YOLOV5 + 双目测距（python）

2023-09-01 19:43

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文章目录

YOLOV5 + 双目相机实现三维测距

YOLOV5 + 双目相机实现三维测距

1. zed + yolov7 实现双目测距
2. zed+yolov4实现双目测距（直接调用，免标定）
3. zed+yolov5实现双目测距（直接调用，免标定）
4. 本文具体实现效果已在哔哩哔哩发布，点击跳转（欢迎投币点赞）
5. 如果有用zed相机的，可以参考我上边的两边文章👆👆👆直接调用内部相机参数，精度比双目测距好很多

下载链接1： https://download.csdn.net/download/qq_45077760/87233955 （CSDN）
下载链接2：https://github.com/up-up-up-up/yolov5_ceju （github）

注：我所做的是在10m以内的检测，没计算过具体误差，当然标定误差越小精度会好一点，其次注意光线、亮度等影响因素，当然检测范围效果跟相机的好坏也有很大关系

在三维测距基础上做了三维跟踪，详见 YOLOv5+双目实现三维跟踪（python）和 YOLOv7+双目实现三维跟踪（python）

1. 项目流程

大致流程: 双目标定→双目校正→立体匹配→结合yolov5→深度测距

找到目标识别源代码中输出物体坐标框的代码段。
找到双目测距代码中计算物体深度的代码段。
将步骤2与步骤1结合，计算得到目标框中物体的深度。
找到目标识别网络中显示障碍物种类的代码段，将深度值添加到里面，进行显示

2. 测距原理

如果想了解双目测距原理，请移步该文章 双目三维测距（python）

3. 代码部分解析

双目相机参数stereoconfig.py
双目相机标定误差越小越好，我这里误差为0.1，尽量使误差在0.2以下

import numpy as np# 双目相机参数class stereoCamera(object):    def __init__(self):        self.cam_matrix_left = np.array([[1101.89299, 0, 1119.89634],             [0, 1100.75252, 636.75282],             [0, 0, 1]])        self.cam_matrix_right = np.array([[1091.11026, 0, 1117.16592],              [0, 1090.53772, 633.28256],              [0, 0, 1]])        self.distortion_l = np.array([[-0.08369, 0.05367, -0.00138, -0.0009, 0]])        self.distortion_r = np.array([[-0.09585, 0.07391, -0.00065, -0.00083, 0]])        self.R = np.array([[1.0000, -0.000603116945856524, 0.00377055351856816],                           [0.000608108737333211, 1.0000, -0.00132288199083992],                           [-0.00376975166958581, 0.00132516525298933, 1.0000]])        self.T = np.array([[-119.99423], [-0.22807], [0.18540]])        self.baseline = 119.99423

