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3231 lines
176 KiB
Python
3231 lines
176 KiB
Python
# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring,missing-class-docstring,too-many-branches
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# pylint: disable=too-many-locals,wrong-import-position,too-many-lines,too-many-statements,chained-comparison,fixme,broad-except,c-extension-no-member
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# pylint: disable=too-many-public-methods,too-many-arguments,too-many-instance-attributes,too-many-public-methods,
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# pylint: disable=consider-using-enumerate
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"""
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document layout analysis (segmentation) with output in PAGE-XML
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"""
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import math
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import os
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import sys
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import time
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import warnings
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from pathlib import Path
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from multiprocessing import Process, Queue, cpu_count
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import gc
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from ocrd_utils import getLogger
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import cv2
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import numpy as np
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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stderr = sys.stderr
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sys.stderr = open(os.devnull, "w")
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import tensorflow as tf
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from tensorflow.python.keras import backend as K
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from tensorflow.keras.models import load_model
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sys.stderr = stderr
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tf.get_logger().setLevel("ERROR")
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warnings.filterwarnings("ignore")
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from scipy.signal import find_peaks
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import matplotlib.pyplot as plt
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from scipy.ndimage import gaussian_filter1d
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from keras.backend import set_session
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from tensorflow.keras import layers
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from .utils.contour import (
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filter_contours_area_of_image,
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filter_contours_area_of_image_tables,
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find_contours_mean_y_diff,
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find_new_features_of_contours,
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find_features_of_contours,
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get_text_region_boxes_by_given_contours,
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get_textregion_contours_in_org_image,
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get_textregion_contours_in_org_image_light,
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return_contours_of_image,
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return_contours_of_interested_region,
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return_contours_of_interested_region_by_min_size,
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return_contours_of_interested_textline,
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return_parent_contours,
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)
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from .utils.rotate import (
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rotate_image,
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rotation_not_90_func,
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rotation_not_90_func_full_layout)
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from .utils.separate_lines import (
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textline_contours_postprocessing,
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separate_lines_new2,
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return_deskew_slop)
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from .utils.drop_capitals import (
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adhere_drop_capital_region_into_corresponding_textline,
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filter_small_drop_capitals_from_no_patch_layout)
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from .utils.marginals import get_marginals
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from .utils.resize import resize_image
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from .utils import (
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boosting_headers_by_longshot_region_segmentation,
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crop_image_inside_box,
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find_num_col,
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otsu_copy_binary,
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put_drop_out_from_only_drop_model,
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putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
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check_any_text_region_in_model_one_is_main_or_header,
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check_any_text_region_in_model_one_is_main_or_header_light,
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small_textlines_to_parent_adherence2,
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order_of_regions,
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find_number_of_columns_in_document,
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return_boxes_of_images_by_order_of_reading_new)
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from .utils.pil_cv2 import check_dpi, pil2cv
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from .utils.xml import order_and_id_of_texts
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from .plot import EynollahPlotter
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from .writer import EynollahXmlWriter
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SLOPE_THRESHOLD = 0.13
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RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45:
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DPI_THRESHOLD = 298
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MAX_SLOPE = 999
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KERNEL = np.ones((5, 5), np.uint8)
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projection_dim = 64
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patch_size = 1
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num_patches =21*21#14*14#28*28#14*14#28*28
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class Patches(layers.Layer):
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def __init__(self, **kwargs):
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super(Patches, self).__init__()
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self.patch_size = patch_size
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def call(self, images):
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batch_size = tf.shape(images)[0]
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patches = tf.image.extract_patches(
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images=images,
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sizes=[1, self.patch_size, self.patch_size, 1],
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strides=[1, self.patch_size, self.patch_size, 1],
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rates=[1, 1, 1, 1],
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padding="VALID",
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)
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patch_dims = patches.shape[-1]
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patches = tf.reshape(patches, [batch_size, -1, patch_dims])
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return patches
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def get_config(self):
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config = super().get_config().copy()
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config.update({
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'patch_size': self.patch_size,
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})
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return config
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class PatchEncoder(layers.Layer):
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def __init__(self, **kwargs):
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super(PatchEncoder, self).__init__()
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self.num_patches = num_patches
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self.projection = layers.Dense(units=projection_dim)
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self.position_embedding = layers.Embedding(
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input_dim=num_patches, output_dim=projection_dim
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)
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def call(self, patch):
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positions = tf.range(start=0, limit=self.num_patches, delta=1)
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encoded = self.projection(patch) + self.position_embedding(positions)
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return encoded
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def get_config(self):
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config = super().get_config().copy()
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config.update({
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'num_patches': self.num_patches,
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'projection': self.projection,
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'position_embedding': self.position_embedding,
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})
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return config
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class Eynollah:
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def __init__(
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self,
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dir_models,
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image_filename=None,
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image_pil=None,
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image_filename_stem=None,
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dir_out=None,
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dir_in=None,
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dir_of_cropped_images=None,
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dir_of_layout=None,
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dir_of_deskewed=None,
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dir_of_all=None,
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dir_save_page=None,
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enable_plotting=False,
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allow_enhancement=False,
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curved_line=False,
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textline_light=False,
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full_layout=False,
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tables=False,
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input_binary=False,
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allow_scaling=False,
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headers_off=False,
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light_version=False,
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ignore_page_extraction=False,
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override_dpi=None,
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logger=None,
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pcgts=None,
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):
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if not 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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self._imgs = self._cache_images(image_filename=image_filename)
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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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self.textline_light = textline_light
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self.full_layout = full_layout
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self.tables = tables
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self.input_binary = input_binary
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self.allow_scaling = allow_scaling
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self.headers_off = headers_off
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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.writer = EynollahXmlWriter(
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dir_out=self.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 if logger else getLogger('eynollah')
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self.dir_models = dir_models
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self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425.h5"
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self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425.h5"
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self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425.h5"
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self.model_region_dir_p = dir_models + "/eynollah-main-regions-aug-scaling_20210425.h5"
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self.model_region_dir_p2 = dir_models + "/eynollah-main-regions-aug-rotation_20210425.h5"
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self.model_region_dir_fully_np = dir_models + "/eynollah-full-regions-1column_20210425.h5"
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self.model_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425.h5"
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self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425.h5"
