In this Python plays Grand Theft Auto tutorial, we're going to incorporate the Hough Line finding functionality from OpenCV. Code up to this point:

import time
from directkeys import ReleaseKey, PressKey, W, A, S, D
import pyautogui


def roi(img, vertices):
    mask = np.zeros_like(img)
    cv2.fillPoly(mask, vertices, 255)
    masked = cv2.bitwise_and(img, mask)
    return masked

def process_img(original_image):
    processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)
    vertices = np.array([[10,500],[10,300], [300,200], [500,200], [800,300], [800,500]], np.int32)
    processed_img = roi(processed_img, [vertices])
    return processed_img


def main():
    last_time = time.time()
    while(True):
        screen =  np.array(ImageGrab.grab(bbox=(0,40, 800, 640)))
        new_screen = process_img(screen)
        print('Loop took {} seconds'.format(time.time()-last_time))
        last_time = time.time()
        cv2.imshow('window', new_screen)
        #cv2.imshow('window2', cv2.cvtColor(screen, cv2.COLOR_BGR2RGB))
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break

Next, we're going to use the HoughLinesP algorithm from OpenCV, which finds lines based on a few parameters. We'll add this to our process image function:

def process_img(original_image):
    processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)
    
    vertices = np.array([[10,500],[10,300],[300,200],[500,200],[800,300],[800,500],
                         ], np.int32)
    processed_img = roi(processed_img, [vertices])


    # more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
    #                          edges       rho   theta   thresh         # min length, max gap:        
    lines = cv2.HoughLinesP(processed_img, 1, np.pi/180, 180,      20,         15)
    draw_lines(processed_img,lines)
    return processed_img

Now, we don't yet have this "draw_lines" function, let's make that:

def draw_lines(img,lines):
    for line in lines:
        coords = line[0]
        cv2.line(img, (coords[0], coords[1]), (coords[2], coords[3]), [255,255,255], 3)

Simple enough, it just simply draws some thicker lines over where it seems to find lines. Full code up to this point:

import numpy as np
from PIL import ImageGrab
import cv2
import time
from directkeys import ReleaseKey, PressKey, W, A, S, D 


def draw_lines(img,lines):
    for line in lines:
        coords = line[0]
        cv2.line(img, (coords[0], coords[1]), (coords[2], coords[3]), [255,255,255], 3)


def process_img(original_image):
    processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)
    vertices = np.array([[10,500],[10,300],[300,200],[500,200],[800,300],[800,500],
                         ], np.int32)
    processed_img = roi(processed_img, [vertices])

    # more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
    #                          edges       rho   theta   thresh         # min length, max gap:        
    lines = cv2.HoughLinesP(processed_img, 1, np.pi/180, 180,      20,         15)
    draw_lines(processed_img,lines)
    return processed_img


def roi(img, vertices):
    #blank mask:
    mask = np.zeros_like(img)
    # fill the mask
    cv2.fillPoly(mask, vertices, 255)
    # now only show the area that is the mask
    masked = cv2.bitwise_and(img, mask)
    return masked


def main():
    last_time = time.time()
    while(True):
        screen =  np.array(ImageGrab.grab(bbox=(0,40, 800, 640)))
        new_screen = process_img(screen)
        print('Loop took {} seconds'.format(time.time()-last_time))
        last_time = time.time()
        cv2.imshow('window', new_screen)
        #cv2.imshow('window2', cv2.cvtColor(screen, cv2.COLOR_BGR2RGB))
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break

If we run this with main(), we get something like:

Image(filename='lines-detected.png')

Okay, hmm, not the strongest lines. This is happening due to all this aliasing going on. Let's smooth this image, then look for lines. For this, we'll blur with cv2.GaussianBlur:

def process_img(original_image):
    processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)
    vertices = np.array([[10,500],[10,300],[300,200],[500,200],[800,300],[800,500],
                         ], np.int32)

    processed_img = cv2.GaussianBlur(processed_img,(5,5),0)
    processed_img = roi(processed_img, [vertices])

    # more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
    #                          edges       rho   theta   thresh         # min length, max gap:        
    lines = cv2.HoughLinesP(processed_img, 1, np.pi/180, 180,      20,         15)
    draw_lines(processed_img,lines)
    return processed_img

Image(filename='blurred-and-lines-detected.jpg')

NOW we're cookin! The longest and most dominant lines are undoubtedly the lanes.