@ -30,6 +30,7 @@ from scipy.signal import find_peaks
import matplotlib . pyplot as plt
from scipy . ndimage import gaussian_filter1d
from keras . backend import set_session
from tensorflow . keras import layers
from . utils . contour import (
filter_contours_area_of_image ,
@ -83,6 +84,60 @@ DPI_THRESHOLD = 298
MAX_SLOPE = 999
KERNEL = np . ones ( ( 5 , 5 ) , np . uint8 )
projection_dim = 64
patch_size = 1
num_patches = 21 * 21 #14*14#28*28#14*14#28*28
class Patches ( layers . Layer ) :
def __init__ ( self , * * kwargs ) :
super ( Patches , self ) . __init__ ( )
self . patch_size = patch_size
def call ( self , images ) :
batch_size = tf . shape ( images ) [ 0 ]
patches = tf . image . extract_patches (
images = images ,
sizes = [ 1 , self . patch_size , self . patch_size , 1 ] ,
strides = [ 1 , self . patch_size , self . patch_size , 1 ] ,
rates = [ 1 , 1 , 1 , 1 ] ,
padding = " VALID " ,
)
patch_dims = patches . shape [ - 1 ]
patches = tf . reshape ( patches , [ batch_size , - 1 , patch_dims ] )
return patches
def get_config ( self ) :
config = super ( ) . get_config ( ) . copy ( )
config . update ( {
' patch_size ' : self . patch_size ,
} )
return config
class PatchEncoder ( layers . Layer ) :
def __init__ ( self , * * kwargs ) :
super ( PatchEncoder , self ) . __init__ ( )
self . num_patches = num_patches
self . projection = layers . Dense ( units = projection_dim )
self . position_embedding = layers . Embedding (
input_dim = num_patches , output_dim = projection_dim
)
def call ( self , patch ) :
positions = tf . range ( start = 0 , limit = self . num_patches , delta = 1 )
encoded = self . projection ( patch ) + self . position_embedding ( positions )
return encoded
def get_config ( self ) :
config = super ( ) . get_config ( ) . copy ( )
config . update ( {
' num_patches ' : self . num_patches ,
' projection ' : self . projection ,
' position_embedding ' : self . position_embedding ,
} )
return config
class Eynollah :
def __init__ (
self ,
@ -100,6 +155,7 @@ class Eynollah:
enable_plotting = False ,
allow_enhancement = False ,
curved_line = False ,
textline_light = False ,
full_layout = False ,
tables = False ,
input_binary = False ,
@ -130,6 +186,7 @@ class Eynollah:
self . enable_plotting = enable_plotting
self . allow_enhancement = allow_enhancement
self . curved_line = curved_line
self . textline_light = textline_light
self . full_layout = full_layout
self . tables = tables
self . input_binary = input_binary
@ -151,6 +208,7 @@ class Eynollah:
dir_out = self . dir_out ,
image_filename = self . image_filename ,
curved_line = self . curved_line ,
textline_light = self . textline_light ,
pcgts = pcgts )
self . logger = logger if logger else getLogger ( ' eynollah ' )
self . dir_models = dir_models
@ -165,6 +223,9 @@ class Eynollah:
self . model_page_dir = dir_models + " /eynollah-page-extraction_20210425.h5 "
self . model_region_dir_p_ens = dir_models + " /eynollah-main-regions-ensembled_20210425.h5 "
self . model_region_dir_p_ens_light = dir_models + " /eynollah-main-regions_20220314.h5 "
if self . textline_light :
self . model_textline_dir = dir_models + " /model_17.h5 "
else :
self . model_textline_dir = dir_models + " /eynollah-textline_20210425.h5 "
self . model_tables = dir_models + " /eynollah-tables_20210319.h5 "
@ -603,7 +664,10 @@ class Eynollah:
gpu_options = tf . compat . v1 . GPUOptions ( allow_growth = True )
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
session = tf . compat . v1 . Session ( config = tf . compat . v1 . ConfigProto ( gpu_options = gpu_options ) )
try :
model = load_model ( model_dir , compile = False )
except :
model = load_model ( model_dir , compile = False , custom_objects = { " PatchEncoder " : PatchEncoder , " Patches " : Patches } )
return model , session
@ -1368,10 +1432,15 @@ class Eynollah:
# plt.imshow(mask_only_con_region)
# plt.show()
if self . textline_light :
all_text_region_raw = np . copy ( textline_mask_tot_ea )