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self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425.h5"
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self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314.h5"
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if self.textline_light:
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self.model_textline_dir = dir_models + "/model_17.h5"
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else:
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self.model_textline_dir = dir_models + "/eynollah-textline_20210425.h5"
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self.model_tables = dir_models + "/eynollah-tables_20210319.h5"
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if 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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set_session(session)
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self.model_page = self.our_load_model(self.model_page_dir)
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self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
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self.model_bin = self.our_load_model(self.model_dir_of_binarization)
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self.model_textline = self.our_load_model(self.model_textline_dir)
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self.model_region = self.our_load_model(self.model_region_dir_p_ens_light)
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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 = os.listdir(self.dir_in)
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if 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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set_session(session)
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self.model_page = self.our_load_model(self.model_page_dir)
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self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
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self.model_bin = self.our_load_model(self.model_dir_of_binarization)
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self.model_textline = self.our_load_model(self.model_textline_dir)
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self.model_region = self.our_load_model(self.model_region_dir_p_ens)
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self.model_region_p2 = self.our_load_model(self.model_region_dir_p2)
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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.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
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self.ls_imgs = os.listdir(self.dir_in)
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def _cache_images(self, image_filename=None, image_pil=None):
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ret = {}
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if image_filename:
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ret['img'] = cv2.imread(image_filename)
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self.dpi = check_dpi(image_filename)
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else:
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ret['img'] = pil2cv(image_pil)
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self.dpi = check_dpi(image_pil)
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ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY)
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for prefix in ('', '_grayscale'):
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ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
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return ret
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def reset_file_name_dir(self, image_filename):
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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.writer = EynollahXmlWriter(
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dir_out=self.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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key += '_grayscale'
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if uint8:
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key += '_uint8'
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return self._imgs[key].copy()
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def isNaN(self, num):
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return num != num
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def predict_enhancement(self, img):
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self.logger.debug("enter predict_enhancement")
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model_enhancement, session_enhancement = self.start_new_session_and_model(self.model_dir_of_enhancement)
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img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
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img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
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if img.shape[0] < img_height_model:
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img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST)
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if img.shape[1] < img_width_model:
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img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST)
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margin = int(0 * img_width_model)
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width_mid = img_width_model - 2 * margin
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height_mid = img_height_model - 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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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
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nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
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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 + img_width_model
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else:
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index_x_d = i * width_mid
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index_x_u = index_x_d + img_width_model
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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 + img_height_model
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else:
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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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 - img_width_model
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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 - img_height_model
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|
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
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verbose=0)
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|
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seg = label_p_pred[0, :, :, :]
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seg = seg * 255
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|
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if i == 0 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg
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elif i == 0 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
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elif i == 0 and j != 0 and j != nyf - 1:
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|
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
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|
elif i == nxf - 1 and j != 0 and j != nyf - 1:
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|
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
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elif i != 0 and i != nxf - 1 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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elif i != 0 and i != nxf - 1 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
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else:
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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prediction_true = prediction_true.astype(int)
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session_enhancement.close()
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del model_enhancement
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del session_enhancement
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gc.collect()
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return prediction_true
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|
|
def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
|
|
self.logger.debug("enter calculate_width_height_by_columns")
|
|
if num_col == 1 and width_early < 1100:
|
|
img_w_new = 2000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
|
|
elif num_col == 1 and width_early >= 2500:
|
|
img_w_new = 2000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
|
|
elif num_col == 1 and width_early >= 1100 and width_early < 2500:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
elif num_col == 2 and width_early < 2000:
|
|
img_w_new = 2400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
|
|
elif num_col == 2 and width_early >= 3500:
|
|
img_w_new = 2400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
|
|
elif num_col == 2 and width_early >= 2000 and width_early < 3500:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
elif num_col == 3 and width_early < 2000:
|
|
img_w_new = 3000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
|
|
elif num_col == 3 and width_early >= 4000:
|
|
img_w_new = 3000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
|
|
elif num_col == 3 and width_early >= 2000 and width_early < 4000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
elif num_col == 4 and width_early < 2500:
|
|
img_w_new = 4000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
|
|
elif num_col == 4 and width_early >= 5000:
|
|
img_w_new = 4000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
|
|
elif num_col == 4 and width_early >= 2500 and width_early < 5000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
elif num_col == 5 and width_early < 3700:
|
|
img_w_new = 5000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
|
|
elif num_col == 5 and width_early >= 7000:
|
|
img_w_new = 5000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
|
|
elif num_col == 5 and width_early >= 3700 and width_early < 7000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
elif num_col == 6 and width_early < 4500:
|
|
img_w_new = 6500 # 5400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500)
|
|
else:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
|
|
img_new = np.copy(img)
|
|
num_column_is_classified = False
|
|
else:
|
|
img_new = resize_image(img, img_h_new, img_w_new)
|
|
num_column_is_classified = True
|
|
|
|
return img_new, num_column_is_classified
|
|
|
|
def resize_image_with_column_classifier(self, is_image_enhanced, img_bin):
|
|
self.logger.debug("enter resize_image_with_column_classifier")
|
|
if self.input_binary:
|
|
img = np.copy(img_bin)
|
|
else:
|
|
img = self.imread()
|
|
|
|
_, page_coord = self.early_page_for_num_of_column_classification(img)
|
|
if not self.dir_in:
|
|
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
|
|
if self.input_binary:
|
|
img_in = np.copy(img)
|
|
img_in = img_in / 255.0
|
|
width_early = img_in.shape[1]
|
|
img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
|
|
img_in = img_in.reshape(1, 448, 448, 3)
|
|
else:
|
|
img_1ch = self.imread(grayscale=True, uint8=False)
|
|
width_early = img_1ch.shape[1]
|
|
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
|
|
# plt.imshow(img_1ch)
|
|
# plt.show()
|
|
img_1ch = img_1ch / 255.0
|
|
|
|
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
|
|
|
|
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
|
|
img_in[0, :, :, 0] = img_1ch[:, :]
|
|
img_in[0, :, :, 1] = img_1ch[:, :]
|
|
img_in[0, :, :, 2] = img_1ch[:, :]
|
|
|
|
if not self.dir_in:
|
|
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
|
|
else:
|
|
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
|
|
num_col = np.argmax(label_p_pred[0]) + 1
|
|
|
|
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
|
|
if not self.dir_in:
|
|
session_col_classifier.close()
|
|
|
|
del model_num_classifier
|
|
del session_col_classifier
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
|
|
|
|
img_new, _ = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
|
|
|
|
if img_new.shape[1] > img.shape[1]:
|
|
img_new = self.predict_enhancement(img_new)
|
|
is_image_enhanced = True
|
|
|
|
return img, img_new, is_image_enhanced
|
|
|
|
def resize_and_enhance_image_with_column_classifier(self,light_version):
|
|
self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
|
|
dpi = self.dpi
|
|
self.logger.info("Detected %s DPI", dpi)
|
|
if self.input_binary:
|
|
img = self.imread()
|
|
if self.dir_in:
|
|
prediction_bin = self.do_prediction(True, img, self.model_bin)
|
|
else:
|
|
|
|
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
|
prediction_bin = self.do_prediction(True, img, model_bin)
|
|
|
|
prediction_bin=prediction_bin[:,:,0]
|
|
prediction_bin = (prediction_bin[:,:]==0)*1
|
|
prediction_bin = prediction_bin*255
|
|
|
|
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
|
|
|
if not self.dir_in:
|
|
session_bin.close()
|
|
del model_bin
|
|
del session_bin
|
|
gc.collect()
|
|
|
|
prediction_bin = prediction_bin.astype(np.uint8)
|
|
img= np.copy(prediction_bin)
|
|
img_bin = np.copy(prediction_bin)
|
|
else:
|
|
img = self.imread()
|
|
img_bin = None
|
|
|
|
_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
|
|
if not self.dir_in:
|
|
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
|
|
|
|
if self.input_binary:
|
|
img_in = np.copy(img)
|
|
width_early = img_in.shape[1]
|
|
img_in = img_in / 255.0
|
|
img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
|
|
img_in = img_in.reshape(1, 448, 448, 3)
|
|
else:
|
|
img_1ch = self.imread(grayscale=True)
|
|
width_early = img_1ch.shape[1]
|
|
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
|
|
img_1ch = img_1ch / 255.0
|
|
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
|
|
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
|
|
img_in[0, :, :, 0] = img_1ch[:, :]
|
|
img_in[0, :, :, 1] = img_1ch[:, :]
|
|
img_in[0, :, :, 2] = img_1ch[:, :]
|
|
|
|
|
|
if self.dir_in:
|
|
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
|
|
else:
|
|
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
|
|
num_col = np.argmax(label_p_pred[0]) + 1
|
|
|
|
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
|
|
if not self.dir_in:
|
|
session_col_classifier.close()
|
|
K.clear_session()
|
|
|
|
if dpi < DPI_THRESHOLD:
|
|
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
|
|
if light_version:
|
|
image_res = np.copy(img_new)
|
|
else:
|
|
image_res = self.predict_enhancement(img_new)
|
|
is_image_enhanced = True
|
|
else:
|
|
num_column_is_classified = True
