mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 12:19:54 +02:00
rfct: introduce EynollahDirs to reduce self.dir_* proliferation
This commit is contained in:
kba
parent
762a7a058e
commit
532ee6fe41
6 changed files with 144 additions and 138 deletions
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@ -2,6 +2,7 @@ import sys
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import click
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from ocrd_utils import getLogger, initLogging, setOverrideLogLevel
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from qurator.eynollah.eynollah import Eynollah
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from qurator.eynollah.utils.dirs import EynollahDirs
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@click.command()
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@ -176,16 +177,18 @@ def main(
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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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eynollah = Eynollah(
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model,
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EynollahDirs(
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dir_models=model,
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dir_out=out,
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dir_in=dir_in,
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dir_of_cropped_images=save_images,
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dir_of_layout=save_layout,
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dir_of_deskewed=save_deskewed,
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dir_of_all=save_all,
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dir_save_page=save_page,
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),
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getLogger('Eynollah'),
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image_filename=image,
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dir_out=out,
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dir_in=dir_in,
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dir_of_cropped_images=save_images,
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dir_of_layout=save_layout,
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dir_of_deskewed=save_deskewed,
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dir_of_all=save_all,
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dir_save_page=save_page,
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enable_plotting=enable_plotting,
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allow_enhancement=allow_enhancement,
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curved_line=curved_line,
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@ -7,6 +7,7 @@ document layout analysis (segmentation) with output in PAGE-XML
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"""
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from logging import Logger
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from dataclasses import dataclass
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import math
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from os import listdir
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from os.path import join
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@ -21,6 +22,8 @@ import numpy as np
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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from .utils.dirs import EynollahDirs
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from .utils.tf import (
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PatchEncoder,
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Patches,
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@ -85,17 +88,10 @@ class Eynollah():
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def __init__(
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self,
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dir_models : str,
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dirs : EynollahDirs,
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logger : Logger,
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image_filename : Optional[str] = None,
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image_pil : Optional[Image] = None,
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dir_out : Optional[str] = None,
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dir_in : Optional[str] = None,
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dir_of_cropped_images : Optional[str] = None,
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dir_of_layout : Optional[str] = None,
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dir_of_deskewed : Optional[str] = None,
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dir_of_all : Optional[str] = None,
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dir_save_page : Optional[str] = None,
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image_filename : Optional[str] = None,
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enable_plotting : bool = False,
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allow_enhancement : bool = False,
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curved_line : bool = False,
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@ -111,7 +107,9 @@ class Eynollah():
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override_dpi : Optional[int] = None,
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pcgts : Optional[OcrdPage] = None,
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):
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if not dir_in:
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self.dirs = dirs
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self.logger = logger
