本文主要是介绍OpenCV+ moviepy + tkinter 视频车道线智能识别项目源码，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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 plt
import numpy as np
import cv2
import os
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
import 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_frame first_frame = 0 else :prev_frame = cachenext_frame = (1-α)*prev_frame+α*present_framecv2.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_frame5、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
:
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 = 1
white_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 tk
from tkinter import *
import cv2
from PIL import Image, ImageTk
import os
import numpy as npglobal last_frame1
last_frame1 = np.zeros((480, 640, 3), dtype=np.uint8)
global last_frame2
last_frame2 = np.zeros((480, 640, 3), dtype=np.uint8)
global cap1
global cap2
cap_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()这篇关于OpenCV+ moviepy + tkinter 视频车道线智能识别项目源码的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
