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Merge branch 'adding-cnn-rnn-training-script' into 2026-01-29-training

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src/eynollah/cli.py
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import sys
import click
import logging
from ocrd_utils import initLogging, getLevelName, getLogger
from eynollah.eynollah import Eynollah, Eynollah_ocr
from eynollah.sbb_binarize import SbbBinarizer
from eynollah.image_enhancer import Enhancer
from eynollah.mb_ro_on_layout import machine_based_reading_order_on_layout
@click.group()
def main():
pass
@main.command()
@click.option(
"--input",
"-i",
help="PAGE-XML input filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--dir_in",
"-di",
help="directory of PAGE-XML input files (instead of --input)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output images",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--model",
"-m",
help="directory of models",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
def machine_based_reading_order(input, dir_in, out, model, log_level):
assert bool(input) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
orderer = machine_based_reading_order_on_layout(model)
if log_level:
orderer.logger.setLevel(getLevelName(log_level))
orderer.run(xml_filename=input,
dir_in=dir_in,
dir_out=out,
)
@main.command()
@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
@click.option('--model_dir', '-m', type=click.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
@click.option(
"--input-image", "--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False)
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--output",
"-o",
help="output image (if using -i) or output image directory (if using -di)",
type=click.Path(file_okay=True, dir_okay=True),
required=True,
)
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
def binarization(patches, model_dir, input_image, dir_in, output, log_level):
assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
binarizer = SbbBinarizer(model_dir)
if log_level:
binarizer.log.setLevel(getLevelName(log_level))
binarizer.run(image_path=input_image, use_patches=patches, output=output, dir_in=dir_in)
@main.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--model",
"-m",
help="directory of models",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--num_col_upper",
"-ncu",
help="lower limit of columns in document image",
)
@click.option(
"--num_col_lower",
"-ncl",
help="upper limit of columns in document image",
)
@click.option(
"--save_org_scale/--no_save_org_scale",
"-sos/-nosos",
is_flag=True,
help="if this parameter set to true, this tool will save the enhanced image in org scale.",
)
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
def enhancement(image, out, overwrite, dir_in, model, num_col_upper, num_col_lower, save_org_scale, log_level):
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
initLogging()
enhancer = Enhancer(
model,
num_col_upper=num_col_upper,
num_col_lower=num_col_lower,
save_org_scale=save_org_scale,
)
if log_level:
enhancer.logger.setLevel(getLevelName(log_level))
enhancer.run(overwrite=overwrite,
dir_in=dir_in,
image_filename=image,
dir_out=out,
)
@main.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--model",
"-m",
help="directory of models",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--model_version",
"-mv",
help="override default versions of model categories",
type=(str, str),
multiple=True,
)
@click.option(
"--save_images",
"-si",
help="if a directory is given, images in documents will be cropped and saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_layout",
"-sl",
help="if a directory is given, plot of layout will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_deskewed",
"-sd",
help="if a directory is given, deskewed image will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_all",
"-sa",
help="if a directory is given, all plots needed for documentation will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_page",
"-sp",
help="if a directory is given, page crop of image will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--enable-plotting/--disable-plotting",
"-ep/-noep",
is_flag=True,
help="If set, will plot intermediary files and images",
)
@click.option(
"--extract_only_images/--disable-extracting_only_images",
"-eoi/-noeoi",
is_flag=True,
help="If a directory is given, only images in documents will be cropped and saved there and the other processing will not be done",
)
@click.option(
"--allow-enhancement/--no-allow-enhancement",
"-ae/-noae",
is_flag=True,
help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory",
)
@click.option(
"--curved-line/--no-curvedline",
"-cl/-nocl",
is_flag=True,
help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.",
)
@click.option(
"--textline_light/--no-textline_light",
"-tll/-notll",
is_flag=True,
help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline with a faster method.",
)
@click.option(
"--full-layout/--no-full-layout",
"-fl/-nofl",
is_flag=True,
help="if this parameter set to true, this tool will try to return all elements of layout.",
)
@click.option(
"--tables/--no-tables",
"-tab/-notab",
is_flag=True,
help="if this parameter set to true, this tool will try to detect tables.",
)
@click.option(
"--right2left/--left2right",
"-r2l/-l2r",
is_flag=True,
help="if this parameter set to true, this tool will extract right-to-left reading order.",
)
@click.option(
"--input_binary/--input-RGB",
"-ib/-irgb",
is_flag=True,
help="in general, eynollah uses RGB as input but if the input document is strongly dark, bright or for any other reason you can turn binarized input on. This option does not mean that you have to provide a binary image, otherwise this means that the tool itself will binarized the RGB input document.",
)
@click.option(
"--allow_scaling/--no-allow-scaling",
"-as/-noas",
is_flag=True,
help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection",
)
@click.option(
"--headers_off/--headers-on",
"-ho/-noho",
is_flag=True,
help="if this parameter set to true, this tool would ignore headers role in reading order",
)
@click.option(
"--light_version/--original",
"-light/-org",
is_flag=True,
help="if this parameter set to true, this tool would use lighter version",
)
@click.option(
"--ignore_page_extraction/--extract_page_included",
"-ipe/-epi",
is_flag=True,
help="if this parameter set to true, this tool would ignore page extraction",
)
@click.option(
"--reading_order_machine_based/--heuristic_reading_order",
"-romb/-hro",
is_flag=True,
help="if this parameter set to true, this tool would apply machine based reading order detection",
)
@click.option(
"--do_ocr",
"-ocr/-noocr",
is_flag=True,
help="if this parameter set to true, this tool will try to do ocr",
)
@click.option(
"--transformer_ocr",
"-tr/-notr",
is_flag=True,
help="if this parameter set to true, this tool will apply transformer ocr",
)
@click.option(
"--batch_size_ocr",
"-bs_ocr",
help="number of inference batch size of ocr model. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively",
)
@click.option(
"--num_col_upper",
"-ncu",
help="lower limit of columns in document image",
)
@click.option(
"--num_col_lower",
"-ncl",
help="upper limit of columns in document image",
)
@click.option(
"--threshold_art_class_layout",
"-tharl",
help="threshold of artifical class in the case of layout detection. The default value is 0.1",
)
@click.option(
"--threshold_art_class_textline",
"-thart",
help="threshold of artifical class in the case of textline detection. The default value is 0.1",
)
@click.option(
"--skip_layout_and_reading_order",
"-slro/-noslro",
is_flag=True,
help="if this parameter set to true, this tool will ignore layout detection and reading order. It means that textline detection will be done within printspace and contours of textline will be written in xml output file.",
)
# TODO move to top-level CLI context
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override 'eynollah' log level globally to this",
)
#
@click.option(
"--setup-logging",
is_flag=True,
help="Setup a basic console logger",
)
def layout(image, out, overwrite, dir_in, model, model_version, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, transformer_ocr, batch_size_ocr, num_col_upper, num_col_lower, threshold_art_class_textline, threshold_art_class_layout, skip_layout_and_reading_order, ignore_page_extraction, log_level, setup_logging):
if setup_logging:
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(message)s')
console_handler.setFormatter(formatter)
getLogger('eynollah').addHandler(console_handler)
getLogger('eynollah').setLevel(logging.INFO)
else:
initLogging()
assert enable_plotting or not save_layout, "Plotting with -sl also requires -ep"
assert enable_plotting or not save_deskewed, "Plotting with -sd also requires -ep"
assert enable_plotting or not save_all, "Plotting with -sa also requires -ep"
assert enable_plotting or not save_page, "Plotting with -sp also requires -ep"
assert enable_plotting or not save_images, "Plotting with -si also requires -ep"
assert enable_plotting or not allow_enhancement, "Plotting with -ae also requires -ep"
assert not enable_plotting or save_layout or save_deskewed or save_all or save_page or save_images or allow_enhancement, \
"Plotting with -ep also requires -sl, -sd, -sa, -sp, -si or -ae"
assert textline_light == light_version, "Both light textline detection -tll and light version -light must be set or unset equally"
assert not extract_only_images or not allow_enhancement, "Image extraction -eoi can not be set alongside allow_enhancement -ae"
assert not extract_only_images or not allow_scaling, "Image extraction -eoi can not be set alongside allow_scaling -as"
assert not extract_only_images or not light_version, "Image extraction -eoi can not be set alongside light_version -light"
assert not extract_only_images or not curved_line, "Image extraction -eoi can not be set alongside curved_line -cl"
assert not extract_only_images or not textline_light, "Image extraction -eoi can not be set alongside textline_light -tll"
assert not extract_only_images or not full_layout, "Image extraction -eoi can not be set alongside full_layout -fl"
assert not extract_only_images or not tables, "Image extraction -eoi can not be set alongside tables -tab"
assert not extract_only_images or not right2left, "Image extraction -eoi can not be set alongside right2left -r2l"
assert not extract_only_images or not headers_off, "Image extraction -eoi can not be set alongside headers_off -ho"
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
eynollah = Eynollah(
model,
model_versions=model_version,
extract_only_images=extract_only_images,
enable_plotting=enable_plotting,
allow_enhancement=allow_enhancement,
curved_line=curved_line,
textline_light=textline_light,
full_layout=full_layout,
tables=tables,
right2left=right2left,
input_binary=input_binary,
allow_scaling=allow_scaling,
headers_off=headers_off,
light_version=light_version,
ignore_page_extraction=ignore_page_extraction,
reading_order_machine_based=reading_order_machine_based,
do_ocr=do_ocr,
transformer_ocr=transformer_ocr,
batch_size_ocr=batch_size_ocr,
num_col_upper=num_col_upper,
num_col_lower=num_col_lower,
skip_layout_and_reading_order=skip_layout_and_reading_order,
threshold_art_class_textline=threshold_art_class_textline,
threshold_art_class_layout=threshold_art_class_layout,
)
if log_level:
eynollah.logger.setLevel(getLevelName(log_level))
eynollah.run(overwrite=overwrite,
image_filename=image,
dir_in=dir_in,
dir_out=out,
dir_of_cropped_images=save_images,
dir_of_layout=save_layout,
dir_of_deskewed=save_deskewed,
dir_of_all=save_all,
dir_save_page=save_page,
)
@main.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--dir_in_bin",
"-dib",
help="directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png' suffix).\nPerform prediction using both RGB and binary images. (This does not necessarily improve results, however it may be beneficial for certain document images.)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--dir_xmls",
"-dx",
help="directory of input PAGE-XML files (in addition to --dir_in; filename stems must match the image files, with '.xml' suffix).",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--dir_out_image_text",
"-doit",
help="directory for output images, newly rendered with predicted text",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--model",
"-m",
help="directory of models",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--model_name",
help="Specific model file path to use for OCR",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--tr_ocr",
"-trocr/-notrocr",
is_flag=True,
help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
)
@click.option(
"--export_textline_images_and_text",
"-etit/-noetit",
is_flag=True,
help="if this parameter set to true, images and text in xml will be exported into output dir. This files can be used for training a OCR engine.",
)
@click.option(
"--do_not_mask_with_textline_contour",
"-nmtc/-mtc",
is_flag=True,
help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
)
@click.option(
"--batch_size",
"-bs",
help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively",
)
@click.option(
"--dataset_abbrevation",
"-ds_pref",
help="in the case of extracting textline and text from a xml GT file user can add an abbrevation of dataset name to generated dataset",
)
@click.option(
"--min_conf_value_of_textline_text",
"-min_conf",
help="minimum OCR confidence value. Text lines with a confidence value lower than this threshold will not be included in the output XML file.",
)
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
def ocr(image, dir_in, dir_in_bin, dir_xmls, out, dir_out_image_text, overwrite, model, model_name, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, batch_size, dataset_abbrevation, min_conf_value_of_textline_text, log_level):
initLogging()
assert bool(model) != bool(model_name), "Either -m (model directory) or --model_name (specific model name) must be provided."
assert not export_textline_images_and_text or not tr_ocr, "Exporting textline and text -etit can not be set alongside transformer ocr -tr_ocr"
assert not export_textline_images_and_text or not model, "Exporting textline and text -etit can not be set alongside model -m"
assert not export_textline_images_and_text or not batch_size, "Exporting textline and text -etit can not be set alongside batch size -bs"
assert not export_textline_images_and_text or not dir_in_bin, "Exporting textline and text -etit can not be set alongside directory of bin images -dib"
assert not export_textline_images_and_text or not dir_out_image_text, "Exporting textline and text -etit can not be set alongside directory of images with predicted text -doit"
assert bool(image) != bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both."
eynollah_ocr = Eynollah_ocr(
dir_models=model,
model_name=model_name,
tr_ocr=tr_ocr,
export_textline_images_and_text=export_textline_images_and_text,
do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
batch_size=batch_size,
pref_of_dataset=dataset_abbrevation,
min_conf_value_of_textline_text=min_conf_value_of_textline_text,
)
if log_level:
eynollah_ocr.logger.setLevel(getLevelName(log_level))
eynollah_ocr.run(overwrite=overwrite,
dir_in=dir_in,
dir_in_bin=dir_in_bin,
image_filename=image,
dir_xmls=dir_xmls,
dir_out_image_text=dir_out_image_text,
dir_out=out,
)
if __name__ == "__main__":
main()

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src/eynollah/cli/__init__.py Normal file
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# NOTE: For predictable order of imports of torch/shapely/tensorflow
# this must be the first import of the CLI!
from ..eynollah_imports import imported_libs
from .cli_models import models_cli
from .cli_binarize import binarize_cli
from .cli import main
from .cli_binarize import binarize_cli
from .cli_enhance import enhance_cli
from .cli_extract_images import extract_images_cli
from .cli_layout import layout_cli
from .cli_ocr import ocr_cli
from .cli_readingorder import readingorder_cli
main.add_command(binarize_cli, 'binarization')
main.add_command(enhance_cli, 'enhancement')
main.add_command(layout_cli, 'layout')
main.add_command(readingorder_cli, 'machine-based-reading-order')
main.add_command(models_cli, 'models')
main.add_command(ocr_cli, 'ocr')
main.add_command(extract_images_cli, 'extract-images')

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src/eynollah/cli/cli.py Normal file
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from dataclasses import dataclass
import logging
import sys
import os
from typing import Union
import click
from ..model_zoo import EynollahModelZoo
from .cli_models import models_cli
@dataclass()
class EynollahCliCtx:
"""
Holds options relevant for all eynollah subcommands
"""
model_zoo: EynollahModelZoo
log_level : Union[str, None] = 'INFO'
@click.group()
@click.option(
"--model-basedir",
"-m",
help="directory of models",
# NOTE: not mandatory to exist so --help for subcommands works but will log a warning
# and raise exception when trying to load models in the CLI
# type=click.Path(exists=True),
default=f'{os.getcwd()}/models_eynollah',
)
@click.option(
"--model-overrides",
"-mv",
help="override default versions of model categories, syntax is 'CATEGORY VARIANT PATH', e.g 'region light /path/to/model'. See eynollah list-models for the full list",
type=(str, str, str),
multiple=True,
)
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
@click.pass_context
def main(ctx, model_basedir, model_overrides, log_level):
"""
eynollah - Document Layout Analysis, Image Enhancement, OCR
"""
# Initialize logging
console_handler = logging.StreamHandler(sys.stderr)
console_handler.setLevel(logging.NOTSET)
formatter = logging.Formatter('%(asctime)s.%(msecs)03d %(levelname)s %(name)s - %(message)s', datefmt='%H:%M:%S')
console_handler.setFormatter(formatter)
logging.getLogger('eynollah').addHandler(console_handler)
logging.getLogger('eynollah').setLevel(log_level or logging.INFO)
# Initialize model zoo
model_zoo = EynollahModelZoo(basedir=model_basedir, model_overrides=model_overrides)
# Initialize CLI context
ctx.obj = EynollahCliCtx(
model_zoo=model_zoo,
log_level=log_level,
)
if __name__ == "__main__":
main()

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src/eynollah/cli/cli_binarize.py Normal file
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import click
@click.command()
@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
@click.option(
"--input-image", "--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False)
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--output",
"-o",
help="output image (if using -i) or output image directory (if using -di)",
type=click.Path(file_okay=True, dir_okay=True),
required=True,
)
@click.pass_context
def binarize_cli(
ctx,
patches,
input_image,
dir_in,
output,
):
"""
Binarize images with a ML model
"""
from ..sbb_binarize import SbbBinarizer
assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo)
binarizer.run(
image_path=input_image,
use_patches=patches,
output=output,
dir_in=dir_in
)

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import click
@click.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--num_col_upper",
"-ncu",
help="lower limit of columns in document image",
)
@click.option(
"--num_col_lower",
"-ncl",
help="upper limit of columns in document image",
)
@click.option(
"--save_org_scale/--no_save_org_scale",
"-sos/-nosos",
is_flag=True,
help="if this parameter set to true, this tool will save the enhanced image in org scale.",
)
@click.pass_context
def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale):
"""
Enhance image
"""
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
from ..image_enhancer import Enhancer
enhancer = Enhancer(
model_zoo=ctx.obj.model_zoo,
num_col_upper=num_col_upper,
num_col_lower=num_col_lower,
save_org_scale=save_org_scale,
)
enhancer.run(overwrite=overwrite,
dir_in=dir_in,
image_filename=image,
dir_out=out,
)

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import click
@click.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_images",
"-si",
help="if a directory is given, images in documents will be cropped and saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--enable-plotting/--disable-plotting",
"-ep/-noep",
is_flag=True,
help="If set, will plot intermediary files and images",
)
@click.option(
"--input_binary/--input-RGB",
"-ib/-irgb",
is_flag=True,
help="In general, eynollah uses RGB as input but if the input document is very dark, very bright or for any other reason you can turn on input binarization. When this flag is set, eynollah will binarize the RGB input document, you should always provide RGB images to eynollah.",
)
@click.option(
"--ignore_page_extraction/--extract_page_included",
"-ipe/-epi",
is_flag=True,
help="if this parameter set to true, this tool would ignore page extraction",
)
@click.option(
"--num_col_upper",
"-ncu",
help="lower limit of columns in document image",
)
@click.option(
"--num_col_lower",
"-ncl",
help="upper limit of columns in document image",
)
@click.pass_context
def extract_images_cli(
ctx,
image,
out,
overwrite,
dir_in,
save_images,
enable_plotting,
input_binary,
num_col_upper,
num_col_lower,
ignore_page_extraction,
):
"""
Detect Layout (with optional image enhancement and reading order detection)
"""
assert enable_plotting or not save_images, "Plotting with -si also requires -ep"
assert not enable_plotting or save_images, "Plotting with -ep also requires -si"
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
from ..extract_images import EynollahImageExtractor
extractor = EynollahImageExtractor(
model_zoo=ctx.obj.model_zoo,
enable_plotting=enable_plotting,
input_binary=input_binary,
ignore_page_extraction=ignore_page_extraction,
num_col_upper=num_col_upper,
num_col_lower=num_col_lower,
)
extractor.run(overwrite=overwrite,
image_filename=image,
dir_in=dir_in,
dir_out=out,
dir_of_cropped_images=save_images,
)

