# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring
"""
tool to extract table form data from alto xml data
"""
import gc
import math
import os
import sys
import time
import warnings
from pathlib import Path
from multiprocessing import Process , Queue , cpu_count
from lxml import etree as ET
from ocrd_utils import getLogger
import cv2
import numpy as np
os . environ [ " TF_CPP_MIN_LOG_LEVEL " ] = " 3 "
stderr = sys . stderr
sys . stderr = open ( os . devnull , " w " )
from keras import backend as K
from keras . models import load_model
sys . stderr = stderr
import tensorflow as tf
tf . get_logger ( ) . setLevel ( " ERROR " )
warnings . filterwarnings ( " ignore " )
from . utils . contour import (
contours_in_same_horizon ,
filter_contours_area_of_image_interiors ,
filter_contours_area_of_image_tables ,
filter_contours_area_of_image ,
find_contours_mean_y_diff ,
find_features_of_contours ,
find_new_features_of_contoures ,
get_text_region_boxes_by_given_contours ,
get_textregion_contours_in_org_image ,
return_bonding_box_of_contours ,
return_contours_of_image ,
return_contours_of_interested_region ,
return_contours_of_interested_region_and_bounding_box ,
return_contours_of_interested_region_by_min_size ,
return_contours_of_interested_textline ,
return_parent_contours ,
return_contours_of_interested_region_by_size ,
)
from . utils . rotate import (
rotate_image ,
rotate_max_area ,
rotate_max_area_new ,
rotatedRectWithMaxArea ,
rotation_image_new ,
rotation_not_90_func ,
rotation_not_90_func_full_layout ,
rotyate_image_different ,
)
from . utils . separate_lines import (
seperate_lines ,
seperate_lines_new_inside_teils ,
seperate_lines_new_inside_teils2 ,
seperate_lines_vertical ,
seperate_lines_vertical_cont ,
textline_contours_postprocessing ,
seperate_lines_new2 ,
return_deskew_slop ,
)
from . utils . drop_capitals import (
adhere_drop_capital_region_into_cprresponding_textline ,
filter_small_drop_capitals_from_no_patch_layout
)
from . utils . marginals import get_marginals
from . utils . resize import resize_image
from . utils import (
boosting_headers_by_longshot_region_segmentation ,
crop_image_inside_box ,
find_features_of_lines ,
find_num_col ,
find_num_col_by_vertical_lines ,
find_num_col_deskew ,
find_num_col_only_image ,
isNaN ,
otsu_copy ,
otsu_copy_binary ,
return_hor_spliter_by_index_for_without_verticals ,
delete_seperator_around ,
return_regions_without_seperators ,
put_drop_out_from_only_drop_model ,
putt_bb_of_drop_capitals_of_model_in_patches_in_layout ,
check_any_text_region_in_model_one_is_main_or_header ,
small_textlines_to_parent_adherence2 ,
order_and_id_of_texts ,
order_of_regions ,
implent_law_head_main_not_parallel ,
return_hor_spliter_by_index ,
combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new ,
return_points_with_boundies ,
find_number_of_columns_in_document ,
return_boxes_of_images_by_order_of_reading_new ,
)
from . utils . xml import create_page_xml , add_textequiv
from . utils . pil_cv2 import check_dpi
from . plot import EynollahPlotter
SLOPE_THRESHOLD = 0.13
class eynollah :
def __init__ (
self ,
image_filename ,
image_filename_stem ,
dir_out ,
dir_models ,
dir_of_cropped_images = None ,
dir_of_layout = None ,
dir_of_deskewed = None ,
dir_of_all = None ,
enable_plotting = False ,
allow_enhancement = False ,
curved_line = False ,
full_layout = False ,
allow_scaling = False ,
headers_off = False
) :
self . image_filename = image_filename # XXX This does not seem to be a directory as the name suggests, but a file
self . cont_page = [ ]
self . dir_out = dir_out
self . image_filename_stem = image_filename_stem
self . allow_enhancement = allow_enhancement
self . curved_line = curved_line
self . full_layout = full_layout
self . allow_scaling = allow_scaling
self . headers_off = headers_off
if not self . image_filename_stem :
self . image_filename_stem = Path ( Path ( image_filename ) . name ) . stem
self . plotter = None if not enable_plotting else EynollahPlotter (
dir_of_all = dir_of_all ,
dir_of_deskewed = dir_of_deskewed ,
dir_of_cropped_images = dir_of_cropped_images ,
dir_of_layout = dir_of_layout ,
image_filename = image_filename ,
image_filename_stem = image_filename_stem ,
)
self . logger = getLogger ( ' eynollah ' )
self . dir_models = dir_models
self . kernel = np . ones ( ( 5 , 5 ) , np . uint8 )
self . model_dir_of_enhancemnet = dir_models + " /model_enhancement.h5 "
self . model_dir_of_col_classifier = dir_models + " /model_scale_classifier.h5 "
self . model_region_dir_p = dir_models + " /model_main_covid19_lr5-5_scale_1_1_great.h5 "
self . model_region_dir_p2 = dir_models + " /model_main_home_corona3_rot.h5 "
self . model_region_dir_fully_np = dir_models + " /model_no_patches_class0_30eopch.h5 "
self . model_region_dir_fully = dir_models + " /model_3up_new_good_no_augmentation.h5 "
self . model_page_dir = dir_models + " /model_page_mixed_best.h5 "
self . model_region_dir_p_ens = dir_models + " /model_ensemble_s.h5 "
self . model_textline_dir = dir_models + " /model_textline_newspapers.h5 "
self . _imgs = { }
def imread ( self , grayscale = False , uint8 = True ) :
key = ' img '
if grayscale :
key + = ' _grayscale '
if uint8 :
key + = ' _uint8 '
if key not in self . _imgs :
if grayscale :
img = cv2 . imread ( self . image_filename , cv2 . IMREAD_GRAYSCALE )
else :
img = cv2 . imread ( self . image_filename )
if uint8 :
img = img . astype ( np . uint8 )
self . _imgs [ key ] = img
return self . _imgs [ key ] . copy ( )
def predict_enhancement ( self , img ) :
self . logger . debug ( " enter predict_enhancement " )
model_enhancement , session_enhancemnet = self . start_new_session_and_model ( self . model_dir_of_enhancemnet )
img_height_model = model_enhancement . layers [ len ( model_enhancement . layers ) - 1 ] . output_shape [ 1 ]
img_width_model = model_enhancement . layers [ len ( model_enhancement . layers ) - 1 ] . output_shape [ 2 ]
# n_classes = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[3]
if img . shape [ 0 ] < img_height_model :
img = cv2 . resize ( img , ( img . shape [ 1 ] , img_width_model ) , interpolation = cv2 . INTER_NEAREST )
if img . shape [ 1 ] < img_width_model :
img = cv2 . resize ( img , ( img_height_model , img . shape [ 0 ] ) , interpolation = cv2 . INTER_NEAREST )
margin = True
if margin :
kernel = np . ones ( ( 5 , 5 ) , np . uint8 )
margin = int ( 0 * img_width_model )
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float ( 255.0 )
img_h = img . shape [ 0 ]
img_w = img . shape [ 1 ]
prediction_true = np . zeros ( ( img_h , img_w , 3 ) )
mask_true = np . zeros ( ( img_h , img_w ) )
nxf = img_w / float ( width_mid )
nyf = img_h / float ( height_mid )
nxf = int ( nxf ) + 1 if nxf > int ( nxf ) else int ( nxf )
nyf = int ( nyf ) + 1 if nyf > int ( nyf ) else int ( nyf )
for i in range ( nxf ) :
for j in range ( nyf ) :
if i == 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
else :
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0 :
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else :
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w :
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h :
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
label_p_pred = model_enhancement . predict ( img_patch . reshape ( 1 , img_patch . shape [ 0 ] , img_patch . shape [ 1 ] , img_patch . shape [ 2 ] ) )
seg = label_p_pred [ 0 , : , : , : ]
seg = seg * 255
if i == 0 and j == 0 :
seg = seg [ 0 : seg . shape [ 0 ] - margin , 0 : seg . shape [ 1 ] - margin ]
prediction_true [ index_y_d + 0 : index_y_u - margin , index_x_d + 0 : index_x_u - margin , : ] = seg
elif i == nxf - 1 and j == nyf - 1 :
seg = seg [ margin : seg . shape [ 0 ] - 0 , margin : seg . shape [ 1 ] - 0 ]
prediction_true [ index_y_d + margin : index_y_u - 0 , index_x_d + margin : index_x_u - 0 , : ] = seg
elif i == 0 and j == nyf - 1 :
seg = seg [ margin : seg . shape [ 0 ] - 0 , 0 : seg . shape [ 1 ] - margin ]
prediction_true [ index_y_d + margin : index_y_u - 0 , index_x_d + 0 : index_x_u - margin , : ] = seg
elif i == nxf - 1 and j == 0 :
seg = seg [ 0 : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - 0 ]
prediction_true [ index_y_d + 0 : index_y_u - margin , index_x_d + margin : index_x_u - 0 , : ] = seg
elif i == 0 and j != 0 and j != nyf - 1 :
seg = seg [ margin : seg . shape [ 0 ] - margin , 0 : seg . shape [ 1 ] - margin ]
prediction_true [ index_y_d + margin : index_y_u - margin , index_x_d + 0 : index_x_u - margin , : ] = seg
elif i == nxf - 1 and j != 0 and j != nyf - 1 :
seg = seg [ margin : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - 0 ]
prediction_true [ index_y_d + margin : index_y_u - margin , index_x_d + margin : index_x_u - 0 , : ] = seg
elif i != 0 and i != nxf - 1 and j == 0 :
seg = seg [ 0 : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - margin ]
prediction_true [ index_y_d + 0 : index_y_u - margin , index_x_d + margin : index_x_u - margin , : ] = seg
elif i != 0 and i != nxf - 1 and j == nyf - 1 :
seg = seg [ margin : seg . shape [ 0 ] - 0 , margin : seg . shape [ 1 ] - margin ]
prediction_true [ index_y_d + margin : index_y_u - 0 , index_x_d + margin : index_x_u - margin , : ] = seg
else :
seg = seg [ margin : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - margin ]
prediction_true [ index_y_d + margin : index_y_u - margin , index_x_d + margin : index_x_u - margin , : ] = seg
prediction_true = prediction_true . astype ( int )
del model_enhancement
del session_enhancemnet
return prediction_true
def calculate_width_height_by_columns ( self , img , num_col , width_early , label_p_pred ) :
self . logger . debug ( " enter calculate_width_height_by_columns " )
if num_col == 1 and width_early < 1100 :
img_w_new = 2000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 2000 )
elif num_col == 1 and width_early > = 2500 :
img_w_new = 2000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 2000 )
elif num_col == 1 and width_early > = 1100 and width_early < 2500 :
img_w_new = width_early
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * width_early )
elif num_col == 2 and width_early < 2000 :
img_w_new = 2400
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 2400 )
elif num_col == 2 and width_early > = 3500 :
img_w_new = 2400
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 2400 )
elif num_col == 2 and width_early > = 2000 and width_early < 3500 :
img_w_new = width_early
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * width_early )
elif num_col == 3 and width_early < 2000 :
img_w_new = 3000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 3000 )
elif num_col == 3 and width_early > = 4000 :
img_w_new = 3000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 3000 )
elif num_col == 3 and width_early > = 2000 and width_early < 4000 :
img_w_new = width_early
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * width_early )
elif num_col == 4 and width_early < 2500 :
img_w_new = 4000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 4000 )
elif num_col == 4 and width_early > = 5000 :
img_w_new = 4000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 4000 )
elif num_col == 4 and width_early > = 2500 and width_early < 5000 :
img_w_new = width_early
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * width_early )
elif num_col == 5 and width_early < 3700 :
img_w_new = 5000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 5000 )
elif num_col == 5 and width_early > = 7000 :
img_w_new = 5000
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 5000 )
elif num_col == 5 and width_early > = 3700 and width_early < 7000 :
img_w_new = width_early
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * width_early )
elif num_col == 6 and width_early < 4500 :
img_w_new = 6500 # 5400
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * 6500 )
else :
img_w_new = width_early
img_h_new = int ( img . shape [ 0 ] / float ( img . shape [ 1 ] ) * width_early )
if label_p_pred [ 0 ] [ int ( num_col - 1 ) ] < 0.9 and img_w_new < width_early :
img_new = np . copy ( img )
num_column_is_classified = False
else :
img_new = resize_image ( img , img_h_new , img_w_new )
num_column_is_classified = True
return img_new , num_column_is_classified
def resize_image_with_column_classifier ( self , is_image_enhanced ) :
self . logger . debug ( " enter resize_image_with_column_classifier " )
img = self . imread ( )
_ , page_coord = self . early_page_for_num_of_column_classification ( )
model_num_classifier , session_col_classifier = self . start_new_session_and_model ( self . model_dir_of_col_classifier )
img_1ch = self . imread ( grayscale = True , uint8 = False )
width_early = img_1ch . shape [ 1 ]
img_1ch = img_1ch [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] ]
# plt.imshow(img_1ch)
# plt.show()
img_1ch = img_1ch / 255.0
img_1ch = cv2 . resize ( img_1ch , ( 448 , 448 ) , interpolation = cv2 . INTER_NEAREST )
img_in = np . zeros ( ( 1 , img_1ch . shape [ 0 ] , img_1ch . shape [ 1 ] , 3 ) )
img_in [ 0 , : , : , 0 ] = img_1ch [ : , : ]
img_in [ 0 , : , : , 1 ] = img_1ch [ : , : ]
img_in [ 0 , : , : , 2 ] = img_1ch [ : , : ]
label_p_pred = model_num_classifier . predict ( img_in )
num_col = np . argmax ( label_p_pred [ 0 ] ) + 1
self . logger . info ( " Found %s columns ( %s ) " , num_col , label_p_pred )
