def lane_fit_on_prediction(Roi_img, src, dst, dst_size):
warped_img, M = sliding_window_approach.perspective_warp(
Roi_img, dst_size, src, dst)
margin = 35
nwindows = 12
# InitialPoints Estimation using K-Means clustering
margin, margin_1, modifiedCenters = sliding_window_approach.initialPoints(
warped_img, margin)
# Sliding Window Search
out_img, curves, lanes, ploty = sliding_window_approach.sliding_window(
warped_img, modifiedCenters, nwindows, margin, margin_1)
return warped_img, out_img, curves, lanes, ploty, modifiedCenters
#!/usr/bin/env python
import rospy
import numpy as np
import cv2
from cv_bridge import CvBridge
from sensor_msgs.msg import Image
import os
os.environ[
'PYGAME_HIDE_SUPPORT_PROMPT'] = "hide" # Hides the pygame version, welcome msg
from os.path import expanduser
import glob
import scipy.signal as signal
import sliding_window_approach
DBASW = sliding_window_approach.sliding_window()
class hilly_nav():
def __init__(self):
self.image = Image()
self.roi_img = Image()
self.final_img = []
self.modifiedCenters_local = []
self.crop_ratio = 0.3 # Ratio to crop the background parts in the image from top
self.centerLine = []
def segment_image(self):
# define range of blue color in HSV