The morphological processing technique for images is a fundamental technique that analyses objects’ shapes, sizes, and structures and processes many other vital functions in an image. Its main two applications are in image processing and computer vision, where it upgrades images by addressing noise reduction, object segmentation, shape analysis, texture analysis, and many more. Though Image editing tools can perform basic morphological operations, dedicated image processing libraries are designed to offer superior capabilities and flexibility for more convoluted image processing. OpenCV is an open-source computer vision library with powerful functions and tools for morphological processing. Morphological processing in OpenCV is generally done on binary or 1-bit images, but you can also apply it to grayscale ones.

 

Performing morphological processing of images using OpenCV involves a series of steps to manipulate the shape and structure of regions within binary images. This process is essential for noise reduction, feature extraction, and object detection in computer vision applications. This blog article is a detailed note on each step, providing explanations and code examples to illustrate the process.

 

Step 1: Import the necessary libraries.

 

To begin with, image processing in Python using OpenCV, we need to import the essential libraries. OpenCV, the Open Source Computer Vision Library, provides powerful functions for various image and video analysis tasks. We’ll import it with the alias “cv2”. Additionally, we’ll import “numpy” as “np” to facilitate numerical operations.

 

python

 

import cv2 import numpy as np

 

OpenCV allows us to access various image manipulation, analysis, and feature extraction functions. On the other hand, “numpy” provides efficient array-based operations, which are advantageous for working with image data represented as NumPy arrays. These libraries enable us to perform advanced image processing techniques, including morphological operations such as dilation, erosion, opening, closing, and gradient.

 

Step 2: Read the input image.

 

Before performing any morphological operations, we must load the input image onto which we’ll apply these operations. OpenCV provides the “cv2.imread()” function for this purpose. We pass the path to the image file as the argument, and the function returns a NumPy array representing the image. Grayscale images are commonly used for morphological operations, as they simplify processing and reduce computational overhead.

 

python

 

image = cv2.imread(“input_image.png”, cv2.IMREAD_GRAYSCALE)

 

The “cv2.imread()” function reads the image as a BGR (Blue, Green, Red) image by default. Since morphological operations are typically performed on binary images (where each pixel is black or white), we use the “cv2.IMREAD_GRAYSCALE” flag to load the image in grayscale mode. Grayscale images have only one channel representing pixel intensities, making them suitable for binary processing.

 

Step 3: Define the structuring element.

 

A structuring element is a small matrix that defines the shape and behavior of morphological operations. It is a reference for how neighboring pixels are considered during the operation. The shape and size of the structuring element play a crucial role in the outcome of morphological processing.

 

In OpenCV, the structuring element is represented by a kernel, which is essentially a matrix of ones and zeros. We use NumPy to create the kernel. For instance, to define a 3×3 square structuring element, we use the following code:

 

python

 

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

 

The above code creates a 3×3 matrix with all elements initialized to 1. This structuring element will be used in morphological operations such as dilation, erosion, opening, and closing.

 

You can create custom kernels for more complex structuring elements by manually setting specific values. For example, to create a cross-shaped kernel, you can use:

 

python

 

kernel = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], np.uint8)

 

Step 4: Perform Dilation.

 

Dilation is a morphological operation that expands the boundaries of regions in a binary image. It involves moving the defined structuring element (kernel) over the image and setting the pixel in the output image to 1 (white) if at least one pixel under the kernel is 1. This operation helps make regions or objects in the image larger and fills small gaps or holes within those regions.

 

In OpenCV, we use the “cv2.dilate()” function to perform dilation. The function takes the input image and the kernel as inputs and returns the dilated image.

 

python

 

dilated_image = cv2.dilate(image, kernel, iterations=1)

 

The above code loads the binary image in grayscale mode and defines a 3×3 square structuring element as the kernel. The “iterations” parameter determines how often the dilation operation is applied. Increasing the iterations can further expand the regions.

 

The resulting “dilated_image” will have expanded regions compared to the original image. Dilation is often used in image processing tasks such as object detection, noise removal, and improving the quality of binary images before applying further operations like contour detection or connected component analysis.

 

Step 5: Perform Erosion.