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import click
@click.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_images",
"-si",
help="if a directory is given, images in documents will be cropped and saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_layout",
"-sl",
help="if a directory is given, plot of layout will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_deskewed",
"-sd",
help="if a directory is given, deskewed image will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_all",
"-sa",
help="if a directory is given, all plots needed for documentation will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--save_page",
"-sp",
help="if a directory is given, page crop of image will be saved there",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--enable-plotting/--disable-plotting",
"-ep/-noep",
is_flag=True,
help="If set, will plot intermediary files and images",
)
@click.option(
"--allow-enhancement/--no-allow-enhancement",
"-ae/-noae",
is_flag=True,
help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory",
)
@click.option(
"--curved-line/--no-curvedline",
"-cl/-nocl",
is_flag=True,
help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.",
)
@click.option(
"--full-layout/--no-full-layout",
"-fl/-nofl",
is_flag=True,
help="if this parameter set to true, this tool will try to return all elements of layout.",
)
@click.option(
"--tables/--no-tables",
"-tab/-notab",
is_flag=True,
help="if this parameter set to true, this tool will try to detect tables.",
)
@click.option(
"--right2left/--left2right",
"-r2l/-l2r",
is_flag=True,
help="if this parameter set to true, this tool will extract right-to-left reading order.",
)
@click.option(
"--input_binary/--input-RGB",
"-ib/-irgb",
is_flag=True,
help="In general, eynollah uses RGB as input but if the input document is very dark, very bright or for any other reason you can turn on input binarization. When this flag is set, eynollah will binarize the RGB input document, you should always provide RGB images to eynollah.",
)
@click.option(
"--allow_scaling/--no-allow-scaling",
"-as/-noas",
is_flag=True,
help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection",
)
@click.option(
"--headers_off/--headers-on",
"-ho/-noho",
is_flag=True,
help="if this parameter set to true, this tool would ignore headers role in reading order",
)
@click.option(
"--ignore_page_extraction/--extract_page_included",
"-ipe/-epi",
is_flag=True,
help="if this parameter set to true, this tool would ignore page extraction",
)
@click.option(
"--reading_order_machine_based/--heuristic_reading_order",
"-romb/-hro",
is_flag=True,
help="if this parameter set to true, this tool would apply machine based reading order detection",
)
@click.option(
"--num_col_upper",
"-ncu",
help="lower limit of columns in document image",
)
@click.option(
"--num_col_lower",
"-ncl",
help="upper limit of columns in document image",
)
@click.option(
"--threshold_art_class_layout",
"-tharl",
help="threshold of artifical class in the case of layout detection. The default value is 0.1",
)
@click.option(
"--threshold_art_class_textline",
"-thart",
help="threshold of artifical class in the case of textline detection. The default value is 0.1",
)
@click.option(
"--skip_layout_and_reading_order",
"-slro/-noslro",
is_flag=True,
help="if this parameter set to true, this tool will ignore layout detection and reading order. It means that textline detection will be done within printspace and contours of textline will be written in xml output file.",
)
@click.pass_context
def layout_cli(
ctx,
image,
out,
overwrite,
dir_in,
save_images,
save_layout,
save_deskewed,
save_all,
save_page,
enable_plotting,
allow_enhancement,
curved_line,
full_layout,
tables,
right2left,
input_binary,
allow_scaling,
headers_off,
reading_order_machine_based,
num_col_upper,
num_col_lower,
threshold_art_class_textline,
threshold_art_class_layout,
skip_layout_and_reading_order,
ignore_page_extraction,
):
"""
Detect Layout (with optional image enhancement and reading order detection)
"""
from ..eynollah import Eynollah
assert enable_plotting or not save_layout, "Plotting with -sl also requires -ep"
assert enable_plotting or not save_deskewed, "Plotting with -sd also requires -ep"
assert enable_plotting or not save_all, "Plotting with -sa also requires -ep"
assert enable_plotting or not save_page, "Plotting with -sp also requires -ep"
assert enable_plotting or not save_images, "Plotting with -si also requires -ep"
assert enable_plotting or not allow_enhancement, "Plotting with -ae also requires -ep"
assert not enable_plotting or save_layout or save_deskewed or save_all or save_page or save_images or allow_enhancement, \
"Plotting with -ep also requires -sl, -sd, -sa, -sp, -si or -ae"
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
eynollah = Eynollah(
model_zoo=ctx.obj.model_zoo,
enable_plotting=enable_plotting,
allow_enhancement=allow_enhancement,
curved_line=curved_line,
full_layout=full_layout,
tables=tables,
right2left=right2left,
input_binary=input_binary,
allow_scaling=allow_scaling,
headers_off=headers_off,
ignore_page_extraction=ignore_page_extraction,
reading_order_machine_based=reading_order_machine_based,
num_col_upper=num_col_upper,
num_col_lower=num_col_lower,
skip_layout_and_reading_order=skip_layout_and_reading_order,
threshold_art_class_textline=threshold_art_class_textline,
threshold_art_class_layout=threshold_art_class_layout,
)
eynollah.run(overwrite=overwrite,
image_filename=image,
dir_in=dir_in,
dir_out=out,
dir_of_cropped_images=save_images,
dir_of_layout=save_layout,
dir_of_deskewed=save_deskewed,
dir_of_all=save_all,
dir_save_page=save_page,
)

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from pathlib import Path
from typing import Set, Tuple
import click
from eynollah.model_zoo.default_specs import MODELS_VERSION
@click.group()
@click.pass_context
def models_cli(
ctx,
):
"""
Organize models for the various runners in eynollah.
"""
assert ctx.obj.model_zoo
@models_cli.command('list')
@click.pass_context
def list_models(
ctx,
):
"""
List all the models in the zoo
"""
print(f"Model basedir: {ctx.obj.model_zoo.model_basedir}")
print(f"Model overrides: {ctx.obj.model_zoo.model_overrides}")
print(ctx.obj.model_zoo)
@models_cli.command('package')
@click.option(
'--set-version', '-V', 'version', help="Version to use for packaging", default=MODELS_VERSION, show_default=True
)
@click.argument('output_dir')
@click.pass_context
def package(
ctx,
version,
output_dir,
):
"""
Generate shell code to copy all the models in the zoo into properly named folders in OUTPUT_DIR for distribution.
eynollah models -m SRC package OUTPUT_DIR
SRC should contain a directory "models_eynollah" containing all the models.
"""
mkdirs: Set[Path] = set([])
copies: Set[Tuple[Path, Path]] = set([])
for spec in ctx.obj.model_zoo.specs.specs:
# skip these as they are dependent on the ocr model
if spec.category in ('num_to_char', 'characters'):
continue
src: Path = ctx.obj.model_zoo.model_path(spec.category, spec.variant)
# Only copy the top-most directory relative to models_eynollah
while src.parent.name != 'models_eynollah':
src = src.parent
for dist in spec.dists:
dist_dir = Path(f"{output_dir}/models_{dist}_{version}/models_eynollah")
copies.add((src, dist_dir))
mkdirs.add(dist_dir)
for dir in mkdirs:
print(f"mkdir -vp {dir}")
for (src, dst) in copies:
print(f"cp -vr {src} {dst}")
for dir in mkdirs:
zip_path = Path(f'../{dir.parent.name}.zip')
print(f"(cd {dir}/..; zip -vr {zip_path} models_eynollah)")

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import click
@click.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--dir_in_bin",
"-dib",
help=("directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png' \n Perform prediction using both RGB and binary images. (This does not necessarily improve results, however it may be beneficial for certain document images."),
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--dir_xmls",
"-dx",
help="directory of input PAGE-XML files (in addition to --dir_in; filename stems must match the image files, with '.xml' suffix).",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--out",
"-o",
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--dir_out_image_text",
"-doit",
help="directory for output images, newly rendered with predicted text",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--overwrite",
"-O",
help="overwrite (instead of skipping) if output xml exists",
is_flag=True,
)
@click.option(
"--tr_ocr",
"-trocr/-notrocr",
is_flag=True,
help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
)
@click.option(
"--do_not_mask_with_textline_contour",
"-nmtc/-mtc",
is_flag=True,
help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
)
@click.option(
"--batch_size",
"-bs",
help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively",
)
@click.option(
"--min_conf_value_of_textline_text",
"-min_conf",
help="minimum OCR confidence value. Text lines with a confidence value lower than this threshold will not be included in the output XML file.",
)
@click.pass_context
def ocr_cli(
ctx,
image,
dir_in,
dir_in_bin,
dir_xmls,
out,
dir_out_image_text,
overwrite,
tr_ocr,
do_not_mask_with_textline_contour,
batch_size,
min_conf_value_of_textline_text,
):
"""
Recognize text with a CNN/RNN or transformer ML model.
"""
assert bool(image) ^ bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both."
from ..eynollah_ocr import Eynollah_ocr
eynollah_ocr = Eynollah_ocr(
model_zoo=ctx.obj.model_zoo,
tr_ocr=tr_ocr,
do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
batch_size=batch_size,
min_conf_value_of_textline_text=min_conf_value_of_textline_text)
eynollah_ocr.run(overwrite=overwrite,
dir_in=dir_in,
dir_in_bin=dir_in_bin,
image_filename=image,
dir_xmls=dir_xmls,
dir_out_image_text=dir_out_image_text,
dir_out=out,
)

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src/eynollah/cli/cli_readingorder.py Normal file
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import click
@click.command()
@click.option(
"--input",
"-i",
help="PAGE-XML input filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--dir_in",
"-di",
help="directory of PAGE-XML input files (instead of --input)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--out",
"-o",
help="directory for output images",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.pass_context
def readingorder_cli(ctx, input, dir_in, out):
"""
Generate ReadingOrder with a ML model
"""
from ..mb_ro_on_layout import machine_based_reading_order_on_layout
assert bool(input) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
orderer = machine_based_reading_order_on_layout(model_zoo=ctx.obj.model_zoo)
orderer.run(xml_filename=input,
dir_in=dir_in,
dir_out=out,
)

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src/eynollah/extract_images.py Normal file
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"""
extract images?
"""
from concurrent.futures import ProcessPoolExecutor
import logging
from multiprocessing import cpu_count
import os
import time
from typing import Optional
from pathlib import Path
import tensorflow as tf
import numpy as np
import cv2
from eynollah.utils.contour import filter_contours_area_of_image, return_contours_of_image, return_contours_of_interested_region
from eynollah.utils.resize import resize_image
from .model_zoo.model_zoo import EynollahModelZoo
from .eynollah import Eynollah
from .utils import box2rect, is_image_filename
from .plot import EynollahPlotter
class EynollahImageExtractor(Eynollah):
def __init__(
self,
*,
model_zoo: EynollahModelZoo,
enable_plotting : bool = False,
input_binary : bool = False,
ignore_page_extraction : bool = False,
num_col_upper : Optional[int] = None,
num_col_lower : Optional[int] = None,
full_layout : bool = False,
tables : bool = False,
curved_line : bool = False,
allow_enhancement : bool = False,
):
self.logger = logging.getLogger('eynollah.extract_images')
self.model_zoo = model_zoo
self.plotter = None
self.tables = tables
self.curved_line = curved_line
self.allow_enhancement = allow_enhancement
self.enable_plotting = enable_plotting
# --input-binary sensible if image is very dark, if layout is not working.
self.input_binary = input_binary
self.ignore_page_extraction = ignore_page_extraction
self.full_layout = full_layout
if num_col_upper:
self.num_col_upper = int(num_col_upper)
else:
self.num_col_upper = num_col_upper
if num_col_lower:
self.num_col_lower = int(num_col_lower)
else:
self.num_col_lower = num_col_lower
# for parallelization of CPU-intensive tasks:
self.executor = ProcessPoolExecutor(max_workers=cpu_count())
t_start = time.time()
try:
for device in tf.config.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(device, True)
except:
self.logger.warning("no GPU device available")
self.logger.info("Loading models...")
self.setup_models()
self.logger.info(f"Model initialization complete ({time.time() - t_start:.1f}s)")
def setup_models(self):
loadable = [
"col_classifier",
"binarization",
"page",
"extract_images",
]
self.model_zoo.load_models(*loadable)
def get_regions_light_v_extract_only_images(self,img, num_col_classifier):
self.logger.debug("enter get_regions_extract_images_only")
erosion_hurts = False
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
if num_col_classifier == 1:
img_w_new = 700
elif num_col_classifier == 2:
img_w_new = 900
elif num_col_classifier == 3:
img_w_new = 1500
elif num_col_classifier == 4:
img_w_new = 1800
elif num_col_classifier == 5:
img_w_new = 2200
elif num_col_classifier == 6:
img_w_new = 2500
else:
raise ValueError("num_col_classifier must be in range 1..6")
img_h_new = int(img.shape[0] / float(img.shape[1]) * img_w_new)
img_resized = resize_image(img,img_h_new, img_w_new )
prediction_regions_org, _ = self.do_prediction_new_concept(True, img_resized, self.model_zoo.get("extract_images"))
prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
image_page, page_coord, cont_page = self.extract_page()
prediction_regions_org = prediction_regions_org[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
prediction_regions_org=prediction_regions_org[:,:,0]
mask_lines_only = (prediction_regions_org[:,:] ==3)*1
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
mask_images_only=(prediction_regions_org[:,:] ==2)*1
polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
polygons_seplines = filter_contours_area_of_image(
mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1,1,1))
text_regions_p_true[text_regions_p_true.shape[0]-15:text_regions_p_true.shape[0], :] = 0
text_regions_p_true[:, text_regions_p_true.shape[1]-15:text_regions_p_true.shape[1]] = 0
##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001)
polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.001)
polygons_of_images_fin = []
for ploy_img_ind in polygons_of_images:
box = _, _, w, h = cv2.boundingRect(ploy_img_ind)
if h < 150 or w < 150:
pass
else:
page_coord_img = box2rect(box) # type: ignore
polygons_of_images_fin.append(np.array([[page_coord_img[2], page_coord_img[0]],
[page_coord_img[3], page_coord_img[0]],
[page_coord_img[3], page_coord_img[1]],
[page_coord_img[2], page_coord_img[1]]]))
self.logger.debug("exit get_regions_extract_images_only")
return (text_regions_p_true,
erosion_hurts,
polygons_seplines,
polygons_of_images_fin,
image_page,
page_coord,
cont_page)
def run(self,
overwrite: bool = False,
image_filename: Optional[str] = None,
dir_in: Optional[str] = None,
dir_out: Optional[str] = None,
dir_of_cropped_images: Optional[str] = None,
dir_of_layout: Optional[str] = None,
dir_of_deskewed: Optional[str] = None,
dir_of_all: Optional[str] = None,
dir_save_page: Optional[str] = None,
):
"""
Get image and scales, then extract the page of scanned image
"""
self.logger.debug("enter run")
t0_tot = time.time()
# Log enabled features directly
enabled_modes = []
if self.full_layout:
enabled_modes.append("Full layout analysis")
if self.tables:
enabled_modes.append("Table detection")
if enabled_modes:
self.logger.info("Enabled modes: " + ", ".join(enabled_modes))
if self.enable_plotting:
self.logger.info("Saving debug plots")
if dir_of_cropped_images:
self.logger.info(f"Saving cropped images to: {dir_of_cropped_images}")
if dir_of_layout:
self.logger.info(f"Saving layout plots to: {dir_of_layout}")
if dir_of_deskewed:
self.logger.info(f"Saving deskewed images to: {dir_of_deskewed}")
if dir_in:
ls_imgs = [os.path.join(dir_in, image_filename)
for image_filename in filter(is_image_filename,
os.listdir(dir_in))]
elif image_filename:
ls_imgs = [image_filename]
else:
raise ValueError("run requires either a single image filename or a directory")
for img_filename in ls_imgs:
self.logger.info(img_filename)
t0 = time.time()
self.reset_file_name_dir(img_filename, dir_out)
if self.enable_plotting:
self.plotter = EynollahPlotter(dir_out=dir_out,
dir_of_all=dir_of_all,
dir_save_page=dir_save_page,
dir_of_deskewed=dir_of_deskewed,
dir_of_cropped_images=dir_of_cropped_images,
dir_of_layout=dir_of_layout,
image_filename_stem=Path(img_filename).stem)
#print("text region early -11 in %.1fs", time.time() - t0)
if os.path.exists(self.writer.output_filename):
if overwrite:
self.logger.warning("will overwrite existing output file '%s'", self.writer.output_filename)
else:
self.logger.warning("will skip input for existing output file '%s'", self.writer.output_filename)
continue
pcgts = self.run_single()
self.logger.info("Job done in %.1fs", time.time() - t0)
self.writer.write_pagexml(pcgts)
if dir_in:
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
def run_single(self):
t0 = time.time()
self.logger.info(f"Processing file: {self.writer.image_filename}")
self.logger.info("Step 1/5: Image Enhancement")
img_res, is_image_enhanced, num_col_classifier, _ = \
self.run_enhancement()
self.logger.info(f"Image: {self.image.shape[1]}x{self.image.shape[0]}, "
f"{self.dpi} DPI, {num_col_classifier} columns")
if is_image_enhanced:
self.logger.info("Enhancement applied")
self.logger.info(f"Enhancement complete ({time.time() - t0:.1f}s)")
# Image Extraction Mode
self.logger.info("Step 2/5: Image Extraction Mode")
_, _, _, polygons_of_images, \
image_page, page_coord, cont_page = \
self.get_regions_light_v_extract_only_images(img_res, num_col_classifier)
pcgts = self.writer.build_pagexml_no_full_layout(
found_polygons_text_region=[],
page_coord=page_coord,
order_of_texts=[],
all_found_textline_polygons=[],
all_box_coord=[],
found_polygons_text_region_img=polygons_of_images,
found_polygons_marginals_left=[],
found_polygons_marginals_right=[],
all_found_textline_polygons_marginals_left=[],
all_found_textline_polygons_marginals_right=[],
all_box_coord_marginals_left=[],
all_box_coord_marginals_right=[],
slopes=[],
slopes_marginals_left=[],
slopes_marginals_right=[],
cont_page=cont_page,
polygons_seplines=[],
found_polygons_tables=[],
)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
self.logger.info("Image extraction complete")
return pcgts

2988
src/eynollah/eynollah.py
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10
src/eynollah/eynollah_imports.py Normal file
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"""
Load libraries with possible race conditions once. This must be imported as the first module of eynollah.
"""
from ocrd_utils import tf_disable_interactive_logs
from torch import *
tf_disable_interactive_logs()
import tensorflow.keras
from shapely import *
imported_libs = True
__all__ = ['imported_libs']