session_col_classifier . close ( )
del model_num_classifier
del session_col_classifier
K . clear_session ( )
gc . collect ( )
img_new , num_column_is_classified = self . calculate_width_height_by_columns ( img , num_col , width_early , label_p_pred )
if img_new . shape [ 1 ] > img . shape [ 1 ] :
img_new = self . predict_enhancement ( img_new )
is_image_enhanced = True
return img , img_new , is_image_enhanced
def resize_and_enhance_image_with_column_classifier ( self ) :
self . logger . debug ( " enter resize_and_enhance_image_with_column_classifier " )
dpi = check_dpi ( self . image_filename )
self . logger . info ( " Detected %s DPI " % dpi )
img = self . imread ( )
_ , page_coord = self . early_page_for_num_of_column_classification ( )
model_num_classifier , session_col_classifier = self . start_new_session_and_model ( self . model_dir_of_col_classifier )
img_1ch = self . imread ( grayscale = True )
width_early = img_1ch . shape [ 1 ]
img_1ch = img_1ch [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] ]
# plt.imshow(img_1ch)
# plt.show()
img_1ch = img_1ch / 255.0
img_1ch = cv2 . resize ( img_1ch , ( 448 , 448 ) , interpolation = cv2 . INTER_NEAREST )
img_in = np . zeros ( ( 1 , img_1ch . shape [ 0 ] , img_1ch . shape [ 1 ] , 3 ) )
img_in [ 0 , : , : , 0 ] = img_1ch [ : , : ]
img_in [ 0 , : , : , 1 ] = img_1ch [ : , : ]
img_in [ 0 , : , : , 2 ] = img_1ch [ : , : ]
# plt.imshow(img_in[0,:,:,:])
# plt.show()
label_p_pred = model_num_classifier . predict ( img_in )
num_col = np . argmax ( label_p_pred [ 0 ] ) + 1
self . logger . info ( " Found %s columns ( %s ) " , num_col , label_p_pred )
session_col_classifier . close ( )
del model_num_classifier
del session_col_classifier
del img_in
del img_1ch
del page_coord
K . clear_session ( )
gc . collect ( )
if dpi < 298 :
img_new , num_column_is_classified = self . calculate_width_height_by_columns ( img , num_col , width_early , label_p_pred )
image_res = self . predict_enhancement ( img_new )
is_image_enhanced = True
else :
is_image_enhanced = False
num_column_is_classified = True
image_res = np . copy ( img )
self . logger . debug ( " exit resize_and_enhance_image_with_column_classifier " )
return is_image_enhanced , img , image_res , num_col , num_column_is_classified
def get_image_and_scales ( self , img_org , img_res , scale ) :
self . logger . debug ( " enter get_image_and_scales " )
self . image = np . copy ( img_res )
self . image_org = np . copy ( img_org )
self . height_org = self . image . shape [ 0 ]
self . width_org = self . image . shape [ 1 ]
self . img_hight_int = int ( self . image . shape [ 0 ] * scale )
self . img_width_int = int ( self . image . shape [ 1 ] * scale )
self . scale_y = self . img_hight_int / float ( self . image . shape [ 0 ] )
self . scale_x = self . img_width_int / float ( self . image . shape [ 1 ] )
self . image = resize_image ( self . image , self . img_hight_int , self . img_width_int )
# Also set for the plotter
# XXX TODO hacky
if self . plotter :
self . plotter . image_org = self . image_org
self . plotter . scale_y = self . scale_y
self . plotter . scale_x = self . scale_x
def get_image_and_scales_after_enhancing ( self , img_org , img_res ) :
self . logger . debug ( " enter get_image_and_scales_after_enhancing " )
self . image = np . copy ( img_res )
self . image = self . image . astype ( np . uint8 )
self . image_org = np . copy ( img_org )
self . height_org = self . image_org . shape [ 0 ]
self . width_org = self . image_org . shape [ 1 ]
self . scale_y = img_res . shape [ 0 ] / float ( self . image_org . shape [ 0 ] )
self . scale_x = img_res . shape [ 1 ] / float ( self . image_org . shape [ 1 ] )
del img_org
del img_res
def start_new_session_and_model ( self , model_dir ) :
self . logger . debug ( " enter start_new_session_and_model (model_dir= %s ) " , model_dir )
config = tf . ConfigProto ( )
config . gpu_options . allow_growth = True
session = tf . InteractiveSession ( )
model = load_model ( model_dir , compile = False )
return model , session
def do_prediction ( self , patches , img , model , marginal_of_patch_percent = 0.1 ) :
self . logger . debug ( " enter do_prediction " )
img_height_model = model . layers [ len ( model . layers ) - 1 ] . output_shape [ 1 ]
img_width_model = model . layers [ len ( model . layers ) - 1 ] . output_shape [ 2 ]
n_classes = model . layers [ len ( model . layers ) - 1 ] . output_shape [ 3 ]
if not patches :
img_h_page = img . shape [ 0 ]
img_w_page = img . shape [ 1 ]
img = img / float ( 255.0 )
img = resize_image ( img , img_height_model , img_width_model )
label_p_pred = model . predict ( img . reshape ( 1 , img . shape [ 0 ] , img . shape [ 1 ] , img . shape [ 2 ] ) )
seg = np . argmax ( label_p_pred , axis = 3 ) [ 0 ]
seg_color = np . repeat ( seg [ : , : , np . newaxis ] , 3 , axis = 2 )
prediction_true = resize_image ( seg_color , img_h_page , img_w_page )
prediction_true = prediction_true . astype ( np . uint8 )
del img
del seg_color
del label_p_pred
del seg
else :
if img . shape [ 0 ] < img_height_model :
img = resize_image ( img , img_height_model , img . shape [ 1 ] )
if img . shape [ 1 ] < img_width_model :
img = resize_image ( img , img . shape [ 0 ] , img_width_model )
self . logger . info ( " Image dimensions: %s x %s " , img_height_model , img_width_model )
margin = int ( marginal_of_patch_percent * img_height_model )
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float ( 255.0 )
img = img . astype ( np . float16 )
img_h = img . shape [ 0 ]
img_w = img . shape [ 1 ]
prediction_true = np . zeros ( ( img_h , img_w , 3 ) )
mask_true = np . zeros ( ( img_h , img_w ) )
nxf = img_w / float ( width_mid )
nyf = img_h / float ( height_mid )
nxf = int ( nxf ) + 1 if nxf > int ( nxf ) else int ( nxf )
nyf = int ( nyf ) + 1 if nyf > int ( nyf ) else int ( nyf )
for i in range ( nxf ) :
for j in range ( nyf ) :
if i == 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
else :
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0 :
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else :
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w :
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h :
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
label_p_pred = model . predict ( img_patch . reshape ( 1 , img_patch . shape [ 0 ] , img_patch . shape [ 1 ] , img_patch . shape [ 2 ] ) )
seg = np . argmax ( label_p_pred , axis = 3 ) [ 0 ]
seg_color = np . repeat ( seg [ : , : , np . newaxis ] , 3 , axis = 2 )
if i == 0 and j == 0 :
seg_color = seg_color [ 0 : seg_color . shape [ 0 ] - margin , 0 : seg_color . shape [ 1 ] - margin , : ]
seg = seg [ 0 : seg . shape [ 0 ] - margin , 0 : seg . shape [ 1 ] - margin ]
mask_true [ index_y_d + 0 : index_y_u - margin , index_x_d + 0 : index_x_u - margin ] = seg
prediction_true [ index_y_d + 0 : index_y_u - margin , index_x_d + 0 : index_x_u - margin , : ] = seg_color
elif i == nxf - 1 and j == nyf - 1 :
seg_color = seg_color [ margin : seg_color . shape [ 0 ] - 0 , margin : seg_color . shape [ 1 ] - 0 , : ]
seg = seg [ margin : seg . shape [ 0 ] - 0 , margin : seg . shape [ 1 ] - 0 ]
mask_true [ index_y_d + margin : index_y_u - 0 , index_x_d + margin : index_x_u - 0 ] = seg
prediction_true [ index_y_d + margin : index_y_u - 0 , index_x_d + margin : index_x_u - 0 , : ] = seg_color
elif i == 0 and j == nyf - 1 :
seg_color = seg_color [ margin : seg_color . shape [ 0 ] - 0 , 0 : seg_color . shape [ 1 ] - margin , : ]
seg = seg [ margin : seg . shape [ 0 ] - 0 , 0 : seg . shape [ 1 ] - margin ]
mask_true [ index_y_d + margin : index_y_u - 0 , index_x_d + 0 : index_x_u - margin ] = seg
prediction_true [ index_y_d + margin : index_y_u - 0 , index_x_d + 0 : index_x_u - margin , : ] = seg_color
elif i == nxf - 1 and j == 0 :
seg_color = seg_color [ 0 : seg_color . shape [ 0 ] - margin , margin : seg_color . shape [ 1 ] - 0 , : ]
seg = seg [ 0 : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - 0 ]
mask_true [ index_y_d + 0 : index_y_u - margin , index_x_d + margin : index_x_u - 0 ] = seg
prediction_true [ index_y_d + 0 : index_y_u - margin , index_x_d + margin : index_x_u - 0 , : ] = seg_color
elif i == 0 and j != 0 and j != nyf - 1 :
seg_color = seg_color [ margin : seg_color . shape [ 0 ] - margin , 0 : seg_color . shape [ 1 ] - margin , : ]
seg = seg [ margin : seg . shape [ 0 ] - margin , 0 : seg . shape [ 1 ] - margin ]
mask_true [ index_y_d + margin : index_y_u - margin , index_x_d + 0 : index_x_u - margin ] = seg
prediction_true [ index_y_d + margin : index_y_u - margin , index_x_d + 0 : index_x_u - margin , : ] = seg_color
elif i == nxf - 1 and j != 0 and j != nyf - 1 :
seg_color = seg_color [ margin : seg_color . shape [ 0 ] - margin , margin : seg_color . shape [ 1 ] - 0 , : ]
seg = seg [ margin : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - 0 ]
mask_true [ index_y_d + margin : index_y_u - margin , index_x_d + margin : index_x_u - 0 ] = seg
prediction_true [ index_y_d + margin : index_y_u - margin , index_x_d + margin : index_x_u - 0 , : ] = seg_color
elif i != 0 and i != nxf - 1 and j == 0 :
seg_color = seg_color [ 0 : seg_color . shape [ 0 ] - margin , margin : seg_color . shape [ 1 ] - margin , : ]
seg = seg [ 0 : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - margin ]
mask_true [ index_y_d + 0 : index_y_u - margin , index_x_d + margin : index_x_u - margin ] = seg
prediction_true [ index_y_d + 0 : index_y_u - margin , index_x_d + margin : index_x_u - margin , : ] = seg_color
elif i != 0 and i != nxf - 1 and j == nyf - 1 :
seg_color = seg_color [ margin : seg_color . shape [ 0 ] - 0 , margin : seg_color . shape [ 1 ] - margin , : ]
seg = seg [ margin : seg . shape [ 0 ] - 0 , margin : seg . shape [ 1 ] - margin ]
mask_true [ index_y_d + margin : index_y_u - 0 , index_x_d + margin : index_x_u - margin ] = seg
prediction_true [ index_y_d + margin : index_y_u - 0 , index_x_d + margin : index_x_u - margin , : ] = seg_color
else :
seg_color = seg_color [ margin : seg_color . shape [ 0 ] - margin , margin : seg_color . shape [ 1 ] - margin , : ]
seg = seg [ margin : seg . shape [ 0 ] - margin , margin : seg . shape [ 1 ] - margin ]
mask_true [ index_y_d + margin : index_y_u - margin , index_x_d + margin : index_x_u - margin ] = seg
prediction_true [ index_y_d + margin : index_y_u - margin , index_x_d + margin : index_x_u - margin , : ] = seg_color
prediction_true = prediction_true . astype ( np . uint8 )
del img
del mask_true
del seg_color
del seg
del img_patch
gc . collect ( )
return prediction_true
def early_page_for_num_of_column_classification ( self ) :
self . logger . debug ( " enter early_page_for_num_of_column_classification " )
img = self . imread ( )
model_page , session_page = self . start_new_session_and_model ( self . model_page_dir )
for ii in range ( 1 ) :
img = cv2 . GaussianBlur ( img , ( 5 , 5 ) , 0 )
img_page_prediction = self . do_prediction ( False , img , model_page )
imgray = cv2 . cvtColor ( img_page_prediction , cv2 . COLOR_BGR2GRAY )
_ , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
thresh = cv2 . dilate ( thresh , self . kernel , iterations = 3 )
contours , _ = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
cnt_size = np . array ( [ cv2 . contourArea ( contours [ j ] ) for j in range ( len ( contours ) ) ] )
cnt = contours [ np . argmax ( cnt_size ) ]
x , y , w , h = cv2 . boundingRect ( cnt )
box = [ x , y , w , h ]
croped_page , page_coord = crop_image_inside_box ( box , img )
session_page . close ( )
del model_page
del session_page
del contours
del thresh
del img
del cnt_size
del cnt
del box
del x
del y
del w
del h
del imgray
del img_page_prediction
gc . collect ( )
self . logger . debug ( " exit early_page_for_num_of_column_classification " )
return croped_page , page_coord
def extract_page ( self ) :
self . logger . debug ( " enter extract_page " )
model_page , session_page = self . start_new_session_and_model ( self . model_page_dir )
for ii in range ( 1 ) :
img = cv2 . GaussianBlur ( self . image , ( 5 , 5 ) , 0 )
img_page_prediction = self . do_prediction ( False , img , model_page )
imgray = cv2 . cvtColor ( img_page_prediction , cv2 . COLOR_BGR2GRAY )
_ , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
thresh = cv2 . dilate ( thresh , self . kernel , iterations = 3 )
contours , _ = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
cnt_size = np . array ( [ cv2 . contourArea ( contours [ j ] ) for j in range ( len ( contours ) ) ] )
cnt = contours [ np . argmax ( cnt_size ) ]
x , y , w , h = cv2 . boundingRect ( cnt )
if x < = 30 :
w + = x
x = 0
if ( self . image . shape [ 1 ] - ( x + w ) ) < = 30 :
w = w + ( self . image . shape [ 1 ] - ( x + w ) )
if y < = 30 :
h = h + y
y = 0
if ( self . image . shape [ 0 ] - ( y + h ) ) < = 30 :
h = h + ( self . image . shape [ 0 ] - ( y + h ) )
box = [ x , y , w , h ]
croped_page , page_coord = crop_image_inside_box ( box , self . image )
self . 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 ] ] ] ) )
session_page . close ( )
del model_page
del session_page
del contours
del thresh
del img
del imgray
K . clear_session ( )
gc . collect ( )
self . logger . debug ( " exit extract_page " )
return croped_page , page_coord
def extract_text_regions ( self , img , patches , cols ) :
