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https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 20:29:55 +02:00
remove unused variables
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commit
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1 changed files with 2 additions and 10 deletions
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@ -6,7 +6,6 @@ tool to extract table form data from alto xml data
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import gc
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import math
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import os
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import random
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import sys
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import time
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import warnings
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@ -1659,7 +1658,6 @@ class eynollah:
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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]:
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arg_text_con.append(jj)
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break
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arg_arg_text_con = np.argsort(arg_text_con)
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args_contours = np.array(range(len(arg_text_con)))
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arg_text_con_h = []
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@ -1668,7 +1666,6 @@ class eynollah:
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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]:
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arg_text_con_h.append(jj)
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break
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arg_arg_text_con = np.argsort(arg_text_con_h)
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args_contours_h = np.array(range(len(arg_text_con_h)))
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order_by_con_head = np.zeros(len(arg_text_con_h))
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@ -1738,7 +1735,6 @@ class eynollah:
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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
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arg_text_con.append(jj)
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break
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arg_arg_text_con = np.argsort(arg_text_con)
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args_contours = np.array(range(len(arg_text_con)))
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order_by_con_main = np.zeros(len(arg_text_con))
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@ -1825,7 +1821,6 @@ class eynollah:
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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]:
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arg_text_con.append(jj)
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break
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arg_arg_text_con = np.argsort(arg_text_con)
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args_contours = np.array(range(len(arg_text_con)))
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order_by_con_main = np.zeros(len(arg_text_con))
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@ -1849,8 +1844,6 @@ class eynollah:
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indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
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indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
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indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
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indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
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zahler = 0
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for mtv in args_contours_box:
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@ -1880,7 +1873,6 @@ class eynollah:
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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
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arg_text_con.append(jj)
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break
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arg_arg_text_con = np.argsort(arg_text_con)
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args_contours = np.array(range(len(arg_text_con)))
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order_by_con_main = np.zeros(len(arg_text_con))
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@ -2397,9 +2389,9 @@ class eynollah:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
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elif self.headers_off:
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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else:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, 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)
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
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# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
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