"""
tool to extract table form data from alto xml data
"""
import gc
import math
import os
import random
import sys
import time
import warnings
from multiprocessing import Process , Queue , cpu_count
from sys import getsizeof
import cv2
import numpy as np
import matplotlib . pyplot as plt
import seaborn as sns
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 scipy . signal import find_peaks
from scipy . ndimage import gaussian_filter1d
from shapely import geometry
from lxml import etree as ET
from matplotlib import pyplot , transforms
import matplotlib . patches as mpatches
import imutils
from . utils import (
boosting_headers_by_longshot_region_segmentation ,
contours_in_same_horizon ,
crop_image_inside_box ,
filter_contours_area_of_image_interiors ,
filter_contours_area_of_image_tables ,
filter_small_drop_capitals_from_no_patch_layout ,
find_contours_mean_y_diff ,
find_features_of_contours ,
find_features_of_lines ,
find_new_features_of_contoures ,
find_num_col ,
find_num_col_by_vertical_lines ,
find_num_col_deskew ,
find_num_col_only_image ,
get_text_region_boxes_by_given_contours ,
get_textregion_contours_in_org_image ,
isNaN ,
otsu_copy ,
otsu_copy_binary ,
resize_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_hor_spliter_by_index_for_without_verticals ,
return_parent_contours ,
rotate_image ,
rotate_max_area ,
rotate_max_area_new ,
rotatedRectWithMaxArea ,
rotation_image_new ,
rotation_not_90_func ,
rotation_not_90_func_full_layout ,
seperate_lines ,
seperate_lines_new_inside_teils ,
seperate_lines_new_inside_teils2 ,
seperate_lines_vertical_cont ,
delete_seperator_around ,
return_regions_without_seperators ,
return_deskew_slop ,
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 ,
return_contours_of_interested_region_by_size ,
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 ,
textline_contours_postprocessing ,
find_number_of_columns_in_document ,
return_boxes_of_images_by_order_of_reading_new ,
)
SLOPE_THRESHOLD = 0.13
VERY_LARGE_NUMBER = 1000000000000000000000
class eynollah :
def __init__ (
self ,
image_dir ,
f_name ,
dir_out ,
dir_models ,
dir_of_cropped_images = None ,
dir_of_layout = None ,
dir_of_deskewed = None ,
dir_of_all = None ,
allow_enhancement = False ,
curved_line = False ,
full_layout = False ,
allow_scaling = False ,
headers_off = False
) :
self . image_dir = image_dir # XXX This does not seem to be a directory as the name suggests, but a file
self . dir_out = dir_out
self . f_name = f_name
self . dir_of_cropped_images = dir_of_cropped_images
self . allow_enhancement = allow_enhancement
self . curved_line = curved_line
self . full_layout = full_layout
self . allow_scaling = allow_scaling
self . dir_of_layout = dir_of_layout
self . headers_off = headers_off
self . dir_of_deskewed = dir_of_deskewed
self . dir_of_all = dir_of_all
if self . f_name is None :
try :
self . f_name = image_dir . split ( " / " ) [ len ( image_dir . split ( " / " ) ) - 1 ]
self . f_name = self . f_name . split ( " . " ) [ 0 ]
except :
self . f_name = self . f_name . split ( " . " ) [ 0 ]
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 " # dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
# self.model_region_dir_p_ens = dir_models +'/model_ensemble_s.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_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 " # "model_3col_p_soft_10_less_aug_binarization_only.h5"
self . model_page_dir = dir_models + " /model_page_mixed_best.h5 "
self . model_region_dir_p_ens = dir_models + " /model_ensemble_s.h5 " # dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5' #dir_models +'/model_ensemble_s.h5'
###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
self . model_textline_dir = dir_models + " /model_textline_newspapers.h5 " #'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'
def predict_enhancement ( self , img ) :
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 )
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
else :
nxf = int ( nxf )
if nyf > int ( nyf ) :
nyf = int ( nyf ) + 1
else :
nyf = 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
elif i > 0 :
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
elif j > 0 :
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 check_dpi ( self ) :
dpi = os . popen ( ' identify -format " %x " ' + self . image_dir ) . read ( )
return int ( float ( dpi ) )
def resize_image_with_column_classifier ( self , is_image_enhanced ) :
dpi = self . check_dpi ( )
img = cv2 . imread ( self . image_dir )
img = img . astype ( np . uint8 )
_ , 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 = cv2 . imread ( self . image_dir , 0 )
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
print ( num_col , label_p_pred , " num_col_classifier " )
session_col_classifier . close ( )
