Erosion and Dilation of images using OpenCV in python
Morphological operations are a set of operations that process images based on shapes. They apply a structuring element to an input image and generate an output image.
The most basic morphological operations are two: Erosion and Dilation
Basics of Erosion:
- Erodes away the boundaries of the foreground object
- Used to diminish the features of an image.
Working of erosion:
- A kernel(a matrix of odd size(3,5,7) is convolved with the image.
- A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel are 1, otherwise, it is eroded (made to zero).
- Thus all the pixels near the boundary will be discarded depending upon the size of the kernel.
- So the thickness or size of the foreground object decreases or simply the white region decreases in the image.
Basics of dilation:
- Increases the object area
- Used to accentuate features
Working of dilation:
- A kernel(a matrix of odd size(3,5,7) is convolved with the image
- A pixel element in the original image is ‘1’ if at least one pixel under the kernel is ‘1’.
- It increases the white region in the image or the size of the foreground object increases
- Python
# Python program to demonstrate erosion and# dilation of images.import cv2import numpy as np# Reading the input imageimg = cv2.imread('input.png', 0)# Taking a matrix of size 5 as the kernelkernel = np.ones((5,5), np.uint8)# 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.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) |
The second image is the eroded form of the original image and the third image is the dilated form.
Last Updated on November 13, 2021 by admin