1 說明：
=====
1.1 完整版：汽車車道動態視頻檢測講解和注釋版代碼，小白秒懂。
1.2 Python/ target=_blank class=infotextkey>Python+OpenCV+moviepy+numpy為主的技術要點。
1.3 代碼來源：
https://github.com/linghugoogle/CarND-Advanced-Lane-Lines
#雖然感覺也是fork別人的，別忘了給他點個贊star
1.4 感謝原作者，并對文件進行修改和代碼進行刪減，注釋等操作，便于操作和理解。
1.5 應用：無人駕駛汽車技術，熱門！
2 效果展示：由于gif≤10MB，所以是節選。
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2.1 原視頻節選：
2.2 處理后視頻節選：
3 準備：
=====
3.1 環境：python3.8+OpenCV4.2.0+deepin-linux操作系統。
3.2 文件結構：
github下載下來
project_video.mp4 ：原始視頻，未進行標注
vedio_out：文件夾為輸出被標注的視頻（處理后的視頻文件夾）
camera_cal：相機參數標定文件夾。
4 代碼講解：
=========
4.1 line.py代碼：
# -*- coding: utf-8 -*-
#導入模塊import numpy as np
#定義line這個類class Line(): #初始化參數 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
#檢測偵測 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代碼省略。
4.3 main-pipeline.py（就是代碼為：pipeline.py）
# -*- coding: utf-8 -*-
#第1步：導入模塊
import os
import cv2import matplotlib.pyplot as plt
import numpy as np
from moviepy.editor import VideoFileClip
import line #自定義模塊import utils #自定義模塊
#第2步：圖片閾值處理
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：視頻擬合和圖片糾正
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：步驟：劃線-校正-讀取原視頻和生成修改后的視頻
#劃線left_line = line.Line() #左線right_line = line.Line() #右線#獲取棋盤格圖片#使用提供的一組棋盤格圖片計算相機校正矩陣(camera calibration matrix)和失真系數(distortion coefficients).cal_imgs = utils.get_images_by_dir('/home/xgj/Desktop/v-carline-good/camera_cal')
#計算object_points,img_pointsobject_points,img_points = utils.calibrate(cal_imgs,grid=(9,6))
M,Minv = utils.get_M_Minv()#需要修改的視頻：原視頻project_video_clip = VideoFileClip("/home/xgj/Desktop/v-carline-good/project_video.mp4")
#輸出修改后的視頻：完成視頻project_outpath = '/home/xgj/Desktop/v-carline-good/vedio_out/project_video_out.mp4'
#制作視頻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 完結：
=====
5.1 以上代碼完整，但制作視頻估計花20分鐘，我也是將代碼最簡化跑起來。
5.2 如果逐步深入分析，可能要從基本開始。
5.3 可以參考這篇文章：
https://zhuanlan.zhihu.com/p/46146266