self . logger . debug ( " enter extract_text_regions " )
img_height_h = img . shape [ 0 ]
img_width_h = img . shape [ 1 ]
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_fully if patches else self . model_region_dir_fully_np )
if not patches :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
prediction_regions2 = None
else :
if cols == 1 :
img2 = otsu_copy_binary ( img )
img2 = img2 . astype ( np . uint8 )
img2 = resize_image ( img2 , int ( img_height_h * 0.7 ) , int ( img_width_h * 0.7 ) )
marginal_of_patch_percent = 0.1
prediction_regions2 = self . do_prediction ( patches , img2 , model_region , marginal_of_patch_percent )
prediction_regions2 = resize_image ( prediction_regions2 , img_height_h , img_width_h )
if cols == 2 :
img2 = otsu_copy_binary ( img )
img2 = img2 . astype ( np . uint8 )
img2 = resize_image ( img2 , int ( img_height_h * 0.4 ) , int ( img_width_h * 0.4 ) )
marginal_of_patch_percent = 0.1
prediction_regions2 = self . do_prediction ( patches , img2 , model_region , marginal_of_patch_percent )
prediction_regions2 = resize_image ( prediction_regions2 , img_height_h , img_width_h )
elif cols > 2 :
img2 = otsu_copy_binary ( img )
img2 = img2 . astype ( np . uint8 )
img2 = resize_image ( img2 , int ( img_height_h * 0.3 ) , int ( img_width_h * 0.3 ) )
marginal_of_patch_percent = 0.1
prediction_regions2 = self . do_prediction ( patches , img2 , model_region , marginal_of_patch_percent )
prediction_regions2 = resize_image ( prediction_regions2 , img_height_h , img_width_h )
if cols == 2 :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
if img_width_h > = 2000 :
img = resize_image ( img , int ( img_height_h * 0.9 ) , int ( img_width_h * 0.9 ) )
img = img . astype ( np . uint8 )
if cols == 1 :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 0.5 ) , int ( img_width_h * 0.5 ) )
img = img . astype ( np . uint8 )
if cols == 3 :
if ( self . scale_x == 1 and img_width_h > 3000 ) or ( self . scale_x != 1 and img_width_h > 2800 ) :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 2800 / float ( img_width_h ) ) , 2800 )
else :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
if cols == 4 :
if ( self . scale_x == 1 and img_width_h > 4000 ) or ( self . scale_x != 1 and img_width_h > 3700 ) :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 3700 / float ( img_width_h ) ) , 3700 )
else :
img = otsu_copy_binary ( img ) #self.otsu_copy(img)
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 0.9 ) , int ( img_width_h * 0.9 ) )
if cols == 5 :
if self . scale_x == 1 and img_width_h > 5000 :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 0.7 ) , int ( img_width_h * 0.7 ) )
else :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 0.9 ) , int ( img_width_h * 0.9 ) )
if cols > = 6 :
if img_width_h > 5600 :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 5600 / float ( img_width_h ) ) , 5600 )
else :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint8 )
img = resize_image ( img , int ( img_height_h * 0.9 ) , int ( img_width_h * 0.9 ) )
marginal_of_patch_percent = 0.1
prediction_regions = self . do_prediction ( patches , img , model_region , marginal_of_patch_percent )
prediction_regions = resize_image ( prediction_regions , img_height_h , img_width_h )
session_region . close ( )
del model_region
del session_region
del img
gc . collect ( )
self . logger . debug ( " exit extract_text_regions " )
return prediction_regions , prediction_regions2
def get_slopes_and_deskew_new ( self , contours , contours_par , textline_mask_tot , image_page_rotated , boxes , slope_deskew ) :
self . logger . debug ( " enter get_slopes_and_deskew_new " )
num_cores = cpu_count ( )
queue_of_all_params = Queue ( )
processes = [ ]
nh = np . linspace ( 0 , len ( boxes ) , num_cores + 1 )
indexes_by_text_con = np . array ( range ( len ( contours_par ) ) )
for i in range ( num_cores ) :
boxes_per_process = boxes [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
contours_per_process = contours [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
contours_par_per_process = contours_par [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
indexes_text_con_per_process = indexes_by_text_con [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
processes . append ( Process ( target = self . do_work_of_slopes_new , args = ( queue_of_all_params , boxes_per_process , textline_mask_tot , contours_per_process , contours_par_per_process , indexes_text_con_per_process , image_page_rotated , slope_deskew ) ) )
for i in range ( num_cores ) :
processes [ i ] . start ( )
slopes = [ ]
all_found_texline_polygons = [ ]
all_found_text_regions = [ ]
all_found_text_regions_par = [ ]
boxes = [ ]
all_box_coord = [ ]
all_index_text_con = [ ]
for i in range ( num_cores ) :
list_all_par = queue_of_all_params . get ( True )
slopes_for_sub_process = list_all_par [ 0 ]
polys_for_sub_process = list_all_par [ 1 ]
boxes_for_sub_process = list_all_par [ 2 ]
contours_for_subprocess = list_all_par [ 3 ]
contours_par_for_subprocess = list_all_par [ 4 ]
boxes_coord_for_subprocess = list_all_par [ 5 ]
indexes_for_subprocess = list_all_par [ 6 ]
for j in range ( len ( slopes_for_sub_process ) ) :
slopes . append ( slopes_for_sub_process [ j ] )
all_found_texline_polygons . append ( polys_for_sub_process [ j ] )
boxes . append ( boxes_for_sub_process [ j ] )
all_found_text_regions . append ( contours_for_subprocess [ j ] )
all_found_text_regions_par . append ( contours_par_for_subprocess [ j ] )
all_box_coord . append ( boxes_coord_for_subprocess [ j ] )
all_index_text_con . append ( indexes_for_subprocess [ j ] )
for i in range ( num_cores ) :
processes [ i ] . join ( )
self . logger . debug ( ' slopes %s ' , slopes )
self . logger . debug ( " exit get_slopes_and_deskew_new " )
return slopes , all_found_texline_polygons , boxes , all_found_text_regions , all_found_text_regions_par , all_box_coord , all_index_text_con
def get_slopes_and_deskew_new_curved ( self , contours , contours_par , textline_mask_tot , image_page_rotated , boxes , mask_texts_only , num_col , scale_par , slope_deskew ) :
self . logger . debug ( " enter get_slopes_and_deskew_new_curved " )
num_cores = cpu_count ( )
queue_of_all_params = Queue ( )
processes = [ ]
nh = np . linspace ( 0 , len ( boxes ) , num_cores + 1 )
indexes_by_text_con = np . array ( range ( len ( contours_par ) ) )
for i in range ( num_cores ) :
boxes_per_process = boxes [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
contours_per_process = contours [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
contours_par_per_process = contours_par [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
indexes_text_con_per_process = indexes_by_text_con [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
processes . append ( Process ( target = self . do_work_of_slopes_new_curved , args = ( queue_of_all_params , boxes_per_process , textline_mask_tot , contours_per_process , contours_par_per_process , image_page_rotated , mask_texts_only , num_col , scale_par , indexes_text_con_per_process , slope_deskew ) ) )
for i in range ( num_cores ) :
processes [ i ] . start ( )
slopes = [ ]
all_found_texline_polygons = [ ]
all_found_text_regions = [ ]
all_found_text_regions_par = [ ]
boxes = [ ]
all_box_coord = [ ]
all_index_text_con = [ ]
for i in range ( num_cores ) :
list_all_par = queue_of_all_params . get ( True )
polys_for_sub_process = list_all_par [ 0 ]
boxes_for_sub_process = list_all_par [ 1 ]
contours_for_subprocess = list_all_par [ 2 ]
contours_par_for_subprocess = list_all_par [ 3 ]
boxes_coord_for_subprocess = list_all_par [ 4 ]
indexes_for_subprocess = list_all_par [ 5 ]
slopes_for_sub_process = list_all_par [ 6 ]
for j in range ( len ( polys_for_sub_process ) ) :
slopes . append ( slopes_for_sub_process [ j ] )
all_found_texline_polygons . append ( polys_for_sub_process [ j ] )
boxes . append ( boxes_for_sub_process [ j ] )
all_found_text_regions . append ( contours_for_subprocess [ j ] )
all_found_text_regions_par . append ( contours_par_for_subprocess [ j ] )
all_box_coord . append ( boxes_coord_for_subprocess [ j ] )
all_index_text_con . append ( indexes_for_subprocess [ j ] )
for i in range ( num_cores ) :
processes [ i ] . join ( )
# print(slopes,'slopes')
return all_found_texline_polygons , boxes , all_found_text_regions , all_found_text_regions_par , all_box_coord , all_index_text_con , slopes
def do_work_of_slopes_new_curved ( self , queue_of_all_params , boxes_text , textline_mask_tot_ea , contours_per_process , contours_par_per_process , image_page_rotated , mask_texts_only , num_col , scale_par , indexes_r_con_per_pro , slope_deskew ) :
self . logger . debug ( " enter do_work_of_slopes_new_curved " )
slopes_per_each_subprocess = [ ]
bounding_box_of_textregion_per_each_subprocess = [ ]
textlines_rectangles_per_each_subprocess = [ ]
contours_textregion_per_each_subprocess = [ ]
contours_textregion_par_per_each_subprocess = [ ]
all_box_coord_per_process = [ ]
index_by_text_region_contours = [ ]
textline_cnt_seperated = np . zeros ( textline_mask_tot_ea . shape )
for mv in range ( len ( boxes_text ) ) :
all_text_region_raw = 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 ] ]
all_text_region_raw = all_text_region_raw . astype ( np . uint8 )
img_int_p = all_text_region_raw [ : , : ]
# img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
# plt.imshow(img_int_p)
# plt.show()
if img_int_p . shape [ 0 ] / img_int_p . shape [ 1 ] < 0.1 :
slopes_per_each_subprocess . append ( 0 )
slope_for_all = [ slope_deskew ] [ 0 ]
else :
try :
textline_con , hierachy = return_contours_of_image ( img_int_p )
textline_con_fil = filter_contours_area_of_image ( img_int_p , textline_con , hierachy , max_area = 1 , min_area = 0.0008 )
y_diff_mean = find_contours_mean_y_diff ( textline_con_fil )
sigma_des = max ( 1 , int ( y_diff_mean * ( 4.0 / 40.0 ) ) )
img_int_p [ img_int_p > 0 ] = 1
slope_for_all = return_deskew_slop ( img_int_p , sigma_des , plotter = self . plotter )
if abs ( slope_for_all ) < 0.5 :
slope_for_all = [ slope_deskew ] [ 0 ]
# old method
# slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
# text_patch_processed=textline_contours_postprocessing(gada)
except :
slope_for_all = 999
if slope_for_all == 999 :
slope_for_all = [ slope_deskew ] [ 0 ]
slopes_per_each_subprocess . append ( slope_for_all )
index_by_text_region_contours . append ( indexes_r_con_per_pro [ mv ] )
crop_img , crop_coor = crop_image_inside_box ( boxes_text [ mv ] , image_page_rotated )
if abs ( slope_for_all ) < 45 :
# all_box_coord.append(crop_coor)
textline_region_in_image = np . zeros ( textline_mask_tot_ea . shape )
cnt_o_t_max = contours_par_per_process [ mv ]
x , y , w , h = cv2 . boundingRect ( cnt_o_t_max )
mask_biggest = np . zeros ( mask_texts_only . shape )
mask_biggest = cv2 . fillPoly ( mask_biggest , pts = [ cnt_o_t_max ] , color = ( 1 , 1 , 1 ) )
mask_region_in_patch_region = mask_biggest [ y : y + h , x : x + w ]
textline_biggest_region = mask_biggest * textline_mask_tot_ea
# print(slope_for_all,'slope_for_all')
textline_rotated_seperated = seperate_lines_new2 ( textline_biggest_region [ y : y + h , x : x + w ] , 0 , num_col , slope_for_all , plotter = self . plotter )
# new line added
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
textline_rotated_seperated [ mask_region_in_patch_region [ : , : ] != 1 ] = 0
# till here
textline_cnt_seperated [ y : y + h , x : x + w ] = textline_rotated_seperated
textline_region_in_image [ y : y + h , x : x + w ] = textline_rotated_seperated
# plt.imshow(textline_region_in_image)
# plt.show()
# plt.imshow(textline_cnt_seperated)
# plt.show()
pixel_img = 1
cnt_textlines_in_image = return_contours_of_interested_textline ( textline_region_in_image , pixel_img )
textlines_cnt_per_region = [ ]
for jjjj in range ( len ( cnt_textlines_in_image ) ) :
mask_biggest2 = np . zeros ( mask_texts_only . shape )
mask_biggest2 = cv2 . fillPoly ( mask_biggest2 , pts = [ cnt_textlines_in_image [ jjjj ] ] , color = ( 1 , 1 , 1 ) )
if num_col + 1 == 1 :
mask_biggest2 = cv2 . dilate ( mask_biggest2 , self . kernel , iterations = 5 )
else :
mask_biggest2 = cv2 . dilate ( mask_biggest2 , self . kernel , iterations = 4 )
pixel_img = 1
mask_biggest2 = resize_image ( mask_biggest2 , int ( mask_biggest2 . shape [ 0 ] * scale_par ) , int ( mask_biggest2 . shape [ 1 ] * scale_par ) )
cnt_textlines_in_image_ind = return_contours_of_interested_textline ( mask_biggest2 , pixel_img )
try :
# textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
textlines_cnt_per_region . append ( cnt_textlines_in_image_ind [ 0 ] )
except :
pass
else :
add_boxes_coor_into_textlines = True
textlines_cnt_per_region = textline_contours_postprocessing ( all_text_region_raw , slope_for_all , contours_par_per_process [ mv ] , boxes_text [ mv ] , add_boxes_coor_into_textlines )
add_boxes_coor_into_textlines = False
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
textlines_rectangles_per_each_subprocess . append ( textlines_cnt_per_region )
bounding_box_of_textregion_per_each_subprocess . append ( boxes_text [ mv ] )
contours_textregion_per_each_subprocess . append ( contours_per_process [ mv ] )
contours_textregion_par_per_each_subprocess . append ( contours_par_per_process [ mv ] )
