系统环境：VMware Fusion 虚拟机 Ubuntu18.04
CPU: intel core i7 8750H
python版本：python3.6.13（anaconda安装的python3.6的虚拟环境）
yolov5模型版本：YOLO v5s
双目摄像头间距：12cm
双目摄像头焦距：100度/3mm
双目摄像头输出分辨率为：2560*720。

1、首先安装YOLO v5

YOLO v5的安装请参考我的另一篇博客：https://blog.csdn.net/qq_40700822/article/details/118487596

2、数据集的标定

参考我的另一篇博客：https://blog.csdn.net/qq_40700822/article/details/118550250

3、双目测距代码的单独运行调试

参考我的另一篇博客：https://blog.csdn.net/qq_40700822/article/details/115765728

4、YOLO v5与双目测距的代码的结合

我用的双目相机长这样，某宝220元购入的。

要想将双目测距的代码加入到YOLO v5中，就需要将YOLO v5检测目标的代码看懂，这部分学起来对我来说是比较吃力的。

我这里的结合用的比较简单，就是把双目测距的代码加入到了yolov5的detect.py中。具体加在了打印目标框的位置，如下代码所示。

detect_and_strereo_video_003.py

 # -*- coding: utf-8 -*-
import argparse
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

import numpy as np 
from PIL import Image, ImageDraw, ImageFont

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
     scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized

from stereo.dianyuntu_yolo import preprocess, undistortion, getRectifyTransform, draw_line, rectifyImage,\
     stereoMatchSGBM, hw3ToN3, DepthColor2Cloud, view_cloud

from stereo import stereoconfig_040_2

num = 210 #207 209 210 211
def detect(save_img=False):
    num = 210
    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://') )

    # Directories
    save_dir = Path( increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) )  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
        print("img_size:")
        print(imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{ 
     frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == 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

                        print("xywh x : %d, y : %d"%(xywh[0],xywh[1]) )
                        line = (cls, *xywh, conf) if opt.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 view_img:  # Add bbox to image
                        label = f'{ 
     names[int(cls)]} { 
     conf:.2f} '
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
                        ##print label x,y zuobiao 

                        x = (xyxy[0] + xyxy[2]) / 2
                        y = (xyxy[1] + xyxy[3]) / 2
                        #print(" %s is x: %d y: %d " %(label,x,y) )
                        height_0, width_0 = im0.shape[0:2]
                        
                        if (x <= int(width_0/2) ):
                            t3 = time_synchronized()
    
                            ################################
                            #stereo code
                            p = num
                            string = ''
                            #print("P is %d" %p )
                            # 读取数据集的图片
                            #iml = cv2.imread('./stereo/yolo/zuo/%szuo%d.bmp' %(string,p) ) # 左图
                            #imr = cv2.imread('./stereo/yolo/you/%syou%d.bmp' %(string,p) ) # 右图

                            #iml = cv2.imread('./stereo/yolo/zuo/%szuo%d.bmp' %(string,p) ) # 左图
                            #imr = cv2.imread('./stereo/yolo/you/%syou%d.bmp' %(string,p) ) # 右图
                            
                            #height_0, width_0 = im0.shape[0:2]

                            #print("width_0 = %d " % width_0)
                            #print("height_0 = %d " % height_0)

                            
                            iml = im0[0:int(height_0), 0:int(width_0/2)]
                            imr = im0[0:int(height_0), int(width_0/2):int(width_0) ]

                            height, width = iml.shape[0:2]

                            #cv2.imshow("iml",iml)
                            #cv2.imshow("imr",im0)
                            #cv2.waitKey(0)

                            #print("width = %d " % width)
                            #print("height = %d " % height)

                            # 读取相机内参和外参
                            config = stereoconfig_040_2.stereoCamera()

                            # 立体校正
                            map1x, map1y, map2x, map2y, Q = getRectifyTransform(height, width, config)  # 获取用于畸变校正和立体校正的映射矩阵以及用于计算像素空间坐标的重投影矩阵
                            #print("Print Q!")
                            #print("Q[2,3]:%.3f"%Q[2,3])
                            iml_rectified, imr_rectified = rectifyImage(iml, imr, map1x, map1y, map2x, map2y)


                                
                            # 绘制等间距平行线，检查立体校正的效果
                            line = draw_line(iml_rectified, imr_rectified)
                            #cv2.imwrite('./yolo/%s检验%d.png' %(string,p), line)

                            # 消除畸变
                            iml = undistortion(iml, config.cam_matrix_left, config.distortion_l)
                            imr = undistortion(imr, config.cam_matrix_right, config.distortion_r)
                        
                            # 立体匹配
                            iml_, imr_ = preprocess(iml, imr)  # 预处理，一般可以削弱光照不均的影响，不做也可以

                            iml_rectified_l, imr_rectified_r = rectifyImage(iml_, imr_, map1x, map1y, map2x, map2y)
                            
