132 lines
4.1 KiB
Markdown
132 lines
4.1 KiB
Markdown
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@[TOC](【小白CV教程】YOLOv5+Deepsort实现车辆行人的检测、追踪和计数)
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# 本文禁止转载!
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本文地址：[https://blog.csdn.net/weixin_44936889/article/details/112002152](https://blog.csdn.net/weixin_44936889/article/details/112002152)
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# 项目简介：
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使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。
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代码地址（欢迎star）：
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[https://github.com/Sharpiless/Yolov5-deepsort-inference](https://github.com/Sharpiless/Yolov5-deepsort-inference)
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最终效果：
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# YOLOv5检测器：
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```python
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class Detector(baseDet):
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def __init__(self):
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super(Detector, self).__init__()
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self.init_model()
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self.build_config()
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def init_model(self):
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self.weights = 'weights/yolov5m.pt'
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self.device = '0' if torch.cuda.is_available() else 'cpu'
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self.device = select_device(self.device)
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model = attempt_load(self.weights, map_location=self.device)
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model.to(self.device).eval()
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model.half()
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# torch.save(model, 'test.pt')
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self.m = model
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self.names = model.module.names if hasattr(
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model, 'module') else model.names
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def preprocess(self, img):
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img0 = img.copy()
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img = letterbox(img, new_shape=self.img_size)[0]
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img = img[:, :, ::-1].transpose(2, 0, 1)
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(self.device)
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img = img.half() # 半精度
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img /= 255.0 # 图像归一化
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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return img0, img
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def detect(self, im):
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im0, img = self.preprocess(im)
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pred = self.m(img, augment=False)[0]
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pred = pred.float()
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pred = non_max_suppression(pred, self.threshold, 0.4)
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pred_boxes = []
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for det in pred:
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if det is not None and len(det):
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det[:, :4] = scale_coords(
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img.shape[2:], det[:, :4], im0.shape).round()
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for *x, conf, cls_id in det:
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lbl = self.names[int(cls_id)]
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if not lbl in ['person', 'car', 'truck']:
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continue
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x1, y1 = int(x[0]), int(x[1])
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x2, y2 = int(x[2]), int(x[3])
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pred_boxes.append(
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(x1, y1, x2, y2, lbl, conf))
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return im, pred_boxes
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```
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调用 self.detect 方法返回图像和预测结果
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# DeepSort追踪器：
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```python
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deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
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max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
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nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
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max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
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use_cuda=True)
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```
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调用 self.update 方法更新追踪结果
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# 运行demo：
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```bash
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python demo.py
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```
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# 训练自己的模型：
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参考我的另一篇博客：
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[【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）](https://blog.csdn.net/weixin_44936889/article/details/110661862)
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训练好后放到 weights 文件夹下
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# 调用接口：
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## 创建检测器：
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```python
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from AIDetector_pytorch import Detector
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det = Detector()
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```
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## 调用检测接口：
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```python
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func_status = {}
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func_status['headpose'] = None
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result = det.feedCap(im, func_status)
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```
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其中 im 为 BGR 图像
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返回的 result 是字典，result['frame'] 返回可视化后的图像
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# 联系作者：
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