# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring """ tool to extract table form data from alto xml data """ import gc import math import os import sys import time import warnings from pathlib import Path from multiprocessing import Process, Queue, cpu_count from lxml import etree as ET from ocrd_utils import getLogger import cv2 import numpy as np os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" stderr = sys.stderr sys.stderr = open(os.devnull, "w") from keras import backend as K from keras.models import load_model sys.stderr = stderr import tensorflow as tf tf.get_logger().setLevel("ERROR") warnings.filterwarnings("ignore") from .utils.contour import ( filter_contours_area_of_image, find_contours_mean_y_diff, find_new_features_of_contoures, get_text_region_boxes_by_given_contours, get_textregion_contours_in_org_image, return_contours_of_image, return_contours_of_interested_region, return_contours_of_interested_region_by_min_size, return_contours_of_interested_textline, return_parent_contours, return_contours_of_interested_region_by_size, ) from .utils.rotate import ( rotate_image, rotation_not_90_func, rotation_not_90_func_full_layout ) from .utils.separate_lines import ( textline_contours_postprocessing, seperate_lines_new2, return_deskew_slop, ) from .utils.drop_capitals import ( adhere_drop_capital_region_into_cprresponding_textline, filter_small_drop_capitals_from_no_patch_layout ) from .utils.marginals import get_marginals from .utils.resize import resize_image from .utils import ( boosting_headers_by_longshot_region_segmentation, crop_image_inside_box, find_num_col, otsu_copy, otsu_copy_binary, delete_seperator_around, return_regions_without_seperators, put_drop_out_from_only_drop_model, putt_bb_of_drop_capitals_of_model_in_patches_in_layout, check_any_text_region_in_model_one_is_main_or_header, small_textlines_to_parent_adherence2, order_and_id_of_texts, order_of_regions, implent_law_head_main_not_parallel, return_hor_spliter_by_index, combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new, return_points_with_boundies, find_number_of_columns_in_document, return_boxes_of_images_by_order_of_reading_new, ) from .utils.xml import create_page_xml, add_textequiv from .utils.pil_cv2 import check_dpi from .plot import EynollahPlotter SLOPE_THRESHOLD = 0.13 RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45: DPI_THRESHOLD = 298 class eynollah: def __init__( self, image_filename, image_filename_stem, dir_out, dir_models, dir_of_cropped_images=None, dir_of_layout=None, dir_of_deskewed=None, dir_of_all=None, enable_plotting=False, allow_enhancement=False, curved_line=False, full_layout=False, allow_scaling=False, headers_off=False ): self.image_filename = image_filename # XXX This does not seem to be a directory as the name suggests, but a file self.cont_page = [] self.dir_out = dir_out self.image_filename_stem = image_filename_stem self.allow_enhancement = allow_enhancement self.curved_line = curved_line self.full_layout = full_layout self.allow_scaling = allow_scaling self.headers_off = headers_off if not self.image_filename_stem: self.image_filename_stem = Path(Path(image_filename).name).stem self.plotter = None if not enable_plotting else EynollahPlotter( dir_of_all=dir_of_all, dir_of_deskewed=dir_of_deskewed, dir_of_cropped_images=dir_of_cropped_images, dir_of_layout=dir_of_layout, image_filename=image_filename, image_filename_stem=image_filename_stem, ) self.logger = getLogger('eynollah') self.dir_models = dir_models self.kernel = np.ones((5, 5), np.uint8) self.model_dir_of_enhancemnet = dir_models + "/model_enhancement.h5" self.model_dir_of_col_classifier = dir_models + "/model_scale_classifier.h5" self.model_region_dir_p = dir_models + "/model_main_covid19_lr5-5_scale_1_1_great.h5" self.model_region_dir_p2 = dir_models + "/model_main_home_corona3_rot.h5" self.model_region_dir_fully_np = dir_models + "/model_no_patches_class0_30eopch.h5" self.model_region_dir_fully = dir_models + "/model_3up_new_good_no_augmentation.h5" self.model_page_dir = dir_models + "/model_page_mixed_best.h5" self.model_region_dir_p_ens = dir_models + "/model_ensemble_s.h5" self.model_textline_dir = dir_models + "/model_textline_newspapers.h5" self._imgs = {} def imread(self, grayscale=False, uint8=True): key = 'img' if grayscale: key += '_grayscale' if uint8: key += '_uint8' if key not in self._imgs: if grayscale: img = cv2.imread(self.image_filename, cv2.IMREAD_GRAYSCALE) else: img = cv2.imread(self.image_filename) if uint8: img = img.astype(np.uint8) self._imgs[key] = img return self._imgs[key].copy() def predict_enhancement(self, img): self.logger.debug("enter predict_enhancement") model_enhancement, _ = self.start_new_session_and_model(self.model_dir_of_enhancemnet) img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1] img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2] if img.shape[0] < img_height_model: img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST) if img.shape[1] < img_width_model: img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST) margin = int(0 * img_width_model) width_mid = img_width_model - 2 * margin height_mid = img_height_model - 2 * margin img = img / float(255.0) img_h = img.shape[0] img_w = img.shape[1] prediction_true = np.zeros((img_h, img_w, 3)) 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_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) seg = label_p_pred[0, :, :, :] seg = seg * 255 if i == 0 and j == 0: seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg elif i == nxf - 1 and j == nyf - 1: seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg elif i == 0 and j == nyf - 1: seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg elif i == nxf - 1 and j == 0: seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg elif i == 0 and j != 0 and j != nyf - 1: seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg elif i == nxf - 1 and j != 0 and j != nyf - 1: seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg elif i != 0 and i != nxf - 1 and j == 0: seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg elif i != 0 and i != nxf - 1 and j == nyf - 1: seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg else: seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg prediction_true = prediction_true.astype(int) return prediction_true def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred): self.logger.debug("enter calculate_width_height_by_columns") if num_col == 1 and width_early < 1100: img_w_new = 2000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000) elif num_col == 1 and width_early >= 2500: img_w_new = 2000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000) elif num_col == 1 and width_early >= 1100 and width_early < 2500: img_w_new = width_early img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early) elif num_col == 2 and width_early < 2000: img_w_new = 2400 img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400) elif num_col == 2 and width_early >= 3500: img_w_new = 2400 img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400) elif num_col == 2 and width_early >= 2000 and