mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 04:09:54 +02:00
remove unused return_hor_spliter_by_index_for_without_verticals
This commit is contained in:
Konstantin Baierer
parent
35838069fc
commit
68d5c0d523
4 changed files with 78 additions and 110 deletions
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@ -28,8 +28,6 @@ tf.get_logger().setLevel("ERROR")
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warnings.filterwarnings("ignore")
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from .utils.contour import (
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contours_in_same_horizon,
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filter_contours_area_of_image_tables,
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filter_contours_area_of_image,
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find_contours_mean_y_diff,
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find_new_features_of_contoures,
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@ -67,15 +65,9 @@ from .utils.resize import resize_image
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from .utils import (
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boosting_headers_by_longshot_region_segmentation,
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crop_image_inside_box,
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find_features_of_lines,
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find_num_col,
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find_num_col_by_vertical_lines,
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find_num_col_deskew,
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find_num_col_only_image,
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isNaN,
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otsu_copy,
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otsu_copy_binary,
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return_hor_spliter_by_index_for_without_verticals,
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delete_seperator_around,
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return_regions_without_seperators,
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put_drop_out_from_only_drop_model,
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@ -3087,4 +3087,82 @@ def filter_contours_area_of_image_interiors(image, contours, hirarchy, max_area,
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jv += 1
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return found_polygons_early
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def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
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# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
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arg_min_hor_sort = np.argsort(x_min_hor_some)
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x_min_hor_some_sort = np.sort(x_min_hor_some)
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x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
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arg_minmax = np.array(range(len(peaks_neg_fin_t)))
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indexer_lines = []
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indexes_to_delete = []
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indexer_lines_deletions_len = []
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indexr_uniq_ind = []
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for i in range(len(x_min_hor_some_sort)):
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min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
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max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
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min_h[0] = min_h[0] # +20
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max_h[len(max_h) - 1] = max_h[len(max_h) - 1] - 20
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min_h_neg = arg_minmax[(min_h < 0)]
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min_h_neg_n = min_h[min_h < 0]
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try:
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min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
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except:
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min_h_neg = []
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max_h_neg = arg_minmax[(max_h > 0)]
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max_h_neg_n = max_h[max_h > 0]
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if len(max_h_neg_n) > 0:
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max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
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else:
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max_h_neg = []
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if len(min_h_neg) > 0 and len(max_h_neg) > 0:
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deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
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unique_delets_int = []
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# print(deletions,len(deletions),'delii')
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if len(deletions) > 0:
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for j in range(len(deletions)):
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indexes_to_delete.append(deletions[j])
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# print(deletions,indexes_to_delete,'badiii')
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unique_delets = np.unique(indexes_to_delete)
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# print(min_h_neg[0],unique_delets)
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unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
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indexer_lines_deletions_len.append(len(deletions))
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indexr_uniq_ind.append([deletions])
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else:
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indexer_lines_deletions_len.append(0)
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indexr_uniq_ind.append(-999)
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index_line_true = min_h_neg[0] - len(unique_delets_int)
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# print(index_line_true)
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if index_line_true > 0 and min_h_neg[0] >= 2:
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index_line_true = index_line_true
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else:
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index_line_true = min_h_neg[0]
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indexer_lines.append(index_line_true)
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if len(unique_delets_int) > 0:
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for dd in range(len(unique_delets_int)):
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indexes_to_delete.append(unique_delets_int[dd])
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else:
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indexer_lines.append(-999)
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indexer_lines_deletions_len.append(-999)
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indexr_uniq_ind.append(-999)
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peaks_true = []
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for m in range(len(peaks_neg_fin_t)):
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if m in indexes_to_delete:
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pass
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else:
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peaks_true.append(peaks_neg_fin_t[m])
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return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
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@ -376,85 +376,6 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
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z = gaussian_filter1d(regions_without_seperators_0, sigma_)
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return np.std(z)
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def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
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# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
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arg_min_hor_sort = np.argsort(x_min_hor_some)
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x_min_hor_some_sort = np.sort(x_min_hor_some)
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x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
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arg_minmax = np.array(range(len(peaks_neg_fin_t)))
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indexer_lines = []
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indexes_to_delete = []
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indexer_lines_deletions_len = []
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indexr_uniq_ind = []
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for i in range(len(x_min_hor_some_sort)):
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min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
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max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
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min_h[0] = min_h[0] # +20
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max_h[len(max_h) - 1] = max_h[len(max_h) - 1] - 20
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min_h_neg = arg_minmax[(min_h < 0)]
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min_h_neg_n = min_h[min_h < 0]
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try:
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min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
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except:
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min_h_neg = []
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max_h_neg = arg_minmax[(max_h > 0)]
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max_h_neg_n = max_h[max_h > 0]
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if len(max_h_neg_n) > 0:
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max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
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else:
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max_h_neg = []
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if len(min_h_neg) > 0 and len(max_h_neg) > 0:
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deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
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unique_delets_int = []
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# print(deletions,len(deletions),'delii')
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if len(deletions) > 0:
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for j in range(len(deletions)):
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indexes_to_delete.append(deletions[j])
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# print(deletions,indexes_to_delete,'badiii')
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unique_delets = np.unique(indexes_to_delete)
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# print(min_h_neg[0],unique_delets)
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unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
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indexer_lines_deletions_len.append(len(deletions))
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indexr_uniq_ind.append([deletions])
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else:
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indexer_lines_deletions_len.append(0)
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indexr_uniq_ind.append(-999)
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index_line_true = min_h_neg[0] - len(unique_delets_int)
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# print(index_line_true)
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if index_line_true > 0 and min_h_neg[0] >= 2:
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index_line_true = index_line_true
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else:
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index_line_true = min_h_neg[0]
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indexer_lines.append(index_line_true)
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if len(unique_delets_int) > 0:
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for dd in range(len(unique_delets_int)):
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indexes_to_delete.append(unique_delets_int[dd])
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else:
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indexer_lines.append(-999)
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indexer_lines_deletions_len.append(-999)
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indexr_uniq_ind.append(-999)
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peaks_true = []
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for m in range(len(peaks_neg_fin_t)):
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if m in indexes_to_delete:
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pass
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else:
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peaks_true.append(peaks_neg_fin_t[m])
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return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
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def find_num_col(regions_without_seperators, multiplier=3.8):
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regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
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@ -13,31 +13,8 @@ from .contour import (
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)
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from .is_nan import isNaN
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from . import (
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boosting_headers_by_longshot_region_segmentation,
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crop_image_inside_box,
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find_features_of_lines,
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find_num_col,
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find_num_col_by_vertical_lines,
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find_num_col_deskew,
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find_num_col_only_image,
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isNaN,
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otsu_copy,
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otsu_copy_binary,
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return_hor_spliter_by_index_for_without_verticals,
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delete_seperator_around,
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return_regions_without_seperators,
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put_drop_out_from_only_drop_model,
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putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
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check_any_text_region_in_model_one_is_main_or_header,
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small_textlines_to_parent_adherence2,
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order_and_id_of_texts,
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order_of_regions,
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implent_law_head_main_not_parallel,
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return_hor_spliter_by_index,
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combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
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return_points_with_boundies,
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find_number_of_columns_in_document,
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return_boxes_of_images_by_order_of_reading_new,
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)
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def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
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