网站底部怎么做,网站备案幕布 下载,做网站得叫什么,广州哪个网站建设公司好1 说明#xff1a;1.1 完整版#xff1a;汽车车道动态视频检测讲解和注释版代码#xff0c;小白秒懂。1.2 pythonOpenCVmoviepynumpy为主的技术要点。1.3 代码来源#xff1a;https://github.com/linghugoogle/CarND-Advanced-Lane-Lines #虽然感觉也是fork别人的#xff…1 说明1.1 完整版汽车车道动态视频检测讲解和注释版代码小白秒懂。1.2 pythonOpenCVmoviepynumpy为主的技术要点。1.3 代码来源https://github.com/linghugoogle/CarND-Advanced-Lane-Lines #虽然感觉也是fork别人的别忘了给他点个赞star1.4 感谢原作者并对文件进行修改和代码进行删减注释等操作便于操作和理解。1.5 应用无人驾驶汽车技术热门2 效果展示由于gif≤10MB所以是节选。2.1 原视频节选2.2 处理后视频节选3 准备3.1 环境python3.8OpenCV4.2.0deepin-linux操作系统。3.2 文件结构github下载下来project_video.mp4 原始视频未进行标注vedio_out文件夹为输出被标注的视频(处理后的视频文件夹)camera_cal相机参数标定文件夹。4 代码讲解4.1 line.py代码# -*- coding: utf-8 -*-#导入模块import numpy as np#定义line这个类class Line(): #初始化参数 def __init__(self): # was the line detected in the last iteration? self.detected False # x values of the last n fits of the line self.recent_fitted [np.array([False])] #average x values of the fitted line over the last n iterations self.bestx None #polynomial coefficients averaged over the last n iterations self.best_fit None #polynomial coefficients for the most recent fit self.current_fit [np.array([False])] #radius of curvature of the line in some units self.radius_of_curvature None #distance in meters of vehicle center from the line self.line_base_pos None #difference in fit coefficients between last and new fits self.diffs np.array([0,0,0], dtypefloat) #x values for detected line pixels self.allx None #y values for detected line pixels self.ally None #检测侦测 def check_detected(self): if (self.diffs[0] 0.01 and self.diffs[1] 10.0 and self.diffs[2] 1000.) and len(self.recent_fitted) 0: return True else: return False #更新 def update(self,fit): if fit is not None: if self.best_fit is not None: self.diffs abs(fit - self.best_fit) if self.check_detected(): self.detected True if len(self.recent_fitted)10: self.recent_fitted self.recent_fitted[1:] self.recent_fitted.append(fit) else: self.recent_fitted.append(fit) self.best_fit np.average(self.recent_fitted, axis0) self.current_fit fit else: self.detected False else: self.best_fit fit self.current_fit fit self.detectedTrue self.recent_fitted.append(fit)4.2 utils.py代码省略。4.3 main-pipeline.py(就是代码为pipeline.py)# -*- coding: utf-8 -*-#第1步导入模块import osimport cv2import matplotlib.pyplot as pltimport numpy as npfrom moviepy.editor import VideoFileClipimport line #自定义模块import utils #自定义模块#第2步图片阈值处理def thresholding(img): #setting all sorts of thresholds x_thresh utils.abs_sobel_thresh(img, orientx, thresh_min10 ,thresh_max230) mag_thresh utils.mag_thresh(img, sobel_kernel3, mag_thresh(30, 150)) dir_thresh utils.dir_threshold(img, sobel_kernel3, thresh(0.7, 1.3)) hls_thresh utils.hls_select(img, thresh(180, 255)) lab_thresh utils.lab_select(img, thresh(155, 200)) luv_thresh utils.luv_select(img, thresh(225, 255)) #Thresholding combination threshholded np.zeros_like(x_thresh) threshholded[((x_thresh 1) (mag_thresh 1)) | ((dir_thresh 1) (hls_thresh 1)) | (lab_thresh 1) | (luv_thresh 1)] 1 return threshholded#第3步视频拟合和图片纠正def processing(img,object_points,img_points,M,Minv,left_line,right_line): #camera calibration, image distortion correction undist utils.cal_undistort(img,object_points,img_points) #get the thresholded binary image thresholded thresholding(undist) #perform perspective transform thresholded_wraped cv2.warpPerspective(thresholded, M, img.shape[1::-1], flagscv2.INTER_LINEAR) #perform detection if left_line.detected and right_line.detected: left_fit, right_fit, left_lane_inds, right_lane_inds utils.find_line_by_previous(thresholded_wraped,left_line.current_fit,right_line.current_fit) else: left_fit, right_fit, left_lane_inds, right_lane_inds utils.find_line(thresholded_wraped) left_line.update(left_fit) right_line.update(right_fit) #draw the detected laneline and the information area_img utils.draw_area(undist,thresholded_wraped,Minv,left_fit, right_fit) curvature,pos_from_center utils.calculate_curv_and_pos(thresholded_wraped,left_fit, right_fit) result utils.draw_values(area_img,curvature,pos_from_center) return result#第4步步骤划线-校正-读取原视频和生成修改后的视频#划线left_line line.Line() #左线right_line line.Line() #右线#获取棋盘格图片#使用提供的一组棋盘格图片计算相机校正矩阵(camera calibration matrix)和失真系数(distortion coefficients).cal_imgs utils.get_images_by_dir(/home/xgj/Desktop/v-carline-good/camera_cal)#计算object_points,img_pointsobject_points,img_points utils.calibrate(cal_imgs,grid(9,6))M,Minv utils.get_M_Minv()#需要修改的视频原视频project_video_clip VideoFileClip(/home/xgj/Desktop/v-carline-good/project_video.mp4)#输出修改后的视频完成视频project_outpath /home/xgj/Desktop/v-carline-good/vedio_out/project_video_out.mp4#制作视频project_video_out_clip project_video_clip.fl_image(lambda clip: processing(clip,object_points,img_points,M,Minv,left_line,right_line))project_video_out_clip.write_videofile(project_outpath, audioFalse)5 完结5.1 以上代码完整但制作视频估计花20分钟我也是将代码最简化跑起来。5.2 如果逐步深入分析可能要从基本开始。5.3 可以参考这篇文章https://zhuanlan.zhihu.com/p/46146266
