InkBleed

class augraphy.augmentations.inkbleed.InkBleed(intensity_range=(0.4, 0.7), kernel_size=(5, 5), severity=(0.3, 0.4), p=1)

Bases: Augmentation

Uses Sobel edge detection to create a mask of all edges, then applies random noise to those edges. When followed by a blur, this creates a fuzzy edge that emulates an ink bleed effect.

Parameters:

• intensity_range – Pair of floats determining the intensity of the ink bleeding effect.

• kernel_size (tuple, optional) – Kernel size to determine area of inkbleed effect.

• severity (tuple, optional) – Severity to determine concentration of inkbleed effect.

• p (float, optional) – The probability this Augmentation will be applied.

Overview

The InkBleed augmentation relies on Sobel edge detection to create a mask of all edges, then applies random noise to those edges. When followed by a blur, this creates a fuzzy edge that emulates an ink bleed effect.

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 InkBleed augmentation instance is initialized and the range of intensity is set to a range of moderate value (0.4, 0.7). The kernel size is set to 5x5 (5,5) to get a moderate spreadness of the effect. The severity of the ink bleed effect is set to a random low value in between 0.2 and 0.4 (0.2, 0.4).

Code example:

inkbleed = InkBleed(intensity_range=(0.4, 0.7),
                    kernel_size=(5, 5),
                    severity=(0.2, 0.4)
                    )

img_inkbleed = inkbleed(image)
cv2.imshow("inkbleed", img_inkbleed)

Augmented image: