@ -29,7 +29,8 @@ warnings.filterwarnings("ignore")
from scipy . signal import find_peaks from scipy . signal import find_peaks
import matplotlib . pyplot as plt import matplotlib . pyplot as plt
from scipy . ndimage import gaussian_filter1d from scipy . ndimage import gaussian_filter1d
from tensorflow . python . keras . backend import set_session # use tf1 compatibility for keras backend
from tensorflow . compat . v1 . keras . backend import set_session
from tensorflow . keras import layers from tensorflow . keras import layers
from . utils . contour import ( from . utils . contour import (
@ -257,7 +258,7 @@ class Eynollah:
config . gpu_options . allow_growth = True
config . gpu_options . allow_growth = True
session = tf . compat . v1 . Session ( config = config )
session = tf . compat . v1 . Session ( config = config )
set_session ( session )
set_session ( session )
self . model_page = self . our_load_model ( self . model_page_dir )
self . model_page = self . our_load_model ( self . model_page_dir )
self . model_classifier = self . our_load_model ( self . model_dir_of_col_classifier )
self . model_classifier = self . our_load_model ( self . model_dir_of_col_classifier )
self . model_bin = self . our_load_model ( self . model_dir_of_binarization )
self . model_bin = self . our_load_model ( self . model_dir_of_binarization )
@ -265,9 +266,9 @@ class Eynollah:
self . model_region = self . our_load_model ( self . model_region_dir_p_ens_light_only_images_extraction )
self . model_region = self . our_load_model ( self . model_region_dir_p_ens_light_only_images_extraction )
#self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
#self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
#self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
#self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
self . ls_imgs = os . listdir ( self . dir_in )
self . ls_imgs = os . listdir ( self . dir_in )
if dir_in and not ( light_version or self . extract_only_images ) :
if dir_in and not ( light_version or self . extract_only_images ) :
config = tf . compat . v1 . ConfigProto ( )
config = tf . compat . v1 . ConfigProto ( )
config . gpu_options . allow_growth = True
config . gpu_options . allow_growth = True
@ -286,7 +287,6 @@ class Eynollah:
self . ls_imgs = os . listdir ( self . dir_in )
self . ls_imgs = os . listdir ( self . dir_in )
def _cache_images ( self , image_filename = None , image_pil = None ) :
def _cache_images ( self , image_filename = None , image_pil = None ) :
ret = { }
ret = { }
@ -482,7 +482,7 @@ class Eynollah:
num_column_is_classified = True
num_column_is_classified = True
return img_new , num_column_is_classified
return img_new , num_column_is_classified
def calculate_width_height_by_columns_extract_only_images ( self , img , num_col , width_early , label_p_pred ) :
def calculate_width_height_by_columns_extract_only_images ( self , img , num_col , width_early , label_p_pred ) :
self . logger . debug ( " enter calculate_width_height_by_columns " )
self . logger . debug ( " enter calculate_width_height_by_columns " )
if num_col == 1 :
if num_col == 1 :
@ -552,7 +552,7 @@ class Eynollah:
is_image_enhanced = True
is_image_enhanced = True
return img , img_new , is_image_enhanced
return img , img_new , is_image_enhanced
def resize_and_enhance_image_with_column_classifier ( self , light_version ) :
def resize_and_enhance_image_with_column_classifier ( self , light_version ) :
self . logger . debug ( " enter resize_and_enhance_image_with_column_classifier " )
self . logger . debug ( " enter resize_and_enhance_image_with_column_classifier " )
dpi = self . dpi
dpi = self . dpi
@ -610,7 +610,7 @@ class Eynollah:
num_col = np . argmax ( label_p_pred [ 0 ] ) + 1
num_col = np . argmax ( label_p_pred [ 0 ] ) + 1
self . logger . info ( " Found %d columns ( %s ) " , num_col , np . around ( label_p_pred , decimals = 5 ) )
self . logger . info ( " Found %d columns ( %s ) " , num_col , np . around ( label_p_pred , decimals = 5 ) )
if not self . extract_only_images :
if not self . extract_only_images :
if dpi < DPI_THRESHOLD :
if dpi < DPI_THRESHOLD :
img_new , num_column_is_classified = self . calculate_width_height_by_columns ( img , num_col , width_early , label_p_pred )
img_new , num_column_is_classified = self . calculate_width_height_by_columns ( img , num_col , width_early , label_p_pred )
@ -624,9 +624,6 @@ class Eynollah:
image_res = np . copy ( img )
image_res = np . copy ( img )
is_image_enhanced = False
is_image_enhanced = False
else :
else :
#img_new, num_column_is_classified = self.calculate_width_height_by_columns_extract_only_images(img, num_col, width_early, label_p_pred)
#image_res = np.copy(img_new)
#is_image_enhanced = True
num_column_is_classified = True
num_column_is_classified = True
image_res = np . copy ( img )
image_res = np . copy ( img )
is_image_enhanced = False
is_image_enhanced = False
@ -925,6 +922,7 @@ class Eynollah:
seg_not_base [ seg_not_base < 1 ] = 0
seg_not_base [ seg_not_base < 1 ] = 0
seg_test = label_p_pred [ 0 , : , : , 1 ]
seg_test = label_p_pred [ 0 , : , : , 1 ]
##seg2 = -label_p_pred[0,:,:,2]
##seg2 = -label_p_pred[0,:,:,2]
@ -1623,7 +1621,7 @@ class Eynollah:
q . put ( slopes_sub )
q . put ( slopes_sub )
poly . put ( poly_sub )
poly . put ( poly_sub )
box_sub . put ( boxes_sub_new )
box_sub . put ( boxes_sub_new )
def get_regions_light_v_extract_only_images ( self , img , is_image_enhanced , num_col_classifier ) :
def get_regions_light_v_extract_only_images ( self , img , is_image_enhanced , num_col_classifier ) :
self . logger . debug ( " enter get_regions_extract_images_only " )
self . logger . debug ( " enter get_regions_extract_images_only " )
erosion_hurts = False
erosion_hurts = False
@ -1646,7 +1644,7 @@ class Eynollah:
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * img_w_new )
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * img_w_new )
img_resized = resize_image ( img , img_h_new , img_w_new )
img_resized = resize_image ( img , img_h_new , img_w_new )
if not self . dir_in :
if not self . dir_in :
@ -1654,61 +1652,61 @@ class Eynollah:
prediction_regions_org = self . do_prediction_new_concept ( True , img_resized , model_region )
prediction_regions_org = self . do_prediction_new_concept ( True , img_resized , model_region )
else :
else :
prediction_regions_org = self . do_prediction_new_concept ( True , img_resized , self . model_region )
prediction_regions_org = self . do_prediction_new_concept ( True , img_resized , self . model_region )
#plt.imshow(prediction_regions_org[:,:,0])
#plt.imshow(prediction_regions_org[:,:,0])
#plt.show()
#plt.show()
prediction_regions_org = resize_image ( prediction_regions_org , img_height_h , img_width_h )
prediction_regions_org = resize_image ( prediction_regions_org , img_height_h , img_width_h )
image_page , page_coord , cont_page = self . extract_page ( )
image_page , page_coord , cont_page = self . extract_page ( )
prediction_regions_org = prediction_regions_org [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] ]
prediction_regions_org = prediction_regions_org [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] ]
prediction_regions_org = prediction_regions_org [ : , : , 0 ]
prediction_regions_org = prediction_regions_org [ : , : , 0 ]
mask_lines_only = ( prediction_regions_org [ : , : ] == 3 ) * 1
mask_lines_only = ( prediction_regions_org [ : , : ] == 3 ) * 1
mask_texts_only = ( prediction_regions_org [ : , : ] == 1 ) * 1
mask_texts_only = ( prediction_regions_org [ : , : ] == 1 ) * 1
mask_images_only = ( prediction_regions_org [ : , : ] == 2 ) * 1
mask_images_only = ( prediction_regions_org [ : , : ] == 2 ) * 1
polygons_lines_xml , hir_lines_xml = return_contours_of_image ( mask_lines_only )
polygons_lines_xml , hir_lines_xml = return_contours_of_image ( mask_lines_only )
polygons_lines_xml = textline_con_fil = filter_contours_area_of_image ( mask_lines_only , polygons_lines_xml , hir_lines_xml , max_area = 1 , min_area = 0.00001 )
polygons_lines_xml = textline_con_fil = filter_contours_area_of_image ( mask_lines_only , polygons_lines_xml , hir_lines_xml , max_area = 1 , min_area = 0.00001 )
polygons_of_only_texts = return_contours_of_interested_region ( mask_texts_only , 1 , 0.00001 )
polygons_of_only_texts = return_contours_of_interested_region ( mask_texts_only , 1 , 0.00001 )
polygons_of_only_lines = return_contours_of_interested_region ( mask_lines_only , 1 , 0.00001 )
polygons_of_only_lines = return_contours_of_interested_region ( mask_lines_only , 1 , 0.00001 )
text_regions_p_true = np . zeros ( prediction_regions_org . shape )
text_regions_p_true = np . zeros ( prediction_regions_org . shape )
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_lines , color = ( 3 , 3 , 3 ) )
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_lines , color = ( 3 , 3 , 3 ) )
text_regions_p_true [ : , : ] [ mask_images_only [ : , : ] == 1 ] = 2
text_regions_p_true [ : , : ] [ mask_images_only [ : , : ] == 1 ] = 2
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_texts , color = ( 1 , 1 , 1 ) )
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_texts , color = ( 1 , 1 , 1 ) )
text_regions_p_true [ text_regions_p_true . shape [ 0 ] - 15 : text_regions_p_true . shape [ 0 ] , : ] = 0
text_regions_p_true [ text_regions_p_true . shape [ 0 ] - 15 : text_regions_p_true . shape [ 0 ] , : ] = 0
text_regions_p_true [ : , text_regions_p_true . shape [ 1 ] - 15 : text_regions_p_true . shape [ 1 ] ] = 0
text_regions_p_true [ : , text_regions_p_true . shape [ 1 ] - 15 : text_regions_p_true . shape [ 1 ] ] = 0
##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001)
##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001)
polygons_of_images = return_contours_of_interested_region ( text_regions_p_true , 2 , 0.001 )
polygons_of_images = return_contours_of_interested_region ( text_regions_p_true , 2 , 0.001 )
image_boundary_of_doc = np . zeros ( ( text_regions_p_true . shape [ 0 ] , text_regions_p_true . shape [ 1 ] ) )
image_boundary_of_doc = np . zeros ( ( text_regions_p_true . shape [ 0 ] , text_regions_p_true . shape [ 1 ] ) )
###image_boundary_of_doc[:6, :] = 1
###image_boundary_of_doc[:6, :] = 1
###image_boundary_of_doc[text_regions_p_true.shape[0]-6:text_regions_p_true.shape[0], :] = 1
###image_boundary_of_doc[text_regions_p_true.shape[0]-6:text_regions_p_true.shape[0], :] = 1
###image_boundary_of_doc[:, :6] = 1
###image_boundary_of_doc[:, :6] = 1
###image_boundary_of_doc[:, text_regions_p_true.shape[1]-6:text_regions_p_true.shape[1]] = 1
###image_boundary_of_doc[:, text_regions_p_true.shape[1]-6:text_regions_p_true.shape[1]] = 1
#plt.imshow(image_boundary_of_doc)
#plt.imshow(image_boundary_of_doc)
#plt.show()
#plt.show()
polygons_of_images_fin = [ ]
polygons_of_images_fin = [ ]
for ploy_img_ind in polygons_of_images :
for ploy_img_ind in polygons_of_images :
"""
"""
@ -1737,9 +1735,9 @@ class Eynollah:
box = [ x , y , w , h ]
box = [ x , y , w , h ]
_ , page_coord_img = crop_image_inside_box ( box , text_regions_p_true )
_ , page_coord_img = crop_image_inside_box ( box , text_regions_p_true )
#cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
#cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
polygons_of_images_fin . append ( np . array ( [ [ page_coord_img [ 2 ] , page_coord_img [ 0 ] ] , [ page_coord_img [ 3 ] , page_coord_img [ 0 ] ] , [ page_coord_img [ 3 ] , page_coord_img [ 1 ] ] , [ page_coord_img [ 2 ] , page_coord_img [ 1 ] ] ] ) )
polygons_of_images_fin . append ( np . array ( [ [ page_coord_img [ 2 ] , page_coord_img [ 0 ] ] , [ page_coord_img [ 3 ] , page_coord_img [ 0 ] ] , [ page_coord_img [ 3 ] , page_coord_img [ 1 ] ] , [ page_coord_img [ 2 ] , page_coord_img [ 1 ] ] ] ) )
return text_regions_p_true , erosion_hurts , polygons_lines_xml , polygons_of_images_fin , image_page , page_coord , cont_page
return text_regions_p_true , erosion_hurts , polygons_lines_xml , polygons_of_images_fin , image_page , page_coord , cont_page
def get_regions_light_v ( self , img , is_image_enhanced , num_col_classifier ) :
def get_regions_light_v ( self , img , is_image_enhanced , num_col_classifier ) :
self . logger . debug ( " enter get_regions_light_v " )
self . logger . debug ( " enter get_regions_light_v " )
@ -2592,7 +2590,7 @@ class Eynollah:
prediction_table_erode = cv2 . erode ( prediction_table [ : , : , 0 ] , KERNEL , iterations = 20 )
prediction_table_erode = cv2 . erode ( prediction_table [ : , : , 0 ] , KERNEL , iterations = 20 )
prediction_table_erode = cv2 . dilate ( prediction_table_erode , KERNEL , iterations = 20 )
prediction_table_erode = cv2 . dilate ( prediction_table_erode , KERNEL , iterations = 20 )
return prediction_table_erode . astype ( np . int16 )
return prediction_table_erode . astype ( np . int16 )
def run_graphics_and_columns_light ( self , text_regions_p_1 , textline_mask_tot_ea , num_col_classifier , num_column_is_classified , erosion_hurts ) :
def run_graphics_and_columns_light ( self , text_regions_p_1 , textline_mask_tot_ea , num_col_classifier , num_column_is_classified , erosion_hurts ) :
img_g = self . imread ( grayscale = True , uint8 = True )
img_g = self . imread ( grayscale = True , uint8 = True )
@ -3004,26 +3002,26 @@ class Eynollah:
if self . dir_in :
if self . dir_in :
self . reset_file_name_dir ( os . path . join ( self . dir_in , img_name ) )
self . reset_file_name_dir ( os . path . join ( self . dir_in , img_name ) )
if self . extract_only_images :
if self . extract_only_images :
img_res , is_image_enhanced , num_col_classifier , num_column_is_classified = self . run_enhancement ( self . light_version )
img_res , is_image_enhanced , num_col_classifier , num_column_is_classified = self . run_enhancement ( self . light_version )
self . logger . info ( " Enhancing took %.1f s " , time . time ( ) - t0 )
self . logger . info ( " Enhancing took %.1f s " , time . time ( ) - t0 )
text_regions_p_1 , erosion_hurts , polygons_lines_xml , polygons_of_images , image_page , page_coord , cont_page = self . get_regions_light_v_extract_only_images ( img_res , is_image_enhanced , num_col_classifier )
text_regions_p_1 , erosion_hurts , polygons_lines_xml , polygons_of_images , image_page , page_coord , cont_page = self . get_regions_light_v_extract_only_images ( img_res , is_image_enhanced , num_col_classifier )
pcgts = self . writer . build_pagexml_no_full_layout ( [ ] , page_coord , [ ] , [ ] , [ ] , [ ] , polygons_of_images , [ ] , [ ] , [ ] , [ ] , [ ] , cont_page , [ ] , [ ] )
pcgts = self . writer . build_pagexml_no_full_layout ( [ ] , page_coord , [ ] , [ ] , [ ] , [ ] , polygons_of_images , [ ] , [ ] , [ ] , [ ] , [ ] , cont_page , [ ] , [ ] )
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 )
#plt.imshow(text_regions_p_1)
#plt.imshow(text_regions_p_1)
#plt.show()
#plt.show()
self . writer . write_pagexml ( pcgts )
self . writer . write_pagexml ( pcgts )
else :
else :
img_res , is_image_enhanced , num_col_classifier , num_column_is_classified = self . run_enhancement ( self . light_version )
img_res , is_image_enhanced , num_col_classifier , num_column_is_classified = self . run_enhancement ( self . light_version )
self . logger . info ( " Enhancing took %.1f s " , time . time ( ) - t0 )
self . logger . info ( " Enhancing took %.1f s " , time . time ( ) - t0 )
t1 = time . time ( )
t1 = time . time ( )
if self . light_version :
if self . light_version :
text_regions_p_1 , erosion_hurts , polygons_lines_xml , textline_mask_tot_ea = self . get_regions_light_v ( img_res , is_image_enhanced , num_col_classifier )
text_regions_p_1 , erosion_hurts , polygons_lines_xml , textline_mask_tot_ea = self . get_regions_light_v ( img_res , is_image_enhanced , num_col_classifier )
@ -3042,7 +3040,7 @@ class Eynollah:
self . run_graphics_and_columns ( text_regions_p_1 , num_col_classifier , num_column_is_classified , erosion_hurts )
self . run_graphics_and_columns ( text_regions_p_1 , num_col_classifier , num_column_is_classified , erosion_hurts )
self . logger . info ( " Graphics detection took %.1f s " , time . time ( ) - t1 )
self . logger . info ( " Graphics detection took %.1f s " , time . time ( ) - t1 )
#self.logger.info('cont_page %s', cont_page)
#self.logger.info('cont_page %s', cont_page)
if not num_col :
if not num_col :
self . logger . info ( " No columns detected, outputting an empty PAGE-XML " )
self . logger . info ( " No columns detected, outputting an empty PAGE-XML " )
pcgts = self . writer . build_pagexml_no_full_layout ( [ ] , page_coord , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , cont_page , [ ] , [ ] )
pcgts = self . writer . build_pagexml_no_full_layout ( [ ] , page_coord , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , cont_page , [ ] , [ ] )
@ -3076,13 +3074,13 @@ class Eynollah:
text_only = ( ( img_revised_tab [ : , : ] == 1 ) ) * 1
text_only = ( ( img_revised_tab [ : , : ] == 1 ) ) * 1
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
text_only_d = ( ( text_regions_p_1_n [ : , : ] == 1 ) ) * 1
text_only_d = ( ( text_regions_p_1_n [ : , : ] == 1 ) ) * 1
min_con_area = 0.000005
min_con_area = 0.000005
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
contours_only_text , hir_on_text = return_contours_of_image ( text_only )
contours_only_text , hir_on_text = return_contours_of_image ( text_only )
contours_only_text_parent = return_parent_contours ( contours_only_text , hir_on_text )
contours_only_text_parent = return_parent_contours ( contours_only_text , hir_on_text )
if len ( contours_only_text_parent ) > 0 :
if len ( contours_only_text_parent ) > 0 :
areas_cnt_text = np . array ( [ cv2 . contourArea ( c ) for c in contours_only_text_parent ] )
areas_cnt_text = np . array ( [ cv2 . contourArea ( c ) for c in contours_only_text_parent ] )
areas_cnt_text = areas_cnt_text / float ( text_only . shape [ 0 ] * text_only . shape [ 1 ] )
areas_cnt_text = areas_cnt_text / float ( text_only . shape [ 0 ] * text_only . shape [ 1 ] )
@ -3102,7 +3100,7 @@ class Eynollah:
areas_cnt_text_d = np . array ( [ cv2 . contourArea ( c ) for c in contours_only_text_parent_d ] )
areas_cnt_text_d = np . array ( [ cv2 . contourArea ( c ) for c in contours_only_text_parent_d ] )
areas_cnt_text_d = areas_cnt_text_d / float ( text_only_d . shape [ 0 ] * text_only_d . shape [ 1 ] )
areas_cnt_text_d = areas_cnt_text_d / float ( text_only_d . shape [ 0 ] * text_only_d . shape [ 1 ] )
if len ( areas_cnt_text_d ) > 0 :
if len ( areas_cnt_text_d ) > 0 :
contours_biggest_d = contours_only_text_parent_d [ np . argmax ( areas_cnt_text_d ) ]
contours_biggest_d = contours_only_text_parent_d [ np . argmax ( areas_cnt_text_d ) ]
index_con_parents_d = np . argsort ( areas_cnt_text_d )
index_con_parents_d = np . argsort ( areas_cnt_text_d )
@ -3122,7 +3120,7 @@ class Eynollah:
cy_biggest_d_last5 = cy_biggest_d [ - len ( cx_bigest_d ) : ]
cy_biggest_d_last5 = cy_biggest_d [ - len ( cx_bigest_d ) : ]
dists_d = [ math . sqrt ( ( cx_bigest_big [ 0 ] - cx_bigest_d_last5 [ j ] ) * * 2 + ( cy_biggest_big [ 0 ] - cy_biggest_d_last5 [ j ] ) * * 2 ) for j in range ( len ( cy_biggest_d_last5 ) ) ]
dists_d = [ math . sqrt ( ( cx_bigest_big [ 0 ] - cx_bigest_d_last5 [ j ] ) * * 2 + ( cy_biggest_big [ 0 ] - cy_biggest_d_last5 [ j ] ) * * 2 ) for j in range ( len ( cy_biggest_d_last5 ) ) ]
