Image Filters - ( Drawing and Image Processing in OpenCV )

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Syntax

cv2.filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]])

Example

import cv2
import numpy as np

# read image
img = cv2.imread('image.jpg')

# create a kernel
kernel = np.ones((5,5), np.float32)/25

# apply the kernel using filter2D function
dst = cv2.filter2D(img, -1, kernel)

# display the original and filtered images
cv2.imshow('Original Image', img)
cv2.imshow('Filtered Image', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

The output will display the original image and the filtered image side by side.

Explanation

Image filters are used in image processing to enhance or modify an image. OpenCV provides the cv2.filter2D() function to apply a 2-dimensional convolution kernel to an image.

In the above example, we read an image and create a kernel of size 5x5 with all values as 1/25. This kernel will average the pixel values in a 5x5 window. We then apply this kernel to the image using the cv2.filter2D() function and display the original and filtered images side by side.

Use

Image filters are used in various image processing tasks, such as noise reduction, edge detection, blurring, etc. They can be used to enhance images for better visualization or to prepare them for further analysis.

Important Points

• Image filters are used in image processing to modify or enhance an image
• OpenCV provides the cv2.filter2D() function to apply a 2-dimensional convolution kernel to an image
• The kernel is a matrix of numbers that is used to modify the pixel values in a neighborhood
• Image filters can be used for tasks such as noise reduction, edge detection, and blurring

Summary

In conclusion, image filters are an essential tool for image processing. OpenCV provides the cv2.filter2D() function to apply a 2-dimensional convolution kernel to an image. Filters are used to enhance images for better visualization or to prepare them for further analysis. They can be used for various image processing tasks such as noise reduction, edge detection, and blurring.