yolov5实现实时目标检测和测距（pycharm）_yolov5摄像头实时检测

首先大家可以参考这篇博客部署好自己的环境：http://t.csdnimg.cn/erGYw

本文前提：已实现yolov5通过摄像头实时目标检测

一、测距文件的编写

首先需要知道自己所用的相机的像素焦距，并将其加入测距代码distance.py文件里

苗的高度18.1（单位英寸→对应cm/2.54）

foc = 933.33        # 镜头焦距
real_hight_miao = 18.1   # 苗高度
 
 
# 自定义函数，单目测距
def miao_distance(h):
    dis_inch = (real_hight_miao * foc) / (h - 2)
    dis_cm = dis_inch * 2.54
    dis_cm = int(dis_cm)
    dis_m = dis_cm/100
    return dis_m

二、测距文件的引入

然后在detect.py文件中加入以下代码

from distance import miao_distance

三、detect.py文件的修改

将if len(det)：到if save_crop:中的代码替换为下面所示

            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
 
                # Print results
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
 
                # Write results
                for *xyxy, conf, cls in reversed(det):
 
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
                    if save_img or save_crop or view_img:  # Add bbox to image
                        x1 = int(xyxy[0])  # 获取四个边框坐标
                        y1 = int(xyxy[1])
                        x2 = int(xyxy[2])
                        y2 = int(xyxy[3])
                        h = y2 - y1
                        if names[int(cls)] == "miao":
                            c = int(cls)  # integer class  整数类 1111111111
                            label = None if hide_labels else (
                                names[c] if hide_conf else f'{names[c]} {conf:.2f}')  # 111
                            dis_m = miao_distance(h)  # 调用函数，计算苗实际高度
                            label += f'  {dis_m}m'  # 将苗的距离显示写在标签后
                            txt = '{0}'.format(label)
                            annotator.box_label(xyxy, txt, color=colors(c, True))
 
 
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

运行代码即可得到最终结果