最近找了一款车道线识别的代码，可以准确识别出车道线，里面的视频路径可以切换为自己的视频，现在我以自带的视频数据展示效果

图片中可以准确识别车的车道线：
下面给出一部分源代码：

import cv2
import numpy as np
import matplotlib.pyplot as plt


def make_line(image, line_parameters):
    slope = line_parameters[0]
    intercept = line_parameters[1]
    y1 = image.shape[0]  #lane detection line starts from bottom of image
    y2 = int(y1*(3/5))  #lane detection line ends at 3/5 of height of image
    x1 = int((y1 - intercept)/slope)
    x2 = int((y2 - intercept)/slope)
    return np.array([x1, y1, x2, y2])

#convert to grayscale, blur and return the canny image
def canny(image):
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) 
    blur = cv2.GaussianBlur(gray, (5, 5), 0) #(image, (kernel size), standard deviation)
    canny_img = cv2.Canny(blur, 50, 150) #(image, low_threshold, high threshold)
    return canny_img

def region_of_interest(image):
    # plt.imshow(image)
    # plt.show()
    mask = np.zeros_like(image)   
    #Defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(image.shape) > 2:
        channel_count = image.shape[2]
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
    #We could have used fixed numbers as the vertices of the polygon,
    #but they will not be applicable to images with different dimesnions.
    rows, cols = image.shape[:2]
    bottom_left  = [cols * 0.1, rows * 0.95]
    top_left     = [cols * 0.4, rows * 0.6]
    bottom_right = [cols * 0.9, rows * 0.95]
    top_right    = [cols * 0.6, rows * 0.6]
    vertices = np.array([[bottom_left, top_left, top_right, bottom_right]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_image = cv2.bitwise_and(image, mask)
    return masked_image
    ~~~
    需要的小伙伴可以参考一下代码