width_early < 3500: img_w_new = width_early img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early) elif num_col == 3 and width_early < 2000: img_w_new = 3000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000) elif num_col == 3 and width_early >= 4000: img_w_new = 3000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000) elif num_col == 3 and width_early >= 2000 and width_early < 4000: img_w_new = width_early img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early) elif num_col == 4 and width_early < 2500: img_w_new = 4000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000) elif num_col == 4 and width_early >= 5000: img_w_new = 4000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000) elif num_col == 4 and width_early >= 2500 and width_early < 5000: img_w_new = width_early img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early) elif num_col == 5 and width_early < 3700: img_w_new = 5000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000) elif num_col == 5 and width_early >= 7000: img_w_new = 5000 img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000) elif num_col == 5 and width_early >= 3700 and width_early < 7000: img_w_new = width_early img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early) elif num_col == 6 and width_early < 4500: img_w_new = 6500 # 5400 img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500) else: img_w_new = width_early img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early) if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early: img_new = np.copy(img) num_column_is_classified = False else: img_new = resize_image(img, img_h_new, img_w_new) num_column_is_classified = True return img_new, num_column_is_classified def resize_image_with_column_classifier(self, is_image_enhanced): self.logger.debug("enter resize_image_with_column_classifier") img = self.imread() _, page_coord = self.early_page_for_num_of_column_classification() model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier) img_1ch = self.imread(grayscale=True, uint8=False) width_early = img_1ch.shape[1] img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] # plt.imshow(img_1ch) # plt.show() img_1ch = img_1ch / 255.0 img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST) img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3)) img_in[0, :, :, 0] = img_1ch[:, :] img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] 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() K.clear_session() img_new, _ = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred) if img_new.shape[1] > img.shape[1]: img_new = self.predict_enhancement(img_new) is_image_enhanced = True return img, img_new, is_image_enhanced def resize_and_enhance_image_with_column_classifier(self): self.logger.debug("enter resize_and_enhance_image_with_column_classifier") dpi = check_dpi(self.image_filename) self.logger.info("Detected %s DPI", dpi) img = self.imread() _, page_coord = self.early_page_for_num_of_column_classification() model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier) img_1ch = self.imread(grayscale=True) width_early = img_1ch.shape[1] img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] # plt.imshow(img_1ch) # plt.show() img_1ch = img_1ch / 255.0 img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST) img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3)) img_in[0, :, :, 0] = img_1ch[:, :] img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] # 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() K.clear_session() if dpi < DPI_THRESHOLD: img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred) 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]) 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] if not patches: img_h_page = img.shape[0] img_w_page = img.shape[1] img = img / float(255.0) img = resize_image(img, img_height_model, img_width_model) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) seg = np.argmax(label_p_pred, axis=3)[0] seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = prediction_true.astype(np.uint8) else: if img.shape[0] < img_height_model: img = resize_image(img, img_height_model, img.shape[1]) if img.shape[1] < img_width_model: img = resize_image(img, img.shape[0], img_width_model) self.logger.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) 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() 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() K.clear_session() 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() 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() 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 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 serialize_lines_in_marginal(self, marginal, all_found_texline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, id_indexer_l): for j in range(len(all_found_texline_polygons_marginals[marginal_idx])): textline = ET.SubElement(marginal, '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[marginal_idx][j])): if not self.curved_line: if len(all_found_texline_polygons_marginals[marginal_idx][j][l]) == 2: points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x)) points_co += ',' points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y)) else: points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x)) points_co += ',' points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0])/self.scale_y)) else: if len(all_found_texline_polygons_marginals[marginal_idx][j][l]) == 2: points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0] + page_coord[2]) / self.scale_x)) points_co += ',' points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][1] + page_coord[0]) / self.scale_y)) else: points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][0] + page_coord[2]) / self.scale_x)) points_co += ',' points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][1] + page_coord[0]) / self.scale_y)) if l < len(all_found_texline_polygons_marginals[marginal_idx][j]) - 1: points_co += ' ' coord.set('points',points_co) return id_indexer_l 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 write_into_page_xml(self, pcgts): self.logger.info("filename stem: '%s'", self.image_filename_stem) tree = ET.ElementTree(pcgts) tree.write(os.path.join(self.dir_out, self.image_filename_stem) + ".xml") def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, 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, slopes, slopes_marginals): self.logger.debug('enter build_pagexml_no_full_layout') # 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)) 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) for marginal_idx in range(len(found_polygons_marginals)): marginal = ET.SubElement(page, 'TextRegion') marginal.set('id', id_of_marginalia[mm]) marginal.set('type', 'marginalia') coord_text = ET.SubElement(marginal, 'Coords') coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord)) self.serialize_lines_in_marginal(marginal, all_found_texline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, id_indexer_l) 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) return pcgts