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src/eynollah/eynollah_ocr.py Normal file
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# FIXME: fix all of those...
# pyright: reportOptionalSubscript=false
from logging import Logger, getLogger
from typing import List, Optional
from pathlib import Path
import os
import gc
import math
from dataclasses import dataclass
import cv2
from cv2.typing import MatLike
from xml.etree import ElementTree as ET
from PIL import Image, ImageDraw
import numpy as np
from eynollah.model_zoo import EynollahModelZoo
from eynollah.utils.font import get_font
from eynollah.utils.xml import etree_namespace_for_element_tag
try:
import torch
except ImportError:
torch = None
from .utils import is_image_filename
from .utils.resize import resize_image
from .utils.utils_ocr import (
break_curved_line_into_small_pieces_and_then_merge,
decode_batch_predictions,
fit_text_single_line,
get_contours_and_bounding_boxes,
get_orientation_moments,
preprocess_and_resize_image_for_ocrcnn_model,
return_textlines_split_if_needed,
rotate_image_with_padding,
)
# TODO: refine typing
@dataclass
class EynollahOcrResult:
extracted_texts_merged: List
extracted_conf_value_merged: Optional[List]
cropped_lines_region_indexer: List
total_bb_coordinates:List
class Eynollah_ocr:
def __init__(
self,
*,
model_zoo: EynollahModelZoo,
tr_ocr=False,
batch_size: Optional[int]=None,
do_not_mask_with_textline_contour: bool=False,
min_conf_value_of_textline_text : Optional[float]=None,
logger: Optional[Logger]=None,
):
self.tr_ocr = tr_ocr
# masking for OCR and GT generation, relevant for skewed lines and bounding boxes
self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour
self.logger = logger if logger else getLogger('eynollah.ocr')
self.model_zoo = model_zoo
self.min_conf_value_of_textline_text = min_conf_value_of_textline_text if min_conf_value_of_textline_text else 0.3
self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size
if tr_ocr:
self.model_zoo.load_model('trocr_processor')
self.model_zoo.load_model('ocr', 'tr')
self.model_zoo.get('ocr').to(self.device)
else:
self.model_zoo.load_model('ocr', '')
self.model_zoo.load_model('num_to_char')
self.model_zoo.load_model('characters')
self.end_character = len(self.model_zoo.get('characters', list)) + 2
@property
def device(self):
assert torch
if torch.cuda.is_available():
self.logger.info("Using GPU acceleration")
return torch.device("cuda:0")
else:
self.logger.info("Using CPU processing")
return torch.device("cpu")
def run_trocr(
self,
*,
img: MatLike,
page_tree: ET.ElementTree,
page_ns,
tr_ocr_input_height_and_width,
) -> EynollahOcrResult:
total_bb_coordinates = []
cropped_lines = []
cropped_lines_region_indexer = []
cropped_lines_meging_indexing = []
extracted_texts = []
indexer_text_region = 0
indexer_b_s = 0
for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'):
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
for child_textlines in child_textregion:
if child_textlines.tag.endswith("Coords"):
cropped_lines_region_indexer.append(indexer_text_region)
p_h=child_textlines.attrib['points'].split(' ')
textline_coords = np.array( [ [int(x.split(',')[0]),
int(x.split(',')[1]) ]
for x in p_h] )
x,y,w,h = cv2.boundingRect(textline_coords)
total_bb_coordinates.append([x,y,w,h])
h2w_ratio = h/float(w)
img_poly_on_img = np.copy(img)
mask_poly = np.zeros(img.shape)
mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
mask_poly = mask_poly[y:y+h, x:x+w, :]
img_crop = img_poly_on_img[y:y+h, x:x+w, :]
img_crop[mask_poly==0] = 255
self.logger.debug("processing %d lines for '%s'",
len(cropped_lines), nn.attrib['id'])
if h2w_ratio > 0.1:
cropped_lines.append(resize_image(img_crop,
tr_ocr_input_height_and_width,
tr_ocr_input_height_and_width) )
cropped_lines_meging_indexing.append(0)
indexer_b_s+=1
if indexer_b_s==self.b_s:
imgs = cropped_lines[:]
cropped_lines = []
indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged
else:
splited_images, _ = return_textlines_split_if_needed(img_crop, None)
#print(splited_images)
if splited_images:
cropped_lines.append(resize_image(splited_images[0],
tr_ocr_input_height_and_width,
tr_ocr_input_height_and_width))
cropped_lines_meging_indexing.append(1)
indexer_b_s+=1
if indexer_b_s==self.b_s:
imgs = cropped_lines[:]
cropped_lines = []
indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged
cropped_lines.append(resize_image(splited_images[1],
tr_ocr_input_height_and_width,
tr_ocr_input_height_and_width))
cropped_lines_meging_indexing.append(-1)
indexer_b_s+=1
if indexer_b_s==self.b_s:
imgs = cropped_lines[:]
cropped_lines = []
indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged
else:
cropped_lines.append(img_crop)
cropped_lines_meging_indexing.append(0)
indexer_b_s+=1
if indexer_b_s==self.b_s:
imgs = cropped_lines[:]
cropped_lines = []
indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged
indexer_text_region = indexer_text_region +1
if indexer_b_s!=0:
imgs = cropped_lines[:]
cropped_lines = []
indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_zoo.get('ocr').generate(pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged
####extracted_texts = []
####n_iterations = math.ceil(len(cropped_lines) / self.b_s)
####for i in range(n_iterations):
####if i==(n_iterations-1):
####n_start = i*self.b_s
####imgs = cropped_lines[n_start:]
####else:
####n_start = i*self.b_s
####n_end = (i+1)*self.b_s
####imgs = cropped_lines[n_start:n_end]
####pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
####generated_ids_merged = self.model_ocr.generate(
#### pixel_values_merged.to(self.device))
####generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
#### generated_ids_merged, skip_special_tokens=True)
####extracted_texts = extracted_texts + generated_text_merged
del cropped_lines
gc.collect()
extracted_texts_merged = [extracted_texts[ind]
if cropped_lines_meging_indexing[ind]==0
else extracted_texts[ind]+" "+extracted_texts[ind+1]
if cropped_lines_meging_indexing[ind]==1
else None
for ind in range(len(cropped_lines_meging_indexing))]
extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
#print(extracted_texts_merged, len(extracted_texts_merged))
return EynollahOcrResult(
extracted_texts_merged=extracted_texts_merged,
extracted_conf_value_merged=None,
cropped_lines_region_indexer=cropped_lines_region_indexer,
total_bb_coordinates=total_bb_coordinates,
)
def run_cnn(
self,
*,
img: MatLike,
img_bin: Optional[MatLike],
page_tree: ET.ElementTree,
page_ns,
image_width,
image_height,
) -> EynollahOcrResult:
total_bb_coordinates = []
cropped_lines = []
img_crop_bin = None
imgs_bin = None
imgs_bin_ver_flipped = None
cropped_lines_bin = []
cropped_lines_ver_index = []
cropped_lines_region_indexer = []
cropped_lines_meging_indexing = []
indexer_text_region = 0
for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'):
try:
type_textregion = nn.attrib['type']
except:
type_textregion = 'paragraph'
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
for child_textlines in child_textregion:
if child_textlines.tag.endswith("Coords"):
cropped_lines_region_indexer.append(indexer_text_region)
p_h=child_textlines.attrib['points'].split(' ')
textline_coords = np.array( [ [int(x.split(',')[0]),
int(x.split(',')[1]) ]
for x in p_h] )
x,y,w,h = cv2.boundingRect(textline_coords)
angle_radians = math.atan2(h, w)
# Convert to degrees
angle_degrees = math.degrees(angle_radians)
if type_textregion=='drop-capital':
angle_degrees = 0
total_bb_coordinates.append([x,y,w,h])
w_scaled = w * image_height/float(h)
img_poly_on_img = np.copy(img)
if img_bin:
img_poly_on_img_bin = np.copy(img_bin)
img_crop_bin = img_poly_on_img_bin[y:y+h, x:x+w, :]
mask_poly = np.zeros(img.shape)
mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
mask_poly = mask_poly[y:y+h, x:x+w, :]
img_crop = img_poly_on_img[y:y+h, x:x+w, :]
# print(file_name, angle_degrees, w*h,
# mask_poly[:,:,0].sum(),
# mask_poly[:,:,0].sum() /float(w*h) ,
# 'didi')
if angle_degrees > 3:
better_des_slope = get_orientation_moments(textline_coords)
img_crop = rotate_image_with_padding(img_crop, better_des_slope)
if img_bin:
img_crop_bin = rotate_image_with_padding(img_crop_bin, better_des_slope)
mask_poly = rotate_image_with_padding(mask_poly, better_des_slope)
mask_poly = mask_poly.astype('uint8')
#new bounding box
x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_poly[:,:,0])
mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :]
img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :]
if not self.do_not_mask_with_textline_contour:
img_crop[mask_poly==0] = 255
if img_bin:
img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :]
if not self.do_not_mask_with_textline_contour:
img_crop_bin[mask_poly==0] = 255
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90:
if img_bin:
img_crop, img_crop_bin = \
break_curved_line_into_small_pieces_and_then_merge(
img_crop, mask_poly, img_crop_bin)
else:
img_crop, _ = \
break_curved_line_into_small_pieces_and_then_merge(
img_crop, mask_poly)
else:
better_des_slope = 0
if not self.do_not_mask_with_textline_contour:
img_crop[mask_poly==0] = 255
if img_bin:
if not self.do_not_mask_with_textline_contour:
img_crop_bin[mask_poly==0] = 255
if type_textregion=='drop-capital':
pass
else:
if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90:
if img_bin:
img_crop, img_crop_bin = \
break_curved_line_into_small_pieces_and_then_merge(
img_crop, mask_poly, img_crop_bin)
else:
img_crop, _ = \
break_curved_line_into_small_pieces_and_then_merge(
img_crop, mask_poly)
if w_scaled < 750:#1.5*image_width:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
img_crop, image_height, image_width)
cropped_lines.append(img_fin)
if abs(better_des_slope) > 45:
cropped_lines_ver_index.append(1)
else:
cropped_lines_ver_index.append(0)
cropped_lines_meging_indexing.append(0)
if img_bin:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
img_crop_bin, image_height, image_width)
cropped_lines_bin.append(img_fin)
else:
splited_images, splited_images_bin = return_textlines_split_if_needed(
img_crop, img_crop_bin if img_bin else None)
if splited_images:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
splited_images[0], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(1)
if abs(better_des_slope) > 45:
cropped_lines_ver_index.append(1)
else:
cropped_lines_ver_index.append(0)
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
splited_images[1], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(-1)
if abs(better_des_slope) > 45:
cropped_lines_ver_index.append(1)
else:
cropped_lines_ver_index.append(0)
if img_bin:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
splited_images_bin[0], image_height, image_width)
cropped_lines_bin.append(img_fin)
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
splited_images_bin[1], image_height, image_width)
cropped_lines_bin.append(img_fin)
else:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
img_crop, image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0)
if abs(better_des_slope) > 45:
cropped_lines_ver_index.append(1)
else:
cropped_lines_ver_index.append(0)
if img_bin:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
img_crop_bin, image_height, image_width)
cropped_lines_bin.append(img_fin)
indexer_text_region = indexer_text_region +1
extracted_texts = []
extracted_conf_value = []
n_iterations = math.ceil(len(cropped_lines) / self.b_s)
# FIXME: copy pasta
for i in range(n_iterations):
if i==(n_iterations-1):
n_start = i*self.b_s
imgs = cropped_lines[n_start:]
imgs = np.array(imgs)
imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3)
ver_imgs = np.array( cropped_lines_ver_index[n_start:] )
indices_ver = np.where(ver_imgs == 1)[0]
#print(indices_ver, 'indices_ver')
if len(indices_ver)>0:
imgs_ver_flipped = imgs[indices_ver, : ,: ,:]
imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:]
#print(imgs_ver_flipped, 'imgs_ver_flipped')
else:
imgs_ver_flipped = None
if img_bin:
imgs_bin = cropped_lines_bin[n_start:]
imgs_bin = np.array(imgs_bin)
imgs_bin = imgs_bin.reshape(imgs_bin.shape[0], image_height, image_width, 3)
if len(indices_ver)>0:
imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:]
imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:]
#print(imgs_ver_flipped, 'imgs_ver_flipped')
else:
imgs_bin_ver_flipped = None
else:
n_start = i*self.b_s
n_end = (i+1)*self.b_s
imgs = cropped_lines[n_start:n_end]
imgs = np.array(imgs).reshape(self.b_s, image_height, image_width, 3)
ver_imgs = np.array( cropped_lines_ver_index[n_start:n_end] )
indices_ver = np.where(ver_imgs == 1)[0]
#print(indices_ver, 'indices_ver')
if len(indices_ver)>0:
imgs_ver_flipped = imgs[indices_ver, : ,: ,:]
imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:]
#print(imgs_ver_flipped, 'imgs_ver_flipped')
else:
imgs_ver_flipped = None
if img_bin:
imgs_bin = cropped_lines_bin[n_start:n_end]
imgs_bin = np.array(imgs_bin).reshape(self.b_s, image_height, image_width, 3)
if len(indices_ver)>0:
imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:]
imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:]
#print(imgs_ver_flipped, 'imgs_ver_flipped')
else:
imgs_bin_ver_flipped = None
self.logger.debug("processing next %d lines", len(imgs))
preds = self.model_zoo.get('ocr').predict(imgs, verbose=0)
if len(indices_ver)>0:
preds_flipped = self.model_zoo.get('ocr').predict(imgs_ver_flipped, verbose=0)
preds_max_fliped = np.max(preds_flipped, axis=2 )
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
masked_means_flipped = \
np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \
np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
masked_means = \
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
np.sum(pred_max_not_unk_mask_bool, axis=1)
masked_means[np.isnan(masked_means)] = 0
masked_means_ver = masked_means[indices_ver]
#print(masked_means_ver, 'pred_max_not_unk')
indices_where_flipped_conf_value_is_higher = \
np.where(masked_means_flipped > masked_means_ver)[0]
#print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher')
if len(indices_where_flipped_conf_value_is_higher)>0:
indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher]
preds[indices_to_be_replaced,:,:] = \
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
if img_bin:
preds_bin = self.model_zoo.get('ocr').predict(imgs_bin, verbose=0)
if len(indices_ver)>0:
preds_flipped = self.model_zoo.get('ocr').predict(imgs_bin_ver_flipped, verbose=0)
preds_max_fliped = np.max(preds_flipped, axis=2 )
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
masked_means_flipped = \
np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \
np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
masked_means = \
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
np.sum(pred_max_not_unk_mask_bool, axis=1)
masked_means[np.isnan(masked_means)] = 0
masked_means_ver = masked_means[indices_ver]
#print(masked_means_ver, 'pred_max_not_unk')
indices_where_flipped_conf_value_is_higher = \
np.where(masked_means_flipped > masked_means_ver)[0]
#print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher')
if len(indices_where_flipped_conf_value_is_higher)>0:
indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher]
preds_bin[indices_to_be_replaced,:,:] = \
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
preds = (preds + preds_bin) / 2.
pred_texts = decode_batch_predictions(preds, self.model_zoo.get('num_to_char'))
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
masked_means = \
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
np.sum(pred_max_not_unk_mask_bool, axis=1)
for ib in range(imgs.shape[0]):
pred_texts_ib = pred_texts[ib].replace("[UNK]", "")
if masked_means[ib] >= self.min_conf_value_of_textline_text:
extracted_texts.append(pred_texts_ib)
extracted_conf_value.append(masked_means[ib])
else:
extracted_texts.append("")
extracted_conf_value.append(0)
del cropped_lines
del cropped_lines_bin
gc.collect()
extracted_texts_merged = [extracted_texts[ind]
if cropped_lines_meging_indexing[ind]==0
else extracted_texts[ind]+" "+extracted_texts[ind+1]
if cropped_lines_meging_indexing[ind]==1
else None
for ind in range(len(cropped_lines_meging_indexing))]
extracted_conf_value_merged = [extracted_conf_value[ind] # type: ignore
if cropped_lines_meging_indexing[ind]==0
else (extracted_conf_value[ind]+extracted_conf_value[ind+1])/2.
if cropped_lines_meging_indexing[ind]==1
else None
for ind in range(len(cropped_lines_meging_indexing))]
extracted_conf_value_merged: List[float] = [extracted_conf_value_merged[ind_cfm]
for ind_cfm in range(len(extracted_texts_merged))
if extracted_texts_merged[ind_cfm] is not None]
extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
return EynollahOcrResult(
extracted_texts_merged=extracted_texts_merged,
extracted_conf_value_merged=extracted_conf_value_merged,
cropped_lines_region_indexer=cropped_lines_region_indexer,
total_bb_coordinates=total_bb_coordinates,
)
def write_ocr(
self,
*,
result: EynollahOcrResult,
page_tree: ET.ElementTree,
out_file_ocr,
page_ns,
img,
out_image_with_text,
):
cropped_lines_region_indexer = result.cropped_lines_region_indexer
total_bb_coordinates = result.total_bb_coordinates
extracted_texts_merged = result.extracted_texts_merged
extracted_conf_value_merged = result.extracted_conf_value_merged
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
if out_image_with_text:
image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white")
draw = ImageDraw.Draw(image_text)
font = get_font()
for indexer_text, bb_ind in enumerate(total_bb_coordinates):
x_bb = bb_ind[0]
y_bb = bb_ind[1]
w_bb = bb_ind[2]
h_bb = bb_ind[3]
font = fit_text_single_line(draw, extracted_texts_merged[indexer_text],
font.path, w_bb, int(h_bb*0.4) )
##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2)
text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font)
text_width = text_bbox[2] - text_bbox[0]
text_height = text_bbox[3] - text_bbox[1]
text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally
text_y = y_bb + (h_bb - text_height) // 2 # Center vertically
# Draw the text
draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font)
image_text.save(out_image_with_text)
text_by_textregion = []
for ind in unique_cropped_lines_region_indexer:
ind = np.array(cropped_lines_region_indexer)==ind
extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
if len(extracted_texts_merged_un)>1:
text_by_textregion_ind = ""
next_glue = ""
for indt in range(len(extracted_texts_merged_un)):
if (extracted_texts_merged_un[indt].endswith('⸗') or
extracted_texts_merged_un[indt].endswith('-') or
extracted_texts_merged_un[indt].endswith('¬')):
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt][:-1]
next_glue = ""
else:
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt]
next_glue = " "
text_by_textregion.append(text_by_textregion_ind)
else:
text_by_textregion.append(" ".join(extracted_texts_merged_un))
indexer = 0
indexer_textregion = 0
for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'):
is_textregion_text = False
for childtest in nn:
if childtest.tag.endswith("TextEquiv"):
is_textregion_text = True
if not is_textregion_text:
text_subelement_textregion = ET.SubElement(nn, 'TextEquiv')
unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode')
has_textline = False
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
is_textline_text = False
for childtest2 in child_textregion:
if childtest2.tag.endswith("TextEquiv"):
is_textline_text = True
if not is_textline_text:
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
if extracted_conf_value_merged:
text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
unicode_textline.text = extracted_texts_merged[indexer]
else:
for childtest3 in child_textregion:
if childtest3.tag.endswith("TextEquiv"):
for child_uc in childtest3:
if child_uc.tag.endswith("Unicode"):
if extracted_conf_value_merged:
childtest3.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
child_uc.text = extracted_texts_merged[indexer]
indexer = indexer + 1
has_textline = True
if has_textline:
if is_textregion_text:
for child4 in nn:
if child4.tag.endswith("TextEquiv"):
for childtr_uc in child4:
if childtr_uc.tag.endswith("Unicode"):
childtr_uc.text = text_by_textregion[indexer_textregion]
else:
unicode_textregion.text = text_by_textregion[indexer_textregion]
indexer_textregion = indexer_textregion + 1
ET.register_namespace("",page_ns)
page_tree.write(out_file_ocr, xml_declaration=True, method='xml', encoding="utf-8", default_namespace=None)
def run(
self,
*,
overwrite: bool = False,
dir_in: Optional[str] = None,
dir_in_bin: Optional[str] = None,
image_filename: Optional[str] = None,
dir_xmls: str,
dir_out_image_text: Optional[str] = None,
dir_out: str,
):
"""
Run OCR.
Args:
dir_in_bin (str): Prediction with RGB and binarized images for selected pages, should not be the default
"""
if dir_in:
ls_imgs = [os.path.join(dir_in, image_filename)
for image_filename in filter(is_image_filename,
os.listdir(dir_in))]
else:
assert image_filename
ls_imgs = [image_filename]
for img_filename in ls_imgs:
file_stem = Path(img_filename).stem
page_file_in = os.path.join(dir_xmls, file_stem+'.xml')
out_file_ocr = os.path.join(dir_out, file_stem+'.xml')
if os.path.exists(out_file_ocr):
if overwrite:
self.logger.warning("will overwrite existing output file '%s'", out_file_ocr)
else:
self.logger.warning("will skip input for existing output file '%s'", out_file_ocr)
return
img = cv2.imread(img_filename)
page_tree = ET.parse(page_file_in, parser = ET.XMLParser(encoding="utf-8"))
page_ns = etree_namespace_for_element_tag(page_tree.getroot().tag)
out_image_with_text = None
if dir_out_image_text:
out_image_with_text = os.path.join(dir_out_image_text, file_stem + '.png')
img_bin = None
if dir_in_bin:
img_bin = cv2.imread(os.path.join(dir_in_bin, file_stem+'.png'))
if self.tr_ocr:
result = self.run_trocr(
img=img,
page_tree=page_tree,
page_ns=page_ns,
tr_ocr_input_height_and_width = 384
)
else:
result = self.run_cnn(
img=img,
page_tree=page_tree,
page_ns=page_ns,
img_bin=img_bin,
image_width=512,
image_height=32,
)
self.write_ocr(
result=result,
img=img,
page_tree=page_tree,
page_ns=page_ns,
out_file_ocr=out_file_ocr,
out_image_with_text=out_image_with_text,
)