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if not dirs.dir_in:
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if image_pil:
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self._imgs = self._cache_images(image_pil=image_pil)
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else:
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@ -119,14 +117,6 @@ class Eynollah():
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if override_dpi:
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self.dpi = override_dpi
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self.image_filename = image_filename
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self.dir_out = dir_out
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self.dir_in = dir_in
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self.dir_of_all = dir_of_all
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self.dir_save_page = dir_save_page
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self.dir_of_deskewed = dir_of_deskewed
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self.dir_of_deskewed = dir_of_deskewed
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self.dir_of_cropped_images=dir_of_cropped_images
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self.dir_of_layout=dir_of_layout
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self.enable_plotting = enable_plotting
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self.allow_enhancement = allow_enhancement
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self.curved_line = curved_line
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@ -140,43 +130,37 @@ class Eynollah():
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self.light_version = light_version
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self.ignore_page_extraction = ignore_page_extraction
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self.pcgts = pcgts
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if not dir_in:
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self.plotter = None if not enable_plotting else EynollahPlotter(
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dir_out=self.dir_out,
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dir_of_all=dir_of_all,
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dir_save_page=dir_save_page,
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dir_of_deskewed=dir_of_deskewed,
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dir_of_cropped_images=dir_of_cropped_images,
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dir_of_layout=dir_of_layout,
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image_filename_stem=Path(Path(image_filename).name).stem)
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# self.batch_mode = bool(self.dirs.dir_in)
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if not dirs.dir_in:
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assert self.image_filename
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self.plotter = None if not self.enable_plotting else EynollahPlotter(self.dirs, image_filename_stem=Path(Path(image_filename).name).stem)
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self.writer = EynollahXmlWriter(
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dir_out=self.dir_out,
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dir_out=self.dirs.dir_out,
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image_filename=self.image_filename,
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curved_line=self.curved_line,
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textline_light = self.textline_light,
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pcgts=pcgts)
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self.logger = logger
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self.dir_models = dir_models
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self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425"
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self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425"
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self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425"
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self.model_region_dir_p = dir_models + "/eynollah-main-regions-aug-scaling_20210425"
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self.model_region_dir_p2 = dir_models + "/eynollah-main-regions-aug-rotation_20210425"
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self.model_region_dir_fully_np = dir_models + "/eynollah-full-regions-1column_20210425"
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self.model_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425"
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self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425"
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self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
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self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
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self.model_dir_of_enhancement = dirs.dir_models + "/eynollah-enhancement_20210425"
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self.model_dir_of_binarization = dirs.dir_models + "/eynollah-binarization_20210425"
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self.model_dir_of_col_classifier = dirs.dir_models + "/eynollah-column-classifier_20210425"
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self.model_region_dir_p = dirs.dir_models + "/eynollah-main-regions-aug-scaling_20210425"
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self.model_region_dir_p2 = dirs.dir_models + "/eynollah-main-regions-aug-rotation_20210425"
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self.model_region_dir_fully_np = dirs.dir_models + "/eynollah-full-regions-1column_20210425"
