强！汽车车道视频检测pythonOpenCV为主实现

　　1 说明：
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　　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 python+OpenCV+moviepy+numpy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star
　　1.4 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。
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　　2.1 原视频节选：
　　2.2 处理后视频节选：
　　3 准备：
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　　3.1 环境：python3.8+OpenCV4.2.0+deepin-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：
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　　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], dtype=＂float＂)          #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, axis=0)                     self.current_fit = fit                 else:                     self.detected = False             else:                 self.best_fit = fit                 self.current_fit = fit                 self.detected=True                 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 os import cv2 import matplotlib.pyplot as plt import numpy as np from moviepy.editor import VideoFileClip  import 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, orient=＂x＂, thresh_min=10 ,thresh_max=230)     mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 150))     dir_thresh = utils.dir_threshold(img, sobel_kernel=3, 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], flags=cv2.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_points object_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, audio=False)
　　5 完结：
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　　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