101
src/eynollah/image_enhancer.py
View file

@ -2,7 +2,12 @@
Image enhancer. The output can be written as same scale of input or in new predicted scale.
"""
from logging import Logger
# FIXME: fix all of those...
# pyright: reportUnboundVariable=false
# pyright: reportCallIssue=false
# pyright: reportArgumentType=false
import logging
import os
import time
from typing import Optional
@ -10,19 +15,18 @@ from pathlib import Path
import gc
import cv2
from keras.models import Model
import numpy as np
from ocrd_utils import getLogger, tf_disable_interactive_logs
import tensorflow as tf
import tensorflow as tf # type: ignore
from skimage.morphology import skeletonize
from tensorflow.keras.models import load_model
from .model_zoo import EynollahModelZoo
from .utils.resize import resize_image
from .utils.pil_cv2 import pil2cv
from .utils import (
is_image_filename,
crop_image_inside_box
)
from .eynollah import PatchEncoder, Patches
DPI_THRESHOLD = 298
KERNEL = np.ones((5, 5), np.uint8)
@ -31,14 +35,13 @@ KERNEL = np.ones((5, 5), np.uint8)
class Enhancer:
def __init__(
self,
dir_models : str,
*,
model_zoo: EynollahModelZoo,
num_col_upper : Optional[int] = None,
num_col_lower : Optional[int] = None,
save_org_scale : bool = False,
logger : Optional[Logger] = None,
):
self.input_binary = False
self.light_version = False
self.save_org_scale = save_org_scale
if num_col_upper:
self.num_col_upper = int(num_col_upper)
@ -49,12 +52,10 @@ class Enhancer:
else:
self.num_col_lower = num_col_lower
self.logger = logger if logger else getLogger('enhancement')
self.dir_models = dir_models
self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425"
self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425"
self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425"
self.model_page_dir = dir_models + "/model_eynollah_page_extraction_20250915"
self.logger = logging.getLogger('eynollah.enhance')
self.model_zoo = model_zoo
for v in ['binarization', 'enhancement', 'col_classifier', 'page']:
self.model_zoo.load_model(v)
try:
for device in tf.config.list_physical_devices('GPU'):
@ -62,25 +63,14 @@ class Enhancer:
except:
self.logger.warning("no GPU device available")
self.model_page = self.our_load_model(self.model_page_dir)
self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
self.model_bin = self.our_load_model(self.model_dir_of_binarization)
def cache_images(self, image_filename=None, image_pil=None, dpi=None):
ret = {}
if image_filename:
ret['img'] = cv2.imread(image_filename)
if self.light_version:
self.dpi = 100
else:
self.dpi = 0#check_dpi(image_filename)
self.dpi = 100
else:
ret['img'] = pil2cv(image_pil)
if self.light_version:
self.dpi = 100
else:
self.dpi = 0#check_dpi(image_pil)
self.dpi = 100
ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY)
for prefix in ('', '_grayscale'):
ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
@ -100,26 +90,11 @@ class Enhancer:
key += '_uint8'
return self._imgs[key].copy()
def isNaN(self, num):
return num != num
@staticmethod
def our_load_model(model_file):
if model_file.endswith('.h5') and Path(model_file[:-3]).exists():
# prefer SavedModel over HDF5 format if it exists
model_file = model_file[:-3]
try:
model = load_model(model_file, compile=False)
except:
model = load_model(model_file, compile=False, custom_objects={
"PatchEncoder": PatchEncoder, "Patches": Patches})
return model
def predict_enhancement(self, img):
self.logger.debug("enter predict_enhancement")
img_height_model = self.model_enhancement.layers[-1].output_shape[1]
img_width_model = self.model_enhancement.layers[-1].output_shape[2]
img_height_model = self.model_zoo.get('enhancement', Model).layers[-1].output_shape[1]
img_width_model = self.model_zoo.get('enhancement', Model).layers[-1].output_shape[2]
if img.shape[0] < img_height_model:
img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST)
if img.shape[1] < img_width_model:
@ -160,7 +135,7 @@ class Enhancer:
index_y_d = img_h - img_height_model
img_patch = img[np.newaxis, index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = self.model_enhancement.predict(img_patch, verbose=0)
label_p_pred = self.model_zoo.get('enhancement', Model).predict(img_patch, verbose='0')
seg = label_p_pred[0, :, :, :] * 255
if i == 0 and j == 0:
@ -246,7 +221,7 @@ class Enhancer:
else:
img = self.imread()
img = cv2.GaussianBlur(img, (5, 5), 0)
img_page_prediction = self.do_prediction(False, img, self.model_page)
img_page_prediction = self.do_prediction(False, img, self.model_zoo.get('page'))
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
@ -285,13 +260,13 @@ class Enhancer:
return img_new, num_column_is_classified
def resize_and_enhance_image_with_column_classifier(self, light_version):
def resize_and_enhance_image_with_column_classifier(self):
self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
dpi = 0#self.dpi
self.logger.info("Detected %s DPI", dpi)
if self.input_binary:
img = self.imread()
prediction_bin = self.do_prediction(True, img, self.model_bin, n_batch_inference=5)
prediction_bin = self.do_prediction(True, img, self.model_zoo.get('binarization'), n_batch_inference=5)
prediction_bin = 255 * (prediction_bin[:,:,0]==0)
prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2).astype(np.uint8)
img= np.copy(prediction_bin)
@ -332,7 +307,7 @@ class Enhancer:
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
label_p_pred = self.model_zoo.get('col_classifier').predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
elif (self.num_col_upper and self.num_col_lower) and (self.num_col_upper!=self.num_col_lower):
if self.input_binary:
@ -352,7 +327,7 @@ class Enhancer:
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
label_p_pred = self.model_zoo.get('col_classifier').predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
if num_col > self.num_col_upper:
@ -368,16 +343,13 @@ class Enhancer:
self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
if dpi < DPI_THRESHOLD:
if light_version and num_col in (1,2):
if num_col in (1,2):
img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2(
img, num_col, width_early, label_p_pred)
else:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(
img, num_col, width_early, label_p_pred)
if light_version:
image_res = np.copy(img_new)
else:
image_res = self.predict_enhancement(img_new)
image_res = np.copy(img_new)
is_image_enhanced = True
else:
@ -671,11 +643,11 @@ class Enhancer:
gc.collect()
return prediction_true
def run_enhancement(self, light_version):
def run_enhancement(self):
t_in = time.time()
self.logger.info("Resizing and enhancing image...")
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = \
self.resize_and_enhance_image_with_column_classifier(light_version)
self.resize_and_enhance_image_with_column_classifier()
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified
@ -683,9 +655,9 @@ class Enhancer:
def run_single(self):
t0 = time.time()
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(light_version=False)
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement()
return img_res
return img_res, is_image_enhanced
def run(self,
@ -723,9 +695,18 @@ class Enhancer:
self.logger.warning("will skip input for existing output file '%s'", self.output_filename)
continue
image_enhanced = self.run_single()
did_resize = False
image_enhanced, did_enhance = self.run_single()
if self.save_org_scale:
image_enhanced = resize_image(image_enhanced, self.h_org, self.w_org)
did_resize = True
self.logger.info(
"Image %s was %senhanced%s.",
img_filename,
'' if did_enhance else 'not ',
'and resized' if did_resize else ''
)
cv2.imwrite(self.output_filename, image_enhanced)

54
src/eynollah/mb_ro_on_layout.py
View file

@ -1,8 +1,12 @@
"""
Image enhancer. The output can be written as same scale of input or in new predicted scale.
Machine learning based reading order detection
"""
from logging import Logger
# pyright: reportCallIssue=false
# pyright: reportUnboundVariable=false
# pyright: reportArgumentType=false
import logging
import os
import time
from typing import Optional
@ -10,12 +14,12 @@ from pathlib import Path
import xml.etree.ElementTree as ET
import cv2
from keras.models import Model
import numpy as np
from ocrd_utils import getLogger
import statistics
import tensorflow as tf
from tensorflow.keras.models import load_model
from .model_zoo import EynollahModelZoo
from .utils.resize import resize_image
from .utils.contour import (
find_new_features_of_contours,
@ -23,7 +27,6 @@ from .utils.contour import (
return_parent_contours,
)
from .utils import is_xml_filename
from .eynollah import PatchEncoder, Patches
DPI_THRESHOLD = 298
KERNEL = np.ones((5, 5), np.uint8)
@ -32,12 +35,12 @@ KERNEL = np.ones((5, 5), np.uint8)
class machine_based_reading_order_on_layout:
def __init__(
self,
dir_models : str,
logger : Optional[Logger] = None,
*,
model_zoo: EynollahModelZoo,
logger : Optional[logging.Logger] = None,
):
self.logger = logger if logger else getLogger('mbreorder')
self.dir_models = dir_models
self.model_reading_order_dir = dir_models + "/model_eynollah_reading_order_20250824"
self.logger = logger or logging.getLogger('eynollah.mbreorder')
self.model_zoo = model_zoo
try:
for device in tf.config.list_physical_devices('GPU'):
@ -45,20 +48,7 @@ class machine_based_reading_order_on_layout:
except:
self.logger.warning("no GPU device available")
self.model_reading_order = self.our_load_model(self.model_reading_order_dir)
self.light_version = True
@staticmethod
def our_load_model(model_file):
if model_file.endswith('.h5') and Path(model_file[:-3]).exists():
# prefer SavedModel over HDF5 format if it exists
model_file = model_file[:-3]
try:
model = load_model(model_file, compile=False)
except:
model = load_model(model_file, compile=False, custom_objects={
"PatchEncoder": PatchEncoder, "Patches": Patches})
return model
self.model_zoo.load_model('reading_order')
def read_xml(self, xml_file):
tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8'))
@ -69,6 +59,7 @@ class machine_based_reading_order_on_layout:
index_tot_regions = []
tot_region_ref = []
y_len, x_len = 0, 0
for jj in root1.iter(link+'Page'):
y_len=int(jj.attrib['imageHeight'])
x_len=int(jj.attrib['imageWidth'])
@ -81,13 +72,13 @@ class machine_based_reading_order_on_layout:
co_printspace = []
if link+'PrintSpace' in alltags:
region_tags_printspace = np.unique([x for x in alltags if x.endswith('PrintSpace')])
elif link+'Border' in alltags:
else:
region_tags_printspace = np.unique([x for x in alltags if x.endswith('Border')])
for tag in region_tags_printspace:
if link+'PrintSpace' in alltags:
tag_endings_printspace = ['}PrintSpace','}printspace']
elif link+'Border' in alltags:
else:
tag_endings_printspace = ['}Border','}border']
if tag.endswith(tag_endings_printspace[0]) or tag.endswith(tag_endings_printspace[1]):
@ -524,7 +515,7 @@ class machine_based_reading_order_on_layout:
min_cont_size_to_be_dilated = 10
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
if len(contours_only_text_parent)>min_cont_size_to_be_dilated:
cx_conts, cy_conts, x_min_conts, x_max_conts, y_min_conts, y_max_conts, _ = find_new_features_of_contours(contours_only_text_parent)
args_cont_located = np.array(range(len(contours_only_text_parent)))
@ -624,13 +615,13 @@ class machine_based_reading_order_on_layout:
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,
int(x_min_main[j]):int(x_max_main[j])] = 1
co_text_all_org = contours_only_text_parent + contours_only_text_parent_h
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
if len(contours_only_text_parent)>min_cont_size_to_be_dilated:
co_text_all = contours_only_dilated + contours_only_text_parent_h
else:
co_text_all = contours_only_text_parent + contours_only_text_parent_h
else:
co_text_all_org = contours_only_text_parent
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
if len(contours_only_text_parent)>min_cont_size_to_be_dilated:
co_text_all = contours_only_dilated
else:
co_text_all = contours_only_text_parent
@ -683,7 +674,7 @@ class machine_based_reading_order_on_layout:
tot_counter += 1
batch.append(j)
if tot_counter % inference_bs == 0 or tot_counter == len(ij_list):
y_pr = self.model_reading_order.predict(input_1 , verbose=0)
y_pr = self.model_zoo.get('reading_order', Model).predict(input_1 , verbose='0')
for jb, j in enumerate(batch):
if y_pr[jb][0]>=0.5:
post_list.append(j)
@ -709,7 +700,7 @@ class machine_based_reading_order_on_layout:
##id_all_text = np.array(id_all_text)[index_sort]
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
if len(contours_only_text_parent)>min_cont_size_to_be_dilated:
org_contours_indexes = []
for ind in range(len(ordered)):
region_with_curr_order = ordered[ind]
@ -802,6 +793,7 @@ class machine_based_reading_order_on_layout:
alltags=[elem.tag for elem in root_xml.iter()]
ET.register_namespace("",name_space)
assert dir_out
tree_xml.write(os.path.join(dir_out, file_name+'.xml'),
xml_declaration=True,
method='xml',

4
src/eynollah/model_zoo/__init__.py Normal file
View file

@ -0,0 +1,4 @@
__all__ = [
'EynollahModelZoo',
]
from .model_zoo import EynollahModelZoo

252
src/eynollah/model_zoo/default_specs.py Normal file
View file

@ -0,0 +1,252 @@
from .specs import EynollahModelSpec, EynollahModelSpecSet
# NOTE: This needs to change whenever models/versions change
ZENODO = "https://zenodo.org/records/17295988/files"
MODELS_VERSION = "v0_7_0"
def dist_url(dist_name: str="layout") -> str:
return f'{ZENODO}/models_{dist_name}_{MODELS_VERSION}.zip'
DEFAULT_MODEL_SPECS = EynollahModelSpecSet([
EynollahModelSpec(
category="enhancement",
variant='',
filename="models_eynollah/eynollah-enhancement_20210425",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="binarization",
variant='hybrid',
filename="models_eynollah/eynollah-binarization-hybrid_20230504/model_bin_hybrid_trans_cnn_sbb_ens",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="binarization",
variant='20210309',
filename="models_eynollah/eynollah-binarization_20210309",
dist_url=dist_url("extra"),
type='Keras',
),
EynollahModelSpec(
category="binarization",
variant='',
filename="models_eynollah/eynollah-binarization_20210425",
dist_url=dist_url("extra"),
type='Keras',
),
EynollahModelSpec(
category="col_classifier",
variant='',
filename="models_eynollah/eynollah-column-classifier_20210425",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="page",
variant='',
filename="models_eynollah/model_eynollah_page_extraction_20250915",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="region",
variant='',
filename="models_eynollah/eynollah-main-regions-ensembled_20210425",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="extract_images",
variant='',
filename="models_eynollah/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="region",
variant='',
filename="models_eynollah/eynollah-main-regions_20220314",
dist_url=dist_url(),
help="early layout",
type='Keras',
),
EynollahModelSpec(
category="region_p2",
variant='non-light',
filename="models_eynollah/eynollah-main-regions-aug-rotation_20210425",
dist_url=dist_url('extra'),
help="early layout, non-light, 2nd part",
type='Keras',
),
EynollahModelSpec(
category="region_1_2",
variant='',
#filename="models_eynollah/modelens_12sp_elay_0_3_4__3_6_n",
#filename="models_eynollah/modelens_earlylayout_12spaltige_2_3_5_6_7_8",
#filename="models_eynollah/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18",
#filename="models_eynollah/modelens_1_2_4_5_early_lay_1_2_spaltige",
#filename="models_eynollah/model_3_eraly_layout_no_patches_1_2_spaltige",
filename="models_eynollah/modelens_e_l_all_sp_0_1_2_3_4_171024",
dist_url=dist_url("layout"),
help="early layout, light, 1-or-2-column",
type='Keras',
),
EynollahModelSpec(
category="region_fl_np",
variant='',
#'filename="models_eynollah/modelens_full_lay_1_3_031124",
#'filename="models_eynollah/modelens_full_lay_13__3_19_241024",
#'filename="models_eynollah/model_full_lay_13_241024",
#'filename="models_eynollah/modelens_full_lay_13_17_231024",
#'filename="models_eynollah/modelens_full_lay_1_2_221024",
#'filename="models_eynollah/eynollah-full-regions-1column_20210425",
filename="models_eynollah/modelens_full_lay_1__4_3_091124",
dist_url=dist_url(),
help="full layout / no patches",
type='Keras',
),
# FIXME: Why is region_fl and region_fl_np the same model?
EynollahModelSpec(
category="region_fl",
variant='',
# filename="models_eynollah/eynollah-full-regions-3+column_20210425",
# filename="models_eynollah/model_2_full_layout_new_trans",
# filename="models_eynollah/modelens_full_lay_1_3_031124",
# filename="models_eynollah/modelens_full_lay_13__3_19_241024",
# filename="models_eynollah/model_full_lay_13_241024",
# filename="models_eynollah/modelens_full_lay_13_17_231024",
# filename="models_eynollah/modelens_full_lay_1_2_221024",
# filename="models_eynollah/modelens_full_layout_24_till_28",
# filename="models_eynollah/model_2_full_layout_new_trans",
filename="models_eynollah/modelens_full_lay_1__4_3_091124",
dist_url=dist_url(),
help="full layout / with patches",
type='Keras',
),
EynollahModelSpec(
category="reading_order",
variant='',
#filename="models_eynollah/model_mb_ro_aug_ens_11",
#filename="models_eynollah/model_step_3200000_mb_ro",
#filename="models_eynollah/model_ens_reading_order_machine_based",
#filename="models_eynollah/model_mb_ro_aug_ens_8",
#filename="models_eynollah/model_ens_reading_order_machine_based",
filename="models_eynollah/model_eynollah_reading_order_20250824",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="textline",
variant='non-light',
#filename="models_eynollah/modelens_textline_1_4_16092024",
#filename="models_eynollah/model_textline_ens_3_4_5_6_artificial",
#filename="models_eynollah/modelens_textline_1_3_4_20240915",
#filename="models_eynollah/model_textline_ens_3_4_5_6_artificial",
#filename="models_eynollah/modelens_textline_9_12_13_14_15",
#filename="models_eynollah/eynollah-textline_20210425",
filename="models_eynollah/modelens_textline_0_1__2_4_16092024",
dist_url=dist_url('extra'),
type='Keras',
),
EynollahModelSpec(
category="textline",
variant='',
#filename="models_eynollah/eynollah-textline_light_20210425",
filename="models_eynollah/modelens_textline_0_1__2_4_16092024",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="table",
variant='non-light',
filename="models_eynollah/eynollah-tables_20210319",
dist_url=dist_url('extra'),
type='Keras',
),
EynollahModelSpec(
category="table",
variant='',
filename="models_eynollah/modelens_table_0t4_201124",
dist_url=dist_url(),
type='Keras',
),
EynollahModelSpec(
category="ocr",
variant='',
filename="models_eynollah/model_eynollah_ocr_cnnrnn_20250930",
dist_url=dist_url("ocr"),
type='Keras',
),
EynollahModelSpec(
category="ocr",
variant='degraded',
filename="models_eynollah/model_eynollah_ocr_cnnrnn__degraded_20250805/",
help="slightly better at degraded Fraktur",
dist_url=dist_url("ocr"),
type='Keras',
),
EynollahModelSpec(
category="num_to_char",
variant='',
filename="characters_org.txt",
dist_url=dist_url("ocr"),
type='decoder',
),
EynollahModelSpec(
category="characters",
variant='',
filename="characters_org.txt",
dist_url=dist_url("ocr"),
type='List[str]',
),
EynollahModelSpec(
category="ocr",
variant='tr',
filename="models_eynollah/model_eynollah_ocr_trocr_20250919",
dist_url=dist_url("ocr"),
help='much slower transformer-based',
type='Keras',
),
EynollahModelSpec(
category="trocr_processor",
variant='',
filename="models_eynollah/model_eynollah_ocr_trocr_20250919",
dist_url=dist_url("ocr"),
type='TrOCRProcessor',
),
EynollahModelSpec(
category="trocr_processor",
variant='htr',
filename="models_eynollah/microsoft/trocr-base-handwritten",
dist_url=dist_url("extra"),
type='TrOCRProcessor',
),
])

203
src/eynollah/model_zoo/model_zoo.py Normal file
View file

@ -0,0 +1,203 @@
import json
import logging
from copy import deepcopy
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Type, Union
from ocrd_utils import tf_disable_interactive_logs
tf_disable_interactive_logs()
from keras.layers import StringLookup
from keras.models import Model as KerasModel
from keras.models import load_model
from tabulate import tabulate
from ..patch_encoder import PatchEncoder, Patches
from .specs import EynollahModelSpecSet
from .default_specs import DEFAULT_MODEL_SPECS
from .types import AnyModel, T
class EynollahModelZoo:
"""
Wrapper class that handles storage and loading of models for all eynollah runners.
"""
model_basedir: Path
specs: EynollahModelSpecSet
def __init__(
self,
basedir: str,
model_overrides: Optional[List[Tuple[str, str, str]]] = None,
) -> None:
self.model_basedir = Path(basedir)
self.logger = logging.getLogger('eynollah.model_zoo')
if not self.model_basedir.exists():
self.logger.warning(f"Model basedir does not exist: {basedir}. Set eynollah --model-basedir to the correct directory.")
self.specs = deepcopy(DEFAULT_MODEL_SPECS)
self._overrides = []
if model_overrides:
self.override_models(*model_overrides)
self._loaded: Dict[str, AnyModel] = {}
@property
def model_overrides(self):
return self._overrides
def override_models(
self,
*model_overrides: Tuple[str, str, str],
):
"""
Override the default model versions
"""
for model_category, model_variant, model_filename in model_overrides:
spec = self.specs.get(model_category, model_variant)
self.logger.warning("Overriding filename for model spec %s to %s", spec, model_filename)
self.specs.get(model_category, model_variant).filename = model_filename
self._overrides += model_overrides
def model_path(
self,
model_category: str,
model_variant: str = '',
absolute: bool = True,
) -> Path:
"""
Translate model_{type,variant} tuple into an absolute (or relative) Path
"""
spec = self.specs.get(model_category, model_variant)
if spec.category in ('characters', 'num_to_char'):
return self.model_path('ocr') / spec.filename
if not Path(spec.filename).is_absolute() and absolute:
model_path = Path(self.model_basedir).joinpath(spec.filename)
else:
model_path = Path(spec.filename)
return model_path
def load_models(
self,
*all_load_args: Union[str, Tuple[str], Tuple[str, str], Tuple[str, str, str]],
) -> Dict:
"""
Load all models by calling load_model and return a dictionary mapping model_category to loaded model
"""
ret = {}
for load_args in all_load_args:
if isinstance(load_args, str):
ret[load_args] = self.load_model(load_args)
else:
ret[load_args[0]] = self.load_model(*load_args)
return ret
def load_model(
self,
model_category: str,
model_variant: str = '',
model_path_override: Optional[str] = None,
) -> AnyModel:
"""
Load any model
"""
if model_path_override:
self.override_models((model_category, model_variant, model_path_override))
model_path = self.model_path(model_category, model_variant)
if model_path.suffix == '.h5' and Path(model_path.stem).exists():
# prefer SavedModel over HDF5 format if it exists
model_path = Path(model_path.stem)
if model_category == 'ocr':
model = self._load_ocr_model(variant=model_variant)
elif model_category == 'num_to_char':
model = self._load_num_to_char()
elif model_category == 'characters':
model = self._load_characters()
elif model_category == 'trocr_processor':
from transformers import TrOCRProcessor
model = TrOCRProcessor.from_pretrained(model_path)
else:
try:
model = load_model(model_path, compile=False)
except Exception as e:
self.logger.exception(e)
model = load_model(
model_path, compile=False, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches}
)
self._loaded[model_category] = model
return model # type: ignore
def get(self, model_category: str, model_type: Optional[Type[T]] = None) -> T:
if model_category not in self._loaded:
raise ValueError(f'Model "{model_category} not previously loaded with "load_model(..)"')
ret = self._loaded[model_category]
if model_type:
assert isinstance(ret, model_type)
return ret # type: ignore # FIXME: convince typing that we're returning generic type
def _load_ocr_model(self, variant: str) -> AnyModel:
"""
Load OCR model
"""
ocr_model_dir = self.model_path('ocr', variant)
if variant == 'tr':
from transformers import VisionEncoderDecoderModel
ret = VisionEncoderDecoderModel.from_pretrained(ocr_model_dir)
assert isinstance(ret, VisionEncoderDecoderModel)
return ret
else:
ocr_model = load_model(ocr_model_dir, compile=False)
assert isinstance(ocr_model, KerasModel)
return KerasModel(
ocr_model.get_layer(name="image").input, # type: ignore
ocr_model.get_layer(name="dense2").output, # type: ignore
)
def _load_characters(self) -> List[str]:
"""
Load encoding for OCR
"""
with open(self.model_path('num_to_char'), "r") as config_file:
return json.load(config_file)
def _load_num_to_char(self) -> StringLookup:
"""
Load decoder for OCR
"""
characters = self._load_characters()
# Mapping characters to integers.
char_to_num = StringLookup(vocabulary=characters, mask_token=None)
# Mapping integers back to original characters.
return StringLookup(vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True)
def __str__(self):
return tabulate(
[
[
spec.type,
spec.category,
spec.variant,
spec.help,
f'Yes, at {self.model_path(spec.category, spec.variant)}'
if self.model_path(spec.category, spec.variant).exists()
else f'No, download {spec.dist_url}',
# self.model_path(spec.category, spec.variant),
]
for spec in sorted(self.specs.specs, key=lambda x: x.dist_url)
],
headers=[
'Type',
'Category',
'Variant',
'Help',
'Used in',
'Installed',
],
tablefmt='github',
)
def shutdown(self):
"""
Ensure that a loaded models is not referenced by ``self._loaded`` anymore
"""
if hasattr(self, '_loaded') and getattr(self, '_loaded'):
for needle in list(self._loaded.keys()):
del self._loaded[needle]

52
src/eynollah/model_zoo/specs.py Normal file
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@ -0,0 +1,52 @@
from dataclasses import dataclass
from typing import Dict, List, Set, Tuple
@dataclass
class EynollahModelSpec():
"""
Describing a single model abstractly.
"""
category: str
# Relative filename to the models_eynollah directory in the dists
filename: str
# URL to the smallest model distribution containing this model (link to Zenodo)
dist_url: str
type: str
variant: str = ''
help: str = ''
class EynollahModelSpecSet():
"""
List of all used models for eynollah.
"""
specs: List[EynollahModelSpec]
def __init__(self, specs: List[EynollahModelSpec]) -> None:
self.specs = sorted(specs, key=lambda x: x.category + '0' + x.variant)
self.categories: Set[str] = set([spec.category for spec in self.specs])
self.variants: Dict[str, Set[str]] = {
spec.category: set([x.variant for x in self.specs if x.category == spec.category])
for spec in self.specs
}
self._index_category_variant: Dict[Tuple[str, str], EynollahModelSpec] = {
(spec.category, spec.variant): spec
for spec in self.specs
}
def asdict(self) -> Dict[str, Dict[str, str]]:
return {
spec.category: {
spec.variant: spec.filename
}
for spec in self.specs
}
def get(self, category: str, variant: str) -> EynollahModelSpec:
if category not in self.categories:
raise ValueError(f"Unknown category '{category}', must be one of {self.categories}")
if variant not in self.variants[category]:
raise ValueError(f"Unknown variant {variant} for {category}. Known variants: {self.variants[category]}")
return self._index_category_variant[(category, variant)]

7
src/eynollah/model_zoo/types.py Normal file
View file

@ -0,0 +1,7 @@
from typing import TypeVar
# NOTE: Creating an actual union type requires loading transformers which is expensive and error-prone
# from transformers import TrOCRProcessor, VisionEncoderDecoderModel
# AnyModel = Union[VisionEncoderDecoderModel, TrOCRProcessor, KerasModel, List]
AnyModel = object
T = TypeVar('T')

26
src/eynollah/ocrd-tool.json
View file

@ -29,16 +29,6 @@
"type": "boolean",
"default": true,
"description": "Try to detect all element subtypes, including drop-caps and headings"
},
"light_version": {
"type": "boolean",
"default": true,
"description": "Try to detect all element subtypes in light version (faster+simpler method for main region detection and deskewing)"
},
"textline_light": {
"type": "boolean",
"default": true,
"description": "Light version need textline light. If this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline with a faster method."
},
"tables": {
"type": "boolean",
@ -83,12 +73,20 @@
},
"resources": [
{
"url": "https://zenodo.org/records/17194824/files/models_layout_v0_5_0.tar.gz?download=1",
"name": "models_layout_v0_5_0",
"url": "https://zenodo.org/records/17580627/files/models_all_v0_7_0.zip?download=1",
"name": "models_layout_v0_7_0",
"type": "archive",
"path_in_archive": "models_layout_v0_5_0",
"size": 6119874002,
"description": "Models for layout detection, reading order detection, textline detection, page extraction, column classification, table detection, binarization, image enhancement and OCR",
"version_range": ">= v0.7.0"
},
{
"url": "https://zenodo.org/records/17295988/files/models_layout_v0_6_0.tar.gz?download=1",
"name": "models_layout_v0_6_0",
"type": "archive",
"path_in_archive": "models_layout_v0_6_0",
"size": 3525684179,
"description": "Models for layout detection, reading order detection, textline detection, page extraction, column classification, table detection, binarization, image enhancement",
"description": "Models for layout detection, reading order detection, textline detection, page extraction, column classification, table detection, binarization, image enhancement and OCR",
"version_range": ">= v0.5.0"
},
{

3
src/eynollah/ocrd_cli.py
View file

@ -1,3 +1,6 @@
# NOTE: For predictable order of imports of torch/shapely/tensorflow
# this must be the first import of the CLI!
from .eynollah_imports import imported_libs
from .processor import EynollahProcessor
from click import command
from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor

13
src/eynollah/ocrd_cli_binarization.py
View file

@ -1,6 +1,8 @@
from functools import cached_property
from typing import Optional
from PIL import Image
from frozendict import frozendict
import numpy as np
import cv2
from click import command
@ -9,6 +11,8 @@ from ocrd import Processor, OcrdPageResult, OcrdPageResultImage
from ocrd_models.ocrd_page import OcrdPage, AlternativeImageType
from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
from eynollah.model_zoo.model_zoo import EynollahModelZoo
from .sbb_binarize import SbbBinarizer
@ -25,7 +29,7 @@ class SbbBinarizeProcessor(Processor):
# already employs GPU (without singleton process atm)
max_workers = 1
@property
@cached_property
def executable(self):
return 'ocrd-sbb-binarize'
@ -34,8 +38,9 @@ class SbbBinarizeProcessor(Processor):
Set up the model prior to processing.
"""
# resolve relative path via OCR-D ResourceManager
model_path = self.resolve_resource(self.parameter['model'])
self.binarizer = SbbBinarizer(model_dir=model_path, logger=self.logger)
assert isinstance(self.parameter, frozendict)
model_zoo = EynollahModelZoo(basedir=self.parameter['model'])
self.binarizer = SbbBinarizer(model_zoo=model_zoo, logger=self.logger)
def process_page_pcgts(self, *input_pcgts: Optional[OcrdPage], page_id: Optional[str] = None) -> OcrdPageResult:
"""
@ -98,7 +103,7 @@ class SbbBinarizeProcessor(Processor):
line_image_bin = cv2pil(self.binarizer.run(image=pil2cv(line_image), use_patches=True))
# update PAGE (reference the image file):
line_image_ref = AlternativeImageType(comments=line_xywh['features'] + ',binarized')
line.add_AlternativeImage(region_image_ref)
line.add_AlternativeImage(line_image_ref)
result.images.append(OcrdPageResultImage(line_image_bin, line.id + '.IMG-BIN', line_image_ref))
return result

52
src/eynollah/patch_encoder.py Normal file
View file

@ -0,0 +1,52 @@
from keras import layers
import tensorflow as tf
projection_dim = 64
patch_size = 1
num_patches =21*21#14*14#28*28#14*14#28*28
class PatchEncoder(layers.Layer):
def __init__(self):
super().__init__()
self.projection = layers.Dense(units=projection_dim)
self.position_embedding = layers.Embedding(input_dim=num_patches, output_dim=projection_dim)
def call(self, patch):
positions = tf.range(start=0, limit=num_patches, delta=1)
encoded = self.projection(patch) + self.position_embedding(positions)
return encoded
def get_config(self):
config = super().get_config().copy()
config.update({
'num_patches': num_patches,
'projection': self.projection,
'position_embedding': self.position_embedding,
})
return config
class Patches(layers.Layer):
def __init__(self, **kwargs):
super(Patches, self).__init__()
self.patch_size = patch_size
def call(self, images):
batch_size = tf.shape(images)[0]
patches = tf.image.extract_patches(
images=images,
sizes=[1, self.patch_size, self.patch_size, 1],
strides=[1, self.patch_size, self.patch_size, 1],
rates=[1, 1, 1, 1],
padding="VALID",
)
patch_dims = patches.shape[-1]
patches = tf.reshape(patches, [batch_size, -1, patch_dims])
return patches
def get_config(self):
config = super().get_config().copy()
config.update({
'patch_size': self.patch_size,
})
return config

4
src/eynollah/plot.py
View file

@ -40,8 +40,8 @@ class EynollahPlotter:
self.image_filename_stem = image_filename_stem
# XXX TODO hacky these cannot be set at init time
self.image_org = image_org
self.scale_x = scale_x
self.scale_y = scale_y
self.scale_x : float = scale_x
self.scale_y : float = scale_y
def save_plot_of_layout_main(self, text_regions_p, image_page):
if self.dir_of_layout is not None:

