YOLOv5+单目测距（python）

• 1. 相关配置
• 2. 测距原理
• 3. 相机标定
• • 3.1：标定方法1
  • 3.2：标定方法2
• 4. 相机测距
• • 4.1 测距添加
  • 4.2 细节修改（可忽略）
  • 4.3 主代码
• 5. 实验效果

相关链接
1. YOLOV7 + 单目测距（python）
2. YOLOV5 + 单目跟踪（python）
3. YOLOV7 + 单目跟踪（python）
4. YOLOV5 + 双目测距（python）
5. YOLOV7 + 双目测距（python）
6. 具体实现效果已在Bilibili发布，点击跳转

本篇博文工程源码下载
链接1：https://download.csdn.net/download/qq_45077760/87708260
链接2：https://github.com/up-up-up-up/yolov5_Monocular_ranging

更多有关单目（尺寸测量，跟踪、碰撞检测等）的文章请见：https://blog.csdn.net/qq_45077760/category_12312107.html

1. 相关配置

系统：win 10
YOLO版本：yolov5 6.1
拍摄视频设备：安卓手机
电脑显卡：NVIDIA 2080Ti（CPU也可以跑，GPU只是起到加速推理效果）

2. 测距原理

单目测距原理相较于双目十分简单，无需进行立体匹配，仅需利用下边公式线性转换即可：

            D = (F*W)/P

其中D是目标到摄像机的距离, Ｆ是摄像机焦距（焦距需要自己进行标定获取）, W是目标的宽度或者高度（行人检测一般以人的身高为基准）, P是指目标在图像中所占据的像素

了解基本原理后，下边就进行实操阶段

3. 相机标定

3.1：标定方法1

可以参考张学友标定法获取相机的焦距

3.2：标定方法2

直接使用代码获得焦距，需要提前拍摄一个矩形物体，拍摄时候相机固定，距离被拍摄物体自行设定，并一直保持此距离，背景为纯色，不要出现杂物；最后将拍摄的视频用以下代码检测：

import cv2win_width = 1920win_height = 1080mid_width = int(win_width / 2)mid_height = int(win_height / 2)foc = 1990.0       # 根据教程调试相机焦距real_wid = 9.05   # A4纸横着的时候的宽度，视频拍摄A4纸要横拍，镜头横，A4纸也横font = cv2.FONT_HERSHEY_SIMPLEXw_ok = 1capture = cv2.VideoCapture('5.mp4')capture.set(3, win_width)capture.set(4, win_height)while (True):    ret, frame = capture.read()    # frame = cv2.flip(frame, 1)    if ret == False:        break    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    gray = cv2.GaussianBlur(gray, (5, 5), 0)    ret, binary = cv2.threshold(gray, 140, 200, 60)    # 扫描不到纸张轮廓时，要更改阈值，直到方框紧密框住纸张    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))    binary = cv2.dilate(binary, kernel, iterations=2)    contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHaiN_APPROX_SIMPLE)    # cv2.drawContours(frame, contours, -1, (0, 255, 0), 2)    # 查看所检测到的轮框    for c in contours:        if cv2.contourArea(c) < 1000:  # 对于矩形区域，只显示大于给定阈值的轮廓，所以一些微小的变化不会显示。对于光照不变和噪声低的摄像头可不设定轮廓最小尺寸的阈值            continue        x, y, w, h = cv2.boundingRect(c)  # 该函数计算矩形的边界框        if x > mid_width or y > mid_height:            continue        if (x + w) < mid_width or (y + h) < mid_height:            continue        if h > w:            continue        if x == 0 or y == 0:            continue        if x == win_width or y == win_height:            continue        w_ok = w        cv2.rectangle(frame, (x + 1, y + 1), (x + w_ok - 1, y + h - 1), (0, 255, 0), 2)    dis_inch = (real_wid * foc) / (w_ok - 2)    dis_cm = dis_inch * 2.54    # os.system("cls")    # print("Distance : ", dis_cm, "cm")    frame = cv2.putText(frame, "%.2fcm" % (dis_cm), (5, 25), font, 0.8, (0, 255, 0), 2)    frame = cv2.putText(frame, "+", (mid_width, mid_height), font, 1.0, (0, 255, 0), 2)    cv2.namedWindow('res', 0)    cv2.namedWindow('gray', 0)    cv2.resizeWindow('res', win_width, win_height)    cv2.resizeWindow('gray', win_width, win_height)    cv2.imshow('res', frame)    cv2.imshow('gray', binary)    c = cv2.waiTKEy(40)    if c == 27:    # 按退出键esc关闭窗口        breakcv2.destroyAllwindows()

反复调节 ret, binary = cv2.threshold(gray, 140, 200, 60)这一行里边的三个参数，直到线条紧紧包裹住你所拍摄视频的物体，然后调整相机焦距直到左上角距离和你拍摄视频时相机到物体的距离接近为止