del model_num_classifier
del session_col_classifier
K . clear_session ( )
gc . collect ( )
# sys.exit()
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
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 , is_image_enhanced ) :
dpi = self . check_dpi ( )
img = cv2 . imread ( self . image_dir )
img = img . astype ( np . uint8 )
_ , 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 = cv2 . imread ( self . image_dir , 0 )
img_1ch = img_1ch . astype ( np . uint8 )
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
print ( num_col , label_p_pred , " num_col_classifier " )
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 ( )
print ( dpi )
if dpi < 298 :
# sys.exit()
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
# img_new=resize_image(img,img_h_new,img_w_new)
image_res = self . predict_enhancement ( img_new )
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
# self.image=self.image.astype(np.uint16)
# self.scale_x=1
# self.scale_y=1
# self.height_org = self.image.shape[0]
# self.width_org = self.image.shape[1]
is_image_enhanced = True
else :
"""
if img . shape [ 0 ] < = 2530 and img . shape [ 0 ] > = img . shape [ 1 ] :
img_h_new = 3000
img_w_new = int ( img . shape [ 1 ] / float ( img . shape [ 0 ] ) * 3000 )
img_new = resize_image ( img , img_h_new , img_w_new )
image_res = self . predict_enhancement ( img_new )
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
#self.image=self.image.astype(np.uint16)
##self.scale_x=1
##self.scale_y=1
##self.height_org = self.image.shape[0]
##self.width_org = self.image.shape[1]
is_image_enhanced = True
else :
is_image_enhanced = False
image_res = np . copy ( img )
"""
is_image_enhanced = False
num_column_is_classified = True
image_res = np . copy ( img )
return is_image_enhanced , img , image_res , num_col , num_column_is_classified
def resize_and_enhance_image ( self , is_image_enhanced ) :
dpi = self . check_dpi ( )
img = cv2 . imread ( self . image_dir )
img = img . astype ( np . uint8 )
# sys.exit()
print ( dpi )
if dpi < 298 :
if img . shape [ 0 ] < 1000 :
img_h_new = int ( img . shape [ 0 ] * 3 )
img_w_new = int ( img . shape [ 1 ] * 3 )
if img_h_new < 2800 :
img_h_new = 3000
img_w_new = int ( img . shape [ 1 ] / float ( img . shape [ 0 ] ) * 3000 )
elif img . shape [ 0 ] > = 1000 and img . shape [ 0 ] < 2000 :
img_h_new = int ( img . shape [ 0 ] * 2 )
img_w_new = int ( img . shape [ 1 ] * 2 )
if img_h_new < 2800 :
img_h_new = 3000
img_w_new = int ( img . shape [ 1 ] / float ( img . shape [ 0 ] ) * 3000 )
else :
img_h_new = int ( img . shape [ 0 ] * 1.5 )
img_w_new = int ( img . shape [ 1 ] * 1.5 )
img_new = resize_image ( img , img_h_new , img_w_new )
image_res = self . predict_enhancement ( img_new )
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
# self.image=self.image.astype(np.uint16)
# self.scale_x=1
# self.scale_y=1
# self.height_org = self.image.shape[0]
# self.width_org = self.image.shape[1]
is_image_enhanced = True
else :
is_image_enhanced = False
image_res = np . copy ( img )
return is_image_enhanced , img , image_res
def resize_and_enhance_image_new ( self , is_image_enhanced ) :
# self.check_dpi()
img = cv2 . imread ( self . image_dir )
img = img . astype ( np . uint8 )
# sys.exit()
image_res = np . copy ( img )
return is_image_enhanced , img , image_res
def get_image_and_scales ( self , img_org , img_res , scale ) :
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 )
del img_res
del img_org
def get_image_and_scales_after_enhancing ( self , img_org , img_res ) :
# self.image = cv2.imread(self.image_dir)
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 get_image_and_scales_deskewd ( self , img_deskewd ) :
self . image = img_deskewd
self . image_org = np . copy ( self . image )
self . height_org = self . image . shape [ 0 ]
self . width_org = self . image . shape [ 1 ]
self . img_hight_int = int ( self . image . shape [ 0 ] * 1 )
self . img_width_int = int ( self . image . shape [ 1 ] * 1 )
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 )
def start_new_session_and_model ( self , 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 ) :
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 patches :
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 )
# print(img_height_model,img_width_model)
# margin = int(0.2 * 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 )
# print(sys.getsizeof(img))
# print(np.max(img))
img = img . astype ( np . float16 )
# print(sys.getsizeof(img))
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 )
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
else :
nxf = int ( nxf )
if nyf > int ( nyf ) :
nyf = int ( nyf ) + 1
else :
nyf = 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
elif i > 0 :
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
