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2441 lines
127 KiB
Python
2441 lines
127 KiB
Python
# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring
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"""
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tool to extract table form data from alto xml data
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"""
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import gc
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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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from lxml import etree as ET
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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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from keras import backend as K
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from keras.models import load_model
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sys.stderr = stderr
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import tensorflow as tf
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tf.get_logger().setLevel("ERROR")
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warnings.filterwarnings("ignore")
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from .utils.contour import (
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contours_in_same_horizon,
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filter_contours_area_of_image_interiors,
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filter_contours_area_of_image_tables,
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filter_contours_area_of_image,
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find_contours_mean_y_diff,
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find_features_of_contours,
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find_new_features_of_contoures,
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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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return_bonding_box_of_contours,
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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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return_contours_of_interested_region_by_size,
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)
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from .utils.rotate import (
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rotate_image,
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rotate_max_area,
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rotate_max_area_new,
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rotatedRectWithMaxArea,
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rotation_image_new,
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rotation_not_90_func,
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rotation_not_90_func_full_layout,
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rotyate_image_different,
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)
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from .utils.separate_lines import (
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seperate_lines,
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seperate_lines_new_inside_teils,
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seperate_lines_new_inside_teils2,
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seperate_lines_vertical,
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seperate_lines_vertical_cont,
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textline_contours_postprocessing,
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seperate_lines_new2,
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return_deskew_slop,
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)
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from .utils.drop_capitals import (
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adhere_drop_capital_region_into_cprresponding_textline,
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filter_small_drop_capitals_from_no_patch_layout
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)
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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_features_of_lines,
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find_num_col,
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find_num_col_by_vertical_lines,
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find_num_col_deskew,
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find_num_col_only_image,
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isNaN,
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otsu_copy,
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otsu_copy_binary,
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return_hor_spliter_by_index_for_without_verticals,
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delete_seperator_around,
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return_regions_without_seperators,
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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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small_textlines_to_parent_adherence2,
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order_and_id_of_texts,
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order_of_regions,
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implent_law_head_main_not_parallel,
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return_hor_spliter_by_index,
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combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
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return_points_with_boundies,
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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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)
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from .utils.xml import create_page_xml, add_textequiv
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from .utils.pil_cv2 import check_dpi
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from .plot import EynollahPlotter
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SLOPE_THRESHOLD = 0.13
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class eynollah:
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def __init__(
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self,
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image_filename,
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image_filename_stem,
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dir_out,
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dir_models,
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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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enable_plotting=False,
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allow_enhancement=False,
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curved_line=False,
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full_layout=False,
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allow_scaling=False,
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headers_off=False
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):
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self.image_filename = image_filename # XXX This does not seem to be a directory as the name suggests, but a file
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self.cont_page = []
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self.dir_out = dir_out
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self.image_filename_stem = image_filename_stem
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self.allow_enhancement = allow_enhancement
