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run_boxes_no_full_layout: also map to fl labels here…

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(because -mbro assumes the label set from -fl)
This commit is contained in:
Robert Sachunsky 2026-04-20 17:09:00 +02:00
parent 0015f2675b
commit 0dfc9d911f
1 changed files with 15 additions and 6 deletions
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21
src/eynollah/eynollah.py
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@ -1672,11 +1672,20 @@ class Eynollah:
slope_deskew, num_col_classifier, table_prediction, erosion_hurts, slope_deskew, num_col_classifier, table_prediction, erosion_hurts,
label_text=1, label_text=1,
label_imgs=2, label_imgs=2,
label_imgs_fl=5,
label_seps=3, label_seps=3,
label_seps_fl=6,
label_marg=4, label_marg=4,
label_marg_fl=8,
label_tabs=10, label_tabs=10,
): ):
self.logger.debug('enter run_boxes_no_full_layout') self.logger.debug('enter run_boxes_no_full_layout')
# map segment labels as in run_boxes_full_layout
# (to ensure we have the same set for do_order_of_regions etc)
text_regions_p[text_regions_p == label_imgs] = label_imgs_fl
text_regions_p[text_regions_p == label_seps] = label_seps_fl
text_regions_p[text_regions_p == label_marg] = label_marg_fl
t_0_box = time.time() t_0_box = time.time()
regions_without_separators = (text_regions_p == label_text) * 1 regions_without_separators = (text_regions_p == label_text) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
@ -1697,10 +1706,10 @@ class Eynollah:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
_, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document( _, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document(
text_regions_p, num_col_classifier, self.tables, label_seps) text_regions_p, num_col_classifier, self.tables, label_seps_fl)
else: else:
_, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document( _, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
text_regions_p_d, num_col_classifier, self.tables, label_seps) text_regions_p_d, num_col_classifier, self.tables, label_seps_fl)
#print(time.time()-t_0_box,'time box in 2') #print(time.time()-t_0_box,'time box in 2')
self.logger.info("num_col_classifier: %s", num_col_classifier) self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -1716,7 +1725,7 @@ class Eynollah:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new( boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new, regions_without_separators, splitter_y_new, regions_without_separators,
text_regions_p == label_seps, matrix_of_seps_ch, text_regions_p == label_seps_fl, matrix_of_seps_ch,
num_col_classifier, erosion_hurts, self.tables, self.right2left) num_col_classifier, erosion_hurts, self.tables, self.right2left)
boxes_d = None boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes)) self.logger.debug("len(boxes): %s", len(boxes))
@ -1725,7 +1734,7 @@ class Eynollah:
else: else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new( boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new_d, regions_without_separators_d, splitter_y_new_d, regions_without_separators_d,
text_regions_p_d == label_seps, matrix_of_seps_ch_d, text_regions_p_d == label_seps_fl, matrix_of_seps_ch_d,
num_col_classifier, erosion_hurts, self.tables, self.right2left) num_col_classifier, erosion_hurts, self.tables, self.right2left)
boxes = None boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d)) self.logger.debug("len(boxes): %s", len(boxes_d))
@ -1736,9 +1745,9 @@ class Eynollah:
min_area_mar = 0.00001 min_area_mar = 0.00001
polygons_of_tables = return_contours_of_interested_region(text_regions_p, label_tabs, min_area_mar) polygons_of_tables = return_contours_of_interested_region(text_regions_p, label_tabs, min_area_mar)
polygons_of_images = return_contours_of_interested_region(text_regions_p, label_imgs) polygons_of_images = return_contours_of_interested_region(text_regions_p, label_imgs_fl)
marginal_mask = (text_regions_p == label_marg).astype(np.uint8) marginal_mask = (text_regions_p == label_marg_fl).astype(np.uint8)
marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2) marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2)
polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar)