Effect

class text_renderer.effect.Effect(p=0.5)

Apply different augmentations to image.

E.g. add noise, add dropout, add padding…

Parameters

p (float) – Probability of apply this effect

abstract apply(img: PIL.Image.Image, text_bbox: text_renderer.utils.bbox.BBox)

Parameters

• img (PILImage) – Image to apply effect

• text_bbox (BBox) – bbox of text on input Image

Returns

• PILImage – Image changed

• BBox – Text bbox on image after apply effect. Some effects (such as Padding) may modify the relative position of the text in the image.

static rand_pick(pim, col, row)

Randomly reset pixel value at [col, row]

new_pixel_value = random.randint(0, pixel_value)

Parameters

• pim (PyAccess) – Get from pil_img.load()

• col (int) –

• row (int) –

class text_renderer.effect.Effects(effects: Union[text_renderer.effect.base_effect.Effect, List[text_renderer.effect.base_effect.Effect], text_renderer.effect.selector.Selector, List[text_renderer.effect.selector.Selector]])

Apply multiple effects

Parameters

effects (Effect or List[Effect]) –

apply_effects(img: PIL.Image.Image, bbox: text_renderer.utils.bbox.BBox) → Tuple[PIL.Image.Image, text_renderer.utils.bbox.BBox]

Parameters

• img –

• bbox – bbox of text on input Image

Returns:

class text_renderer.effect.NoEffects

Placeholder when you don’t want to apply effects for multi corpus

class text_renderer.effect.OneOf(effects: List[Effect])

Parameters

effects (list of Effect) –