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self.curved_line = curved_line
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self.full_layout = full_layout
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self.allow_scaling = allow_scaling
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self.headers_off = headers_off
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if not self.image_filename_stem:
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self.image_filename_stem = Path(Path(image_filename).name).stem
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self.plotter = None if not enable_plotting else EynollahPlotter(
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dir_of_all=dir_of_all,
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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=image_filename,
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image_filename_stem=image_filename_stem,
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)
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self.logger = getLogger('eynollah')
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self.dir_models = dir_models
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self.kernel = np.ones((5, 5), np.uint8)
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self.model_dir_of_enhancemnet = dir_models + "/model_enhancement.h5"
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self.model_dir_of_col_classifier = dir_models + "/model_scale_classifier.h5"
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self.model_region_dir_p = dir_models + "/model_main_covid19_lr5-5_scale_1_1_great.h5"
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self.model_region_dir_p2 = dir_models + "/model_main_home_corona3_rot.h5"
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self.model_region_dir_fully_np = dir_models + "/model_no_patches_class0_30eopch.h5"
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self.model_region_dir_fully = dir_models + "/model_3up_new_good_no_augmentation.h5"
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self.model_page_dir = dir_models + "/model_page_mixed_best.h5"
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self.model_region_dir_p_ens = dir_models + "/model_ensemble_s.h5"
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self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
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self._imgs = {}
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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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if key not in self._imgs:
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if grayscale:
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img = cv2.imread(self.image_filename, cv2.IMREAD_GRAYSCALE)
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else:
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img = cv2.imread(self.image_filename)
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if uint8:
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img = img.astype(np.uint8)
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self._imgs[key] = img
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return self._imgs[key].copy()
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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_enhancemnet = self.start_new_session_and_model(self.model_dir_of_enhancemnet)
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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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# n_classes = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[3]
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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 = True
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if margin:
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kernel = np.ones((5, 5), np.uint8)
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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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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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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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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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seg = label_p_pred[0, :, :, :]
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seg = seg * 255
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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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del model_enhancement
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del session_enhancemnet
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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):
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self.logger.debug("enter calculate_width_height_by_columns")
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if num_col == 1 and width_early < 1100:
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img_w_new = 2000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
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elif num_col == 1 and width_early >= 2500:
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img_w_new = 2000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
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elif num_col == 1 and width_early >= 1100 and width_early < 2500:
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img_w_new = width_early
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img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
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elif num_col == 2 and width_early < 2000:
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img_w_new = 2400
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
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elif num_col == 2 and width_early >= 3500:
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img_w_new = 2400
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
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elif num_col == 2 and width_early >= 2000 and width_early < 3500:
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img_w_new = width_early
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img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
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elif num_col == 3 and width_early < 2000:
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img_w_new = 3000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
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elif num_col == 3 and width_early >= 4000:
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img_w_new = 3000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
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elif num_col == 3 and width_early >= 2000 and width_early < 4000:
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img_w_new = width_early
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img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
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elif num_col == 4 and width_early < 2500:
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img_w_new = 4000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
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elif num_col == 4 and width_early >= 5000:
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img_w_new = 4000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
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elif num_col == 4 and width_early >= 2500 and width_early < 5000:
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img_w_new = width_early
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img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
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elif num_col == 5 and width_early < 3700:
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img_w_new = 5000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
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elif num_col == 5 and width_early >= 7000:
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img_w_new = 5000
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
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elif num_col == 5 and width_early >= 3700 and width_early < 7000:
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img_w_new = width_early
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img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
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elif num_col == 6 and width_early < 4500:
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img_w_new = 6500 # 5400
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img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500)
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else:
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img_w_new = width_early
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img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
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if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
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img_new = np.copy(img)
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num_column_is_classified = False
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else:
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img_new = resize_image(img, img_h_new, img_w_new)
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num_column_is_classified = True
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return img_new, num_column_is_classified
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def resize_image_with_column_classifier(self, is_image_enhanced):
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self.logger.debug("enter resize_image_with_column_classifier")
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img = self.imread()
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_, page_coord = self.early_page_for_num_of_column_classification()
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
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img_1ch = self.imread(grayscale=True, uint8=False)
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width_early = img_1ch.shape[1]
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img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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# plt.imshow(img_1ch)
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# plt.show()
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img_1ch = img_1ch / 255.0
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img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
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img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
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img_in[0, :, :, 0] = img_1ch[:, :]
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img_in[0, :, :, 1] = img_1ch[:, :]
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img_in[0, :, :, 2] = img_1ch[:, :]
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label_p_pred = model_num_classifier.predict(img_in)
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num_col = np.argmax(label_p_pred[0]) + 1
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self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
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session_col_classifier.close()
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del model_num_classifier
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del session_col_classifier
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K.clear_session()
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gc.collect()
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img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
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if img_new.shape[1] > img.shape[1]:
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img_new = self.predict_enhancement(img_new)
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is_image_enhanced = True
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return img, img_new, is_image_enhanced
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def resize_and_enhance_image_with_column_classifier(self):
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self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
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dpi = check_dpi(self.image_filename)
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self.logger.info("Detected %s DPI" % dpi)
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img = self.imread()
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_, page_coord = self.early_page_for_num_of_column_classification()
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
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img_1ch = self.imread(grayscale=True)
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width_early = img_1ch.shape[1]
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img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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# plt.imshow(img_1ch)
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# plt.show()
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img_1ch = img_1ch / 255.0
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|
|
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[:, :]
|
|
|
|
# plt.imshow(img_in[0,:,:,:])
|
|
# plt.show()
|
|
|
|
label_p_pred = model_num_classifier.predict(img_in)
|
|
num_col = np.argmax(label_p_pred[0]) + 1
|
|
|
|
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
|
|
|
|
session_col_classifier.close()
|
|
del model_num_classifier
|
|
del session_col_classifier
|
|
del img_in
|
|
del img_1ch
|
|
del page_coord
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
if dpi < 298:
|
|
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
|
|
image_res = self.predict_enhancement(img_new)
|
|
is_image_enhanced = True
|
|
else:
|
|
is_image_enhanced = False
|
|
num_column_is_classified = True
|
|
image_res = np.copy(img)
|
|
|
|
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
|
|
return is_image_enhanced, img, image_res, num_col, num_column_is_classified
|
|
|
|
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
|
|
# XXX TODO hacky
|
|
if self.plotter:
|
|
self.plotter.image_org = self.image_org
|
|
self.plotter.scale_y = self.scale_y
|
|
self.plotter.scale_x = self.scale_x
|
|
|
|
|
|
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])
|
|
|
|
|
|
del img_org
|
|
del img_res
|
|
|
|
def start_new_session_and_model(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 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]
|
|
n_classes = model.layers[len(model.layers) - 1].output_shape[3]
|
|
|
|
|
|
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)
|
|
|
|
del img
|
|
del seg_color
|
|
del label_p_pred
|
|
del seg
|
|
|
|
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.info("Image dimensions: %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_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 img
|
|
del mask_true
|
|
del seg_color
|
|
del seg
|
|
del img_patch
|
|
gc.collect()
|
|
return prediction_true
|
|
|
|
def early_page_for_num_of_column_classification(self):
|
|
self.logger.debug("enter early_page_for_num_of_column_classification")
|
|
img = self.imread()
|
|
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
|
for ii in range(1):
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