然后将相机焦距写进测距代码distance.py文件里，这里行人用高度表示，根据公式 D = (F*W)/P，知道相机焦距F、行人的高度66.9（单位英寸→170cm/2.54）、像素点距离 h，即可求出相机到物体距离D。 这里用到h-2是因为框的上下边界像素点不接触物体

foc = 1990.0        # 镜头焦距real_hight_person = 66.9   # 行人高度real_hight_car = 57.08      # 轿车高度# 自定义函数，单目测距def person_distance(h):    dis_inch = (real_hight_person * foc) / (h - 2)    dis_cm = dis_inch * 2.54    dis_cm = int(dis_cm)    dis_m = dis_cm/100    return dis_mdef car_distance(h):    dis_inch = (real_hight_car * foc) / (h - 2)    dis_cm = dis_inch * 2.54    dis_cm = int(dis_cm)    dis_m = dis_cm/100    return dis_m

4. 相机测距

4.1 测距添加

主要是把测距部分加在了画框附近，首先提取边框的像素点坐标，然后计算边框像素点高度，在根据 公式 D = (F*W)/P 计算目标距离

 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)] == "person":             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 = person_distance(h)   # 调用函数，计算行人实际高度             label += f'  {dis_m}m'       # 将行人距离显示写在标签后             txt = '{0}'.format(label)             annotator.box_label(xyxy, txt, color=colors(c, True))         if names[int(cls)] == "car":             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 = car_distance(h)      # 调用函数，计算汽车实际高度             label += f'  {dis_m}m'       # 将汽车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)

4.2 细节修改（可忽略）

到上述步骤就已经实现了单目测距过程，下边是一些小细节修改，可以不看
为了实时显示画面，对运行的py文件点击编辑配置，在形参那里输入–view-img --save-txt

但实时显示画面太大，我们对显示部分做了修改，这部分也可以不要，具体是把代码

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

替换成

if view_img:     cv2.namedWindow("WEBcam", cv2.WINDOW_NORMAL)     cv2.resizeWindow("Webcam", 1280, 720)     cv2.moveWindow("Webcam", 0, 100)     cv2.imshow("Webcam", im0)     cv2.waitKey(1)