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self.model_region_dir_fully = dirs.dir_models + "/eynollah-full-regions-3+column_20210425"
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self.model_page_dir = dirs.dir_models + "/eynollah-page-extraction_20210425"
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self.model_region_dir_p_ens = dirs.dir_models + "/eynollah-main-regions-ensembled_20210425"
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self.model_region_dir_p_ens_light = dirs.dir_models + "/eynollah-main-regions_20220314"
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if self.textline_light:
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self.model_textline_dir = dir_models + "/eynollah-textline_light_20210425"
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self.model_textline_dir = dirs.dir_models + "/eynollah-textline_light_20210425"
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else:
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self.model_textline_dir = dir_models + "/eynollah-textline_20210425"
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self.model_tables = dir_models + "/eynollah-tables_20210319"
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self.model_textline_dir = dirs.dir_models + "/eynollah-textline_20210425"
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self.model_tables = dirs.dir_models + "/eynollah-tables_20210319"
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self.models : dict[str, tf.keras.Model] = {}
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if dir_in and light_version:
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if self.dirs.dir_in and light_version:
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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@ -190,9 +174,9 @@ class Eynollah():
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self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
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self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
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self.ls_imgs = listdir(self.dir_in)
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self.ls_imgs = listdir(self.dirs.dir_in)
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if dir_in and not light_version:
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if self.dirs.dir_in and not light_version:
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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@ -208,7 +192,7 @@ class Eynollah():
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self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
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self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
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self.ls_imgs = listdir(self.dir_in)
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self.ls_imgs = listdir(self.dirs.dir_in)
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def _cache_images(self, image_filename=None, image_pil=None):
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@ -228,21 +212,14 @@ class Eynollah():
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self._imgs = self._cache_images(image_filename=image_filename)
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self.image_filename = image_filename
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self.plotter = None if not self.enable_plotting else EynollahPlotter(
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dir_out=self.dir_out,
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dir_of_all=self.dir_of_all,
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dir_save_page=self.dir_save_page,
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dir_of_deskewed=self.dir_of_deskewed,
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dir_of_cropped_images=self.dir_of_cropped_images,
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dir_of_layout=self.dir_of_layout,
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image_filename_stem=Path(Path(image_filename).name).stem)
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self.plotter = None if not self.enable_plotting else EynollahPlotter(self.dirs, image_filename_stem=Path(Path(image_filename).name).stem)
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self.writer = EynollahXmlWriter(
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dir_out=self.dir_out,
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dir_out=self.dirs.dir_out,
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image_filename=self.image_filename,
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curved_line=self.curved_line,
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textline_light = self.textline_light,
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pcgts=self.pcgts)
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def imread(self, grayscale=False, uint8=True):
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key = 'img'
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if grayscale:
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@ -415,7 +392,7 @@ class Eynollah():
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img = self.imread()
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_, page_coord = self.early_page_for_num_of_column_classification(img)
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_num_classifier = self.load_model(self.model_dir_of_col_classifier)
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if self.input_binary:
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img_in = np.copy(img)
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@ -439,7 +416,7 @@ class Eynollah():
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img_in[0, :, :, 1] = img_1ch[:, :]
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img_in[0, :, :, 2] = img_1ch[:, :]
