在视频上运行车道检测算法时出现“NoneType”错误。它适用于视频中的单帧

Question

对于某些上下文，我正在关注本教程： https://www.youtube.com/watch?v=eLTLtUVuuy4 （OpenCV Python 教程 - 为自动驾驶汽车寻找车道（计算机视觉基础教程））

这是我正在执行车道检测的视频： https://www.istockphoto.com/video/exciting-journey-on-road-through-the-desert-california-usa-gm820334744-133238849

我得到的错误： 文件“c:\Users\hello\Desktop inding-lanes\image_test.py”，第 93 行，位于 平均线 = average_slope_intercept（帧，线） ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 文件“c:\Users\hello\Desktop inding-lanes\image_test.py”，第 23 行，在 average_slope_intercept 中 对于行中的行： TypeError: 'NoneType' 对象不可迭代

代码在单帧上运行时有效，但在视频上运行时停止工作 我已经尝试过弄乱 HoughLinesP 函数，但那并没有真正做任何事情。

谢谢！

这是我的代码：

import cv2
import numpy as np


#make coordinates from slope and intercept given the image
def make_coordinates(image, line_parameters):
    slope, intercept = line_parameters
    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:
        #reshape all lines into a one dimentional array with 4 elements, which will be x1, y1, x2, y2
        x1, y1, x2, y2 = line.reshape(4)
        parameters = np.polyfit((x1, x2),(y1, y2), 1)
        #get the slope and intercept from parameters
        slope = parameters[0]
        intercept = parameters [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_coordinates(image, left_fit_average)
    right_line = make_coordinates(image, right_fit_average)
    return np.array([left_line, right_line])


def canny(lane_image):
    #apply canny edge detection technique: first make it black and white, then blur it, and finally apply canny function
    gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)
    canny = cv2.Canny(gray, 50, 150)
    return canny


#outputs a display image based on source image and lines from hough transformation
def display_lines(image, lines):
    line_image = np.zeros_like(image)
    if lines is not None:#if lines are not empty
        for x1, y1, x2, y2 in lines: 
            #draws line segment connecting two points (2nd and 3rd argument )over an image (1st argument),
            #then gives it a colour and thickeness (last two arguments)
            cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10)
    return line_image


#this function makes a region of interest, i.e a black mask over the main image that hides unimportant stuff
def region_of_interest(image):
    height = image.shape [0]
    polygons = np.array([
    #these are the coordinates of the trianle taken by printing the image using matlibplot
    [(300, height), (1200, 500), (790, 300)]
    ])
    mask = np.zeros_like(image)
    #superimpose the polygon on the mask, filling the area of the polygon with pixels of 255 intensity (white)
    cv2.fillPoly(mask, polygons, 255)
    masked_image = cv2.bitwise_and(image, mask)
    return masked_image


cap = cv2.VideoCapture("test3.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=30, 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) == ord('t'):
        break
cap.release()
cv2.destroyAllWindows()