ind_largest = len ( cx_bigest_d ) - len ( cx_bigest_d ) + np . argmin ( dists_d )
ind_largest = len ( cx_bigest_d ) - len ( cx_bigest_d ) + np . argmin ( dists_d )
cx_bigest_d_big [ 0 ] = cx_bigest_d [ ind_largest ]
cx_bigest_d_big [ 0 ] = cx_bigest_d [ ind_largest ]
cy_biggest_d_big [ 0 ] = cy_biggest_d [ ind_largest ]
cy_biggest_d_big [ 0 ] = cy_biggest_d [ ind_largest ]
except Exception as why :
except Exception as why :
@ -3151,7 +3149,7 @@ class Eynollah:
contours_only_text_parent_d_ordered = [ ]
contours_only_text_parent_d_ordered = [ ]
contours_only_text_parent_d = [ ]
contours_only_text_parent_d = [ ]
contours_only_text_parent = [ ]
contours_only_text_parent = [ ]
else :
else :
contours_only_text_parent_d_ordered = [ ]
contours_only_text_parent_d_ordered = [ ]
contours_only_text_parent_d = [ ]
contours_only_text_parent_d = [ ]
@ -3159,7 +3157,7 @@ class Eynollah:
else :
else :
contours_only_text , hir_on_text = return_contours_of_image ( text_only )
contours_only_text , hir_on_text = return_contours_of_image ( text_only )
contours_only_text_parent = return_parent_contours ( contours_only_text , hir_on_text )
contours_only_text_parent = return_parent_contours ( contours_only_text , hir_on_text )
if len ( contours_only_text_parent ) > 0 :
if len ( contours_only_text_parent ) > 0 :
areas_cnt_text = np . array ( [ cv2 . contourArea ( c ) for c in contours_only_text_parent ] )
areas_cnt_text = np . array ( [ cv2 . contourArea ( c ) for c in contours_only_text_parent ] )
areas_cnt_text = areas_cnt_text / float ( text_only . shape [ 0 ] * text_only . shape [ 1 ] )
areas_cnt_text = areas_cnt_text / float ( text_only . shape [ 0 ] * text_only . shape [ 1 ] )
@ -3185,7 +3183,7 @@ class Eynollah:
txt_con_org = get_textregion_contours_in_org_image ( contours_only_text_parent , self . image , slope_first )
txt_con_org = get_textregion_contours_in_org_image ( contours_only_text_parent , self . image , slope_first )
boxes_text , _ = get_text_region_boxes_by_given_contours ( contours_only_text_parent )
boxes_text , _ = get_text_region_boxes_by_given_contours ( contours_only_text_parent )
boxes_marginals , _ = get_text_region_boxes_by_given_contours ( polygons_of_marginals )
boxes_marginals , _ = get_text_region_boxes_by_given_contours ( polygons_of_marginals )
if not self . curved_line :
if not self . curved_line :
if self . light_version :
if self . light_version :
if self . textline_light :
if self . textline_light :
@ -3199,13 +3197,13 @@ class Eynollah:
slopes_marginals , all_found_textline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , _ = self . get_slopes_and_deskew_new ( polygons_of_marginals , polygons_of_marginals , textline_mask_tot_ea , image_page_rotated , boxes_marginals , slope_deskew )
slopes_marginals , all_found_textline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , _ = self . get_slopes_and_deskew_new ( polygons_of_marginals , polygons_of_marginals , textline_mask_tot_ea , image_page_rotated , boxes_marginals , slope_deskew )
else :
else :
scale_param = 1
scale_param = 1
all_found_textline_polygons , boxes_text , txt_con_org , contours_only_text_parent , all_box_coord , index_by_text_par_con , slopes = self . get_slopes_and_deskew_new_curved ( txt_con_org , contours_only_text_parent , cv2 . erode ( textline_mask_tot_ea , kernel = KERNEL , iterations = 1 ) , image_page_rotated , boxes_text , text_only , num_col_classifier , scale_param , slope_deskew )
all_found_textline_polygons , boxes_text , txt_con_org , contours_only_text_parent , all_box_coord , index_by_text_par_con , slopes = self . get_slopes_and_deskew_new_curved ( txt_con_org , contours_only_text_parent , cv2 . erode ( textline_mask_tot_ea , kernel = KERNEL , iterations = 1 ) , image_page_rotated , boxes_text , text_only , num_col_classifier , scale_param , slope_deskew )