all_box_coord_per_process . append ( crop_coor )
queue_of_all_params . put ( [ textlines_rectangles_per_each_subprocess , bounding_box_of_textregion_per_each_subprocess , contours_textregion_per_each_subprocess , contours_textregion_par_per_each_subprocess , all_box_coord_per_process , index_by_text_region_contours , slopes_per_each_subprocess ] )
def do_work_of_slopes_new ( self , queue_of_all_params , boxes_text , textline_mask_tot_ea , contours_per_process , contours_par_per_process , indexes_r_con_per_pro , image_page_rotated , slope_deskew ) :
self . logger . debug ( ' enter do_work_of_slopes_new ' )
slopes_per_each_subprocess = [ ]
bounding_box_of_textregion_per_each_subprocess = [ ]
textlines_rectangles_per_each_subprocess = [ ]
contours_textregion_per_each_subprocess = [ ]
contours_textregion_par_per_each_subprocess = [ ]
all_box_coord_per_process = [ ]
index_by_text_region_contours = [ ]
for mv in range ( len ( boxes_text ) ) :
crop_img , crop_coor = crop_image_inside_box ( boxes_text [ mv ] , image_page_rotated )
mask_textline = np . zeros ( ( textline_mask_tot_ea . shape ) )
mask_textline = cv2 . fillPoly ( mask_textline , pts = [ contours_per_process [ mv ] ] , color = ( 1 , 1 , 1 ) )
denoised = None
all_text_region_raw = ( textline_mask_tot_ea * mask_textline [ : , : ] ) [ 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 = all_text_region_raw . astype ( np . uint8 )
img_int_p = all_text_region_raw [ : , : ] #self.all_text_region_raw[mv]
img_int_p = cv2 . erode ( img_int_p , self . kernel , iterations = 2 )
if img_int_p . shape [ 0 ] / img_int_p . shape [ 1 ] < 0.1 :
slopes_per_each_subprocess . append ( 0 )
slope_for_all = [ slope_deskew ] [ 0 ]
all_text_region_raw = 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 ] ]
cnt_clean_rot = textline_contours_postprocessing ( all_text_region_raw , slope_for_all , contours_par_per_process [ mv ] , boxes_text [ mv ] , 0 )
textlines_rectangles_per_each_subprocess . append ( cnt_clean_rot )
index_by_text_region_contours . append ( indexes_r_con_per_pro [ mv ] )
bounding_box_of_textregion_per_each_subprocess . append ( boxes_text [ mv ] )
else :
try :
textline_con , hierachy = return_contours_of_image ( img_int_p )
textline_con_fil = filter_contours_area_of_image ( img_int_p , textline_con , hierachy , max_area = 1 , min_area = 0.00008 )
y_diff_mean = find_contours_mean_y_diff ( textline_con_fil )
sigma_des = int ( y_diff_mean * ( 4.0 / 40.0 ) )
if sigma_des < 1 :
sigma_des = 1
img_int_p [ img_int_p > 0 ] = 1
slope_for_all = return_deskew_slop ( img_int_p , sigma_des , plotter = self . plotter )
if abs ( slope_for_all ) < = 0.5 :
slope_for_all = [ slope_deskew ] [ 0 ]
except :
slope_for_all = 999
if slope_for_all == 999 :
slope_for_all = [ slope_deskew ] [ 0 ]
slopes_per_each_subprocess . append ( slope_for_all )
mask_only_con_region = np . zeros ( textline_mask_tot_ea . shape )
mask_only_con_region = cv2 . fillPoly ( mask_only_con_region , pts = [ contours_par_per_process [ mv ] ] , color = ( 1 , 1 , 1 ) )
# plt.imshow(mask_only_con_region)
# plt.show()
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 ] ]
##plt.imshow(textline_mask_tot_ea)
##plt.show()
##plt.imshow(all_text_region_raw)
##plt.show()
##plt.imshow(mask_only_con_region)
##plt.show()
all_text_region_raw [ mask_only_con_region == 0 ] = 0
cnt_clean_rot = textline_contours_postprocessing ( all_text_region_raw , slope_for_all , contours_par_per_process [ mv ] , boxes_text [ mv ] )
textlines_rectangles_per_each_subprocess . append ( cnt_clean_rot )
index_by_text_region_contours . append ( indexes_r_con_per_pro [ mv ] )
bounding_box_of_textregion_per_each_subprocess . append ( boxes_text [ mv ] )
contours_textregion_per_each_subprocess . append ( contours_per_process [ mv ] )
contours_textregion_par_per_each_subprocess . append ( contours_par_per_process [ mv ] )
all_box_coord_per_process . append ( crop_coor )
queue_of_all_params . put ( [ slopes_per_each_subprocess , textlines_rectangles_per_each_subprocess , bounding_box_of_textregion_per_each_subprocess , contours_textregion_per_each_subprocess , contours_textregion_par_per_each_subprocess , all_box_coord_per_process , index_by_text_region_contours ] )
def textline_contours ( self , img , patches , scaler_h , scaler_w ) :
self . logger . debug ( ' enter textline_contours ' )
model_textline , session_textline = self . start_new_session_and_model ( self . model_textline_dir if patches else self . model_textline_dir_np )
img = img . astype ( np . uint8 )
img_org = np . copy ( img )
img_h = img_org . shape [ 0 ]
img_w = img_org . shape [ 1 ]
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * scaler_h ) , int ( img_org . shape [ 1 ] * scaler_w ) )
prediction_textline = self . do_prediction ( patches , img , model_textline )
prediction_textline = resize_image ( prediction_textline , img_h , img_w )
prediction_textline_longshot = self . do_prediction ( False , img , model_textline )
prediction_textline_longshot_true_size = resize_image ( prediction_textline_longshot , img_h , img_w )
##plt.imshow(prediction_textline_streched[:,:,0])
##plt.show()
session_textline . close ( )
del model_textline
del session_textline
del img
del img_org
gc . collect ( )
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 ) :
self . logger . debug ( ' enter do_work_of_slopes ' )
slope_biggest = 0
slopes_sub = [ ]
boxes_sub_new = [ ]
poly_sub = [ ]
for mv in range ( len ( boxes_per_process ) ) :
crop_img , _ = crop_image_inside_box ( boxes_per_process [ mv ] , np . repeat ( textline_mask_tot [ : , : , np . newaxis ] , 3 , axis = 2 ) )
crop_img = crop_img [ : , : , 0 ]
crop_img = cv2 . erode ( crop_img , self . kernel , iterations = 2 )
try :
textline_con , hierachy = return_contours_of_image ( crop_img )
textline_con_fil = filter_contours_area_of_image ( crop_img , textline_con , hierachy , max_area = 1 , min_area = 0.0008 )
y_diff_mean = find_contours_mean_y_diff ( textline_con_fil )
sigma_des = int ( y_diff_mean * ( 4.0 / 40.0 ) )
if sigma_des < 1 :
sigma_des = 1
crop_img [ crop_img > 0 ] = 1
slope_corresponding_textregion = return_deskew_slop ( crop_img , sigma_des , plotter = self . plotter )
except :
slope_corresponding_textregion = 999
if slope_corresponding_textregion == 999 :
slope_corresponding_textregion = slope_biggest
slopes_sub . append ( slope_corresponding_textregion )
cnt_clean_rot = textline_contours_postprocessing ( crop_img , slope_corresponding_textregion , contours_per_process [ mv ] , boxes_per_process [ mv ] )
poly_sub . append ( cnt_clean_rot )
boxes_sub_new . append ( boxes_per_process [ mv ] )
q . put ( slopes_sub )
poly . put ( poly_sub )
box_sub . put ( boxes_sub_new )
def serialize_lines_in_region ( self , textregion , all_found_texline_polygons , region_idx , page_coord , all_box_coord , slopes , id_indexer_l ) :
self . logger . debug ( ' enter serialize_lines_in_region ' )
for j in range ( len ( all_found_texline_polygons [ region_idx ] ) ) :
textline = ET . SubElement ( textregion , ' TextLine ' )
textline . set ( ' id ' , ' l %s ' % id_indexer_l )
id_indexer_l + = 1
coord = ET . SubElement ( textline , ' Coords ' )
add_textequiv ( textline )
points_co = ' '
for l in range ( len ( all_found_texline_polygons [ region_idx ] [ j ] ) ) :
if not self . curved_line :
if len ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] ) == 2 :
textline_x_coord = max ( 0 , int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] + all_box_coord [ region_idx ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
textline_y_coord = max ( 0 , int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 1 ] + all_box_coord [ region_idx ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
textline_x_coord = max ( 0 , int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord [ region_idx ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
textline_y_coord = max ( 0 , int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord [ region_idx ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
points_co + = str ( textline_x_coord )
points_co + = ' , '
points_co + = str ( textline_y_coord )
if self . curved_line and np . abs ( slopes [ region_idx ] ) < = 45 :
if len ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
elif self . curved_line and np . abs ( slopes [ region_idx ] ) > 45 :
if len ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] + all_box_coord [ region_idx ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 1 ] + all_box_coord [ region_idx ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord [ region_idx ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ region_idx ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord [ region_idx ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
if l < len ( all_found_texline_polygons [ region_idx ] [ j ] ) - 1 :
points_co + = ' '
coord . set ( ' points ' , points_co )
return id_indexer_l
def calculate_polygon_coords ( self , contour_list , i , page_coord ) :
self . logger . debug ( ' enter calculate_polygon_coords ' )
coords = ' '
for j in range ( len ( contour_list [ i ] ) ) :
if len ( contour_list [ i ] [ j ] ) == 2 :
coords + = str ( int ( ( contour_list [ i ] [ j ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
coords + = ' , '
coords + = str ( int ( ( contour_list [ i ] [ j ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
coords + = str ( int ( ( contour_list [ i ] [ j ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
coords + = ' , '
coords + = str ( int ( ( contour_list [ i ] [ j ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if j < len ( contour_list [ i ] ) - 1 :
coords = coords + ' '
#print(coords)
return coords
def calculate_page_coords ( self ) :
self . logger . debug ( ' enter calculate_page_coords ' )
points_page_print = " "
for lmm in range ( len ( self . cont_page [ 0 ] ) ) :
if len ( self . cont_page [ 0 ] [ lmm ] ) == 2 :
points_page_print + = str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] ) / self . scale_x ) )
points_page_print + = ' , '
points_page_print + = str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 1 ] ) / self . scale_y ) )
else :
points_page_print + = str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] [ 0 ] ) / self . scale_x ) )
points_page_print + = ' , '
points_page_print + = str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] [ 1 ] ) / self . scale_y ) )
if lmm < len ( self . cont_page [ 0 ] ) - 1 :
points_page_print = points_page_print + ' '
return points_page_print
def xml_reading_order ( self , page , order_of_texts , id_of_texts , id_of_marginalia , found_polygons_marginals ) :
"""
XXX side - effect : extends id_of_marginalia
"""
region_order = ET . SubElement ( page , ' ReadingOrder ' )
region_order_sub = ET . SubElement ( region_order , ' OrderedGroup ' )
region_order_sub . set ( ' id ' , " ro357564684568544579089 " )
indexer_region = 0
for vj in order_of_texts :
name = " coord_text_ %s " % vj
name = ET . SubElement ( region_order_sub , ' RegionRefIndexed ' )
name . set ( ' index ' , str ( indexer_region ) )
name . set ( ' regionRef ' , id_of_texts [ vj ] )
indexer_region + = 1
for vm in range ( len ( found_polygons_marginals ) ) :
id_of_marginalia . append ( ' r %s ' % indexer_region )
name = " coord_text_ %s " % indexer_region
name = ET . SubElement ( region_order_sub , ' RegionRefIndexed ' )
name . set ( ' index ' , str ( indexer_region ) )
name . set ( ' regionRef ' , ' r %s ' % indexer_region )
indexer_region + = 1
def write_into_page_xml ( self , found_polygons_text_region , page_coord , dir_of_image , order_of_texts , id_of_texts , all_found_texline_polygons , all_box_coord , found_polygons_text_region_img , found_polygons_marginals , all_found_texline_polygons_marginals , all_box_coord_marginals , curved_line , slopes , slopes_marginals ) :
self . logger . debug ( ' enter write_into_page_xml ' )
# create the file structure
pcgts , page = create_page_xml ( self . image_filename , self . height_org , self . width_org )
page_print_sub = ET . SubElement ( page , " Border " )
coord_page = ET . SubElement ( page_print_sub , " Coords " )
coord_page . set ( ' points ' , self . calculate_page_coords ( ) )
id_of_marginalia = [ ]
id_indexer = 0
id_indexer_l = 0
if len ( found_polygons_text_region ) > 0 :
self . xml_reading_order ( page , order_of_texts , id_of_texts , id_of_marginalia , found_polygons_marginals )
for mm in range ( len ( found_polygons_text_region ) ) :
textregion = ET . SubElement ( page , ' TextRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
textregion . set ( ' type ' , ' paragraph ' )
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_text_region , mm , page_coord ) )
for j in range ( len ( all_found_texline_polygons [ mm ] ) ) :
textline = ET . SubElement ( textregion , ' TextLine ' )
textline . set ( ' id ' , ' l %s ' % id_indexer_l )
id_indexer_l + = 1
coord = ET . SubElement ( textline , ' Coords ' )
add_textequiv ( textline )
points_co = ' '
for l in range ( len ( all_found_texline_polygons [ mm ] [ j ] ) ) :
if not curved_line :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
textline_x_coord = max ( 0 , int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] + all_box_coord [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
