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"""Iam paragraph stem class."""
import torchvision.transforms as T
import text_recognizer.metadata.iam_paragraphs as metadata
from text_recognizer.data.transforms.image import ImageStem
IMAGE_HEIGHT, IMAGE_WIDTH = metadata.IMAGE_HEIGHT, metadata.IMAGE_WIDTH
IMAGE_SHAPE = metadata.IMAGE_SHAPE
MAX_LABEL_LENGTH = metadata.MAX_LABEL_LENGTH
class ParagraphStem(ImageStem):
"""A stem for handling images that contain a paragraph of text."""
def __init__(
self,
augment=False,
color_jitter_kwargs=None,
random_affine_kwargs=None,
random_perspective_kwargs=None,
gaussian_blur_kwargs=None,
sharpness_kwargs=None,
):
super().__init__()
if not augment:
self.pil_transform = T.Compose([T.CenterCrop(IMAGE_SHAPE)])
else:
if color_jitter_kwargs is None:
color_jitter_kwargs = {"brightness": 0.4, "contrast": 0.4}
if random_affine_kwargs is None:
random_affine_kwargs = {
"degrees": 3,
"shear": 6,
"scale": (0.95, 1),
"interpolation": T.InterpolationMode.BILINEAR,
}
if random_perspective_kwargs is None:
random_perspective_kwargs = {
"distortion_scale": 0.2,
"p": 0.5,
"interpolation": T.InterpolationMode.BILINEAR,
}
if gaussian_blur_kwargs is None:
gaussian_blur_kwargs = {"kernel_size": (3, 3), "sigma": (0.1, 1.0)}
if sharpness_kwargs is None:
sharpness_kwargs = {"sharpness_factor": 2, "p": 0.5}
self.pil_transform = T.Compose(
[
T.ColorJitter(**color_jitter_kwargs),
T.RandomCrop(
size=IMAGE_SHAPE,
padding=None,
pad_if_needed=True,
fill=0,
padding_mode="constant",
),
T.RandomAffine(**random_affine_kwargs),
T.RandomPerspective(**random_perspective_kwargs),
T.GaussianBlur(**gaussian_blur_kwargs),
T.RandomAdjustSharpness(**sharpness_kwargs),
]
)
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