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Yolov5 deepsort inference，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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# **YOLOv5 + DeepSort 用于目标跟踪与计数**
🚗🚶♂️ **使用 YOLOv5 和 DeepSort 实现车辆与行人实时跟踪与计数**
[](https://github.com/Sharpiless/Yolov5-deepsort-inference) [](https://github.com/Sharpiless/Yolov5-deepsort-inference) [](https://github.com/Sharpiless/Yolov5-deepsort-inference/blob/main/LICENSE)
最新版本：[https://github.com/Sharpiless/YOLOv11-DeepSort](https://github.com/Sharpiless/YOLOv11-DeepSort)
---
## **📌 项目简介**
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。
🔗 **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)
---
## **🚀 核心功能**
- **目标跟踪**：实时跟踪车辆与行人。
- **计数功能**：轻松统计视频流中的车辆或行人数。
- **封装式接口**：`Detector` 类封装了检测与跟踪逻辑，便于集成。
- **高度自定义**：支持训练自己的 YOLOv5 模型并无缝接入框架。
---
## **🔧 使用说明**
### **安装依赖**
```bash
pip install -r requirements.txt
```
确保安装了 `requirements.txt` 文件中列出的所有依赖。
### **运行 Demo**
```bash
python demo.py
```
---
## **🛠️ 开发说明**
### **YOLOv5 检测器**
```python
class Detector(baseDet):
def __init__(self):
super(Detector, self).__init__()
self.init_model()
self.build_config()
def init_model(self):
self.weights = 'weights/yolov5m.pt'
self.device = '0' if torch.cuda.is_available() else 'cpu'
self.device = select_device(self.device)
model = attempt_load(self.weights, map_location=self.device)
model.to(self.device).eval()
model.half()
# torch.save(model, 'test.pt')
self.m = model
self.names = model.module.names if hasattr(
model, 'module') else model.names
def preprocess(self, img):
img0 = img.copy()
img = letterbox(img, new_shape=self.img_size)[0]
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() # 半精度
img /= 255.0 # 图像归一化
if img.ndimension() == 3:
img = img.unsqueeze(0)
return img0, img
def detect(self, im):
im0, img = self.preprocess(im)
pred = self.m(img, augment=False)[0]
pred = pred.float()
pred = non_max_suppression(pred, self.threshold, 0.4)
pred_boxes = []
for det in pred:
if det is not None and len(det):
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], im0.shape).round()
for *x, conf, cls_id in det:
lbl = self.names[int(cls_id)]
if not lbl in ['person', 'car', 'truck']:
continue
x1, y1 = int(x[0]), int(x[1])
x2, y2 = int(x[2]), int(x[3])
pred_boxes.append(
(x1, y1, x2, y2, lbl, conf))
return im, pred_boxes
```
- 调用 `self.detect()` 方法返回图像和预测结果
### **DeepSort 追踪器**
```python
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
```
- 调用 `self.update()` 方法更新追踪结果
---
## **📊 训练自己的模型**
如果需要训练自定义的 YOLOv5 模型，请参考以下教程：
[【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)
训练完成后，将模型权重文件放置于 `weights` 文件夹中。
---
## **📦 API 调用**
### **初始化检测器**
```python
from AIDetector_pytorch import Detector
det = Detector()
```
### **调用检测接口**
```python
func_status = {}
func_staExcerpt of 5,454 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:77be382506c503e6, topic:object-detection