项目完整源代码，使用 OpenCV 的Hough 直线检测算法，提取出道路车道线并绘制出来。通过tkinter 提供GUI界面展示效果。

1、导入相关模块

import matplotlib.pyplot as pltimport numpy as npimport cv2import osimport matplotlib.image as mpimgfrom moviepy.editor import VideoFileClipimport math

2. 用掩码获取ROI区域

def interested_region(img, vertices):if len(img.shape) > 2: mask_color_ignore = (255,) * img.shape[2]else:mask_color_ignore = 255cv2.fillPoly(np.zeros_like(img), vertices, mask_color_ignore)return cv2.bitwise_and(img, np.zeros_like(img))

3、Hough变换空间, 转换像素到直线

def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)lines_drawn(line_img,lines)return line_img

4、在Hough变换后，每帧增加两条线

def lines_drawn(img, lines, color=[255, 0, 0], thickness=6):global cacheglobal first_frameslope_l, slope_r = [],[]lane_l,lane_r = [],[]α =0.2# 希腊字母阿尔法for line in lines:for x1,y1,x2,y2 in line:slope = (y2-y1)/(x2-x1)if slope > 0.4:slope_r.append(slope)lane_r.append(line)elif slope < -0.4:slope_l.append(slope)lane_l.append(line)img.shape[0] = min(y1,y2,img.shape[0])if((len(lane_l) == 0) or (len(lane_r) == 0)):print ('no lane detected')return 1slope_mean_l = np.mean(slope_l,axis =0)slope_mean_r = np.mean(slope_r,axis =0)mean_l = np.mean(np.array(lane_l),axis=0)mean_r = np.mean(np.array(lane_r),axis=0)if ((slope_mean_r == 0) or (slope_mean_l == 0 )):print('dividing by zero')return 1x1_l = int((img.shape[0] - mean_l[0][1] - (slope_mean_l * mean_l[0][0]))/slope_mean_l) x2_l = int((img.shape[0] - mean_l[0][1] - (slope_mean_l * mean_l[0][0]))/slope_mean_l) x1_r = int((img.shape[0] - mean_r[0][1] - (slope_mean_r * mean_r[0][0]))/slope_mean_r)x2_r = int((img.shape[0] - mean_r[0][1] - (slope_mean_r * mean_r[0][0]))/slope_mean_r) if x1_l > x1_r:x1_l = int((x1_l+x1_r)/2)x1_r = x1_ly1_l = int((slope_mean_l * x1_l ) + mean_l[0][1] - (slope_mean_l * mean_l[0][0]))y1_r = int((slope_mean_r * x1_r ) + mean_r[0][1] - (slope_mean_r * mean_r[0][0]))y2_l = int((slope_mean_l * x2_l ) + mean_l[0][1] - (slope_mean_l * mean_l[0][0]))y2_r = int((slope_mean_r * x2_r ) + mean_r[0][1] - (slope_mean_r * mean_r[0][0]))else:y1_l = img.shape[0]y2_l = img.shape[0]y1_r = img.shape[0]y2_r = img.shape[0]present_frame = np.array([x1_l,y1_l,x2_l,y2_l,x1_r,y1_r,x2_r,y2_r],dtype ="float32")if first_frame == 1:next_frame = present_framefirst_frame = 0else :prev_frame = cachenext_frame = (1-α)*prev_frame+α*present_frame cv2.line(img, (int(next_frame[0]), int(next_frame[1])), (int(next_frame[2]),int(next_frame[3])), color, thickness)cv2.line(img, (int(next_frame[4]), int(next_frame[5])), (int(next_frame[6]),int(next_frame[7])), color, thickness)cache = next_frame

5、处理每帧画面

def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):return cv2.addWeighted(initial_img, α, img, β, λ)def process_image(image):global first_framegray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)img_hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)lower_yellow = np.array([20, 100, 100], dtype = "uint8")upper_yellow = np.array([30, 255, 255], dtype="uint8")mask_yellow = cv2.inRange(img_hsv, lower_yellow, upper_yellow)mask_white = cv2.inRange(gray_image, 200, 255)mask_yw = cv2.bitwise_or(mask_white, mask_yellow)mask_yw_image = cv2.bitwise_and(gray_image, mask_yw)# gause blue gauss_gray= cv2.GaussianBlur(mask_yw_image, (5, 5), 0)# detect edgecanny_edges=cv2.Canny(gauss_gray, 50, 150，apertureSize=3)imshape = image.shapelower_left = [imshape[1]/9,imshape[0]]lower_right = [imshape[1]-imshape[1]/9,imshape[0]]top_left = [imshape[1]/2-imshape[1]/8,imshape[0]/2+imshape[0]/10]top_right = [imshape[1]/2+imshape[1]/8,imshape[0]/2+imshape[0]/10]vertices = [np.array([lower_left,top_left,top_right,lower_right],dtype=np.int32)]roi_image = interested_region(canny_edges, vertices)theta = np.pi/180line_image = hough_lines(roi_image, 4, theta, 30, 100, 180)result = weighted_img(line_image, image, α=0.8, β=1., λ=0.)return result

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6、用moviepy的videofileClip 读出视频，调用process_image方法处理后保存至文件

first_frame = 1white_output = '__path_to_output_file__'clip1 = VideoFileClip("__path_to_input_file__")new_clip = clip1.fl_image(process_image)new_clip.write_videofile(white_output, audio=False)

7、用tkinter编写车道线检测项目的GUI图形界面

import tkinter as tkfrom tkinter import *import cv2from PIL import Image, ImageTkimport osimport numpy as npglobal last_frame1 last_frame1 = np.zeros((480, 640, 3), dtype=np.uint8)global last_frame2last_frame2 = np.zeros((480, 640, 3), dtype=np.uint8)global cap1global cap2cap_input = cv2.VideoCapture("path_to_input_test_video")cap_drawlane = cv2.VideoCapture("path_to_resultant_lane_detected_video")def show_input_video(): if not cap_input.isOpened(): print("无法打开原始视频")flag1, frame1 = cap_input.read()frame1 = cv2.resize(frame1,(400,500))if flag1 is None:print ("原视频读帧错误")elif flag1:global last_frame1last_frame1 = frame1.copy()pic = cv2.cvtColor(last_frame1, cv2.COLOR_BGR2RGB) img = Image.fromarray(pic)imgtk = ImageTk.PhotoImage(image=img)lmain.imgtk = imgtklmain.configure(image=imgtk)lmain.after(10, show_input_video)def show_drawlane_video():if not cap_drawlane.isOpened(): print("无法打开车道线视频")flag2, frame2 = cap_drawlane.read()frame2 = cv2.resize(frame2,(400,500))if flag2 is None:print ("车道线视频读帧错误")elif flag2:global last_frame2last_frame2 = frame2.copy()pic2 = cv2.cvtColor(last_frame2, cv2.COLOR_BGR2RGB)img2 = Image.fromarray(pic2)img2tk = ImageTk.PhotoImage(image=img2)lmain2.img2tk = img2tklmain2.configure(image=img2tk)lmain2.after(10, show_drawlane_video)if __name__ == '__main__':root=tk.Tk() lmain = tk.Label(master=root)lmain2 = tk.Label(master=root)lmain.pack(side = LEFT)lmain2.pack(side = RIGHT)root.title("车道线检测")root.geometry("900x700+100+10") exitbutton = Button(root, text='退出',fg="red",command= root.destroy).pack(side = BOTTOM,)show_input_video()show_drawlane_video()root.mainloop()cap.release()