lanes2.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

def make_coordinates(image, line_parameters):
	slope,intercept = line_parameters
	y1 = image.shape[0]
	y2 = int(y1*(3/5))
	x1 = int((y1-intercept)/slope)
	x2 = int((y2-intercept)/slope)
	#print(image.shape)
	return np.array([x1,y1,x2,y2])

def averaged_slope_intercept (image,lines):
	left_fit = []
	right_fit = []
	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))
	left_fit_avg = np.average(left_fit,axis=0)
	right_fit_avg = np.average(right_fit,axis=0)
	left_line = make_coordinates(image,left_fit_avg)
	right_line = make_coordinates(image,right_fit_avg)
	print (parameters)

def canny(image):

	gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
	blur = cv2.GaussianBlur(gray, (5, 5),0)
	canny = cv2.Canny(blur,80,400)
	return canny
def display_lines (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),(255,0,0),10)
			print (line)
	return line_image

def region_of_interest(image):
	height = image.shape[0]
	polygons = np.array([
	[(170,height),(810,height),(500,250)]
	])
	mask = np.zeros_like(image)
	cv2.fillPoly(mask,polygons, 255)
	masked_image = cv2.bitwise_and(image, mask)       # used to mask the image only to show the	region of interest
	return masked_image

image = cv2.imread('test_image3.jpg')
lane_image = np.copy(image)
canny = canny(lane_image)
cropped_image = region_of_interest(canny)
lines = cv2.HoughLinesP(cropped_image, 2, np.pi/180,100,np.array([]),minLineLength = 40, maxLineGap = 8)
averaged_lines = averaged_slope_intercept(lane_image, lines)
line_image = display_lines(lane_image,lines)
combo_img = cv2.addWeighted(lane_image,0.8, line_image,1,1 )
#plt.imshow (canny)
#plt.show()
cv2.imshow('result',combo_img)
cv2.waitKey(0)