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+"""Class for loading the IAM dataset, which encompasses both paragraphs and lines, with associated utilities."""
+import os
+from typing import Any, Dict, List
+import zipfile
+
+from boltons.cacheutils import cachedproperty
+import defusedxml.ElementTree as ET
+from loguru import logger
+import toml
+
+from text_recognizer.datasets.util import _download_raw_dataset, DATA_DIRNAME
+
+RAW_DATA_DIRNAME = DATA_DIRNAME / "raw" / "iam"
+METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
+EXTRACTED_DATASET_DIRNAME = RAW_DATA_DIRNAME / "iamdb"
+RAW_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
+
+DOWNSAMPLE_FACTOR = 2 # If images were downsampled, the regions must also be.
+LINE_REGION_PADDING = 0 # Add this many pixels around the exact coordinates.
+
+
+class IamDataset:
+ """IAM dataset.
+
+ "The IAM Lines dataset, first published at the ICDAR 1999, contains forms of unconstrained handwritten text,
+ which were scanned at a resolution of 300dpi and saved as PNG images with 256 gray levels."
+ From http://www.fki.inf.unibe.ch/databases/iam-handwriting-database
+
+ The data split we will use is
+ IAM lines Large Writer Independent Text Line Recognition Task (lwitlrt): 9,862 text lines.
+ The validation set has been merged into the train set.
+ The train set has 7,101 lines from 326 writers.
+ The test set has 1,861 lines from 128 writers.
+ The text lines of all data sets are mutually exclusive, thus each writer has contributed to one set only.
+
+ """
+
+ def __init__(self) -> None:
+ self.metadata = toml.load(METADATA_FILENAME)
+
+ def load_or_generate_data(self) -> None:
+ """Downloads IAM dataset if xml files does not exist."""
+ if not self.xml_filenames:
+ self._download_iam()
+
+ @property
+ def xml_filenames(self) -> List:
+ """List of xml filenames."""
+ return list((EXTRACTED_DATASET_DIRNAME / "xml").glob("*.xml"))
+
+ @property
+ def form_filenames(self) -> List:
+ """List of forms filenames."""
+ return list((EXTRACTED_DATASET_DIRNAME / "forms").glob("*.jpg"))
+
+ def _download_iam(self) -> None:
+ curdir = os.getcwd()
+ os.chdir(RAW_DATA_DIRNAME)
+ _download_raw_dataset(self.metadata)
+ _extract_raw_dataset(self.metadata)
+ os.chdir(curdir)
+
+ @property
+ def form_filenames_by_id(self) -> Dict:
+ """Creates a dictionary with filenames as keys and forms as values."""
+ return {filename.stem: filename for filename in self.form_filenames}
+
+ @cachedproperty
+ def line_strings_by_id(self) -> Dict:
+ """Return a dict from name of IAM form to a list of line texts in it."""
+ return {
+ filename.stem: _get_line_strings_from_xml_file(filename)
+ for filename in self.xml_filenames
+ }
+
+ @cachedproperty
+ def line_regions_by_id(self) -> Dict:
+ """Return a dict from name of IAM form to a list of (x1, x2, y1, y2) coordinates of all lines in it."""
+ return {
+ filename.stem: _get_line_regions_from_xml_file(filename)
+ for filename in self.xml_filenames
+ }
+
+ def __repr__(self) -> str:
+ """Print info about dataset."""
+ return "IAM Dataset\n" f"Number of forms: {len(self.xml_filenames)}\n"
+
+
+def _extract_raw_dataset(metadata: Dict) -> None:
+ logger.info("Extracting IAM data.")
+ with zipfile.ZipFile(metadata["filename"], "r") as zip_file:
+ zip_file.extractall()
+
+
+def _get_line_strings_from_xml_file(filename: str) -> List[str]:
+ """Get the text content of each line. Note that we replace " with "."""
+ xml_root_element = ET.parse(filename).getroot() # nosec
+ xml_line_elements = xml_root_element.findall("handwritten-part/line")
+ return [el.attrib["text"].replace(""", '"') for el in xml_line_elements]
+
+
+def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]:
+ """Get the line region dict for each line."""
+ xml_root_element = ET.parse(filename).getroot() # nosec
+ xml_line_elements = xml_root_element.findall("handwritten-part/line")
+ return [_get_line_region_from_xml_element(el) for el in xml_line_elements]
+
+
+def _get_line_region_from_xml_element(xml_line: Any) -> Dict[str, int]:
+ """Extracts coordinates for each line of text."""
+ # TODO: fix input!
+ word_elements = xml_line.findall("word/cmp")
+ x1s = [int(el.attrib["x"]) for el in word_elements]
+ y1s = [int(el.attrib["y"]) for el in word_elements]
+ x2s = [int(el.attrib["x"]) + int(el.attrib["width"]) for el in word_elements]
+ y2s = [int(el.attrib["y"]) + int(el.attrib["height"]) for el in word_elements]
+ return {
+ "x1": min(x1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING,
+ "y1": min(y1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING,
+ "x2": max(x2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
+ "y2": max(y2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
+ }
+
+
+def main() -> None:
+ """Initializes the dataset and print info about the dataset."""
+ dataset = IamDataset()
+ dataset.load_or_generate_data()
+ print(dataset)
+
+
+if __name__ == "__main__":
+ main()