13
src/eynollah/processor.py
View file

@ -3,6 +3,8 @@ from typing import Optional
from ocrd_models import OcrdPage
from ocrd import OcrdPageResultImage, Processor, OcrdPageResult
from eynollah.model_zoo.model_zoo import EynollahModelZoo
from .eynollah import Eynollah, EynollahXmlWriter
class EynollahProcessor(Processor):
@ -16,24 +18,20 @@ class EynollahProcessor(Processor):
def setup(self) -> None:
assert self.parameter
if self.parameter['textline_light'] != self.parameter['light_version']:
raise ValueError("Error: You must set or unset both parameter 'textline_light' (to enable light textline detection), "
"and parameter 'light_version' (faster+simpler method for main region detection and deskewing)")
model_zoo = EynollahModelZoo(basedir=self.parameter['models'])
self.eynollah = Eynollah(
self.resolve_resource(self.parameter['models']),
model_zoo=model_zoo,
allow_enhancement=self.parameter['allow_enhancement'],
curved_line=self.parameter['curved_line'],
right2left=self.parameter['right_to_left'],
reading_order_machine_based=self.parameter['reading_order_machine_based'],
ignore_page_extraction=self.parameter['ignore_page_extraction'],
light_version=self.parameter['light_version'],
textline_light=self.parameter['textline_light'],
full_layout=self.parameter['full_layout'],
allow_scaling=self.parameter['allow_scaling'],
headers_off=self.parameter['headers_off'],
tables=self.parameter['tables'],
logger=self.logger
)
self.eynollah.logger = self.logger
self.eynollah.plotter = None
def shutdown(self):
@ -90,7 +88,6 @@ class EynollahProcessor(Processor):
dir_out=None,
image_filename=image_filename,
curved_line=self.eynollah.curved_line,
textline_light=self.eynollah.textline_light,
pcgts=pcgts)
self.eynollah.run_single()
return result

116
src/eynollah/sbb_binarize.py
View file

@ -2,18 +2,22 @@
Tool to load model and binarize a given image.
"""
import sys
from glob import glob
# pyright: reportIndexIssue=false
# pyright: reportCallIssue=false
# pyright: reportArgumentType=false
# pyright: reportPossiblyUnboundVariable=false
import os
import logging
from typing import Optional
import numpy as np
from PIL import Image
import cv2
from ocrd_utils import tf_disable_interactive_logs
from eynollah.model_zoo import EynollahModelZoo
tf_disable_interactive_logs()
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.python.keras import backend as tensorflow_backend
from .utils import is_image_filename
@ -23,40 +27,32 @@ def resize_image(img_in, input_height, input_width):
class SbbBinarizer:
def __init__(self, model_dir, logger=None):
self.model_dir = model_dir
self.log = logger if logger else logging.getLogger('SbbBinarizer')
self.start_new_session()
self.model_files = glob(self.model_dir+"/*/", recursive = True)
self.models = []
for model_file in self.model_files:
self.models.append(self.load_model(model_file))
def __init__(
self,
*,
model_zoo: EynollahModelZoo,
logger: Optional[logging.Logger] = None,
):
self.logger = logger if logger else logging.getLogger('eynollah.binarization')
self.models = (model_zoo.model_path('binarization'), model_zoo.load_model('binarization'))
self.session = self.start_new_session()
def start_new_session(self):
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
tensorflow_backend.set_session(self.session)
session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
tensorflow_backend.set_session(session)
return session
def end_session(self):
tensorflow_backend.clear_session()
self.session.close()
del self.session
def load_model(self, model_name):
model = load_model(os.path.join(self.model_dir, model_name), compile=False)
def predict(self, model, img, use_patches, n_batch_inference=5):
model_height = model.layers[len(model.layers)-1].output_shape[1]
model_width = model.layers[len(model.layers)-1].output_shape[2]
n_classes = model.layers[len(model.layers)-1].output_shape[3]
return model, model_height, model_width, n_classes
def predict(self, model_in, img, use_patches, n_batch_inference=5):
tensorflow_backend.set_session(self.session)
model, model_height, model_width, n_classes = model_in
img_org_h = img.shape[0]
img_org_w = img.shape[1]
@ -324,9 +320,38 @@ class SbbBinarizer:
if image_path is not None:
image = cv2.imread(image_path)
img_last = 0
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
model_file, model = self.models
self.logger.info('Predicting %s with model %s', image_path if image_path else '[image]', model_file)
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
res[:, :][res[:, :] == 0] = 2
res = res - 1
res = res * 255
img_fin[:, :, 0] = res
img_fin[:, :, 1] = res
img_fin[:, :, 2] = res
img_fin = img_fin.astype(np.uint8)
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
if output:
self.logger.info('Writing binarized image to %s', output)
cv2.imwrite(output, img_last)
return img_last
else:
ls_imgs = list(filter(is_image_filename, os.listdir(dir_in)))
self.logger.info("Found %d image files to binarize in %s", len(ls_imgs), dir_in)
for i, image_path in enumerate(ls_imgs):
self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_path)
image_stem = image_path.split('.')[0]
image = cv2.imread(os.path.join(dir_in,image_path) )
img_last = 0
model_file, model = self.models
self.logger.info('Predicting %s with model %s', image_path if image_path else '[image]', model_file)
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
@ -341,38 +366,9 @@ class SbbBinarizer:
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
if output:
cv2.imwrite(output, img_last)
return img_last
else:
ls_imgs = list(filter(is_image_filename, os.listdir(dir_in)))
for image_name in ls_imgs:
image_stem = image_name.split('.')[0]
print(image_name,'image_name')
image = cv2.imread(os.path.join(dir_in,image_name) )
img_last = 0
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
res[:, :][res[:, :] == 0] = 2
res = res - 1
res = res * 255
img_fin[:, :, 0] = res
img_fin[:, :, 1] = res
img_fin[:, :, 2] = res
img_fin = img_fin.astype(np.uint8)
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
cv2.imwrite(os.path.join(output, image_stem + '.png'), img_last)
output_filename = os.path.join(output, image_stem + '.png')
self.logger.info('Writing binarized image to %s', output_filename)
cv2.imwrite(output_filename, img_last)

2
src/eynollah/training/cli.py
View file

@ -8,6 +8,7 @@ from .build_model_load_pretrained_weights_and_save import build_model_load_pretr
from .generate_gt_for_training import main as generate_gt_cli
from .inference import main as inference_cli
from .train import ex
from .extract_line_gt import linegt_cli
@click.command(context_settings=dict(
ignore_unknown_options=True,
@ -24,3 +25,4 @@ main.add_command(build_model_load_pretrained_weights_and_save)
main.add_command(generate_gt_cli, 'generate-gt')
main.add_command(inference_cli, 'inference')
main.add_command(train_cli, 'train')
main.add_command(linegt_cli, 'export_textline_images_and_text')

134
src/eynollah/training/extract_line_gt.py Normal file
View file

@ -0,0 +1,134 @@
from logging import Logger, getLogger
from typing import Optional
from pathlib import Path
import os
import click
import cv2
import xml.etree.ElementTree as ET
import numpy as np
from ..utils import is_image_filename
@click.command()
@click.option(
"--image",
"-i",
help="input image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--dir_in",
"-di",
help="directory of input images (instead of --image)",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--dir_xmls",
"-dx",
help="directory of input PAGE-XML files (in addition to --dir_in; filename stems must match the image files, with '.xml' suffix).",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--out",
"-o",
'dir_out',
help="directory for output PAGE-XML files",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--dataset_abbrevation",
"-ds_pref",
'pref_of_dataset',
help="in the case of extracting textline and text from a xml GT file user can add an abbrevation of dataset name to generated dataset",
)
@click.option(
"--do_not_mask_with_textline_contour",
"-nmtc/-mtc",
is_flag=True,
help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
)
def linegt_cli(
image,
dir_in,
dir_xmls,
dir_out,
pref_of_dataset,
do_not_mask_with_textline_contour,
):
assert bool(dir_in) ^ bool(image), "Set --dir-in or --image-filename, not both"
if dir_in:
ls_imgs = [
os.path.join(dir_in, image) for image in filter(is_image_filename, os.listdir(dir_in))
]
else:
assert image
ls_imgs = [image]
for dir_img in ls_imgs:
file_name = Path(dir_img).stem
dir_xml = os.path.join(dir_xmls, file_name + '.xml')
img = cv2.imread(dir_img)
total_bb_coordinates = []
tree1 = ET.parse(dir_xml, parser=ET.XMLParser(encoding="utf-8"))
root1 = tree1.getroot()
alltags = [elem.tag for elem in root1.iter()]
name_space = alltags[0].split('}')[0]
name_space = name_space.split('{')[1]
region_tags = np.unique([x for x in alltags if x.endswith('TextRegion')])
cropped_lines_region_indexer = []
indexer_text_region = 0
indexer_textlines = 0
# FIXME: non recursive, use OCR-D PAGE generateDS API. Or use an existing tool for this purpose altogether
for nn in root1.iter(region_tags):
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
for child_textlines in child_textregion:
if child_textlines.tag.endswith("Coords"):
cropped_lines_region_indexer.append(indexer_text_region)
p_h = child_textlines.attrib['points'].split(' ')
textline_coords = np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])
x, y, w, h = cv2.boundingRect(textline_coords)
total_bb_coordinates.append([x, y, w, h])
img_poly_on_img = np.copy(img)
mask_poly = np.zeros(img.shape)
mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
mask_poly = mask_poly[y : y + h, x : x + w, :]
img_crop = img_poly_on_img[y : y + h, x : x + w, :]
if not do_not_mask_with_textline_contour:
img_crop[mask_poly == 0] = 255
if img_crop.shape[0] == 0 or img_crop.shape[1] == 0:
continue
if child_textlines.tag.endswith("TextEquiv"):
for cheild_text in child_textlines:
if cheild_text.tag.endswith("Unicode"):
textline_text = cheild_text.text
if textline_text:
base_name = os.path.join(
dir_out, file_name + '_line_' + str(indexer_textlines)
)
if pref_of_dataset:
base_name += '_' + pref_of_dataset
if not do_not_mask_with_textline_contour:
base_name += '_masked'
with open(base_name + '.txt', 'w') as text_file:
text_file.write(textline_text)
cv2.imwrite(base_name + '.png', img_crop)
indexer_textlines += 1

101
src/eynollah/training/models.py
View file

@ -33,6 +33,25 @@ resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_
IMAGE_ORDERING = 'channels_last'
MERGE_AXIS = -1
class CTCLayer(tf.keras.layers.Layer):
def __init__(self, name=None):
super().__init__(name=name)
self.loss_fn = tf.keras.backend.ctc_batch_cost
def call(self, y_true, y_pred):
batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64")
input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64")
label_length = tf.cast(tf.shape(y_true)[1], dtype="int64")
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64")
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64")
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
# At test time, just return the computed predictions.
return y_pred
def mlp(x, hidden_units, dropout_rate):
for units in hidden_units:
x = Dense(units, activation=tf.nn.gelu)(x)
@ -779,3 +798,85 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
model = Model(img_input , o)
return model
def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_seq=None):
input_img = tf.keras.Input(shape=(image_height, image_width, 3), name="image")
labels = tf.keras.layers.Input(name="label", shape=(None,))
x = tf.keras.layers.Conv2D(64,kernel_size=(3,3),padding="same")(input_img)
x = tf.keras.layers.BatchNormalization(name="bn1")(x)
x = tf.keras.layers.Activation("relu", name="relu1")(x)
x = tf.keras.layers.Conv2D(64,kernel_size=(3,3),padding="same")(x)
x = tf.keras.layers.BatchNormalization(name="bn2")(x)
x = tf.keras.layers.Activation("relu", name="relu2")(x)
x = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x)
x = tf.keras.layers.Conv2D(128,kernel_size=(3,3),padding="same")(x)
x = tf.keras.layers.BatchNormalization(name="bn3")(x)
x = tf.keras.layers.Activation("relu", name="relu3")(x)
x = tf.keras.layers.Conv2D(128,kernel_size=(3,3),padding="same")(x)
x = tf.keras.layers.BatchNormalization(name="bn4")(x)
x = tf.keras.layers.Activation("relu", name="relu4")(x)
x = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x)
x = tf.keras.layers.Conv2D(256,kernel_size=(3,3),padding="same")(x)
x = tf.keras.layers.BatchNormalization(name="bn5")(x)
x = tf.keras.layers.Activation("relu", name="relu5")(x)
x = tf.keras.layers.Conv2D(256,kernel_size=(3,3),padding="same")(x)
x = tf.keras.layers.BatchNormalization(name="bn6")(x)
x = tf.keras.layers.Activation("relu", name="relu6")(x)
x = tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2))(x)
x = tf.keras.layers.Conv2D(image_width,kernel_size=(3,3),padding="same")(x)
x = tf.keras.layers.BatchNormalization(name="bn7")(x)
x = tf.keras.layers.Activation("relu", name="relu7")(x)
x = tf.keras.layers.Conv2D(image_width,kernel_size=(16,1))(x)
x = tf.keras.layers.BatchNormalization(name="bn8")(x)
x = tf.keras.layers.Activation("relu", name="relu8")(x)
x2d = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x)
x4d = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x2d)
new_shape = (x.shape[1]*x.shape[2], x.shape[3])
new_shape2 = (x2d.shape[1]*x2d.shape[2], x2d.shape[3])
new_shape4 = (x4d.shape[1]*x4d.shape[2], x4d.shape[3])
x = tf.keras.layers.Reshape(target_shape=new_shape, name="reshape")(x)
x2d = tf.keras.layers.Reshape(target_shape=new_shape2, name="reshape2")(x2d)
x4d = tf.keras.layers.Reshape(target_shape=new_shape4, name="reshape4")(x4d)
xrnnorg = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(x)
xrnn2d = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(x2d)
xrnn4d = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(x4d)
xrnn2d = tf.keras.layers.Reshape(target_shape=(1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
xrnn4d = tf.keras.layers.Reshape(target_shape=(1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
xrnn2dup = tf.keras.layers.UpSampling2D(size=(1, 2), interpolation="nearest")(xrnn2d)
xrnn4dup = tf.keras.layers.UpSampling2D(size=(1, 4), interpolation="nearest")(xrnn4d)
xrnn2dup = tf.keras.layers.Reshape(target_shape=(xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
xrnn4dup = tf.keras.layers.Reshape(target_shape=(xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
addition = tf.keras.layers.Add()([xrnnorg, xrnn2dup, xrnn4dup])
addition_rnn = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(addition)
out = tf.keras.layers.Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn)
out = tf.keras.layers.BatchNormalization(name="bn9")(out)
out = tf.keras.layers.Activation("relu", name="relu9")(out)
#out = tf.keras.layers.Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)
out = tf.keras.layers.Dense(
n_classes, activation="softmax", name="dense2"
)(out)
# Add CTC layer for calculating CTC loss at each step.
output = CTCLayer(name="ctc_loss")(labels, out)
model = tf.keras.models.Model(inputs=[input_img, labels], outputs=output, name="handwriting_recognizer")
return model

115
src/eynollah/training/train.py
View file

@ -15,10 +15,13 @@ from eynollah.training.models import (
resnet50_classifier,
resnet50_unet,
vit_resnet50_unet,
vit_resnet50_unet_transformer_before_cnn
vit_resnet50_unet_transformer_before_cnn,
cnn_rnn_ocr_model
)
from eynollah.training.utils import (
data_gen,
data_gen_ocr,
return_multiplier_based_on_augmnentations,
generate_arrays_from_folder_reading_order,
generate_data_from_folder_evaluation,
generate_data_from_folder_training,
@ -36,6 +39,7 @@ from tensorflow.keras.models import load_model
from tqdm import tqdm
from sklearn.metrics import f1_score
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.layers import StringLookup
import numpy as np
import cv2
@ -62,6 +66,7 @@ class SaveWeightsAfterSteps(Callback):
print(f"saved model as steps {self.step_count} to {save_file}")
def configuration():
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
@ -89,6 +94,7 @@ def config_params():
input_width = 224 * 1 # Width of model's input in pixels.
weight_decay = 1e-6 # Weight decay of l2 regularization of model layers.
n_batch = 1 # Number of batches at each iteration.
max_len = None # max len for ocr output.
learning_rate = 1e-4 # Set the learning rate.
patches = False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false.
augmentation = False # To apply any kind of augmentation, this parameter must be set to true.
@ -101,6 +107,20 @@ def config_params():
degrading = False # If true, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" in config_params.json.
brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
image_inversion = False # If true, and if the binarized images are avilable the image inevrsion will be applied.
white_noise_strap = False # If true, white noise will be applied on some straps on the textline image.
textline_skewing = False # If true, textline images will be skewed for augmentation.
textline_skewing_bin = False # If true, textline image skewing augmentation for binarized images will be applied if already are available.
textline_left_in_depth = False # If true, left side of textline image will be displayed in depth.
textline_left_in_depth_bin = False # If true, left side of textline binarized image (if available) will be displayed in depth.
textline_right_in_depth = False # If true, right side of textline image will be displayed in depth.
textline_right_in_depth_bin = False # If true, right side of textline binarized image (if available) will be displayed in depth.
textline_up_in_depth = False # If true, upper side of textline image will be displayed in depth.
textline_up_in_depth_bin = False # If true, upper side of textline binarized image (if available) will be displayed in depth.
textline_down_in_depth = False # If true, lower side of textline image will be displayed in depth.
textline_down_in_depth_bin = False # If true, lower side of textline binarized image (if available) will be displayed in depth.
pepper_bin_aug = False # If true, pepper noise will be added to textline binarized image (if available).
pepper_aug = False # If true, pepper noise will be added to textline image.
adding_rgb_background = False
adding_rgb_foreground = False
add_red_textlines = False
@ -111,14 +131,21 @@ def config_params():
pretraining = False # Set to true to load pretrained weights of ResNet50 encoder.
scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image.
scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image.
bin_deg = False # If true, a combination of degrading and binarization will be applied to the image.
rotation = False # If true, a 90 degree rotation will be implemeneted.
color_padding_rotation = False # If true, rotation and padding will be implemeneted.
rotation_not_90 = False # If true rotation based on provided angles with thetha will be implemeneted.
scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image.
scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
thetha = None # Rotate image by these angles for augmentation.
shuffle_indexes = None
thetha_padd = None # List of angles used for rotation alongside padding
shuffle_indexes = None # List of shuffling indexes like [[0,2,1], [1,2,0], [1,0,2]]
pepper_indexes = None # List of pepper noise indexes like [0.01, 0.005]
white_padds = None # List of padding size in the case of white padding
skewing_amplitudes = None # List of skewing augmentation amplitudes like [5, 8]
blur_k = None # Blur image for augmentation.
scales = None # Scale patches for augmentation.
padd_colors = None # padding colors. A list elements can be only white and black. like ["white", "black"] or only one of them ["white"]
degrade_scales = None # Degrade image for augmentation.
brightness = None # Brighten image for augmentation.
flip_index = None # Flip image for augmentation.
@ -145,6 +172,7 @@ def config_params():
number_of_backgrounds_per_image = 1
dir_rgb_backgrounds = None
dir_rgb_foregrounds = None
characters_txt_file = None # Directory of characters text file needed for cnn_rnn_ocr model training. The file ends with .txt
@ex.automain
def run(_config, n_classes, n_epochs, input_height,
@ -155,11 +183,14 @@ def run(_config, n_classes, n_epochs, input_height,
brightening, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_projection_dim,
thetha, thetha_padd, scaling_flip, continue_training, transformer_projection_dim,
transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
transformer_patchsize_x, transformer_patchsize_y,
transformer_num_patches_xy, backbone_type, save_interval, flip_index, dir_eval, dir_output,
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds,
dir_rgb_foregrounds, characters_txt_file, color_padding_rotation, bin_deg, image_inversion, white_noise_strap, textline_skewing, textline_skewing_bin,
textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin,
textline_down_in_depth, textline_down_in_depth_bin, pepper_bin_aug, pepper_aug, padd_colors, pepper_indexes, white_padds, skewing_amplitudes, max_len):
if dir_rgb_backgrounds:
list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
@ -375,6 +406,82 @@ def run(_config, n_classes, n_epochs, input_height,
#os.system('rm -rf '+dir_eval_flowing)
#model.save(dir_output+'/'+'model'+'.h5')
elif task=="cnn-rnn-ocr":
dir_img, dir_lab = get_dirs_or_files(dir_train)
with open(characters_txt_file, 'r') as char_txt_f:
characters = json.load(char_txt_f)
AUTOTUNE = tf.data.AUTOTUNE
# Mapping characters to integers.
char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
# Mapping integers back to original characters.
##num_to_char = StringLookup(
##vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
##)
padding_token = len(characters) + 5
ls_files_images = os.listdir(dir_img)
n_classes = len(char_to_num.get_vocabulary()) + 2
if continue_training:
model = load_model(dir_of_start_model)
else:
index_start = 0
model = cnn_rnn_ocr_model(image_height=input_height, image_width=input_width, n_classes=n_classes, max_seq=max_len)
print(model.summary())
aug_multip = return_multiplier_based_on_augmnentations(augmentation, color_padding_rotation, rotation_not_90, blur_aug, degrading, bin_deg,
brightening, padding_white, adding_rgb_foreground, adding_rgb_background, binarization,
image_inversion, channels_shuffling, add_red_textlines, white_noise_strap, textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin, pepper_bin_aug, pepper_aug, degrade_scales, number_of_backgrounds_per_image, thetha, thetha_padd, brightness, padd_colors, shuffle_indexes, pepper_indexes, skewing_amplitudes, blur_k, white_padds)
len_dataset = aug_multip*len(ls_files_images)
train_ds = data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir_train, ls_files_images,
augmentation, color_padding_rotation, rotation_not_90, blur_aug, degrading, bin_deg, brightening, padding_white,
adding_rgb_foreground, adding_rgb_background, binarization, image_inversion, channels_shuffling, add_red_textlines, white_noise_strap,
textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth,
textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin,
pepper_bin_aug, pepper_aug, degrade_scales, number_of_backgrounds_per_image, thetha, thetha_padd, brightness, padd_colors,
shuffle_indexes, pepper_indexes, skewing_amplitudes, blur_k, char_to_num, list_all_possible_background_images, list_all_possible_foreground_rgbs,
dir_rgb_backgrounds, dir_rgb_foregrounds, white_padds, dir_img_bin)
initial_learning_rate = 1e-4
decay_steps = int (n_epochs * ( len_dataset / n_batch ))
alpha = 0.01
lr_schedule = 1e-4#tf.keras.optimizers.schedules.CosineDecay(initial_learning_rate, decay_steps, alpha)
opt = tf.keras.optimizers.Adam(learning_rate=learning_rate)#1e-4)#(lr_schedule)
model.compile(optimizer=opt)
if save_interval:
save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
for i in tqdm(range(index_start, n_epochs + index_start)):
if save_interval:
model.fit(
train_ds,
steps_per_epoch=len_dataset / n_batch,
epochs=1,
callbacks=[save_weights_callback]
)
else:
model.fit(
train_ds,
steps_per_epoch=len_dataset / n_batch,
epochs=1
)
if i >=0:
model.save( os.path.join(dir_output,'model_'+str(i) ))
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON
elif task=='classification':
configuration()
model = resnet50_classifier(n_classes, input_height, input_width, weight_decay, pretraining)

