mirror of
https://github.com/qurator-spk/eynollah.git
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resolving merge conflict of machine based reading order and extracting only images branches
This commit is contained in:
vahidrezanezhad
commit
f7e5fb917f
10 changed files with 3578 additions and 888 deletions
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@ -1,8 +0,0 @@
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# ocrd includes opencv, numpy, shapely, click
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ocrd >= 2.23.3
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numpy <1.24.0
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scikit-learn >= 0.23.2
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tensorflow == 2.12.1
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imutils >= 0.5.3
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matplotlib
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setuptools >= 50
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@ -2,15 +2,95 @@ import sys
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import click
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from ocrd_utils import initLogging, setOverrideLogLevel
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from eynollah.eynollah import Eynollah
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from eynollah.eynollah import Eynollah
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from eynollah.sbb_binarize import SbbBinarizer
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@click.group()
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def main():
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pass
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@click.command()
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@main.command()
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@click.option(
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"--dir_xml",
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"-dx",
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help="directory of GT page-xml files",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--dir_out_modal_image",
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"-domi",
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help="directory where ground truth images would be written",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--dir_out_classes",
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"-docl",
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help="directory where ground truth classes would be written",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--input_height",
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"-ih",
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help="input height",
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)
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@click.option(
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"--input_width",
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"-iw",
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help="input width",
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)
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@click.option(
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"--min_area_size",
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"-min",
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help="min area size of regions considered for reading order training.",
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)
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def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size):
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xml_files_ind = os.listdir(dir_xml)
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@main.command()
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@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
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@click.option('--model_dir', '-m', type=click.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
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@click.argument('input_image')
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@click.argument('output_image')
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@click.option(
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"--dir_in",
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"-di",
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help="directory of images",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--dir_out",
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"-do",
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help="directory where the binarized images will be written",
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type=click.Path(exists=True, file_okay=False),
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)
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def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out):
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if not dir_out and (dir_in):
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print("Error: You used -di but did not set -do")
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sys.exit(1)
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elif dir_out and not (dir_in):
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print("Error: You used -do to write out binarized images but have not set -di")
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sys.exit(1)
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SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image, dir_in=dir_in, dir_out=dir_out)
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@main.command()
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@click.option(
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"--image",
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"-i",
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help="image filename",
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type=click.Path(exists=True, dir_okay=False),
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)
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@click.option(
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"--out",
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"-o",
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@ -140,36 +220,41 @@ from eynollah.eynollah import Eynollah
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help="if this parameter set to true, this tool would ignore page extraction",
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)
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@click.option(
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"--log-level",
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"--reading_order_machine_based/--heuristic_reading_order",
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"-romb/-hro",
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is_flag=True,
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help="if this parameter set to true, this tool would apply machine based reading order detection",
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)
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@click.option(
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"--do_ocr",
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"-ocr/-noocr",
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is_flag=True,
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help="if this parameter set to true, this tool will try to do ocr",
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)
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@click.option(
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"--num_col_upper",
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"-ncu",
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help="lower limit of columns in document image",
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)
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@click.option(
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"--num_col_lower",
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"-ncl",
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help="upper limit of columns in document image",
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)
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@click.option(
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"--skip_layout_and_reading_order",
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"-slro/-noslro",
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is_flag=True,
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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.",
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)
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@click.option(
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"--log_level",
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"-l",
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type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
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help="Override log level globally to this",
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)
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def main(
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image,
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out,
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dir_in,
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model,
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save_images,
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save_layout,
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save_deskewed,
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save_all,
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extract_only_images,
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save_page,
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enable_plotting,
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allow_enhancement,
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curved_line,
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textline_light,
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full_layout,
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tables,
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right2left,
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input_binary,
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allow_scaling,
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headers_off,
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light_version,
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ignore_page_extraction,
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log_level
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):
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def layout(image, out, dir_in, model, 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, num_col_upper, num_col_lower, skip_layout_and_reading_order, ignore_page_extraction, log_level):
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if log_level:
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setOverrideLogLevel(log_level)
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initLogging()
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@ -182,8 +267,11 @@ def main(
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if textline_light and not light_version:
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print('Error: You used -tll to enable light textline detection but -light is not enabled')
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sys.exit(1)
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if extract_only_images and (allow_enhancement or allow_scaling or light_version or curved_line or textline_light or full_layout or tables or right2left or headers_off) :
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print('Error: You used -eoi which can not be enabled alongside light_version -light or allow_scaling -as or allow_enhancement -ae or curved_line -cl or textline_light -tll or full_layout -fl or tables -tab or right2left -r2l or headers_off -ho')
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if light_version and not textline_light:
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print('Error: You used -light without -tll. Light version need light textline to be enabled.')
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sys.exit(1)
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eynollah = Eynollah(
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image_filename=image,
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@ -208,6 +296,11 @@ def main(
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headers_off=headers_off,
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light_version=light_version,
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ignore_page_extraction=ignore_page_extraction,
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reading_order_machine_based=reading_order_machine_based,
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do_ocr=do_ocr,
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num_col_upper=num_col_upper,
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num_col_lower=num_col_lower,
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skip_layout_and_reading_order=skip_layout_and_reading_order,
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)
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if dir_in:
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eynollah.run()
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File diff suppressed because it is too large
Load diff
383
src/eynollah/sbb_binarize.py
Normal file
383
src/eynollah/sbb_binarize.py
Normal file
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@ -0,0 +1,383 @@
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"""
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Tool to load model and binarize a given image.
