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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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本文地址:https://blog.csdn.net/weixin_44936889/article/details/112002152

注意:

本项目使用Yolov5 3.0版本,最新版本5.0请移步:

https://github.com/Sharpiless/yolov5-deepsort

注:新版本添加了类别显示功能

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

最终效果: 在这里插入图片描述

YOLOv5检测器:

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:

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:

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)

训练好后放到 weights 文件夹下

调用接口:

创建检测器:

from AIDetector_pytorch import Detector

det = Detector()

调用检测接口:

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

关注我的公众号:

感兴趣的同学关注我的公众号——可达鸭的深度学习教程:

在这里插入图片描述

联系作者:

B站:https://space.bilibili.com/470550823

CSDN:https://blog.csdn.net/weixin_44936889

AI Studio:https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156

Github: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/

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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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