4.3 主代码

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license"""Run inference on images, videos, directories, streams, etc.Usage - sources:    $ Python path/to/detect.py --weights yolov5s.pt --source 0              # webcam     img.jpg        # image     vid.mp4        # video     path/          # directory     path/*.jpg     # glob     'https://youtu.be/Zgi9g1ksQHc'  # YouTube     'rtsp://example.com/media.mp4'  # RTSP, RTMP, Http streamUsage - formats:    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch             yolov5s.torchscript        # TorchScript             yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn             yolov5s.xml                # OpenVINO             yolov5s.engine             # TensorRT             yolov5s.mlmodel            # CoreML (MacOS-only)             yolov5s_saved_model        # Tensorflow SavedModel             yolov5s.pb                 # TensorFlow GraphDef             yolov5s.tflite             # TensorFlow Lite             yolov5s_edgetpu.tflite     # TensorFlow Edge TPU"""import argparseimport osimport sysfrom pathlib import Pathimport cv2import torchimport torch.backends.cudnn as cudnnFILE = Path(__file__).resolve()ROOT = FILE.parents[0]  # YOLOv5 root directoryif str(ROOT) not in sys.path:    sys.path.append(str(ROOT))  # add ROOT to PATHROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom models.common import DetectMultiBackendfrom utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreamsfrom utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)from utils.plots import Annotator, colors, save_one_boxfrom utils.torch_utils import select_device, time_syncfrom distance import person_distance,car_distance@torch.no_grad()def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path        imgsz=(640, 640),  # inference size (height, width)        conf_thres=0.25,  # confidence threshold        iou_thres=0.45,  # NMS IOU threshold        max_det=1000,  # maximum detections per image        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu        view_img=False,  # show results        save_txt=False,  # save results to *.txt        save_conf=False,  # save confidences in --save-txt labels        save_crop=False,  # save cropped prediction boxes        nosave=False,  # do not save images/videos        classes=None,  # filter by class: --class 0, or --class 0 2 3        agnostic_nms=False,  # class-agnostic NMS        augment=False,  # augmented inference        visualize=False,  # visualize features        update=False,  # update all models        project=ROOT / 'runs/detect',  # save results to project/name        name='exp',  # save results to project/name        exist_ok=False,  # existing project/name ok, do not increment        line_thickness=3,  # bounding box thickness (pixels)        hide_labels=False,  # hide labels        hide_conf=False,  # hide confidences        half=False,  # use FP16 half-precision inference        dnn=False,  # use OpenCV DNN for ONNX inference        ):    source = str(source)    save_img = not nosave and not source.endswith('.txt')  # save inference images    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)    if is_url and is_file:        source = check_file(source)  # download    # Directories    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir    # Load model    device = select_device(device)    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)    stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine    imgsz = check_img_size(imgsz, s=stride)  # check image size    # Half    half &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16 supported on limited backends with CUDA    if pt or jit:        model.model.half() if half else model.model.float()    # Dataloader    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, auto=pt)        bs = len(dataset)  # batch_size    else:        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)        bs = 1  # batch_size    vid_path, vid_writer = [None] * bs, [None] * bs    # Run inference    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=half)  # warmup    dt, seen = [0.0, 0.0, 0.0], 0    for path, im, im0s, vid_cap, s in dataset:        t1 = time_sync()        im = torch.from_numpy(im).to(device)        im = im.half() if half else im.float()  # uint8 to fp16/32        im /= 255  # 0 - 255 to 0.0 - 1.0        if len(im.shape) == 3:            im = im[None]  # expand for batch dim        t2 = time_sync()        dt[0] += t2 - t1        # Inference        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False        pred = model(im, augment=augment, visualize=visualize)        t3 = time_sync()        dt[1] += t3 - t2        # NMS        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)        dt[2] += time_sync() - t3        # Second-stage classifier (optional)        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)        # Process predictions        for i, det in enumerate(pred):  # per image            seen += 1            if webcam:  # batch_size >= 1                p, im0, frame = path[i], im0s[i].copy(), dataset.count                s += f'{i}: '            else:                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)            p = Path(p)  # to Path            save_path = str(save_dir / p.name)  # im.jpg            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt            s += '%gx%g ' % im.shape[2:]  # print string            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh            imc = im0.copy() if save_crop else im0  # for save_crop            annotator = Annotator(im0, line_width=line_thickness, example=str(names))            if len(det):                # Rescale boxes from img_size to im0 size                det[:, :4] = scale_coords(im.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                        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])                        y1 = int(xyxy[1])                        x2 = int(xyxy[2])                        y2 = int(xyxy[3])                        h = y2-y1                        if names[int(cls)] == "person":c = int(cls)  # integer class  整数类 1111111111label = None if hide_labels else (    names[c] if hide_conf else f'{names[c]} {conf:.2f}')  # 111dis_m = person_distance(h)label += f'  {dis_m}m'txt = '{0}'.format(label)# annotator.box_label(xyxy, txt, color=(255, 0, 255))annotator.box_label(xyxy, txt, color=colors(c, True))                        if names[int(cls)] == "car":c = int(cls)  # integer class  整数类 1111111111label = None if hide_labels else (    names[c] if hide_conf else f'{names[c]} {conf:.2f}')  # 111dis_m = car_distance(h)label += f'  {dis_m}m'txt = '{0}'.format(label)# annotator.box_label(xyxy, txt, color=(255, 0, 255))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)            # Stream results            im0 = annotator.result()            '''if view_img:                cv2.imshow(str(p), im0)                cv2.waitKey(1)  # 1 millisecond'''            if view_img:                cv2.namedWindow("Webcam", cv2.WINDOW_NORMAL)                cv2.resizeWindow("Webcam", 1280, 720)                cv2.moveWindow("Webcam", 0, 100)                cv2.imshow("Webcam", im0)                cv2.waitKey(1)            # Save results (image with detections)            if save_img:                if dataset.mode == 'image':                    cv2.imwrite(save_path, im0)                else:  # 'video' or 'stream'                    if vid_path[i] != save_path:  # new video                        vid_path[i] = save_path                        if isinstance(vid_writer[i], cv2.VideoWriter):vid_writer[i].release()  # release previous video writer                        if vid_cap:  # videofps = 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))                        else:  # streamfps, w, h = 30, im0.shape[1], im0.shape[0]                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))                    vid_writer[i].write(im0)        # Print time (inference-only)        LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')    # Print results    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)    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 ''        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")    if update:        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)def parse_opt():    parser = argparse.ArgumentParser()    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')    parser.add_argument('--source', type=str, default=ROOT / 'data/images/1.mp4', help='file/dir/URL/glob, 0 for webcam')    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')    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='show 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('--save-crop', action='store_true', help='save cropped prediction boxes')    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features')    parser.add_argument('--update', action='store_true', help='update all models')    parser.add_argument('--project', default=ROOT / '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')    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')    opt = parser.parse_args()    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand    print_args(FILE.stem, opt)    return optdef main(opt):    check_requirements(exclude=('tensorboard', 'thop'))    run(**vars(opt))if __name__ == "__main__":    opt = parse_opt()    main(opt)

5. 实验效果

实验效果如下

更多有关单目（尺寸测量，跟踪、碰撞检测等）的文章请见：https://blog.csdn.net/qq_45077760/category_12312107.html

来源地址：https://blog.csdn.net/qq_45077760/article/details/130261489