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README.md
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README.md
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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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# 注意：
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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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## 本项目使用Yolov5 3.0版本，最新版本5.0请移步：
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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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[https://github.com/Sharpiless/yolov5-deepsort](https://github.com/Sharpiless/yolov5-deepsort)
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---
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## 注：新版本添加了类别显示功能
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## **📌 项目简介**
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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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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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代码地址（欢迎star）：
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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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[https://github.com/Sharpiless/Yolov5-deepsort-inference](https://github.com/Sharpiless/Yolov5-deepsort-inference)
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---
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最终效果：
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# YOLOv5检测器：
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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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@ -79,12 +101,9 @@ class Detector(baseDet):
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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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- 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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@ -93,34 +112,28 @@ deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
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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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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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## **📊 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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训练好后放到 weights 文件夹下
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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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# 调用接口：
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---
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## 创建检测器：
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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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### **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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@ -128,25 +141,34 @@ 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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- `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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返回的 result 是字典，result['frame'] 返回可视化后的图像
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---
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# 关注我的公众号：
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## **✨ 可视化效果**
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感兴趣的同学关注我的公众号——可达鸭的深度学习教程：
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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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# 联系作者：
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---
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> B站：[https://space.bilibili.com/470550823](https://space.bilibili.com/470550823)
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## **🎉 关注我**
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> CSDN：[https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889)
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关注我的微信公众号，获取更多深度学习教程：
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**公众号：可达鸭的深度学习教程**
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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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---
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> Github：[https://github.com/Sharpiless](https://github.com/Sharpiless)
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## **💡 许可证**
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遵循 GNU General Public License v3.0 协议，标明目标检测部分来源：https://github.com/ultralytics/yolov5/
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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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