import glob
import skimage
import matplotlib.pyplot as plt

image='/home/ncbc/Desktop/WorkSpace_NCBC/IQRA /dataset/SB/archive/pest/stem_borer/jpg_53.jpg'
# 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(image)

# 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()