|
|
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, self.kernel, iterations=3)
|
|
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))])
|
|
cnt = contours[np.argmax(cnt_size)]
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
box = [x, y, w, h]
|
|
croped_page, page_coord = crop_image_inside_box(box, img)
|
|
session_page.close()
|
|
del model_page
|
|
del session_page
|
|
del contours
|
|
del thresh
|
|
del img
|
|
del cnt_size
|
|
del cnt
|
|
del box
|
|
del x
|
|
del y
|
|
del w
|
|
del h
|
|
del imgray
|
|
del img_page_prediction
|
|
|
|
gc.collect()
|
|
self.logger.debug("exit early_page_for_num_of_column_classification")
|
|
return croped_page, page_coord
|
|
|
|
def extract_page(self):
|
|
self.logger.debug("enter extract_page")
|
|
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
|
for ii in range(1):
|
|
img = cv2.GaussianBlur(self.image, (5, 5), 0)
|
|
|
|
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, self.kernel, iterations=3)
|
|
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))])
|
|
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]
|
|
croped_page, page_coord = crop_image_inside_box(box, self.image)
|
|
|
|
self.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]]]))
|
|
|
|
session_page.close()
|
|
del model_page
|
|
del session_page
|
|
del contours
|
|
del thresh
|
|
del img
|
|
del imgray
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
self.logger.debug("exit extract_page")
|
|
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]
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_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)#self.otsu_copy(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)
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
del img
|
|
gc.collect()
|
|
self.logger.debug("exit extract_text_regions")
|
|
return prediction_regions, prediction_regions2
|
|
|
|
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])
|
|
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_seperated = 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,self.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, hierachy = return_contours_of_image(img_int_p)
|
|
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, 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)))
|
|
|
|
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]
|
|
# old method
|
|
# slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
|
|
# text_patch_processed=textline_contours_postprocessing(gada)
|
|
except:
|
|
slope_for_all = 999
|
|
|
|
if slope_for_all == 999:
|
|
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_img, 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_seperated = seperate_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_seperated),np.shape(mask_biggest))
|
|
textline_rotated_seperated[mask_region_in_patch_region[:, :] != 1] = 0
|
|
# till here
|
|
|
|
textline_cnt_seperated[y : y + h, x : x + w] = textline_rotated_seperated
|
|
textline_region_in_image[y : y + h, x : x + w] = textline_rotated_seperated
|
|
|
|
# plt.imshow(textline_region_in_image)
|
|
# plt.show()
|
|
# plt.imshow(textline_cnt_seperated)
|
|
# 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, self.kernel, iterations=5)
|
|
else:
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, self.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]/scale_par)
|
|
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
|
|
except:
|
|
pass
|
|
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(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_img,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))
|
|
denoised=None
|
|
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,self.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, hierachy = return_contours_of_image(img_int_p)
|
|
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.00008)
|
|
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
|
|
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:
|
|
slope_for_all = 999
|
|
|
|
if slope_for_all == 999:
|
|
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')
|
|
|
|
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))
|
|
prediction_textline = self.do_prediction(patches, img, model_textline)
|
|
prediction_textline = resize_image(prediction_textline, img_h, img_w)
|
|
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
|
|
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
|
|
##plt.imshow(prediction_textline_streched[:,:,0])
|
|
##plt.show()
|
|
|
|
session_textline.close()
|
|
del model_textline
|
|
del session_textline
|
|
del img
|
|
del img_org
|
|
|
|
gc.collect()
|
|
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, self.kernel, iterations=2)
|
|
|
|
try:
|
|
textline_con, hierachy = return_contours_of_image(crop_img)
|
|
textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_des = int(y_diff_mean * (4.0 / 40.0))
|
|
|
|
if sigma_des < 1:
|
|
sigma_des = 1
|
|
|
|
crop_img[crop_img > 0] = 1
|
|
slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, plotter=self.plotter)
|
|
|
|
except:
|
|
slope_corresponding_textregion = 999
|
|
|
|
if slope_corresponding_textregion == 999:
|
|
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 serialize_lines_in_region(self, textregion, all_found_texline_polygons, region_idx, page_coord, all_box_coord, slopes, id_indexer_l):
|
|
self.logger.debug('enter serialize_lines_in_region')
|
|
for j in range(len(all_found_texline_polygons[region_idx])):
|
|
textline = ET.SubElement(textregion, 'TextLine')
|
|
textline.set('id', 'l%s' % id_indexer_l)
|
|
id_indexer_l += 1
|
|
coord = ET.SubElement(textline, 'Coords')
|
|
add_textequiv(textline)
|
|
|
|
points_co = ''
|
|
for l in range(len(all_found_texline_polygons[region_idx][j])):
|
|
if not self.curved_line:
|
|
if len(all_found_texline_polygons[region_idx][j][l])==2:
|
|
textline_x_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][0] + all_box_coord[region_idx][2] + page_coord[2]) / self.scale_x))
|
|
textline_y_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][1] + all_box_coord[region_idx][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
textline_x_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][0][0] + all_box_coord[region_idx][2] + page_coord[2]) / self.scale_x))
|
|
textline_y_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][0][1] + all_box_coord[region_idx][0] + page_coord[0]) / self.scale_y))
|
|
points_co += str(textline_x_coord)
|
|
points_co += ','
|
|
points_co += str(textline_y_coord)
|
|
|
|
if self.curved_line and np.abs(slopes[region_idx]) <= 45:
|
|
if len(all_found_texline_polygons[region_idx][j][l]) == 2:
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][1] + page_coord[0])/self.scale_y))
|
|
elif self.curved_line and np.abs(slopes[region_idx]) > 45:
|
|
if len(all_found_texline_polygons[region_idx][j][l])==2:
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0] + all_box_coord[region_idx][2]+page_coord[2])/self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][1] + all_box_coord[region_idx][0]+page_coord[0])/self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][0] + all_box_coord[region_idx][2]+page_coord[2])/self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][1] + all_box_coord[region_idx][0]+page_coord[0])/self.scale_y))
|
|
|
|
if l < len(all_found_texline_polygons[region_idx][j]) - 1:
|
|
points_co += ' '
|
|
coord.set('points',points_co)
|
|
return id_indexer_l
|
|
|
|
def calculate_polygon_coords(self, contour_list, i, page_coord):
|
|
self.logger.debug('enter calculate_polygon_coords')
|
|
coords = ''
|
|
for j in range(len(contour_list[i])):
|
|
if len(contour_list[i][j]) == 2:
|
|
coords += str(int((contour_list[i][j][0] + page_coord[2]) / self.scale_x))
|
|
coords += ','
|
|
coords += str(int((contour_list[i][j][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
coords += str(int((contour_list[i][j][0][0] + page_coord[2]) / self.scale_x))
|
|
coords += ','
|
|
coords += str(int((contour_list[i][j][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if j < len(contour_list[i]) - 1:
|
|
coords=coords+' '
|
|
#print(coords)
|
|
return coords
|
|
|
|
def calculate_page_coords(self):
|
|
self.logger.debug('enter calculate_page_coords')
|
|
points_page_print = ""
|
|
for lmm in range(len(self.cont_page[0])):
|
|
if len(self.cont_page[0][lmm]) == 2:
|
|
points_page_print += str(int((self.cont_page[0][lmm][0] ) / self.scale_x))
|
|
points_page_print += ','
|
|
points_page_print += str(int((self.cont_page[0][lmm][1] ) / self.scale_y))
|
|
else:
|
|
points_page_print += str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
|
|
points_page_print += ','
|
|
points_page_print += str(int((self.cont_page[0][lmm][0][1] ) / self.scale_y))
|
|
|
|
if lmm < len( self.cont_page[0] ) - 1:
|
|
points_page_print = points_page_print + ' '
|
|
return points_page_print
|
|
|
|
def xml_reading_order(self, page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals):
|
|
"""
|
|
XXX side-effect: extends id_of_marginalia
|
|
"""
|
|
region_order = ET.SubElement(page, 'ReadingOrder')
|
|
region_order_sub = ET.SubElement(region_order, 'OrderedGroup')
|
|
region_order_sub.set('id', "ro357564684568544579089")
|
|
indexer_region = 0
|
|
for vj in order_of_texts:
|
|
name = "coord_text_%s" % vj
|
|
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
|
|
name.set('index', str(indexer_region))
|
|
name.set('regionRef', id_of_texts[vj])
|
|
indexer_region+=1
|
|
for vm in range(len(found_polygons_marginals)):
|
|
id_of_marginalia.append('r%s' % indexer_region)
|
|
name = "coord_text_%s" % indexer_region
|
|
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
|
|
name.set('index', str(indexer_region))
|
|
name.set('regionRef', 'r%s' % indexer_region)
|
|
indexer_region += 1
|
|
|
|
|
|
def write_into_page_xml(self, found_polygons_text_region, page_coord, dir_of_image, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, curved_line, slopes, slopes_marginals):
|
|
self.logger.debug('enter write_into_page_xml')
|
|
|
|
# create the file structure
|
|
pcgts, page = create_page_xml(self.image_filename, self.height_org, self.width_org)
|
|
page_print_sub = ET.SubElement(page, "Border")
|
|
coord_page = ET.SubElement(page_print_sub, "Coords")
|
|
coord_page.set('points', self.calculate_page_coords())
|
|
|
|
id_of_marginalia = []
|
|
id_indexer = 0
|
|
id_indexer_l = 0
|
|
if len(found_polygons_text_region) > 0:
|
|
self.xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals)
|
|
|
|
for mm in range(len(found_polygons_text_region)):
|
|
textregion = ET.SubElement(page, 'TextRegion')
|
|
textregion.set('id', 'r%s' % id_indexer)
|
|
id_indexer += 1
|
|
textregion.set('type', 'paragraph')
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region, mm, page_coord))
|
|
for j in range(len(all_found_texline_polygons[mm])):
|
|
textline = ET.SubElement(textregion, 'TextLine')
|
|
textline.set('id', 'l%s' % id_indexer_l)
|
|
id_indexer_l += 1
|
|
coord = ET.SubElement(textline, 'Coords')
|
|
add_textequiv(textline)
|
|
points_co = ''
|
|
for l in range(len(all_found_texline_polygons[mm][j])):
|
|
if not curved_line:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
textline_x_coord = max(0, int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
textline_y_coord = max(0, int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
textline_x_coord = max(0, int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2]+page_coord[2]) / self.scale_x))
|
|
textline_y_coord = max(0, int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0]+page_coord[0]) / self.scale_y))
|
|
points_co += str(textline_x_coord) + ',' + str(textline_y_coord)
|
|
if curved_line and abs(slopes[mm]) <= 45:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co += str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ','
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
elif curved_line and abs(slopes[mm]) > 45:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co += str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < len(all_found_texline_polygons[mm][j]) - 1:
|
|
points_co += ' '
|
|
coord.set('points', points_co)
|
|
|
|
add_textequiv(textregion)
|
|
|
|
for mm in range(len(found_polygons_marginals)):
|
|
textregion = ET.SubElement(page, 'TextRegion')
|
|
textregion.set('id', id_of_marginalia[mm])
|
|
textregion.set('type', 'marginalia')
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord))
|
|
for j in range(len(all_found_texline_polygons_marginals[mm])):
|
|
textline = ET.SubElement(textregion, 'TextLine')
|
|
textline.set('id','l'+str(id_indexer_l))
|
|
id_indexer_l += 1
|
|
coord = ET.SubElement(textline, 'Coords')
|
|
add_textequiv(textline)
|
|
points_co = ''
|
|
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
|
|
if not curved_line:
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0])/self.scale_y))
|
|
else:
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
if l < len(all_found_texline_polygons_marginals[mm][j]) - 1:
|
|
points_co += ' '
|
|
coord.set('points',points_co)
|
|
|
|
id_indexer = len(found_polygons_text_region) + len(found_polygons_marginals)
|
|
for mm in range(len(found_polygons_text_region_img)):
|
|
textregion=ET.SubElement(page, 'ImageRegion')
|
|
textregion.set('id', 'r%s' % id_indexer)
|
|
id_indexer += 1
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
points_co = ''
|
|
for lmm in range(len(found_polygons_text_region_img[mm])):
|
|
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
|
|
if lmm < len(found_polygons_text_region_img[mm]) - 1:
|
|
points_co += ' '
|
|
coord_text.set('points', points_co)
|
|
|
|
self.logger.info("filename stem: '%s'", self.image_filename_stem)
|
|
tree = ET.ElementTree(pcgts)
|
|
tree.write(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
|
|
|
|
def write_into_page_xml_full(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, dir_of_image, order_of_texts, id_of_texts, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals):
|
|
self.logger.debug('enter write_into_page_xml_full')
|
|
|
|
# create the file structure
|
|
pcgts, page = create_page_xml(self.image_filename, self.height_org, self.width_org)
|
|
page_print_sub = ET.SubElement(page, "Border")
|
|
coord_page = ET.SubElement(page_print_sub, "Coords")
|
|
coord_page.set('points', self.calculate_page_coords())
|
|
|
|
id_indexer = 0
|
|
id_indexer_l = 0
|
|
id_of_marginalia = []
|
|
|
|
if len(found_polygons_text_region) > 0:
|
|
self.xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals)
|
|
for mm in range(len(found_polygons_text_region)):
|
|
textregion=ET.SubElement(page, 'TextRegion')
|
|
textregion.set('id', 'r%s' % id_indexer)
|
|
id_indexer += 1
|
|
textregion.set('type', 'paragraph')
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region, mm, page_coord))
|
|
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons, mm, page_coord, all_box_coord, slopes, id_indexer_l)
|
|
add_textequiv(textregion)
|
|
|
|
self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h))
|
|
if len(found_polygons_text_region_h) > 0:
|
|
for mm in range(len(found_polygons_text_region_h)):
|
|
textregion=ET.SubElement(page, 'TextRegion')
|
|
textregion.set('id', 'r%s' % id_indexer)
|
|
id_indexer += 1
|
|
textregion.set('type','header')
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region_h, mm, page_coord))
|
|
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons_h, mm, page_coord, all_box_coord_h, slopes, id_indexer_l)
|
|
add_textequiv(textregion)
|
|
|
|
if len(found_polygons_drop_capitals) > 0:
|
|
id_indexer = len(found_polygons_text_region) + len(found_polygons_text_region_h) + len(found_polygons_marginals)
|
|
for mm in range(len(found_polygons_drop_capitals)):
|
|
textregion=ET.SubElement(page, 'TextRegion')
|
|
textregion.set('id',' r%s' % id_indexer)
|
|
id_indexer += 1
|
|
textregion.set('type', 'drop-capital')
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_drop_capitals, mm, page_coord))
|
|
add_textequiv(textregion)
|
|
|
|
for mm in range(len(found_polygons_marginals)):
|
|
textregion = ET.SubElement(page, 'TextRegion')
|
|
textregion.set('id', id_of_marginalia[mm])
|
|
textregion.set('type', 'marginalia')
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord))
|
|
|
|
for j in range(len(all_found_texline_polygons_marginals[mm])):
|
|
textline = ET.SubElement(textregion, 'TextLine')
|
|
textline.set('id', 'l%s' % id_indexer_l)
|
|
id_indexer_l += 1
|
|
coord = ET.SubElement(textline, 'Coords')
|
|
add_textequiv(textline)
|
|
points_co = ''
|
|
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
|
|
if not self.curved_line:
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co+= str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co += ','
|
|
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < len(all_found_texline_polygons_marginals[mm][j]) - 1:
|
|
points_co = points_co+' '
|
|
coord.set('points',points_co)
|
|
add_textequiv(textregion)
|
|
|
|
id_indexer = len(found_polygons_text_region) + len(found_polygons_text_region_h) + len(found_polygons_marginals) + len(found_polygons_drop_capitals)
|
|
for mm in range(len(found_polygons_text_region_img)):
|
|
textregion=ET.SubElement(page, 'ImageRegion')
|
|
textregion.set('id', 'r%s' % id_indexer)
|
|
id_indexer += 1
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region_img, mm, page_coord))
|
|
|
|
for mm in range(len(found_polygons_tables)):
|
|
textregion = ET.SubElement(page, 'TableRegion')
|
|
textregion.set('id', 'r%s' %id_indexer)
|
|
id_indexer += 1
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
coord_text.set('points', self.calculate_polygon_coords(found_polygons_tables, mm, page_coord))
|
|
|
|
self.logger.info("filename stem: '%s'", self.image_filename_stem)
|
|
tree = ET.ElementTree(pcgts)
|
|
tree.write(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
|
|
|
|
def get_regions_from_xy_2models(self,img,is_image_enhanced):
|
|
self.logger.debug("enter get_regions_from_xy_2models")
|
|
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)
|
|
|
|
gaussian_filter=False
|
|
binary=False
|
|
ratio_y=1.3
|
|
ratio_x=1
|
|
median_blur=False
|
|
|
|
img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
|
|
if binary:
|
|
img = otsu_copy_binary(img)
|
|
img = img.astype(np.uint16)
|
|
if median_blur:
|
|
img = cv2.medianBlur(img,5)
|
|
if gaussian_filter:
|
|
img= cv2.GaussianBlur(img,(5,5),0)
|
|
img = img.astype(np.uint16)
|
|
|
|
prediction_regions_org_y = self.do_prediction(True, img, 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
|
|
if is_image_enhanced:
|
|
ratio_x = 1.2
|
|
else:
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
median_blur=False
|
|
|
|
img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
|
|
if binary:
|
|
img = otsu_copy_binary(img)#self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5,5 ), 0)
|
|
img = img.astype(np.uint16)
|
|
|
|
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
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
|
|
|
|
gaussian_filter=False
|
|
binary=False
|
|
ratio_x=1
|
|
ratio_y=1
|
|
median_blur=False
|
|
|
|
img= resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
|
|
if binary:
|
|
img = otsu_copy_binary(img)#self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img=cv2.medianBlur(img,5)
|
|
if gaussian_filter:
|
|
img= cv2.GaussianBlur(img,(5,5),0)
|
|
img = img.astype(np.uint16)
|
|
|
|
marginal_patch=0.2
|
|
prediction_regions_org2=self.do_prediction(True, img, model_region, marginal_patch)
|
|
|
|
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
|
|
|
|
#plt.imshow(prediction_regions_org2[:,:,0])
|
|
#plt.show()
|
|
##prediction_regions_org=prediction_regions_org[:,:,0]
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
|
|
mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
|
|
|
|
text_sume_early=( (prediction_regions_org[:,:]==1)*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<95.50):#98.45:
|
|
prediction_regions_org=np.copy(prediction_regions_org_copy)
|
|
|
|
##prediction_regions_org[mask_lines2[:,:]==1]=3
|
|
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
|
|
|
|
|
|
del mask_lines2
|
|
del mask_zeros2
|
|
del prediction_regions_org2
|
|
|
|
mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
|
|
|
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)
|
|
|
|
#plt.imshow(text_region2_1st_channel)
|
|
#plt.show()
|
|
|
|
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)
|
|
mask_texts_only=(prediction_regions_org[:,:]==1)*1
|
|
mask_images_only=(prediction_regions_org[:,:]==2)*1
|
|
|
|
pixel_img=1
|
|
min_area_text=0.00001
|
|
polygons_of_only_texts=return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)
|
|
polygons_of_only_images=return_contours_of_interested_region(mask_images_only,pixel_img)
|
|
polygons_of_only_lines=return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)
|
|
|
|
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))
|
|
|
|
del polygons_of_only_texts
|
|
del polygons_of_only_images
|
|
del polygons_of_only_lines
|
|
del mask_images_only
|
|
del prediction_regions_org
|
|
del img
|
|
del mask_zeros_y
|
|
|
|
del prediction_regions_org_y
|
|
del img_org
|
|
gc.collect()
|
|
|
|
K.clear_session()
|
|
return text_regions_p_true
|
|
|
|
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_contoures(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_contoures(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 i in range(len(args_contours_box)):
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
|
|
for i in range(len(args_contours_box_h)):
|
|
con_inter_box_h.append(contours_only_text_parent_h[args_contours_box_h[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]
|
|
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]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box_h:
|
|
arg_order_v = indexes_sorted_head[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
# print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
|
|
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
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 = 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)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
|
|
except:
|
|
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
|
|
arg_arg_text_con_h = np.argsort(arg_text_con_h)
|
|
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 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 i in range(len(args_contours_box)):
|
|
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
for i in range(len(args_contours_box_h)):
|
|
|
|
con_inter_box_h.append(contours_only_text_parent_h[args_contours_box_h[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]
|
|
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]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box_h:
|
|
arg_order_v = indexes_sorted_head[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
# print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
|
|
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
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 = 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)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
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_contoures(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]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
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 = 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)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
|
|
except:
|
|
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]
|
|
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]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
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 = 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)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
|
|
return order_text_new, id_of_texts_tot
|
|
|
|
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 run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified):
|
|
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 = self.extract_page()
|
|
if self.plotter:
|
|
self.plotter.save_page_image(image_page)
|
|
|
|
img_g3_page = img_g3[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
|
|
|
|
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[:, :], self.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)
|
|
img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], self.kernel, iterations=6)
|
|
|
|
try:
|
|
num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0)
|
|
num_col = num_col + 1
|
|
if not num_column_is_classified:
|
|
num_col_classifier = num_col + 1
|
|
except:
|
|
num_col = None
|
|
peaks_neg_fin = []
|
|
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1
|
|
|
|
def run_enhancement(self):
|
|
self.logger.info("resize and enhance image")
|
|
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified = self.resize_and_enhance_image_with_column_classifier()
|
|
self.logger.info("Image is %senhanced", '' if is_image_enhanced else 'not ')
|
|
K.clear_session()
|
|
scale = 1
|
|
if is_image_enhanced:
|
|
if self.allow_enhancement:
|
|
cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem) + ".tif", img_res)
|
|
img_res = img_res.astype(np.uint8)
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
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)
|
|
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, textline_mask_tot_long_shot = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
#print(np.unique(textline_mask_tot_ea[:, :]), "textline")
|
|
# plt.imshow(textline_mask_tot_ea)
|
|
# plt.show()
|
|
if self.plotter:
|
|
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
|
|
return textline_mask_tot_ea, textline_mask_tot_long_shot
|
|
|
|
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, self.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):
|
|
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :]
|
|
textline_mask_tot[mask_images[:, :] == 1] = 0
|
|
|
|
pixel_img = 1
|
|
min_area = 0.00001
|
|
max_area = 0.0006
|
|
textline_mask_tot_small_size = return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
|
|
text_regions_p_1[mask_lines[:, :] == 1] = 3
|
|
text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
|
|
text_regions_p = np.array(text_regions_p)
|
|
|
|
if num_col_classifier == 1 or num_col_classifier == 2:
|
|
try:
|
|
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
|
|
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
|
text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel)
|
|
except:
|
|
pass
|
|
|
|
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):
|
|
self.logger.debug('enter run_boxes_no_full_layout')
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, 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_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
|
regions_without_seperators = (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)
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
text_regions_p_1_n = None
|
|
textline_mask_tot_d = None
|
|
regions_without_seperators_d = None
|
|
pixel_lines = 3
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
|
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_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, pixel_lines)
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
self.logger.info("num_col_classifier: %s", num_col_classifier)
|
|
|
|
if num_col_classifier >= 3:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
|
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
|
|
#random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
|
|
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
|
|
#random_pixels_for_image[random_pixels_for_image != 0] = 1
|
|
#regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
|
|
else:
|
|
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
|
|
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
|
|
#random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
|
|
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
|
|
#random_pixels_for_image[random_pixels_for_image != 0] = 1
|
|
#regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
|
|
|
|
t1 = time.time()
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
|
|
boxes_d = None
|
|
self.logger.debug("len(boxes): %s", len(boxes))
|
|
else:
|
|
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
|
|
boxes = None
|
|
self.logger.debug("len(boxes): %s", len(boxes_d))
|
|
|
|
self.logger.info("detecting boxes took %ss", str(time.time() - t1))
|
|
img_revised_tab = text_regions_p[:, :]
|
|
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 2)
|
|
|
|
# plt.imshow(img_revised_tab)
|
|
# plt.show()
|
|
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_seperators_d, boxes, boxes_d
|
|
|
|
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions):
|
|
self.logger.debug('enter run_boxes_full_layout')
|
|
# 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
|
|
|
|
K.clear_session()
|
|
# gc.collect()
|
|
image_page = image_page.astype(np.uint8)
|
|
|
|
# print(type(image_page))
|
|
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
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# 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()
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
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()
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# 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 np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
image_page_rotated_n, 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])
|
|
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
|
else:
|
|
text_regions_p_1_n = None
|
|
textline_mask_tot_d = None
|
|
regions_without_seperators_d = None
|
|
|
|
regions_without_seperators = (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)
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
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_seperators_d, regions_fully, regions_without_seperators
|
|
|
|
def run(self):
|
|
"""
|
|
Get image and scales, then extract the page of scanned image
|
|
"""
|
|
self.logger.debug("enter run")
|
|
|
|
t1 = time.time()
|
|
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement()
|
|
self.logger.info("Enhancing took %ss ", str(time.time() - t1))
|
|
|
|
t1 = time.time()
|
|
text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced)
|
|
self.logger.info("Textregion detection took %ss ", str(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 = \
|
|
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified)
|
|
self.logger.info("Graphics detection took %ss ", str(time.time() - t1))
|
|
|
|
if not num_col:
|
|
self.logger.info("No columns detected, outputting an empty PAGE-XML")
|
|
self.write_into_page_xml([], page_coord, self.dir_out, [], [], [], [], [], [], [], [], self.curved_line, [], [])
|
|
self.logger.info("Job done in %ss", str(time.time() - t1))
|
|
return
|
|
|
|
t1 = time.time()
|
|
textline_mask_tot_ea, textline_mask_tot_long_shot = self.run_textline(image_page)
|
|
self.logger.info("textline detection took %ss", str(time.time() - t1))
|
|
|
|
t1 = time.time()
|
|
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
|
|
self.logger.info("deskewing took %ss", str(time.time() - t1))
|
|
t1 = time.time()
|
|
|
|
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)
|
|
self.logger.info("detection of marginals took %ss", str(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_seperators_d, boxes, boxes_d = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier)
|
|
|
|
pixel_img = 4
|
|
min_area_mar = 0.00001
|
|
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
|
|
|
if self.full_layout:
|
|
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_seperators_d, regions_fully, regions_without_seperators = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions)
|
|
# plt.imshow(img_revised_tab)
|
|
# plt.show()
|
|
|
|
# print(img_revised_tab.shape,text_regions_p_1_n.shape)
|
|
# text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
|
|
# print(np.unique(text_regions_p_1_n),'uni')
|
|
|
|
text_only = ((img_revised_tab[:, :] == 1)) * 1
|
|
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
|
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
|
|
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
|
|
|
|
# print(text_only.shape,text_only_d.shape)
|
|
# plt.imshow(text_only)
|
|
# plt.show()
|
|
|
|
# plt.imshow(text_only_d)
|
|
# plt.show()
|
|
|
|
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)
|
|
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_contoures([contours_biggest])
|
|
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(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])
|
|
|
|
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_contoures([contours_biggest_d])
|
|
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_d)
|
|
try:
|
|
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)
|
|
cx_bigest_d_big[0]=cx_bigest_d[ind_largest]
|
|
cy_biggest_d_big[0]=cy_biggest_d[ind_largest]
|
|
except:
|
|
pass
|
|
|
|
(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
|
|
|
|
# print(p_big)
|
|
# print(cx_bigest_d_big,cy_biggest_d_big)
|
|
# print(x_diff,y_diff)
|
|
|
|
contours_only_text_parent_d_ordered = []
|
|
for i in range(len(contours_only_text_parent)):
|
|
# img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
|
|
# img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
|
|
# plt.imshow(img1[:,:,0])
|
|
# plt.show()
|
|
|
|
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
|
|
# print(p)
|
|
p[0] = p[0] - x_diff[0]
|
|
p[1] = p[1] - y_diff[0]
|
|
# print(p)
|
|
# print(cx_bigest_d)
|
|
# print(cy_biggest_d)
|
|
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))]
|
|
# print(np.argmin(dists))
|
|
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, hir_on_text = return_contours_of_image(text_only)
|
|
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
|
|
|
|
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_contoures([contours_biggest])
|
|
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(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))
|
|
|
|
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:
|
|
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, index_by_text_par_con_marginal = 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=self.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, index_by_text_par_con_marginal, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=self.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)
|
|
index_of_vertical_text_contours = np.array(range(len(slopes)))[(abs(np.array(slopes)) > 60)]
|
|
contours_text_vertical = [contours_only_text_parent[i] for i in index_of_vertical_text_contours]
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
# print(index_by_text_par_con,'index_by_text_par_con')
|
|
|
|
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])
|
|
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:
|
|
contours_only_text_parent_d_ordered = None
|
|
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)
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
polygons_of_tabels = []
|
|
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_cprresponding_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=self.kernel, curved_line=self.curved_line)
|
|
|
|
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
|
|
pixel_lines = 6
|
|
|
|
if not self.headers_off:
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h)
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else:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
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elif self.headers_off:
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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else:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
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# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
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# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
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if num_col_classifier >= 3:
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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regions_without_seperators = regions_without_seperators.astype(np.uint8)
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regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
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random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
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random_pixels_for_image[random_pixels_for_image < -0.5] = 0
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random_pixels_for_image[random_pixels_for_image != 0] = 1
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regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
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else:
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regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
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regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
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random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
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random_pixels_for_image[random_pixels_for_image < -0.5] = 0
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random_pixels_for_image[random_pixels_for_image != 0] = 1
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regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
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else:
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boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
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if self.plotter:
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self.plotter.write_images_into_directory(polygons_of_images, image_page)
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if self.full_layout:
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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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)
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else:
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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)
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self.write_into_page_xml_full(contours_only_text_parent, contours_only_text_parent_h, page_coord, self.dir_out, 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, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals)
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else:
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contours_only_text_parent_h = None
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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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)
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else:
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contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
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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)
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self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, 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, self.curved_line, slopes, slopes_marginals)
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self.logger.info("Job done in %ss", str(time.time() - t1))
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