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"""
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import sys
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from glob import glob
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from os import environ, devnull
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from os.path import join
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from warnings import catch_warnings, simplefilter
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import os
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import numpy as np
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from PIL import Image
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import cv2
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environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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stderr = sys.stderr
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sys.stderr = open(devnull, 'w')
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.python.keras import backend as tensorflow_backend
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sys.stderr = stderr
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import logging
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def resize_image(img_in, input_height, input_width):
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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class SbbBinarizer:
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def __init__(self, model_dir, logger=None):
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self.model_dir = model_dir
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self.log = logger if logger else logging.getLogger('SbbBinarizer')
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self.start_new_session()
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self.model_files = glob(self.model_dir+"/*/", recursive = True)
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self.models = []
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for model_file in self.model_files:
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self.models.append(self.load_model(model_file))
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def start_new_session(self):
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
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tensorflow_backend.set_session(self.session)
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def end_session(self):
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tensorflow_backend.clear_session()
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self.session.close()
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del self.session
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def load_model(self, model_name):
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model = load_model(join(self.model_dir, model_name), compile=False)
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model_height = model.layers[len(model.layers)-1].output_shape[1]
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model_width = model.layers[len(model.layers)-1].output_shape[2]
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n_classes = model.layers[len(model.layers)-1].output_shape[3]
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return model, model_height, model_width, n_classes
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def predict(self, model_in, img, use_patches, n_batch_inference=5):
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tensorflow_backend.set_session(self.session)
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model, model_height, model_width, n_classes = model_in
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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if img.shape[0] < model_height and img.shape[1] >= model_width:
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img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = 0
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img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
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elif img.shape[0] >= model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
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index_start_h = 0
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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elif img.shape[0] < model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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else:
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index_start_h = 0
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index_start_w = 0
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img_padded = np.copy(img)
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img = np.copy(img_padded)
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if use_patches:
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margin = int(0.1 * model_width)
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width_mid = model_width - 2 * margin
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height_mid = model_height - 2 * margin
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img = img / float(255.0)
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img_h = img.shape[0]
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img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
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for i in range(nxf):
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for j in range(nyf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - model_width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - model_height
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list_i_s.append(i)
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list_j_s.append(j)
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list_x_u.append(index_x_u)
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list_x_d.append(index_x_d)
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list_y_d.append(index_y_d)
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list_y_u.append(index_y_u)
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img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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batch_indexer = batch_indexer + 1
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if batch_indexer == n_batch_inference:
