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Merge pull request #102 from qurator-spk/right2left_reading_order

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Right2left reading order
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vahidrezanezhad 2023-06-05 18:59:33 +02:00 • committed by GitHub
commit 68923e0a5d
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GPG key ID: 4AEE18F83AFDEB23
3 changed files with 57 additions and 11 deletions
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8
qurator/eynollah/cli.py
View file

@ -97,6 +97,12 @@ from qurator.eynollah.eynollah import Eynollah
is_flag=True,
help="if this parameter set to true, this tool will try to detect tables.",
)
@click.option(
"--right2left/--left2right",
"-r2l/-l2r",
is_flag=True,
help="if this parameter set to true, this tool will extract right-to-left reading order.",
)
@click.option(
"--input_binary/--input-RGB",
"-ib/-irgb",
@ -149,6 +155,7 @@ def main(
textline_light,
full_layout,
tables,
right2left,
input_binary,
allow_scaling,
headers_off,
@ -184,6 +191,7 @@ def main(
textline_light=textline_light,
full_layout=full_layout,
tables=tables,
right2left=right2left,
input_binary=input_binary,
allow_scaling=allow_scaling,
headers_off=headers_off,

27
qurator/eynollah/eynollah.py
View file

@ -158,6 +158,7 @@ class Eynollah:
textline_light=False,
full_layout=False,
tables=False,
right2left=False,
input_binary=False,
allow_scaling=False,
headers_off=False,
@ -189,6 +190,7 @@ class Eynollah:
self.textline_light = textline_light
self.full_layout = full_layout
self.tables = tables
self.right2left = right2left
self.input_binary = input_binary
self.allow_scaling = allow_scaling
self.headers_off = headers_off
@ -2069,6 +2071,7 @@ class Eynollah:
arg_text_con = []
for ii in range(len(cx_text_only)):
for jj in range(len(boxes)):
print(cx_text_only[ii],cy_text_only[ii],'markaz')
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
@ -2104,6 +2107,9 @@ class Eynollah:
ref_point += len(id_of_texts)
order_of_texts_tot = []
print(len(contours_only_text_parent),'contours_only_text_parent')
print(len(order_by_con_main),'order_by_con_main')
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1]))
@ -2618,7 +2624,7 @@ 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, self.tables)
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)
boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes))
@ -2628,7 +2634,7 @@ 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, self.tables)
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)
boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d))
@ -2713,7 +2719,7 @@ class Eynollah:
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_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
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)
text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
pixel_line = 3
@ -2722,7 +2728,7 @@ 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_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
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)
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
@ -3065,10 +3071,17 @@ class Eynollah:
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, self.tables)
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)
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, self.tables)
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)
#print(boxes_d,'boxes_d')
#img_once = np.zeros((textline_mask_tot_d.shape[0],textline_mask_tot_d.shape[1]))
#for box_i in boxes_d:
#img_once[int(box_i[2]):int(box_i[3]),int(box_i[0]):int(box_i[1]) ] =1
#plt.imshow(img_once)
#plt.show()
#print(np.unique(img_once),'img_once')
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
t_order = time.time()

33
qurator/eynollah/utils/__init__.py
View file

@ -1672,7 +1672,9 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
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, tables):
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):
if right2left_readingorder:
regions_without_separators = cv2.flip(regions_without_separators,1)
boxes=[]
peaks_neg_tot_tables = []
@ -1763,6 +1765,13 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
cy_hor_diff=matrix_new[:,7][ (matrix_new[:,9]==0) ]
arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
if right2left_readingorder:
x_max_hor_some_new = regions_without_separators.shape[1] - x_min_hor_some
x_min_hor_some_new = regions_without_separators.shape[1] - x_max_hor_some
x_min_hor_some =list(np.copy(x_min_hor_some_new))
x_max_hor_some =list(np.copy(x_max_hor_some_new))
@ -1774,7 +1783,6 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
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)
if (reading_order_type==1) or (reading_order_type==0 and (len(y_lines_without_mother)>=2 or there_is_sep_with_child==1)):
@ -2027,6 +2035,7 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
columns_not_covered_child_no_mother=np.sort(columns_not_covered_child_no_mother)
ind_args=np.array(range(len(y_type_2)))
@ -2281,7 +2290,6 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
ind_args=np.array(range(len(y_type_2)))
#ind_args=np.array(ind_args)
#print(ind_args,'ind_args')
for column in range(len(peaks_neg_tot)-1):
#print(column,'column')
ind_args_in_col=ind_args[x_starting==column]
@ -2337,4 +2345,21 @@ 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
if right2left_readingorder:
peaks_neg_tot_tables_new = []
if len(peaks_neg_tot_tables)>=1:
for peaks_tab_ind in peaks_neg_tot_tables:
peaks_neg_tot_tables_ind = regions_without_separators.shape[1] - np.array(peaks_tab_ind)
peaks_neg_tot_tables_ind = list(peaks_neg_tot_tables_ind[::-1])
peaks_neg_tot_tables_new.append(peaks_neg_tot_tables_ind)
for i in range(len(boxes)):
x_start_new = regions_without_separators.shape[1] - boxes[i][1]
x_end_new = regions_without_separators.shape[1] - boxes[i][0]
boxes[i][0] = x_start_new
boxes[i][1] = x_end_new
return boxes, peaks_neg_tot_tables_new
else:
return boxes, peaks_neg_tot_tables