我遇到此错误,无法理解为什么出现此问题。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
中,该行是
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()
和完整的错误消息:
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)
,并收到以下错误消息
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)
答案 0 :(得分:0)
在您的代码中有一种情况,其中line_parameters
可以是单个值np.nan
,而不是一对(slope, intercept)
值。如果拟合的斜率始终为> 0
,则left_fit
将最终成为一个空列表[]
:
if slope < 0:
left_fit.append((slope, intercept))
else:
right_fit.append((slope, intercept))
在一个空列表上运行的np.average
的输出为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,在某些情况下为left_fit_average = np.average(left_fit) == np.average([]) == np.nan
。 np.nan
的类型为numpy.float64
。然后您的代码将调用:
left_line = make_coordinates(image, line_parameters=left_fit_average)
因此,当对make_coordinates
的呼叫到达线路时:
slope, intercept = line_parameters
line_parameters
可能是np.nan
,在这种情况下,您会收到有关以下内容的错误消息:
TypeError: 'numpy.float64' object is not iterable
您可以通过确保为slope
和intercept
分配合理的值来修复该错误。您可以通过将分配行包装在line_parameters=np.nan
子句中来完成此操作:
try... except
您必须确定此行为是否符合您的需求。
或者,当其中一个try:
slope, intercept = line_parameters
except TypeError:
slope, intercept = 0,0
值没有任何趣味时,您可以阻止average_slope_intercept
函数首先调用make_coordinates
:
x_fit
答案 1 :(得分:0)
我找到了解决方案,在您的代码中有错误的缩进: 而不是您的代码:
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,应减少缩进量,直到函数结束。
因此,您的代码必须为:
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])
答案 2 :(得分:0)
按照@tel的答案,我想添加一些,
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,当通道之间的距离太大时,可以增加maxLineGap的值来捕获通道