类型错误:无法解包不可迭代的 numpy.float64 对象 [英] TypeError: cannot unpack non-iterable numpy.float64 object

问题描述

以下代码使用 openCV 模块来识别道路上的车道.我使用 python 3.6 进行编码(我使用 atom IDE 进行开发.提供此信息是因为 stackoverflow 不允许我在没有不必要的信息行的情况下发布信息.所以请忽略括号中的注释)代码在给定的示例视频中运行良好.但是当我为另一个视频运行它时，它会引发以下错误:

The below piece of code uses openCV module to identify lanes on road. I use the python 3.6 for coding (I use atom IDE for development. This info is being provided because stackoverflow isn't letting me post the info without unnecessary lines of info. so please ignore the comments in bracket) The code runs fine with a given sample video. But when I run it for another video it throws the following error:

(base) D:\Self-Driving course\finding-lanes>RayanFindingLanes.py
C:\Users\Tarun\Anaconda3\lib\site-packages\numpy\lib\function_base.py:392: RuntimeWarning: Mean of empty slice.
  avg = a.mean(axis)
C:\Users\Tarun\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
Traceback (most recent call last):
  File "D:\Self-Driving course\finding-lanes\RayanFindinglanes.py", line 81, in <module>
    averaged_lines = average_slope_intercept(frame, lines)
  File "D:\Self-Driving course\finding-lanes\RayanFindinglanes.py", line 51, in average_slope_intercept
    right_line = make_points(image, right_fit_average)
  File "D:\Self-Driving course\finding-lanes\RayanFindinglanes.py", line 56, in make_points
    slope, intercept = line
TypeError: cannot unpack non-iterable numpy.float64 object

错误是什么意思以及如何解决?

What does the error mean and how to solve it?

代码:

import cv2
import numpy as np

def canny(img):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    kernel = 5
    blur = cv2.GaussianBlur(gray,(kernel, kernel),0)
    canny = cv2.Canny(blur, 50, 150)
    return canny

def region_of_interest(canny):
    height = canny.shape[0]
    width = canny.shape[1]
    mask = np.zeros_like(canny)

    triangle = np.array([[
    (200, height),
    (550, 250),
    (1100, height),]], np.int32)

    cv2.fillPoly(mask, triangle, 255)
    masked_image = cv2.bitwise_and(canny, mask)
    return masked_image

def display_lines(img,lines):
    line_image = np.zeros_like(img)
    if lines is not None:
        for line in lines:
            for x1, y1, x2, y2 in line:
                cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
    return line_image

def average_slope_intercept(image, lines):
    left_fit    = []
    right_fit   = []
    if lines is None:
        return None
    for line in lines:
        for x1, y1, x2, y2 in line:
            fit = np.polyfit((x1,x2), (y1,y2), 1)
            slope = fit[0]
            intercept = fit[1]
            if slope < 0: # y is reversed in image
                left_fit.append((slope, intercept))
            else:
                right_fit.append((slope, intercept))
    # add more weight to longer lines
    left_fit_average  = np.average(left_fit, axis=0)
    right_fit_average = np.average(right_fit, axis=0)
    left_line  = make_points(image, left_fit_average)
    right_line = make_points(image, right_fit_average)
    averaged_lines = [left_line, right_line]
    return averaged_lines

def make_points(image, line):
    slope, intercept = line
    y1 = int(image.shape[0])# bottom of the image
    y2 = int(y1*3/5)         # slightly lower than the middle
    x1 = int((y1 - intercept)/slope)
    x2 = int((y2 - intercept)/slope)
    return [[x1, y1, x2, y2]]

cap = cv2.VideoCapture("test3.mp4")
while(cap.isOpened()):
    _, frame = cap.read()
    canny_image = canny(frame)
    cropped_canny = region_of_interest(canny_image)
    lines = cv2.HoughLinesP(cropped_canny, 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) == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

推荐答案

在一些帧中所有的斜率 > 0 因此 left_fit 列表是空的.因此，在计算 left_fit 平均值时会出错.解决此问题的方法之一是使用前一帧的 left_fit 平均值.我已经使用相同的方法解决了它.请查看下面的代码，让我知道它是否解决了您的问题.

In some of the frames all the slope are > 0 hence left_fit list is empty. Because of that you are getting error when you are calculating left_fit average. One of the way to solve this problem to use left_fit average from previous frame. I have solved it using the same approach. Please see the code below and let me know if it has solved your issue.

global_left_fit_average = []
global_right_fit_average = []
def average_slope_intercept(image, lines):
    left_fit = []
    right_fit = []
    global global_left_fit_average
    global global_right_fit_average

    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line.reshape(4)
            parameters = np.polyfit((x1, x2), (y1,y2), 1)
            slope = parameters[0]
            intercept = parameters[1]
            if (slope < 0):
                left_fit.append((slope, intercept))
            else:
                right_fit.append((slope, intercept))
    if (len(left_fit) == 0):
        left_fit_average = global_left_fit_average
    else:
        left_fit_average = np.average(left_fit, axis=0)
        global_left_fit_average = left_fit_average

    right_fit_average = np.average(right_fit, axis=0)
    global_right_fit_average = right_fit_average
    left_line = make_corordinates(image, left_fit_average)
    right_line = make_corordinates(image, right_fit_average)
    return np.array([left_line, right_line])

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