11_Image_thresholding.py

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt

######################### Simple thresholding ##############################
# img = cv.imread('sudoku.jpeg', cv.IMREAD_GRAYSCALE)

# # cv.imshow('window', img)

# ret,thresh1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
# ret,thresh2 = cv.threshold(img,127,255,cv.THRESH_BINARY_INV)
# ret,thresh3 = cv.threshold(img,127,255,cv.THRESH_TRUNC)
# ret,thresh4 = cv.threshold(img,127,255,cv.THRESH_TOZERO)
# ret,thresh5 = cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)

# titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
# images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

# for i in range(6):
#     plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
#     plt.title(titles[i])
#     plt.xticks([]),plt.yticks([])
# plt.show()
# cv.waitKey(0)
# cv.destroyAllWindows()


############################# Adaptive Thresholding ######################

# img = cv.imread('sudoku.jpeg', cv.IMREAD_GRAYSCALE)
# # img = cv.medianBlur(img,3)

# ret, th1 = cv.threshold(img,127, 255, cv.THRESH_BINARY)
# th2 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,\
#             cv.THRESH_BINARY,11,2)
# th3 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,\
#             cv.THRESH_BINARY,11,2)
# titles = ['Original Image', 'Global Thresholding (v = 127)',
#             'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
# images = [img, th1, th2, th3]
# for i in range(4):
#     plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
#     plt.title(titles[i])
#     plt.xticks([]),plt.yticks([])
# plt.show()

# cv.waitKey(0)
# cv.destroyAllWindows()

################### Otsu's Binarization ######################

img = cv.imread('image.jpg', cv.IMREAD_GRAYSCALE)


# global thresholding
ret1,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
# Otsu's thresholding
ret2,th2 = cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)

# Otsu's thresholding after Gaussian filtering
blur = cv.GaussianBlur(img,(5,5),0)
ret3,th3 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
# plot all the images and their histograms
images = [img, 0, th1,
          img, 0, th2,
          blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
          'Original Noisy Image','Histogram',"Otsu's Thresholding",
          'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
for i in range(3):
    plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
    plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
    plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
    plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])
plt.show()