elif j > 0 :
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
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
del model
gc . collect ( )
return prediction_true
def early_page_for_num_of_column_classification ( self ) :
img = cv2 . imread ( self . image_dir )
img = img . astype ( np . uint8 )
patches = False
model_page , session_page = self . start_new_session_and_model ( self . model_page_dir )
###img = otsu_copy(self.image)
for ii in range ( 1 ) :
img = cv2 . GaussianBlur ( img , ( 5 , 5 ) , 0 )
img_page_prediction = self . do_prediction ( patches , 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 ( )
return croped_page , page_coord
def extract_page ( self ) :
patches = False
model_page , session_page = self . start_new_session_and_model ( self . model_page_dir )
###img = otsu_copy(self.image)
for ii in range ( 1 ) :
img = cv2 . GaussianBlur ( self . image , ( 5 , 5 ) , 0 )
img_page_prediction = self . do_prediction ( patches , 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 = 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 = [ ]
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
gc . collect ( )
return croped_page , page_coord
def extract_drop_capital_13 ( self , img , patches , cols ) :
img_height_h = img . shape [ 0 ]
img_width_h = img . shape [ 1 ]
patches = False
img = otsu_copy_binary ( img ) # otsu_copy(img)
img = img . astype ( np . uint16 )
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_fully_np )
img_1 = img [ : int ( img . shape [ 0 ] / 3.0 ) , : , : ]
img_2 = img [ int ( img . shape [ 0 ] / 3.0 ) : int ( 2 * img . shape [ 0 ] / 3.0 ) , : , : ]
img_3 = img [ int ( 2 * img . shape [ 0 ] / 3.0 ) : , : , : ]
# img_1 = otsu_copy_binary(img_1)#otsu_copy(img)
# img_1 = img_1.astype(np.uint16)
plt . imshow ( img_1 )
plt . show ( )
# img_2 = otsu_copy_binary(img_2)#otsu_copy(img)
# img_2 = img_2.astype(np.uint16)
plt . imshow ( img_2 )
plt . show ( )
# img_3 = otsu_copy_binary(img_3)#otsu_copy(img)
# img_3 = img_3.astype(np.uint16)
plt . imshow ( img_3 )
plt . show ( )
prediction_regions_1 = self . do_prediction ( patches , img_1 , model_region )
plt . imshow ( prediction_regions_1 )
plt . show ( )
prediction_regions_2 = self . do_prediction ( patches , img_2 , model_region )
plt . imshow ( prediction_regions_2 )
plt . show ( )
prediction_regions_3 = self . do_prediction ( patches , img_3 , model_region )
plt . imshow ( prediction_regions_3 )
plt . show ( )
prediction_regions = np . zeros ( ( img_height_h , img_width_h ) )
prediction_regions [ : int ( img . shape [ 0 ] / 3.0 ) , : ] = prediction_regions_1 [ : , : , 0 ]
prediction_regions [ int ( img . shape [ 0 ] / 3.0 ) : int ( 2 * img . shape [ 0 ] / 3.0 ) , : ] = prediction_regions_2 [ : , : , 0 ]
prediction_regions [ int ( 2 * img . shape [ 0 ] / 3.0 ) : , : ] = prediction_regions_3 [ : , : , 0 ]
session_region . close ( )
del img_1
del img_2
del img_3
del prediction_regions_1
del prediction_regions_2
del prediction_regions_3
del model_region
del session_region
del img
gc . collect ( )
return prediction_regions
def extract_text_regions ( self , img , patches , cols ) :
img_height_h = img . shape [ 0 ]
img_width_h = img . shape [ 1 ]
###if patches and cols>=3 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
###if not patches:
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
###if patches and cols==2 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
###if patches and cols==1 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
###if patches and cols>=2:
###img = otsu_copy_binary(img)#otsu_copy(img)
###img = img.astype(np.uint8)
###if patches and cols==1:
###img = otsu_copy_binary(img)#otsu_copy(img)
###img = img.astype(np.uint8)
###img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
if patches :
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_fully )
if not patches :
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_fully_np )
if patches and cols == 1 :
img2 = otsu_copy_binary ( img ) # otsu_copy(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 patches and cols == 2 :
img2 = otsu_copy_binary ( img ) # otsu_copy(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 patches and cols > 2 :
img2 = otsu_copy_binary ( img ) # otsu_copy(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 patches and cols == 2 :
img = otsu_copy_binary ( img ) # otsu_copy(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 ) )
else :
pass # img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
img = img . astype ( np . uint8 )
if patches and cols == 1 :
img = otsu_copy_binary ( img ) # otsu_copy(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 patches and cols == 3 :
img = otsu_copy_binary ( img ) # 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 patches and cols == 4 :
