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https://github.com/qurator-spk/eynollah.git
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Merge pull request #102 from qurator-spk/right2left_reading_order
Right2left reading order
This commit is contained in:
commit
68923e0a5d
3 changed files with 57 additions and 11 deletions
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@ -97,6 +97,12 @@ from qurator.eynollah.eynollah import Eynollah
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is_flag=True,
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help="if this parameter set to true, this tool will try to detect tables.",
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)
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@click.option(
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"--right2left/--left2right",
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"-r2l/-l2r",
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is_flag=True,
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help="if this parameter set to true, this tool will extract right-to-left reading order.",
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)
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@click.option(
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"--input_binary/--input-RGB",
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"-ib/-irgb",
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@ -149,6 +155,7 @@ def main(
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textline_light,
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full_layout,
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tables,
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right2left,
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input_binary,
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allow_scaling,
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headers_off,
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@ -184,6 +191,7 @@ def main(
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textline_light=textline_light,
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full_layout=full_layout,
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tables=tables,
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right2left=right2left,
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input_binary=input_binary,
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allow_scaling=allow_scaling,
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headers_off=headers_off,
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@ -158,6 +158,7 @@ class Eynollah:
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textline_light=False,
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full_layout=False,
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tables=False,
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right2left=False,
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input_binary=False,
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allow_scaling=False,
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headers_off=False,
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@ -189,6 +190,7 @@ class Eynollah:
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self.textline_light = textline_light
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self.full_layout = full_layout
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self.tables = tables
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self.right2left = right2left
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self.input_binary = input_binary
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self.allow_scaling = allow_scaling
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self.headers_off = headers_off
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@ -2069,6 +2071,7 @@ class Eynollah:
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arg_text_con = []
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for ii in range(len(cx_text_only)):
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for jj in range(len(boxes)):
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print(cx_text_only[ii],cy_text_only[ii],'markaz')
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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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@ -2104,6 +2107,9 @@ class Eynollah:
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ref_point += len(id_of_texts)
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order_of_texts_tot = []
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print(len(contours_only_text_parent),'contours_only_text_parent')
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print(len(order_by_con_main),'order_by_con_main')
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for tj1 in range(len(contours_only_text_parent)):
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order_of_texts_tot.append(int(order_by_con_main[tj1]))
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@ -2618,7 +2624,7 @@ class Eynollah:
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regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
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t1 = time.time()
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables, self.right2left)
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boxes_d = None
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self.logger.debug("len(boxes): %s", len(boxes))
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@ -2628,7 +2634,7 @@ class Eynollah:
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img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables , num_col_classifier , 0.000005, pixel_line)
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img_revised_tab2, contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2,table_prediction, 10, num_col_classifier)
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else:
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables, self.right2left)
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boxes = None
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self.logger.debug("len(boxes): %s", len(boxes_d))
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@ -2713,7 +2719,7 @@ class Eynollah:
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pass
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables, self.right2left)
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text_regions_p_tables = np.copy(text_regions_p)
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text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
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pixel_line = 3
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@ -2722,7 +2728,7 @@ class Eynollah:
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img_revised_tab2,contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2, table_prediction, 10, num_col_classifier)
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else:
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables, self.right2left)
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text_regions_p_tables = np.copy(text_regions_p_1_n)
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text_regions_p_tables = np.round(text_regions_p_tables)
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text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10
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@ -3065,10 +3071,17 @@ class Eynollah:
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables, self.right2left)
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else:
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables, self.right2left)
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#print(boxes_d,'boxes_d')
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#img_once = np.zeros((textline_mask_tot_d.shape[0],textline_mask_tot_d.shape[1]))
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#for box_i in boxes_d:
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#img_once[int(box_i[2]):int(box_i[3]),int(box_i[0]):int(box_i[1]) ] =1
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#plt.imshow(img_once)
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#plt.show()
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#print(np.unique(img_once),'img_once')
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if self.plotter:
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self.plotter.write_images_into_directory(polygons_of_images, image_page)
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t_order = time.time()
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@ -1672,7 +1672,9 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
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return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,splitter_y_new,separators_closeup_n
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def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, tables):
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def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, tables, right2left_readingorder):
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if right2left_readingorder:
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regions_without_separators = cv2.flip(regions_without_separators,1)
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boxes=[]
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peaks_neg_tot_tables = []
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@ -1763,6 +1765,13 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
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cy_hor_diff=matrix_new[:,7][ (matrix_new[:,9]==0) ]
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arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
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if right2left_readingorder:
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x_max_hor_some_new = regions_without_separators.shape[1] - x_min_hor_some
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x_min_hor_some_new = regions_without_separators.shape[1] - x_max_hor_some
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x_min_hor_some =list(np.copy(x_min_hor_some_new))
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x_max_hor_some =list(np.copy(x_max_hor_some_new))
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@ -1774,7 +1783,6 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
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reading_order_type,x_starting,x_ending,y_type_2,y_diff_type_2,y_lines_without_mother,x_start_without_mother,x_end_without_mother,there_is_sep_with_child,y_lines_with_child_without_mother,x_start_with_child_without_mother,x_end_with_child_without_mother,new_main_sep_y=return_x_start_end_mothers_childs_and_type_of_reading_order(x_min_hor_some,x_max_hor_some,cy_hor_some,peaks_neg_tot,cy_hor_diff)
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if (reading_order_type==1) or (reading_order_type==0 and (len(y_lines_without_mother)>=2 or there_is_sep_with_child==1)):
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@ -2028,6 +2036,7 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
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columns_not_covered_child_no_mother=np.sort(columns_not_covered_child_no_mother)
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ind_args=np.array(range(len(y_type_2)))
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@ -2281,7 +2290,6 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
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ind_args=np.array(range(len(y_type_2)))
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#ind_args=np.array(ind_args)
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#print(ind_args,'ind_args')
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for column in range(len(peaks_neg_tot)-1):
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#print(column,'column')
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ind_args_in_col=ind_args[x_starting==column]
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@ -2337,4 +2345,21 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
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#else:
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#boxes.append([ 0, regions_without_separators[:,:].shape[1] ,splitter_y_new[i],splitter_y_new[i+1]])
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if right2left_readingorder:
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peaks_neg_tot_tables_new = []
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if len(peaks_neg_tot_tables)>=1:
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for peaks_tab_ind in peaks_neg_tot_tables:
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peaks_neg_tot_tables_ind = regions_without_separators.shape[1] - np.array(peaks_tab_ind)
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peaks_neg_tot_tables_ind = list(peaks_neg_tot_tables_ind[::-1])
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peaks_neg_tot_tables_new.append(peaks_neg_tot_tables_ind)
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for i in range(len(boxes)):
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x_start_new = regions_without_separators.shape[1] - boxes[i][1]
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x_end_new = regions_without_separators.shape[1] - boxes[i][0]
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boxes[i][0] = x_start_new
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boxes[i][1] = x_end_new
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return boxes, peaks_neg_tot_tables_new
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else:
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return boxes, peaks_neg_tot_tables
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