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if not self.dir_in:
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if not self.dirs.dir_in:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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@ -462,7 +439,7 @@ class Eynollah():
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self.logger.info("Detected %s DPI", dpi)
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if self.input_binary:
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img = self.imread()
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if self.dir_in:
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if self.dirs.dir_in:
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prediction_bin = self.do_prediction(True, img, self.model_bin)
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else:
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@ -484,7 +461,7 @@ class Eynollah():
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t1 = time.time()
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_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_num_classifier = self.load_model(self.model_dir_of_col_classifier)
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if self.input_binary:
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@ -506,7 +483,7 @@ class Eynollah():
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img_in[0, :, :, 2] = img_1ch[:, :]
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if self.dir_in:
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if self.dirs.dir_in:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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@ -896,10 +873,10 @@ class Eynollah():
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if not self.ignore_page_extraction:
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_page = self.load_model(self.model_page_dir)
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if not self.dir_in:
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if not self.dirs.dir_in:
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img_page_prediction = self.do_prediction(False, img, model_page)
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else:
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img_page_prediction = self.do_prediction(False, img, self.model_page)
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@ -944,11 +921,11 @@ class Eynollah():
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img = img.astype(np.uint8)
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else:
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img = self.imread()
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_page = self.load_model(self.model_page_dir)
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img = cv2.GaussianBlur(img, (5, 5), 0)
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if self.dir_in:
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if self.dirs.dir_in:
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img_page_prediction = self.do_prediction(False, img, self.model_page)
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else:
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img_page_prediction = self.do_prediction(False, img, model_page)
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@ -977,7 +954,7 @@ class Eynollah():
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self.logger.debug("enter extract_text_regions")
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img_height_h = img.shape[0]
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img_width_h = img.shape[1]
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_region = self.load_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
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else:
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model_region = self.model_region_fl if patches else self.model_region_fl_np
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@ -1444,19 +1421,19 @@ class Eynollah():
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def textline_contours(self, img, patches, scaler_h, scaler_w):
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self.logger.debug('enter textline_contours')
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_textline = self.load_model(self.model_textline_dir if patches else self.model_textline_dir_np)
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img = img.astype(np.uint8)
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img_org = np.copy(img)
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img_h = img_org.shape[0]
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img_w = img_org.shape[1]
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img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
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if not self.dir_in:
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if not self.dirs.dir_in:
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prediction_textline = self.do_prediction(patches, img, model_textline)
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else:
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prediction_textline = self.do_prediction(patches, img, self.model_textline)
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prediction_textline = resize_image(prediction_textline, img_h, img_w)
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if not self.dir_in:
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if not self.dirs.dir_in:
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prediction_textline_longshot = self.do_prediction(False, img, model_textline)
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else:
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prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
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@ -1502,6 +1479,7 @@ class Eynollah():
