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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)
+
+# 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-inference](https://github.com/Sharpiless/Yolov5-deepsort-inference)
+
+最终效果：
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20201231090541223.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDkzNjg4OQ==,size_16,color_FFFFFF,t_70)
+# 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
+func_status = {}
+func_status['headpose'] = None
+
+result = det.feedCap(im, func_status)
+```
+
+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：
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20201120115403928.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDkzNjg4OQ==,size_16,color_FFFFFF,t_70#pic_center)
+