https://github.com/Sharpiless/Yolov5-Deepsort

最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集
https://github.com/Sharpiless/Yolov5-Deepsort

computer-vision deepsort object-detection object-tracking pytorch yolov5

Last synced: 12 days ago
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最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集

• Host: GitHub
• URL: https://github.com/Sharpiless/Yolov5-Deepsort
• Owner: Sharpiless
• License: gpl-3.0
• Created: 2021-05-26T13:34:50.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-06T08:33:23.000Z (over 1 year ago)
• Last Synced: 2024-02-29T09:34:03.366Z (4 months ago)
• Topics: computer-vision, deepsort, object-detection, object-tracking, pytorch, yolov5
• Language: Python
• Homepage:
• Size: 61.9 MB
• Stars: 799
• Watchers: 6
• Forks: 139
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-yolo-object-detection - Sharpiless/Yolov5-Deepsort - Deepsort?style=social"/> : 最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集。 (Applications)

README

        
# 本文禁止转载!

本文地址：[https://blog.csdn.net/weixin_44936889/article/details/112002152](https://blog.csdn.net/weixin_44936889/article/details/112002152)

# 项目简介：

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。

代码地址（欢迎star）：

[https://github.com/Sharpiless/yolov5-deepsort/](https://github.com/Sharpiless/yolov5-deepsort/)

最终效果：

![在这里插入图片描述](https://github.com/Sharpiless/Yolov5-Deepsort/blob/main/image.png)

# 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

# 运行demo：

```bash

python demo.py

```

# 训练自己的模型：

参考我的另一篇博客：

[【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)

训练好后放到 weights 文件夹下

# 调用接口：

## 创建检测器：

```python

from AIDetector_pytorch import Detector

det = Detector()

```

## 调用检测接口：

```python

result = det.feedCap(im)

```

其中 im 为 BGR 图像

返回的 result 是字典，result['frame'] 返回可视化后的图像

# 联系作者：

> B站：[https://space.bilibili.com/470550823](https://space.bilibili.com/470550823)

> CSDN：[https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889)

> AI Studio：[https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156)

> Github：[https://github.com/Sharpiless](https://github.com/Sharpiless)

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/