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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-24 22:15:54 +0100
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-24 22:15:54 +0100
commit8248f173132dfb7e47ec62b08e9235990c8626e3 (patch)
tree2f3ff85602cbc08b7168bf4f0d3924d32a689852 /text_recognizer/data/iam.py
parent74c907a17379688967dc4b3f41a44ba83034f5e0 (diff)
renamed datasets to data, added iam refactor
Diffstat (limited to 'text_recognizer/data/iam.py')
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+"""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)