#!/usr/bin/env python3
import numpy as np
import cv2
import matplotlib.pyplot as plt
from processor import Processor
processor = Processor()
cap = cv2.VideoCapture("test.mp4")
while (cap.isOpened()):
_, frame = cap.read()
canny_img = processor.canny(frame)
cropped_img = processor.region_of_interest(canny_img)
lines = cv2.HoughLinesP(cropped_img,
2,
np.pi / 180,
100,
np.array([]),
minLineLength=40,
maxLineGap=1)
avg_lines = processor.average_slope_intercept(frame, lines)
# Display the lines on top of our coloured image
line_img = processor.display_lines(frame, avg_lines)
combo_img = cv2.addWeighted(frame, 0.8, line_img, 1, 0)
# Present image in a window - top-right of your monitor.
cv2.imshow('result', combo_img)
cv2.moveWindow('result', 0, 0)
cv2.waitKey(1)
if cv2.waitKey(1) == ord('q'):
#!/usr/bin/env python3
import numpy as np
import cv2
import matplotlib.pyplot as plt
from processor import Processor
processor = Processor()
# Format image
img = cv2.imread('test_image.jpg')
lane_img = np.copy(img)
canny_img = processor.canny(lane_img)
cropped_img = processor.region_of_interest(canny_img)
# Create a single line for each side of the road, averaged from the HoughLines algorithm
lines = cv2.HoughLinesP(cropped_img,
2,
np.pi / 180,
100,
np.array([]),
minLineLength=40,
maxLineGap=1)
avg_lines = processor.average_slope_intercept(lane_img, lines)
# Display the lines on top of our coloured image
line_img = processor.display_lines(img, avg_lines)
combo_img = cv2.addWeighted(img, 0.8, line_img, 1, 0)
# Present image in a window - top-right of your monitor.
cv2.imshow('result', combo_img)
cv2.moveWindow('result', 0, 0)
