Understanding Morphological Operations with OpenCV

       Morphology, in the context of image processing, refers to a set of operations that process images based on their shape or structure. These operations are typically applied to binary (black and white) images, where pixels are either white or black. Morphological operations analyze and manipulate the spatial arrangement of pixels in an image to extract features or modify its structure.

    In this tutorial, we will delve into two fundamental morphological operations: opening and closing. These operations are essential tools in the image processing, and we will explain how they work, their applications, and how to use them in Python with OpenCV. The tutorial covers:

1. What Are Morphological Operations?
   
2. Implementation in OpenCV
3. Conclusion
   

     Let's get started.

 

What Are Morphological Operations

      Morphological operations consist of a set of image processing techniques that operate on images based on their shape or structure. These operations analyze and manipulate the spatial arrangement of pixels in an image to extract meaningful features or modify its structure. Essentially, they help us investigate and enhance the form and layout of objects within an image. Common morphological operations include erosion, opening, closing, dilation, and more. This tutorial will specifically delve into opening and closing.

Morphological Opening
    Opening is a sequence of two fundamental operations: erosion followed by dilation. It is particularly useful when we want to remove noise or small objects from binary images while preserving the shape and size of larger objects. Consider it as tidying up the image—removing unwanted specks while preserving the essentials.

Morphological Closing
    Closing is the counterpart to opening, consisting of dilation followed by erosion. This operation is perfect for closing small holes or gaps in the foreground of binary images. It ensures that the overall shape and size of larger objects remain unaltered while filling in the gaps.

Implementation in OpenCV

 

   OpenCV provides the cv2.morphologyEx() function, facilitating the execution of morphological operations on binary or grayscale images. Here's a breakdown of its key parameters:
    image: This parameter is the input image on which you want to apply the morphological operation. It's typically a binary image (containing only black and white pixels) or a grayscale image.
    op: This parameter specifies the type of morphological operation to be performed. In our examples, we use cv2.MORPH_OPEN for opening and cv2.MORPH_CLOSE for closing. These constants indicate the type of operation you want to perform.
    kernel: The kernel is a small matrix that defines the neighborhood for the morphological operation. It specifies the size and shape of the structuring element used for erosion and dilation. A larger kernel can have a broader effect on the image, while a smaller kernel is more focused.

 

# Apply morphological opening to reduce noise

noise_reduced_image_o = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)

# Apply morphological closing to fill gaps

noise_reduced_image_c = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)

    Now, let's take a look at an example of step-by-step morphological open and close operation example with cv2.morphologyEx() function. We'll start loading the target image and convert it to grayscale.  We define kernel with the size (3, 3) and apply morphological open and close operation with defined kernel. Finally, we display the results. 

  

 

import cv2

import numpy as np

from matplotlib import pyplot as plt

# Load the binary image with noise

image = cv2.imread('map.jpg', cv2.IMREAD_GRAYSCALE)

# Define the kernel (structuring element) for morphological operations

kernel = np.ones((3, 3), np.uint8)

# Apply morphological opening to reduce noise

noise_reduced_image_o = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)

# Apply morphological closing to fill gaps

noise_reduced_image_c = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)

# Display the original and noise-reduced images

plt.figure(figsize=(12, 12))

plt.subplot(131)

plt.imshow(image, cmap='gray')

plt.title('Original Image')

plt.subplot(132)

plt.imshow(noise_reduced_image_o, cmap='gray')

plt.title('Noise-Reduced Morph-open')

plt.subplot(133)

plt.imshow(noise_reduced_image_c, cmap='gray')

plt.title('Noise-Reduced Morph-close')

plt.show()

 

 

 

 Conclusion

  

    In this tutorial, we've briefly learned how to implement morphological open and close operations with OpenCV.  Morphological operations play an important role in image processing, providing robust methods to enhance images, segment objects, and analyze shapes. Understanding the opening and closing operations provides valuable tools for improving the quality of images and extracting meaningful information.

 

 

References:

1. OpenCV image processing