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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#print(seg.shape, len(seg), len(list_i_s))
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
else:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
|
||||
indexer_inside_batch = indexer_inside_batch +1
|
||||
|
||||
|
||||
list_i_s = []
|
||||
list_j_s = []
|
||||
list_x_u = []
|
||||
list_x_d = []
|
||||
list_y_u = []
|
||||
list_y_d = []
|
||||
|
||||
batch_indexer = 0
|
||||
|
||||
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
|
||||
|
||||
elif i==(nxf-1) and j==(nyf-1):
|
||||
label_p_pred = model.predict(img_patch,verbose=0)
|
||||
|
||||
seg = np.argmax(label_p_pred, axis=3)
|
||||
|
||||
#print(seg.shape, len(seg), len(list_i_s))
|
||||
|
||||
indexer_inside_batch = 0
|
||||
for i_batch, j_batch in zip(list_i_s, list_j_s):
|
||||
seg_in = seg[indexer_inside_batch,:,:]
|
||||
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
index_y_u_in = list_y_u[indexer_inside_batch]
|
||||
index_y_d_in = list_y_d[indexer_inside_batch]
|
||||
|
||||
index_x_u_in = list_x_u[indexer_inside_batch]
|
||||
index_x_d_in = list_x_d[indexer_inside_batch]
|
||||
|
||||
if i_batch == 0 and j_batch == 0:
|
||||
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch == nxf - 1 and j_batch == nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
|
||||
elif i_batch == 0 and j_batch == nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch == nxf - 1 and j_batch == 0:
|
||||
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
||||
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
|
||||
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
|
||||
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
|
||||
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
else:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
|
||||
indexer_inside_batch = indexer_inside_batch +1
|
||||
|
||||
|
||||
list_i_s = []
|
||||
list_j_s = []
|
||||
list_x_u = []
|
||||
list_x_d = []
|
||||
list_y_u = []
|
||||
list_y_d = []
|
||||
|
||||
batch_indexer = 0
|
||||
|
||||
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
|
||||
|
||||
|
||||
|
||||
prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
|
||||
prediction_true = prediction_true.astype(np.uint8)
|
||||
|
||||
else:
|
||||
img_h_page = img.shape[0]
|
||||
img_w_page = img.shape[1]
|
||||
img = img / float(255.0)
|
||||
img = resize_image(img, model_height, model_width)
|
||||
|
||||
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
|
||||
|
||||
seg = np.argmax(label_p_pred, axis=3)[0]
|
||||
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
||||
prediction_true = resize_image(seg_color, img_h_page, img_w_page)
|
||||
prediction_true = prediction_true.astype(np.uint8)
|
||||
return prediction_true[:,:,0]
|
||||
|
||||
def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None):
|
||||
print(dir_in,'dir_in')
|
||||
if not dir_in:
|
||||
if (image is not None and image_path is not None) or \
|
||||
(image is None and image_path is None):
|
||||
raise ValueError("Must pass either a opencv2 image or an image_path")
|
||||
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)))
|
||||
|
||||
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
|
||||
if save:
|
||||
cv2.imwrite(save, img_last)
|
||||
return img_last
|
||||
else:
|
||||
ls_imgs = 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(dir_out,image_stem+'.png'), img_last)
|
||||
|
|
@ -7,7 +7,7 @@ import cv2
|
|||
import imutils
|
||||
from scipy.signal import find_peaks
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
|
||||
import time
|
||||
from .is_nan import isNaN
|
||||
from .contour import (contours_in_same_horizon,
|
||||
find_new_features_of_contours,
|
||||
|
|
@ -775,9 +775,8 @@ def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
|
|||
|
||||
return layout_no_patch
|
||||
|
||||
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
||||
|
||||
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
|
||||
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch, drop_capital_label):
|
||||
drop_only = (layout_in_patch[:, :, 0] == drop_capital_label) * 1
|
||||
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
||||
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
||||
|
||||
|
|
@ -786,13 +785,18 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
|||
|
||||
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
||||
|
||||
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
||||
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001]
|
||||
|
||||
contours_drop_parent_final = []
|
||||
|
||||
for jj in range(len(contours_drop_parent)):
|
||||
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
||||
layout_in_patch[y : y + h, x : x + w, 0] = 4
|
||||
|
||||
if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.4:
|
||||
|
||||
layout_in_patch[y : y + h, x : x + w, 0] = drop_capital_label
|
||||
else:
|
||||
layout_in_patch[y : y + h, x : x + w, 0][layout_in_patch[y : y + h, x : x + w, 0] == drop_capital_label] = 1#drop_capital_label
|
||||
|
||||
return layout_in_patch
|
||||
|
||||
|
|
@ -1200,17 +1204,12 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
|
|||
top = peaks_neg_new[i]
|
||||
down = peaks_neg_new[i + 1]
|
||||
|
||||
# print(top,down,'topdown')
|
||||
|
||||
indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
||||
cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
||||
cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
||||
types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
||||
index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
||||
|
||||
# print(top,down)
|
||||
# print(cys_in,'cyyyins')
|
||||
# print(indexes_in,'indexes')
|
||||
sorted_inside = np.argsort(cxs_in)
|
||||
|
||||
ind_in_int = indexes_in[sorted_inside]
|
||||
|
|
@ -1224,11 +1223,17 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
|
|||
|
||||
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
|
||||
|
||||
# print(peaks_neg_new,'peaks')
|
||||
# print(final_indexers_sorted,'indexsorted')
|
||||
# print(final_types,'types')
|
||||
# print(final_index_type,'final_index_type')
|
||||
|
||||
# This fix is applied if the sum of the lengths of contours and contours_h does not match final_indexers_sorted. However, this is not the optimal solution..
|
||||
if (len(cy_main)+len(cy_header) ) == len(final_index_type):
|
||||
pass
|
||||
else:
|
||||
indexes_missed = set(list( np.array( range((len(cy_main)+len(cy_header) ) )) )) - set(final_indexers_sorted)
|
||||
for ind_missed in indexes_missed:
|
