Move stems to transforms

1 files changed, 66 insertions, 0 deletions

diff --git a/text_recognizer/data/transforms/paragraph.py b/text_recognizer/data/transforms/paragraph.py
new file mode 100644
index 0000000..39e1e59
--- /dev/null
+++ b/text_recognizer/data/transforms/paragraph.py
@@ -0,0 +1,66 @@
+"""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),
+                ]
+            )