# Python program to illustrate

# simple thresholding type on an image

# organizing imports

import cv2

import numpy as np

# path to input image is specified and 

# image is loaded with imread command

image1 = cv2.imread('input1.jpg')

# cv2.cvtColor is applied over the

# image input with applied parameters

# to convert the image in grayscale

img = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)

# applying different thresholding

# techniques on the input image

# all pixels value above 120 will

# be set to 255

ret, thresh1 = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY)

ret, thresh2 = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY_INV)

ret, thresh3 = cv2.threshold(img, 120, 255, cv2.THRESH_TRUNC)

ret, thresh4 = cv2.threshold(img, 120, 255, cv2.THRESH_TOZERO)

ret, thresh5 = cv2.threshold(img, 120, 255, cv2.THRESH_TOZERO_INV)

# the window showing output images

# with the corresponding thresholding

# techniques applied to the input images

cv2.imshow('Binary Threshold', thresh1)

cv2.imshow('Binary Threshold Inverted', thresh2)

cv2.imshow('Truncated Threshold', thresh3)

cv2.imshow('Set to 0', thresh4)

cv2.imshow('Set to 0 Inverted', thresh5)

# De-allocate any associated memory usage 

if cv2.waitKey(0) & 0xff == 27:

    cv2.destroyAllWindows()