||||
final_indexers_sorted.append(ind_missed)
|
||||
final_types.append(1)
|
||||
final_index_type.append(ind_missed)
|
||||
|
||||
|
||||
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
|
||||
|
||||
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor,num_col_classifier):
|
||||
|
|
@ -1338,7 +1343,7 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
|
|||
return peaks_neg_tot
|
||||
|
||||
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None):
|
||||
|
||||
t_ins_c0 = time.time()
|
||||
separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
|
||||
|
||||
separators_closeup[0:110,:,:]=0
|
||||
|
|
@ -1352,84 +1357,47 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
|
||||
|
||||
separators_closeup_new=np.zeros((separators_closeup.shape[0] ,separators_closeup.shape[1] ))
|
||||
|
||||
|
||||
|
||||
##_,separators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0])
|
||||
separators_closeup_n=np.copy(separators_closeup)
|
||||
|
||||
separators_closeup_n=separators_closeup_n.astype(np.uint8)
|
||||
##plt.imshow(separators_closeup_n[:,:,0])
|
||||
##plt.show()
|
||||
|
||||
separators_closeup_n_binary=np.zeros(( separators_closeup_n.shape[0],separators_closeup_n.shape[1]) )
|
||||
separators_closeup_n_binary[:,:]=separators_closeup_n[:,:,0]
|
||||
|
||||
separators_closeup_n_binary[:,:][separators_closeup_n_binary[:,:]!=0]=1
|
||||
#separators_closeup_n_binary[:,:][separators_closeup_n_binary[:,:]==0]=255
|
||||
#separators_closeup_n_binary[:,:][separators_closeup_n_binary[:,:]==-255]=0
|
||||
|
||||
|
||||
#separators_closeup_n_binary=(separators_closeup_n_binary[:,:]==2)*1
|
||||
|
||||
#gray = cv2.cvtColor(separators_closeup_n, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
###
|
||||
|
||||
#print(separators_closeup_n_binary.shape)
|
||||
|
||||
gray_early=np.repeat(separators_closeup_n_binary[:, :, np.newaxis], 3, axis=2)
|
||||
gray_early=gray_early.astype(np.uint8)
|
||||
|
||||
#print(gray_early.shape,'burda')
|
||||
imgray_e = cv2.cvtColor(gray_early, cv2.COLOR_BGR2GRAY)
|
||||
#print('burda2')
|
||||
ret_e, thresh_e = cv2.threshold(imgray_e, 0, 255, 0)
|
||||
|
||||
#print('burda3')
|
||||
contours_line_e,hierarchy_e=cv2.findContours(thresh_e,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
#slope_lines_e,dist_x_e, x_min_main_e ,x_max_main_e ,cy_main_e,slope_lines_org_e,y_min_main_e, y_max_main_e, cx_main_e=self.find_features_of_lines(contours_line_e)
|
||||
|
||||
slope_linese,dist_xe, x_min_maine ,x_max_maine ,cy_maine,slope_lines_orge,y_min_maine, y_max_maine, cx_maine=find_features_of_lines(contours_line_e)
|
||||
|
||||
dist_ye=y_max_maine-y_min_maine
|
||||
#print(y_max_maine-y_min_maine,'y')
|
||||
#print(dist_xe,'x')
|
||||
|
||||
|
||||
args_e=np.array(range(len(contours_line_e)))
|
||||
args_hor_e=args_e[(dist_ye<=50) & (dist_xe>=3*dist_ye)]
|
||||
|
||||
#print(args_hor_e,'jidi',len(args_hor_e),'jilva')
|
||||
|
||||
cnts_hor_e=[]
|
||||
for ce in args_hor_e:
|
||||
cnts_hor_e.append(contours_line_e[ce])
|
||||
#print(len(slope_linese),'lieee')
|
||||
|
||||
figs_e=np.zeros(thresh_e.shape)
|
||||
figs_e=cv2.fillPoly(figs_e,pts=cnts_hor_e,color=(1,1,1))
|
||||
|
||||
#plt.imshow(figs_e)
|
||||
#plt.show()
|
||||
|
||||
###
|
||||
|
||||
separators_closeup_n_binary=cv2.fillPoly(separators_closeup_n_binary,pts=cnts_hor_e,color=(0,0,0))
|
||||
|
||||
gray = cv2.bitwise_not(separators_closeup_n_binary)
|
||||
gray=gray.astype(np.uint8)
|
||||
|
||||
|
||||
#plt.imshow(gray)
|
||||
#plt.show()
|
||||
|
||||
|
||||
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
|
||||
cv2.THRESH_BINARY, 15, -2)
|
||||
##plt.imshow(bw[:,:])
|
||||
##plt.show()
|
||||
|
||||
|
||||
horizontal = np.copy(bw)
|
||||
vertical = np.copy(bw)
|
||||
|
||||
|
|
@ -1447,16 +1415,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
horizontal = cv2.dilate(horizontal,kernel,iterations = 2)
|
||||
horizontal = cv2.erode(horizontal,kernel,iterations = 2)
|
||||
|
||||
|
||||
###
|
||||
#print(np.unique(horizontal),'uni')
|
||||
horizontal=cv2.fillPoly(horizontal,pts=cnts_hor_e,color=(255,255,255))
|
||||
###
|
||||
|
||||
|
||||
|
||||
#plt.imshow(horizontal)
|
||||
#plt.show()
|
||||
|
||||
rows = vertical.shape[0]
|
||||
verticalsize = rows // 30
|
||||
|
|
@ -1467,35 +1426,21 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
vertical = cv2.dilate(vertical, verticalStructure)
|
||||
|
||||
vertical = cv2.dilate(vertical,kernel,iterations = 1)
|
||||
# Show extracted vertical lines
|
||||
|
||||
horizontal,special_separators=combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical,horizontal,num_col_classifier)
|
||||
|
||||
|
||||
#plt.imshow(horizontal)
|
||||
#plt.show()
|
||||
#print(vertical.shape,np.unique(vertical),'verticalvertical')
|
||||
separators_closeup_new[:,:][vertical[:,:]!=0]=1
|
||||
separators_closeup_new[:,:][horizontal[:,:]!=0]=1
|
||||
|
||||
##plt.imshow(separators_closeup_new)
|
||||
##plt.show()
|
||||
##separators_closeup_n
|
||||
vertical=np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
|
||||
vertical=vertical.astype(np.uint8)
|
||||
|
||||
##plt.plot(vertical[:,:,0].sum(axis=0))
|
||||
##plt.show()
|
||||
|
||||
#plt.plot(vertical[:,:,0].sum(axis=1))
|
||||
#plt.show()
|
||||
|
||||
imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_line_vers,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
||||
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=find_features_of_lines(contours_line_vers)
|
||||
#print(slope_lines,'vertical')
|
||||
|
||||
args=np.array( range(len(slope_lines) ))
|
||||
args_ver=args[slope_lines==1]
|
||||
dist_x_ver=dist_x[slope_lines==1]
|
||||
|
|
@ -1508,9 +1453,6 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
len_y=separators_closeup.shape[0]/3.0
|
||||
|
||||
|
||||
#plt.imshow(horizontal)
|
||||
#plt.show()
|
||||
|
||||
horizontal=np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
|
||||
horizontal=horizontal.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
|
||||
|
|
@ -1578,8 +1520,6 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
|
||||
matrix_of_lines_ch[len(cy_main_hor):,9]=1
|
||||
|
||||
|
||||
|
||||
if contours_h is not None:
|
||||
slope_lines_head,dist_x_head, x_min_main_head ,x_max_main_head ,cy_main_head,slope_lines_org_head,y_min_main_head, y_max_main_head, cx_main_head=find_features_of_lines(contours_h)
|
||||
matrix_l_n=np.zeros((matrix_of_lines_ch.shape[0]+len(cy_main_head),matrix_of_lines_ch.shape[1]))
|
||||
|
|
@ -1625,8 +1565,6 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
|
||||