textline_y_coord = max ( 0 , int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 1 ] + all_box_coord [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
textline_x_coord = max ( 0 , int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
textline_y_coord = max ( 0 , int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
points_co + = str ( textline_x_coord ) + ' , ' + str ( textline_y_coord )
if curved_line and abs ( slopes [ mm ] ) < = 45 :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + ' , '
points_co = points_co + str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
elif curved_line and abs ( slopes [ mm ] ) > 45 :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] + all_box_coord [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 1 ] + all_box_coord [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
if l < len ( all_found_texline_polygons [ mm ] [ j ] ) - 1 :
points_co + = ' '
coord . set ( ' points ' , points_co )
add_textequiv ( textregion )
for mm in range ( len ( found_polygons_marginals ) ) :
textregion = ET . SubElement ( page , ' TextRegion ' )
textregion . set ( ' id ' , id_of_marginalia [ mm ] )
textregion . set ( ' type ' , ' marginalia ' )
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_marginals , mm , page_coord ) )
for j in range ( len ( all_found_texline_polygons_marginals [ mm ] ) ) :
textline = ET . SubElement ( textregion , ' TextLine ' )
textline . set ( ' id ' , ' l ' + str ( id_indexer_l ) )
id_indexer_l + = 1
coord = ET . SubElement ( textline , ' Coords ' )
add_textequiv ( textline )
points_co = ' '
for l in range ( len ( all_found_texline_polygons_marginals [ mm ] [ j ] ) ) :
if not curved_line :
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] + all_box_coord_marginals [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 1 ] + all_box_coord_marginals [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord_marginals [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord_marginals [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if l < len ( all_found_texline_polygons_marginals [ mm ] [ j ] ) - 1 :
points_co + = ' '
coord . set ( ' points ' , points_co )
id_indexer = len ( found_polygons_text_region ) + len ( found_polygons_marginals )
for mm in range ( len ( found_polygons_text_region_img ) ) :
textregion = ET . SubElement ( page , ' ImageRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
coord_text = ET . SubElement ( textregion , ' Coords ' )
points_co = ' '
for lmm in range ( len ( found_polygons_text_region_img [ mm ] ) ) :
points_co + = str ( int ( ( found_polygons_text_region_img [ mm ] [ lmm , 0 , 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( found_polygons_text_region_img [ mm ] [ lmm , 0 , 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < len ( found_polygons_text_region_img [ mm ] ) - 1 :
points_co + = ' '
coord_text . set ( ' points ' , points_co )
self . logger . info ( " filename stem: ' %s ' " , self . image_filename_stem )
tree = ET . ElementTree ( pcgts )
tree . write ( os . path . join ( dir_of_image , self . image_filename_stem ) + " .xml " )
def write_into_page_xml_full ( self , found_polygons_text_region , found_polygons_text_region_h , page_coord , dir_of_image , order_of_texts , id_of_texts , all_found_texline_polygons , all_found_texline_polygons_h , all_box_coord , all_box_coord_h , found_polygons_text_region_img , found_polygons_tables , found_polygons_drop_capitals , found_polygons_marginals , all_found_texline_polygons_marginals , all_box_coord_marginals , slopes , slopes_marginals ) :
self . logger . debug ( ' enter write_into_page_xml_full ' )
# create the file structure
pcgts , page = create_page_xml ( self . image_filename , self . height_org , self . width_org )
page_print_sub = ET . SubElement ( page , " Border " )
coord_page = ET . SubElement ( page_print_sub , " Coords " )
coord_page . set ( ' points ' , self . calculate_page_coords ( ) )
id_indexer = 0
id_indexer_l = 0
id_of_marginalia = [ ]
if len ( found_polygons_text_region ) > 0 :
self . xml_reading_order ( page , order_of_texts , id_of_texts , id_of_marginalia , found_polygons_marginals )
for mm in range ( len ( found_polygons_text_region ) ) :
textregion = ET . SubElement ( page , ' TextRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
textregion . set ( ' type ' , ' paragraph ' )
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_text_region , mm , page_coord ) )
id_indexer_l = self . serialize_lines_in_region ( textregion , all_found_texline_polygons , mm , page_coord , all_box_coord , slopes , id_indexer_l )
add_textequiv ( textregion )
self . logger . debug ( ' len(found_polygons_text_region_h) %s ' , len ( found_polygons_text_region_h ) )
if len ( found_polygons_text_region_h ) > 0 :
for mm in range ( len ( found_polygons_text_region_h ) ) :
textregion = ET . SubElement ( page , ' TextRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
textregion . set ( ' type ' , ' header ' )
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_text_region_h , mm , page_coord ) )
id_indexer_l = self . serialize_lines_in_region ( textregion , all_found_texline_polygons_h , mm , page_coord , all_box_coord_h , slopes , id_indexer_l )
add_textequiv ( textregion )
if len ( found_polygons_drop_capitals ) > 0 :
id_indexer = len ( found_polygons_text_region ) + len ( found_polygons_text_region_h ) + len ( found_polygons_marginals )
for mm in range ( len ( found_polygons_drop_capitals ) ) :
textregion = ET . SubElement ( page , ' TextRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
textregion . set ( ' type ' , ' drop-capital ' )
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_drop_capitals , mm , page_coord ) )
add_textequiv ( textregion )
for mm in range ( len ( found_polygons_marginals ) ) :
textregion = ET . SubElement ( page , ' TextRegion ' )
textregion . set ( ' id ' , id_of_marginalia [ mm ] )
textregion . set ( ' type ' , ' marginalia ' )
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_marginals , mm , page_coord ) )
for j in range ( len ( all_found_texline_polygons_marginals [ mm ] ) ) :
textline = ET . SubElement ( textregion , ' TextLine ' )
textline . set ( ' id ' , ' l %s ' % id_indexer_l )
id_indexer_l + = 1
coord = ET . SubElement ( textline , ' Coords ' )
add_textequiv ( textline )
points_co = ' '
for l in range ( len ( all_found_texline_polygons_marginals [ mm ] [ j ] ) ) :
if not self . curved_line :
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] + all_box_coord_marginals [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 1 ] + all_box_coord_marginals [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord_marginals [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord_marginals [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co + = ' , '
points_co + = str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if l < len ( all_found_texline_polygons_marginals [ mm ] [ j ] ) - 1 :
points_co = points_co + ' '
coord . set ( ' points ' , points_co )
add_textequiv ( textregion )
id_indexer = len ( found_polygons_text_region ) + len ( found_polygons_text_region_h ) + len ( found_polygons_marginals ) + len ( found_polygons_drop_capitals )
for mm in range ( len ( found_polygons_text_region_img ) ) :
textregion = ET . SubElement ( page , ' ImageRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_text_region_img , mm , page_coord ) )
for mm in range ( len ( found_polygons_tables ) ) :
textregion = ET . SubElement ( page , ' TableRegion ' )
textregion . set ( ' id ' , ' r %s ' % id_indexer )
id_indexer + = 1
coord_text = ET . SubElement ( textregion , ' Coords ' )
coord_text . set ( ' points ' , self . calculate_polygon_coords ( found_polygons_tables , mm , page_coord ) )
self . logger . info ( " filename stem: ' %s ' " , self . image_filename_stem )
tree = ET . ElementTree ( pcgts )
tree . write ( os . path . join ( dir_of_image , self . image_filename_stem ) + " .xml " )
def get_regions_from_xy_2models ( self , img , is_image_enhanced ) :
self . logger . debug ( " enter get_regions_from_xy_2models " )
img_org = np . copy ( img )
img_height_h = img_org . shape [ 0 ]
img_width_h = img_org . shape [ 1 ]
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_p_ens )
gaussian_filter = False
binary = False
ratio_y = 1.3
ratio_x = 1
median_blur = False
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * ratio_y ) , int ( img_org . shape [ 1 ] * ratio_x ) )
if binary :
img = otsu_copy_binary ( img )
img = img . astype ( np . uint16 )
if median_blur :
img = cv2 . medianBlur ( img , 5 )
if gaussian_filter :
img = cv2 . GaussianBlur ( img , ( 5 , 5 ) , 0 )
img = img . astype ( np . uint16 )
prediction_regions_org_y = self . do_prediction ( True , img , model_region )
prediction_regions_org_y = resize_image ( prediction_regions_org_y , img_height_h , img_width_h )
#plt.imshow(prediction_regions_org_y[:,:,0])
#plt.show()
prediction_regions_org_y = prediction_regions_org_y [ : , : , 0 ]
mask_zeros_y = ( prediction_regions_org_y [ : , : ] == 0 ) * 1
if is_image_enhanced :
ratio_x = 1.2
else :
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * ratio_y ) , int ( img_org . shape [ 1 ] * ratio_x ) )
if binary :
img = otsu_copy_binary ( img ) #self.otsu_copy(img)
img = img . astype ( np . uint16 )
if median_blur :
img = cv2 . medianBlur ( img , 5 )
if gaussian_filter :
img = cv2 . GaussianBlur ( img , ( 5 , 5 ) , 0 )
img = img . astype ( np . uint16 )
prediction_regions_org = self . do_prediction ( True , img , model_region )
prediction_regions_org = resize_image ( prediction_regions_org , img_height_h , img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
prediction_regions_org = prediction_regions_org [ : , : , 0 ]
prediction_regions_org [ ( prediction_regions_org [ : , : ] == 1 ) & ( mask_zeros_y [ : , : ] == 1 ) ] = 0
session_region . close ( )
del model_region
del session_region
gc . collect ( )
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_p2 )
gaussian_filter = False
binary = False
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * ratio_y ) , int ( img_org . shape [ 1 ] * ratio_x ) )
if binary :
img = otsu_copy_binary ( img ) #self.otsu_copy(img)
img = img . astype ( np . uint16 )
if median_blur :
img = cv2 . medianBlur ( img , 5 )
if gaussian_filter :
img = cv2 . GaussianBlur ( img , ( 5 , 5 ) , 0 )
img = img . astype ( np . uint16 )
marginal_patch = 0.2
prediction_regions_org2 = self . do_prediction ( True , img , model_region , marginal_patch )
prediction_regions_org2 = resize_image ( prediction_regions_org2 , img_height_h , img_width_h )
#plt.imshow(prediction_regions_org2[:,:,0])
#plt.show()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region . close ( )
del model_region
del session_region
gc . collect ( )
mask_zeros2 = ( prediction_regions_org2 [ : , : , 0 ] == 0 ) * 1
mask_lines2 = ( prediction_regions_org2 [ : , : , 0 ] == 3 ) * 1
text_sume_early = ( ( prediction_regions_org [ : , : ] == 1 ) * 1 ) . sum ( )
prediction_regions_org_copy = np . copy ( prediction_regions_org )
prediction_regions_org_copy [ ( prediction_regions_org_copy [ : , : ] == 1 ) & ( mask_zeros2 [ : , : ] == 1 ) ] = 0
text_sume_second = ( ( prediction_regions_org_copy [ : , : ] == 1 ) * 1 ) . sum ( )
rate_two_models = text_sume_second / float ( text_sume_early ) * 100
self . logger . info ( " ratio_of_two_models: %s " , rate_two_models )
if not ( is_image_enhanced and rate_two_models < 95.50 ) : #98.45:
prediction_regions_org = np . copy ( prediction_regions_org_copy )
##prediction_regions_org[mask_lines2[:,:]==1]=3
prediction_regions_org [ ( mask_lines2 [ : , : ] == 1 ) & ( prediction_regions_org [ : , : ] == 0 ) ] = 3
del mask_lines2
del mask_zeros2
del prediction_regions_org2
mask_lines_only = ( prediction_regions_org [ : , : ] == 3 ) * 1
prediction_regions_org = cv2 . erode ( prediction_regions_org [ : , : ] , self . kernel , iterations = 2 )
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2 . dilate ( prediction_regions_org [ : , : ] , self . kernel , iterations = 2 )
mask_texts_only = ( prediction_regions_org [ : , : ] == 1 ) * 1
mask_images_only = ( prediction_regions_org [ : , : ] == 2 ) * 1
pixel_img = 1
min_area_text = 0.00001
polygons_of_only_texts = return_contours_of_interested_region ( mask_texts_only , pixel_img , min_area_text )
polygons_of_only_images = return_contours_of_interested_region ( mask_images_only , pixel_img )
polygons_of_only_lines = return_contours_of_interested_region ( mask_lines_only , pixel_img , min_area_text )
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 [ : , : ] [ mask_images_only [ : , : ] == 1 ] = 2
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_texts , color = ( 1 , 1 , 1 ) )
del polygons_of_only_texts
del polygons_of_only_images
del polygons_of_only_lines
del mask_images_only
del prediction_regions_org
del img
del mask_zeros_y
del prediction_regions_org_y
del img_org
gc . collect ( )
K . clear_session ( )
return text_regions_p_true
def do_order_of_regions_full_layout ( self , contours_only_text_parent , contours_only_text_parent_h , boxes , textline_mask_tot ) :
self . logger . debug ( " enter do_order_of_regions_full_layout " )
