qurator-spk
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eynollah
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more modifications for tables

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pull/48/head
vahid 3 years ago
parent 9f64110513
commit 254abf4d3d
2 changed files with 92 additions and 57 deletions
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110
qurator/eynollah/eynollah.py
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@ -1174,7 +1174,7 @@ class Eynollah:
try:
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=20)
_, _ = find_num_col(img_only_regions, multiplier=6.0)
_, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
@ -1976,7 +1976,7 @@ class Eynollah:
try:
num_col, _ = find_num_col(img_only_regions, multiplier=6.0)
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
num_col = num_col + 1
if not num_column_is_classified:
num_col_classifier = num_col + 1
@ -2071,10 +2071,10 @@ class Eynollah:
regions_without_separators_d = None
pixel_lines = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, splitter_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)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
K.clear_session()
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -2088,7 +2088,7 @@ class Eynollah:
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
t1 = time.time()
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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)
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)
boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes))
@ -2098,7 +2098,7 @@ class Eynollah:
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)
img_revised_tab2, contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2,table_prediction, 10, num_col_classifier)
else:
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)
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)
boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d))
@ -2156,34 +2156,34 @@ class Eynollah:
textline_mask_tot_d = resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])
table_prediction_n = resize_image(table_prediction_n,text_regions_p.shape[0],text_regions_p.shape[1])
regions_without_seperators_d=(text_regions_p_1_n[:,:] == 1)*1
regions_without_seperators_d[table_prediction_n[:,:] == 1] = 1
regions_without_separators_d=(text_regions_p_1_n[:,:] == 1)*1
regions_without_separators_d[table_prediction_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)
regions_without_seperators[table_prediction == 1] = 1
regions_without_separators = (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)
regions_without_separators[table_prediction == 1] = 1
pixel_lines=3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, splitter_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, splitter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_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, splitter_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, self.tables, pixel_lines)
K.clear_session()
gc.collect()
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[:,:], KERNEL, iterations=6)
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:,:], KERNEL, iterations=6)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:,:], KERNEL, iterations=6)
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:,:], KERNEL, iterations=6)
else:
pass
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier, erosion_hurts)
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)
text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
pixel_line = 3
@ -2192,7 +2192,7 @@ class Eynollah:
img_revised_tab2,contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2, table_prediction, 10, num_col_classifier)
else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts)
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)
text_regions_p_tables = np.copy(text_regions_p_1_n)
text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10
@ -2271,20 +2271,20 @@ class Eynollah:
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
#plt.imshow(text_regions_p)
#plt.show()
if not self.tables:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, 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)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, 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_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
else:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
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_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
else:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions)
regions_without_separators = (text_regions_p[:, :] == 1) * 1
K.clear_session()
img_revised_tab = np.copy(text_regions_p[:, :])
@ -2327,6 +2327,8 @@ class Eynollah:
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
self.logger.info("deskewing took %ss", str(time.time() - t1))
t1 = time.time()
#plt.imshow(table_prediction)
#plt.show()
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, table_prediction)
self.logger.info("detection of marginals took %ss", str(time.time() - t1))
@ -2482,14 +2484,14 @@ class Eynollah:
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_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)
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h)
else:
_, _, matrix_of_lines_ch_d, splitter_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)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h_d_ordered)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
else:
_, _, matrix_of_lines_ch_d, splitter_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)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
# print(splitter_y_new,splitter_y_new_d,'num_col_classifier')
@ -2499,22 +2501,42 @@ class Eynollah:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.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_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
#regions_without_separators_0 = regions_without_separators[:, :].sum(axis=0)
#meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
#first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
#last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
#last_nonzero = len(regions_without_separators_0) - last_nonzero
#random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.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_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
#regions_without_separators[:, 0:first_nonzero] = 0
#regions_without_separators[:, last_nonzero:] = 0
else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_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_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
#regions_without_separators_0 = regions_without_separators_d[:, :].sum(axis=0)
#meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
#first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
#last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
#last_nonzero = len(regions_without_separators_0) - last_nonzero
#random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_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_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
#regions_without_separators_d[:, 0:first_nonzero] = 0
#regions_without_separators_d[:, last_nonzero:] = 0
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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)
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)
else:
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)
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)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)

39
qurator/eynollah/utils/__init__.py
Unescape Escape View File

@ -360,7 +360,7 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8):
return np.std(z)
def find_num_col(regions_without_separators, multiplier=3.8):
def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8):
regions_without_separators_0 = regions_without_separators[:, :].sum(axis=0)
##plt.plot(regions_without_separators_0)
##plt.show()
@ -417,6 +417,19 @@ def find_num_col(regions_without_separators, multiplier=3.8):
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
# interest_neg_fin=interest_neg[(interest_neg<grenze)]
if not tables:
if ( num_col_classifier - ( (len(interest_neg_fin))+1 ) ) >= 3:
index_sort_interest_neg_fin= np.argsort(interest_neg_fin)
peaks_neg_sorted = np.array(peaks_neg)[index_sort_interest_neg_fin]
interest_neg_fin_sorted = np.array(interest_neg_fin)[index_sort_interest_neg_fin]
if len(index_sort_interest_neg_fin)>=num_col_classifier:
peaks_neg_fin = list( peaks_neg_sorted[:num_col_classifier] )
interest_neg_fin = list( interest_neg_fin_sorted[:num_col_classifier] )
else:
peaks_neg_fin = peaks_neg[:]
interest_neg_fin = interest_neg[:]
num_col = (len(interest_neg_fin)) + 1
# print(peaks_neg_fin,'peaks_neg_fin')
@ -489,9 +502,9 @@ def find_num_col(regions_without_separators, multiplier=3.8):
num_col = 1
peaks_neg_true = []
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
diff_peaks_abnormal = diff_peaks[diff_peaks < 360]
if len(diff_peaks_annormal) > 0:
if len(diff_peaks_abnormal) > 0:
arg_help = np.array(range(len(diff_peaks)))
arg_help_ann = arg_help[diff_peaks < 360]
@ -1248,7 +1261,7 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
peaks_neg_tot.append(last_point)
return peaks_neg_tot
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_lines, contours_h=None):
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None):
separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
@ -1561,7 +1574,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
#regions_without_separators_tile=cv2.erode(regions_without_separators_tile,kernel,iterations = 3)
#
try:
num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile,multiplier=7.0)
num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile, num_col_classifier, tables, multiplier=7.0)
except:
num_col = 0
peaks_neg_fin = []
@ -1583,7 +1596,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,splitter_y_new,separators_closeup_n
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):
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):
boxes=[]
peaks_neg_tot_tables = []
@ -1599,20 +1612,21 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
try:
if erosion_hurts:
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=6.)
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:], num_col_classifier, tables, multiplier=6.)
else:
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=7.)
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],num_col_classifier, tables, multiplier=7.)
except:
peaks_neg_fin=[]
num_col = 0
try:
peaks_neg_fin_org=np.copy(peaks_neg_fin)
if (len(peaks_neg_fin)+1)<num_col_classifier:
if (len(peaks_neg_fin)+1)<num_col_classifier or num_col_classifier==6:
#print('burda')
if len(peaks_neg_fin)==0:
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=3.)
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],num_col_classifier, tables, multiplier=3.)
peaks_neg_fin_early=[]
peaks_neg_fin_early.append(0)
#print(peaks_neg_fin,'peaks_neg_fin')
@ -1628,12 +1642,12 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
#plt.plot(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]].sum(axis=0) )
#plt.show()
try:
num_col, peaks_neg_fin1=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=7.)
num_col, peaks_neg_fin1=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],num_col_classifier,tables, multiplier=7.)
except:
peaks_neg_fin1=[]
try:
num_col, peaks_neg_fin2=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=5.)
num_col, peaks_neg_fin2=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],num_col_classifier,tables, multiplier=5.)
except:
peaks_neg_fin2=[]
@ -2238,5 +2252,4 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
#else:
#boxes.append([ 0, regions_without_separators[:,:].shape[1] ,splitter_y_new[i],splitter_y_new[i+1]])
return boxes, peaks_neg_tot_tables

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