all_found_textline_polygons = small_textlines_to_parent_adherence2 ( all_found_textline_polygons , textline_mask_tot_ea , num_col_classifier )
all_found_textline_polygons = small_textlines_to_parent_adherence2 ( all_found_textline_polygons , textline_mask_tot_ea , num_col_classifier )
all_found_textline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , _ , slopes_marginals = self . get_slopes_and_deskew_new_curved ( polygons_of_marginals , polygons_of_marginals , cv2 . erode ( textline_mask_tot_ea , kernel = KERNEL , iterations = 1 ) , image_page_rotated , boxes_marginals , text_only , num_col_classifier , scale_param , slope_deskew )
all_found_textline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , _ , slopes_marginals = self . get_slopes_and_deskew_new_curved ( polygons_of_marginals , polygons_of_marginals , cv2 . erode ( textline_mask_tot_ea , kernel = KERNEL , iterations = 1 ) , image_page_rotated , boxes_marginals , text_only , num_col_classifier , scale_param , slope_deskew )
all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2 ( all_found_textline_polygons_marginals , textline_mask_tot_ea , num_col_classifier )
all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2 ( all_found_textline_polygons_marginals , textline_mask_tot_ea , num_col_classifier )
if self . full_layout :
if self . full_layout :
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
contours_only_text_parent_d_ordered = list ( np . array ( contours_only_text_parent_d_ordered , dtype = object ) [ index_by_text_par_con ] )
contours_only_text_parent_d_ordered = list ( np . array ( contours_only_text_parent_d_ordered , dtype = object ) [ index_by_text_par_con ] )
@ -3224,12 +3222,12 @@ class Eynollah:
if self . plotter :
if self . plotter :
self . plotter . save_plot_of_layout ( text_regions_p , image_page )
self . plotter . save_plot_of_layout ( text_regions_p , image_page )
self . plotter . save_plot_of_layout_all ( text_regions_p , image_page )
self . plotter . save_plot_of_layout_all ( text_regions_p , image_page )
pixel_img = 4
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size ( text_regions_p , pixel_img )
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size ( text_regions_p , pixel_img )
all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_textline ( text_regions_p , polygons_of_drop_capitals , contours_only_text_parent , contours_only_text_parent_h , all_box_coord , all_box_coord_h , all_found_textline_polygons , all_found_textline_polygons_h , kernel = KERNEL , curved_line = self . curved_line )
all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_textline ( text_regions_p , polygons_of_drop_capitals , contours_only_text_parent , contours_only_text_parent_h , all_box_coord , all_box_coord_h , all_found_textline_polygons , all_found_textline_polygons_h , kernel = KERNEL , curved_line = self . curved_line )
pixel_lines = 6
pixel_lines = 6
if not self . headers_off :
if not self . headers_off :
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
@ -3250,12 +3248,12 @@ class Eynollah:
else :
else :
regions_without_separators_d = regions_without_separators_d . astype ( np . uint8 )
regions_without_separators_d = regions_without_separators_d . astype ( np . uint8 )
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 )
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 ( splitter_y_new , regions_without_separators , matrix_of_lines_ch , num_col_classifier , erosion_hurts , self . tables , self . right2left )
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 ( splitter_y_new_d , regions_without_separators_d , matrix_of_lines_ch_d , num_col_classifier , erosion_hurts , self . tables , self . right2left )
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')
#print(boxes_d,'boxes_d')
#img_once = np.zeros((textline_mask_tot_d.shape[0],textline_mask_tot_d.shape[1]))
#img_once = np.zeros((textline_mask_tot_d.shape[0],textline_mask_tot_d.shape[1]))
@ -3273,19 +3271,23 @@ class Eynollah:
else :
else :
order_text_new , id_of_texts_tot = self . do_order_of_regions ( contours_only_text_parent_d_ordered , contours_only_text_parent_h_d_ordered , boxes_d , textline_mask_tot_d )
order_text_new , id_of_texts_tot = self . do_order_of_regions ( contours_only_text_parent_d_ordered , contours_only_text_parent_h_d_ordered , boxes_d , textline_mask_tot_d )
pcgts = self . writer . build_pagexml_full_layout ( contours_only_text_parent , contours_only_text_parent_h , page_coord , order_text_new , id_of_texts_tot , all_found_textline_polygons , all_found_textline_polygons_h , all_box_coord , all_box_coord_h , polygons_of_images , contours_tables , polygons_of_drop_capitals , polygons_of_marginals , all_found_textline_polygons_marginals , all_box_coord_marginals , slopes , slopes_h , slopes_marginals , cont_page , polygons_lines_xml )
pcgts = self . writer . build_pagexml_full_layout ( contours_only_text_parent , contours_only_text_parent_h , page_coord , order_text_new , id_of_texts_tot , all_found_textline_polygons , all_found_textline_polygons_h , all_box_coord , all_box_coord_h , polygons_of_images , contours_tables , polygons_of_drop_capitals , polygons_of_marginals , all_found_textline_polygons_marginals , all_box_coord_marginals , slopes , slopes_h , slopes_marginals , cont_page , polygons_lines_xml )
self . logger . info ( " Job done in %.1f s " , time . time ( ) - t0 )
self . logger . info ( " Job done in %.1f s " , time . time ( ) - t0 )
##return pcgts
if not self . dir_in :
return pcgts
else :
contours_only_text_parent_h = None
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
order_text_new , id_of_texts_tot = self . do_order_of_regions ( contours_only_text_parent , contours_only_text_parent_h , boxes , textline_mask_tot )
else :
else :
contours_only_text_parent_h = None
contours_only_text_parent_d_ordered = list ( np . array ( contours_only_text_parent_d_ordered , dtype = object ) [ index_by_text_par_con ] )
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
order_text_new , id_of_texts_tot = self . do_order_of_regions ( contours_only_text_parent_d_ordered , contours_only_text_parent_h , boxes_d , textline_mask_tot_d )
order_text_new , id_of_texts_tot = self . do_order_of_regions ( contours_only_text_parent , contours_only_text_parent_h , boxes , textline_mask_tot )
pcgts = self . writer . build_pagexml_no_full_layout ( txt_con_org , page_coord , order_text_new , id_of_texts_tot , all_found_textline_polygons , all_box_coord , polygons_of_images , polygons_of_marginals , all_found_textline_polygons_marginals , all_box_coord_marginals , slopes , slopes_marginals , cont_page , polygons_lines_xml , contours_tables )
else :
self . logger . info ( " Job done in %.1f s " , time . time ( ) - t0 )
contours_only_text_parent_d_ordered = list ( np . array ( contours_only_text_parent_d_ordered , dtype = object ) [ index_by_text_par_con ] )
if not self . dir_in :
order_text_new , id_of_texts_tot = self . do_order_of_regions ( contours_only_text_parent_d_ordered , contours_only_text_parent_h , boxes_d , textline_mask_tot_d )
return pcgts
pcgts = self . writer . build_pagexml_no_full_layout ( txt_con_org , page_coord , order_text_new , id_of_texts_tot , all_found_textline_polygons , all_box_coord , polygons_of_images , polygons_of_marginals , all_found_textline_polygons_marginals , all_box_coord_marginals , slopes , slopes_marginals , cont_page , polygons_lines_xml , contours_tables )
self . logger . info ( " Job done in %.1f s " , time . time ( ) - t0 )
if self . dir_in :
##return pcgts
self . writer . write_pagexml ( pcgts )
self . writer . write_pagexml ( pcgts )
#self.logger.info("Job done in %.1fs", time.time() - t0)
#self.logger.info("Job done in %.1fs", time.time() - t0)
if self . dir_in :
if self . dir_in :
cneud
4 months ago