf, cls in reversed(det): xywh = (torch.tensor(xyxy).view(1, 4)).clone().detach()[0] c1, c2 = (int(xywh[0]), int(xywh[1])), (int(xywh[2]), int(xywh[3])) box_w = c2[0] - c1[0] box_h = c2[1] - c1[1] x_c, y_c = xywh[0], xywh[1] obj = [ x_c.item(), y_c.item(), box_w.item(), box_h.item() ] bbox_xywh.append(obj) confidences.append(conf.item()) outputs = deepsort.update(torch.Tensor(bbox_xywh), confidences, im0) for output in list(outputs): draw_boxes(im0, output) return im0 # 绘制边框函数定义省略... ``` 上述代码片段描述了一次完整的检测加跟踪操作，其中包含了从原始图片读取到最后绘制跟踪结果的过程[^4]。