img = otsu_copy_binary ( img ) # 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 patches and cols > = 5 :
img = otsu_copy_binary ( img ) # 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 not patches :
img = otsu_copy_binary ( img ) # otsu_copy(img)
img = img . astype ( np . uint8 )
prediction_regions2 = None
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 ( )
return prediction_regions , prediction_regions2
def extract_only_text_regions ( self , img , patches ) :
model_region , session_region = self . start_new_session_and_model ( self . model_only_text )
img = otsu_copy_binary ( img ) # otsu_copy(img)
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 ] * 1 ) , int ( img_org . shape [ 1 ] * 1 ) )
prediction_regions1 = self . do_prediction ( patches , img , model_region )
prediction_regions1 = resize_image ( prediction_regions1 , img_h , img_w )
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4)
# prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7)
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2)
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * 1 ) , int ( img_org . shape [ 1 ] * 1 ) )
prediction_regions2 = self . do_prediction ( patches , img , model_region )
prediction_regions2 = resize_image ( prediction_regions2 , img_h , img_w )
# prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
prediction_regions2 = cv2 . erode ( prediction_regions2 , self . kernel , iterations = 2 )
prediction_regions2 = cv2 . dilate ( prediction_regions2 , self . kernel , iterations = 2 )
# prediction_regions=( (prediction_regions2[:,:,0]==1) & (prediction_regions1[:,:,0]==1) )
# prediction_regions=(prediction_regions1[:,:,0]==1)
session_region . close ( )
del model_region
del session_region
gc . collect ( )
return prediction_regions1 [ : , : , 0 ]
def extract_binarization ( self , img , patches ) :
model_bin , session_bin = self . start_new_session_and_model ( self . model_binafrization )
img_h = img . shape [ 0 ]
img_w = img . shape [ 1 ]
img = resize_image ( img , int ( img . shape [ 0 ] * 1 ) , int ( img . shape [ 1 ] * 1 ) )
prediction_regions = self . do_prediction ( patches , img , model_bin )
res = ( prediction_regions [ : , : , 0 ] != 0 ) * 1
img_fin = np . zeros ( ( res . shape [ 0 ] , res . shape [ 1 ] , 3 ) )
res [ : , : ] [ res [ : , : ] == 0 ] = 2
res = res - 1
res = res * 255
img_fin [ : , : , 0 ] = res
img_fin [ : , : , 1 ] = res
img_fin [ : , : , 2 ] = res
session_bin . close ( )
del model_bin
del session_bin
gc . collect ( )
# plt.imshow(img_fin[:,:,0])
# plt.show()
return img_fin
def get_slopes_and_deskew_new ( self , contours , contours_par , textline_mask_tot , image_page_rotated , boxes , slope_deskew ) :
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 ( )
# print(slopes,'slopes')
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 ) :
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 ) :
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 = [ ]
slope_biggest = 0
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 [ : , : ] # self.all_text_region_raw[mv]
##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_first = 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 = 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=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = return_deskew_slop ( img_int_p , sigma_des , dir_of_all = self . dir_of_all , f_name = self . f_name )
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
##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if slope_for_all == 999 :
slope_for_all = [ slope_deskew ] [ 0 ]
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
##elif slope_for_all==999:
##slope_for_all=slope_biggest
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 = self . seperate_lines_new2 ( textline_biggest_region [ y : y + h , x : x + w ] , 0 , num_col , slope_for_all )
# 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 :
slope_first = 0
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 ] , slope_first , add_boxes_coor_into_textlines )
add_boxes_coor_into_textlines = False
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
# textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
# index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
textlines_rectangles_per_each_subprocess . append ( textlines_cnt_per_region )
# all_found_texline_polygons.append(cnt_clean_rot)
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 ) :
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 = [ ]
slope_biggest = 0
for mv in range ( len ( boxes_text ) ) :
crop_img , crop_coor = crop_image_inside_box ( boxes_text [ mv ] , image_page_rotated )
# all_box_coord.append(crop_coor)
denoised = None
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 [ : , : ] # 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,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
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 ] )
# all_found_texline_polygons.append(cnt_clean_rot)
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=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = return_deskew_slop ( img_int_p , sigma_des , dir_of_all = self . dir_of_all , f_name = self . f_name )
if abs ( slope_for_all ) < = 0.5 :
slope_for_all = [ slope_deskew ] [ 0 ]
except :
slope_for_all = 999
##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if slope_for_all == 999 :
slope_for_all = [ slope_deskew ] [ 0 ]
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
##elif slope_for_all==999:
##slope_for_all=slope_biggest
slopes_per_each_subprocess . append ( slope_for_all )
slope_first = 0
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,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot = textline_contours_postprocessing ( all_text_region_raw , slope_for_all , contours_par_per_process [ mv ] , boxes_text [ mv ] , slope_first )
textlines_rectangles_per_each_subprocess . append ( cnt_clean_rot )
index_by_text_region_contours . append ( indexes_r_con_per_pro [ mv ] )
# all_found_texline_polygons.append(cnt_clean_rot)
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 get_text_region_contours_and_boxes ( self , image ) :
rgb_class_of_texts = ( 1 , 1 , 1 )
mask_texts = np . all ( image == rgb_class_of_texts , axis = - 1 )
image = np . repeat ( mask_texts [ : , : , np . newaxis ] , 3 , axis = 2 ) * 255
image = image . astype ( np . uint8 )
image = cv2 . morphologyEx ( image , cv2 . MORPH_OPEN , self . kernel )
image = cv2 . morphologyEx ( image , cv2 . MORPH_CLOSE , self . kernel )
imgray = cv2 . cvtColor ( image , cv2 . COLOR_BGR2GRAY )
_ , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours , hirarchy = cv2 . findContours ( thresh . copy ( ) , cv2 . cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
main_contours = filter_contours_area_of_image ( thresh , contours , hirarchy , max_area = 1 , min_area = 0.00001 )
self . boxes = [ ]
for jj in range ( len ( main_contours ) ) :
x , y , w , h = cv2 . boundingRect ( main_contours [ jj ] )
self . boxes . append ( [ x , y , w , h ] )
return main_contours
def textline_contours ( self , img , patches , scaler_h , scaler_w ) :
if patches :
model_textline , session_textline = self . start_new_session_and_model ( self . model_textline_dir )
if not patches :
model_textline , session_textline = self . start_new_session_and_model ( self . model_textline_dir_np )
##img = otsu_copy(img)
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 )
patches = False
prediction_textline_longshot = self . do_prediction ( patches , img , model_textline )
prediction_textline_longshot_true_size = resize_image ( prediction_textline_longshot , img_h , img_w )
# scaler_w=1.5
# scaler_h=1.5
# patches=True
# img= resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w))
# prediction_textline_streched=self.do_prediction(patches,img,model_textline)
# prediction_textline_streched= resize_image(prediction_textline_streched, img_h, img_w)
##plt.imshow(prediction_textline_streched[:,:,0])
##plt.show()
# sys.exit()
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 seperate_lines_new ( self , img_path , thetha , num_col ) :
if num_col == 1 :
num_patches = int ( img_path . shape [ 1 ] / 200.0 )
else :
num_patches = int ( img_path . shape [ 1 ] / 100.0 )
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0 :
num_patches = 1
( h , w ) = img_path . shape [ : 2 ]
center = ( w / / 2 , h / / 2 )
M = cv2 . getRotationMatrix2D ( center , - thetha , 1.0 )
x_d = M [ 0 , 2 ]
y_d = M [ 1 , 2 ]
thetha = thetha / 180.0 * np . pi
rotation_matrix = np . array ( [ [ np . cos ( thetha ) , - np . sin ( thetha ) ] , [ np . sin ( thetha ) , np . cos ( thetha ) ] ] )
x_min_cont = 0
x_max_cont = img_path . shape [ 1 ]
y_min_cont = 0
y_max_cont = img_path . shape [ 0 ]
xv = np . linspace ( x_min_cont , x_max_cont , 1000 )
mada_n = img_path . sum ( axis = 1 )
##plt.plot(mada_n)
##plt.show()
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = mada_n [ : ] # [first_nonzero:last_nonzero]
y_help = np . zeros ( len ( y ) + 40 )
y_help [ 20 : len ( y ) + 20 ] = y
x = np . array ( range ( len ( y ) ) )
peaks_real , _ = find_peaks ( gaussian_filter1d ( y , 3 ) , height = 0 )
if len ( peaks_real ) < = 2 and len ( peaks_real ) > 1 :
sigma_gaus = 10
else :
sigma_gaus = 6
z = gaussian_filter1d ( y_help , sigma_gaus )
zneg_rev = - y_help + np . max ( y_help )
zneg = np . zeros ( len ( zneg_rev ) + 40 )
zneg [ 20 : len ( zneg_rev ) + 20 ] = zneg_rev
zneg = gaussian_filter1d ( zneg , sigma_gaus )
peaks , _ = find_peaks ( z , height = 0 )
peaks_neg , _ = find_peaks ( zneg , height = 0 )
for nn in range ( len ( peaks_neg ) ) :
