lane_detection.py

import matplotlib.pylab 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
    match_mask_color = 255
    cv2.fillPoly(mask, vertices, match_mask_color)
    plt.imshow(mask)
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def draw_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), 3)

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


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

height = image.shape[0]
width = image.shape[1]

region_of_interest_vertices = [
    (0 - width/5, height),
    (width/2, height/1.8),
    (width + width/5, height)
]


gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
canny_image = cv2.Canny(gray_image, 80, 200)
cropped_image = region_of_interest(canny_image, np.array([region_of_interest_vertices], np.int32),)

lines = cv2.HoughLinesP(cropped_image,
                        rho=6,
                        theta=np.pi/180,
                        threshold=160,
                        lines=np.array([]),
                        minLineLength=40,
                        maxLineGap=25)
image_with_lines = draw_lines(image, lines)
plt.imshow(image_with_lines)

plt.show()