 

Erosion is a morphological operation that shrinks the boundaries of regions in a binary image. It involves moving the defined structuring element (kernel) over the image and setting the pixel in the output image to 0 (black) if all the pixels under the kernel are 1. This operation helps to remove small noise, fine details, and narrow regions from the image.

 

In OpenCV, we use the “cv2.erode()” function to perform erosion. The function takes the input image and the kernel as inputs and returns the eroded image.

 

python

 

eroded_image = cv2.erode(image, kernel, iterations=1)

 

The above code loads the binary image in grayscale mode and defines a 3×3 square structuring element as the kernel. The “iterations” parameter determines how often the erosion operation is applied. Increasing the iterations can lead to a stronger erosion effect.

 

The resulting “eroded_image” will have reduced regions compared to the original image. Erosion is useful in removing small noises or unwanted details and is often used in conjunction with dilation (closing operation) to remove small gaps or holes in regions.

 

Step 6: Perform Opening.

 

The opening is a morphological operation that consists of an erosion followed by a dilation. It removes small noise and fine details from the regions in a binary image while preserving the overall structure of larger regions. Opening is especially effective in removing salt-and-pepper noise and small isolated objects.

 

In OpenCV, we use the “cv2.morphologyEx()” function with the “cv2.MORPH_OPEN” parameter to perform opening. The function takes the input image, the kernel, and the number of iterations as inputs and returns the opened image.

 

python

 

opened_image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, iterations=1)

 

The above code loads the binary image in grayscale mode and defines a 3×3 square structuring element as the kernel. The “iterations” parameter determines how often the opening operation is applied.

 

The resulting “opened_image” will reduce noise and remove small regions, while the larger structures will remain intact. Opening is a valuable preprocessing step before further analysis, such as object recognition or feature extraction, as it simplifies the image while preserving important structures.

 

Step 7: Perform Closing.

 

Closing is a morphological operation that consists of a dilation followed by an erosion. It is used to close small gaps or holes in the regions of a binary image while preserving the overall structure of larger regions. Closing is particularly effective in removing dark spots or small black regions within the larger white regions.

 

In OpenCV, we use the “cv2.morphologyEx()” function with the “cv2.MORPH_CLOSE” parameter to perform closing. The function takes the input image, the kernel, and the number of iterations as inputs and returns the closed image.

 

python

 

closed_image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)

 

The above code loads the binary image in grayscale mode and defines a 3×3 square structuring element as the kernel. The “iterations” parameter determines how often the closing operation is applied.

 

The resulting “closed_image” will have closed gaps or holes within the regions while the overall shape and structure of the larger regions are preserved.

 

Step 8: Perform Gradient.

 

The gradient is a morphological operation used to highlight the boundaries of objects or regions in a binary image. It provides valuable information about the edges and boundaries by calculating the difference between the dilation and erosion of the image.

 

In OpenCV, we use the “cv2.morphologyEx()” function with the “cv2.MORPH_GRADIENT” parameter to perform gradient. The function takes the input image, the kernel, and the number of iterations as inputs and returns the gradient image.

 

python

 

gradient_image = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel, iterations=1)

 

The above code loads the binary image in grayscale mode and defines a 3×3 square structuring element as the kernel. The “iterations” parameter determines how often the gradient operation is applied.

 

The resulting “gradient_image” will highlight the edges and boundaries of objects in the binary image. It can be useful for edge detection and feature extraction tasks.

 

By combining the morphological operations of dilation, erosion, opening, closing, and gradient, we can perform various image processing tasks and enhance the quality of binary images for a wide range of computer vision applications.

 

Conclusion

 

In conclusion, morphological processing is an immaculate approach that results in accurate image modifications like image smoothness, shape and texture analysis, noise reduction, image restoration, etc. Using OpenCV libraries makes morphological image processing more convenient and accessible for users. OpenCV proffers more precision than other image manipulation programs, which are reliable for various editing tasks, but OpenCV libraries can work even better for challenging image processing tasks. Additionally, morphological processing using OpenCV is highly advantageous for researchers, engineers, and image-processing fanatics to thoroughly discover the prospects of morphological processing, leading to innovations in computer vision, medical imaging, robotics, and elsewhere.