Exp_4.py

# Practical 4
# simple thresholding type on an image
	
#%%
# Import Libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt

#%%
# path to input image is specified and
# image is loaded with imread command
image1 = cv2.imread('F:\DIP-Lab\Org_Img.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)

#%%
# Please Check for Pop up Windows

#%%
# De-allocate any associated memory usage
if cv2.waitKey(0) & 0xff == 27:
	cv2.destroyAllWindows()