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eynollah
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right2left reading order detection accomplished

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pull/102/head
vahid 2 years ago
parent 0cda1f3c7a
commit 0b35011847
3 changed files with 51 additions and 259 deletions
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8
qurator/eynollah/cli.py
Unescape Escape View File

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

25
qurator/eynollah/eynollah.py
Unescape Escape View File

@ -72,8 +72,7 @@ from .utils import (
small_textlines_to_parent_adherence2, small_textlines_to_parent_adherence2,
order_of_regions, order_of_regions,
find_number_of_columns_in_document, find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new, return_boxes_of_images_by_order_of_reading_new)
return_boxes_of_images_by_order_of_reading_new_right2left)
from .utils.pil_cv2 import check_dpi, pil2cv from .utils.pil_cv2 import check_dpi, pil2cv
from .utils.xml import order_and_id_of_texts from .utils.xml import order_and_id_of_texts
from .plot import EynollahPlotter from .plot import EynollahPlotter
@ -159,6 +158,7 @@ class Eynollah:
textline_light=False, textline_light=False,
full_layout=False, full_layout=False,
tables=False, tables=False,
right2left=False,
input_binary=False, input_binary=False,
allow_scaling=False, allow_scaling=False,
headers_off=False, headers_off=False,
@ -190,6 +190,7 @@ class Eynollah:
self.textline_light = textline_light self.textline_light = textline_light
self.full_layout = full_layout self.full_layout = full_layout
self.tables = tables self.tables = tables
self.right2left = right2left
self.input_binary = input_binary self.input_binary = input_binary
self.allow_scaling = allow_scaling self.allow_scaling = allow_scaling
self.headers_off = headers_off self.headers_off = headers_off
@ -2623,7 +2624,7 @@ class Eynollah:
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6) regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
t1 = time.time() t1 = time.time()
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_right2left(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 boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes)) self.logger.debug("len(boxes): %s", len(boxes))
@ -2633,7 +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 = 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) 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: else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new_right2left(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 boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d)) self.logger.debug("len(boxes): %s", len(boxes_d))
@ -2718,7 +2719,7 @@ class Eynollah:
pass pass
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_right2left(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 = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10 text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
pixel_line = 3 pixel_line = 3
@ -2727,7 +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) 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: else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new_right2left(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.copy(text_regions_p_1_n)
text_regions_p_tables = np.round(text_regions_p_tables) text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10 text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10
@ -3070,11 +3071,17 @@ 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_right2left(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: else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new_right2left(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: if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page) self.plotter.write_images_into_directory(polygons_of_images, image_page)
t_order = time.time() t_order = time.time()

279
qurator/eynollah/utils/__init__.py
Unescape Escape 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 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=[] boxes=[]
peaks_neg_tot_tables = [] 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) ] cy_hor_diff=matrix_new[:,7][ (matrix_new[:,9]==0) ]
arg_org_hor_some=matrix_new[:,0][ (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))
@ -2027,6 +2036,7 @@ 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) columns_not_covered_child_no_mother=np.sort(columns_not_covered_child_no_mother)
ind_args=np.array(range(len(y_type_2))) ind_args=np.array(range(len(y_type_2)))
@ -2335,254 +2345,21 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
#else: #else:
#boxes.append([ 0, regions_without_separators[:,:].shape[1] ,splitter_y_new[i],splitter_y_new[i+1]]) #boxes.append([ 0, regions_without_separators[:,:].shape[1] ,splitter_y_new[i],splitter_y_new[i+1]])
return boxes, peaks_neg_tot_tables
def return_boxes_of_images_by_order_of_reading_new_right2left(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, tables):
boxes=[]
peaks_neg_tot_tables = []
for i in range(len(splitter_y_new)-1):
#print(splitter_y_new[i],splitter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> splitter_y_new[i] ) & (matrix_of_lines_ch[:,7]< splitter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical separator to find holes.
if 1>0:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(splitter_y_new[i+1]-splitter_y_new[i] )):
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]),:], 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]),:],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 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]),:],num_col_classifier, tables, multiplier=3.)
peaks_neg_fin_early=[]
peaks_neg_fin_early.append(0)
#print(peaks_neg_fin,'peaks_neg_fin')
for p_n in peaks_neg_fin:
peaks_neg_fin_early.append(p_n)
peaks_neg_fin_early.append(regions_without_separators.shape[1]-1)
#print(peaks_neg_fin_early,'burda2')
peaks_neg_fin_rev=[]
for i_n in range(len(peaks_neg_fin_early)-1):
#print(i_n,'i_n')
#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]],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]],num_col_classifier,tables, multiplier=5.)
except:
peaks_neg_fin2=[]
if len(peaks_neg_fin1)>=len(peaks_neg_fin2):
peaks_neg_fin=list(np.copy(peaks_neg_fin1))
else:
peaks_neg_fin=list(np.copy(peaks_neg_fin2))
peaks_neg_fin=list(np.array(peaks_neg_fin)+peaks_neg_fin_early[i_n])
if i_n!=(len(peaks_neg_fin_early)-2):
peaks_neg_fin_rev.append(peaks_neg_fin_early[i_n+1])
#print(peaks_neg_fin,'peaks_neg_fin')
peaks_neg_fin_rev=peaks_neg_fin_rev+peaks_neg_fin
if len(peaks_neg_fin_rev)>=len(peaks_neg_fin_org):
peaks_neg_fin=list(np.sort(peaks_neg_fin_rev))
num_col=len(peaks_neg_fin)
else:
peaks_neg_fin=list(np.copy(peaks_neg_fin_org))
num_col=len(peaks_neg_fin)
#print(peaks_neg_fin,'peaks_neg_fin')
except:
pass
#num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some=matrix_new[:,2][ (matrix_new[:,9]==0) ]
x_max_hor_some=matrix_new[:,3][ (matrix_new[:,9]==0) ]
cy_hor_some=matrix_new[:,5][ (matrix_new[:,9]==0) ]
cy_hor_diff=matrix_new[:,7][ (matrix_new[:,9]==0) ]
arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
peaks_neg_tot=return_points_with_boundies(peaks_neg_fin,0, regions_without_separators[:,:].shape[1])
peaks_neg_tot_tables.append(peaks_neg_tot)
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)
y_lines_by_order=[]
x_start_by_order=[]
x_end_by_order=[]
if len(x_starting)>0:
all_columns = np.array(range(len(peaks_neg_tot)-1))
columns_covered_by_lines_covered_more_than_2col=[]
for dj in range(len(x_starting)):
if set( list(np.array(range(x_starting[dj],x_ending[dj])) ) ) == set(all_columns):
pass
else:
columns_covered_by_lines_covered_more_than_2col=columns_covered_by_lines_covered_more_than_2col+list(np.array(range(x_starting[dj],x_ending[dj])) )
columns_covered_by_lines_covered_more_than_2col=list(set(columns_covered_by_lines_covered_more_than_2col))
columns_not_covered=list( set(all_columns)-set(columns_covered_by_lines_covered_more_than_2col) )
y_type_2=list(y_type_2)
x_starting=list(x_starting)
x_ending=list(x_ending)
for lj in columns_not_covered:
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(lj)
x_ending.append(lj+1)
##y_lines_by_order.append(int(splitter_y_new[i]))
##x_start_by_order.append(0)
#y_type_2.append(int(splitter_y_new[i]))
#x_starting.append(x_starting[0])
#x_ending.append(x_ending[0])
if len(new_main_sep_y)>0:
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(0)
x_ending.append(len(peaks_neg_tot)-1)
else:
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(x_starting[0])
x_ending.append(x_ending[0])
y_type_2=np.array(y_type_2)
x_starting=np.array(x_starting)
x_ending=np.array(x_ending)
else:
all_columns=np.array(range(len(peaks_neg_tot)-1))
columns_not_covered=list( set(all_columns) )
y_type_2=list(y_type_2)
x_starting=list(x_starting)
x_ending=list(x_ending)
for lj in columns_not_covered:
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(lj)
x_ending.append(lj+1)
##y_lines_by_order.append(int(splitter_y_new[i]))
##x_start_by_order.append(0)
y_type_2=np.array(y_type_2)
x_starting=np.array(x_starting)
x_ending=np.array(x_ending)
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)-1,0,-1):
#print(column,'column')
ind_args_in_col=ind_args[x_ending==column]
ind_args_in_col=np.array(ind_args_in_col)
#print(len(y_type_2))
y_column=y_type_2[ind_args_in_col]
x_start_column=x_starting[ind_args_in_col]
x_end_column=x_ending[ind_args_in_col]
ind_args_col_sorted=np.argsort(y_column)
y_col_sort=y_column[ind_args_col_sorted]
x_start_column_sort=x_start_column[ind_args_col_sorted]
x_end_column_sort=x_end_column[ind_args_col_sorted]
#print('babali4')
for ii in range(len(y_col_sort)):
#print('babali5')
y_lines_by_order.append(y_col_sort[ii])
x_start_by_order.append(x_start_column_sort[ii])
x_end_by_order.append(x_end_column_sort[ii]-1)
for il in range(len(y_lines_by_order)):
y_copy=list( np.copy(y_lines_by_order) )
x_start_copy=list( np.copy(x_start_by_order) )
x_end_copy=list ( np.copy(x_end_by_order) )
#print(y_copy,'y_copy')
y_itself=y_copy.pop(il)
x_start_itself=x_start_copy.pop(il)
x_end_itself=x_end_copy.pop(il)
#print(y_copy,'y_copy2')
for column in range(x_end_itself+1-1,x_start_itself-1,-1):
#print(column,'cols')
y_in_cols=[]
for yic in range(len(y_copy)):
#print('burda')
if y_copy[yic]>y_itself and column>=x_start_copy[yic] and column<=x_end_copy[yic]:
y_in_cols.append(y_copy[yic])
#print('burda2')
#print(y_in_cols,'y_in_cols')
if len(y_in_cols)>0:
y_down=np.min(y_in_cols)
else:
y_down=[int(splitter_y_new[i+1])][0]
#print(y_itself,'y_itself')
boxes.append([peaks_neg_tot[column],peaks_neg_tot[column+1],y_itself,y_down])
#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

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