車道線檢測是自動(dòng)駕駛汽車以及一般計(jì)算機(jī)視覺的關(guān)鍵組件。這個(gè)概念用于描述自動(dòng)駕駛汽車的路徑并避免進(jìn)入另一條車道的風(fēng)險(xiǎn)。
在本文中，我們將構(gòu)建一個(gè)機(jī)器學(xué)習(xí)項(xiàng)目來實(shí)時(shí)檢測車道線。我們將使用 OpenCV 庫使用計(jì)算機(jī)視覺的概念來做到這一點(diǎn)。為了檢測車道，我們必須檢測車道兩側(cè)的白色標(biāo)記。
使用 Python 和 OpenCV 進(jìn)行道路車道線檢測
使用 Python 中的計(jì)算機(jī)視覺技術(shù)，我們將識(shí)別自動(dòng)駕駛汽車必須行駛的道路車道線。這將是自動(dòng)駕駛汽車的關(guān)鍵部分，因?yàn)樽詣?dòng)駕駛汽車不應(yīng)該越過它的車道，也不應(yīng)該進(jìn)入對(duì)面車道以避免事故。
幀掩碼和霍夫線變換
要檢測車道中的白色標(biāo)記，首先，我們需要屏蔽幀的其余部分。我們使用幀屏蔽來做到這一點(diǎn)。該幀只不過是圖像像素值的 NumPy 數(shù)組。為了掩蓋幀中不必要的像素，我們只需將 NumPy 數(shù)組中的這些像素值更新為 0。
制作后我們需要檢測車道線。用于檢測此類數(shù)學(xué)形狀的技術(shù)稱為霍夫變換?；舴蜃儞Q可以檢測矩形、圓形、三角形和直線等形狀。
代碼下載
源碼請(qǐng)下載：車道線檢測項(xiàng)目代碼
按照以下步驟在 Python 中進(jìn)行車道線檢測：
1.導(dǎo)入包
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. 應(yīng)用幀屏蔽并找到感興趣的區(qū)域：
def interested_region(img, vertices):
if len(img.shape) > 2:
mask_color_ignore = (255,) * img.shape[2]
else:
mask_color_ignore = 255
cv2.fillPoly(np.zeros_like(img), vertices, mask_color_ignore)
return cv2.bitwise_and(img, np.zeros_like(img))
3.霍夫變換空間中像素到線的轉(zhuǎn)換：
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. 霍夫變換后在每一幀中創(chuàng)建兩條線：
def lines_drawn(img, lines, color=[255, 0, 0], thickness=6):
global cache
global first_frame
slope_l, slope_r = [],[]
lane_l,lane_r = [],[]
α =0.2
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 1
slope_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 1
x1_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_l
y1_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 = cache
next_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.處理每一幀視頻以檢測車道：
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_frame
gray_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)
gauss_gray= cv2.GaussianBlur(mask_yw_image, (5, 5), 0)
canny_edges=cv2.Canny(gauss_gray, 50, 150)
imshape = image.shape
lower_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/180
line_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. 將輸入視頻剪輯成幀并得到結(jié)果輸出視頻文件：
first_frame = 1
white_output = '__path_to_output_file__'
clip1 = VideoFileClip("__path_to_input_file__")
white_clip = clip1.fl_image(process_image)
white_clip.write_videofile(white_output, audio=False)
車道線檢測項(xiàng)目 GUI 代碼：
import tkinter as tk
from tkinter import *
import cv2
from PIL import Image, ImageTk
import os
import numpy as np
global 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
cap1 = cv2.VideoCapture("path_to_input_test_video")
cap2 = cv2.VideoCapture("path_to_resultant_lane_detected_video")
def show_vid():
if not cap1.isOpened():
print("cant open the camera1")
flag1, frame1 = cap1.read()
frame1 = cv2.resize(frame1,(400,500))
if flag1 is None:
print ("Major error!")
elif flag1:
global last_frame1
last_frame1 = frame1.copy()
pic = cv2.cvtColor(last_frame1, cv2.COLOR_BGR2RGB)
img = Image.fromarray(pic)
imgtk = ImageTk.PhotoImage(image=img)
lmain.imgtk = imgtk
lmain.configure(image=imgtk)
lmain.after(10, show_vid)
def show_vid2():
if not cap2.isOpened():
print("cant open the camera2")
flag2, frame2 = cap2.read()
frame2 = cv2.resize(frame2,(400,500))
if flag2 is None:
print ("Major error2!")
elif flag2:
global last_frame2
last_frame2 = frame2.copy()
pic2 = cv2.cvtColor(last_frame2, cv2.COLOR_BGR2RGB)
img2 = Image.fromarray(pic2)
img2tk = ImageTk.PhotoImage(image=img2)
lmain2.img2tk = img2tk
lmain2.configure(image=img2tk)
lmain2.after(10, show_vid2)
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("Lane-line detection")
root.geometry("900x700+100+10")
exitbutton = Button(root, text='Quit',fg="red",command= root.destroy).pack(side = BOTTOM,)
show_vid()
show_vid2()
root.mainloop()
cap.release()
到此這篇關(guān)于Python道路車道線檢測的實(shí)現(xiàn)的文章就介紹到這了,更多相關(guān)Python 道路車道線檢測內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
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