Created an abstract Dataset class for common methods.

1 files changed, 120 insertions, 5 deletions

diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py
index dd16bed..3acf5db 100644
--- a/src/text_recognizer/datasets/util.py
+++ b/src/text_recognizer/datasets/util.py
@@ -1,6 +1,7 @@
 """Util functions for datasets."""
 import hashlib
 import importlib
+import json
 import os
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Type, Union
@@ -11,15 +12,129 @@ from loguru import logger
 import numpy as np
 from PIL import Image
 from torch.utils.data import DataLoader, Dataset
+from torchvision.datasets import EMNIST
 from tqdm import tqdm
 
+DATA_DIRNAME = Path(__file__).resolve().parents[3] / "data"
+ESSENTIALS_FILENAME = Path(__file__).resolve().parents[0] / "emnist_essentials.json"
 
-class Transpose:
-    """Transposes the EMNIST image to the correct orientation."""
 
-    def __call__(self, image: Image) -> np.ndarray:
-        """Swaps axis."""
-        return np.array(image).swapaxes(0, 1)
+def save_emnist_essentials(emnsit_dataset: type = EMNIST) -> None:
+    """Extract and saves EMNIST essentials."""
+    labels = emnsit_dataset.classes
+    labels.sort()
+    mapping = [(i, str(label)) for i, label in enumerate(labels)]
+    essentials = {
+        "mapping": mapping,
+        "input_shape": tuple(emnsit_dataset[0][0].shape[:]),
+    }
+    logger.info("Saving emnist essentials...")
+    with open(ESSENTIALS_FILENAME, "w") as f:
+        json.dump(essentials, f)
+
+
+def download_emnist() -> None:
+    """Download the EMNIST dataset via the PyTorch class."""
+    logger.info(f"Data directory is: {DATA_DIRNAME}")
+    dataset = EMNIST(root=DATA_DIRNAME, split="byclass", download=True)
+    save_emnist_essentials(dataset)
+
+
+class EmnistMapper:
+    """Mapper between network output to Emnist character."""
+
+    def __init__(self) -> None:
+        """Loads the emnist essentials file with the mapping and input shape."""
+        self.essentials = self._load_emnist_essentials()
+        # Load dataset infromation.
+        self._mapping = self._augment_emnist_mapping(dict(self.essentials["mapping"]))
+        self._inverse_mapping = {v: k for k, v in self.mapping.items()}
+        self._num_classes = len(self.mapping)
+        self._input_shape = self.essentials["input_shape"]
+
+    def __call__(self, token: Union[str, int, np.uint8]) -> Union[str, int]:
+        """Maps the token to emnist character or character index.
+
+        If the token is an integer (index), the method will return the Emnist character corresponding to that index.
+        If the token is a str (Emnist character), the method will return the corresponding index for that character.
+
+        Args:
+            token (Union[str, int, np.uint8]): Eihter a string or index (integer).
+
+        Returns:
+            Union[str, int]: The mapping result.
+
+        Raises:
+            KeyError: If the index or string does not exist in the mapping.
+
+        """
+        if (isinstance(token, np.uint8) or isinstance(token, int)) and int(
+            token
+        ) in self.mapping:
+            return self.mapping[int(token)]
+        elif isinstance(token, str) and token in self._inverse_mapping:
+            return self._inverse_mapping[token]
+        else:
+            raise KeyError(f"Token {token} does not exist in the mappings.")
+
+    @property
+    def mapping(self) -> Dict:
+        """Returns the mapping between index and character."""
+        return self._mapping
+
+    @property
+    def inverse_mapping(self) -> Dict:
+        """Returns the mapping between character and index."""
+        return self._inverse_mapping
+
+    @property
+    def num_classes(self) -> int:
+        """Returns the number of classes in the dataset."""
+        return self._num_classes
+
+    @property
+    def input_shape(self) -> List[int]:
+        """Returns the input shape of the Emnist characters."""
+        return self._input_shape
+
+    def _load_emnist_essentials(self) -> Dict:
+        """Load the EMNIST mapping."""
+        with open(str(ESSENTIALS_FILENAME)) as f:
+            essentials = json.load(f)
+        return essentials
+
+    def _augment_emnist_mapping(self, mapping: Dict) -> Dict:
+        """Augment the mapping with extra symbols."""
+        # Extra symbols in IAM dataset
+        extra_symbols = [
+            " ",
+            "!",
+            '"',
+            "#",
+            "&",
+            "'",
+            "(",
+            ")",
+            "*",
+            "+",
+            ",",
+            "-",
+            ".",
+            "/",
+            ":",
+            ";",
+            "?",
+        ]
+
+        # padding symbol
+        extra_symbols.append("_")
+
+        max_key = max(mapping.keys())
+        extra_mapping = {}
+        for i, symbol in enumerate(extra_symbols):
+            extra_mapping[max_key + 1 + i] = symbol
+
+        return {**mapping, **extra_mapping}
 
 
 def compute_sha256(filename: Union[Path, str]) -> str: