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In the context of image processing, Convolution involves applying a small matrix (known as convolution kernel) of values to an image. This process results in various filtering effects such as blurring, sharpening, embossing, and edge detection. Each value in the kernel represents a weight or coefficient. This kernel is applied to a corresponding neighborhood of pixels in the image to produce the output pixel value at the corresponding position in the output image.
Python”s Pillow library provides a specific class known as “kernel” within its ImageFilter module. This class is used to create convolution kernels of sizes that extend beyond the conventional 5×5 matrices.
Creating the Convolution kernel
To create a convolution kernel you can use the Kernel() class from the ImageFilter module.
It”s important to note that the current version of Pillow supports 3×3 and 5×5 integer and floating-point kernels. And these kernels apply exclusively to images in “L” and “RGB” modes.
Following is the syntax of this ImageFilter.Kernel() class −
class PIL.ImageFilter.Kernel(size, kernel, scale=None, offset=0)
Here are the details of the class parameters −
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size − Represents the kernel size, specified as (width, height). In the current version, the valid sizes are (3,3) or (5,5).
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kernel − A sequence containing the kernel weights. The kernel is vertically flipped before applying it to the image.
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scale − Denotes the scale factor. If provided, the result for each pixel is divided by this value. The default value is the sum of the kernel weights.
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offset − Signifies an offset value. If provided, this value is added to the result after being divided by the scale factor.
Example
This example demonstrates how to apply a convolution kernel filter to an image using the Image.filter() method.
from PIL import Image, ImageFilter
# Create an image object
original_image = Image.open(''Images/Car_2.jpg'')
# Apply the Kernel filter
filtered_image = original_image.filter(ImageFilter.Kernel((3, 3), (0, -1, 0, -1, 5, -1, 0, -1, 0)))
# Display the original image
original_image.show()
# Display the filtered image
filtered_image.show()
Input Image
Output
Output of the convolution kernel filter −
Example
Here”s an example of applying a 5×5 emboss convolution kernel filter to an image.
from PIL import Image, ImageFilter
# Create an image object
original_image = Image.open(''Images/Car_2.jpg'')
# Define a 5x5 convolution kernel
kernel_5x5 = [-2, 0, -1, 0, 0,
0, -2, -1, 0, 0,
-1, -1, 1, 1, 1,
0, 0, 1, 2, 0,
0, 0, 1, 0, 2]
# Apply the 5x5 convolution kernel filter
filtered_image = original_image.filter(ImageFilter.Kernel((5, 5), kernel_5x5, 1, 0))
# Display the original image
original_image.show()
# Display the filtered image
filtered_image.show()
Input Image
Output Image
Output of the convolution kernel filter of size 5X5 −
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