                            disp, _ = stereoMatchSGBM(iml_rectified_l, imr_rectified_r, True) 
                            #cv2.imwrite('./yolo/%s视差%d.png' %(string,p), disp)


                            # 计算像素点的3D坐标（左相机坐标系下）
                            points_3d = cv2.reprojectImageTo3D(disp, Q)  # 可以使用上文的stereo_config.py给出的参数

                            #points_3d = points_3d

                            ''' #print("x is :%.3f" %points_3d[int(y), int(x), 0] ) print('点 (%d, %d) 的三维坐标 (x:%.3fcm, y:%.3fcm, z:%.3fcm)' % (int(x), int(y), points_3d[int(y), int(x), 0]/10, points_3d[int(y), int(x), 1]/10, points_3d[int(y), int(x), 2]/10) ) '''
                            count = 0
                            #try:
                            while( (points_3d[int(y), int(x), 2] < 0) | (points_3d[int(y), int(x), 2] > 2500) ):

                                count += 1
                                x += count
                                if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
                                    break
                                y += count
                                if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
                                    break

                                count += 1
                                x -= count
                                if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
                                    break
                                y -= count
                                if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
                                    break

                                #if(count%2==1):
                                # x += 1
                                #else:
                                # y += 1

                                

                            text_cxy = "*"
                            cv2.putText(im0, text_cxy, (x, y) ,  cv2.FONT_ITALIC, 1.2, (0,0,255), 3)
                            
                            #print("count is %d" %count)
                            print('点 (%d, %d) 的三维坐标 (x:%.1fcm, y:%.1fcm, z:%.1fcm)' % (int(x), int(y), 
                                points_3d[int(y), int(x), 0]/10, 
                                points_3d[int(y), int(x), 1]/10, 
                                points_3d[int(y), int(x), 2]/10) )


                            dis = ( (points_3d[int(y), int(x), 0] ** 2 + points_3d[int(y), int(x), 1] ** 2 + points_3d[int(y), int(x), 2] **2) ** 0.5 ) / 10
                            print('点 (%d, %d) 的 %s 距离左摄像头的相对距离为 %0.1f cm' %(x, y,label, dis) )
                        

                            text_x = "x:%.1fcm" %(points_3d[int(y), int(x), 0]/10)
                            text_y = "y:%.1fcm" %(points_3d[int(y), int(x), 1]/10)
                            text_z = "z:%.1fcm" %(points_3d[int(y), int(x), 2]/10)
                            text_dis = "dis:%.1fcm" %dis

                            cv2.rectangle(im0,(xyxy[0]+(xyxy[2]-xyxy[0]),xyxy[1]),(xyxy[0]+(xyxy[2]-xyxy[0])+5+220,xyxy[1]+150),colors[int(cls)],-1);
                            cv2.putText(im0, text_x, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+30),  cv2.FONT_ITALIC, 1.2, (255,255,255), 3)
                            cv2.putText(im0, text_y, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+65),  cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)
                            cv2.putText(im0, text_z, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+100), cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)
                            cv2.putText(im0, text_dis, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+145), cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)


                            t4 = time_synchronized()
                            print(f'Done. ({ 
     t4 - t3:.3f}s)')




            # Print time (inference + NMS)
            print(f'{ 
     s}Done. ({ 
     t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fourcc = 'mp4v'  # output video codec
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        s = f"\n{ 
     len(list(save_dir.glob('labels/*.txt')))} labels saved to { 
     save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to { 
     save_dir}{ 
     s}")

    print(f'Done. ({ 
     time.time() - t0:.3f}s)')




if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='last_dead_fish_1000.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='./shuangmu_dead_fish_011.mp4' , help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    opt = parser.parse_args()
    print(opt)
    check_requirements()

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()

5、最终识别测距效果

由于我使用的死鱼的模型，所以测试就使用的死鱼照片测试的。
效果如下图所示。

近景效果。

远景效果。

6、代码下载调试运行

yolov5加双目测距的代码，下载后直接运行detect_and_stereo_video_003.py即可开始识别定位。注意是在yolov5的环境运行的。
资源包下载地址：
https://download.csdn.net/download/qq_40700822/87226878?spm=1001.2014.3001.5503

运行detect_and_stereo_video_003.py程序后出现以下情况表示，运行成功，可以把自己的模型替换掉我的模型，实现其他物体的识别测距和定位。注意摄像头的型号规格。

7、最终演示视频

欢迎各位点赞投币收藏哦🤣🤣🤣！【毕业设计-基于YOLO V5智能追踪记录装置的设计-哔哩哔哩】https://b23.tv/iTkkRJ

8、需要指导的小伙伴可以加我QQ哦～

$ QQ=1024062844
$ 加QQ群免费获取资料哦：544210336

今天的文章YOLO v5与双目测距结合，实现目标的识别和定位测距分享到此就结束了，感谢您的阅读。