From 732a27fe778940fa069d0bae390d12c490c0962a Mon Sep 17 00:00:00 2001 From: Konstantin Baierer Date: Tue, 23 Feb 2021 16:36:11 +0100 Subject: [PATCH] remove unused variables --- sbb_newspapers_org_image/eynollah.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/sbb_newspapers_org_image/eynollah.py b/sbb_newspapers_org_image/eynollah.py index 797de1b..c7ae090 100644 --- a/sbb_newspapers_org_image/eynollah.py +++ b/sbb_newspapers_org_image/eynollah.py @@ -6,7 +6,6 @@ tool to extract table form data from alto xml data import gc import math import os -import random import sys import time import warnings @@ -1659,7 +1658,6 @@ class eynollah: 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 - arg_arg_text_con = np.argsort(arg_text_con) args_contours = np.array(range(len(arg_text_con))) arg_text_con_h = [] @@ -1668,7 +1666,6 @@ class eynollah: 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 - arg_arg_text_con = 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)) @@ -1738,7 +1735,6 @@ class eynollah: 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 - arg_arg_text_con = np.argsort(arg_text_con) args_contours = np.array(range(len(arg_text_con))) order_by_con_main = np.zeros(len(arg_text_con)) @@ -1825,7 +1821,6 @@ class eynollah: 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 - arg_arg_text_con = np.argsort(arg_text_con) args_contours = np.array(range(len(arg_text_con))) order_by_con_main = np.zeros(len(arg_text_con)) @@ -1849,8 +1844,6 @@ class eynollah: indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] - indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2] - indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2] zahler = 0 for mtv in args_contours_box: @@ -1880,7 +1873,6 @@ class eynollah: 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 - arg_arg_text_con = np.argsort(arg_text_con) args_contours = np.array(range(len(arg_text_con))) order_by_con_main = np.zeros(len(arg_text_con)) @@ -2397,9 +2389,9 @@ class eynollah: 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) elif self.headers_off: 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) + 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, 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) + 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')