morphological_techniques.py

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('sample.png',0) # Load an color image in grayscale
mask = cv2.threshold(img, 220, 255, cv2.THRESH_BINARY_INV) #mask returns a Array of two values with threshold value and the the second value is the thresholded image.

#For every pixel, the same threshold value is applied. If the pixel value is smaller than the threshold, it is set to 0, otherwise it is set to a maximum value

kernal = np.ones((5,5), np.uint8) #Return a new array of given shape and type, filled with ones.
#(5,5) -- type of array
# np.uint8 -- datatype of the values in array
# int8 and unit8 --> unsigned and signed integers

dilation = cv2.dilate(mask[1], kernal, iterations=2)
# The first parameter is the original image,
# kernel is the matrix with which image is convolved and 
# third parameter is the number of iterations, which will determine how much you want to erode/dilate a given image.
erosion = cv2.erode(mask[1], kernal, iterations=1)
opening = cv2.morphologyEx(mask[1], cv2.MORPH_OPEN, kernal)
closing = cv2.morphologyEx(mask[1], cv2.MORPH_CLOSE, kernal)
morphgradient = cv2.morphologyEx(mask[1], cv2.MORPH_GRADIENT, kernal)
tophat = cv2.morphologyEx(mask[1], cv2.MORPH_TOPHAT, kernal)

titles = ['image', 'mask', 'dilation', 'erosion', 'opening', 'closing', 'morphgradient', 'tophat']
images = [img, mask[1], dilation, erosion, opening, closing, morphgradient, tophat]

for i in range(8):
    plt.subplot(2, 4, i+1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()