下面的代码使用openCV模块识别道路上的车道。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，因此请忽略括号中的注释)
对于给定的示例视频，代码可以正常运行。但是，当我为另一个视频运行它时，它将引发以下错误:
(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，如何解决？
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()
最佳答案
在某些框架中，所有斜率均> 0，因此left_fit列表为空。因此，在计算left_fit平均值时会出错。解决此问题的一种方法是使用前一帧的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。
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])