def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_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 build_pagexml_full_layout') # 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 marginal_idx in range(len(found_polygons_marginals)): marginal = ET.SubElement(page, 'TextRegion') add_textequiv(textregion) marginal.set('id', id_of_marginalia[mm]) marginal.set('type', 'marginalia') coord_text = ET.SubElement(marginal, 'Coords') coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord)) self.serialize_lines_in_marginal(marginal, all_found_texline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, id_indexer_l) 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)) return pcgts 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) ratio_y=1.3 ratio_x=1 img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x)) 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 img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1))) prediction_regions_org = self.do_prediction(True, img, model_region) prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) ##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() model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2) img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1])) prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2) prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h ) #plt.imshow(prediction_regions_org2[:,:,0]) #plt.show() ##prediction_regions_org=prediction_regions_org[:,:,0] session_region.close() mask_zeros2 = (prediction_regions_org2[:,:,0] == 0) mask_lines2 = (prediction_regions_org2[:,:,0] == 3) text_sume_early = (prediction_regions_org[:,:] == 1).sum() prediction_regions_org_copy = np.copy(prediction_regions_org) prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)] = 0 text_sume_second = ((prediction_regions_org_copy[:,:]==1)*1).sum() rate_two_models = text_sume_second / float(text_sume_early) * 100 self.logger.info("ratio_of_two_models: %s", rate_two_models) if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD): prediction_regions_org = np.copy(prediction_regions_org_copy) prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3 mask_lines_only=(prediction_regions_org[:,:]==3)*1 prediction_regions_org = cv2.erode(prediction_regions_org[:,:], 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 polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001) polygons_of_only_images = return_contours_of_interested_region(mask_images_only, 1) polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001) text_regions_p_true = np.zeros(prediction_regions_org.shape) text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3)) text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1)) 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] for zahler, _ in enumerate(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 for zahler, _ in enumerate(args_contours_box_h): arg_order_v = indexes_sorted_head[zahler] tartib = np.where(indexes_sorted == arg_order_v)[0][0] order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point for jji in range(len(id_of_texts)): order_of_texts_tot.append(order_of_texts[jji] + ref_point) id_of_texts_tot.append(id_of_texts[jji]) ref_point = 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 box in args_contours_box: con_inter_box.append(contours_only_text_parent[box]) for box in args_contours_box_h: con_inter_box_h.append(contours_only_text_parent_h[box]) indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2]) order_of_texts, id_of_texts = order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point) indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2] indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2] for zahler, _ in enumerate(args_contours_box): arg_order_v = indexes_sorted_main[zahler] 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 for zahler, _ in enumerate(args_contours_box_h): arg_order_v = indexes_sorted_head[zahler] tartib = np.where(indexes_sorted == arg_order_v)[0][0] order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point for jji in range(len(id_of_texts)): order_of_texts_tot.append(order_of_texts[jji] + ref_point) id_of_texts_tot.append(id_of_texts[jji]) ref_point = 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] for zahler, mtv in enumerate(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 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] for zahler, mtv in enumerate(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 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, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline) K.clear_session() #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 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 in (1, 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 Exception as e: self.logger.error("exception %s", e) 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() 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() 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() # 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() 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() # 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() 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(self.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [])) self.logger.info("Job done in %ss", str(time.time() - t1)) return t1 = time.time() textline_mask_tot_ea = 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, _ = 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)] K.clear_session() # 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, _, 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() 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, _, 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) else: _, _, 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) elif 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) else: 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) # print(peaks_neg_fin,peaks_neg_fin_d,'num_col2') # print(spliter_y_new,spliter_y_new_d,'num_col_classifier') # print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch') 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[:, :] == 5)] = 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[:, :] == 5)] = 1 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) 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) if self.plotter: self.plotter.write_images_into_directory(polygons_of_images, image_page) if self.full_layout: if np.abs(slope_deskew) < SLOPE_THRESHOLD: order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) else: order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d) self.write_into_page_xml(self.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals)) else: contours_only_text_parent_h = None if np.abs(slope_deskew) < SLOPE_THRESHOLD: order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) else: contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con]) order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d) self.write_into_page_xml(self.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals)) self.logger.info("Job done in %ss", str(time.time() - t1))