args_big_parts=np.array(range(len(splitter_y_new_diff))) [ splitter_y_new_diff>22 ]
|
||||
|
||||
|
||||
|
||||
regions_without_separators=return_regions_without_separators(region_pre_p)
|
||||
|
||||
|
||||
|
|
@ -1636,19 +1574,8 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
peaks_neg_fin_fin=[]
|
||||
|
||||
for itiles in args_big_parts:
|
||||
|
||||
|
||||
regions_without_separators_tile=regions_without_separators[int(splitter_y_new[itiles]):int(splitter_y_new[itiles+1]),:,0]
|
||||
#image_page_background_zero_tile=image_page_background_zero[int(splitter_y_new[itiles]):int(splitter_y_new[itiles+1]),:]
|
||||
|
||||
#print(regions_without_separators_tile.shape)
|
||||
##plt.imshow(regions_without_separators_tile)
|
||||
##plt.show()
|
||||
|
||||
#num_col, peaks_neg_fin=self.find_num_col(regions_without_separators_tile,multiplier=6.0)
|
||||
|
||||
#regions_without_separators_tile=cv2.erode(regions_without_separators_tile,kernel,iterations = 3)
|
||||
#
|
||||
|
||||
try:
|
||||
num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile, num_col_classifier, tables, multiplier=7.0)
|
||||
except:
|
||||
|
|
@ -1666,9 +1593,6 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
|
|||
peaks_neg_fin=peaks_neg_fin[peaks_neg_fin<=(vertical.shape[1]-500)]
|
||||
peaks_neg_fin_fin=peaks_neg_fin[:]
|
||||
|
||||
#print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza')
|
||||
|
||||
|
||||
return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,splitter_y_new,separators_closeup_n
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -263,7 +263,7 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
|
|||
|
||||
return cnts_org
|
||||
|
||||
def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
||||
def get_textregion_contours_in_org_image_light_old(cnts, img, slope_first):
|
||||
|
||||
h_o = img.shape[0]
|
||||
w_o = img.shape[1]
|
||||
|
|
@ -278,14 +278,7 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
|||
img_copy = np.zeros(img.shape)
|
||||
img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
|
||||
|
||||
# plt.imshow(img_copy)
|
||||
# plt.show()
|
||||
|
||||
# print(img.shape,'img')
|
||||
img_copy = rotation_image_new(img_copy, -slope_first)
|
||||
##print(img_copy.shape,'img_copy')
|
||||
# plt.imshow(img_copy)
|
||||
# plt.show()
|
||||
|
||||
img_copy = img_copy.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
||||
|
|
@ -300,6 +293,70 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
|||
|
||||
return cnts_org
|
||||
|
||||
def return_list_of_contours_with_desired_order(ls_cons, sorted_indexes):
|
||||
return [ls_cons[sorted_indexes[index]] for index in range(len(sorted_indexes))]
|
||||
def do_back_rotation_and_get_cnt_back(queue_of_all_params, contours_par_per_process,indexes_r_con_per_pro, img, slope_first):
|
||||
contours_textregion_per_each_subprocess = []
|
||||
index_by_text_region_contours = []
|
||||
for mv in range(len(contours_par_per_process)):
|
||||
img_copy = np.zeros(img.shape)
|
||||
img_copy = cv2.fillPoly(img_copy, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
|
||||
|
||||
img_copy = rotation_image_new(img_copy, -slope_first)
|
||||
|
||||
img_copy = img_copy.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
|
||||
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# print(np.shape(cont_int[0]))
|
||||
contours_textregion_per_each_subprocess.append(cont_int[0]*6)
|
||||
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
||||
|
||||
queue_of_all_params.put([contours_textregion_per_each_subprocess, index_by_text_region_contours])
|
||||
|
||||
def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
||||
num_cores = cpu_count()
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(cnts), num_cores + 1)
|
||||
indexes_by_text_con = np.array(range(len(cnts)))
|
||||
|
||||
h_o = img.shape[0]
|
||||
w_o = img.shape[1]
|
||||
|
||||
img = cv2.resize(img, (int(img.shape[1]/6.), int(img.shape[0]/6.)), interpolation=cv2.INTER_NEAREST)
|
||||
##cnts = list( (np.array(cnts)/2).astype(np.int16) )
|
||||
#cnts = cnts/2
|
||||
cnts = [(i/ 6).astype(np.int32) for i in cnts]
|
||||
|
||||
for i in range(num_cores):
|
||||
contours_par_per_process = cnts[int(nh[i]) : int(nh[i + 1])]
|
||||
indexes_text_con_per_process = indexes_by_text_con[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_back_rotation_and_get_cnt_back, args=(queue_of_all_params, contours_par_per_process, indexes_text_con_per_process, img, slope_first)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
cnts_org = []
|
||||
all_index_text_con = []
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
contours_for_subprocess = list_all_par[0]
|
||||
indexes_for_subprocess = list_all_par[1]
|
||||
for j in range(len(contours_for_subprocess)):
|
||||
cnts_org.append(contours_for_subprocess[j])
|
||||
all_index_text_con.append(indexes_for_subprocess[j])
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
cnts_org = return_list_of_contours_with_desired_order(cnts_org, all_index_text_con)
|
||||
|
||||
return cnts_org
|
||||
|
||||
def return_contours_of_interested_textline(region_pre_p, pixel):
|
||||
|
||||
# pixels of images are identified by 5
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ 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, kernel=None):
|
||||
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_version=False, kernel=None):
|
||||
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
|
||||
mask_marginals=mask_marginals.astype(np.uint8)
|
||||
|
||||
|
|
@ -49,27 +49,14 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
if thickness_along_y_percent>=14:
|
||||
|
||||
text_with_lines_y_rev=-1*text_with_lines_y[:]
|
||||
#print(text_with_lines_y)
|
||||
#print(text_with_lines_y_rev)
|
||||
|
||||
|
||||
|
||||
|
||||
#plt.plot(text_with_lines_y)
|
||||
#plt.show()
|
||||
|
||||
|
||||
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
|
||||
|
||||
#plt.plot(text_with_lines_y_rev)
|
||||
#plt.show()
|
||||
sigma_gaus=1
|
||||
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
|
||||
|
||||
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
|
||||
|
||||
#plt.plot(region_sum_0_rev)
|
||||
#plt.show()
|
||||
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
|
||||
|
||||
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
|
||||
|
|
@ -78,43 +65,17 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
|
||||
last_nonzero=len(region_sum_0)-last_nonzero
|
||||
|
||||
##img_sum_0_smooth_rev=-region_sum_0
|
||||
|
||||
|
||||
mid_point=(last_nonzero+first_nonzero)/2.