cx_text_only , cy_text_only , x_min_text_only , _ , _ , _ , y_cor_x_min_main = find_new_features_of_contoures ( contours_only_text_parent )
cx_text_only_h , cy_text_only_h , x_min_text_only_h , _ , _ , _ , y_cor_x_min_main_h = find_new_features_of_contoures ( contours_only_text_parent_h )
try :
arg_text_con = [ ]
for ii in range ( len ( cx_text_only ) ) :
for jj in range ( len ( boxes ) ) :
if ( x_min_text_only [ ii ] + 80 ) > = boxes [ jj ] [ 0 ] and ( x_min_text_only [ ii ] + 80 ) < boxes [ jj ] [ 1 ] and y_cor_x_min_main [ ii ] > = boxes [ jj ] [ 2 ] and y_cor_x_min_main [ ii ] < boxes [ jj ] [ 3 ] :
arg_text_con . append ( jj )
break
args_contours = np . array ( range ( len ( arg_text_con ) ) )
arg_text_con_h = [ ]
for ii in range ( len ( cx_text_only_h ) ) :
for jj in range ( len ( boxes ) ) :
if ( x_min_text_only_h [ ii ] + 80 ) > = boxes [ jj ] [ 0 ] and ( x_min_text_only_h [ ii ] + 80 ) < boxes [ jj ] [ 1 ] and y_cor_x_min_main_h [ ii ] > = boxes [ jj ] [ 2 ] and y_cor_x_min_main_h [ ii ] < boxes [ jj ] [ 3 ] :
arg_text_con_h . append ( jj )
break
args_contours_h = np . array ( range ( len ( arg_text_con_h ) ) )
order_by_con_head = np . zeros ( len ( arg_text_con_h ) )
order_by_con_main = np . zeros ( len ( arg_text_con ) )
ref_point = 0
order_of_texts_tot = [ ]
id_of_texts_tot = [ ]
for iij in range ( len ( boxes ) ) :
args_contours_box = args_contours [ np . array ( arg_text_con ) == iij ]
args_contours_box_h = args_contours_h [ np . array ( arg_text_con_h ) == iij ]
con_inter_box = [ ]
con_inter_box_h = [ ]
for i in range ( len ( args_contours_box ) ) :
con_inter_box . append ( contours_only_text_parent [ args_contours_box [ i ] ] )
for i in range ( len ( args_contours_box_h ) ) :
con_inter_box_h . append ( contours_only_text_parent_h [ args_contours_box_h [ i ] ] )
indexes_sorted , matrix_of_orders , kind_of_texts_sorted , index_by_kind_sorted = order_of_regions ( textline_mask_tot [ int ( boxes [ iij ] [ 2 ] ) : int ( boxes [ iij ] [ 3 ] ) , int ( boxes [ iij ] [ 0 ] ) : int ( boxes [ iij ] [ 1 ] ) ] , con_inter_box , con_inter_box_h , boxes [ iij ] [ 2 ] )
order_of_texts , id_of_texts = order_and_id_of_texts ( con_inter_box , con_inter_box_h , matrix_of_orders , indexes_sorted , index_by_kind_sorted , kind_of_texts_sorted , ref_point )
indexes_sorted_main = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_by_type_main = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_sorted_head = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 2 ]
indexes_by_type_head = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 2 ]
zahler = 0
for mtv in args_contours_box :
arg_order_v = indexes_sorted_main [ zahler ]
tartib = np . where ( indexes_sorted == arg_order_v ) [ 0 ] [ 0 ]
order_by_con_main [ args_contours_box [ indexes_by_type_main [ zahler ] ] ] = tartib + ref_point
zahler = zahler + 1
zahler = 0
for mtv in args_contours_box_h :
arg_order_v = indexes_sorted_head [ zahler ]
tartib = np . where ( indexes_sorted == arg_order_v ) [ 0 ] [ 0 ]
# print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
order_by_con_head [ args_contours_box_h [ indexes_by_type_head [ zahler ] ] ] = tartib + ref_point
zahler = zahler + 1
for jji in range ( len ( id_of_texts ) ) :
order_of_texts_tot . append ( order_of_texts [ jji ] + ref_point )
id_of_texts_tot . append ( id_of_texts [ jji ] )
ref_point = ref_point + len ( id_of_texts )
order_of_texts_tot = [ ]
for tj1 in range ( len ( contours_only_text_parent ) ) :
order_of_texts_tot . append ( int ( order_by_con_main [ tj1 ] ) )
for tj1 in range ( len ( contours_only_text_parent_h ) ) :
order_of_texts_tot . append ( int ( order_by_con_head [ tj1 ] ) )
order_text_new = [ ]
for iii in range ( len ( order_of_texts_tot ) ) :
tartib_new = np . where ( np . array ( order_of_texts_tot ) == iii ) [ 0 ] [ 0 ]
order_text_new . append ( tartib_new )
except :
arg_text_con = [ ]
for ii in range ( len ( cx_text_only ) ) :
for jj in range ( len ( boxes ) ) :
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
args_contours = np . array ( range ( len ( arg_text_con ) ) )
order_by_con_main = np . zeros ( len ( arg_text_con ) )
############################# head
arg_text_con_h = [ ]
for ii in range ( len ( cx_text_only_h ) ) :
for jj in range ( len ( boxes ) ) :
if cx_text_only_h [ ii ] > = boxes [ jj ] [ 0 ] and cx_text_only_h [ ii ] < boxes [ jj ] [ 1 ] and cy_text_only_h [ ii ] > = boxes [ jj ] [ 2 ] and cy_text_only_h [ ii ] < boxes [ jj ] [ 3 ] : # this is valid if the center of region identify in which box it is located
arg_text_con_h . append ( jj )
break
arg_arg_text_con_h = np . argsort ( arg_text_con_h )
args_contours_h = np . array ( range ( len ( arg_text_con_h ) ) )
order_by_con_head = np . zeros ( len ( arg_text_con_h ) )
ref_point = 0
order_of_texts_tot = [ ]
id_of_texts_tot = [ ]
for iij in range ( len ( boxes ) ) :
args_contours_box = args_contours [ np . array ( arg_text_con ) == iij ]
args_contours_box_h = args_contours_h [ np . array ( arg_text_con_h ) == iij ]
con_inter_box = [ ]
con_inter_box_h = [ ]
for i in range ( len ( args_contours_box ) ) :
con_inter_box . append ( contours_only_text_parent [ args_contours_box [ i ] ] )
for i in range ( len ( args_contours_box_h ) ) :
con_inter_box_h . append ( contours_only_text_parent_h [ args_contours_box_h [ i ] ] )
indexes_sorted , matrix_of_orders , kind_of_texts_sorted , index_by_kind_sorted = order_of_regions ( textline_mask_tot [ int ( boxes [ iij ] [ 2 ] ) : int ( boxes [ iij ] [ 3 ] ) , int ( boxes [ iij ] [ 0 ] ) : int ( boxes [ iij ] [ 1 ] ) ] , con_inter_box , con_inter_box_h , boxes [ iij ] [ 2 ] )
order_of_texts , id_of_texts = order_and_id_of_texts ( con_inter_box , con_inter_box_h , matrix_of_orders , indexes_sorted , index_by_kind_sorted , kind_of_texts_sorted , ref_point )
indexes_sorted_main = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_by_type_main = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_sorted_head = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 2 ]
indexes_by_type_head = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 2 ]
zahler = 0
for mtv in args_contours_box :
arg_order_v = indexes_sorted_main [ zahler ]
tartib = np . where ( indexes_sorted == arg_order_v ) [ 0 ] [ 0 ]
order_by_con_main [ args_contours_box [ indexes_by_type_main [ zahler ] ] ] = tartib + ref_point
zahler = zahler + 1
zahler = 0
for mtv in args_contours_box_h :
arg_order_v = indexes_sorted_head [ zahler ]
tartib = np . where ( indexes_sorted == arg_order_v ) [ 0 ] [ 0 ]
# print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
order_by_con_head [ args_contours_box_h [ indexes_by_type_head [ zahler ] ] ] = tartib + ref_point
zahler = zahler + 1
for jji in range ( len ( id_of_texts ) ) :
order_of_texts_tot . append ( order_of_texts [ jji ] + ref_point )
id_of_texts_tot . append ( id_of_texts [ jji ] )
ref_point = ref_point + len ( id_of_texts )
order_of_texts_tot = [ ]
for tj1 in range ( len ( contours_only_text_parent ) ) :
order_of_texts_tot . append ( int ( order_by_con_main [ tj1 ] ) )
for tj1 in range ( len ( contours_only_text_parent_h ) ) :
order_of_texts_tot . append ( int ( order_by_con_head [ tj1 ] ) )
order_text_new = [ ]
for iii in range ( len ( order_of_texts_tot ) ) :
tartib_new = np . where ( np . array ( order_of_texts_tot ) == iii ) [ 0 ] [ 0 ]
order_text_new . append ( tartib_new )
return order_text_new , id_of_texts_tot
def do_order_of_regions_no_full_layout ( self , contours_only_text_parent , contours_only_text_parent_h , boxes , textline_mask_tot ) :
self . logger . debug ( " enter do_order_of_regions_no_full_layout " )
cx_text_only , cy_text_only , x_min_text_only , _ , _ , _ , y_cor_x_min_main = find_new_features_of_contoures ( contours_only_text_parent )
try :
arg_text_con = [ ]
for ii in range ( len ( cx_text_only ) ) :
for jj in range ( len ( boxes ) ) :
if ( x_min_text_only [ ii ] + 80 ) > = boxes [ jj ] [ 0 ] and ( x_min_text_only [ ii ] + 80 ) < boxes [ jj ] [ 1 ] and y_cor_x_min_main [ ii ] > = boxes [ jj ] [ 2 ] and y_cor_x_min_main [ ii ] < boxes [ jj ] [ 3 ] :
arg_text_con . append ( jj )
break
args_contours = np . array ( range ( len ( arg_text_con ) ) )
order_by_con_main = np . zeros ( len ( arg_text_con ) )
ref_point = 0
order_of_texts_tot = [ ]
id_of_texts_tot = [ ]
for iij in range ( len ( boxes ) ) :
args_contours_box = args_contours [ np . array ( arg_text_con ) == iij ]
con_inter_box = [ ]
con_inter_box_h = [ ]
for i in range ( len ( args_contours_box ) ) :
con_inter_box . append ( contours_only_text_parent [ args_contours_box [ i ] ] )
indexes_sorted , matrix_of_orders , kind_of_texts_sorted , index_by_kind_sorted = order_of_regions ( textline_mask_tot [ int ( boxes [ iij ] [ 2 ] ) : int ( boxes [ iij ] [ 3 ] ) , int ( boxes [ iij ] [ 0 ] ) : int ( boxes [ iij ] [ 1 ] ) ] , con_inter_box , con_inter_box_h , boxes [ iij ] [ 2 ] )
order_of_texts , id_of_texts = order_and_id_of_texts ( con_inter_box , con_inter_box_h , matrix_of_orders , indexes_sorted , index_by_kind_sorted , kind_of_texts_sorted , ref_point )
indexes_sorted_main = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_by_type_main = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
zahler = 0
for mtv in args_contours_box :
arg_order_v = indexes_sorted_main [ zahler ]
tartib = np . where ( indexes_sorted == arg_order_v ) [ 0 ] [ 0 ]
order_by_con_main [ args_contours_box [ indexes_by_type_main [ zahler ] ] ] = tartib + ref_point
zahler = zahler + 1
for jji in range ( len ( id_of_texts ) ) :
order_of_texts_tot . append ( order_of_texts [ jji ] + ref_point )
id_of_texts_tot . append ( id_of_texts [ jji ] )
ref_point = ref_point + len ( id_of_texts )
order_of_texts_tot = [ ]
for tj1 in range ( len ( contours_only_text_parent ) ) :
order_of_texts_tot . append ( int ( order_by_con_main [ tj1 ] ) )
order_text_new = [ ]
for iii in range ( len ( order_of_texts_tot ) ) :
tartib_new = np . where ( np . array ( order_of_texts_tot ) == iii ) [ 0 ] [ 0 ]
order_text_new . append ( tartib_new )
except :
arg_text_con = [ ]
for ii in range ( len ( cx_text_only ) ) :
for jj in range ( len ( boxes ) ) :
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
args_contours = np . array ( range ( len ( arg_text_con ) ) )
order_by_con_main = np . zeros ( len ( arg_text_con ) )
ref_point = 0
order_of_texts_tot = [ ]
id_of_texts_tot = [ ]
for iij in range ( len ( boxes ) ) :
args_contours_box = args_contours [ np . array ( arg_text_con ) == iij ]
con_inter_box = [ ]
con_inter_box_h = [ ]
for i in range ( len ( args_contours_box ) ) :
con_inter_box . append ( contours_only_text_parent [ args_contours_box [ i ] ] )
indexes_sorted , matrix_of_orders , kind_of_texts_sorted , index_by_kind_sorted = order_of_regions ( textline_mask_tot [ int ( boxes [ iij ] [ 2 ] ) : int ( boxes [ iij ] [ 3 ] ) , int ( boxes [ iij ] [ 0 ] ) : int ( boxes [ iij ] [ 1 ] ) ] , con_inter_box , con_inter_box_h , boxes [ iij ] [ 2 ] )
order_of_texts , id_of_texts = order_and_id_of_texts ( con_inter_box , con_inter_box_h , matrix_of_orders , indexes_sorted , index_by_kind_sorted , kind_of_texts_sorted , ref_point )
indexes_sorted_main = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_by_type_main = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 1 ]
indexes_sorted_head = np . array ( indexes_sorted ) [ np . array ( kind_of_texts_sorted ) == 2 ]
indexes_by_type_head = np . array ( index_by_kind_sorted ) [ np . array ( kind_of_texts_sorted ) == 2 ]
zahler = 0
for mtv in args_contours_box :
arg_order_v = indexes_sorted_main [ zahler ]
tartib = np . where ( indexes_sorted == arg_order_v ) [ 0 ] [ 0 ]
order_by_con_main [ args_contours_box [ indexes_by_type_main [ zahler ] ] ] = tartib + ref_point
zahler = zahler + 1
for jji in range ( len ( id_of_texts ) ) :
order_of_texts_tot . append ( order_of_texts [ jji ] + ref_point )
id_of_texts_tot . append ( id_of_texts [ jji ] )
ref_point = ref_point + len ( id_of_texts )
order_of_texts_tot = [ ]
for tj1 in range ( len ( contours_only_text_parent ) ) :
order_of_texts_tot . append ( int ( order_by_con_main [ tj1 ] ) )
order_text_new = [ ]
for iii in range ( len ( order_of_texts_tot ) ) :
tartib_new = np . where ( np . array ( order_of_texts_tot ) == iii ) [ 0 ] [ 0 ]
order_text_new . append ( tartib_new )
return order_text_new , id_of_texts_tot
def do_order_of_regions ( self , * args , * * kwargs ) :
if self . full_layout :
return self . do_order_of_regions_full_layout ( * args , * * kwargs )
return self . do_order_of_regions_no_full_layout ( * args , * * kwargs )
def run_graphics_and_columns ( self , text_regions_p_1 , num_col_classifier , num_column_is_classified ) :
img_g = self . imread ( grayscale = True , uint8 = True )
img_g3 = np . zeros ( ( img_g . shape [ 0 ] , img_g . shape [ 1 ] , 3 ) )
img_g3 = img_g3 . astype ( np . uint8 )
img_g3 [ : , : , 0 ] = img_g [ : , : ]
img_g3 [ : , : , 1 ] = img_g [ : , : ]
img_g3 [ : , : , 2 ] = img_g [ : , : ]
image_page , page_coord = self . extract_page ( )
if self . plotter :
self . plotter . save_page_image ( image_page )
img_g3_page = img_g3 [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] , : ]
text_regions_p_1 = text_regions_p_1 [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] ]
mask_images = ( text_regions_p_1 [ : , : ] == 2 ) * 1
