188 lines
6.2 KiB
Markdown
188 lines
6.2 KiB
Markdown
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# **YOLOv5 + DeepSort 用于目标跟踪与计数**
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🚗🚶♂️ **使用 YOLOv5 和 DeepSort 实现车辆与行人实时跟踪与计数**
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[](https://github.com/Sharpiless/Yolov5-deepsort-inference) [](https://github.com/Sharpiless/Yolov5-deepsort-inference) [](https://github.com/Sharpiless/Yolov5-deepsort-inference/blob/main/LICENSE)
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最新版本：[https://github.com/Sharpiless/YOLOv11-DeepSort](https://github.com/Sharpiless/YOLOv11-DeepSort)
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---
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## **📌 项目简介**
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本项目将 **YOLOv5** 与 **DeepSort** 相结合，实现了对目标的实时跟踪与计数。提供了一个封装的 `Detector` 类，方便将此功能嵌入到自定义项目中。
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🔗 **阅读完整博客**：[【小白CV教程】YOLOv5+Deepsort实现车辆行人的检测、追踪和计数](https://blog.csdn.net/weixin_44936889/article/details/112002152)
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---
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## **🚀 核心功能**
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- **目标跟踪**：实时跟踪车辆与行人。
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- **计数功能**：轻松统计视频流中的车辆或行人数。
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- **封装式接口**：`Detector` 类封装了检测与跟踪逻辑，便于集成。
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- **高度自定义**：支持训练自己的 YOLOv5 模型并无缝接入框架。
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---
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## **🔧 使用说明**
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### **安装依赖**
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```bash
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pip install -r requirements.txt
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```
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确保安装了 `requirements.txt` 文件中列出的所有依赖。
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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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---
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## **🛠️ 开发说明**
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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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---
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## **📊 训练自己的模型**
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如果需要训练自定义的 YOLOv5 模型，请参考以下教程：
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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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训练完成后，将模型权重文件放置于 `weights` 文件夹中。
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---
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## **📦 API 调用**
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### **初始化检测器**
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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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- `im`: 输入的 BGR 图像。
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- `result['frame']`: 检测结果的可视化图像。
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---
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## **✨ 可视化效果**
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---
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## **📚 联系作者**
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- Bilibili: [https://space.bilibili.com/470550823](https://space.bilibili.com/470550823)
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- CSDN: [https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889)
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- AI Studio: [https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156)
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- GitHub: [https://github.com/Sharpiless](https://github.com/Sharpiless)
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---
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<picture>
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<source
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media="(prefers-color-scheme: dark)"
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srcset="
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https://api.star-history.com/svg?repos=Sharpiless/Yolov5-deepsort-inference&type=Date&theme=dark
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"
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/>
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<source
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media="(prefers-color-scheme: light)"
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srcset="
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https://api.star-history.com/svg?repos=Sharpiless/Yolov5-deepsort-inference&type=Date
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"
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/>
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<img
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alt="Star History Chart"
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src="https://api.star-history.com/svg?repos=Sharpiless/Yolov5-deepsort-inference&type=Date"
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/>
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</picture>
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## **💡 许可证**
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本项目遵循 **GNU General Public License v3.0** 协议。
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**标明目标检测部分来源**：[https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
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