YOLOV9 + 双目测距

积极向上的mr.d2024-04-08 18:05

YOLOV9 + 双目测距

相关文章
1. YOLOV5 + 双目测距（python）
2. YOLOv7+双目测距（python）
3. YOLOv8+双目测距（python）

如果有用zed相机的，可以进我主页👇👇👇直接调用内部相机参数，精度比双目测距好很多
https://blog.csdn.net/qq_45077760

下载链接（求STAR）：https://github.com/up-up-up-up/YOLOv9-stereo

1. 环境配置

python==3.8

Windows-pycharm

yolov9代码和yolov5类似，感觉还可以，挺好写

2. 测距流程和原理

2.1 测距流程

大致流程: 双目标定→双目校正→立体匹配→结合yolov9→深度测距

  1. 找到目标识别源代码中输出物体坐标框的代码段。
  2. 找到双目测距代码中计算物体深度的代码段。
  3. 将步骤2与步骤1结合，计算得到目标框中物体的深度。
  4. 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

注：我所做的是在20m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

2.2 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

3.1 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

python 复制代码 
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

3.2 测距部分

这一部分我用了多线程加快速度，计算目标检测框中心点的深度值

python 复制代码 
config = stereoconfig_040_2.stereoCamera()
    # 立体校正
map1x, map1y, map2x, map2y, Q = getRectifyTransform(720, 1280, config)
for path, im, im0s, vid_cap, s in dataset:
    with dt[0]:
        im = torch.from_numpy(im).to(model.device)
        im = im.half() if model.fp16 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

    # Inference
    with dt[1]:
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)

    # NMS
    with dt[2]:
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

    # 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)
        thread = MyThread(stereo_threading, args=(config, im0, map1x, map1y, map2x, map2y, Q))
        thread.start()
        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_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 (0 < xyxy[2] < 1280):
                    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(f'{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
                        c = int(cls)  # integer class
                        x_center = (xyxy[0] + xyxy[2]) / 2
                        y_center = (xyxy[1] + xyxy[3]) / 2
                        x_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]

                            thread.join()
                            points_3d = thread.get_result()
                            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
                            distance = ((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} '
                                annotator.box_label(xyxy, label, color=colors(c, True))

                                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.putText(im0, text_dis_avg, (int(x2 + 5), int(y1 + 30)),
                                            cv2.FONT_ITALIC, 1.2,
                                            (0, 255, 255), 3)

3.3 主代码yolov9-stereo.py

python 复制代码 
import argparse
import os
import platform
import sys
from pathlib import Path
from stereo import stereoconfig_040_2
from stereo.stereo import stereo_40
from stereo.stereo import stereo_threading, MyThread
from stereo.dianyuntu_yolo import preprocess, undistortion, getRectifyTransform, draw_line, rectifyImage, \
    stereoMatchSGBM
import torch

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLO root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode


@smart_inference_mode()
def run(
        weights=ROOT / 'yolo.pt',  # model path or triton URL
        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
        data=ROOT / 'data/coco.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
        vid_stride=1,  # video frame-rate stride
):
    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)
    screenshot = source.lower().startswith('screen')
    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, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    bs = 1  # batch_size
    if webcam:
        view_img = check_imshow(warn=True)
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset)
    elif screenshot:
        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
    config = stereoconfig_040_2.stereoCamera()
    # 立体校正
    map1x, map1y, map2x, map2y, Q = getRectifyTransform(720, 1280, config)
    for path, im, im0s, vid_cap, s in dataset:
        with dt[0]:
            im = torch.from_numpy(im).to(model.device)
            im = im.half() if model.fp16 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

        # Inference
        with dt[1]:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            pred = model(im, augment=augment, visualize=visualize)

        # NMS
        with dt[2]:
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

        # 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)
            thread = MyThread(stereo_threading, args=(config, im0, map1x, map1y, map2x, map2y, Q))
            thread.start()
            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_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 (0 < xyxy[2] < 1280):
                        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(f'{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
                            c = int(cls)  # integer class
                            x_center = (xyxy[0] + xyxy[2]) / 2
                            y_center = (xyxy[1] + xyxy[3]) / 2
                            x_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]

                                thread.join()
                                points_3d = thread.get_result()
                                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
                                distance = ((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} '
                                    annotator.box_label(xyxy, label, color=colors(c, True))

                                    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.putText(im0, text_dis_avg, (int(x2 + 5), int(y1 + 30)),
                                                cv2.FONT_ITALIC, 1.2,
                                                (0, 255, 255), 3)


                        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:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                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' 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:  # video
                            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))
                        else:  # stream
                            fps, 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}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")

    # Print results
    t = tuple(x.t / 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[0])  # update model (to fix SourceChangeWarning)


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'gelan-c-det.pt', help='model path or triton URL')
    parser.add_argument('--source', type=str, default=ROOT / 'data/images/a1.mp4', help='file/dir/URL/glob/screen/0(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', default=True,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')
    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(vars(opt))
    return opt


def main(opt):
    # check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)

4. 实验结果

4.1 测距

4.2 视频展示

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