Add synthetic iam paragraphs dataset

1 files changed, 8 insertions, 8 deletions

diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index 402a8d4..f588587 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -93,14 +93,14 @@ class IAMParagraphs(BaseDataModule):
 
         def _load_dataset(split: str, augment: bool) -> BaseDataset:
             crops, labels = _load_processed_crops_and_labels(split)
-            data = [_resize_image(crop, IMAGE_SCALE_FACTOR) for crop in crops]
+            data = [resize_image(crop, IMAGE_SCALE_FACTOR) for crop in crops]
             targets = convert_strings_to_labels(
                 strings=labels, mapping=self.inverse_mapping, length=self.output_dims[0]
             )
             return BaseDataset(
                 data,
                 targets,
-                transform=_get_transform(image_shape=self.dims[1:], augment=augment),
+                transform=get_transform(image_shape=self.dims[1:], augment=augment),
             )
 
         logger.info(f"Loading IAM paragraph regions and lines for {stage}...")
@@ -142,7 +142,7 @@ class IAMParagraphs(BaseDataModule):
         return basic + data
 
 
-def _get_dataset_properties() -> Dict:
+def get_dataset_properties() -> Dict:
     """Return properties describing the overall dataset."""
     with (PROCESSED_DATA_DIRNAME / "_properties.json").open("r") as f:
         properties = json.load(f)
@@ -173,7 +173,7 @@ def _validate_data_dims(
     input_dims: Optional[Tuple[int, ...]], output_dims: Optional[Tuple[int, ...]]
 ) -> None:
     """Validates input and output dimensions against the properties of the dataset."""
-    properties = _get_dataset_properties()
+    properties = get_dataset_properties()
 
     max_image_shape = properties["crop_shape"]["max"] / IMAGE_SCALE_FACTOR
     if (
@@ -192,7 +192,7 @@ def _validate_data_dims(
         )
 
 
-def _resize_image(image: Image.Image, scale_factor: int) -> Image.Image:
+def resize_image(image: Image.Image, scale_factor: int) -> Image.Image:
     """Resize image by scale factor."""
     if scale_factor == 1:
         return image
@@ -219,7 +219,7 @@ def _get_paragraph_crops_and_labels(
         image = ImageOps.invert(image)
 
         line_regions = iam.line_regions_by_id[id_]
-        parameter_box = [
+        paragraph_box = [
             min([region["x1"] for region in line_regions]),
             min([region["y1"] for region in line_regions]),
             max([region["x2"] for region in line_regions]),
@@ -227,7 +227,7 @@ def _get_paragraph_crops_and_labels(
         ]
         lines = iam.line_strings_by_id[id_]
 
-        crops[id_] = image.crop(parameter_box)
+        crops[id_] = image.crop(paragraph_box)
         labels[id_] = NEW_LINE_TOKEN.join(lines)
 
     if len(crops) != len(labels):
@@ -269,7 +269,7 @@ def _load_processed_crops_and_labels(
     return ordered_crops, ordered_labels
 
 
-def _get_transform(image_shape: Tuple[int, int], augment: bool) -> transforms.Compose:
+def get_transform(image_shape: Tuple[int, int], augment: bool) -> transforms.Compose:
     """Get transformations for images."""
     if augment:
         transforms_list = [