The below code is for a lane detection algo, I have used Canny Edge detection to work the follow code. However, there are a few errors that I am facing if you could check and let me know it would be of great help. I feel the errors are somewhere around the various arguments and parameters in Python since I might not entirely be thorough with the maths of the problem.
import cv2 import numpy as np import matplotlib.pyplot as plt #108 300 def empty(a) : pass
cv2.namedWindow('image')
cv2.resizeWindow('image',640,240)
#---track-bars
cv2.createTrackbar('MIN-VAL','image', 0, 300, empty)
cv2.createTrackbar('MAX-VAL','image',0, 300, empty)
# while True:
def ROI1(image):
height = image.shape[0]
triangle = np.array([
[(200, height), (1150, height), (700, 400)]
])
mask = np.zeros_like(image)
cv2.fillPoly(mask, triangle, 255)
return mask
def ROI2(image):
height = image.shape[0]
triangle = np.array([
[(0, 650), (1050, height), (750, 445)]
])
mask = np.zeros_like(image)
cv2.fillPoly(mask, triangle, 255)
return mask
def avergaeLines(image, linez):
left_fit = []
right_fit = []
for line in linez:
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)
left_line = coord(image, left_Fit_Average)
right_line = coord(image, right_Fit_Average)
return np.array([left_line, right_line])
def displayLines(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), (0, 255, 0), 5)
return line_image
def coord(image, linePara):
try:
slope, intercept = linePara
except TypeError:
slope, intercept = 0.001, 0
y1 = image.shape[0]
y2 = int(y1*(4/5))
x1 = int((y1 - intercept)/slope)
x2 = int((y2-intercept)/slope)
return np.array([x1, y1, x2, y2])
cap = cv2.VideoCapture("challenge_video.mp4")
#img = cv2.imread("test3.jpg")
while True:
success, img = cap.read()
while True:
lane_image = np.copy(img)
# cv2.imshow("a", lane_image)
gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)
# cv2.imshow("b", gray)
blur_gray = cv2.GaussianBlur(gray, (5, 5), 0)
# cv2.imshow("c", blur_gray)
min_val = cv2.getTrackbarPos('MIN-VAL', 'image')
max_val = cv2.getTrackbarPos('MAX-VAL', 'image')
canny_blur_gray = cv2.Canny(blur_gray, 0, 159)
# cv2.imshow("f", canny_blur_gray)
mask = ROI2(canny_blur_gray)
ADDED = cv2.bitwise_and(canny_blur_gray, mask)
lines = cv2.HoughLinesP(ADDED, 2, np.pi/180, 100, np.array([]), minLineLength=70, maxLineGap=4)
#70 AND 7 WORKS FINE
averagedLines = avergaeLines(lane_image, lines)
linesImage = displayLines(lane_image, averagedLines)
comboImage = cv2.addWeighted(lane_image, 0.8, linesImage, 1, 1)
key = cv2.waitKey(100)
cv2.waitKey(0) & 0xFF
cv2.imshow("RESULT", comboImage)
if cv2.waitKey(0) & 0xFF == ord('c'):
break
#188 but 209 seems perfect
# print(min_wal, max_val)
#ROI_image = ROI(canny_blur_gray)
Below are the various errors that come -
packages\numpy\lib\function_base.py:380: RuntimeWarning: Mean of empty slice.
avg = a.mean(axis)
RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
Traceback (most recent call last):
linesImage = displayLines(lane_image, averagedLines)
in displayLines
cv2.line(line_image, (x1, y1), (x2, y2), (0, 255, 0), 5)
OverflowError: Python int too large to convert to C long
