问题
I am getting this error and cant understand why the issue is appearing. Below will be the code and error.
The result of the last printable workout
[-8.54582258e-01 9.83741381e+02] left
[ 0.776281243 -160.77584028] right
The code error happens in make_coordinates and the line is
slope, intercept = line_parameters
Here's the full code:
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()
and the complete error message:
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)
Now receiving another error, line 27, first error was fixed
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)
I change line 27 to cv2.line(line_image, int(x1, y1), int(x2, y2), (255,0,0), 10) and get the following error
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)
回答1:
The problem
There's a case in your code where line_parameters can be a single value, np.nan, instead of a pair of (slope, intercept) values. If the slope of your fits is always > 0, then left_fit will end up being an empty list []:
if slope < 0:
left_fit.append((slope, intercept))
else:
right_fit.append((slope, intercept))
The output of np.average run on an empty list is NaN:
np.average([])
# output: np.nan
# also raises two warnings: "RuntimeWarning: Mean of empty slice." and
# "RuntimeWarning: invalid value encountered in double_scalars"
Thus, in some cases left_fit_average = np.average(left_fit) == np.average([]) == np.nan. np.nan has a type of numpy.float64. Your code then calls:
left_line = make_coordinates(image, line_parameters=left_fit_average)
Thus, when the call to make_coordinates gets to the line:
slope, intercept = line_parameters
it's possible for line_parameters to be np.nan, in which case you get the error message about:
TypeError: 'numpy.float64' object is not iterable
A fix
You can fix the bug by making sure that sensible values get assigned to slope and intercept even if line_parameters=np.nan. You can accomplished this by wrapping the assignment line in a try... except clause:
try:
slope, intercept = line_parameters
except TypeError:
slope, intercept = 0,0
You'll have to decide if this behavior is correct for your needs.
Alternatively, you could prevent the average_slope_intercept function from calling make_coordinates in the first place when one of the x_fit values doesn't have anything interesting in it:
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)
回答2:
I found the solution, in your code there is the wrong indent: instead of your code:
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])**
after right_fit.append((slope, intercept)) you should make one less indent till the end of the function.
So, your code must be:
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])
回答3:
As per @tel answer, I like to add some,
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)
Again, you can increase the value of maxLineGap to catch the lane when there is so much distance between lanes
来源：https://stackoverflow.com/questions/54273077/cannot-unpack-non-iterable-numpy-float64-object-python3-opencv