以下我stereo.py里的对图像进行处理的代码
这些都是网上现成的，直接套用就可以

class stereo_dd:    def __init__(self,imgl,imgr):        self.left  = imgl        self.right = imgr        # 预处理    def preprocess(self, img1, img2):        # 彩色图->灰度图        if(img1.ndim == 3):#判断为三维数组            img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)  # 通过OpenCV加载的图像通道顺序是BGR        if(img2.ndim == 3):            img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)        # 直方图均衡        img1 = cv2.equalizeHist(img1)        img2 = cv2.equalizeHist(img2)        return img1, img2    '''    # 消除畸变    def undistortion(self, image, camera_matrix, dist_coeff):        undistortion_image = cv2.undistort(image, camera_matrix, dist_coeff)        return undistortion_image    '''    # 消除畸变    def undistortion(self, imagleft,imagright, camera_matrix_left, camera_matrix_right, dist_coeff_left,dist_coeff_right):        undistortion_imagleft  = cv2.undistort(imagleft,  camera_matrix_left,  dist_coeff_left )        undistortion_imagright = cv2.undistort(imagright, camera_matrix_right, dist_coeff_right)        return undistortion_imagleft, undistortion_imagright    # 畸变校正和立体校正    def rectifyImage(self, image1, image2, map1x, map1y, map2x, map2y):        rectifyed_img1 = cv2.remap(image1, map1x, map1y, cv2.INTER_AREA)        rectifyed_img2 = cv2.remap(image2, map2x, map2y, cv2.INTER_AREA)        return rectifyed_img1, rectifyed_img2            # 立体校正检验----画线    def draw_line(self, image1, image2):        # 建立输出图像        height = max(image1.shape[0], image2.shape[0])        width = image1.shape[1] + image2.shape[1]        output = np.zeros((height, width, 3), dtype=np.uint8)        output[0:image1.shape[0], 0:image1.shape[1]] = image1        output[0:image2.shape[0], image1.shape[1]:] = image2        # 绘制等间距平行线        line_interval = 50  # 直线间隔：50        for k in range(height // line_interval):            cv2.line(output, (0, line_interval * (k + 1)), (2 * width, line_interval * (k + 1)), (0, 255, 0), thickness=2, lineType=cv2.LINE_AA)        return output    # 视差计算    def stereoMatchSGBM(self, left_image, right_image, down_scale=False):        # SGBM匹配参数设置        if left_image.ndim == 2:            img_channels = 1        else:            img_channels = 3        blockSize = 3        paraml = {'minDisparity': 0,                 'numDisparities': 128,                 'blockSize': blockSize,                 'P1': 8 * img_channels * blockSize ** 2,                 'P2': 32 * img_channels * blockSize ** 2,                 'disp12MaxDiff': -1,                 'preFilterCap': 63,                 'uniquenessRatio': 10,                 'speckleWindowSize': 100,                 'speckleRange': 1,                 'mode': cv2.STEREO_SGBM_MODE_SGBM_3WAY                 }        # 构建SGBM对象        left_matcher = cv2.StereoSGBM_create(**paraml)        paramr = paraml        paramr['minDisparity'] = -paraml['numDisparities']        right_matcher = cv2.StereoSGBM_create(**paramr)        # 计算视差图        size = (left_image.shape[1], left_image.shape[0])        if down_scale == False:            disparity_left = left_matcher.compute(left_image, right_image)            disparity_right = right_matcher.compute(right_image, left_image)        else:            left_image_down = cv2.pyrDown(left_image)            right_image_down = cv2.pyrDown(right_image)            factor = left_image.shape[1] / left_image_down.shape[1]                        disparity_left_half = left_matcher.compute(left_image_down, right_image_down)            disparity_right_half = right_matcher.compute(right_image_down, left_image_down)            disparity_left = cv2.resize(disparity_left_half, size, interpolation=cv2.INTER_AREA)            disparity_right = cv2.resize(disparity_right_half, size, interpolation=cv2.INTER_AREA)            disparity_left = factor * disparity_left            disparity_right = factor * disparity_right                    trueDisp_left = disparity_left.astype(np.float32) / 16.        trueDisp_right = disparity_right.astype(np.float32) / 16.        return trueDisp_left, trueDisp_right

测距代码部分解析

这一部分我直接计算了目标检测框中心点的深度值，把中心点的深度值当作了距离
你也可以写个循环，计算平均值或者中位数，把他们当作深度值

if (accel_frame % fps_set == 0):    t3 = time.time()      thread.join()    points_3d = thread.get_result()    t4 = time.time()      a = points_3d[int(y_0), int(x_0), 0] / 1000    b = points_3d[int(y_0), int(x_0), 1] / 1000    c = points_3d[int(y_0), int(x_0), 2] / 1000    dis = ((a**2+b**2+c**2)**0.5)

这里我加入了检测人与车之间的三维距离，分为了正常、中等、高风险三个距离等级
你也可以替换成人与人或者车与车等等

##########  plot_one_box 系列 ###########if (distance != 0):  ## Add bbox to image    label = f'{names[int(cls)]} {conf:.2f} '    '''下边这几行如果不需要，可以改成    plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)    我是做了分类，是为了计算人与汽车之间的距离写的'''    if label is not None:        if (label.split())[0] == 'person':            people_coords.append(xyxy)        if (label.split())[0] == 'car' or (label.split())[0] =='truck':            car_coords.append(xyxy)            #plot_dots_on_car(xyxy, im0)        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)],line_thickness=3)##########  annotator.box_label 系列 ###########if names[int(cls)] == "person":     people_coords.append(xyxy)     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     print("x:", point_cloud_value[0], "y:", point_cloud_value[1], "z:",           point_cloud_value[2], "dis:", distance, '', label)     # print("dis:", distance,  "W:", wide)     txt = '{0}  dis:{1} '.format(label, distance)     # annotator.box_label(xyxy, txt, color=(255, 0, 255))     annotator.box_label(xyxy, txt, color=colors(c, True)) if names[int(cls)] == "chair":     car_coords.append(xyxy)     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     print("x:", point_cloud_value[0], "y:", point_cloud_value[1], "z:",           point_cloud_value[2], "dis:", distance, '',label)     #print("dis:", distance,  "W:", wide)     txt = '{0}  dis:{1} '.format(label,distance)     #annotator.box_label(xyxy, txt, color=(255, 0, 255))     annotator.box_label(xyxy, txt, color = colors(c, True))

normal, intermediate, high = distancing(people_coords,car_coords, im0, intermediate, high,normal,dist_thres_lim=(2, 3))

主代码
加入了多线程处理，加快处理速度

import argparseimport timefrom pathlib import Pathimport threadingfrom threading import Threadimport cv2import torchimport torch.backends.cudnn as cudnnfrom numpy import randomimport numpy as npfrom utils.datasets import *  ##1111111111#from utils.utils import *  # 111111111111from PIL import Image, ImageDraw, ImageFontfrom models.experimental import attempt_loadfrom utils.datasets import LoadStreams, LoadImagesfrom utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_pathfrom utils.plots import plot_one_boxfrom utils.torch_utils import select_device, load_classifier, time_synchronizedfrom stereo.dianyuntu_yolo import preprocess, undistortion, getRectifyTransform, draw_line, rectifyImage, \    stereoMatchSGBM  # , hw3ToN3, view_cloud ,DepthColor2Cloudfrom stereo import stereoconfigfrom stereo.stereo import stereo_ddfrom stereo.stereo import get_median, stereo_threading, MyThreadintermediate = 0high = 0normal = 0people_label = " "normal_label = " "inter_label = " "high_label = " "def detect(save_img=False):    accel_frame = 0    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://'))    # Initialize    set_logging()    device = select_device(opt.device)    half = device.type != 'cpu'    # Load model    model = attempt_load(weights, map_location=device)    stride = int(model.stride.max())    imgsz = check_img_size(imgsz,s=stride)    if half:        model.half()    # Second-stage classifier    classify = False    if classify:        modelc = load_classifier(name='resnet101', n=2)        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:        save_stream_dir = Path(            increment_path(Path("./runs/streams") / opt.name, exist_ok=opt.exist_ok))        (save_stream_dir / 'labels' if save_txt else save_stream_dir).mkdir(parents=True,                    exist_ok=True)        flag = 0        view_img = check_imshow()        cudnn.benchmark = True        dataset = LoadStreams(source, img_size=imgsz, stride=stride)        # 获取视频信息，线程抓取图片dataset类中imgs[0]是0个摄像头的图片，LoadStreams是迭代类---》dataset是一个迭代器    else:        # Directories        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)        save_img = True        dataset = LoadImages(source, img_size=imgsz, stride=stride)        print("img_size:")        print(imgsz)    names = model.module.names if hasattr(model, 'module') else model.names    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]    ################################ stereo #############################    if device.type != 'cpu':        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once    t0 = time.time()  # 整个推理过程的开始计时    config = stereoconfig_040_2.stereoCamera()    # 立体校正    map1x, map1y, map2x, map2y, Q = getRectifyTransform(720, 1280, config)    # -----------------摄像头从此处开始反复循环-dataset为迭代器类--------------------------------    for path, img, im0s, vid_cap in dataset:        img = torch.from_numpy(img).to(device)        img = img.half() if half else img.float()        img /= 255.0        if img.ndimension() == 3:            img = img.unsqueeze(0)        t1 = time_synchronized()        pred = model(img, augment=opt.augment)[0]        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)        # List to store bounding coordinates of people      1111111        people_coords = []  # 1111111111        car_coords = []        # Process detections        for i, det in enumerate(pred):            if webcam:                p, s, im0, frame = path[i], '%g: ' % i, im0s[                    i].copy(), dataset.count            else:                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)            fps_set = 10  # setting the frame            if (accel_frame % fps_set == 0):                thread = MyThread(stereo_threading,args=(config, im0, map1x, map1y, map2x, map2y, Q))                thread.start()                print(threading.active_count())                print(threading.enumerate())                print("############## Frame is %d !##################" % accel_frame)            p = Path(p)            if webcam:                save_stream_path = str(save_stream_dir / "stream0.mp4")            else:                save_path = str(save_dir / p.name)                txt_path = str(save_dir / 'labels' / p.stem) + (                    '' if dataset.mode == 'image' else f'_{frame}')            s += '%gx%g ' % img.shape[2:]            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]            if len(det):                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()                for c in det[:, -1].unique():                    n = (det[:, -1] == c).sum()                    s += f"{n} {names[int(c)]} {'s' * (n > 1)} , "                for *xyxy, conf, cls in reversed(det):                    if (0 < xyxy[2] < 1280):                        if save_txt:xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()print("xywh  x : %d, y : %d" % (xywh[0], xywh[1]))line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)with open(txt_path + '.txt', 'a') as f:    f.write(('%g ' * len(line)).rstrip() % line + '\n')                        if save_img or view_img:x_center = (xyxy[0] + xyxy[2]) / 2y_center = (xyxy[1] + xyxy[3]) / 2x_0 = int(x_center)y_0 = int(y_center)if (0 < x_0 < 1280):    x1 = xyxy[0]    x2 = xyxy[2]    y1 = xyxy[1]    y2 = xyxy[3]    if (accel_frame % fps_set == 0):        t3 = time.time()          thread.join()        points_3d = thread.get_result()        t4 = time.time()         print(f'{s}Stereo Done. ({t4 - t3:.3f}s)')        a = points_3d[int(y_0), int(x_0), 0] / 1000        b = points_3d[int(y_0), int(x_0), 1] / 1000        c = points_3d[int(y_0), int(x_0), 2] / 1000        dis = ((a**2+b**2+c**2)**0.5)    distance = []    distance.append(dis)    if (distance != 0):  ## Add bbox to image        label = f'{names[int(cls)]} {conf:.2f} '        '''下边这几行如果不需要，可以改成        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)        我是做了分类，是为了计算人与汽车之间的距离写的'''        if label is not None:            if (label.split())[0] == 'person':                people_coords.append(xyxy)            if (label.split())[0] == 'car' or (label.split())[0] =='truck':                car_coords.append(xyxy)                #plot_dots_on_car(xyxy, im0)            plot_one_box(xyxy, im0, label=label, color=colors[int(cls)],line_thickness=3)        text_xy_0 = "*"        cv2.putText(im0, text_xy_0, (int(x_0), int(y_0)), cv2.FONT_ITALIC, 1.2,(0, 0, 255), 3)        print()        print('点 (%d, %d) 的 %s 距离左摄像头的相对距离为 %0.2f m' % (x_center, y_center, label, distance))        text_dis_avg = "dis:%0.2fm" % distance        # only put dis on frame        cv2.rectangle(im0, (int(x1 + (x2 - x1)), int(y1)),(int(x1 + (x2 - x1) + 5 + 210), int(y1 + 40)), colors[int(cls)],-1)  # 画框存三维坐标        cv2.putText(im0, text_dis_avg, (int(x1 + (x2 - x1) + 5), int(y1 + 30)),cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)        '''同理，下边这一行如果不需要可以去除，我也是做行人与车辆之间距离用的'''        normal, intermediate, high = distancing(people_coords,car_coords, im0, intermediate, high,normal,dist_thres_lim=(2, 3))            t5 = time_synchronized()  # stereo time end            print(f'{s}yolov5 Done. ({t2 - t1:.3f}s)')            if (accel_frame % fps_set == 0):                print(f'{s}yolov5+stereo Done. ({t5 - t1:.3f}s)')            if cv2.waitKey(1) & 0xFF == ord('q'):                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 ''                if view_img:                    s = save_stream_path                print(f"Results saved to {s}")                print(f'All Done. ({time.time() - t0:.3f}s)')                vid_writer.release()                exit()            # Stream results            if view_img:                if (dataset.mode == 'stream') & (flag == 0):                    if isinstance(vid_writer, cv2.VideoWriter):                        vid_writer.release()  # release previous video writer                    fourcc = 'mp4v'  # output video codec                    fps = 24  # vid_cap.get(cv2.CAP_PROP_FPS)                    w = 2560  # int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))                    h = 720  # int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))                    print("save_stream_dir is %s" % save_stream_dir)                    print("save_stream_path is %s" % save_stream_path)                    vid_writer = cv2.VideoWriter(save_stream_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))                    flag = 1                vid_writer.write(im0)                cv2.namedWindow("Webcam", cv2.WINDOW_NORMAL)                cv2.resizeWindow("Webcam", 1280, 480)                cv2.moveWindow("Webcam", 0, 100)                cv2.imshow("Webcam", im0)                cv2.waitKey(1)            # Save results            if save_img:                if dataset.mode == 'image':                    cv2.imwrite(save_path, im0)                else:                    if vid_path != save_path:                        vid_path = save_path                        if isinstance(vid_writer, cv2.VideoWriter):vid_writer.release()                        fourcc = 'mp4v'                        fps = 24                        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)                    cv2.namedWindow("Video", cv2.WINDOW_NORMAL)                    cv2.resizeWindow("Video", 1280, 480)                    cv2.moveWindow("Video", 0, 0)                    cv2.imshow("Video", im0)                    cv2.waitKey(1)        print("frame %d is done!" % accel_frame)        accel_frame += 1    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'All Done. ({time.time() - t0:.3f}s)')if __name__ == '__main__':    parser = argparse.ArgumentParser()    parser.add_argument('--weights', nargs='+', type=str, default='./yolov5s.pt', help='model.pt path(s)')    parser.add_argument('--source', type=str, default='./data/video/test.mp4',help='source')    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:            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:                detect()                strip_optimizer(opt.weights)        else:            detect()