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无法解包非可迭代的numpy.float64对象 Python 3 OpenCV

pythonnumpyopencv
10
我遇到了这个错误，不知道为什么会出现这个问题。下面是代码和错误信息。
上一个可打印的训练结果。
[-8.54582258e-01  9.83741381e+02] left
[   0.776281243  -160.77584028] right

代码错误发生在 make_coordinates 中，出错的行是

slope, intercept = line_parameters

这是完整代码：

import cv2
import numpy as np

vid = cv2.VideoCapture('carDriving.mp4')

def processImage(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)
    canny = cv2.Canny(blur, 50, 150)
    return canny

def region_of_interest(image):
    height = image.shape[0]
    polygons = np.array([
    [(200,height), (1200,height), (750,300)]
    ])
    mask = np.zeros_like(image)
    cv2.fillPoly(mask, polygons, 255)
    masked_image = cv2.bitwise_and(image, mask) 
    return masked_image

def display_lines(image, lines):
    line_image = np.zeros_like(image)
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line.reshape(4)
            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 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))
        left_fit_average = np.average(left_fit, axis=0)
        right_fit_average = np.average(right_fit, axis=0)
        print(left_fit_average, 'left')
        print(right_fit_average, 'right')
        left_line = make_coordinates(image, left_fit_average)
        right_line = make_coordinates(image, right_fit_average)
        #return np.array([left_line, right_line])

def make_coordinates(image, line_parameters):
    slope, intercept = line_parameters
    y1 = image.shape[0]
    y2 = int(y1*3/5)
    x1 = int(y1 - intercept)/slope
    x1 = int(y2 - intercept)/slope
    return np.array([x1, y1, x2, y2])

while True:
    ret, frame = vid.read()
    grayFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    processed_image = processImage(frame)
    cropped_image = region_of_interest(processed_image)
    lines = cv2.HoughLinesP(cropped_image, 2, np.pi/180, 100, np.array([]), minLineLength=40, maxLineGap=5)
    averaged_lines = average_slope_intercept(grayFrame, lines)
    line_image = display_lines(cropped_image,lines) 
    combo_image = cv2.addWeighted(grayFrame, .6, line_image, 1, 1)
    cv2.imshow('result', combo_image)
    print(lines)
    if cv2.waitKey(30) & 0xFF == ord('q'):
        break

vid.release()
cv2.destroyAllWindows()

以及完整的错误信息：

Message=cannot unpack non-iterable numpy.float64 object
Source=C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py
  StackTrace:
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 52, in make_coordinates
    slope, intercept = line_parameters
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 47, in average_slope_intercept
    left_line = make_coordinates(image, left_fit_average)
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 65, in <module>
    averaged_lines = average_slope_intercept(grayFrame, lines)

现在收到另一个错误，第一条错误已被修复，此为第27行。

Message=integer argument expected, got float
  Source=C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py
  StackTrace:
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 27, in display_lines
    cv2.line(line_image, (x1, y1), (x2, y2), (255,0,0), 10)
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 76, in <module>
    line_image = display_lines(cropped_image,averaged_lines)

我将第27行更改为cv2.line(line_image, int(x1, y1), int(x2, y2), (255,0,0), 10)，然后出现了以下错误

  Message='numpy.float64' object cannot be interpreted as an integer
  Source=C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py
  StackTrace:
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 27, in display_lines
    cv2.line(line_image, int(x1, y1), int(x2, y2), (255,0,0), 10)
  File "C:\Users\Andre\source\repos\SelfDrivingCarTest\SelfDrivingCarTest\SelfDrivingCarTest.py", line 76, in <module>
    line_image = display_lines(cropped_image,averaged_lines)
1
你在调用 make_coordinates 之前打印了 left_fit_average。你能否将在出现错误之前最后一次打印该值的内容添加到你的问题中？ - tel
当然，我刚刚把它添加到帖子中了。 - Andrey Cronenwett
您可以通过将其更改为cv2.line(line_image，（int（x1），int（y1）），（int（x2），int（y2）），（255,0,0），10)来解决新问题。如果将来遇到新问题，请提出新问题。将新问题附加到已回答的问题上对于问题回答者（即我）或以后在谷歌上搜索此问题的人都没有帮助。 - tel
嗨，这是几年前为Manas项目做的吗？ - lafeo_007
3个回答
6

问题

您的代码中存在这样一种情况，即line_parameters可能是单个值np.nan，而不是一对(slope, intercept)值。如果拟合的斜率始终为> 0，则left_fit最终将为空列表[]：

        if slope < 0:
            left_fit.append((slope, intercept))
        else:
            right_fit.append((slope, intercept))

np.average 对空列表的输出是 NaN（Not a Number）：

np.average([])
# output: np.nan
# also raises two warnings: "RuntimeWarning: Mean of empty slice." and 
#                           "RuntimeWarning: invalid value encountered in double_scalars"

因此，在某些情况下，left_fit_average = np.average(left_fit) == np.average([]) == np.nan。 np.nan 的类型为numpy.float64。然后，您的代码调用：

left_line = make_coordinates(image, line_parameters=left_fit_average)

因此，当调用make_coordinates到达以下行时：
slope, intercept = line_parameters

如果line_parameters为np.nan，那么就会出现关于以下错误的消息：

TypeError: 'numpy.float64' object is not iterable

修复

如果line_parameters=np.nan，你可以通过确保将合理的值分配给slope和intercept来修复错误。你可以通过在赋值行上包装一个try... except子句来实现：

try:
    slope, intercept = line_parameters
except TypeError:
    slope, intercept = 0,0

您需要决定这种行为是否符合您的需求。

或者，当其中一个 x_fit 值不包含任何有趣的内容时，您可以阻止 average_slope_intercept 函数首先调用 make_coordinates：

if left_fit:
    left_fit_average = np.average(left_fit, axis=0)
    print(left_fit_average, 'left')
    left_line = make_coordinates(image, left_fit_average)
if right_fit:
    right_fit_average = np.average(right_fit, axis=0)
    print(right_fit_average, 'right')
    right_line = make_coordinates(image, right_fit_average)
好的，第二个解决方案可行，但又出现了另一个问题。该问题将在主要问题中发布。 - Andrey Cronenwett
1
根据@tel的回答，我想补充一些内容：
try:
    slope, intercept = line_parameters
except TypeError:
    slope, intercept = 0.001, 0 // It will minimize the error detecting the lane (putting 0, give you a math error)

同样地，您可以增加maxLineGap的值，在车道之间距离较远时捕捉车道。

0
我找到了解决方案，在你的代码中缩进有误： 与你的代码不同：
def average_slope_intercept(image, lines):
    left_fit = []
    right_fit = []
    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))
        **left_fit_average = np.average(left_fit, axis=0)
        right_fit_average = np.average(right_fit, axis=0)
        print(left_fit_average, 'left')
        print(right_fit_average, 'right')
        left_line = make_coordinates(image, left_fit_average)
        right_line = make_coordinates(image, right_fit_average)
        #return np.array([left_line, right_line])**

在right_fit.append((slope, intercept))之后，你应该减少一个缩进直到函数结束。

因此，你的代码应该是：

def average_slope_intercept(image, lines):
    left_fit = []
    right_fit = []
    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))
    left_fit_average = np.average(left_fit, axis=0)
    right_fit_average = np.average(right_fit, axis=0)
    print(left_fit_average, 'left')
    print(right_fit_average, 'right')
    left_line = make_coordinates(image, left_fit_average)
    right_line = make_coordinates(image, right_fit_average)
    return np.array([left_line, right_line])
您可以考虑发布已更正的代码块，以最小化可能与您的答案混淆的困惑。 - AfroThundr
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