lane_detection_video.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


def process(image):
    height = image.shape[0]
    width = image.shape[1]

    region_of_interest_vertices = [
        (0, height/1.4),
        (width/3, height/2), (width - width/3, height/2),
        (width, height/1.4)
    ]


    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=50,
                            lines=np.array([]),
                            minLineLength=40,
                            maxLineGap=25)
    image_with_lines = draw_lines(image, lines)
    #will not use matplotlib for vidoe processing
    return image_with_lines

cap = cv2.VideoCapture('videoplayback_Trim.mp4')
while(cap.isOpened()):
    ret, frame = cap.read()
    frame = process(frame)
    cv2.imshow('test',frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()