894
src/eynollah/training/utils.py
View file

@ -9,8 +9,238 @@ from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
from tqdm import tqdm
import imutils
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from PIL import Image, ImageEnhance
from PIL import Image, ImageFile, ImageEnhance
ImageFile.LOAD_TRUNCATED_IMAGES = True
def vectorize_label(label, char_to_num, padding_token, max_len):
label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
length = tf.shape(label)[0]
pad_amount = max_len - length
label = tf.pad(label, paddings=[[0, pad_amount]], constant_values=padding_token)
return label
def scale_padd_image_for_ocr(img, height, width):
ratio = height /float(img.shape[0])
w_ratio = int(ratio * img.shape[1])
if w_ratio<=width:
width_new = w_ratio
else:
width_new = width
img_res= resize_image (img, height, width_new)
img_fin = np.ones((height, width, 3))*255
img_fin[:,:width_new,:] = img_res[:,:,:]
return img_fin
def add_salt_and_pepper_noise(img, salt_prob, pepper_prob):
"""
Add salt-and-pepper noise to an image.
Parameters:
image: ndarray
Input image.
salt_prob: float
Probability of salt noise.
pepper_prob: float
Probability of pepper noise.
Returns:
noisy_image: ndarray
Image with salt-and-pepper noise.
"""
# Make a copy of the image
noisy_image = np.copy(img)
# Generate random noise
total_pixels = img.size
num_salt = int(salt_prob * total_pixels)
num_pepper = int(pepper_prob * total_pixels)
# Add salt noise
coords = [np.random.randint(0, i - 1, num_salt) for i in img.shape[:2]]
noisy_image[coords[0], coords[1]] = 255 # white pixels
# Add pepper noise
coords = [np.random.randint(0, i - 1, num_pepper) for i in img.shape[:2]]
noisy_image[coords[0], coords[1]] = 0 # black pixels
return noisy_image
def invert_image(img):
img_inv = 255 - img
return img_inv
def return_image_with_strapped_white_noises(img):
img_w_noised = np.copy(img)
img_h, img_width = img.shape[0], img.shape[1]
n = 9
p = 0.3
num_windows = np.random.binomial(n, p, 1)[0]
if num_windows<1:
num_windows = 1
loc_of_windows = np.random.uniform(0,img_width,num_windows).astype(np.int64)
width_windows = np.random.uniform(10,50,num_windows).astype(np.int64)
for i, loc in enumerate(loc_of_windows):
noise = np.random.normal(0, 50, (img_h, width_windows[i], 3))
try:
img_w_noised[:, loc:loc+width_windows[i], : ] = noise[:,:,:]
except:
pass
return img_w_noised
def do_padding_for_ocr(img, percent_height, padding_color):
padding_size = int( img.shape[0]*percent_height/2. )
height_new = img.shape[0] + 2*padding_size
width_new = img.shape[1] + 2*padding_size
h_start = padding_size
w_start = padding_size
if padding_color == 'white':
img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
if padding_color == 'black':
img_new = np.zeros((height_new, width_new, img.shape[2])).astype(float)
img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
return img_new
def do_deskewing(img, amplitude):
height, width = img.shape[:2]
# Generate sinusoidal wave distortion with reduced amplitude
#amplitude = 8 # 5 # Reduce the amplitude for less curvature
frequency = 300 # Increase frequency to stretch the curve
x_indices = np.tile(np.arange(width), (height, 1))
y_indices = np.arange(height).reshape(-1, 1) + amplitude * np.sin(2 * np.pi * x_indices / frequency)
# Convert indices to float32 for remapping
map_x = x_indices.astype(np.float32)
map_y = y_indices.astype(np.float32)
# Apply the remap to create the curve
curved_image = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
return curved_image
def do_left_in_depth(img):
height, width = img.shape[:2]
# Define the original corner points of the image
src_points = np.float32([
[0, 0], # Top-left corner
[width, 0], # Top-right corner
[0, height], # Bottom-left corner
[width, height] # Bottom-right corner
])
# Define the new corner points for a subtle right-to-left tilt
dst_points = np.float32([
[2, 13], # Slight inward shift for top-left
[width, 0], # Slight downward shift for top-right
[2, height-13], # Slight inward shift for bottom-left
[width, height] # Slight upward shift for bottom-right
])
# Compute the perspective transformation matrix
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
# Apply the perspective warp
warped_image = cv2.warpPerspective(img, matrix, (width, height))
return warped_image
def do_right_in_depth(img):
height, width = img.shape[:2]
# Define the original corner points of the image
src_points = np.float32([
[0, 0], # Top-left corner
[width, 0], # Top-right corner
[0, height], # Bottom-left corner
[width, height] # Bottom-right corner
])
# Define the new corner points for a subtle right-to-left tilt
dst_points = np.float32([
[0, 0], # Slight inward shift for top-left
[width, 13], # Slight downward shift for top-right
[0, height], # Slight inward shift for bottom-left
[width, height - 13] # Slight upward shift for bottom-right
])
# Compute the perspective transformation matrix
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
# Apply the perspective warp
warped_image = cv2.warpPerspective(img, matrix, (width, height))
return warped_image
def do_up_in_depth(img):
# Get the dimensions of the image
height, width = img.shape[:2]
# Define the original corner points of the image
src_points = np.float32([
[0, 0], # Top-left corner
[width, 0], # Top-right corner
[0, height], # Bottom-left corner
[width, height] # Bottom-right corner
])
# Define the new corner points to simulate a tilted perspective
# Make the top part appear closer and the bottom part farther
dst_points = np.float32([
[50, 0], # Top-left moved inward
[width - 50, 0], # Top-right moved inward
[0, height], # Bottom-left remains the same
[width, height] # Bottom-right remains the same
])
# Compute the perspective transformation matrix
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
# Apply the perspective warp
warped_image = cv2.warpPerspective(img, matrix, (width, height))
return warped_image
def do_down_in_depth(img):
# Get the dimensions of the image
height, width = img.shape[:2]
# Define the original corner points of the image
src_points = np.float32([
[0, 0], # Top-left corner
[width, 0], # Top-right corner
[0, height], # Bottom-left corner
[width, height] # Bottom-right corner
])
# Define the new corner points to simulate a tilted perspective
# Make the top part appear closer and the bottom part farther
dst_points = np.float32([
[0, 0], # Top-left moved inward
[width, 0], # Top-right moved inward
[50, height], # Bottom-left remains the same
[width - 50, height] # Bottom-right remains the same
])
# Compute the perspective transformation matrix
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
# Apply the perspective warp
warped_image = cv2.warpPerspective(img, matrix, (width, height))
return warped_image
def return_shuffled_channels(img, channels_order):
@ -208,7 +438,7 @@ def generate_data_from_folder_evaluation(path_classes, height, width, n_classes,
return ret_x/255., ret_y
def generate_data_from_folder_training(path_classes, batchsize, height, width, n_classes, list_classes):
def generate_data_from_folder_training(path_classes, n_batch, height, width, n_classes, list_classes):
#sub_classes = os.listdir(path_classes)
#n_classes = len(sub_classes)
@ -234,8 +464,8 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n
shuffled_labels = np.array(labels)[ids]
shuffled_files = np.array(all_imgs)[ids]
categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ]
ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
ret_x= np.zeros((n_batch, height,width, 3)).astype(np.int16)
ret_y= np.zeros((n_batch, n_classes)).astype(np.int16)
batchcount = 0
while True:
for i in range(len(shuffled_files)):
@ -259,11 +489,11 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n
batchcount+=1
if batchcount>=batchsize:
if batchcount>=n_batch:
ret_x = ret_x/255.
yield ret_x, ret_y
ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
ret_x= np.zeros((n_batch, height,width, 3)).astype(np.int16)
ret_y= np.zeros((n_batch, n_classes)).astype(np.int16)
batchcount = 0
def do_brightening(img_in_dir, factor):
@ -428,10 +658,10 @@ def IoU(Yi, y_predi):
#print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes, thetha, augmentation=False):
def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, n_batch, height, width, n_classes, thetha, augmentation=False):
all_labels_files = os.listdir(classes_file_dir)
ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
ret_x= np.zeros((n_batch, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((n_batch, n_classes)).astype(np.int16)
batchcount = 0
while True:
for i in all_labels_files:
@ -446,10 +676,10 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
ret_y[batchcount, :] = label_class
batchcount+=1
if batchcount>=batchsize:
if batchcount>=n_batch:
yield ret_x, ret_y
ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
ret_x= np.zeros((n_batch, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((n_batch, n_classes)).astype(np.int16)
batchcount = 0
if augmentation:
@ -464,10 +694,10 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
ret_y[batchcount, :] = label_class
batchcount+=1
if batchcount>=batchsize:
if batchcount>=n_batch:
yield ret_x, ret_y
ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
ret_x= np.zeros((n_batch, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((n_batch, n_classes)).astype(np.int16)
batchcount = 0
def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
@ -1055,3 +1285,635 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
cv2.flip( cv2.imread(dir_img + '/' + im), f_i),
cv2.flip(cv2.imread(dir_of_label_file), f_i),
input_height, input_width, indexer=indexer, scaler=sc_ind)
def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir_train, ls_files_images,
augmentation, color_padding_rotation, rotation_not_90, blur_aug, degrading, bin_deg, brightening, padding_white,
adding_rgb_foreground, adding_rgb_background, binarization, image_inversion, channels_shuffling, add_red_textlines, white_noise_strap,
textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth,
textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin,
pepper_bin_aug, pepper_aug, degrade_scales, number_of_backgrounds_per_image, thetha, thetha_padd, brightness, padd_colors,
shuffle_indexes, pepper_indexes, skewing_amplitudes, blur_k, char_to_num, list_all_possible_background_images,
list_all_possible_foreground_rgbs, dir_rgb_backgrounds, dir_rgb_foregrounds, white_padds, dir_img_bin=None):
random.shuffle(ls_files_images)
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
while True:
for i in ls_files_images:
f_name = i.split('.')[0]
txt_inp = open(os.path.join(dir_train, "labels/"+f_name+'.txt'),'r').read().split('\n')[0]
img = cv2.imread(os.path.join(dir_train, "images/"+i) )
if dir_img_bin:
img_bin_corr = cv2.imread(os.path.join(dir_img_bin, f_name+'.png') )
else:
img_bin_corr = None
if augmentation:
img_out = scale_padd_image_for_ocr(img, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if color_padding_rotation:
for index, thetha_ind in enumerate(thetha_padd):
for padd_col in padd_colors:
img_out = rotation_not_90_func_single_image(do_padding_for_ocr(img, 1.2, padd_col), thetha_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if rotation_not_90:
for index, thetha_ind in enumerate(thetha):
img_out = rotation_not_90_func_single_image(img, thetha_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if blur_aug:
for index, blur_type in enumerate(blur_k):
img_out = bluring(img, blur_type)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if degrading:
for index, deg_scale_ind in enumerate(degrade_scales):
try:
img_out = do_degrading(img, deg_scale_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if bin_deg:
for index, deg_scale_ind in enumerate(degrade_scales):
try:
img_out = do_degrading(img_bin_corr, deg_scale_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if brightening:
for index, bright_scale_ind in enumerate(brightness):
try:
img_out = do_brightening(dir_img, bright_scale_ind)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if padding_white:
for index, padding_size in enumerate(white_padds):
for padd_col in padd_colors:
img_out = do_padding_for_ocr(img, padding_size, padd_col)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if adding_rgb_foreground:
for i_n in range(number_of_backgrounds_per_image):
background_image_chosen_name = random.choice(list_all_possible_background_images)
foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs)
img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name)
img_with_overlayed_background = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen)
img_out = scale_padd_image_for_ocr(img_with_overlayed_background, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if adding_rgb_background:
for i_n in range(number_of_backgrounds_per_image):
background_image_chosen_name = random.choice(list_all_possible_background_images)
img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
img_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_chosen)
img_out = scale_padd_image_for_ocr(img_with_overlayed_background, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if binarization:
img_out = scale_padd_image_for_ocr(img_bin_corr, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if image_inversion:
img_out = invert_image(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :, :, :] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x = np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y = np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if channels_shuffling:
for shuffle_index in shuffle_indexes:
img_out = return_shuffled_channels(img, shuffle_index)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if add_red_textlines:
img_red_context = return_image_with_red_elements(img, img_bin_corr)
img_out = scale_padd_image_for_ocr(img_red_context, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if white_noise_strap:
img_out = return_image_with_strapped_white_noises(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_skewing:
for index, des_scale_ind in enumerate(skewing_amplitudes):
try:
img_out = do_deskewing(img, des_scale_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_skewing_bin:
for index, des_scale_ind in enumerate(skewing_amplitudes):
try:
img_out = do_deskewing(img_bin_corr, des_scale_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_left_in_depth:
try:
img_out = do_left_in_depth(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_left_in_depth_bin:
try:
img_out = do_left_in_depth(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_right_in_depth:
try:
img_out = do_right_in_depth(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_right_in_depth_bin:
try:
img_out = do_right_in_depth(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_up_in_depth:
try:
img_out = do_up_in_depth(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_up_in_depth_bin:
try:
img_out = do_up_in_depth(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_down_in_depth:
try:
img_out = do_down_in_depth(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if textline_down_in_depth_bin:
try:
img_out = do_down_in_depth(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if pepper_bin_aug:
for index, pepper_ind in enumerate(pepper_indexes):
img_out = add_salt_and_pepper_noise(img_bin_corr, pepper_ind, pepper_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
if pepper_aug:
for index, pepper_ind in enumerate(pepper_indexes):
img_out = add_salt_and_pepper_noise(img, pepper_ind, pepper_ind)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
else:
img_out = scale_padd_image_for_ocr(img, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
batchcount+=1
if batchcount>=n_batch:
ret_x = ret_x/255.
yield {"image": ret_x, "label": ret_y}
ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32)
ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token
batchcount = 0
def return_multiplier_based_on_augmnentations(augmentation, color_padding_rotation, rotation_not_90, blur_aug,
degrading, bin_deg, brightening, padding_white,adding_rgb_foreground, adding_rgb_background, binarization, image_inversion, channels_shuffling, add_red_textlines, white_noise_strap,
textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin, pepper_bin_aug, pepper_aug, degrade_scales, number_of_backgrounds_per_image, thetha, thetha_padd, brightness, padd_colors, shuffle_indexes, pepper_indexes, skewing_amplitudes, blur_k, white_padds):
aug_multip = 1
if augmentation:
if binarization:
aug_multip = aug_multip + 1
if image_inversion:
aug_multip = aug_multip + 1
if add_red_textlines:
aug_multip = aug_multip + 1
if white_noise_strap:
aug_multip = aug_multip + 1
if textline_right_in_depth:
aug_multip = aug_multip + 1
if textline_left_in_depth:
aug_multip = aug_multip + 1
if textline_up_in_depth:
aug_multip = aug_multip + 1
if textline_down_in_depth:
aug_multip = aug_multip + 1
if textline_right_in_depth_bin:
aug_multip = aug_multip + 1
if textline_left_in_depth_bin:
aug_multip = aug_multip + 1
if textline_up_in_depth_bin:
aug_multip = aug_multip + 1
if textline_down_in_depth_bin:
aug_multip = aug_multip + 1
if adding_rgb_foreground:
aug_multip = aug_multip + number_of_backgrounds_per_image
if adding_rgb_background:
aug_multip = aug_multip + number_of_backgrounds_per_image
if bin_deg:
aug_multip = aug_multip + len(degrade_scales)
if degrading:
aug_multip = aug_multip + len(degrade_scales)
if rotation_not_90:
aug_multip = aug_multip + len(thetha)
if textline_skewing:
aug_multip = aug_multip + len(skewing_amplitudes)
if textline_skewing_bin:
aug_multip = aug_multip + len(skewing_amplitudes)
if color_padding_rotation:
aug_multip = aug_multip + len(thetha_padd)*len(padd_colors)
if channels_shuffling:
aug_multip = aug_multip + len(shuffle_indexes)
if blur_aug:
aug_multip = aug_multip + len(blur_k)
if brightening:
aug_multip = aug_multip + len(brightness)
if padding_white:
aug_multip = aug_multip + len(white_padds)*len(padd_colors)
if pepper_aug:
aug_multip = aug_multip + len(pepper_indexes)
if pepper_bin_aug:
aug_multip = aug_multip + len(pepper_indexes)
return aug_multip

8
src/eynollah/utils/__init__.py
View file

@ -19,7 +19,6 @@ from .contour import (contours_in_same_horizon,
find_new_features_of_contours,
return_contours_of_image,
return_parent_contours)
def pairwise(iterable):
# pairwise('ABCDEFG') → AB BC CD DE EF FG
@ -393,7 +392,12 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8):
z = gaussian_filter1d(regions_without_separators_0, sigma_)
return np.std(z)
def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8):
def find_num_col(
regions_without_separators,
num_col_classifier,
tables,
multiplier=3.8,
):
if not regions_without_separators.any():
return 0, []
#plt.imshow(regions_without_separators)

2
src/eynollah/utils/contour.py
View file

@ -357,7 +357,7 @@ def join_polygons(polygons: Sequence[Polygon], scale=20) -> Polygon:
assert jointp.geom_type == 'Polygon', jointp.wkt
# follow-up calculations will necessarily be integer;
# so anticipate rounding here and then ensure validity
jointp2 = set_precision(jointp, 1.0)
jointp2 = set_precision(jointp, 1.0, mode="keep_collapsed")
if jointp2.geom_type != 'Polygon' or not jointp2.is_valid:
jointp2 = Polygon(np.round(jointp.exterior.coords))
jointp2 = make_valid(jointp2)

3
src/eynollah/utils/drop_capitals.py
View file

@ -19,7 +19,6 @@ def adhere_drop_capital_region_into_corresponding_textline(
all_found_textline_polygons_h,
kernel=None,
curved_line=False,
textline_light=False,
):
# print(np.shape(all_found_textline_polygons),np.shape(all_found_textline_polygons[3]),'all_found_textline_polygonsshape')
# print(all_found_textline_polygons[3])
@ -79,7 +78,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
# region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
# print(np.unique(img_con_all_copy[:,:,0]))
if curved_line or textline_light:
if curved_line:
if len(region_with_intersected_drop) > 1:
sum_pixels_of_intersection = []

16
src/eynollah/utils/font.py Normal file
View file

@ -0,0 +1,16 @@
# cannot use importlib.resources until we move to 3.9+ forimportlib.resources.files
import sys
from PIL import ImageFont
if sys.version_info < (3, 10):
import importlib_resources
else:
import importlib.resources as importlib_resources
def get_font():
#font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
font = importlib_resources.files(__package__) / "../Charis-Regular.ttf"
with importlib_resources.as_file(font) as font:
return ImageFont.truetype(font=font, size=40)

107
src/eynollah/utils/marginals.py
View file

@ -6,7 +6,7 @@ from .contour import find_new_features_of_contours, return_contours_of_intereste
from .resize import resize_image
from .rotate import rotate_image
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_version=False, kernel=None):
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
mask_marginals=mask_marginals.astype(np.uint8)
@ -27,9 +27,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
if light_version:
kernel_hor = np.ones((1, 5), dtype=np.uint8)
text_with_lines = cv2.erode(text_with_lines,kernel_hor,iterations=6)
kernel_hor = np.ones((1, 5), dtype=np.uint8)
text_with_lines = cv2.erode(text_with_lines,kernel_hor,iterations=6)
text_with_lines_y=text_with_lines.sum(axis=0)
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
@ -43,10 +42,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
min_textline_thickness=20
else:
if light_version:
min_textline_thickness=45
else:
min_textline_thickness=40
min_textline_thickness=45
if thickness_along_y_percent>=14:
@ -128,92 +124,39 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
if max_point_of_right_marginal>=text_regions.shape[1]:
max_point_of_right_marginal=text_regions.shape[1]-1
if light_version:
text_regions_org = np.copy(text_regions)
text_regions[text_regions[:,:]==1]=4
pixel_img=4
min_area_text=0.00001
polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals,1,min_area_text)
polygon_mask_marginals_rotated = polygon_mask_marginals_rotated[0]
text_regions_org = np.copy(text_regions)
text_regions[text_regions[:,:]==1]=4
pixel_img=4
min_area_text=0.00001
polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals,1,min_area_text)
polygon_mask_marginals_rotated = polygon_mask_marginals_rotated[0]
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals)
text_regions[(text_regions[:,:]==4)]=1
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text=[]
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
for i in range(len(cx_text_only)):
results = cv2.pointPolygonTest(polygon_mask_marginals_rotated, (cx_text_only[i], cy_text_only[i]), False)
for i in range(len(cx_text_only)):
results = cv2.pointPolygonTest(polygon_mask_marginals_rotated, (cx_text_only[i], cy_text_only[i]), False)
if results == -1:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if results == -1:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
text_regions_org=cv2.fillPoly(text_regions_org, pts =marginlas_should_be_main_text, color=(4,4))
text_regions = np.copy(text_regions_org)
text_regions_org=cv2.fillPoly(text_regions_org, pts =marginlas_should_be_main_text, color=(4,4))
text_regions = np.copy(text_regions_org)
else:
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
pixel_img=4
min_area_text=0.00001
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals)
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
for i in range(len(cx_text_only)):
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
if x_width_mar>16 and y_height_mar/x_width_mar<18:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i]<(mid_point-one_third_left):
x_min_marginals_left_new=x_min_text_only[i]
if len(x_min_marginals_left)==0:
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
else:
x_min_marginals_right_new=x_min_text_only[i]
if len(x_min_marginals_right)==0:
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
if len(x_min_marginals_left)==0:
x_min_marginals_left=[0]
if len(x_min_marginals_right)==0:
x_min_marginals_right=[text_regions.shape[1]-1]
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4

81
src/eynollah/utils/separate_lines.py
View file

@ -5,8 +5,6 @@ import numpy as np
import cv2
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
from multiprocessing import Process, Queue, cpu_count
from multiprocessing import Pool
from .rotate import rotate_image
from .resize import resize_image
from .contour import (
@ -20,9 +18,7 @@ from .contour import (
from .shm import share_ndarray, wrap_ndarray_shared
from . import (
find_num_col_deskew,
crop_image_inside_box,
box2rect,
box2slice,
)
def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
@ -1590,65 +1586,6 @@ def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, map=map
var = 0
return angle, var
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
def do_work_of_slopes_new(
box_text, contour, contour_par,
textline_mask_tot_ea=None, slope_deskew=0.0,
logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None
):
if KERNEL is None:
KERNEL = np.ones((5, 5), np.uint8)
if logger is None:
logger = getLogger(__package__)
logger.debug('enter do_work_of_slopes_new')
x, y, w, h = box_text
crop_coor = box2rect(box_text)
mask_textline = np.zeros(textline_mask_tot_ea.shape)
mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1))
all_text_region_raw = textline_mask_tot_ea * mask_textline
all_text_region_raw = all_text_region_raw[y: y + h, x: x + w].astype(np.uint8)
img_int_p = all_text_region_raw[:,:]
img_int_p = cv2.erode(img_int_p, KERNEL, iterations=2)
if not np.prod(img_int_p.shape) or img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
slope = 0
slope_for_all = slope_deskew
all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w]
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, 0)
else:
try:
textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con,
hierarchy,
max_area=1, min_area=0.00008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) if len(textline_con_fil) > 1 else np.NaN
if np.isnan(y_diff_mean):
slope_for_all = MAX_SLOPE
else:
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, logger=logger, plotter=plotter)
if abs(slope_for_all) <= 0.5:
slope_for_all = slope_deskew
except:
logger.exception("cannot determine angle of contours")
slope_for_all = MAX_SLOPE
if slope_for_all == MAX_SLOPE:
slope_for_all = slope_deskew
slope = slope_for_all
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1))
all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].copy()
mask_only_con_region = mask_only_con_region[y: y + h, x: x + w]
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text)
return cnt_clean_rot, crop_coor, slope
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
@wrap_ndarray_shared(kw='mask_texts_only')
def do_work_of_slopes_new_curved(
@ -1748,7 +1685,7 @@ def do_work_of_slopes_new_curved(
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
def do_work_of_slopes_new_light(
box_text, contour, contour_par,
textline_mask_tot_ea=None, slope_deskew=0, textline_light=True,
textline_mask_tot_ea=None, slope_deskew=0,
logger=None
):
if logger is None:
@ -1765,16 +1702,10 @@ def do_work_of_slopes_new_light(
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1))
if textline_light:
all_text_region_raw = np.copy(textline_mask_tot_ea)
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(all_text_region_raw)
cnt_clean_rot = filter_contours_area_of_image(all_text_region_raw, cnt_clean_rot_raw, hir_on_cnt_clean_rot,
max_area=1, min_area=0.00001)
else:
all_text_region_raw = np.copy(textline_mask_tot_ea[y: y + h, x: x + w])
mask_only_con_region = mask_only_con_region[y: y + h, x: x + w]
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_deskew, contour_par, box_text)
all_text_region_raw = np.copy(textline_mask_tot_ea)
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(all_text_region_raw)
cnt_clean_rot = filter_contours_area_of_image(all_text_region_raw, cnt_clean_rot_raw, hir_on_cnt_clean_rot,
max_area=1, min_area=0.00001)
return cnt_clean_rot, crop_coor, slope_deskew

4
src/eynollah/utils/utils_ocr.py
View file

@ -128,6 +128,7 @@ def return_textlines_split_if_needed(textline_image, textline_image_bin=None):
return [image1, image2], None
else:
return None, None
def preprocess_and_resize_image_for_ocrcnn_model(img, image_height, image_width):
if img.shape[0]==0 or img.shape[1]==0:
img_fin = np.ones((image_height, image_width, 3))
@ -379,7 +380,6 @@ def return_rnn_cnn_ocr_of_given_textlines(image,
all_box_coord,
prediction_model,
b_s_ocr, num_to_char,
textline_light=False,
curved_line=False):
max_len = 512
padding_token = 299
@ -404,7 +404,7 @@ def return_rnn_cnn_ocr_of_given_textlines(image,
else:
for indexing2, ind_poly in enumerate(ind_poly_first):
cropped_lines_region_indexer.append(indexer_text_region)
if not (textline_light or curved_line):
if not curved_line:
ind_poly = copy.deepcopy(ind_poly)
box_ind = all_box_coord[indexing]

4
src/eynollah/utils/xml.py
View file

@ -88,3 +88,7 @@ def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region
order_of_texts.append(interest)
return order_of_texts, id_of_texts
def etree_namespace_for_element_tag(tag: str):
right = tag.find('}')
return tag[1:right]

146
src/eynollah/writer.py
View file

@ -2,15 +2,14 @@
# pylint: disable=import-error
from pathlib import Path
import os.path
import xml.etree.ElementTree as ET
from typing import Optional
import logging
from .utils.xml import create_page_xml, xml_reading_order
from .utils.counter import EynollahIdCounter
from ocrd_utils import getLogger
from ocrd_models.ocrd_page import (
BorderType,
CoordsType,
PcGtsType,
TextLineType,
TextEquivType,
TextRegionType,
@ -19,23 +18,21 @@ from ocrd_models.ocrd_page import (
SeparatorRegionType,
to_xml
)
import numpy as np
class EynollahXmlWriter:
def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None):
self.logger = getLogger('eynollah.writer')
def __init__(self, *, dir_out, image_filename, curved_line, pcgts=None):
self.logger = logging.getLogger('eynollah.writer')
self.counter = EynollahIdCounter()
self.dir_out = dir_out
self.image_filename = image_filename
self.output_filename = os.path.join(self.dir_out or "", self.image_filename_stem) + ".xml"
self.curved_line = curved_line
self.textline_light = textline_light
self.pcgts = pcgts
self.scale_x = None # XXX set outside __init__
self.scale_y = None # XXX set outside __init__
self.height_org = None # XXX set outside __init__
self.width_org = None # XXX set outside __init__
self.scale_x: Optional[float] = None # XXX set outside __init__
self.scale_y: Optional[float] = None # XXX set outside __init__
self.height_org: Optional[int] = None # XXX set outside __init__
self.width_org: Optional[int] = None # XXX set outside __init__
@property
def image_filename_stem(self):
@ -73,13 +70,9 @@ class EynollahXmlWriter:
point = point[0]
point_x = point[0] + page_coord[2]
point_y = point[1] + page_coord[0]
# FIXME: or actually... not self.textline_light and not self.curved_line or np.abs(slopes[region_idx]) > 45?
if not self.textline_light and not (self.curved_line and np.abs(slopes[region_idx]) <= 45):
point_x += region_bboxes[2]
point_y += region_bboxes[0]
point_x = max(0, int(point_x / self.scale_x))
point_y = max(0, int(point_y / self.scale_y))
points_co += str(point_x) + ',' + str(point_y) + ' '
points_co += f'{point_x},{point_y} '
coords.set_points(points_co[:-1])
def write_pagexml(self, pcgts):
@ -88,53 +81,94 @@ class EynollahXmlWriter:
f.write(to_xml(pcgts))
def build_pagexml_no_full_layout(
self, found_polygons_text_region,
page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons,
all_box_coord,
found_polygons_text_region_img,
found_polygons_marginals_left, found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left, all_box_coord_marginals_right,
slopes, slopes_marginals_left, slopes_marginals_right,
cont_page, polygons_seplines,
found_polygons_tables,
**kwargs):
self,
*,
found_polygons_text_region,
page_coord,
order_of_texts,
all_found_textline_polygons,
all_box_coord,
found_polygons_text_region_img,
found_polygons_marginals_left,
found_polygons_marginals_right,
all_found_textline_polygons_marginals_left,
all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left,
all_box_coord_marginals_right,
slopes,
slopes_marginals_left,
slopes_marginals_right,
cont_page,
polygons_seplines,
found_polygons_tables,
):
return self.build_pagexml_full_layout(
found_polygons_text_region, [],
page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons, [],
all_box_coord, [],
found_polygons_text_region_img, found_polygons_tables, [],
found_polygons_marginals_left, found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left, all_box_coord_marginals_right,
slopes, [], slopes_marginals_left, slopes_marginals_right,
cont_page, polygons_seplines,
**kwargs)
found_polygons_text_region=found_polygons_text_region,
found_polygons_text_region_h=[],
page_coord=page_coord,
order_of_texts=order_of_texts,
all_found_textline_polygons=all_found_textline_polygons,
all_found_textline_polygons_h=[],
all_box_coord=all_box_coord,
all_box_coord_h=[],
found_polygons_text_region_img=found_polygons_text_region_img,
found_polygons_tables=found_polygons_tables,
found_polygons_drop_capitals=[],
found_polygons_marginals_left=found_polygons_marginals_left,
found_polygons_marginals_right=found_polygons_marginals_right,
all_found_textline_polygons_marginals_left=all_found_textline_polygons_marginals_left,
all_found_textline_polygons_marginals_right=all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left=all_box_coord_marginals_left,
all_box_coord_marginals_right=all_box_coord_marginals_right,
slopes=slopes,
slopes_h=[],
slopes_marginals_left=slopes_marginals_left,
slopes_marginals_right=slopes_marginals_right,
cont_page=cont_page,
polygons_seplines=polygons_seplines,
)
def build_pagexml_full_layout(
self,
found_polygons_text_region, found_polygons_text_region_h,
page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons, all_found_textline_polygons_h,
all_box_coord, all_box_coord_h,
found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals,
found_polygons_marginals_left,found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left, all_box_coord_marginals_right,
slopes, slopes_h, slopes_marginals_left, slopes_marginals_right,
cont_page, polygons_seplines,
ocr_all_textlines=None, ocr_all_textlines_h=None,
ocr_all_textlines_marginals_left=None, ocr_all_textlines_marginals_right=None,
ocr_all_textlines_drop=None,
conf_contours_textregions=None, conf_contours_textregions_h=None,
skip_layout_reading_order=False):
self,
*,
found_polygons_text_region,
found_polygons_text_region_h,
page_coord,
order_of_texts,
all_found_textline_polygons,
all_found_textline_polygons_h,
all_box_coord,
all_box_coord_h,
found_polygons_text_region_img,
found_polygons_tables,
found_polygons_drop_capitals,
found_polygons_marginals_left,
found_polygons_marginals_right,
all_found_textline_polygons_marginals_left,
all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left,
all_box_coord_marginals_right,
slopes,
slopes_h,
slopes_marginals_left,
slopes_marginals_right,
cont_page,
polygons_seplines,
ocr_all_textlines=None,
ocr_all_textlines_h=None,
ocr_all_textlines_marginals_left=None,
ocr_all_textlines_marginals_right=None,
ocr_all_textlines_drop=None,
conf_contours_textregions=None,
conf_contours_textregions_h=None,
skip_layout_reading_order=False,
):
self.logger.debug('enter build_pagexml')
# create the file structure
pcgts = self.pcgts if self.pcgts else create_page_xml(self.image_filename, self.height_org, self.width_org)
page = pcgts.get_Page()
assert page
page.set_Border(BorderType(Coords=CoordsType(points=self.calculate_page_coords(cont_page))))
counter = EynollahIdCounter()
@ -152,6 +186,7 @@ class EynollahXmlWriter:
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord,
skip_layout_reading_order))
)
assert textregion.Coords
if conf_contours_textregions:
textregion.Coords.set_conf(conf_contours_textregions[mm])
page.add_TextRegion(textregion)
@ -168,6 +203,7 @@ class EynollahXmlWriter:
id=counter.next_region_id, type_='heading',
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))
)
assert textregion.Coords
if conf_contours_textregions_h:
textregion.Coords.set_conf(conf_contours_textregions_h[mm])
page.add_TextRegion(textregion)