无法解压不可迭代的 numpy.float64 对象 python3 opencv

Question

我收到此错误，但不明白为什么会出现此问题。下面是代码和错误。

上次可打印锻炼的结果

[-8.54582258e-01  9.83741381e+02] left
[   0.776281243  -160.77584028] right

代码错误发生在make_coordinates，行是

slope, intercept = line_parameters

这是完整的代码：

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 27 行，第一个错误已修复

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) 并得到以下错误

  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)

Answer 1

问题

在您的代码中，line_parameters 可以是单个值 np.nan，而不是一对 (slope, intercept) 值。如果您的拟合斜率始终为> 0，那么left_fit 最终将是一个空列表[]：

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

np.average 在空列表上运行的输出是 NaN：

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，当对make_coordinates 的调用到达线路时：

slope, intercept = line_parameters

line_parameters 可能是np.nan，在这种情况下，您会收到以下错误消息：

TypeError: 'numpy.float64' object is not iterable

修复

您可以通过确保将合理的值分配给slope 和intercept 来修复该错误，即使line_parameters=np.nan 也是如此。您可以通过将赋值行包装在 try... except 子句中来完成此操作：

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

您必须确定此行为是否适合您的需求。

或者，当x_fit 值之一没有任何有趣的内容时，您可以首先阻止average_slope_intercept 函数调用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)

Answer 2

根据@tel 的回答，我想补充一些，

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，当车道之间的距离很大时，您可以增加 maxLineGap 的值来捕捉车道

Answer 3

我找到了解决方案，在您的代码中有错误的缩进： 而不是你的代码：

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。

所以，你的代码必须是：

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])