|
||||
|
||||
|
||||
one_third_right=(last_nonzero-mid_point)/3.0
|
||||
one_third_left=(mid_point-first_nonzero)/3.0
|
||||
|
||||
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
|
||||
|
||||
|
||||
|
||||
|
||||
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
|
||||
|
||||
|
||||
peaks=np.array(peaks)
|
||||
|
||||
|
||||
#print(region_sum_0[peaks])
|
||||
##plt.plot(region_sum_0)
|
||||
##plt.plot(peaks,region_sum_0[peaks],'*')
|
||||
##plt.show()
|
||||
#print(first_nonzero,last_nonzero,peaks)
|
||||
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
|
||||
|
||||
#print(first_nonzero,last_nonzero,peaks)
|
||||
|
||||
|
||||
#print(region_sum_0[peaks]<10)
|
||||
####peaks=peaks[region_sum_0[peaks]<25 ]
|
||||
|
||||
#print(region_sum_0[peaks])
|
||||
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
|
||||
#print(peaks)
|
||||
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
|
||||
|
||||
|
||||
if num_col==1:
|
||||
peaks_right=peaks[peaks>mid_point]
|
||||
|
|
@ -137,9 +98,6 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
|
||||
|
||||
|
||||
|
||||
#print(point_left,point_right)
|
||||
#print(text_regions.shape)
|
||||
if point_right>=mask_marginals.shape[1]:
|
||||
point_right=mask_marginals.shape[1]-1
|
||||
|
||||
|
|
@ -148,10 +106,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
except:
|
||||
mask_marginals[:,:]=1
|
||||
|
||||
#print(mask_marginals.shape,point_left,point_right,'nadosh')
|
||||
mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew)
|
||||
|
||||
#print(mask_marginals_rotated.shape,'nadosh')
|
||||
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
|
||||
|
||||
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
|
||||
|
|
@ -168,11 +124,6 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
max_point_of_right_marginal=text_regions.shape[1]-1
|
||||
|
||||
|
||||
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
|
||||
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
|
||||
#plt.imshow(mask_marginals)
|
||||
#plt.show()
|
||||
|
||||
#plt.imshow(mask_marginals_rotated)
|
||||
#plt.show()
|
||||
|
||||
|
|
@ -195,10 +146,9 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
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])
|
||||
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
|
||||
|
||||
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):
|
||||
|
|
@ -220,18 +170,13 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
|
|||
x_min_marginals_right=[text_regions.shape[1]-1]
|
||||
|
||||
|
||||
|
||||
|
||||
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
||||
|
||||
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
|
||||
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
|
||||
|
||||
#print(np.unique(text_regions))
|
||||
|
||||
#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
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,8 @@ import cv2
|
|||
from scipy.signal import find_peaks
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
import os
|
||||
|
||||
from multiprocessing import Process, Queue, cpu_count
|
||||
from multiprocessing import Pool
|
||||
from .rotate import rotate_image
|
||||
from .contour import (
|
||||
return_parent_contours,
|
||||
|
|
@ -1569,8 +1570,21 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, plotter=None):
|
|||
# plt.show()
|
||||
return img_patch_ineterst_revised
|
||||
|
||||
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
||||
def do_image_rotation(queue_of_all_params,angels_per_process, img_resized, sigma_des):
|
||||
angels_per_each_subprocess = []
|
||||
for mv in range(len(angels_per_process)):
|
||||
img_rot=rotate_image(img_resized,angels_per_process[mv])
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
angels_per_each_subprocess.append(var_spectrum)
|
||||
|
||||
queue_of_all_params.put([angels_per_each_subprocess])
|
||||
|
||||
def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100, main_page=False, plotter=None):
|
||||
num_cores = cpu_count()
|
||||
if main_page and plotter:
|
||||
plotter.save_plot_of_textline_density(img_patch_org)
|
||||
|
||||
|
|
@ -1603,22 +1617,44 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
#plt.imshow(img_resized)
|
||||
#plt.show()
|
||||
angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
|
||||
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
#plt.imshow(img_rot)
|
||||
#plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
##print(rot,var_spectrum,'var_spectrum')
|
||||
except:
|
||||
var_spectrum=0
|
||||
var_res.append(var_spectrum)
|
||||
###for rot in angels:
|
||||
###img_rot=rotate_image(img_resized,rot)
|
||||
####plt.imshow(img_rot)
|
||||
####plt.show()
|
||||
###img_rot[img_rot!=0]=1
|
||||
####neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
####print(var_spectrum,'var_spectrum')
|
||||
###try:
|
||||
###var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#####print(rot,var_spectrum,'var_spectrum')
|
||||
###except:
|
||||
###var_spectrum=0
|
||||
###var_res.append(var_spectrum)
|
||||
|
||||
|
||||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
|
|
@ -1626,19 +1662,40 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
ang_int=0
|
||||
|
||||
|
||||
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
|
||||
angels=np.linspace(ang_int-22.5,ang_int+22.5,n_tot_angles)
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
var_res.append(var_spectrum)
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
##var_res=[]
|
||||
##for rot in angels:
|
||||
##img_rot=rotate_image(img_resized,rot)
|
||||
####plt.imshow(img_rot)
|
||||
####plt.show()
|
||||
##img_rot[img_rot!=0]=1
|
||||
##try:
|
||||
##var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
##except:
|
||||
##var_spectrum=0
|
||||
##var_res.append(var_spectrum)
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
|
|
@ -1649,25 +1706,47 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
|
||||
#plt.imshow(img_resized)
|
||||
#plt.show()
|
||||
