DirtyScreen

class augraphy.augmentations.dirtyscreen.DirtyScreen(n_clusters=(50, 100), n_samples=(2, 20), std_range=(1, 5), value_range=(150, 250), p=1)

Bases: Augmentation

Creates a dirty screen effect by applying blobs of different size in the entire image.

Parameters:

• n_clusters (tuple, optional) – Pair of ints determining number of clusters. Each cluster represents a dirty area.

• n_samples (tuple, optional) – Pair of ints determining number of points in a cluster. Higher point value contributes to a denser dirty area.

• std_range (tuple, optional) – Pair of ints determining the size of a cluster.

• value_range (tuple, optional) – Pair of ints determining the value of the points in a the cluster.

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

Overview

The DirtyScreen augmentation creates a dirty screen effect by applying blobs of different size in the entire image.

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 DirtyScreen augmentation instance is initialized. The number of noise cluster is set to random value in between 50 and 100 (50, 100). The number of points in each cluster is set to random value in between 2 and 20 (2,20). The size of each cluster is set to small size (std_range = (1,5)). The value of points is set to random value in between 150 and 250 (150,250).

Code example:

dirtyscreen = DirtyScreen(n_clusters = (50,100),
                                                      n_samples = (2,20),
                                                      std_range = (1,5),
                                                      value_range = (150,250),
                          )

img_dirtyscreen = dirtyscreen(image)
cv2.imshow("dirtyscreen", img_dirtyscreen)

Augmented image: