lane_detection_opencv.py

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

def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    # channel_count = img.shape[2]
    match_mask_color = 255  #, ) * channel_count
    cv2.fillPoly(mask, vertices, match_mask_color)
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def draw_the_lines(img, lines):
    img = np.copy(img)
    blank_image = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)

    for line in lines:
        for x1, y1, x2, y2 in line:
            cv2.line(blank_image, (x1, y1), (x2, y2), (0,255,0), thickness=3)


    img = cv2.addWeighted(img, 0.8, blank_image,  1, 0.0)
    return img

# image = cv2.imread('road.jpg')
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

def process(image):

    # print(image.shape)
    height = image.shape[0]
    width = image.shape[1]

    region_of_interest_vertices = [

        (0, height),
        (width/2, height/2),
        (width,height)

        # (120, height),
        # (width / 2, height / 2),
        # (width, 180)
    ]

    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    canny_image = cv2.Canny(gray_image, 100, 120)

    cropped_image = region_of_interest(canny_image,
            np.array([region_of_interest_vertices], np.int32))
    lines = cv2.HoughLinesP(cropped_image, rho=2, theta=np.pi/60, threshold=50, lines=np.array([]), minLineLength=40, maxLineGap=100)

    image_with_lines = draw_the_lines(image, lines)
    if lines is None:
        image_with_lines = image

    return image_with_lines


cap = cv2.VideoCapture('test_images/test_video.mp4')

while(cap.isOpened()):
    ret, frame = cap.read()
    frame = process(frame)
    cv2.imshow('frame',frame)
    if cv2.waitKeyEx(1) & 0xff == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()