python 实现显著性检测_强！汽车车道视频检测：python+OpenCV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 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, 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_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, 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：