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
|
||||
|
||||
|
||||
angels=np.linspace(-12,12,n_tot_angles)#np.array([0 , 45 , 90 , -45])
|
||||
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
#plt.imshow(img_rot)
|
||||
#plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
|
||||
except:
|
||||
var_spectrum=0
|
||||
##var_res=[]
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
##for rot in angels:
|
||||
##img_rot=rotate_image(img_resized,rot)
|
||||
###plt.imshow(img_rot)
|
||||
###plt.show()
|
||||
##img_rot[img_rot!=0]=1
|
||||
###neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
###print(var_spectrum,'var_spectrum')
|
||||
##try:
|
||||
##var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
|
||||
##except:
|
||||
##var_spectrum=0
|
||||
|
||||
##var_res.append(var_spectrum)
|
||||
|
||||
|
||||
if plotter:
|
||||
|
|
@ -1680,18 +1759,39 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
|
||||
early_slope_edge=11
|
||||
if abs(ang_int)>early_slope_edge and ang_int<0:
|
||||
angels=np.linspace(-90,-12,100)
|
||||
angels=np.linspace(-90,-12,n_tot_angles)
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
var_res.append(var_spectrum)
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
##var_res=[]
|
||||
##for rot in angels:
|
||||
##img_rot=rotate_image(img_resized,rot)
|
||||
####plt.imshow(img_rot)
|
||||
####plt.show()
|
||||
##img_rot[img_rot!=0]=1
|
||||
##try:
|
||||
##var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
##except:
|
||||
##var_spectrum=0
|
||||
##var_res.append(var_spectrum)
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
|
|
@ -1700,40 +1800,85 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
|
||||
elif abs(ang_int)>early_slope_edge and ang_int>0:
|
||||
|
||||
angels=np.linspace(90,12,100)
|
||||
angels=np.linspace(90,12,n_tot_angles)
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(indexer,'indexer')
|
||||
except:
|
||||
var_spectrum=0
|
||||
var_res.append(var_spectrum)
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
|
||||
###var_res=[]
|
||||
###for rot in angels:
|
||||
###img_rot=rotate_image(img_resized,rot)
|
||||
#####plt.imshow(img_rot)
|
||||
#####plt.show()
|
||||
###img_rot[img_rot!=0]=1
|
||||
###try:
|
||||
###var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
####print(indexer,'indexer')
|
||||
###except:
|
||||
###var_spectrum=0
|
||||
###var_res.append(var_spectrum)
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
else:
|
||||
angels=np.linspace(-25,25,60)
|
||||
var_res=[]
|
||||
angels=np.linspace(-25,25,int(n_tot_angles/2.)+10)
|
||||
indexer=0
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
#plt.imshow(img_rot)
|
||||
#plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
####var_res=[]
|
||||
|
||||
####for rot in angels:
|
||||
####img_rot=rotate_image(img_resized,rot)
|
||||
#####plt.imshow(img_rot)
|
||||
#####plt.show()
|
||||
####img_rot[img_rot!=0]=1
|
||||
#####neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#####print(var_spectrum,'var_spectrum')
|
||||
####try:
|
||||
####var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
####except:
|
||||
####var_spectrum=0
|
||||
####var_res.append(var_spectrum)
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
|
|
@ -1749,20 +1894,41 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
early_slope_edge=22
|
||||
if abs(ang_int)>early_slope_edge and ang_int<0:
|
||||
|
||||
angels=np.linspace(-90,-25,60)
|
||||
|
||||
angels=np.linspace(-90,-25,int(n_tot_angles/2.)+10)
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
var_res.append(var_spectrum)
|
||||
###var_res=[]
|
||||
|
||||
###for rot in angels:
|
||||
###img_rot=rotate_image(img_resized,rot)
|
||||
#####plt.imshow(img_rot)
|
||||
#####plt.show()
|
||||
###img_rot[img_rot!=0]=1
|
||||
###try:
|
||||
###var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
###except:
|
||||
###var_spectrum=0
|
||||
###var_res.append(var_spectrum)
|
||||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
|
@ -1772,23 +1938,45 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
|
|||
|
||||
elif abs(ang_int)>early_slope_edge and ang_int>0:
|
||||
|
||||
angels=np.linspace(90,25,60)
|
||||
|
||||
var_res=[]
|
||||
|
||||
angels=np.linspace(90,25,int(n_tot_angles/2.)+10)
|
||||
indexer=0
|
||||
for rot in angels:
|
||||
img_rot=rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(indexer,'indexer')
|
||||
except:
|
||||
var_spectrum=0
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angels), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angels_per_process = angels[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation, args=(queue_of_all_params, angels_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
angles_for_subprocess = list_all_par[0]
|
||||
for j in range(len(angles_for_subprocess)):
|
||||
var_res.append(angles_for_subprocess[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
###var_res=[]
|
||||
|
||||
|
||||
###for rot in angels:
|
||||
###img_rot=rotate_image(img_resized,rot)
|
||||
#####plt.imshow(img_rot)
|
||||
#####plt.show()
|
||||
###img_rot[img_rot!=0]=1
|
||||
###try:
|
||||
###var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
####print(indexer,'indexer')
|
||||
###except:
|
||||
###var_spectrum=0
|
||||
|
||||
###var_res.append(var_spectrum)
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region
|
|||
|
||||
index_of_types_2 = index_of_types[kind_of_texts == 2]
|
||||
indexes_sorted_2 = indexes_sorted[kind_of_texts == 2]
|
||||
|
||||
|
||||
counter = EynollahIdCounter(region_idx=ref_point)
|
||||
for idx_textregion, _ in enumerate(found_polygons_text_region):
|
||||
id_of_texts.append(counter.next_region_id)
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
# pylint: disable=import-error
|
||||
from pathlib import Path
|
||||
import os.path
|
||||
|
||||
import xml.etree.ElementTree as ET
|
||||
from .utils.xml import create_page_xml, xml_reading_order
|
||||
from .utils.counter import EynollahIdCounter
|
||||
|
||||
|
|
@ -12,6 +12,7 @@ from ocrd_models.ocrd_page import (
|
|||
CoordsType,
|
||||
PcGtsType,
|
||||
TextLineType,
|
||||
TextEquivType,
|
||||
TextRegionType,
|
||||
ImageRegionType,
|
||||
TableRegionType,
|
||||
|
|
@ -93,11 +94,13 @@ class EynollahXmlWriter():
|
|||
points_co += ' '
|
||||
coords.set_points(points_co[:-1])
|
||||
|
||||
def serialize_lines_in_region(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter):
|
||||
def serialize_lines_in_region(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter, ocr_all_textlines_textregion):
|
||||
self.logger.debug('enter serialize_lines_in_region')
|
||||
for j in range(len(all_found_textline_polygons[region_idx])):
|
||||
coords = CoordsType()
|
||||
textline = TextLineType(id=counter.next_line_id, Coords=coords)
|
||||
if ocr_all_textlines_textregion:
|
||||
textline.set_TextEquiv( [ TextEquivType(Unicode=ocr_all_textlines_textregion[j]) ] )
|
||||
text_region.add_TextLine(textline)
|
||||
region_bboxes = all_box_coord[region_idx]
|
||||
points_co = ''
|
||||
|
|
@ -133,6 +136,29 @@ class EynollahXmlWriter():
|
|||
points_co += str(int((contour_textline[0][1] + region_bboxes[0]+page_coord[0])/self.scale_y))
|
||||
points_co += ' '
|
||||
coords.set_points(points_co[:-1])
|
||||
|
||||
def serialize_lines_in_dropcapital(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter, ocr_all_textlines_textregion):
|
||||
self.logger.debug('enter serialize_lines_in_region')
|
||||
for j in range(1):
|
||||
coords = CoordsType()
|
||||
textline = TextLineType(id=counter.next_line_id, Coords=coords)
|
||||
if ocr_all_textlines_textregion:
|
||||
textline.set_TextEquiv( [ TextEquivType(Unicode=ocr_all_textlines_textregion[j]) ] )
|
||||
text_region.add_TextLine(textline)
|
||||
#region_bboxes = all_box_coord[region_idx]
|
||||
points_co = ''
|
||||
for idx_contour_textline, contour_textline in enumerate(all_found_textline_polygons[j]):
|
||||
if len(contour_textline) == 2:
|
||||
points_co += str(int((contour_textline[0] + page_coord[2]) / self.scale_x))
|
||||
points_co += ','
|
||||
points_co += str(int((contour_textline[1] + page_coord[0]) / self.scale_y))
|
||||
else:
|
||||
points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x))
|
||||
points_co += ','
|
||||
points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y))
|
||||
|
||||
points_co += ' '
|
||||
coords.set_points(points_co[:-1])
|
||||
|
||||
def write_pagexml(self, pcgts):
|
||||
out_fname = os.path.join(self.dir_out, self.image_filename_stem) + ".xml"
|
||||
|
|
@ -140,7 +166,7 @@ class EynollahXmlWriter():
|
|||
with open(out_fname, 'w') as f:
|
||||
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, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, found_polygons_tables):
|
||||
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, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, found_polygons_tables, ocr_all_textlines):
|
||||
self.logger.debug('enter build_pagexml_no_full_layout')
|
||||
|
||||
# create the file structure
|
||||
|
|
@ -159,7 +185,11 @@ class EynollahXmlWriter():
|
|||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord)),
|
||||
)
|
||||
page.add_TextRegion(textregion)
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter)
|
||||
if ocr_all_textlines:
|
||||
ocr_textlines = ocr_all_textlines[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_marginals)):
|
||||
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
|
||||
|
|
@ -209,7 +239,7 @@ class EynollahXmlWriter():
|
|||
|
||||
return pcgts
|
||||
|
||||
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, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml):
|
||||
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, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, ocr_all_textlines):
|
||||
self.logger.debug('enter build_pagexml_full_layout')
|
||||
|
||||
# create the file structure
|
||||
|
|
@ -226,14 +256,24 @@ class EynollahXmlWriter():
|
|||
textregion = TextRegionType(id=counter.next_region_id, type_='paragraph',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord)))
|
||||
page.add_TextRegion(textregion)
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter)
|
||||
|
||||
if ocr_all_textlines:
|
||||
ocr_textlines = ocr_all_textlines[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines)
|
||||
|
||||
self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h))
|
||||
for mm in range(len(found_polygons_text_region_h)):
|
||||
textregion = TextRegionType(id=counter.next_region_id, type_='header',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_h[mm], page_coord)))
|
||||
page.add_TextRegion(textregion)
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h, slopes_h, counter)
|
||||
|
||||
if ocr_all_textlines:
|
||||
ocr_textlines = ocr_all_textlines[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h, slopes_h, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_marginals)):
|
||||
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
|
||||
|
|
@ -242,8 +282,12 @@ class EynollahXmlWriter():
|
|||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter)
|
||||
|
||||
for mm in range(len(found_polygons_drop_capitals)):
|
||||
page.add_TextRegion(TextRegionType(id=counter.next_region_id, type_='drop-capital',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_drop_capitals[mm], page_coord))))
|
||||
dropcapital = TextRegionType(id=counter.next_region_id, type_='drop-capital',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_drop_capitals[mm], page_coord)))
|
||||
page.add_TextRegion(dropcapital)
|
||||
all_box_coord_drop = None
|
||||
slopes_drop = None
|
||||
self.serialize_lines_in_dropcapital(dropcapital, [found_polygons_drop_capitals[mm]], mm, page_coord, all_box_coord_drop, slopes_drop, counter, ocr_all_textlines_textregion=None)
|
||||
|
||||
for mm in range(len(found_polygons_text_region_img)):
|
||||
page.add_ImageRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_img[mm], page_coord))))
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue