Erosion and Dilation of Images using OpenCV in Python

This post will be helpful in learning OpenCV using Python programming. Here I will show how to implement OpenCV functions and apply it in various aspects using some examples. Then the output will be shown with some comparisons as well.

Requirements:

OpenCV 3.4
Python 3.6+
Numpy
Image, Webcam or Video input
Documentation Source: OpenCV Official Documentation

First, you need to setup your Python Environment with OpenCV. You can easily do it by following Life2Coding’s tutorial on YouTube: Linking OpenCV 3 with Python 3

Goals:

In this tutorial, I will show you how to erode and dilate images using OpenCV and Python coding.

Documentation:

Python: cv2.erode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) → dst

Erodes an image by using a specific structuring element.

Parameters:

src – input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F` or “CV_64F.
dst – output image of the same size and type as src.
kernel – structuring element used for erosion; if element=Mat() , a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement().
anchor – position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations – number of times erosion is applied.
borderType – pixel extrapolation method (see borderInterpolate for details).

Python: cv2.dilate(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) → dst

Dilates an image by using a specific structuring element.

Parameters:

src – input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F` or “CV_64F.
dst – output image of the same size and type as src.
kernel – structuring element used for dilation; if elemenat=Mat() , a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement()
anchor – position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations – number of times dilation is applied.
borderType – pixel extrapolation method (see borderInterpolate for details).
borderValue – border value in case of a constant border

Steps:

Load the Original image using cv2.imread()
Apply erode and dilate using cv2.erode() and cv2.dialate()
Display all the images using cv2.imshow()
Wait for keyboard button press using cv2.waitKey()
Exit window and destroy all windows using cv2.destroyAllWindows()

Example Code:

import cv2
import numpy as np


def erodeDilate(imagePath):
    # Read the input image
    frame = cv2.imread('hanif.jpg')

    # kernel is used to control the amount of eroding and dilating
    kernel = np.ones((5, 5), np.uint8)

    # Repeat the erosion and dilation by changing iterations.
    img_erode = cv2.erode(frame, kernel, iterations=2)
    img_dilate = cv2.dilate(frame, kernel, iterations=2)

    cv2.imshow('Input', frame)
    cv2.imshow('Erosion', img_erode)
    cv2.imshow('Dilation', img_dilate)
    cv2.waitKey(0)

def main():
    imagePath='hanif.jpg'
    erodeDilate(imagePath)
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()