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BlurNoise & RandomBlurNoise Modules

Note

BlurNoise Module

The BlurNoise class is designed to apply Gaussian blur noise to images using the Pillow library. It inherits from the abstract base class Noise and implements the add_noise method.

Attributes

  • blur_radius : float

    The radius of the Gaussian blur to be applied. A higher value results in a more blurred image.

Methods

add_noise()

add_noise(self, image: Image) -> Image:
Applies Gaussian blur noise to the input image.

  • Parameters:

    • image : Image

      The input image to which the noise will be added.

  • Returns:

    • Image:

      The image with Gaussian blur applied.

_blur_noise

_blur_noise(self, image: Image) -> Image:
An internal method that applies the Gaussian blur to the image.

  • Parameters:

    • image : Image

      The image to which the Gaussian blur will be applied.

  • Returns:

    • Image:

      The blurred image.

Usage Example

from PIL import Image
from iftg.noises import BlurNoise

# Open an image
image = Image.open('path/to/image.tif')

# Create a BlurNoise object with a specific blur radius
blur_noise = BlurNoise(blur_radius=5.0)

# Apply the blur noise to the image
blurred_image = blur_noise.add_noise(image)

# Save the blurred image
blurred_image.save('path/to/blurred_image.tif')


RandomBlurNoise Module

The RandomBlurNoise class extends BlurNoise by introducing randomness to the blur radius. Instead of using a fixed blur radius, it selects a random value within a specified range each time the noise is applied.

Attributes

  • blur_radius_range : tuple[float, float]

    A tuple representing the minimum and maximum values between which the blur radius will be randomly selected.

Methods

add_noise()

Note

Each call to add_noise() results in a different blur radius, adding variability to the blur effect. This is particularly useful when processing multiple images to prevent uniform blur across all images.

add_noise(self, image: Image) -> Image:
Applies Gaussian blur noise with a random radius to the input image.

  • Parameters:

    • image : Image

      The input image to which the noise will be added.

  • Returns:

    • Image:

      The image with random Gaussian blur applied.

Usage Example

from PIL import Image
from iftg.noises import RandomBlurNoise

# Open an image
image = Image.open('path/to/image.tif')

# Create a RandomBlurNoise object with a blur radius range
random_blur_noise = RandomBlurNoise(blur_radius_range=(2.0, 6.0))

# Apply random blur noise to the image
random_blurred_image = random_blur_noise.add_noise(image)

# Save the randomly blurred image
random_blurred_image.save('path/to/random_blurred_image.tif')