实现车道线检测

原创

陶陶name

发布于 2022-05-13 08:39:15

3580

发布于 2022-05-13 08:39:15

文章被收录于专栏：陶陶计算机陶陶计算机

当今计算机视觉在我们的日常生活中运用的十分广泛，例如人脸识别、自动驾驶、等等由于对自动驾驶十分感兴趣，因此就花了一些时间实现了车道线检测

环境

笔者的环境配置如下：

ubuntu 16.04

python3.7

opencv >=4.0

说明：这里的系统以及python的版本都不是固定的，读者使用win10 win7也是可以的

但是python的版本一定要是3.x的版本

第三方库

这里使用的第三方库，是大家比较熟悉的opencv以及numpy

import cv2
import numpy as np

函数

def make_coordinate(image,line_parameters):
    slope,intercept=line_parameters
    # print(image.shape)
    y1=image.shape[0]
    y2=int(y1*(3/5))
    x1=int((y1-intercept)/slope)
    x2 = int((y2 - intercept) / slope)
    return np.array([x1,y1,x2,y2])
def average_slope_intercept(image,lines):
    left_fit=[]
    right_fit=[]
    for line in lines:
        x1,y1,x2,y2=line.reshape(4)
        paraneters=np.polyfit((x1,x2),(y1,y2),1)
        slope=paraneters[0]
        intercept=paraneters[1]
        if slope<0:
            left_fit.append((slope,intercept))
        else:
            right_fit.append((slope,intercept))
    left_fit_average=np.average(left_fit,axis=0)
    right_fit_average=np.average(right_fit,axis=0)
    left_line=make_coordinate(image,left_fit_average)
    right_line=make_coordinate(image,right_fit_average)

    return np.array([left_line,right_line])

def canny(image):
    gray=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
    blur=cv2.GaussianBlur(gray,(5,5),0)
    canny=cv2.Canny(blur,50,150)
    return canny
def display_lines(image,lines):
    line_image=np.zeros_like(image)
    if lines is not None:
        for x1,y1,x2,y2 in lines:
            # x1,y1,x2,y2=line.reshape(4)
            cv2.line(line_image,(x1,y1),(x2,y2),(0,255,0),10)
    return line_image
def region_of_interest(image):
    height=image.shape[0]
    polygons=np.array([
        [(200,height),(1100,height),(550,250)]
    ])
    mask=np.zeros_like(image)
    cv2.fillPoly(mask,polygons,255)
    masked_image=cv2.bitwise_and(image,mask)
    return masked_image

测试（图片）

image = cv2.imread("test_image.jpg")
lane_image = np.copy(image)
canny_image=canny(lane_image)
cropped_image=region_of_interest(canny_image)
lines=cv2.HoughLinesP(cropped_image,2,np.pi/180,100,np.array([]),minLineLength=40,maxLineGap=5)
averaged_lines=average_slope_intercept(lane_image,lines)
line_image=display_lines(lane_image,averaged_lines)
combo_image=cv2.addWeighted(lane_image,0.8,line_image,1,1)
cv2.imshow("result",combo_image)
cv2.waitKey(0)

最终的效果如下图所示：

测试（视频）

cap=cv2.VideoCapture("test2.mp4")
while(cap.isOpened()):
    _,frame=cap.read()
    canny_image = canny(frame)
    cropped_image = region_of_interest(canny_image)
    lines = cv2.HoughLinesP(cropped_image, 2, np.pi / 180, 100, np.array([]), minLineLength=40, maxLineGap=5)
    averaged_lines = average_slope_intercept(frame, lines)
    line_image = display_lines(frame, averaged_lines)
    combo_image = cv2.addWeighted(frame, 0.8, line_image, 1, 1)
    cv2.imshow("result", combo_image)
    if cv2.waitKey(1) & 0xFF==ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

效果如下图所示：