Move stems to transforms

1 files changed, 0 insertions, 93 deletions

diff --git a/text_recognizer/data/stems/line.py b/text_recognizer/data/stems/line.py
deleted file mode 100644
index 4f0ce05..0000000
--- a/text_recognizer/data/stems/line.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import random
-from typing import Any, Dict
-
-from PIL import Image
-import torchvision.transforms as T
-
-import text_recognizer.metadata.iam_lines as metadata
-from text_recognizer.data.stems.image import ImageStem
-
-
-class LineStem(ImageStem):
-    """A stem for handling images containing a line of text."""
-
-    def __init__(
-        self,
-        augment: bool = False,
-        color_jitter_kwargs: Dict[str, Any] = None,
-        random_affine_kwargs: Dict[str, Any] = None,
-    ) -> None:
-        super().__init__()
-        if color_jitter_kwargs is None:
-            color_jitter_kwargs = {"brightness": (0.5, 1)}
-        if random_affine_kwargs is None:
-            random_affine_kwargs = {
-                "degrees": 3,
-                "translate": (0, 0.05),
-                "scale": (0.4, 1.1),
-                "shear": (-40, 50),
-                "interpolation": T.InterpolationMode.BILINEAR,
-                "fill": 0,
-            }
-
-        if augment:
-            self.pil_transforms = T.Compose(
-                [
-                    T.ColorJitter(**color_jitter_kwargs),
-                    T.RandomAffine(**random_affine_kwargs),
-                ]
-            )
-
-
-class IamLinesStem(ImageStem):
-    """A stem for handling images containing lines of text from the IAMLines dataset."""
-
-    def __init__(
-        self,
-        augment: bool = False,
-        color_jitter_kwargs: Dict[str, Any] = None,
-        random_affine_kwargs: Dict[str, Any] = None,
-    ) -> None:
-        super().__init__()
-
-        def embed_crop(crop, augment=augment):
-            # crop is PIL.image of dtype="L" (so values range from 0 -> 255)
-            image = Image.new("L", (metadata.IMAGE_WIDTH, metadata.IMAGE_HEIGHT))
-
-            # Resize crop
-            crop_width, crop_height = crop.size
-            new_crop_height = metadata.IMAGE_HEIGHT
-            new_crop_width = int(new_crop_height * (crop_width / crop_height))
-            if augment:
-                # Add random stretching
-                new_crop_width = int(new_crop_width * random.uniform(0.9, 1.1))
-                new_crop_width = min(new_crop_width, metadata.IMAGE_WIDTH)
-            crop_resized = crop.resize(
-                (new_crop_width, new_crop_height), resample=Image.BILINEAR
-            )
-
-            # Embed in the image
-            x = min(metadata.CHAR_WIDTH, metadata.IMAGE_WIDTH - new_crop_width)
-            y = metadata.IMAGE_HEIGHT - new_crop_height
-
-            image.paste(crop_resized, (x, y))
-
-            return image
-
-        if color_jitter_kwargs is None:
-            color_jitter_kwargs = {"brightness": (0.8, 1.6)}
-        if random_affine_kwargs is None:
-            random_affine_kwargs = {
-                "degrees": 1,
-                "shear": (-30, 20),
-                "interpolation": T.InterpolationMode.BILINEAR,
-                "fill": 0,
-            }
-
-        pil_transform_list = [T.Lambda(embed_crop)]
-        if augment:
-            pil_transform_list += [
-                T.ColorJitter(**color_jitter_kwargs),
-                T.RandomAffine(**random_affine_kwargs),
-            ]
-        self.pil_transform = T.Compose(pil_transform_list)