Move stems to transforms

1 files changed, 0 insertions, 66 deletions

diff --git a/text_recognizer/data/stems/paragraph.py b/text_recognizer/data/stems/paragraph.py
deleted file mode 100644
index 39e1e59..0000000
--- a/text_recognizer/data/stems/paragraph.py
+++ /dev/null
@@ -1,66 +0,0 @@
-"""Iam paragraph stem class."""
-import torchvision.transforms as T
-
-import text_recognizer.metadata.iam_paragraphs as metadata
-from text_recognizer.data.stems.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),
-                ]
-            )