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q.put(slopes_sub)
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poly.put(poly_sub)
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box_sub.put(boxes_sub_new)
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def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier):
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self.logger.debug("enter get_regions_light_v")
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erosion_hurts = False
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@ -1536,7 +1514,7 @@ class Eynollah():
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img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
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img_resized = resize_image(img,img_h_new, img_w_new )
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_bin = self.load_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img_resized, model_bin)
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else:
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@ -1555,7 +1533,7 @@ class Eynollah():
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textline_mask_tot_ea = self.run_textline(img_bin)
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_region = self.load_model(self.model_region_dir_p_ens_light)
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prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
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else:
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@ -1600,14 +1578,14 @@ class Eynollah():
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img_height_h = img_org.shape[0]
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img_width_h = img_org.shape[1]
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if not self.dir_in:
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if not self.dirs.dir_in:
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model_region = self.load_model(self.model_region_dir_p_ens)
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ratio_y=1.3
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ratio_x=1
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img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if not self.dir_in:
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if not self.dirs.dir_in:
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prediction_regions_org_y = self.do_prediction(True, img, model_region)
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else:
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prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
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@ -1629,7 +1607,7 @@ class Eynollah():
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img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
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if self.dir_in:
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if self.dirs.dir_in:
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prediction_regions_org = self.do_prediction(True, img, self.model_region)
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else:
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prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
|
|
@ -1639,12 +1617,12 @@ class Eynollah():
|
|||
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
|
||||
|
||||
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
model_region = self.load_model(self.model_region_dir_p2)
|
||||
|
||||
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
|
||||
|
||||
if self.dir_in:
|
||||
if self.dirs.dir_in:
|
||||
prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
|
||||
else:
|
||||
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
|
||||
|
|
@ -1678,7 +1656,7 @@ class Eynollah():
|
|||
if self.input_binary:
|
||||
prediction_bin = np.copy(img_org)
|
||||
else:
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
model_bin = self.load_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
||||
else:
|
||||
|
|
@ -1691,7 +1669,7 @@ class Eynollah():
|
|||
|
||||
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
model_region = self.load_model(self.model_region_dir_p_ens)
|
||||
ratio_y=1
|
||||
ratio_x=1
|
||||
|
|
@ -1699,7 +1677,7 @@ class Eynollah():
|
|||
|
||||
img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
||||
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
else:
|
||||
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
|
|
@ -1731,7 +1709,7 @@ class Eynollah():
|
|||
if self.input_binary:
|
||||
prediction_bin = np.copy(img_org)
|
||||
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
model_bin = self.load_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
||||
else:
|
||||
|
|
@ -1746,7 +1724,7 @@ class Eynollah():
|
|||
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
model_region = self.load_model(self.model_region_dir_p_ens)
|
||||
|
||||
else:
|
||||
|
|
@ -1756,7 +1734,7 @@ class Eynollah():
|
|||
|
||||
|
||||
img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
else:
|
||||
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
|
|
@ -2755,13 +2733,13 @@ class Eynollah():
|
|||
|
||||
t0_tot = time.time()
|
||||
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
self.ls_imgs = [1]
|
||||
|
||||
for img_name in self.ls_imgs:
|
||||
t0 = time.time()
|
||||
if self.dir_in:
|
||||
self.reset_file_name_dir(join(self.dir_in,img_name))
|
||||
if self.dirs.dir_in:
|
||||
self.reset_file_name_dir(join(self.dirs.dir_in,img_name))
|
||||
|
||||
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
|
||||
self.logger.info("Enhancing took %.1fs ", time.time() - t0)
|
||||
|
|
@ -2789,7 +2767,7 @@ class Eynollah():
|
|||
self.logger.info("No columns detected, outputting an empty PAGE-XML")
|
||||
pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [], cont_page, [], [])
|
||||
self.logger.info("Job done in %.1fs", time.time() - t1)
|
||||
if self.dir_in:
|
||||
if self.dirs.dir_in:
|
||||
self.writer.write_pagexml(pcgts)
|
||||
continue
|
||||
else:
|
||||
|
|
@ -3017,7 +2995,7 @@ class Eynollah():
|
|||
|
||||
pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
|
||||
self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
return pcgts
|
||||
else:
|
||||
contours_only_text_parent_h = None
|
||||
|
|
@ -3028,11 +3006,11 @@ class Eynollah():
|
|||
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
|
||||
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
|
||||
self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
if not self.dir_in:
|
||||
if not self.dirs.dir_in:
|
||||
return pcgts
|
||||
|
||||
if self.dir_in:
|
||||
if self.dirs.dir_in:
|
||||
self.writer.write_pagexml(pcgts)
|
||||
#self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
if self.dir_in:
|
||||
if self.dirs.dir_in:
|
||||
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ from scipy.ndimage import gaussian_filter1d
|
|||
from .utils import crop_image_inside_box
|
||||
from .utils.rotate import rotate_image_different
|
||||
from .utils.resize import resize_image
|
||||
from .utils.dirs import EynollahDirs
|
||||
|
||||
class EynollahPlotter():
|
||||
"""
|
||||
|
|
@ -17,23 +18,13 @@ class EynollahPlotter():
|
|||
def __init__(
|
||||
self,
|
||||
*,
|
||||
dir_out,
|
||||
dir_of_all,
|
||||
dir_save_page,
|
||||
dir_of_deskewed,
|
||||
dir_of_layout,
|
||||
dir_of_cropped_images,
|
||||
dirs : EynollahDirs,
|
||||
image_filename_stem,
|
||||
image_org=None,
|
||||
scale_x=1,
|
||||
scale_y=1,
|
||||
):
|
||||
self.dir_out = dir_out
|
||||
self.dir_of_all = dir_of_all
|
||||
self.dir_save_page = dir_save_page
|
||||
self.dir_of_layout = dir_of_layout
|
||||
self.dir_of_cropped_images = dir_of_cropped_images
|
||||
self.dir_of_deskewed = dir_of_deskewed
|
||||
self.dirs = dirs
|
||||
self.image_filename_stem = image_filename_stem
|
||||
# XXX TODO hacky these cannot be set at init time
|
||||
self.image_org = image_org
|
||||
|
|
@ -41,7 +32,7 @@ class EynollahPlotter():
|
|||
self.scale_y = scale_y
|
||||
|
||||
def save_plot_of_layout_main(self, text_regions_p, image_page):
|
||||
if self.dir_of_layout is not None:
|
||||
if self.dirs.dir_of_layout is not None:
|
||||
values = np.unique(text_regions_p[:, :])
|
||||
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
||||
pixels=['Background' , 'Main text' , 'Image' , 'Separator','Marginalia']
|
||||
|
|
@ -52,11 +43,11 @@ class EynollahPlotter():
|
|||
colors = [im.cmap(im.norm(value)) for value in values]
|
||||
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
||||
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
|
||||
plt.savefig(os.path.join(self.dir_of_layout, self.image_filename_stem + "_layout_main.png"))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_layout, self.image_filename_stem + "_layout_main.png"))
|
||||
|
||||
|
||||
def save_plot_of_layout_main_all(self, text_regions_p, image_page):
|
||||
if self.dir_of_all is not None:
|
||||
if self.dirs.dir_of_all is not None:
|
||||
values = np.unique(text_regions_p[:, :])
|
||||
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
||||
pixels=['Background' , 'Main text' , 'Image' , 'Separator','Marginalia']
|
||||
|
|
@ -70,10 +61,10 @@ class EynollahPlotter():
|
|||
colors = [im.cmap(im.norm(value)) for value in values]
|
||||
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
||||
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
|
||||
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_layout_main_and_page.png"))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_all, self.image_filename_stem + "_layout_main_and_page.png"))
|
||||
|
||||
def save_plot_of_layout(self, text_regions_p, image_page):
|
||||
if self.dir_of_layout is not None:
|
||||
if self.dirs.dir_of_layout is not None:
|
||||
values = np.unique(text_regions_p[:, :])
|
||||
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
||||
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capital", "Image", "Separator", "Tables"]
|
||||
|
|
@ -84,10 +75,10 @@ class EynollahPlotter():
|
|||
colors = [im.cmap(im.norm(value)) for value in values]
|
||||
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
||||
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
|
||||
plt.savefig(os.path.join(self.dir_of_layout, self.image_filename_stem + "_layout.png"))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_layout, self.image_filename_stem + "_layout.png"))
|
||||
|
||||
def save_plot_of_layout_all(self, text_regions_p, image_page):
|
||||
if self.dir_of_all is not None:
|
||||
if self.dirs.dir_of_all is not None:
|
||||
values = np.unique(text_regions_p[:, :])
|
||||
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
||||
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capital", "Image", "Separator", "Tables"]
|
||||
|
|
@ -101,10 +92,10 @@ class EynollahPlotter():
|
|||
colors = [im.cmap(im.norm(value)) for value in values]
|
||||
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
||||
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
|
||||
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_layout_and_page.png"))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_all, self.image_filename_stem + "_layout_and_page.png"))
|
||||
|
||||
def save_plot_of_textlines(self, textline_mask_tot_ea, image_page):
|
||||
if self.dir_of_all is not None:
|
||||
if self.dirs.dir_of_all is not None:
|
||||
values = np.unique(textline_mask_tot_ea[:, :])
|
||||
pixels = ["Background", "Textlines"]
|
||||
values_indexes = [0, 1]
|
||||
|
|
@ -117,25 +108,25 @@ class EynollahPlotter():
|
|||
colors = [im.cmap(im.norm(value)) for value in values]
|
||||
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
||||
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
|
||||
