threshold.py

import argparse

import cv2
import numpy as np

parser = argparse.ArgumentParser(description='White Lane Detection')
parser.add_argument('-i', '--input_image', help='Input Image', default="0.png", type=str)

args = parser.parse_args()

img = cv2.imread(args.input_image)


def nothing(x):
    pass


# Creating a window for later use
cv2.namedWindow('result')

# Creating track bar
cv2.createTrackbar('h_min', 'result', 0, 179, nothing)
cv2.createTrackbar('s_min', 'result', 0, 255, nothing)
cv2.createTrackbar('v_min', 'result', 0, 255, nothing)

cv2.createTrackbar('h_max', 'result', 0, 179, nothing)
cv2.createTrackbar('s_max', 'result', 0, 255, nothing)
cv2.createTrackbar('v_max', 'result', 0, 255, nothing)

while True:

    frame = cv2.imread(args.input_image)

    # frame = cv2.resize(frame, (500,500))
    # converting to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)

    # get info from track bar and appy to result
    h_min = cv2.getTrackbarPos('h_min', 'result')
    s_min = cv2.getTrackbarPos('s_min', 'result')
    v_min = cv2.getTrackbarPos('v_min', 'result')

    h_max = cv2.getTrackbarPos('h_max', 'result')
    s_max = cv2.getTrackbarPos('s_max', 'result')
    v_max = cv2.getTrackbarPos('v_max', 'result')

    # Normal masking algorithm
    lower = np.array([h_min, s_min, v_min])
    upper = np.array([h_max, s_max, v_max])

    mask = cv2.inRange(hsv, lower, upper)

    result = cv2.bitwise_and(frame, frame, mask=mask)

    cv2.imshow('result', result)

    k = cv2.waitKey(0) & 0xFF
    if k in [27, 113]:
        cv2.destroyAllWindows()
        break