Erosion and dilation

Erosion and dilation are morphological image processing operations. Morphological image processing basically deals with modifying geometric structures in the image. These operations are primarily defined for binary images, but we can also use them on grayscale images. Erosion basically strips out the outermost layer of pixels in a structure, whereas dilation adds an extra layer of pixels to a structure.

Let's see what these operations look like:

Following is the code to achieve this:

import cv2 
import numpy as np 
 
img = cv2.imread('images/input.jpg', 0) 
 
kernel = np.ones((5,5), np.uint8) 
 
img_erosion = cv2.erode(img, kernel, iterations=1) 
img_dilation = cv2.dilate(img, kernel, iterations=1) 
 
cv2.imshow('Input', img) 
cv2.imshow('Erosion', img_erosion) 
cv2.imshow('Dilation', img_dilation) 
 
cv2.waitKey(0) 

Afterthought

OpenCV provides functions to directly erode and dilate an image. They are called erode and dilate, respectively. The interesting thing to note is...