mask_images = mask_images . astype ( np . uint8 )
mask_images = cv2 . erode ( mask_images [ : , : ] , self . kernel , iterations = 10 )
mask_lines = ( text_regions_p_1 [ : , : ] == 3 ) * 1
mask_lines = mask_lines . astype ( np . uint8 )
img_only_regions_with_sep = ( ( text_regions_p_1 [ : , : ] != 3 ) & ( text_regions_p_1 [ : , : ] != 0 ) ) * 1
img_only_regions_with_sep = img_only_regions_with_sep . astype ( np . uint8 )
img_only_regions = cv2 . erode ( img_only_regions_with_sep [ : , : ] , self . kernel , iterations = 6 )
try :
num_col , peaks_neg_fin = find_num_col ( img_only_regions , multiplier = 6.0 )
num_col = num_col + 1
if not num_column_is_classified :
num_col_classifier = num_col + 1
except :
num_col = None
peaks_neg_fin = [ ]
return num_col , num_col_classifier , img_only_regions , page_coord , image_page , mask_images , mask_lines , text_regions_p_1
def run_enhancement ( self ) :
self . logger . info ( " resize and enhance image " )
is_image_enhanced , img_org , img_res , num_col_classifier , num_column_is_classified = self . resize_and_enhance_image_with_column_classifier ( )
self . logger . info ( " Image is %s enhanced " , ' ' if is_image_enhanced else ' not ' )
K . clear_session ( )
scale = 1
if is_image_enhanced :
if self . allow_enhancement :
cv2 . imwrite ( os . path . join ( self . dir_out , self . image_filename_stem ) + " .tif " , img_res )
img_res = img_res . astype ( np . uint8 )
self . get_image_and_scales ( img_org , img_res , scale )
else :
self . get_image_and_scales_after_enhancing ( img_org , img_res )
else :
if self . allow_enhancement :
self . get_image_and_scales ( img_org , img_res , scale )
else :
self . get_image_and_scales ( img_org , img_res , scale )
if self . allow_scaling :
img_org , img_res , is_image_enhanced = self . resize_image_with_column_classifier ( is_image_enhanced )
self . get_image_and_scales_after_enhancing ( img_org , img_res )
return img_res , is_image_enhanced , num_col_classifier , num_column_is_classified
def run_textline ( self , image_page ) :
scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea , textline_mask_tot_long_shot = self . textline_contours ( image_page , True , scaler_h_textline , scaler_w_textline )
K . clear_session ( )
gc . collect ( )
#print(np.unique(textline_mask_tot_ea[:, :]), "textline")
# plt.imshow(textline_mask_tot_ea)
# plt.show()
if self . plotter :
self . plotter . save_plot_of_textlines ( textline_mask_tot_ea , image_page )
return textline_mask_tot_ea , textline_mask_tot_long_shot
def run_deskew ( self , textline_mask_tot_ea ) :
sigma = 2
main_page_deskew = True
slope_deskew = return_deskew_slop ( cv2 . erode ( textline_mask_tot_ea , self . kernel , iterations = 2 ) , sigma , main_page_deskew , plotter = self . plotter )
slope_first = 0
if self . plotter :
self . plotter . save_deskewed_image ( slope_deskew )
self . logger . info ( " slope_deskew: %s " , slope_deskew )
return slope_deskew , slope_first
def run_marginals ( self , image_page , textline_mask_tot_ea , mask_images , mask_lines , num_col_classifier , slope_deskew , text_regions_p_1 ) :
image_page_rotated , textline_mask_tot = image_page [ : , : ] , textline_mask_tot_ea [ : , : ]
textline_mask_tot [ mask_images [ : , : ] == 1 ] = 0
pixel_img = 1
min_area = 0.00001
max_area = 0.0006
textline_mask_tot_small_size = return_contours_of_interested_region_by_size ( textline_mask_tot , pixel_img , min_area , max_area )
text_regions_p_1 [ mask_lines [ : , : ] == 1 ] = 3
text_regions_p = text_regions_p_1 [ : , : ] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
text_regions_p = np . array ( text_regions_p )
if num_col_classifier == 1 or num_col_classifier == 2 :
try :
regions_without_seperators = ( text_regions_p [ : , : ] == 1 ) * 1
regions_without_seperators = regions_without_seperators . astype ( np . uint8 )
text_regions_p = get_marginals ( rotate_image ( regions_without_seperators , slope_deskew ) , text_regions_p , num_col_classifier , slope_deskew , kernel = self . kernel )
except :
pass
if self . plotter :
self . plotter . save_plot_of_layout_main_all ( text_regions_p , image_page )
self . plotter . save_plot_of_layout_main ( text_regions_p , image_page )
return textline_mask_tot , text_regions_p , image_page_rotated
def run_boxes_no_full_layout ( self , image_page , textline_mask_tot , text_regions_p , slope_deskew , num_col_classifier ) :
self . logger . debug ( ' enter run_boxes_no_full_layout ' )
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
image_page_rotated_n , textline_mask_tot_d , text_regions_p_1_n = rotation_not_90_func ( image_page , textline_mask_tot , text_regions_p , slope_deskew )
text_regions_p_1_n = resize_image ( text_regions_p_1_n , text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] )
textline_mask_tot_d = resize_image ( textline_mask_tot_d , text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] )
regions_without_seperators_d = ( text_regions_p_1_n [ : , : ] == 1 ) * 1
regions_without_seperators = ( text_regions_p [ : , : ] == 1 ) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_seperators_d = None
pixel_lines = 3
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
num_col , peaks_neg_fin , matrix_of_lines_ch , spliter_y_new , seperators_closeup_n = find_number_of_columns_in_document ( np . repeat ( text_regions_p [ : , : , np . newaxis ] , 3 , axis = 2 ) , num_col_classifier , pixel_lines )
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
num_col_d , peaks_neg_fin_d , matrix_of_lines_ch_d , spliter_y_new_d , seperators_closeup_n_d = find_number_of_columns_in_document ( np . repeat ( text_regions_p_1_n [ : , : , np . newaxis ] , 3 , axis = 2 ) , num_col_classifier , pixel_lines )
K . clear_session ( )
gc . collect ( )
self . logger . info ( " num_col_classifier: %s " , num_col_classifier )
if num_col_classifier > = 3 :
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
regions_without_seperators = regions_without_seperators . astype ( np . uint8 )
regions_without_seperators = cv2 . erode ( regions_without_seperators [ : , : ] , self . kernel , iterations = 6 )
#random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
#regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
else :
regions_without_seperators_d = regions_without_seperators_d . astype ( np . uint8 )
regions_without_seperators_d = cv2 . erode ( regions_without_seperators_d [ : , : ] , self . kernel , iterations = 6 )
#random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
#regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
t1 = time . time ( )
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
boxes = return_boxes_of_images_by_order_of_reading_new ( spliter_y_new , regions_without_seperators , matrix_of_lines_ch , num_col_classifier )
boxes_d = None
self . logger . debug ( " len(boxes): %s " , len ( boxes ) )
else :
boxes_d = return_boxes_of_images_by_order_of_reading_new ( spliter_y_new_d , regions_without_seperators_d , matrix_of_lines_ch_d , num_col_classifier )
boxes = None
self . logger . debug ( " len(boxes): %s " , len ( boxes_d ) )
self . logger . info ( " detecting boxes took %s s " , str ( time . time ( ) - t1 ) )
img_revised_tab = text_regions_p [ : , : ]
polygons_of_images = return_contours_of_interested_region ( img_revised_tab , 2 )
# plt.imshow(img_revised_tab)
# plt.show()
K . clear_session ( )
self . logger . debug ( ' exit run_boxes_no_full_layout ' )
return polygons_of_images , img_revised_tab , text_regions_p_1_n , textline_mask_tot_d , regions_without_seperators_d , boxes , boxes_d
def run_boxes_full_layout ( self , image_page , textline_mask_tot , text_regions_p , slope_deskew , num_col_classifier , img_only_regions ) :
self . logger . debug ( ' enter run_boxes_full_layout ' )
# set first model with second model
text_regions_p [ : , : ] [ text_regions_p [ : , : ] == 2 ] = 5
text_regions_p [ : , : ] [ text_regions_p [ : , : ] == 3 ] = 6
text_regions_p [ : , : ] [ text_regions_p [ : , : ] == 4 ] = 8
K . clear_session ( )
# gc.collect()
image_page = image_page . astype ( np . uint8 )
# print(type(image_page))
regions_fully , regions_fully_only_drop = self . extract_text_regions ( image_page , True , cols = num_col_classifier )
text_regions_p [ : , : ] [ regions_fully [ : , : , 0 ] == 6 ] = 6
regions_fully_only_drop = put_drop_out_from_only_drop_model ( regions_fully_only_drop , text_regions_p )
regions_fully [ : , : , 0 ] [ regions_fully_only_drop [ : , : , 0 ] == 4 ] = 4
K . clear_session ( )
gc . collect ( )
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout ( regions_fully )
# plt.imshow(regions_fully[:,:,0])
# plt.show()
K . clear_session ( )
gc . collect ( )
regions_fully_np , _ = self . extract_text_regions ( image_page , False , cols = num_col_classifier )
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
if num_col_classifier > 2 :
regions_fully_np [ : , : , 0 ] [ regions_fully_np [ : , : , 0 ] == 4 ] = 0
else :
regions_fully_np = filter_small_drop_capitals_from_no_patch_layout ( regions_fully_np , text_regions_p )
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
K . clear_session ( )
gc . collect ( )
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = boosting_headers_by_longshot_region_segmentation ( regions_fully , regions_fully_np , img_only_regions )
# plt.imshow(regions_fully[:,:,0])
# plt.show()
text_regions_p [ : , : ] [ regions_fully [ : , : , 0 ] == 4 ] = 4
text_regions_p [ : , : ] [ regions_fully_np [ : , : , 0 ] == 4 ] = 4
#plt.imshow(text_regions_p)
#plt.show()
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
image_page_rotated_n , textline_mask_tot_d , text_regions_p_1_n , regions_fully_n = rotation_not_90_func_full_layout ( image_page , textline_mask_tot , text_regions_p , regions_fully , slope_deskew )
text_regions_p_1_n = resize_image ( text_regions_p_1_n , text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] )
textline_mask_tot_d = resize_image ( textline_mask_tot_d , text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] )
regions_fully_n = resize_image ( regions_fully_n , text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] )
regions_without_seperators_d = ( text_regions_p_1_n [ : , : ] == 1 ) * 1
else :
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_seperators_d = None
regions_without_seperators = ( text_regions_p [ : , : ] == 1 ) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
K . clear_session ( )
gc . collect ( )
img_revised_tab = np . copy ( text_regions_p [ : , : ] )
polygons_of_images = return_contours_of_interested_region ( img_revised_tab , 5 )
self . logger . debug ( ' exit run_boxes_full_layout ' )
return polygons_of_images , img_revised_tab , text_regions_p_1_n , textline_mask_tot_d , regions_without_seperators_d , regions_fully , regions_without_seperators
def run ( self ) :
"""
Get image and scales , then extract the page of scanned image
"""
self . logger . debug ( " enter run " )
t1 = time . time ( )
img_res , is_image_enhanced , num_col_classifier , num_column_is_classified = self . run_enhancement ( )
self . logger . info ( " Enhancing took %s s " , str ( time . time ( ) - t1 ) )
t1 = time . time ( )
text_regions_p_1 = self . get_regions_from_xy_2models ( img_res , is_image_enhanced )
self . logger . info ( " Textregion detection took %s s " , str ( time . time ( ) - t1 ) )
t1 = time . time ( )
num_col , num_col_classifier , img_only_regions , page_coord , image_page , mask_images , mask_lines , text_regions_p_1 = \
self . run_graphics_and_columns ( text_regions_p_1 , num_col_classifier , num_column_is_classified )
self . logger . info ( " Graphics detection took %s s " , str ( time . time ( ) - t1 ) )
if not num_col :
self . logger . info ( " No columns detected, outputting an empty PAGE-XML " )
self . write_into_page_xml ( [ ] , page_coord , self . dir_out , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , [ ] , self . curved_line , [ ] , [ ] )
self . logger . info ( " Job done in %s s " , str ( time . time ( ) - t1 ) )
return
t1 = time . time ( )
textline_mask_tot_ea , textline_mask_tot_long_shot = self . run_textline ( image_page )
self . logger . info ( " textline detection took %s s " , str ( time . time ( ) - t1 ) )
t1 = time . time ( )
slope_deskew , slope_first = self . run_deskew ( textline_mask_tot_ea )
self . logger . info ( " deskewing took %s s " , str ( time . time ( ) - t1 ) )
t1 = time . time ( )
textline_mask_tot , text_regions_p , image_page_rotated = self . run_marginals ( image_page , textline_mask_tot_ea , mask_images , mask_lines , num_col_classifier , slope_deskew , text_regions_p_1 )
self . logger . info ( " detection of marginals took %s s " , str ( time . time ( ) - t1 ) )
t1 = time . time ( )
if not self . full_layout :
polygons_of_images , img_revised_tab , text_regions_p_1_n , textline_mask_tot_d , regions_without_seperators_d , boxes , boxes_d = self . run_boxes_no_full_layout ( image_page , textline_mask_tot , text_regions_p , slope_deskew , num_col_classifier )
pixel_img = 4
min_area_mar = 0.00001
polygons_of_marginals = return_contours_of_interested_region ( text_regions_p , pixel_img , min_area_mar )
if self . full_layout :
polygons_of_images , img_revised_tab , text_regions_p_1_n , textline_mask_tot_d , regions_without_seperators_d , regions_fully , regions_without_seperators = self . run_boxes_full_layout ( image_page , textline_mask_tot , text_regions_p , slope_deskew , num_col_classifier , img_only_regions )
# plt.imshow(img_revised_tab)
# plt.show()
# print(img_revised_tab.shape,text_regions_p_1_n.shape)
# text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
# print(np.unique(text_regions_p_1_n),'uni')
text_only = ( ( img_revised_tab [ : , : ] == 1 ) ) * 1
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
text_only_d = ( ( text_regions_p_1_n [ : , : ] == 1 ) ) * 1
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
# print(text_only.shape,text_only_d.shape)
# plt.imshow(text_only)
# plt.show()
# plt.imshow(text_only_d)
# plt.show()
min_con_area = 0.000005
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
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 )
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_only_text_parent [ j ] ) for j in range ( len ( contours_only_text_parent ) ) ] )
areas_cnt_text = areas_cnt_text / float ( text_only . shape [ 0 ] * text_only . shape [ 1 ] )
self . logger . info ( ' areas_cnt_text %s ' , areas_cnt_text )
contours_biggest = contours_only_text_parent [ np . argmax ( areas_cnt_text ) ]
contours_only_text_parent = [ contours_only_text_parent [ jz ] for jz in range ( len ( contours_only_text_parent ) ) if areas_cnt_text [ jz ] > min_con_area ]
areas_cnt_text_parent = [ areas_cnt_text [ jz ] for jz in range ( len ( areas_cnt_text ) ) if areas_cnt_text [ jz ] > min_con_area ]
index_con_parents = np . argsort ( areas_cnt_text_parent )
contours_only_text_parent = list ( np . array ( contours_only_text_parent ) [ index_con_parents ] )
areas_cnt_text_parent = list ( np . array ( areas_cnt_text_parent ) [ index_con_parents ] )
cx_bigest_big , cy_biggest_big , _ , _ , _ , _ , _ = find_new_features_of_contoures ( [ contours_biggest ] )
cx_bigest , cy_biggest , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent )
contours_only_text_d , hir_on_text_d = return_contours_of_image ( text_only_d )
contours_only_text_parent_d = return_parent_contours ( contours_only_text_d , hir_on_text_d )
areas_cnt_text_d = np . array ( [ cv2 . contourArea ( contours_only_text_parent_d [ j ] ) for j in range ( len ( contours_only_text_parent_d ) ) ] )
areas_cnt_text_d = areas_cnt_text_d / float ( text_only_d . shape [ 0 ] * text_only_d . shape [ 1 ] )
contours_biggest_d = contours_only_text_parent_d [ np . argmax ( areas_cnt_text_d ) ]
index_con_parents_d = np . argsort ( areas_cnt_text_d )
contours_only_text_parent_d = list ( np . array ( contours_only_text_parent_d ) [ index_con_parents_d ] )
areas_cnt_text_d = list ( np . array ( areas_cnt_text_d ) [ index_con_parents_d ] )
cx_bigest_d_big , cy_biggest_d_big , _ , _ , _ , _ , _ = find_new_features_of_contoures ( [ contours_biggest_d ] )
cx_bigest_d , cy_biggest_d , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent_d )
try :
cx_bigest_d_last5 = cx_bigest_d [ - 5 : ]
cy_biggest_d_last5 = cy_biggest_d [ - 5 : ]
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 ) - 5 + np . argmin ( dists_d )
cx_bigest_d_big [ 0 ] = cx_bigest_d [ ind_largest ]
cy_biggest_d_big [ 0 ] = cy_biggest_d [ ind_largest ]
except :
pass
( h , w ) = text_only . shape [ : 2 ]
center = ( w / / 2.0 , h / / 2.0 )
M = cv2 . getRotationMatrix2D ( center , slope_deskew , 1.0 )
M_22 = np . array ( M ) [ : 2 , : 2 ]
p_big = np . dot ( M_22 , [ cx_bigest_big , cy_biggest_big ] )
x_diff = p_big [ 0 ] - cx_bigest_d_big
y_diff = p_big [ 1 ] - cy_biggest_d_big
# print(p_big)
# print(cx_bigest_d_big,cy_biggest_d_big)
# print(x_diff,y_diff)
contours_only_text_parent_d_ordered = [ ]
for i in range ( len ( contours_only_text_parent ) ) :
# img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
# plt.imshow(img1[:,:,0])
# plt.show()
p = np . dot ( M_22 , [ cx_bigest [ i ] , cy_biggest [ i ] ] )
# print(p)
p [ 0 ] = p [ 0 ] - x_diff [ 0 ]
p [ 1 ] = p [ 1 ] - y_diff [ 0 ]
# print(p)
# print(cx_bigest_d)
# print(cy_biggest_d)
dists = [ math . sqrt ( ( p [ 0 ] - cx_bigest_d [ j ] ) * * 2 + ( p [ 1 ] - cy_biggest_d [ j ] ) * * 2 ) for j in range ( len ( cx_bigest_d ) ) ]
# print(np.argmin(dists))
contours_only_text_parent_d_ordered . append ( contours_only_text_parent_d [ np . argmin ( dists ) ] )
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
# plt.imshow(img2[:,:,0])
# plt.show()
else :
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 )
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_only_text_parent [ j ] ) for j in range ( len ( contours_only_text_parent ) ) ] )
areas_cnt_text = areas_cnt_text / float ( text_only . shape [ 0 ] * text_only . shape [ 1 ] )
contours_biggest = contours_only_text_parent [ np . argmax ( areas_cnt_text ) ]
contours_only_text_parent = [ contours_only_text_parent [ jz ] for jz in range ( len ( contours_only_text_parent ) ) if areas_cnt_text [ jz ] > min_con_area ]
areas_cnt_text_parent = [ areas_cnt_text [ jz ] for jz in range ( len ( areas_cnt_text ) ) if areas_cnt_text [ jz ] > min_con_area ]
index_con_parents = np . argsort ( areas_cnt_text_parent )
contours_only_text_parent = list ( np . array ( contours_only_text_parent ) [ index_con_parents ] )
areas_cnt_text_parent = list ( np . array ( areas_cnt_text_parent ) [ index_con_parents ] )
cx_bigest_big , cy_biggest_big , _ , _ , _ , _ , _ = find_new_features_of_contoures ( [ contours_biggest ] )
cx_bigest , cy_biggest , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent )
self . logger . debug ( ' areas_cnt_text_parent %s ' , areas_cnt_text_parent )
# self.logger.debug('areas_cnt_text_parent_d %s', areas_cnt_text_parent_d)
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
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_marginals , _ = get_text_region_boxes_by_given_contours ( polygons_of_marginals )
if not self . curved_line :
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 ( 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 , index_by_text_par_con_marginal = self . get_slopes_and_deskew_new ( polygons_of_marginals , polygons_of_marginals , textline_mask_tot_ea , image_page_rotated , boxes_marginals , slope_deskew )
else :
scale_param = 1
all_found_texline_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 = self . kernel , iterations = 1 ) , image_page_rotated , boxes_text , text_only , num_col_classifier , scale_param , slope_deskew )
all_found_texline_polygons = small_textlines_to_parent_adherence2 ( all_found_texline_polygons , textline_mask_tot_ea , num_col_classifier )
all_found_texline_polygons_marginals , boxes_marginals , _ , polygons_of_marginals , all_box_coord_marginals , index_by_text_par_con_marginal , slopes_marginals = self . get_slopes_and_deskew_new_curved ( polygons_of_marginals , polygons_of_marginals , cv2 . erode ( textline_mask_tot_ea , kernel = self . kernel , iterations = 1 ) , image_page_rotated , boxes_marginals , text_only , num_col_classifier , scale_param , slope_deskew )
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2 ( all_found_texline_polygons_marginals , textline_mask_tot_ea , num_col_classifier )
index_of_vertical_text_contours = np . array ( range ( len ( slopes ) ) ) [ ( abs ( np . array ( slopes ) ) > 60 ) ]
contours_text_vertical = [ contours_only_text_parent [ i ] for i in index_of_vertical_text_contours ]
K . clear_session ( )
gc . collect ( )
# print(index_by_text_par_con,'index_by_text_par_con')
if self . full_layout :
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
contours_only_text_parent_d_ordered = list ( np . array ( contours_only_text_parent_d_ordered ) [ index_by_text_par_con ] )
text_regions_p , contours_only_text_parent , contours_only_text_parent_h , all_box_coord , all_box_coord_h , all_found_texline_polygons , all_found_texline_polygons_h , slopes , slopes_h , contours_only_text_parent_d_ordered , contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header ( text_regions_p , regions_fully , contours_only_text_parent , all_box_coord , all_found_texline_polygons , slopes , contours_only_text_parent_d_ordered )
else :
contours_only_text_parent_d_ordered = None
text_regions_p , contours_only_text_parent , contours_only_text_parent_h , all_box_coord , all_box_coord_h , all_found_texline_polygons , all_found_texline_polygons_h , slopes , slopes_h , contours_only_text_parent_d_ordered , contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header ( text_regions_p , regions_fully , contours_only_text_parent , all_box_coord , all_found_texline_polygons , slopes , contours_only_text_parent_d_ordered )
if self . plotter :
self . plotter . save_plot_of_layout ( text_regions_p , image_page )
self . plotter . save_plot_of_layout_all ( text_regions_p , image_page )
K . clear_session ( )
gc . collect ( )
polygons_of_tabels = [ ]
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size ( text_regions_p , pixel_img )
all_found_texline_polygons = adhere_drop_capital_region_into_cprresponding_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_texline_polygons , all_found_texline_polygons_h , kernel = self . kernel , curved_line = self . curved_line )
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines = 6
if not self . headers_off :
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
num_col , peaks_neg_fin , matrix_of_lines_ch , spliter_y_new , _ = find_number_of_columns_in_document ( np . repeat ( text_regions_p [ : , : , np . newaxis ] , 3 , axis = 2 ) , num_col_classifier , pixel_lines , contours_only_text_parent_h )
else :
num_col_d , peaks_neg_fin_d , matrix_of_lines_ch_d , spliter_y_new_d , _ = find_number_of_columns_in_document ( np . repeat ( text_regions_p_1_n [ : , : , np . newaxis ] , 3 , axis = 2 ) , num_col_classifier , pixel_lines , contours_only_text_parent_h_d_ordered )
elif self . headers_off :
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
num_col , peaks_neg_fin , matrix_of_lines_ch , spliter_y_new , _ = find_number_of_columns_in_document ( np . repeat ( text_regions_p [ : , : , np . newaxis ] , 3 , axis = 2 ) , num_col_classifier , pixel_lines )
else :
num_col_d , peaks_neg_fin_d , matrix_of_lines_ch_d , spliter_y_new_d , _ = find_number_of_columns_in_document ( np . repeat ( text_regions_p_1_n [ : , : , np . newaxis ] , 3 , axis = 2 ) , num_col_classifier , pixel_lines )
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
if num_col_classifier > = 3 :
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
regions_without_seperators = regions_without_seperators . astype ( np . uint8 )
regions_without_seperators = cv2 . erode ( regions_without_seperators [ : , : ] , self . kernel , iterations = 6 )
random_pixels_for_image = np . random . randn ( regions_without_seperators . shape [ 0 ] , regions_without_seperators . shape [ 1 ] )
random_pixels_for_image [ random_pixels_for_image < - 0.5 ] = 0
random_pixels_for_image [ random_pixels_for_image != 0 ] = 1
regions_without_seperators [ ( random_pixels_for_image [ : , : ] == 1 ) & ( text_regions_p [ : , : ] == 5 ) ] = 1
else :
regions_without_seperators_d = regions_without_seperators_d . astype ( np . uint8 )
regions_without_seperators_d = cv2 . erode ( regions_without_seperators_d [ : , : ] , self . kernel , iterations = 6 )
random_pixels_for_image = np . random . randn ( regions_without_seperators_d . shape [ 0 ] , regions_without_seperators_d . shape [ 1 ] )
random_pixels_for_image [ random_pixels_for_image < - 0.5 ] = 0
random_pixels_for_image [ random_pixels_for_image != 0 ] = 1
regions_without_seperators_d [ ( random_pixels_for_image [ : , : ] == 1 ) & ( text_regions_p_1_n [ : , : ] == 5 ) ] = 1
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
boxes = return_boxes_of_images_by_order_of_reading_new ( spliter_y_new , regions_without_seperators , matrix_of_lines_ch , num_col_classifier )
else :
boxes_d = return_boxes_of_images_by_order_of_reading_new ( spliter_y_new_d , regions_without_seperators_d , matrix_of_lines_ch_d , num_col_classifier )
if self . plotter :
self . plotter . write_images_into_directory ( polygons_of_images , image_page )
if self . full_layout :
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 :
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 )
self . write_into_page_xml_full ( contours_only_text_parent , contours_only_text_parent_h , page_coord , self . dir_out , order_text_new , id_of_texts_tot , all_found_texline_polygons , all_found_texline_polygons_h , all_box_coord , all_box_coord_h , polygons_of_images , polygons_of_tabels , polygons_of_drop_capitals , polygons_of_marginals , all_found_texline_polygons_marginals , all_box_coord_marginals , slopes , slopes_marginals )
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 :
contours_only_text_parent_d_ordered = list ( np . array ( contours_only_text_parent_d_ordered ) [ index_by_text_par_con ] )
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 )
self . write_into_page_xml ( txt_con_org , page_coord , self . dir_out , order_text_new , id_of_texts_tot , all_found_texline_polygons , all_box_coord , polygons_of_images , polygons_of_marginals , all_found_texline_polygons_marginals , all_box_coord_marginals , self . curved_line , slopes , slopes_marginals )
self . logger . info ( " Job done in %s s " , str ( time . time ( ) - t1 ) )
