renamed datasets to data, added iam refactor

1 files changed, 120 insertions, 0 deletions

diff --git a/text_recognizer/data/iam.py b/text_recognizer/data/iam.py
new file mode 100644
index 0000000..fcfe9a7
--- /dev/null
+++ b/text_recognizer/data/iam.py
@@ -0,0 +1,120 @@
+"""Class for loading the IAM dataset, which encompasses both paragraphs and lines, with associated utilities."""
+import os
+from pathlib import Path
+from typing import Any, Dict, List
+import xml.etree.ElementTree as ElementTree
+import zipfile
+
+from boltons.cacheutils import cachedproperty
+from loguru import logger
+from PIL import Image
+import toml
+
+from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
+from text_recognizer.data.download_utils import download_dataset
+
+
+RAW_DATA_DIRNAME = BaseDataModule.data_dirname() / "raw" / "iam"
+METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
+DL_DATA_DIRNAME = BaseDataModule.data_dirname() / "downloaded" / "iam"
+EXTRACTED_DATASET_DIRNAME = DL_DATA_DIRNAME / "iamdb"
+
+DOWNSAMPLE_FACTOR = 2  # If images were downsampled, the regions must also be.
+LINE_REGION_PADDING = 16  # Add this many pixels around the exact coordinates.
+
+
+class IAM(BaseDataModule):
+    """
+    "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, batch_size: int = 128, num_workers: int = 0) -> None:
+        super().__init__(batch_size, num_workers)
+        self.metadata = toml.load(METADATA_FILENAME)
+
+    def prepare_data(self) -> None:
+        if self.xml_filenames:
+            return
+        filename = download_dataset(self.metadata, DL_DATA_DIRNAME)
+        _extract_raw_dataset(filename, DL_DATA_DIRNAME)
+
+    @property
+    def xml_filenames(self) -> List[Path]:
+        return list((EXTRACTED_DATASET_DIRNAME / "xml").glob("*.xml"))
+
+    @property
+    def form_filenames(self) -> List[Path]:
+        return list((EXTRACTED_DATASET_DIRNAME / "forms").glob("*.jpg"))
+
+    @property
+    def form_filenames_by_id(self) -> Dict[str, Path]:
+        return {filename.stem: filename for filename in self.form_filenames}
+
+    @property
+    def split_by_id(self) -> Dict[str, str]:
+        return {filename.stem: "test" if filename.stem in self.metadata["test_ids"] else "trainval" for filename in self.form_filenames}
+
+    @cachedproperty
+    def line_strings_by_id(self) -> Dict[str, List[str]]:
+        """Return a dict from name of IAM form to 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[str, List[Dict[str, int]]]:
+        """Return a dict from name IAM form to 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:
+        """Return info about the dataset."""
+        return ("IAM Dataset\n"
+                f"Num forms total: {len(self.xml_filenames)}\n"
+                f"Num in test set: {len(self.metadata['test_ids'])}\n")
+
+
+def _extract_raw_dataset(filename: Path, dirname: Path) -> None:
+    logger.info("Extracting IAM data...")
+    curdir = os.getcwd()
+    os.chdir(dirname)
+    with zipfile.ZipFile(filename, "r") as f:
+        f.extractall()
+    os.chdir(curdir)
+
+
+def _get_line_strings_from_xml_file(filename: str) -> List[str]:
+    """Get the text content of each line. Note that we replace &quot: with "."""
+    xml_root_element = ElementTree.parse(filename).getroot()  # nosec
+    xml_line_elements = xml_root_element.findall("handwritten-part/line")
+    return [el.attrib["text"].replace("&quot", '"') for el in xml_line_elements]
+
+
+def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]:
+    """Get line region dict for each line."""
+    xml_root_element = ElementTree.parse(filename).getroot()  # nosec
+    xml_line_elements = xml_root_element.findall("handwritten-part/line")
+    return [_get_line_region_from_xml_file(el) for el in xml_line_elements]
+
+
+def _get_line_region_from_xml_file(xml_line: Any) -> Dict[str, int]:
+    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["x"]) + 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": min(x2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
+            "y2": min(y2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
+            }
+
+
+def download_iam() -> None:
+    load_and_print_info(IAM)