all_text_region_raw [ mask_only_con_region == 0 ] = 0
cnt_clean_rot_raw , hir_on_cnt_clean_rot = return_contours_of_image ( all_text_region_raw )
cnt_clean_rot = filter_contours_area_of_image ( all_text_region_raw , cnt_clean_rot_raw , hir_on_cnt_clean_rot , max_area = 1 , min_area = 0.00001 )
else :
all_text_region_raw = np . copy ( textline_mask_tot_ea [ boxes_text [ mv ] [ 1 ] : boxes_text [ mv ] [ 1 ] + boxes_text [ mv ] [ 3 ] , boxes_text [ mv ] [ 0 ] : boxes_text [ mv ] [ 0 ] + boxes_text [ mv ] [ 2 ] ] )
mask_only_con_region = mask_only_con_region [ boxes_text [ mv ] [ 1 ] : boxes_text [ mv ] [ 1 ] + boxes_text [ mv ] [ 3 ] , boxes_text [ mv ] [ 0 ] : boxes_text [ mv ] [ 0 ] + boxes_text [ mv ] [ 2 ] ]
all_text_region_raw [ mask_only_con_region == 0 ] = 0
cnt_clean_rot = textline_contours_postprocessing ( all_text_region_raw , [ slope_deskew ] [ 0 ] , contours_par_per_process [ mv ] , boxes_text [ mv ] )
@ -1481,7 +1550,9 @@ class Eynollah:
if not self . dir_in :
session_textline . close ( )
if self . textline_light :
return ( prediction_textline [ : , : , 0 ] == 1 ) * 1 , ( prediction_textline_longshot_true_size [ : , : , 0 ] == 1 ) * 1
else :
return prediction_textline [ : , : , 0 ] , prediction_textline_longshot_true_size [ : , : , 0 ]
def do_work_of_slopes ( self , q , poly , box_sub , boxes_per_process , textline_mask_tot , contours_per_process ) :
@ -2562,6 +2633,8 @@ class Eynollah:
scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea , _ = self . textline_contours ( image_page , True , scaler_h_textline , scaler_w_textline )
if self . textline_light :
textline_mask_tot_ea = textline_mask_tot_ea . astype ( np . int16 )
if not self . dir_in :
K . clear_session ( )
if self . plotter :
@ -2870,7 +2943,6 @@ class Eynollah:
self . ls_imgs = [ 1 ]
for img_name in self . ls_imgs :
print ( img_name , ' img_name ' )
t0 = time . time ( )
if self . dir_in :
self . reset_file_name_dir ( os . path . join ( self . dir_in , img_name ) )
@ -2887,6 +2959,7 @@ class Eynollah:
num_col , num_col_classifier , img_only_regions , page_coord , image_page , mask_images , mask_lines , text_regions_p_1 , cont_page , table_prediction , textline_mask_tot_ea = \
self . run_graphics_and_columns_light ( text_regions_p_1 , textline_mask_tot_ea , num_col_classifier , num_column_is_classified , erosion_hurts )
#self.logger.info("run graphics %.1fs ", time.time() - t1t)
textline_mask_tot_ea_org = np . copy ( textline_mask_tot_ea )
else :
text_regions_p_1 , erosion_hurts , polygons_lines_xml = self . get_regions_from_xy_2models ( img_res , is_image_enhanced , num_col_classifier )
self . logger . info ( " Textregion detection took %.1f s " , time . time ( ) - t1 )
@ -3043,6 +3116,10 @@ class Eynollah:
if not self . curved_line :
if self . light_version :
if self . textline_light :
slopes , all_found_texline_polygons , boxes_text , txt_con_org , contours_only_text_parent , all_box_coord , index_by_text_par_con = self . get_slopes_and_deskew_new_light ( txt_con_org , contours_only_text_parent , textline_mask_tot_ea_org , image_page_rotated , boxes_text , slope_deskew )
slopes_marginals , all_found_texline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , _ = self . get_slopes_and_deskew_new_light ( polygons_of_marginals , polygons_of_marginals , textline_mask_tot_ea_org , image_page_rotated , boxes_marginals , slope_deskew )
else :
slopes , all_found_texline_polygons , boxes_text , txt_con_org , contours_only_text_parent , all_box_coord , index_by_text_par_con = self . get_slopes_and_deskew_new_light ( txt_con_org , contours_only_text_parent , textline_mask_tot_ea , image_page_rotated , boxes_text , slope_deskew )
slopes_marginals , all_found_texline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , _ = self . get_slopes_and_deskew_new_light ( polygons_of_marginals , polygons_of_marginals , textline_mask_tot_ea , image_page_rotated , boxes_marginals , slope_deskew )
else :
vahid
2 years ago