Delete Yolov5_Deepsort/README.md

Yolov5_Deepsort/README.md

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-# 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/
-
-