OpenCV Python ŌĆō How to Perform SQRBox Filter Operation on An Image

If you are working on image processing tasks, you are most likely familiar with OpenCV. OpenCV is a powerful open-source library that can help you perform various tasks related to computer vision, image processing, and machine learning. In this article, we will explore how to perform SQRBox filter operation on an image using OpenCV in Python.

What is a SQRBox Filter?

A SQRBox filter is a type of filter that can help you to enhance the edges in an image. It works by computing the local variance of image pixels within a certain window size. The filter then replaces each pixel with the local variance value. The local variance is computed by subtracting the mean value of all pixels within the window from each pixel within the same window.

How to Implement SQRBox Filter Using OpenCV in Python?

Here’s the Python code to perform SQRBox filter operation on an image using OpenCV:

import cv2
import numpy as np

# Read the input image
img = cv2.imread('input.jpg')

# Define SQRBox filter size
kernel_size = 15

# Create SQRBox filter kernel
kernel = np.ones((kernel_size, kernel_size), np.float32)/(kernel_size*kernel_size)

# Apply SQRBox filter operation on the input image
filtered_image = cv2.filter2D(img, -1, kernel)

# Display the original and filtered images side-by-side
cv2.imshow('Original Image', img)
cv2.imshow('Filtered Image', filtered_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

In this code, we first read the input image using the cv2.imread() function. We then define the size of the SQRBox filter, which determines the size of the sliding window that will be used to compute the local variance. In this example, we have set the kernel size to 15.

Next, we create the SQRBox filter kernel using the np.ones() and np.float32() functions. We then divide each element of the kernel by the total number of elements in the kernel to normalize it and ensure that the sum of all kernel elements is 1.0.

Finally, we apply the SQRBox filter operation on the input image using the cv2.filter2D() function. This function computes the convolution of the input image with the SQRBox filter kernel. We then display the original and filtered images side-by-side using the cv2.imshow() function.

Conclusion

In this article, we have explored how to perform SQRBox filter operation on an image using OpenCV in Python. The SQRBox filter is a simple yet powerful filter that can help you to enhance the edges in an image. By applying this filter to your images, you can improve their visual quality and make them more suitable for further processing or analysis. If you are working on an image processing task and need to enhance the edges in your images, consider using the SQRBox filter and OpenCV in Python.