|
|
image_res = np.copy(img)
|
|
is_image_enhanced = False
|
|
if not self.dir_in:
|
|
session_col_classifier.close()
|
|
|
|
|
|
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
|
|
return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
|
|
|
|
# pylint: disable=attribute-defined-outside-init
|
|
def get_image_and_scales(self, img_org, img_res, scale):
|
|
self.logger.debug("enter get_image_and_scales")
|
|
self.image = np.copy(img_res)
|
|
self.image_org = np.copy(img_org)
|
|
self.height_org = self.image.shape[0]
|
|
self.width_org = self.image.shape[1]
|
|
|
|
self.img_hight_int = int(self.image.shape[0] * scale)
|
|
self.img_width_int = int(self.image.shape[1] * scale)
|
|
self.scale_y = self.img_hight_int / float(self.image.shape[0])
|
|
self.scale_x = self.img_width_int / float(self.image.shape[1])
|
|
|
|
self.image = resize_image(self.image, self.img_hight_int, self.img_width_int)
|
|
|
|
# Also set for the plotter
|
|
if self.plotter:
|
|
self.plotter.image_org = self.image_org
|
|
self.plotter.scale_y = self.scale_y
|
|
self.plotter.scale_x = self.scale_x
|
|
# Also set for the writer
|
|
self.writer.image_org = self.image_org
|
|
self.writer.scale_y = self.scale_y
|
|
self.writer.scale_x = self.scale_x
|
|
self.writer.height_org = self.height_org
|
|
self.writer.width_org = self.width_org
|
|
|
|
def get_image_and_scales_after_enhancing(self, img_org, img_res):
|
|
self.logger.debug("enter get_image_and_scales_after_enhancing")
|
|
self.image = np.copy(img_res)
|
|
self.image = self.image.astype(np.uint8)
|
|
self.image_org = np.copy(img_org)
|
|
self.height_org = self.image_org.shape[0]
|
|
self.width_org = self.image_org.shape[1]
|
|
|
|
self.scale_y = img_res.shape[0] / float(self.image_org.shape[0])
|
|
self.scale_x = img_res.shape[1] / float(self.image_org.shape[1])
|
|
|
|
# Also set for the plotter
|
|
if self.plotter:
|
|
self.plotter.image_org = self.image_org
|
|
self.plotter.scale_y = self.scale_y
|
|
self.plotter.scale_x = self.scale_x
|
|
# Also set for the writer
|
|
self.writer.image_org = self.image_org
|
|
self.writer.scale_y = self.scale_y
|
|
self.writer.scale_x = self.scale_x
|
|
self.writer.height_org = self.height_org
|
|
self.writer.width_org = self.width_org
|
|
|
|
def start_new_session_and_model_old(self, model_dir):
|
|
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
|
|
config = tf.ConfigProto()
|
|
config.gpu_options.allow_growth = True
|
|
|
|
session = tf.InteractiveSession()
|
|
model = load_model(model_dir, compile=False)
|
|
|
|
return model, session
|
|
|
|
|
|
def start_new_session_and_model(self, model_dir):
|
|
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
|
|
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
|
|
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
|
|
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
|
|
|
|
# try:
|
|
# model = load_model(model_dir, compile=False)
|
|
# except:
|
|
# model = load_model(model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
|
|
if model_dir.endswith('.h5') and Path(model_dir[:-3]).exists():
|
|
# prefer SavedModel over HDF5 format if it exists
|
|
model_dir = model_dir[:-3]
|
|
model = load_model(model_dir, compile=False)
|
|
|
|
return model, session
|
|
|
|
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
|
|
self.logger.debug("enter do_prediction")
|
|
|
|
img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
|
|
img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
|
|
|
|
if not patches:
|
|
img_h_page = img.shape[0]
|
|
img_w_page = img.shape[1]
|
|
img = img / float(255.0)
|
|
img = resize_image(img, img_height_model, img_width_model)
|
|
|
|
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]),
|
|
verbose=0)
|
|
|
|
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)
|
|
|
|
|
|
else:
|
|
if img.shape[0] < img_height_model:
|
|
img = resize_image(img, img_height_model, img.shape[1])
|
|
|
|
if img.shape[1] < img_width_model:
|
|
img = resize_image(img, img.shape[0], img_width_model)
|
|
|
|
self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model)
|
|
margin = int(marginal_of_patch_percent * img_height_model)
|
|
width_mid = img_width_model - 2 * margin
|
|
height_mid = img_height_model - 2 * margin
|
|
img = img / float(255.0)
|
|
img = img.astype(np.float16)
|
|
img_h = img.shape[0]
|
|
img_w = img.shape[1]
|
|
prediction_true = np.zeros((img_h, img_w, 3))
|
|
mask_true = np.zeros((img_h, img_w))
|
|
nxf = img_w / float(width_mid)
|
|
nyf = img_h / float(height_mid)
|
|
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
|
|
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
|
|
|
|
for i in range(nxf):
|
|
for j in range(nyf):
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + img_width_model
|
|
else:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + img_width_model
|
|
if j == 0:
|
|
index_y_d = j * height_mid
|
|
index_y_u = index_y_d + img_height_model
|
|
else:
|
|
index_y_d = j * height_mid
|
|
index_y_u = index_y_d + img_height_model
|
|
if index_x_u > img_w:
|
|
index_x_u = img_w
|
|
index_x_d = img_w - img_width_model
|
|
if index_y_u > img_h:
|
|
index_y_u = img_h
|
|
index_y_d = img_h - img_height_model
|
|
|
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
|
|
verbose=0)
|
|
seg = np.argmax(label_p_pred, axis=3)[0]
|
|
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
|
|
|
if i == 0 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
|
#seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
|
|
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
elif i == nxf - 1 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
|
|
#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
|
|
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
elif i == 0 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
|
|
#seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
|
|
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
elif i == nxf - 1 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
|
#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
|
|
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
elif i == 0 and j != 0 and j != nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
|
#seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
|
|
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
elif i == nxf - 1 and j != 0 and j != nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
|
#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
|
|
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
elif i != 0 and i != nxf - 1 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
|
#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
|
|
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
elif i != 0 and i != nxf - 1 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
|
|
#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
|
|
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
else:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
|
#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
|
|
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
|
|
prediction_true = prediction_true.astype(np.uint8)
|
|
del model
|
|
gc.collect()
|
|
return prediction_true
|
|
def do_prediction_new_concept(self, patches, img, model, marginal_of_patch_percent=0.1):
|
|
self.logger.debug("enter do_prediction")
|
|
|
|
img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
|
|
img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
|
|
|
|
if not patches:
|
|
img_h_page = img.shape[0]
|
|
img_w_page = img.shape[1]
|
|
img = img / float(255.0)
|
|
img = resize_image(img, img_height_model, img_width_model)
|
|
|
|
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)
|
|
|
|
|
|
else:
|
|
if img.shape[0] < img_height_model:
|
|
img = resize_image(img, img_height_model, img.shape[1])
|
|
|
|
if img.shape[1] < img_width_model:
|
|
img = resize_image(img, img.shape[0], img_width_model)
|
|
|
|
self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model)
|
|
margin = int(marginal_of_patch_percent * img_height_model)
|
|
width_mid = img_width_model - 2 * margin
|
|
height_mid = img_height_model - 2 * margin
|
|
img = img / float(255.0)
|
|
img = img.astype(np.float16)
|
|
img_h = img.shape[0]
|
|
img_w = img.shape[1]
|
|
prediction_true = np.zeros((img_h, img_w, 3))
|
|
mask_true = np.zeros((img_h, img_w))
|
|
nxf = img_w / float(width_mid)
|
|
nyf = img_h / float(height_mid)
|
|
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
|
|
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
|
|
|
|
for i in range(nxf):
|
|
for j in range(nyf):
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + img_width_model
|
|
else:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + img_width_model
|
|
if j == 0:
|
|
index_y_d = j * height_mid
|
|
index_y_u = index_y_d + img_height_model
|
|
else:
|
|
index_y_d = j * height_mid
|
|
index_y_u = index_y_d + img_height_model
|
|
if index_x_u > img_w:
|
|
index_x_u = img_w
|
|
index_x_d = img_w - img_width_model
|
|
if index_y_u > img_h:
|
|
index_y_u = img_h
|
|
index_y_d = img_h - img_height_model
|
|
|
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
|
|
seg = np.argmax(label_p_pred, axis=3)[0]
|
|
|
|
|
|
seg_not_base = label_p_pred[0,:,:,4]
|
|
##seg2 = -label_p_pred[0,:,:,2]
|
|
|
|
|
|
seg_not_base[seg_not_base>0.03] =1
|
|
seg_not_base[seg_not_base<1] =0
|
|
|
|
|
|
|
|
seg_test = label_p_pred[0,:,:,1]
|
|
##seg2 = -label_p_pred[0,:,:,2]
|
|
|
|
|
|
seg_test[seg_test>0.75] =1
|
|
seg_test[seg_test<1] =0
|
|
|
|
|
|
seg_line = label_p_pred[0,:,:,3]
|
|
##seg2 = -label_p_pred[0,:,:,2]
|
|
|
|
|
|
seg_line[seg_line>0.1] =1
|
|
seg_line[seg_line<1] =0
|
|
|
|
|
|
seg_background = label_p_pred[0,:,:,0]
|
|
##seg2 = -label_p_pred[0,:,:,2]
|
|
|
|
|
|
seg_background[seg_background>0.25] =1
|
|
seg_background[seg_background<1] =0
|
|
##seg = seg+seg2
|
|
#seg = label_p_pred[0,:,:,2]
|
|
#seg[seg>0.4] =1
|
|
#seg[seg<1] =0
|
|
|
|
##plt.imshow(seg_test)
|
|
##plt.show()
|
|
|
|
##plt.imshow(seg_background)
|
|
##plt.show()
|
|
#seg[seg==1]=0
|
|
#seg[seg_test==1]=1
|
|
seg[seg_not_base==1]=4
|
|
seg[seg_background==1]=0
|
|
seg[(seg_line==1) & (seg==0)]=3
|
|
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
|
|
|
if i == 0 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
|
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
|
|
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
elif i == nxf - 1 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
|
|
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
|
|
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
elif i == 0 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
|
|
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
elif i == nxf - 1 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
|
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
|
|
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
elif i == 0 and j != 0 and j != nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
|
|
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
elif i == nxf - 1 and j != 0 and j != nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
|
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
|
|
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
elif i != 0 and i != nxf - 1 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
|
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
|
|
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
elif i != 0 and i != nxf - 1 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
|
|
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
else:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
|
|
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
|
|
prediction_true = prediction_true.astype(np.uint8)
|
|
del model
|
|
gc.collect()
|
|
return prediction_true
|
|
|
|
def extract_page(self):
|
|
self.logger.debug("enter extract_page")
|
|
cont_page = []
|
|
if not self.ignore_page_extraction:
|
|
if not self.dir_in:
|
|
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
|
img = cv2.GaussianBlur(self.image, (5, 5), 0)
|
|
if not self.dir_in:
|
|
img_page_prediction = self.do_prediction(False, img, model_page)
|
|
else:
|
|
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
|
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
|
|
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
if len(contours)>0:
|
|
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
|
|
cnt = contours[np.argmax(cnt_size)]
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
if x <= 30:
|
|
w += x
|
|
x = 0
|
|
if (self.image.shape[1] - (x + w)) <= 30:
|
|
w = w + (self.image.shape[1] - (x + w))
|
|
if y <= 30:
|
|
h = h + y
|
|
y = 0
|
|
if (self.image.shape[0] - (y + h)) <= 30:
|
|
h = h + (self.image.shape[0] - (y + h))
|
|
|
|
box = [x, y, w, h]
|
|
else:
|
|
box = [0, 0, img.shape[1], img.shape[0]]
|
|
croped_page, page_coord = crop_image_inside_box(box, self.image)
|
|
cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
|
|
if not self.dir_in:
|
|
session_page.close()
|
|
del model_page
|
|
del session_page
|
|
K.clear_session()
|
|
gc.collect()
|
|
self.logger.debug("exit extract_page")
|
|
else:
|
|
box = [0, 0, self.image.shape[1], self.image.shape[0]]
|
|
croped_page, page_coord = crop_image_inside_box(box, self.image)
|
|
cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
|
|
return croped_page, page_coord, cont_page
|
|
|
|
def early_page_for_num_of_column_classification(self,img_bin):
|
|
if not self.ignore_page_extraction:
|
|
self.logger.debug("enter early_page_for_num_of_column_classification")
|
|
if self.input_binary:
|
|
img =np.copy(img_bin)
|
|
img = img.astype(np.uint8)
|
|
else:
|
|
img = self.imread()
|
|
if not self.dir_in:
|
|
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
|
|
if self.dir_in:
|
|
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
|
else:
|
|
img_page_prediction = self.do_prediction(False, img, model_page)
|
|
|
|
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
|
|
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
if len(contours)>0:
|
|
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
|
|
cnt = contours[np.argmax(cnt_size)]
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
box = [x, y, w, h]
|
|
else:
|
|
box = [0, 0, img.shape[1], img.shape[0]]
|
|
croped_page, page_coord = crop_image_inside_box(box, img)
|
|
|
|
if not self.dir_in:
|
|
session_page.close()
|
|
del model_page
|
|
del session_page
|
|
K.clear_session()
|
|
|
|
gc.collect()
|
|
|
|
self.logger.debug("exit early_page_for_num_of_column_classification")
|
|
else:
|
|
img = self.imread()
|
|
box = [0, 0, img.shape[1], img.shape[0]]
|
|
croped_page, page_coord = crop_image_inside_box(box, img)
|
|
return croped_page, page_coord
|
|
|
|
|
|
def extract_text_regions(self, img, patches, cols):
|
|
self.logger.debug("enter extract_text_regions")
|
|
img_height_h = img.shape[0]
|
|
img_width_h = img.shape[1]
|
|
if not self.dir_in:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
|
|
else:
|
|
model_region = self.model_region_fl if patches else self.model_region_fl_np
|
|
|
|
if not patches:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
prediction_regions2 = None
|
|
else:
|
|
if cols == 1:
|
|
img2 = otsu_copy_binary(img)
|
|
img2 = img2.astype(np.uint8)
|
|
img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
|
|
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
|
|
|
|
if cols == 2:
|
|
img2 = otsu_copy_binary(img)
|
|
img2 = img2.astype(np.uint8)
|
|
img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
|
|
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
|
|
|
|
elif cols > 2:
|
|
img2 = otsu_copy_binary(img)
|
|
img2 = img2.astype(np.uint8)
|
|
img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
|
|
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
|
|
|
|
if cols == 2:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
if img_width_h >= 2000:
|
|
img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
|
|
img = img.astype(np.uint8)
|
|
|
|
if cols == 1:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
|
|
img = img.astype(np.uint8)
|
|
|
|
if cols == 3:
|
|
if (self.scale_x == 1 and img_width_h > 3000) or (self.scale_x != 1 and img_width_h > 2800):
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img = resize_image(img, int(img_height_h * 2800 / float(img_width_h)), 2800)
|
|
else:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
|
|
if cols == 4:
|
|
if (self.scale_x == 1 and img_width_h > 4000) or (self.scale_x != 1 and img_width_h > 3700):
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img= resize_image(img, int(img_height_h * 3700 / float(img_width_h)), 3700)
|
|
else:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
|
|
|
|
if cols == 5:
|
|
if self.scale_x == 1 and img_width_h > 5000:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img= resize_image(img, int(img_height_h * 0.7), int(img_width_h * 0.7))
|
|
else:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9) )
|
|
|
|
if cols >= 6:
|
|
if img_width_h > 5600:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img= resize_image(img, int(img_height_h * 5600 / float(img_width_h)), 5600)
|
|
else:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint8)
|
|
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
|
|
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
|
|
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
|
|
|
|
if not self.dir_in:
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
self.logger.debug("exit extract_text_regions")
|
|
return prediction_regions, prediction_regions2
|
|
|
|
def get_slopes_and_deskew_new_light(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew):
|
|
self.logger.debug("enter get_slopes_and_deskew_new")
|
|
num_cores = cpu_count()
|
|
queue_of_all_params = Queue()
|
|
|
|
processes = []
|
|
nh = np.linspace(0, len(boxes), num_cores + 1)
|
|
indexes_by_text_con = np.array(range(len(contours_par)))
|
|
for i in range(num_cores):
|
|
boxes_per_process = boxes[int(nh[i]) : int(nh[i + 1])]
|
|
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
|
|
contours_par_per_process = contours_par[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=self.do_work_of_slopes_new_light, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process, contours_par_per_process, indexes_text_con_per_process, image_page_rotated, slope_deskew)))
|
|
for i in range(num_cores):
|
|
processes[i].start()
|
|
|
|
slopes = []
|
|
all_found_texline_polygons = []
|
|
all_found_text_regions = []
|
|
all_found_text_regions_par = []
|
|
boxes = []
|
|
all_box_coord = []
|
|
all_index_text_con = []
|
|
for i in range(num_cores):
|
|
list_all_par = queue_of_all_params.get(True)
|
|
slopes_for_sub_process = list_all_par[0]
|
|
polys_for_sub_process = list_all_par[1]
|
|
boxes_for_sub_process = list_all_par[2]
|
|
contours_for_subprocess = list_all_par[3]
|
|
contours_par_for_subprocess = list_all_par[4]
|
|
boxes_coord_for_subprocess = list_all_par[5]
|
|
indexes_for_subprocess = list_all_par[6]
|
|
for j in range(len(slopes_for_sub_process)):
|
|
slopes.append(slopes_for_sub_process[j])
|
|
all_found_texline_polygons.append(polys_for_sub_process[j])
|
|
boxes.append(boxes_for_sub_process[j])
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
all_found_text_regions_par.append(contours_par_for_subprocess[j])
|
|
all_box_coord.append(boxes_coord_for_subprocess[j])
|
|
all_index_text_con.append(indexes_for_subprocess[j])
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
self.logger.debug('slopes %s', slopes)
|
|
self.logger.debug("exit get_slopes_and_deskew_new")
|
|
return slopes, all_found_texline_polygons, boxes, all_found_text_regions, all_found_text_regions_par, all_box_coord, all_index_text_con
|
|
|
|
def get_slopes_and_deskew_new(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew):
|
|
self.logger.debug("enter get_slopes_and_deskew_new")
|
|
num_cores = cpu_count()
|
|
queue_of_all_params = Queue()
|
|
|
|
processes = []
|
|
nh = np.linspace(0, len(boxes), num_cores + 1)
|
|
indexes_by_text_con = np.array(range(len(contours_par)))
|
|
for i in range(num_cores):
|
|
boxes_per_process = boxes[int(nh[i]) : int(nh[i + 1])]
|
|
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
|
|
contours_par_per_process = contours_par[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=self.do_work_of_slopes_new, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process, contours_par_per_process, indexes_text_con_per_process, image_page_rotated, slope_deskew)))
|
|
for i in range(num_cores):
|
|
processes[i].start()
|
|
|
|
slopes = []
|
|
all_found_texline_polygons = []
|
|
all_found_text_regions = []
|
|
all_found_text_regions_par = []
|
|
boxes = []
|
|
all_box_coord = []
|
|
all_index_text_con = []
|
|
for i in range(num_cores):
|
|
list_all_par = queue_of_all_params.get(True)
|
|
slopes_for_sub_process = list_all_par[0]
|
|
polys_for_sub_process = list_all_par[1]
|
|
boxes_for_sub_process = list_all_par[2]
|
|
contours_for_subprocess = list_all_par[3]
|
|
contours_par_for_subprocess = list_all_par[4]
|
|
boxes_coord_for_subprocess = list_all_par[5]
|
|
indexes_for_subprocess = list_all_par[6]
|
|
for j in range(len(slopes_for_sub_process)):
|
|
slopes.append(slopes_for_sub_process[j])
|
|
all_found_texline_polygons.append(polys_for_sub_process[j])
|
|
boxes.append(boxes_for_sub_process[j])
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
all_found_text_regions_par.append(contours_par_for_subprocess[j])
|
|
all_box_coord.append(boxes_coord_for_subprocess[j])
|
|
all_index_text_con.append(indexes_for_subprocess[j])
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
self.logger.debug('slopes %s', slopes)
|
|
self.logger.debug("exit get_slopes_and_deskew_new")
|
|
return slopes, all_found_texline_polygons, boxes, all_found_text_regions, all_found_text_regions_par, all_box_coord, all_index_text_con
|
|
|
|
def get_slopes_and_deskew_new_curved(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, mask_texts_only, num_col, scale_par, slope_deskew):
|
|
self.logger.debug("enter get_slopes_and_deskew_new_curved")
|
|
num_cores = cpu_count()
|
|
queue_of_all_params = Queue()
|
|
|
|
processes = []
|
|
nh = np.linspace(0, len(boxes), num_cores + 1)
|
|
indexes_by_text_con = np.array(range(len(contours_par)))
|
|
|
|
for i in range(num_cores):
|
|
boxes_per_process = boxes[int(nh[i]) : int(nh[i + 1])]
|
|
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
|
|
contours_par_per_process = contours_par[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=self.do_work_of_slopes_new_curved, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process, contours_par_per_process, image_page_rotated, mask_texts_only, num_col, scale_par, indexes_text_con_per_process, slope_deskew)))
|
|
|
|
for i in range(num_cores):
|
|
processes[i].start()
|
|
|
|
slopes = []
|
|
all_found_texline_polygons = []
|
|
all_found_text_regions = []
|
|
all_found_text_regions_par = []
|
|
boxes = []
|
|
all_box_coord = []
|
|
all_index_text_con = []
|
|
|
|
for i in range(num_cores):
|
|
list_all_par = queue_of_all_params.get(True)
|
|
polys_for_sub_process = list_all_par[0]
|
|
boxes_for_sub_process = list_all_par[1]
|
|
contours_for_subprocess = list_all_par[2]
|
|
contours_par_for_subprocess = list_all_par[3]
|
|
boxes_coord_for_subprocess = list_all_par[4]
|
|
indexes_for_subprocess = list_all_par[5]
|
|
slopes_for_sub_process = list_all_par[6]
|
|
for j in range(len(polys_for_sub_process)):
|
|
slopes.append(slopes_for_sub_process[j])
|
|
all_found_texline_polygons.append(polys_for_sub_process[j][::-1])
|
|
boxes.append(boxes_for_sub_process[j])
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
all_found_text_regions_par.append(contours_par_for_subprocess[j])
|
|
all_box_coord.append(boxes_coord_for_subprocess[j])
|
|
all_index_text_con.append(indexes_for_subprocess[j])
|
|
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
# print(slopes,'slopes')
|
|
return all_found_texline_polygons, boxes, all_found_text_regions, all_found_text_regions_par, all_box_coord, all_index_text_con, slopes
|
|
|
|
def do_work_of_slopes_new_curved(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, image_page_rotated, mask_texts_only, num_col, scale_par, indexes_r_con_per_pro, slope_deskew):
|
|
self.logger.debug("enter do_work_of_slopes_new_curved")
|
|
slopes_per_each_subprocess = []
|
|
bounding_box_of_textregion_per_each_subprocess = []
|
|
textlines_rectangles_per_each_subprocess = []
|
|
contours_textregion_per_each_subprocess = []
|
|
contours_textregion_par_per_each_subprocess = []
|
|
all_box_coord_per_process = []
|
|
index_by_text_region_contours = []
|
|
|
|
textline_cnt_separated = np.zeros(textline_mask_tot_ea.shape)
|
|
|
|
for mv in range(len(boxes_text)):
|
|
|
|
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
all_text_region_raw = all_text_region_raw.astype(np.uint8)
|
|
img_int_p = all_text_region_raw[:, :]
|
|
|
|
# img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2)
|
|
# plt.imshow(img_int_p)
|
|
# plt.show()
|
|
|
|
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
|
|
slopes_per_each_subprocess.append(0)
|
|
slope_for_all = [slope_deskew][0]
|
|
else:
|
|
try:
|
|
textline_con, hierarchy = return_contours_of_image(img_int_p)
|
|
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
|
|
if self.isNaN(y_diff_mean):
|
|
slope_for_all = MAX_SLOPE
|
|
else:
|
|
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
|
|
img_int_p[img_int_p > 0] = 1
|
|
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
|
|
|
|
if abs(slope_for_all) < 0.5:
|
|
slope_for_all = [slope_deskew][0]
|
|
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
slope_for_all = MAX_SLOPE
|
|
|
|
if slope_for_all == MAX_SLOPE:
|
|
slope_for_all = [slope_deskew][0]
|
|
slopes_per_each_subprocess.append(slope_for_all)
|
|
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
_, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
|
|
|
|
if abs(slope_for_all) < 45:
|
|
# all_box_coord.append(crop_coor)
|
|
textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
|
|
cnt_o_t_max = contours_par_per_process[mv]
|
|
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
|
|
mask_biggest = np.zeros(mask_texts_only.shape)
|
|
mask_biggest = cv2.fillPoly(mask_biggest, pts=[cnt_o_t_max], color=(1, 1, 1))
|
|
mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
|
|
textline_biggest_region = mask_biggest * textline_mask_tot_ea
|
|
|
|
# print(slope_for_all,'slope_for_all')
|
|
textline_rotated_separated = separate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all, plotter=self.plotter)
|
|
|
|
# new line added
|
|
##print(np.shape(textline_rotated_separated),np.shape(mask_biggest))
|
|
textline_rotated_separated[mask_region_in_patch_region[:, :] != 1] = 0
|
|
# till here
|
|
|
|
textline_cnt_separated[y : y + h, x : x + w] = textline_rotated_separated
|
|
textline_region_in_image[y : y + h, x : x + w] = textline_rotated_separated
|
|
|
|
# plt.imshow(textline_region_in_image)
|
|
# plt.show()
|
|
# plt.imshow(textline_cnt_separated)
|
|
# plt.show()
|
|
|
|
pixel_img = 1
|
|
cnt_textlines_in_image = return_contours_of_interested_textline(textline_region_in_image, pixel_img)
|
|
|
|
textlines_cnt_per_region = []
|
|
for jjjj in range(len(cnt_textlines_in_image)):
|
|
mask_biggest2 = np.zeros(mask_texts_only.shape)
|
|
mask_biggest2 = cv2.fillPoly(mask_biggest2, pts=[cnt_textlines_in_image[jjjj]], color=(1, 1, 1))
|
|
if num_col + 1 == 1:
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=5)
|
|
else:
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=4)
|
|
|
|
pixel_img = 1
|
|
mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
|
|
cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img)
|
|
try:
|
|
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
else:
|
|
add_boxes_coor_into_textlines = True
|
|
textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], add_boxes_coor_into_textlines)
|
|
add_boxes_coor_into_textlines = False
|
|
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
|
|
|
|
textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
|
|
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
|
|
all_box_coord_per_process.append(crop_coor)
|
|
|
|
queue_of_all_params.put([textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours, slopes_per_each_subprocess])
|
|
def do_work_of_slopes_new_light(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, indexes_r_con_per_pro, image_page_rotated, slope_deskew):
|
|
self.logger.debug('enter do_work_of_slopes_new_light')
|
|
slopes_per_each_subprocess = []
|
|
bounding_box_of_textregion_per_each_subprocess = []
|
|
textlines_rectangles_per_each_subprocess = []
|
|
contours_textregion_per_each_subprocess = []
|
|
contours_textregion_par_per_each_subprocess = []
|
|
all_box_coord_per_process = []
|
|
index_by_text_region_contours = []
|
|
for mv in range(len(boxes_text)):
|
|
_, crop_coor = crop_image_inside_box(boxes_text[mv],image_page_rotated)
|
|
mask_textline = np.zeros((textline_mask_tot_ea.shape))
|
|
mask_textline = cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
|
|
all_text_region_raw = (textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
|
|
all_text_region_raw=all_text_region_raw.astype(np.uint8)
|
|
|
|
slopes_per_each_subprocess.append([slope_deskew][0])
|
|
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
|
|
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
|
|
|
|
# plt.imshow(mask_only_con_region)
|
|
# plt.show()
|
|
|
|
if self.textline_light:
|
|
all_text_region_raw = np.copy(textline_mask_tot_ea)
|
|
all_text_region_raw[mask_only_con_region == 0] = 0
|
|
cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(all_text_region_raw)
|
|
cnt_clean_rot = filter_contours_area_of_image(all_text_region_raw, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001)
|
|
else:
|
|
all_text_region_raw = np.copy(textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]])
|
|
mask_only_con_region = mask_only_con_region[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
all_text_region_raw[mask_only_con_region == 0] = 0
|
|
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, [slope_deskew][0], contours_par_per_process[mv], boxes_text[mv])
|
|
|
|
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
|
|
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
|
|
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
|
|
all_box_coord_per_process.append(crop_coor)
|
|
queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours])
|
|
|
|
def do_work_of_slopes_new(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, indexes_r_con_per_pro, image_page_rotated, slope_deskew):
|
|
self.logger.debug('enter do_work_of_slopes_new')
|
|
slopes_per_each_subprocess = []
|
|
bounding_box_of_textregion_per_each_subprocess = []
|
|
textlines_rectangles_per_each_subprocess = []
|
|
contours_textregion_per_each_subprocess = []
|
|
contours_textregion_par_per_each_subprocess = []
|
|
all_box_coord_per_process = []
|
|
index_by_text_region_contours = []
|
|
for mv in range(len(boxes_text)):
|
|
_, crop_coor = crop_image_inside_box(boxes_text[mv],image_page_rotated)
|
|
mask_textline = np.zeros((textline_mask_tot_ea.shape))
|
|
mask_textline = cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
|
|
all_text_region_raw = (textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
|
|
all_text_region_raw=all_text_region_raw.astype(np.uint8)
|
|
img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
|
|
img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2)
|
|
|
|
if img_int_p.shape[0]/img_int_p.shape[1]<0.1:
|
|
slopes_per_each_subprocess.append(0)
|
|
slope_for_all = [slope_deskew][0]
|
|
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], 0)
|
|
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
else:
|
|
try:
|
|
textline_con, hierarchy = return_contours_of_image(img_int_p)
|
|
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008)
|
|
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
|
|
if self.isNaN(y_diff_mean):
|
|
slope_for_all = MAX_SLOPE
|
|
else:
|
|
sigma_des = int(y_diff_mean * (4.0 / 40.0))
|
|
if sigma_des < 1:
|
|
sigma_des = 1
|
|
img_int_p[img_int_p > 0] = 1
|
|
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
|
|
if abs(slope_for_all) <= 0.5:
|
|
slope_for_all = [slope_deskew][0]
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
slope_for_all = MAX_SLOPE
|
|
if slope_for_all == MAX_SLOPE:
|
|
slope_for_all = [slope_deskew][0]
|
|
slopes_per_each_subprocess.append(slope_for_all)
|
|
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
|
|
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
|
|
|
|
# plt.imshow(mask_only_con_region)
|
|
# plt.show()
|
|
all_text_region_raw = np.copy(textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]])
|
|
mask_only_con_region = mask_only_con_region[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
|
|
##plt.imshow(textline_mask_tot_ea)
|
|
##plt.show()
|
|
##plt.imshow(all_text_region_raw)
|
|
##plt.show()
|
|
##plt.imshow(mask_only_con_region)
|
|
##plt.show()
|
|
|
|
all_text_region_raw[mask_only_con_region == 0] = 0
|
|
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv])
|
|
|
|
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
|
|
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
|
|
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
|
|
all_box_coord_per_process.append(crop_coor)
|
|
queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours])
|
|
|
|
def textline_contours(self, img, patches, scaler_h, scaler_w):
|
|
self.logger.debug('enter textline_contours')
|
|
if not self.dir_in:
|
|
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
|
|
img = img.astype(np.uint8)
|
|
img_org = np.copy(img)
|
|
img_h = img_org.shape[0]
|
|
img_w = img_org.shape[1]
|
|
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
|
|
if not self.dir_in:
|
|
prediction_textline = self.do_prediction(patches, img, model_textline)
|
|
else:
|
|
prediction_textline = self.do_prediction(patches, img, self.model_textline)
|
|
prediction_textline = resize_image(prediction_textline, img_h, img_w)
|
|
if not self.dir_in:
|
|
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
|
|
else:
|
|
prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
|
|
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
|
|
|
|
if not self.dir_in:
|
|
session_textline.close()
|
|
|
|
if self.textline_light:
|
|
return (prediction_textline[:, :, 0]==1)*1, (prediction_textline_longshot_true_size[:, :, 0]==1)*1
|
|
else:
|
|
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
|
|
|
|
def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
|
|
self.logger.debug('enter do_work_of_slopes')
|
|
slope_biggest = 0
|
|
slopes_sub = []
|
|
boxes_sub_new = []
|
|
poly_sub = []
|
|
for mv in range(len(boxes_per_process)):
|
|
crop_img, _ = crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
|
crop_img = crop_img[:, :, 0]
|
|
crop_img = cv2.erode(crop_img, KERNEL, iterations=2)
|
|
try:
|
|
textline_con, hierarchy = return_contours_of_image(crop_img)
|
|
textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierarchy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
|
|
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
|
|
crop_img[crop_img > 0] = 1
|
|
slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, plotter=self.plotter)
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
slope_corresponding_textregion = MAX_SLOPE
|
|
|
|
if slope_corresponding_textregion == MAX_SLOPE:
|
|
slope_corresponding_textregion = slope_biggest
|
|
slopes_sub.append(slope_corresponding_textregion)
|
|
|
|
cnt_clean_rot = textline_contours_postprocessing(crop_img, slope_corresponding_textregion, contours_per_process[mv], boxes_per_process[mv])
|
|
|
|
poly_sub.append(cnt_clean_rot)
|
|
boxes_sub_new.append(boxes_per_process[mv])
|
|
|
|
q.put(slopes_sub)
|
|
poly.put(poly_sub)
|
|
box_sub.put(boxes_sub_new)
|
|
def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier):
|
|
self.logger.debug("enter get_regions_light_v")
|
|
erosion_hurts = False
|
|
img_org = np.copy(img)
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
|
|
|
|
|
|
|
if num_col_classifier == 1:
|
|
img_w_new = 1000
|
|
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
|
|
|
elif num_col_classifier == 2:
|
|
img_w_new = 1500
|
|
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
|
|
|
elif num_col_classifier == 3:
|
|
img_w_new = 2000
|
|
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
|
|
|
elif num_col_classifier == 4:
|
|
img_w_new = 2500
|
|
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
|
elif num_col_classifier == 5:
|
|
img_w_new = 3000
|
|
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
|
else:
|
|
img_w_new = 4000
|
|
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
|
gc.collect()
|
|
##img_resized = resize_image(img_bin,img_height_h, img_width_h )
|
|
img_resized = resize_image(img,img_h_new, img_w_new )
|
|
|
|
if not self.dir_in:
|
|
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
|
prediction_bin = self.do_prediction(True, img_resized, model_bin)
|
|
else:
|
|
prediction_bin = self.do_prediction(True, img_resized, self.model_bin)
|
|
prediction_bin=prediction_bin[:,:,0]
|
|
prediction_bin = (prediction_bin[:,:]==0)*1
|
|
prediction_bin = prediction_bin*255
|
|
|
|
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
|
if not self.dir_in:
|
|
session_bin.close()
|
|
del model_bin
|
|
del session_bin
|
|
gc.collect()
|
|
|
|
prediction_bin = prediction_bin.astype(np.uint16)
|
|
#img= np.copy(prediction_bin)
|
|
img_bin = np.copy(prediction_bin)
|
|
|
|
|
|
|
|
textline_mask_tot_ea = self.run_textline(img_bin)
|
|
|
|
if not self.dir_in:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
|
|
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
|
|
else:
|
|
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
|
|
|
|
#plt.imshow(prediction_regions_org[:,:,0])
|
|
#plt.show()
|
|
|
|
prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
|
|
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
|
|
|
|
prediction_regions_org=prediction_regions_org[:,:,0]
|
|
|
|
mask_lines_only = (prediction_regions_org[:,:] ==3)*1
|
|
|
|
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
|
|
|
|
mask_images_only=(prediction_regions_org[:,:] ==2)*1
|
|
|
|
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
|
|
polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
|
|
|
|
|
|
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
|
|
|
|
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
|
|
|
|
text_regions_p_true = np.zeros(prediction_regions_org.shape)
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
|
|
|
|
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
|
|
|
|
#erosion_hurts = True
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea
|
|
|
|
def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier):
|
|
self.logger.debug("enter get_regions_from_xy_2models")
|
|
erosion_hurts = False
|
|
img_org = np.copy(img)
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
if not self.dir_in:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
|
|
|
ratio_y=1.3
|
|
ratio_x=1
|
|
|
|
img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
if not self.dir_in:
|
|
prediction_regions_org_y = self.do_prediction(True, img, model_region)
|
|
else:
|
|
prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
|
|
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h )
|
|
|
|
#plt.imshow(prediction_regions_org_y[:,:,0])
|
|
#plt.show()
|
|
prediction_regions_org_y = prediction_regions_org_y[:,:,0]
|
|
mask_zeros_y = (prediction_regions_org_y[:,:]==0)*1
|
|
|
|
##img_only_regions_with_sep = ( (prediction_regions_org_y[:,:] != 3) & (prediction_regions_org_y[:,:] != 0) )*1
|
|
img_only_regions_with_sep = ( prediction_regions_org_y[:,:] == 1 )*1
|
|
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
|
|
|
|
try:
|
|
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=20)
|
|
|
|
_, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
|
|
|
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
|
|
|
|
if self.dir_in:
|
|
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
|
else:
|
|
prediction_regions_org = self.do_prediction(True, img, model_region)
|
|
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
|
|
|
##plt.imshow(prediction_regions_org[:,:,0])
|
|
##plt.show()
|
|
prediction_regions_org=prediction_regions_org[:,:,0]
|
|
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
|
|
|
|
if not self.dir_in:
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
if not self.dir_in:
|
|
model_region, session_region = self.start_new_session_and_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:
|
|
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)
|
|
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
|
|
|
|
if not self.dir_in:
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
|
|
mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
|
|
text_sume_early = (prediction_regions_org[:,:] == 1).sum()
|
|
prediction_regions_org_copy = np.copy(prediction_regions_org)
|
|
prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)] = 0
|
|
text_sume_second = ((prediction_regions_org_copy[:,:]==1)*1).sum()
|
|
|
|
rate_two_models = text_sume_second / float(text_sume_early) * 100
|
|
|
|
self.logger.info("ratio_of_two_models: %s", rate_two_models)
|
|
if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD):
|
|
prediction_regions_org = np.copy(prediction_regions_org_copy)
|
|
|
|
|
|
|
|
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
|
|
mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
|
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
|
|
|
|
#plt.imshow(text_region2_1st_channel)
|
|
#plt.show()
|
|
|
|
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
|
|
|
|
|
|
if rate_two_models<=40:
|
|
if self.input_binary:
|
|
prediction_bin = np.copy(img_org)
|
|
else:
|
|
if not self.dir_in:
|
|
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
|
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
|
else:
|
|
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
|
|
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
|
|
|
|
prediction_bin=prediction_bin[:,:,0]
|
|
prediction_bin = (prediction_bin[:,:]==0)*1
|
|
prediction_bin = prediction_bin*255
|
|
|
|
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
|
|
|
if not self.dir_in:
|
|
session_bin.close()
|
|
del model_bin
|
|
del session_bin
|
|
gc.collect()
|
|
|
|
|
|
if not self.dir_in:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
|
ratio_y=1
|
|
ratio_x=1
|
|
|
|
|
|
img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
|
|
if not self.dir_in:
|
|
prediction_regions_org = self.do_prediction(True, img, model_region)
|
|
else:
|
|
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
|
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
|
prediction_regions_org=prediction_regions_org[:,:,0]
|
|
|
|
mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
|
if not self.dir_in:
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
|
|
mask_texts_only=(prediction_regions_org[:,:]==1)*1
|
|
mask_images_only=(prediction_regions_org[:,:]==2)*1
|
|
|
|
|
|
|
|
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
|
|
polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
|
|
|
|
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
|
|
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
|
|
|
|
text_regions_p_true = np.zeros(prediction_regions_org.shape)
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3))
|
|
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
|
|
|
|
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
|
|
|
|
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
return text_regions_p_true, erosion_hurts, polygons_lines_xml
|
|
except:
|
|
|
|
if self.input_binary:
|
|
prediction_bin = np.copy(img_org)
|
|
else:
|
|
if not self.dir_in:
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
if not self.dir_in:
|
|
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
|
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
|
else:
|
|
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
|
|
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
|
|
prediction_bin=prediction_bin[:,:,0]
|
|
|
|
prediction_bin = (prediction_bin[:,:]==0)*1
|
|
|
|
prediction_bin = prediction_bin*255
|
|
|
|
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
|
|
|
|
|
if not self.dir_in:
|
|
session_bin.close()
|
|
del model_bin
|
|
del session_bin
|
|
gc.collect()
|
|
|
|
|
|
if not self.dir_in:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
|
ratio_y=1
|
|
ratio_x=1
|
|
|
|
|
|
img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
if not self.dir_in:
|
|
prediction_regions_org = self.do_prediction(True, img, model_region)
|
|
else:
|
|
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
|
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
|
prediction_regions_org=prediction_regions_org[:,:,0]
|
|
|
|
#mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
|
if not self.dir_in:
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
|
|
|
|
#prediction_regions_org = self.do_prediction(True, img, model_region)
|
|
|
|
#prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
|
|
|
#prediction_regions_org = prediction_regions_org[:,:,0]
|
|
|
|
#prediction_regions_org[(prediction_regions_org[:,:] == 1) & (mask_zeros_y[:,:] == 1)]=0
|
|
#session_region.close()
|
|
#del model_region
|
|
#del session_region
|
|
#gc.collect()
|
|
|
|
|
|
|
|
|
|
mask_lines_only = (prediction_regions_org[:,:] ==3)*1
|
|
|
|
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
|
|
|
|
mask_images_only=(prediction_regions_org[:,:] ==2)*1
|
|
|
|