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_textline_and_page.png"))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_all, self.image_filename_stem + "_textline_and_page.png"))
|
||||
|
||||
def save_deskewed_image(self, slope_deskew):
|
||||
if self.dir_of_all is not None:
|
||||
cv2.imwrite(os.path.join(self.dir_of_all, self.image_filename_stem + "_org.png"), self.image_org)
|
||||
if self.dir_of_deskewed is not None:
|
||||
if self.dirs.dir_of_all is not None:
|
||||
cv2.imwrite(os.path.join(self.dirs.dir_of_all, self.image_filename_stem + "_org.png"), self.image_org)
|
||||
if self.dirs.dir_of_deskewed is not None:
|
||||
img_rotated = rotate_image_different(self.image_org, slope_deskew)
|
||||
cv2.imwrite(os.path.join(self.dir_of_deskewed, self.image_filename_stem + "_deskewed.png"), img_rotated)
|
||||
cv2.imwrite(os.path.join(self.dirs.dir_of_deskewed, self.image_filename_stem + "_deskewed.png"), img_rotated)
|
||||
|
||||
def save_page_image(self, image_page):
|
||||
if self.dir_of_all is not None:
|
||||
cv2.imwrite(os.path.join(self.dir_of_all, self.image_filename_stem + "_page.png"), image_page)
|
||||
if self.dir_save_page is not None:
|
||||
cv2.imwrite(os.path.join(self.dir_save_page, self.image_filename_stem + "_page.png"), image_page)
|
||||
if self.dirs.dir_of_all is not None:
|
||||
cv2.imwrite(os.path.join(self.dirs.dir_of_all, self.image_filename_stem + "_page.png"), image_page)
|
||||
if self.dirs.dir_save_page is not None:
|
||||
cv2.imwrite(os.path.join(self.dirs.dir_save_page, self.image_filename_stem + "_page.png"), image_page)
|
||||
def save_enhanced_image(self, img_res):
|
||||
cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem + "_enhanced.png"), img_res)
|
||||
cv2.imwrite(os.path.join(self.dirs.dir_out, self.image_filename_stem + "_enhanced.png"), img_res)
|
||||
|
||||
def save_plot_of_textline_density(self, img_patch_org):
|
||||
if self.dir_of_all is not None:
|
||||
if self.dirs.dir_of_all is not None:
|
||||
plt.figure(figsize=(80,40))
|
||||
plt.rcParams['font.size']='50'
|
||||
plt.subplot(1,2,1)
|
||||
|
|
@ -146,10 +137,10 @@ class EynollahPlotter():
|
|||
plt.ylabel('Height',fontsize=60)
|
||||
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
|
||||
plt.gca().invert_yaxis()
|
||||
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem+'_density_of_textline.png'))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_all, self.image_filename_stem+'_density_of_textline.png'))
|
||||
|
||||
def save_plot_of_rotation_angle(self, angels, var_res):
|
||||
if self.dir_of_all is not None:
|
||||
if self.dirs.dir_of_all is not None:
|
||||
plt.figure(figsize=(60,30))
|
||||
plt.rcParams['font.size']='50'
|
||||
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
|
||||
|
|
@ -157,10 +148,10 @@ class EynollahPlotter():
|
|||
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
|
||||
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
|
||||
plt.legend(loc='best')
|
||||
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem+'_rotation_angle.png'))
|
||||
plt.savefig(os.path.join(self.dirs.dir_of_all, self.image_filename_stem+'_rotation_angle.png'))
|
||||
|
||||
def write_images_into_directory(self, img_contours, image_page):
|
||||
if self.dir_of_cropped_images is not None:
|
||||
if self.dirs.dir_of_cropped_images is not None:
|
||||
index = 0
|
||||
for cont_ind in img_contours:
|
||||
x, y, w, h = cv2.boundingRect(cont_ind)
|
||||
|
|
@ -169,7 +160,7 @@ class EynollahPlotter():
|
|||
|
||||
croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
|
||||
|
||||
path = os.path.join(self.dir_of_cropped_images, self.image_filename_stem + "_" + str(index) + ".jpg")
|
||||
path = os.path.join(self.dirs.dir_of_cropped_images, self.image_filename_stem + "_" + str(index) + ".jpg")
|
||||
cv2.imwrite(path, croped_page)
|
||||
index += 1
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ from ocrd.processor.ocrd_page_result import OcrdPageResult
|
|||
from ocrd_models import OcrdPage
|
||||
from ocrd import Processor
|
||||
|
||||
from qurator.eynollah.utils.dirs import EynollahDirs
|
||||
|
||||
from .eynollah import Eynollah
|
||||
|
||||
class EynollahProcessor(Processor):
|
||||
|
|
@ -20,7 +22,9 @@ class EynollahProcessor(Processor):
|
|||
# page_image, _, _ = self.workspace.image_from_page(page, page_id, feature_filter='binarized')
|
||||
image_filename = self.workspace.download_file(next(self.workspace.mets.find_files(local_filename=page.imageFilename))).local_filename
|
||||
Eynollah(
|
||||
self.resolve_resource(self.parameter['models']),
|
||||
EynollahDirs(
|
||||
dir_models=self.resolve_resource(self.parameter['models']),
|
||||
),
|
||||
self.logger,
|
||||
allow_enhancement=self.parameter['allow_enhancement'],
|
||||
curved_line=self.parameter['curved_line'],
|
||||
|
|
|
|||
19
qurator/eynollah/utils/dirs.py
Normal file
19
qurator/eynollah/utils/dirs.py
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
|
||||
@dataclass()
|
||||
class EynollahDirs():
|
||||
"""
|
||||
Wrapper for all the dir_ kwargs everywhere
|
||||
"""
|
||||
dir_models : str
|
||||
dir_out : Optional[str] = None
|
||||
dir_in : 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
|
||||
|
||||
|
||||
|
|
@ -2,6 +2,9 @@
|
|||
# pylint: disable=import-error
|
||||
from pathlib import Path
|
||||
import os.path
|
||||
from typing import Optional
|
||||
|
||||
from ocrd_models import OcrdPage
|
||||
|
||||
from .utils.xml import create_page_xml, xml_reading_order
|
||||
from .utils.counter import EynollahIdCounter
|
||||
|
|
@ -22,7 +25,15 @@ import numpy as np
|
|||
|
||||
class EynollahXmlWriter():
|
||||
|
||||
def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
image_filename : str,
|
||||
dir_out : Optional[str],
|
||||
curved_line : bool,
|
||||
textline_light : bool,
|
||||
pcgts : Optional[OcrdPage] = None
|
||||
):
|
||||
self.logger = getLogger('eynollah.writer')
|
||||
self.counter = EynollahIdCounter()
|
||||
self.dir_out = dir_out
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue