温州手机网站制作哪家便宜,企业网站的主要内容,怎么做新网站上线通稿,网络规划设计方案模板YOLOV8 双目测距 1. 环境配置2. 测距流程和原理2.1 测距流程2.2 测距原理 3. 代码部分解析3.1 相机参数stereoconfig.py3.2 测距部分3.3 主代码yolov8-stereo.py 4. 实验结果4.1 测距4.2 测距跟踪4.3 测距跟踪分割4.4 视频展示 相关文章 1. YOLOv5双目测距#xff08;python 双目测距 1. 环境配置2. 测距流程和原理2.1 测距流程2.2 测距原理 3. 代码部分解析3.1 相机参数stereoconfig.py3.2 测距部分3.3 主代码yolov8-stereo.py 4. 实验结果4.1 测距4.2 测距跟踪4.3 测距跟踪分割4.4 视频展示 相关文章 1. YOLOv5双目测距python 2. YOLOv7双目测距python
如果有用zed相机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STARhttps://github.com/up-up-up-up/YOLOv8-stereo
1. 环境配置
具体可见 WindowsYOLOV8环境配置
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yolov8→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
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
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
config stereoconfig_040_2.stereoCamera()
map1x, map1y, map2x, map2y, Q getRectifyTransform(720, 1280, config)
thread MyThread(stereo_threading, args(config, im0, map1x, map1y, map2x, map2y, Q))
thread.start()
results model.predict(im0, saveFalse, conf0.5)
annotated_frame results[0].plot()
boxes results[0].boxes.xywh.cpu()
for i, box in enumerate(boxes):# for box, class_idx in zip(boxes, classes):x_center, y_center, width, height box.tolist()x1 x_center - width / 2y1 y_center - height / 2x2 x_center width / 2y2 y_center height / 2if (0 x2 1280):thread.join()points_3d thread.get_result()# gol.set_value(points_3d, points_3d)a points_3d[int(y_center), int(x_center), 0] / 1000b points_3d[int(y_center), int(x_center), 1] / 1000c points_3d[int(y_center), int(x_center), 2] / 1000distance ((a ** 2 b ** 2 c ** 2) ** 0.5)3.3 主代码yolov8-stereo.py
1加入了多线程处理加快处理速度 2如果想打开相机直接把cap cv2.VideoCapture(‘a1.mp4’)改成cap cv2.VideoCapture(0)即可
import cv2
import torch
import argparse
from ultralytics import YOLO
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, \stereoMatchSGBMdef main():cap cv2.VideoCapture(ultralytics/assets/a1.mp4)model YOLO(yolov8n.pt)cv2.namedWindow(00, cv2.WINDOW_NORMAL)cv2.resizeWindow(00, 1280, 360) # 设置宽高out_video cv2.VideoWriter(output.avi, cv2.VideoWriter_fourcc(*XVID), 30, (2560, 720))while True:ret, im0 cap.read()if not ret:print(Video frame is empty or video processing has been successfully completed.)break# img cv2.cvtColor(image_net, cv2.COLOR_BGRA2BGR)config stereoconfig_040_2.stereoCamera()map1x, map1y, map2x, map2y, Q getRectifyTransform(720, 1280, config)thread MyThread(stereo_threading, args(config, im0, map1x, map1y, map2x, map2y, Q))thread.start()results model.predict(im0, saveFalse, conf0.5)annotated_frame results[0].plot()boxes results[0].boxes.xywh.cpu()for i, box in enumerate(boxes):# for box, class_idx in zip(boxes, classes):x_center, y_center, width, height box.tolist()x1 x_center - width / 2y1 y_center - height / 2x2 x_center width / 2y2 y_center height / 2if (0 x2 1280):thread.join()points_3d thread.get_result()# gol.set_value(points_3d, points_3d)a points_3d[int(y_center), int(x_center), 0] / 1000b points_3d[int(y_center), int(x_center), 1] / 1000c points_3d[int(y_center), int(x_center), 2] / 1000distance ((a ** 2 b ** 2 c ** 2) ** 0.5)if (distance ! 0):text_dis_avg dis:%0.2fm % distancecv2.putText(annotated_frame, text_dis_avg, (int(x2 5), int(y1 30)), cv2.FONT_ITALIC, 1.2,(0, 255, 255), 3)cv2.imshow(00, annotated_frame)out_video.write(annotated_frame)key cv2.waitKey(1)if key q:breakout_video.release()cap.release()cv2.destroyAllWindows()if __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--weights, typestr, defaultyolov8n.pt, helpmodel.pt path(s))parser.add_argument(--svo, typestr, defaultNone, helpoptional svo file)parser.add_argument(--img_size, typeint, default416, helpinference size (pixels))parser.add_argument(--conf_thres, typefloat, default0.4, helpobject confidence threshold)opt parser.parse_args()with torch.no_grad():main()4. 实验结果
可实现测距、跟踪和分割功能实现不同功能仅需修改以下代码具体见 此篇文章
4.1 测距 4.2 测距跟踪 4.3 测距跟踪分割 4.4 视频展示
