InkMottling#

class augraphy.augmentations.inkmottling.InkMottling(ink_mottling_alpha_range=(0.2, 0.3), ink_mottling_noise_scale_range=(2, 2), ink_mottling_gaussian_kernel_range=(3, 5), p=1)[source]#

Bases: Augmentation

Create a random pattern effect in the detected ink by blending a layer of random Gaussian noise.

Parameters:

ink_mottling_alpha_range (tuple, optional) – Tuple of floats determining the alpha value of the added effect.
ink_mottling_noise_scale_range (tuple, optional) – Tuple of ints determining the size of Gaussian noise pattern.
ink_mottling_gaussian_kernel_range (tuple, optional) – Tuple of ints determining the Gaussian kernel value.
p (float, optional) – The probability that this Augmentation will be applied.

Overview#

The InkMottling augmentation create a random pattern effect in the detected ink by blending a layer of random Gaussian noise.

Initially, a clean image with single line of text is created.

Code example:

# import libraries
import cv2
import numpy as np
from augraphy import *


# create a clean image with single line of text
image = np.full((500, 1500,3), 250, dtype="uint8")
cv2.putText(
    image,
    "Lorem ipsum dolor sit amet, consectetur adipiscing elit",
    (80, 250),
    cv2.FONT_HERSHEY_SIMPLEX,
    1.5,
    0,
    3,
)

cv2.imshow("Input image", image)

Clean image:

Example 1#

In this example, a InkMottling augmentation instance is initialized and the alpha value of the ink mottling effect is set to 0.5 (0.5, 0.5). The generated scale of Gaussian noise pattern is set to 1 (1,1). The Gaussian kernel size is set to random number between 3 and 5 (3,5).

Code example:

inkmottling= InkMottling(ink_mottling_alpha_range=(0.5, 0.5),
                         ink_mottling_noise_scale_range=(1,1),
                         ink_mottling_gaussian_kernel_range=(3,5),
                         )

img_inkmottling = inkmottling(image)

cv2.imshow("inkmottling", img_inkmottling)

Augmented image: