Python Pillow - Blur an Image

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Blurring an Image is a fundamental concept in image processing, used to reduce the level of detail or sharpness in an image. The primary purpose of blurring is to make an image appear smoother or less sharp. Blurring can be achieved through various mathematical operations, convolution kernels, or filters.

Python's Pillow library offers several standard image filters within the ImageFilter module to perform the different blurring operations on the image by calling the image.filter() method. In this tutorial we will see different image blurring filters provided by the ImageFilter module.

Applying Blur Filter to an Image

Blurring an image can be done by using the ImageFilter.BLUR filter, which is one of the built-in filter options available in the current version of the Pillow library and is used to create a blurred effect in the image.

Following are the steps for applying image blurring −

• Load the input image using the Image.open() function.

• Apply the filter() function to the loaded Image object and provide ImageFilter.BLUR as an argument to the function. The function will return the blurred image as a PIL.Image.Image object.

Following is the input image used in all the examples of this chapter.

Example

Here is the example using the Image.filter() method with ImageFilter.BLUR kernel filter.

from PIL import Image, ImageFilter

# Open an existing image
original_image = Image.open('Images/car_2.jpg')

# Apply a blur filter to the image
blurred_image = original_image.filter(ImageFilter.BLUR)

# Display the original image 
original_image.show()

# Display the blurred mage
blurred_image.show()

Output Image

Applying the BoxBlur Filter

The BoxBlur filter is used to blur an image by setting each pixel's value to the average value of the pixels within a square box that extends a specified number of pixels in each direction. For this yo can use the BoxBlur() class from the Pillow's ImageFilter module.

Following is the syntax of the ImageFilter.BoxBlur() class −

class PIL.ImageFilter.BoxBlur(radius)

The PIL.ImageFilter.BoxBlur() class accepts a single parameter −

• radius − This parameter specifies the size of the square box used for blurring in each direction. It can be provided as either a sequence of two numbers (for the x and y directions) or a single number that applies to both directions.

If radius is set to 1, it takes the average of the pixel values within a 3x3 square box (1 pixel in each direction, resulting in 9 pixels in total). A radius value of 0 doesn't blur the image and returns an identical image.

Example

Here's an example that demonstrates how to use the BoxBlur() class for image blurring.

from PIL import Image, ImageFilter

# Open an existing image
original_image = Image.open('Images/car_2.jpg')

# Apply a Box Blur filter to the image
box_blurred_image = original_image.filter(ImageFilter.BoxBlur(radius=3))

# Display the Box Blurred image
box_blurred_image.show()

Output Image

Applying the Gaussian Blur to an Image

The GaussianBlur filter is used to blur an image by applying a Gaussian blur, which is a specific type of blur that approximates a Gaussian distribution. This can be done by using the Pillow's ImageFilter.GaussianBlur() class.

Below is the syntax of the ImageFilter.GaussianBlur() class −

class PIL.ImageFilter.GaussianBlur(radius=2)

The ImageFilter.GaussianBlur() class accepts a single parameter −

• radius − This parameter specifies the standard deviation of the Gaussian kernel. The standard deviation controls the extent of blurring. You can provide it as either a sequence of two numbers (for the x and y directions) or a single number that applies to both directions.

Example

Here's an example that demonstrates how to use the GaussianBlur() class for image blurring.

from PIL import Image, ImageFilter

# Open an existing image
original_image = Image.open('Images/car_2.jpg')

# Apply a Gaussian Blur filter to the image
gaussian_blurred_image = original_image.filter(ImageFilter.GaussianBlur(radius=2))

# Display the Gaussian Blurred image
gaussian_blurred_image.show()

Output Image