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'): break cap.release() cv2.destroyAllWindows()
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) cv2.waitKey()