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
|
|
polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
|
|
|
|
|
|
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
|
|
|
|
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
|
|
|
|
|
|
text_regions_p_true = np.zeros(prediction_regions_org.shape)
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
|
|
|
|
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
|
|
|
|
erosion_hurts = True
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
return text_regions_p_true, erosion_hurts, polygons_lines_xml
|
|
|
|
def do_order_of_regions_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
|
|
self.logger.debug("enter do_order_of_regions_full_layout")
|
|
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours(contours_only_text_parent)
|
|
cx_text_only_h, cy_text_only_h, x_min_text_only_h, _, _, _, y_cor_x_min_main_h = find_new_features_of_contours(contours_only_text_parent_h)
|
|
|
|
try:
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
|
|
arg_text_con.append(jj)
|
|
break
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
arg_text_con_h = []
|
|
for ii in range(len(cx_text_only_h)):
|
|
for jj in range(len(boxes)):
|
|
if (x_min_text_only_h[ii] + 80) >= boxes[jj][0] and (x_min_text_only_h[ii] + 80) < boxes[jj][1] and y_cor_x_min_main_h[ii] >= boxes[jj][2] and y_cor_x_min_main_h[ii] < boxes[jj][3]:
|
|
arg_text_con_h.append(jj)
|
|
break
|
|
args_contours_h = np.array(range(len(arg_text_con_h)))
|
|
|
|
order_by_con_head = np.zeros(len(arg_text_con_h))
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for box in args_contours_box:
|
|
con_inter_box.append(contours_only_text_parent[box])
|
|
|
|
for box in args_contours_box_h:
|
|
con_inter_box_h.append(contours_only_text_parent_h[box])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
|
|
for zahler, _ in enumerate(args_contours_box):
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
|
|
|
|
for zahler, _ in enumerate(args_contours_box_h):
|
|
arg_order_v = indexes_sorted_head[zahler]
|
|
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
|
|
|
|
for jji in range(len(id_of_texts)):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point += len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
for tj1 in range(len(contours_only_text_parent_h)):
|
|
order_of_texts_tot.append(int(order_by_con_head[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
|
|
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
|
|
arg_text_con.append(jj)
|
|
break
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
############################# head
|
|
|
|
arg_text_con_h = []
|
|
for ii in range(len(cx_text_only_h)):
|
|
for jj in range(len(boxes)):
|
|
if cx_text_only_h[ii] >= boxes[jj][0] and cx_text_only_h[ii] < boxes[jj][1] and cy_text_only_h[ii] >= boxes[jj][2] and cy_text_only_h[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
|
|
arg_text_con_h.append(jj)
|
|
break
|
|
args_contours_h = np.array(range(len(arg_text_con_h)))
|
|
|
|
order_by_con_head = np.zeros(len(arg_text_con_h))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij, _ in enumerate(boxes):
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for box in args_contours_box:
|
|
con_inter_box.append(contours_only_text_parent[box])
|
|
|
|
for box in args_contours_box_h:
|
|
con_inter_box_h.append(contours_only_text_parent_h[box])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
|
|
for zahler, _ in enumerate(args_contours_box):
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
|
|
|
|
for zahler, _ in enumerate(args_contours_box_h):
|
|
arg_order_v = indexes_sorted_head[zahler]
|
|
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
|
|
|
|
for jji, _ in enumerate(id_of_texts):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point += len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
for tj1 in range(len(contours_only_text_parent_h)):
|
|
order_of_texts_tot.append(int(order_by_con_head[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
|
|
return order_text_new, id_of_texts_tot
|
|
|
|
def do_order_of_regions_no_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
|
|
self.logger.debug("enter do_order_of_regions_no_full_layout")
|
|
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours(contours_only_text_parent)
|
|
|
|
try:
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
|
|
arg_text_con.append(jj)
|
|
break
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
for i in range(len(args_contours_box)):
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
|
|
for zahler, _ in enumerate(args_contours_box):
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
|
|
|
|
for jji, _ in enumerate(id_of_texts):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point += len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
|
|
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
|
|
arg_text_con.append(jj)
|
|
break
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for i in range(len(args_contours_box)):
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
|
|
for zahler, _ in enumerate(args_contours_box):
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
|
|
|
|
for jji, _ in enumerate(id_of_texts):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point += len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
|
|
|
|
return order_text_new, id_of_texts_tot
|
|
def check_iou_of_bounding_box_and_contour_for_tables(self, layout, table_prediction_early, pixel_tabel, num_col_classifier):
|
|
layout_org = np.copy(layout)
|
|
layout_org[:,:,0][layout_org[:,:,0]==pixel_tabel] = 0
|
|
layout = (layout[:,:,0]==pixel_tabel)*1
|
|
|
|
layout =np.repeat(layout[:, :, np.newaxis], 3, axis=2)
|
|
layout = layout.astype(np.uint8)
|
|
imgray = cv2.cvtColor(layout, cv2.COLOR_BGR2GRAY )
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
|
|
|
|
contours_new = []
|
|
for i in range(len(contours)):
|
|
x, y, w, h = cv2.boundingRect(contours[i])
|
|
iou = cnt_size[i] /float(w*h) *100
|
|
|
|
if iou<80:
|
|
layout_contour = np.zeros((layout_org.shape[0], layout_org.shape[1]))
|
|
layout_contour= cv2.fillPoly(layout_contour,pts=[contours[i]] ,color=(1,1,1))
|
|
|
|
|
|
layout_contour_sum = layout_contour.sum(axis=0)
|
|
layout_contour_sum_diff = np.diff(layout_contour_sum)
|
|
layout_contour_sum_diff= np.abs(layout_contour_sum_diff)
|
|
layout_contour_sum_diff_smoothed= gaussian_filter1d(layout_contour_sum_diff, 10)
|
|
|
|
peaks, _ = find_peaks(layout_contour_sum_diff_smoothed, height=0)
|
|
peaks= peaks[layout_contour_sum_diff_smoothed[peaks]>4]
|
|
|
|
for j in range(len(peaks)):
|
|
layout_contour[:,peaks[j]-3+1:peaks[j]+1+3] = 0
|
|
|
|
layout_contour=cv2.erode(layout_contour[:,:], KERNEL, iterations=5)
|
|
layout_contour=cv2.dilate(layout_contour[:,:], KERNEL, iterations=5)
|
|
|
|
layout_contour =np.repeat(layout_contour[:, :, np.newaxis], 3, axis=2)
|
|
layout_contour = layout_contour.astype(np.uint8)
|
|
|
|
imgray = cv2.cvtColor(layout_contour, cv2.COLOR_BGR2GRAY )
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_sep, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
for ji in range(len(contours_sep) ):
|
|
contours_new.append(contours_sep[ji])
|
|
if num_col_classifier>=2:
|
|
only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1]))
|
|
only_recent_contour_image= cv2.fillPoly(only_recent_contour_image,pts=[contours_sep[ji]] ,color=(1,1,1))
|
|
table_pixels_masked_from_early_pre = only_recent_contour_image[:,:]*table_prediction_early[:,:]
|
|
iou_in = table_pixels_masked_from_early_pre.sum() /float(only_recent_contour_image.sum()) *100
|
|
#print(iou_in,'iou_in_in1')
|
|
|
|
if iou_in>30:
|
|
layout_org= cv2.fillPoly(layout_org,pts=[contours_sep[ji]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
|
|
else:
|
|
pass
|
|
else:
|
|
|
|
layout_org= cv2.fillPoly(layout_org,pts=[contours_sep[ji]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
|
|
|
|
else:
|
|
contours_new.append(contours[i])
|
|
if num_col_classifier>=2:
|
|
only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1]))
|
|
only_recent_contour_image= cv2.fillPoly(only_recent_contour_image,pts=[contours[i]] ,color=(1,1,1))
|
|
|
|
table_pixels_masked_from_early_pre = only_recent_contour_image[:,:]*table_prediction_early[:,:]
|
|
iou_in = table_pixels_masked_from_early_pre.sum() /float(only_recent_contour_image.sum()) *100
|
|
#print(iou_in,'iou_in')
|
|
if iou_in>30:
|
|
layout_org= cv2.fillPoly(layout_org,pts=[contours[i]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
|
|
else:
|
|
pass
|
|
else:
|
|
layout_org= cv2.fillPoly(layout_org,pts=[contours[i]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
|
|
|
|
return layout_org, contours_new
|
|
def delete_separator_around(self,spliter_y,peaks_neg,image_by_region, pixel_line, pixel_table):
|
|
# format of subboxes: box=[x1, x2 , y1, y2]
|
|
pix_del = 100
|
|
if len(image_by_region.shape)==3:
|
|
for i in range(len(spliter_y)-1):
|
|
for j in range(1,len(peaks_neg[i])-1):
|
|
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0]==pixel_line ]=0
|
|
image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,1]==pixel_line ]=0
|
|
image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,2]==pixel_line ]=0
|
|
|
|
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0]==pixel_table ]=0
|
|
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,1]==pixel_table ]=0
|
|
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,2]==pixel_table ]=0
|
|
else:
|
|
for i in range(len(spliter_y)-1):
|
|
for j in range(1,len(peaks_neg[i])-1):
|
|
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del]==pixel_line ]=0
|
|
|
|
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del]==pixel_table ]=0
|
|
return image_by_region
|
|
def add_tables_heuristic_to_layout(self, image_regions_eraly_p,boxes, slope_mean_hor, spliter_y,peaks_neg_tot, image_revised, num_col_classifier, min_area, pixel_line):
|
|
pixel_table =10
|
|
image_revised_1 = self.delete_separator_around(spliter_y, peaks_neg_tot, image_revised, pixel_line, pixel_table)
|
|
|
|
try:
|
|
image_revised_1[:,:30][image_revised_1[:,:30]==pixel_line] = 0
|
|
image_revised_1[:,image_revised_1.shape[1]-30:][image_revised_1[:,image_revised_1.shape[1]-30:]==pixel_line] = 0
|
|
except:
|
|
pass
|
|
|
|
img_comm_e = np.zeros(image_revised_1.shape)
|
|
img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
|
|
|
|
for indiv in np.unique(image_revised_1):
|
|
image_col=(image_revised_1==indiv)*255
|
|
img_comm_in=np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
|
|
img_comm_in=img_comm_in.astype(np.uint8)
|
|
|
|
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
if indiv==pixel_table:
|
|
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area = 1, min_area = 0.001)
|
|
else:
|
|
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area = 1, min_area = min_area)
|
|
|
|
img_comm = cv2.fillPoly(img_comm, pts = main_contours, color = (indiv, indiv, indiv))
|
|
img_comm = img_comm.astype(np.uint8)
|
|
|
|
if not self.isNaN(slope_mean_hor):
|
|
image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1],3))
|
|
for i in range(len(boxes)):
|
|
image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
|
|
try:
|
|
image_box_tabels_1=(image_box[:,:,0]==pixel_table)*1
|
|
contours_tab,_=return_contours_of_image(image_box_tabels_1)
|
|
contours_tab=filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.003)
|
|
image_box_tabels_1=(image_box[:,:,0]==pixel_line)*1
|
|
|
|
image_box_tabels_and_m_text=( (image_box[:,:,0]==pixel_table) | (image_box[:,:,0]==1) )*1
|
|
image_box_tabels_and_m_text=image_box_tabels_and_m_text.astype(np.uint8)
|
|
|
|
image_box_tabels_1=image_box_tabels_1.astype(np.uint8)
|
|
image_box_tabels_1 = cv2.dilate(image_box_tabels_1,KERNEL,iterations = 5)
|
|
|
|
contours_table_m_text,_=return_contours_of_image(image_box_tabels_and_m_text)
|
|
image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
|
|
|
|
image_box_tabels=image_box_tabels.astype(np.uint8)
|
|
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
y_min_main_line ,y_max_main_line=find_features_of_contours(contours_line)
|
|
y_min_main_tab ,y_max_main_tab=find_features_of_contours(contours_tab)
|
|
|
|
cx_tab_m_text,cy_tab_m_text ,x_min_tab_m_text , x_max_tab_m_text, y_min_tab_m_text ,y_max_tab_m_text, _= find_new_features_of_contours(contours_table_m_text)
|
|
cx_tabl,cy_tabl ,x_min_tabl , x_max_tabl, y_min_tabl ,y_max_tabl,_= find_new_features_of_contours(contours_tab)
|
|
|
|
if len(y_min_main_tab )>0:
|
|
y_down_tabs=[]
|
|
y_up_tabs=[]
|
|
|
|
for i_t in range(len(y_min_main_tab )):
|
|
y_down_tab=[]
|
|
y_up_tab=[]
|
|
for i_l in range(len(y_min_main_line)):
|
|
if y_min_main_tab[i_t]>y_min_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l] and y_min_main_tab[i_t]>y_max_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l]:
|
|
pass
|
|
elif y_min_main_tab[i_t]<y_max_main_line[i_l] and y_max_main_tab[i_t]<y_max_main_line[i_l] and y_max_main_tab[i_t]<y_min_main_line[i_l] and y_min_main_tab[i_t]<y_min_main_line[i_l]:
|
|
pass
|
|
elif np.abs(y_max_main_line[i_l]-y_min_main_line[i_l])<100:
|
|
pass
|
|
else:
|
|
y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t] ]) )
|
|
y_down_tab.append( np.max([ y_max_main_line[i_l],y_max_main_tab[i_t] ]) )
|
|
|
|
if len(y_up_tab)==0:
|
|
y_up_tabs.append(y_min_main_tab[i_t])
|
|
y_down_tabs.append(y_max_main_tab[i_t])
|
|
else:
|
|
y_up_tabs.append(np.min(y_up_tab))
|
|
y_down_tabs.append(np.max(y_down_tab))
|
|
else:
|
|
y_down_tabs=[]
|
|
y_up_tabs=[]
|
|
pass
|
|
except:
|
|
y_down_tabs=[]
|
|
y_up_tabs=[]
|
|
|
|
for ii in range(len(y_up_tabs)):
|
|
image_box[y_up_tabs[ii]:y_down_tabs[ii],:,0]=pixel_table
|
|
|
|
image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]
|
|
else:
|
|
for i in range(len(boxes)):
|
|
|
|
image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
|
|
image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]
|
|
|
|
if num_col_classifier==1:
|
|
img_tables_col_1=( image_revised_last[:,:,0]==pixel_table )*1
|
|
img_tables_col_1=img_tables_col_1.astype(np.uint8)
|
|
contours_table_col1,_=return_contours_of_image(img_tables_col_1)
|
|
|
|
_,_ ,_ , _, y_min_tab_col1 ,y_max_tab_col1, _= find_new_features_of_contours(contours_table_col1)
|
|
|
|
if len(y_min_tab_col1)>0:
|
|
for ijv in range(len(y_min_tab_col1)):
|
|
image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv]),:,:]=pixel_table
|
|
return image_revised_last
|
|
def do_order_of_regions(self, *args, **kwargs):
|
|
if self.full_layout:
|
|
return self.do_order_of_regions_full_layout(*args, **kwargs)
|
|
return self.do_order_of_regions_no_full_layout(*args, **kwargs)
|
|
|
|
def get_tables_from_model(self, img, num_col_classifier):
|
|
img_org = np.copy(img)
|
|
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_tables)
|
|
|
|
patches = False
|
|
|
|
if num_col_classifier < 4 and num_col_classifier > 2:
|
|
prediction_table = self.do_prediction(patches, img, model_region)
|
|
pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
|
|
pre_updown = cv2.flip(pre_updown, -1)
|
|
|
|
prediction_table[:,:,0][pre_updown[:,:,0]==1]=1
|
|
prediction_table = prediction_table.astype(np.int16)
|
|
|
|
elif num_col_classifier ==2:
|
|
height_ext = 0#int( img.shape[0]/4. )
|
|
h_start = int(height_ext/2.)
|
|
width_ext = int( img.shape[1]/8. )
|
|
w_start = int(width_ext/2.)
|
|
|
|
height_new = img.shape[0]+height_ext
|
|
width_new = img.shape[1]+width_ext
|
|
|
|
img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
|
|
img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
|
|
|
|
prediction_ext = self.do_prediction(patches, img_new, model_region)
|
|
|
|
pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
|
|
pre_updown = cv2.flip(pre_updown, -1)
|
|
|
|
prediction_table = prediction_ext[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
|
|
prediction_table_updown = pre_updown[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
|
|
|
|
prediction_table[:,:,0][prediction_table_updown[:,:,0]==1]=1
|
|
prediction_table = prediction_table.astype(np.int16)
|
|
|
|
elif num_col_classifier ==1:
|
|
height_ext = 0# int( img.shape[0]/4. )
|
|
h_start = int(height_ext/2.)
|
|
width_ext = int( img.shape[1]/4. )
|
|
w_start = int(width_ext/2.)
|
|
|
|
height_new = img.shape[0]+height_ext
|
|
width_new = img.shape[1]+width_ext
|
|
|
|
img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
|
|
img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
|
|
|
|
prediction_ext = self.do_prediction(patches, img_new, model_region)
|
|
|
|
pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
|
|
pre_updown = cv2.flip(pre_updown, -1)
|
|
|
|
prediction_table = prediction_ext[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
|
|
prediction_table_updown = pre_updown[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
|
|
|
|
prediction_table[:,:,0][prediction_table_updown[:,:,0]==1]=1
|
|
prediction_table = prediction_table.astype(np.int16)
|
|
|
|
else:
|
|
prediction_table = np.zeros(img.shape)
|
|
img_w_half = int(img.shape[1]/2.)
|
|
|
|
pre1 = self.do_prediction(patches, img[:,0:img_w_half,:], model_region)
|
|
pre2 = self.do_prediction(patches, img[:,img_w_half:,:], model_region)
|
|
|
|
pre_full = self.do_prediction(patches, img[:,:,:], model_region)
|
|
|
|
pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
|
|
pre_updown = cv2.flip(pre_updown, -1)
|
|
|
|
prediction_table_full_erode = cv2.erode(pre_full[:,:,0], KERNEL, iterations=4)
|
|
prediction_table_full_erode = cv2.dilate(prediction_table_full_erode, KERNEL, iterations=4)
|
|
|
|
prediction_table_full_updown_erode = cv2.erode(pre_updown[:,:,0], KERNEL, iterations=4)
|
|
prediction_table_full_updown_erode = cv2.dilate(prediction_table_full_updown_erode, KERNEL, iterations=4)
|
|
|
|
prediction_table[:,0:img_w_half,:] = pre1[:,:,:]
|
|
prediction_table[:,img_w_half:,:] = pre2[:,:,:]
|
|
|
|
prediction_table[:,:,0][prediction_table_full_erode[:,:]==1]=1
|
|
prediction_table[:,:,0][prediction_table_full_updown_erode[:,:]==1]=1
|
|
prediction_table = prediction_table.astype(np.int16)
|
|
|
|
#prediction_table_erode = cv2.erode(prediction_table[:,:,0], self.kernel, iterations=6)
|
|
#prediction_table_erode = cv2.dilate(prediction_table_erode, self.kernel, iterations=6)
|
|
|
|
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
|
|
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
|
|
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
|
|
return prediction_table_erode.astype(np.int16)
|
|
def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts):
|
|
img_g = self.imread(grayscale=True, uint8=True)
|
|
|
|
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
|
|
img_g3 = img_g3.astype(np.uint8)
|
|
img_g3[:, :, 0] = img_g[:, :]
|
|
img_g3[:, :, 1] = img_g[:, :]
|
|
img_g3[:, :, 2] = img_g[:, :]
|
|
|
|
image_page, page_coord, cont_page = self.extract_page()
|
|
|
|
if self.tables:
|
|
table_prediction = self.get_tables_from_model(image_page, num_col_classifier)
|
|
else:
|
|
table_prediction = (np.zeros((image_page.shape[0], image_page.shape[1]))).astype(np.int16)
|
|
|
|
if self.plotter:
|
|
self.plotter.save_page_image(image_page)
|
|
|
|
text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
textline_mask_tot_ea = textline_mask_tot_ea[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
mask_images = (text_regions_p_1[:, :] == 2) * 1
|
|
mask_images = mask_images.astype(np.uint8)
|
|
mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
|
|
mask_lines = (text_regions_p_1[:, :] == 3) * 1
|
|
mask_lines = mask_lines.astype(np.uint8)
|
|
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
|
|
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
|
|
|
|
|
|
if erosion_hurts:
|
|
img_only_regions = np.copy(img_only_regions_with_sep[:,:])
|
|
else:
|
|
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
|
|
|
|
##print(img_only_regions.shape,'img_only_regions')
|
|
##plt.imshow(img_only_regions[:,:])
|
|
##plt.show()
|
|
##num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
|
try:
|
|
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
|
num_col = num_col + 1
|
|
if not num_column_is_classified:
|
|
num_col_classifier = num_col + 1
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
num_col = None
|
|
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea
|
|
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
|
|
img_g = self.imread(grayscale=True, uint8=True)
|
|
|
|
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
|
|
img_g3 = img_g3.astype(np.uint8)
|
|
img_g3[:, :, 0] = img_g[:, :]
|
|
img_g3[:, :, 1] = img_g[:, :]
|
|
img_g3[:, :, 2] = img_g[:, :]
|
|
|
|
image_page, page_coord, cont_page = self.extract_page()
|
|
|
|
if self.tables:
|
|
table_prediction = self.get_tables_from_model(image_page, num_col_classifier)
|
|
else:
|
|
table_prediction = (np.zeros((image_page.shape[0], image_page.shape[1]))).astype(np.int16)
|
|
|
|
if self.plotter:
|
|
self.plotter.save_page_image(image_page)
|
|
|
|
text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
mask_images = (text_regions_p_1[:, :] == 2) * 1
|
|
mask_images = mask_images.astype(np.uint8)
|
|
mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
|
|
mask_lines = (text_regions_p_1[:, :] == 3) * 1
|
|
mask_lines = mask_lines.astype(np.uint8)
|
|
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
|
|
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
|
|
|
|
|
|
if erosion_hurts:
|
|
img_only_regions = np.copy(img_only_regions_with_sep[:,:])
|
|
else:
|
|
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
|
|
|
|
|
|
try:
|
|
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
|
num_col = num_col + 1
|
|
if not num_column_is_classified:
|
|
num_col_classifier = num_col + 1
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
num_col = None
|
|
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction
|
|
|
|
def run_enhancement(self,light_version):
|
|
self.logger.info("Resizing and enhancing image...")
|
|
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = self.resize_and_enhance_image_with_column_classifier(light_version)
|
|
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
scale = 1
|
|
if is_image_enhanced:
|
|
if self.allow_enhancement:
|
|
img_res = img_res.astype(np.uint8)
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
if self.plotter:
|
|
self.plotter.save_enhanced_image(img_res)
|
|
else:
|
|
self.get_image_and_scales_after_enhancing(img_org, img_res)
|
|
else:
|
|
if self.allow_enhancement:
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
else:
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
if self.allow_scaling:
|
|
img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced, img_bin)
|
|
self.get_image_and_scales_after_enhancing(img_org, img_res)
|
|
return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified
|
|
|
|
def run_textline(self, image_page):
|
|
scaler_h_textline = 1 # 1.2#1.2
|
|
scaler_w_textline = 1 # 0.9#1
|
|
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
|
|
if self.textline_light:
|
|
textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16)
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
if self.plotter:
|
|
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
|
|
return textline_mask_tot_ea
|
|
|
|
def run_deskew(self, textline_mask_tot_ea):
|
|
sigma = 2
|
|
main_page_deskew = True
|
|
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
|
|
slope_first = 0
|
|
|
|
if self.plotter:
|
|
self.plotter.save_deskewed_image(slope_deskew)
|
|
self.logger.info("slope_deskew: %s", slope_deskew)
|
|
return slope_deskew, slope_first
|
|
|
|
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction):
|
|
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :]
|
|
textline_mask_tot[mask_images[:, :] == 1] = 0
|
|
|
|
text_regions_p_1[mask_lines[:, :] == 1] = 3
|
|
text_regions_p = text_regions_p_1[:, :]
|
|
text_regions_p = np.array(text_regions_p)
|
|
|
|
if num_col_classifier in (1, 2):
|
|
try:
|
|
regions_without_separators = (text_regions_p[:, :] == 1) * 1
|
|
if self.tables:
|
|
regions_without_separators[table_prediction==1] = 1
|
|
regions_without_separators = regions_without_separators.astype(np.uint8)
|
|
text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL)
|
|
except Exception as e:
|
|
self.logger.error("exception %s", e)
|
|
|
|
if self.plotter:
|
|
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
|
|
self.plotter.save_plot_of_layout_main(text_regions_p, image_page)
|
|
return textline_mask_tot, text_regions_p, image_page_rotated
|
|
|
|
def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts):
|
|
self.logger.debug('enter run_boxes_no_full_layout')
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
_, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
|
|
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
table_prediction_n = resize_image(table_prediction_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
|
if self.tables:
|
|
regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
|
|
regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions)
|
|
if self.tables:
|
|
regions_without_separators[table_prediction ==1 ] = 1
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
text_regions_p_1_n = None
|
|
textline_mask_tot_d = None
|
|
regions_without_separators_d = None
|
|
pixel_lines = 3
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
|
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
|
|
self.logger.info("num_col_classifier: %s", num_col_classifier)
|
|
|
|
if num_col_classifier >= 3:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
regions_without_separators = regions_without_separators.astype(np.uint8)
|
|
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
|
|
else:
|
|
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
|
|
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
|
|
t1 = time.time()
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
|
|
boxes_d = None
|
|
self.logger.debug("len(boxes): %s", len(boxes))
|
|
|
|
text_regions_p_tables = np.copy(text_regions_p)
|
|
text_regions_p_tables[:,:][(table_prediction[:,:] == 1)] = 10
|
|
pixel_line = 3
|
|
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables , num_col_classifier , 0.000005, pixel_line)
|
|
img_revised_tab2, contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2,table_prediction, 10, num_col_classifier)
|
|
else:
|
|
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
|
|
boxes = None
|
|
self.logger.debug("len(boxes): %s", len(boxes_d))
|
|
|
|
text_regions_p_tables = np.copy(text_regions_p_1_n)
|
|
text_regions_p_tables =np.round(text_regions_p_tables)
|
|
text_regions_p_tables[:,:][(text_regions_p_tables[:,:] != 3) & (table_prediction_n[:,:] == 1)] = 10
|
|
|
|
pixel_line = 3
|
|
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables,boxes_d,0,splitter_y_new_d,peaks_neg_tot_tables_d,text_regions_p_tables, num_col_classifier, 0.000005, pixel_line)
|
|
img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2,table_prediction_n, 10, num_col_classifier)
|
|
|
|
img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew)
|
|
img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated)
|
|
img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8)
|
|
img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
|
|
self.logger.info("detecting boxes took %.1fs", time.time() - t1)
|
|
|
|
if self.tables:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
img_revised_tab = np.copy(img_revised_tab2[:,:,0])
|
|
img_revised_tab[:,:][(text_regions_p[:,:] == 1) & (img_revised_tab[:,:] != 10)] = 1
|
|
else:
|
|
img_revised_tab = np.copy(text_regions_p[:,:])
|
|
img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0
|
|
img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10
|
|
|
|
text_regions_p[:,:][text_regions_p[:,:]==10] = 0
|
|
text_regions_p[:,:][img_revised_tab[:,:]==10] = 10
|
|
else:
|
|
img_revised_tab=text_regions_p[:,:]
|
|
#img_revised_tab = text_regions_p[:, :]
|
|
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 2)
|
|
|
|
pixel_img = 4
|
|
min_area_mar = 0.00001
|
|
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
|
|
|
pixel_img = 10
|
|
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
|
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
self.logger.debug('exit run_boxes_no_full_layout')
|
|
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables
|
|
|
|
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts):
|
|
self.logger.debug('enter run_boxes_full_layout')
|
|
|
|
if self.tables:
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n , table_prediction_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
|
|
|
|
text_regions_p_1_n = resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1])
|
|
textline_mask_tot_d = resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])
|
|
table_prediction_n = resize_image(table_prediction_n,text_regions_p.shape[0],text_regions_p.shape[1])
|
|
|
|
regions_without_separators_d=(text_regions_p_1_n[:,:] == 1)*1
|
|
regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
|
|
else:
|
|
text_regions_p_1_n = None
|
|
textline_mask_tot_d = None
|
|
regions_without_separators_d = None
|
|
|
|
regions_without_separators = (text_regions_p[:,:] == 1)*1#( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
|
|
regions_without_separators[table_prediction == 1] = 1
|
|
|
|
pixel_lines=3
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
num_col, peaks_neg_fin, matrix_of_lines_ch, splitter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
|
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier, self.tables, pixel_lines)
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
if num_col_classifier>=3:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
regions_without_separators = regions_without_separators.astype(np.uint8)
|
|
regions_without_separators = cv2.erode(regions_without_separators[:,:], KERNEL, iterations=6)
|
|
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
|
|
regions_without_separators_d = cv2.erode(regions_without_separators_d[:,:], KERNEL, iterations=6)
|
|
else:
|
|
pass
|
|
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
|
|
text_regions_p_tables = np.copy(text_regions_p)
|
|
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
|
|
pixel_line = 3
|
|
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables , num_col_classifier , 0.000005, pixel_line)
|
|
|
|
img_revised_tab2,contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2, table_prediction, 10, num_col_classifier)
|
|
|
|
else:
|
|
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
|
|
text_regions_p_tables = np.copy(text_regions_p_1_n)
|
|
text_regions_p_tables = np.round(text_regions_p_tables)
|
|
text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10
|
|
|
|
pixel_line = 3
|
|
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables,boxes_d,0,splitter_y_new_d,peaks_neg_tot_tables_d,text_regions_p_tables, num_col_classifier, 0.000005, pixel_line)
|
|
|
|
img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2, table_prediction_n, 10, num_col_classifier)
|
|
img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew)
|
|
|
|
|
|
img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated)
|
|
img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8)
|
|
|
|
img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
|
|
|
|
if np.abs(slope_deskew) < 0.13:
|
|
img_revised_tab = np.copy(img_revised_tab2[:,:,0])
|
|
else:
|
|
img_revised_tab = np.copy(text_regions_p[:,:])
|
|
img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0
|
|
img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10
|
|
|
|
|
|
##img_revised_tab=img_revised_tab2[:,:,0]
|
|
#img_revised_tab=text_regions_p[:,:]
|
|
text_regions_p[:,:][text_regions_p[:,:]==10] = 0
|
|
text_regions_p[:,:][img_revised_tab[:,:]==10] = 10
|
|
#img_revised_tab[img_revised_tab2[:,:,0]==10] =10
|
|
|
|
pixel_img = 4
|
|
min_area_mar = 0.00001
|
|
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
|
|
|
pixel_img = 10
|
|
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
|
|
|
# set first model with second model
|
|
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
|
|
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
|
|
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
image_page = image_page.astype(np.uint8)
|
|
|
|
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, True, cols=num_col_classifier)
|
|
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
|
|
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
|
|
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
|
|
# plt.imshow(regions_fully_np[:,:,0])
|
|
# plt.show()
|
|
if num_col_classifier > 2:
|
|
regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
|
|
else:
|
|
regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
|
|
|
|
# plt.imshow(regions_fully_np[:,:,0])
|
|
# plt.show()
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4
|
|
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
|
|
#plt.imshow(text_regions_p)
|
|
#plt.show()
|
|
####if not self.tables:
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
_, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
|
|
|
|
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
if not self.tables:
|
|
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
|
else:
|
|
text_regions_p_1_n = None
|
|
textline_mask_tot_d = None
|
|
regions_without_separators_d = None
|
|
if not self.tables:
|
|
regions_without_separators = (text_regions_p[:, :] == 1) * 1
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
img_revised_tab = np.copy(text_regions_p[:, :])
|
|
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
|
|
self.logger.debug('exit run_boxes_full_layout')
|
|
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables
|
|
|
|
def our_load_model(self, model_file):
|
|
|
|
try:
|
|
model = load_model(model_file, compile=False)
|
|
except:
|
|
model = load_model(model_file , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
|
|
|
|
return model
|
|
|
|
def run(self):
|
|
"""
|
|
Get image and scales, then extract the page of scanned image
|
|
"""
|
|
self.logger.debug("enter run")
|
|
|
|
t0_tot = time.time()
|
|
|
|
if not self.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(os.path.join(self.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)
|
|
|
|
t1 = time.time()
|
|
if self.light_version:
|
|
text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea = self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier)
|
|
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
|
|
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
|
|
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea = \
|
|
self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts)
|
|
#self.logger.info("run graphics %.1fs ", time.time() - t1t)
|
|
textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea)
|
|
else:
|
|
text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier)
|
|
self.logger.info("Textregion detection took %.1fs ", time.time() - t1)
|
|
|
|
t1 = time.time()
|
|
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction = \
|
|
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts)
|
|
self.logger.info("Graphics detection took %.1fs ", time.time() - t1)
|
|
#self.logger.info('cont_page %s', cont_page)
|
|
|
|
if not num_col:
|
|
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:
|
|
self.writer.write_pagexml(pcgts)
|
|
continue
|
|
else:
|
|
return pcgts
|
|
|
|
t1 = time.time()
|
|
if not self.light_version:
|
|
textline_mask_tot_ea = self.run_textline(image_page)
|
|
self.logger.info("textline detection took %.1fs", time.time() - t1)
|
|
|
|
t1 = time.time()
|
|
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
|
|
self.logger.info("deskewing took %.1fs", time.time() - t1)
|
|
t1 = time.time()
|
|
#plt.imshow(table_prediction)
|
|
#plt.show()
|
|
|
|
textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction)
|
|
self.logger.info("detection of marginals took %.1fs", time.time() - t1)
|
|
t1 = time.time()
|
|
if not self.full_layout:
|
|
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts)
|
|
|
|
if self.full_layout:
|
|
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts)
|
|
text_only = ((img_revised_tab[:, :] == 1)) * 1
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
|
|
|
|
|
|
min_con_area = 0.000005
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
contours_only_text, hir_on_text = return_contours_of_image(text_only)
|
|
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
|
|
|
|
if len(contours_only_text_parent) > 0:
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
|
|
#self.logger.info('areas_cnt_text %s', areas_cnt_text)
|
|
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
|
|
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
|
|
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
|
|
|
|
index_con_parents = np.argsort(areas_cnt_text_parent)
|
|
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
|
|
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
|
|
|
|
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
|
|
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
|
|
|
|
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
|
|
contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
|
|
|
|
areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
|
|
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
|
|
|
|
if len(areas_cnt_text_d)>0:
|
|
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
|
|
index_con_parents_d=np.argsort(areas_cnt_text_d)
|
|
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
|
|
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
|
|
|
|
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d])
|
|
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d)
|
|
try:
|
|
if len(cx_bigest_d) >= 5:
|
|
cx_bigest_d_last5 = cx_bigest_d[-5:]
|
|
cy_biggest_d_last5 = cy_biggest_d[-5:]
|
|
dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) for j in range(len(cy_biggest_d_last5))]
|
|
ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d)
|
|
else:
|
|
cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):]
|
|
cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):]
|
|
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))]
|
|
ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d)
|
|
|
|
cx_bigest_d_big[0] = cx_bigest_d[ind_largest]
|
|
cy_biggest_d_big[0] = cy_biggest_d[ind_largest]
|
|
except Exception as why:
|
|
self.logger.error(why)
|
|
|
|
(h, w) = text_only.shape[:2]
|
|
center = (w // 2.0, h // 2.0)
|
|
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
|
|
M_22 = np.array(M)[:2, :2]
|
|
p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
|
|
x_diff = p_big[0] - cx_bigest_d_big
|
|
y_diff = p_big[1] - cy_biggest_d_big
|
|
|
|
contours_only_text_parent_d_ordered = []
|
|
for i in range(len(contours_only_text_parent)):
|
|
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
|
|
p[0] = p[0] - x_diff[0]
|
|
p[1] = p[1] - y_diff[0]
|
|
dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
|
|
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
|
|
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
|
|
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
|
|
# plt.imshow(img2[:,:,0])
|
|
# plt.show()
|
|
else:
|
|
contours_only_text_parent_d_ordered = []
|
|
contours_only_text_parent_d = []
|
|
contours_only_text_parent = []
|
|
|
|
else:
|
|
contours_only_text_parent_d_ordered = []
|
|
contours_only_text_parent_d = []
|
|
contours_only_text_parent = []
|
|
else:
|
|
contours_only_text, hir_on_text = return_contours_of_image(text_only)
|
|
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
|
|
|
|
if len(contours_only_text_parent) > 0:
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
|
|
|
|
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
|
|
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
|
|
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
|
|
|
|
index_con_parents = np.argsort(areas_cnt_text_parent)
|
|
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
|
|
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
|
|
|
|
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
|
|
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
|
|
#self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
|
|
# self.logger.debug('areas_cnt_text_parent_d %s', areas_cnt_text_parent_d)
|
|
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
|
|
else:
|
|
pass
|
|
if self.light_version:
|
|
txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
|
|
else:
|
|
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
|
|
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
|
|
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
|
|
|
|
if not self.curved_line:
|
|
if self.light_version:
|
|
if self.textline_light:
|
|
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea_org, image_page_rotated, boxes_text, slope_deskew)
|
|
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new_light(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea_org, image_page_rotated, boxes_marginals, slope_deskew)
|
|
else:
|
|
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
|
|
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new_light(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
|
|
else:
|
|
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
|
|
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
|
|
else:
|
|
|
|
scale_param = 1
|
|
all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_curved(txt_con_org, contours_only_text_parent, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
|
|
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
|
|
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
|
|
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
|
|
if self.full_layout:
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
|
|
if self.light_version:
|
|
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header_light(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
|
else:
|
|
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
|
else:
|
|
#takes long timee
|
|
contours_only_text_parent_d_ordered = None
|
|
if self.light_version:
|
|
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header_light(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
|
else:
|
|
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
|
|
|
if self.plotter:
|
|
self.plotter.save_plot_of_layout(text_regions_p, image_page)
|
|
self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
|
|
if not self.dir_in:
|
|
K.clear_session()
|
|
|
|
pixel_img = 4
|
|
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
|
|
all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, kernel=KERNEL, curved_line=self.curved_line)
|
|
pixel_lines = 6
|
|
|
|
|
|
if not self.headers_off:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h)
|
|
else:
|
|
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h_d_ordered)
|
|
elif self.headers_off:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
|
else:
|
|
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
|
|
|
if num_col_classifier >= 3:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
regions_without_separators = regions_without_separators.astype(np.uint8)
|
|
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
|
|
|
|
else:
|
|
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
|
|
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
|
|
|
|
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
|
|
else:
|
|
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
|
|
|
|
if self.plotter:
|
|
self.plotter.write_images_into_directory(polygons_of_images, image_page)
|
|
t_order = time.time()
|
|
if self.full_layout:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
|
|
else:
|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d)
|
|
|
|
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_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_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)
|
|
##return pcgts
|
|
else:
|
|
contours_only_text_parent_h = None
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
|
|
else:
|
|
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
|
|
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_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_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)
|
|
##return pcgts
|
|
self.writer.write_pagexml(pcgts)
|
|
#self.logger.info("Job done in %.1fs", time.time() - t0)
|
|
if self.dir_in:
|
|
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
|