if peaks_neg [ nn ] > len ( z ) - 1 :
peaks_neg [ nn ] = len ( z ) - 1
if peaks_neg [ nn ] < 0 :
peaks_neg [ nn ] = 0
diff_peaks = np . abs ( np . diff ( peaks_neg ) )
cut_off = 20
peaks_neg_true = [ ]
forest = [ ]
for i in range ( len ( peaks_neg ) ) :
if i == 0 :
forest . append ( peaks_neg [ i ] )
if i < ( len ( peaks_neg ) - 1 ) :
if diff_peaks [ i ] < = cut_off :
forest . append ( peaks_neg [ i + 1 ] )
if diff_peaks [ i ] > cut_off :
# print(forest[np.argmin(z[forest]) ] )
if not isNaN ( forest [ np . argmin ( z [ forest ] ) ] ) :
# print(len(z),forest)
peaks_neg_true . append ( forest [ np . argmin ( z [ forest ] ) ] )
forest = [ ]
forest . append ( peaks_neg [ i + 1 ] )
if i == ( len ( peaks_neg ) - 1 ) :
# print(print(forest[np.argmin(z[forest]) ] ))
if not isNaN ( forest [ np . argmin ( z [ forest ] ) ] ) :
peaks_neg_true . append ( forest [ np . argmin ( z [ forest ] ) ] )
peaks_neg_true = np . array ( peaks_neg_true )
"""
#plt.figure(figsize=(40,40))
#plt.subplot(1,2,1)
#plt.title('Textline segmentation von Textregion')
#plt.imshow(img_path)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.subplot(1,2,2)
#plt.title('Dichte entlang X')
#base = pyplot.gca().transData
#rot = transforms.Affine2D().rotate_deg(90)
#plt.plot(zneg,np.array(range(len(zneg))))
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
#plt.gca().invert_yaxis()
#plt.xlabel('Dichte')
#plt.ylabel('Y')
##plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
"""
peaks_neg_true = peaks_neg_true - 20 - 20
peaks = peaks - 20
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path [ : , : ] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
##plt.imshow(img_patch_ineterst)
##plt.show()
length_x = int ( img_path . shape [ 1 ] / float ( num_patches ) )
margin = int ( 0.04 * length_x )
width_mid = length_x - 2 * margin
nxf = img_path . shape [ 1 ] / float ( width_mid )
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
else :
nxf = int ( nxf )
slopes_tile_wise = [ ]
for i in range ( nxf ) :
if i == 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path . shape [ 1 ] :
index_x_u = img_path . shape [ 1 ]
index_x_d = img_path . shape [ 1 ] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst [ : , index_x_d : index_x_u ]
sigma = 2
try :
slope_xline = return_deskew_slop ( img_xline , sigma , dir_of_all = self . dir_of_all , f_name = self . f_name )
except :
slope_xline = 0
slopes_tile_wise . append ( slope_xline )
# print(slope_xline,'xlineeee')
img_line_rotated = rotate_image ( img_xline , slope_xline )
img_line_rotated [ : , : ] [ img_line_rotated [ : , : ] != 0 ] = 1
"""
xline = np . linspace ( 0 , img_path . shape [ 1 ] , nx )
slopes_tile_wise = [ ]
for ui in range ( nx - 1 ) :
img_xline = img_patch_ineterst [ : , int ( xline [ ui ] ) : int ( xline [ ui + 1 ] ) ]
##plt.imshow(img_xline)
##plt.show()
sigma = 3
try :
slope_xline = return_deskew_slop ( img_xline , sigma , dir_of_all = self . dir_of_all , f_name = self . f_name )
except :
slope_xline = 0
slopes_tile_wise . append ( slope_xline )
print ( slope_xline , ' xlineeee ' )
img_line_rotated = rotate_image ( img_xline , slope_xline )
##plt.imshow(img_line_rotated)
##plt.show()
"""
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path [ : , : ] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np . zeros ( img_patch_ineterst . shape )
for i in range ( nxf ) :
if i == 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path . shape [ 1 ] :
index_x_u = img_path . shape [ 1 ]
index_x_d = img_path . shape [ 1 ] - length_x
img_xline = img_patch_ineterst [ : , index_x_d : index_x_u ]
img_int = np . zeros ( ( img_xline . shape [ 0 ] , img_xline . shape [ 1 ] ) )
img_int [ : , : ] = img_xline [ : , : ] # img_patch_org[:,:,0]
img_resized = np . zeros ( ( int ( img_int . shape [ 0 ] * ( 1.2 ) ) , int ( img_int . shape [ 1 ] * ( 3 ) ) ) )
img_resized [ int ( img_int . shape [ 0 ] * ( 0.1 ) ) : int ( img_int . shape [ 0 ] * ( 0.1 ) ) + img_int . shape [ 0 ] , int ( img_int . shape [ 1 ] * ( 1 ) ) : int ( img_int . shape [ 1 ] * ( 1 ) ) + img_int . shape [ 1 ] ] = img_int [ : , : ]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated = rotate_image ( img_resized , slopes_tile_wise [ i ] )
img_line_rotated [ : , : ] [ img_line_rotated [ : , : ] != 0 ] = 1
img_patch_seperated = seperate_lines_new_inside_teils ( img_line_rotated , 0 )
##plt.imshow(img_patch_seperated)
##plt.show()
img_patch_seperated_returned = rotate_image ( img_patch_seperated , - slopes_tile_wise [ i ] )
img_patch_seperated_returned [ : , : ] [ img_patch_seperated_returned [ : , : ] != 0 ] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned [ int ( img_int . shape [ 0 ] * ( 0.1 ) ) : int ( img_int . shape [ 0 ] * ( 0.1 ) ) + img_int . shape [ 0 ] , int ( img_int . shape [ 1 ] * ( 1 ) ) : int ( img_int . shape [ 1 ] * ( 1 ) ) + img_int . shape [ 1 ] ]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size [ : , margin : length_x - margin ]
img_patch_ineterst_revised [ : , index_x_d + margin : index_x_u - margin ] = img_patch_seperated_returned_true_size
"""
for ui in range ( nx - 1 ) :
img_xline = img_patch_ineterst [ : , int ( xline [ ui ] ) : int ( xline [ ui + 1 ] ) ]
img_int = np . zeros ( ( img_xline . shape [ 0 ] , img_xline . shape [ 1 ] ) )
img_int [ : , : ] = img_xline [ : , : ] #img_patch_org[:,:,0]
img_resized = np . zeros ( ( int ( img_int . shape [ 0 ] * ( 1.2 ) ) , int ( img_int . shape [ 1 ] * ( 3 ) ) ) )
img_resized [ int ( img_int . shape [ 0 ] * ( .1 ) ) : int ( img_int . shape [ 0 ] * ( .1 ) ) + img_int . shape [ 0 ] , int ( img_int . shape [ 1 ] * ( 1 ) ) : int ( img_int . shape [ 1 ] * ( 1 ) ) + img_int . shape [ 1 ] ] = img_int [ : , : ]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated = rotate_image ( img_resized , slopes_tile_wise [ ui ] )
#img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated = seperate_lines_new_inside_teils ( img_line_rotated , 0 )
img_patch_seperated_returned = rotate_image ( img_patch_seperated , - slopes_tile_wise [ ui ] )
##plt.imshow(img_patch_seperated)
##plt.show()
print ( img_patch_seperated_returned . shape )
#plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
#plt.show()
img_patch_ineterst_revised [ : , int ( xline [ ui ] ) : int ( xline [ ui + 1 ] ) ] = img_patch_seperated_returned [ int ( img_int . shape [ 0 ] * ( .1 ) ) : int ( img_int . shape [ 0 ] * ( .1 ) ) + img_int . shape [ 0 ] , int ( img_int . shape [ 1 ] * ( 1 ) ) : int ( img_int . shape [ 1 ] * ( 1 ) ) + img_int . shape [ 1 ] ]
"""
# print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
##plt.imshow(img_patch_ineterst_revised)
##plt.show()
return img_patch_ineterst_revised
def seperate_lines_new2 ( self , img_path , thetha , num_col , slope_region ) :
if num_col == 1 :
num_patches = int ( img_path . shape [ 1 ] / 200.0 )
else :
num_patches = int ( img_path . shape [ 1 ] / 140.0 )
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0 :
num_patches = 1
img_patch_ineterst = img_path [ : , : ] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
# plt.imshow(img_patch_ineterst)
# plt.show()
length_x = int ( img_path . shape [ 1 ] / float ( num_patches ) )
# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
margin = int ( 0.04 * length_x )
# print(margin,'margin')
# if margin<=4:
# margin = int(0.08 * length_x)
# margin=0
width_mid = length_x - 2 * margin
nxf = img_path . shape [ 1 ] / float ( width_mid )
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
else :
nxf = int ( nxf )
slopes_tile_wise = [ ]
for i in range ( nxf ) :
if i == 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path . shape [ 1 ] :
index_x_u = img_path . shape [ 1 ]
index_x_d = img_path . shape [ 1 ] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst [ : , index_x_d : index_x_u ]
sigma = 2
try :
slope_xline = return_deskew_slop ( img_xline , sigma , dir_of_all = self . dir_of_all , f_name = self . f_name )
except :
slope_xline = 0
if abs ( slope_region ) < 25 and abs ( slope_xline ) > 25 :
slope_xline = [ slope_region ] [ 0 ]
# if abs(slope_region)>70 and abs(slope_xline)<25:
# slope_xline=[slope_region][0]
slopes_tile_wise . append ( slope_xline )
# print(slope_xline,'xlineeee')
img_line_rotated = rotate_image ( img_xline , slope_xline )
img_line_rotated [ : , : ] [ img_line_rotated [ : , : ] != 0 ] = 1
# print(slopes_tile_wise,'slopes_tile_wise')
img_patch_ineterst = img_path [ : , : ] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np . zeros ( img_patch_ineterst . shape )
for i in range ( nxf ) :
if i == 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0 :
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path . shape [ 1 ] :
index_x_u = img_path . shape [ 1 ]
index_x_d = img_path . shape [ 1 ] - length_x
img_xline = img_patch_ineterst [ : , index_x_d : index_x_u ]
img_int = np . zeros ( ( img_xline . shape [ 0 ] , img_xline . shape [ 1 ] ) )
img_int [ : , : ] = img_xline [ : , : ] # img_patch_org[:,:,0]
img_resized = np . zeros ( ( int ( img_int . shape [ 0 ] * ( 1.2 ) ) , int ( img_int . shape [ 1 ] * ( 3 ) ) ) )
img_resized [ int ( img_int . shape [ 0 ] * ( 0.1 ) ) : int ( img_int . shape [ 0 ] * ( 0.1 ) ) + img_int . shape [ 0 ] , int ( img_int . shape [ 1 ] * ( 1 ) ) : int ( img_int . shape [ 1 ] * ( 1 ) ) + img_int . shape [ 1 ] ] = img_int [ : , : ]
# plt.imshow(img_xline)
# plt.show()
img_line_rotated = rotate_image ( img_resized , slopes_tile_wise [ i ] )
img_line_rotated [ : , : ] [ img_line_rotated [ : , : ] != 0 ] = 1
img_patch_seperated = seperate_lines_new_inside_teils2 ( img_line_rotated , 0 )
img_patch_seperated_returned = rotate_image ( img_patch_seperated , - slopes_tile_wise [ i ] )
img_patch_seperated_returned [ : , : ] [ img_patch_seperated_returned [ : , : ] != 0 ] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned [ int ( img_int . shape [ 0 ] * ( 0.1 ) ) : int ( img_int . shape [ 0 ] * ( 0.1 ) ) + img_int . shape [ 0 ] , int ( img_int . shape [ 1 ] * ( 1 ) ) : int ( img_int . shape [ 1 ] * ( 1 ) ) + img_int . shape [ 1 ] ]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size [ : , margin : length_x - margin ]
img_patch_ineterst_revised [ : , index_x_d + margin : index_x_u - margin ] = img_patch_seperated_returned_true_size
# plt.imshow(img_patch_ineterst_revised)
# plt.show()
return img_patch_ineterst_revised
def textline_contours_to_get_slope_correctly ( self , textline_mask , img_patch , contour_interest ) :
slope_new = 0 # deskew_images(img_patch)
textline_mask = np . repeat ( textline_mask [ : , : , np . newaxis ] , 3 , axis = 2 ) * 255
textline_mask = textline_mask . astype ( np . uint8 )
textline_mask = cv2 . morphologyEx ( textline_mask , cv2 . MORPH_OPEN , self . kernel )
textline_mask = cv2 . morphologyEx ( textline_mask , cv2 . MORPH_CLOSE , self . kernel )
textline_mask = cv2 . erode ( textline_mask , self . kernel , iterations = 1 )
imgray = cv2 . cvtColor ( textline_mask , cv2 . COLOR_BGR2GRAY )
_ , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
thresh = cv2 . morphologyEx ( thresh , cv2 . MORPH_OPEN , self . kernel )
thresh = cv2 . morphologyEx ( thresh , cv2 . MORPH_CLOSE , self . kernel )
contours , hirarchy = cv2 . findContours ( thresh . copy ( ) , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
main_contours = filter_contours_area_of_image_tables ( thresh , contours , hirarchy , max_area = 1 , min_area = 0.003 )
textline_maskt = textline_mask [ : , : , 0 ]
textline_maskt [ textline_maskt != 0 ] = 1
peaks_point , _ = seperate_lines ( textline_maskt , contour_interest , slope_new )
mean_dis = np . mean ( np . diff ( peaks_point ) )
len_x = thresh . shape [ 1 ]
slope_lines = [ ]
contours_slope_new = [ ]
for kk in range ( len ( main_contours ) ) :
if len ( main_contours [ kk ] . shape ) == 2 :
xminh = np . min ( main_contours [ kk ] [ : , 0 ] )
xmaxh = np . max ( main_contours [ kk ] [ : , 0 ] )
yminh = np . min ( main_contours [ kk ] [ : , 1 ] )
ymaxh = np . max ( main_contours [ kk ] [ : , 1 ] )
elif len ( main_contours [ kk ] . shape ) == 3 :
xminh = np . min ( main_contours [ kk ] [ : , 0 , 0 ] )
xmaxh = np . max ( main_contours [ kk ] [ : , 0 , 0 ] )
yminh = np . min ( main_contours [ kk ] [ : , 0 , 1 ] )
ymaxh = np . max ( main_contours [ kk ] [ : , 0 , 1 ] )
if ymaxh - yminh < = mean_dis and ( xmaxh - xminh ) > = 0.3 * len_x : # xminh>=0.05*len_x and xminh<=0.4*len_x and xmaxh<=0.95*len_x and xmaxh>=0.6*len_x:
contours_slope_new . append ( main_contours [ kk ] )
rows , cols = thresh . shape [ : 2 ]
[ vx , vy , x , y ] = cv2 . fitLine ( main_contours [ kk ] , cv2 . DIST_L2 , 0 , 0.01 , 0.01 )
slope_lines . append ( ( vy / vx ) / np . pi * 180 )
if len ( slope_lines ) > = 2 :
slope = np . mean ( slope_lines ) # slope_true/np.pi*180
else :
slope = 999
else :
slope = 0
return slope
def return_deskew_slope_new ( self , img_patch , sigma_des ) :
max_x_y = max ( img_patch . shape [ 0 ] , img_patch . shape [ 1 ] )
##img_patch=resize_image(img_patch,max_x_y,max_x_y)
img_patch_copy = np . zeros ( ( img_patch . shape [ 0 ] , img_patch . shape [ 1 ] ) )
img_patch_copy [ : , : ] = img_patch [ : , : ] # img_patch_org[:,:,0]
img_patch_padded = np . zeros ( ( int ( max_x_y * ( 1.4 ) ) , int ( max_x_y * ( 1.4 ) ) ) )
img_patch_padded_center_p = int ( img_patch_padded . shape [ 0 ] / 2.0 )
len_x_org_patch_half = int ( img_patch_copy . shape [ 1 ] / 2.0 )
len_y_org_patch_half = int ( img_patch_copy . shape [ 0 ] / 2.0 )
img_patch_padded [ img_patch_padded_center_p - len_y_org_patch_half : img_patch_padded_center_p - len_y_org_patch_half + img_patch_copy . shape [ 0 ] , img_patch_padded_center_p - len_x_org_patch_half : img_patch_padded_center_p - len_x_org_patch_half + img_patch_copy . shape [ 1 ] ] = img_patch_copy [ : , : ]
# img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
angles = np . linspace ( - 25 , 25 , 80 )
res = [ ]
num_of_peaks = [ ]
index_cor = [ ]
var_res = [ ]
# plt.imshow(img_patch)
# plt.show()
indexer = 0
for rot in angles :
# print(rot,'rot')
img_rotated = rotate_image ( img_patch_padded , rot )
img_rotated [ img_rotated != 0 ] = 1
# plt.imshow(img_rotated)
# plt.show()
try :
neg_peaks , var_spectrum = self . get_standard_deviation_of_summed_textline_patch_along_width ( img_rotated , sigma_des , 20.3 )
res_me = np . mean ( neg_peaks )
if res_me == 0 :
res_me = VERY_LARGE_NUMBER
else :
pass
res_num = len ( neg_peaks )
except :
res_me = VERY_LARGE_NUMBER
res_num = 0
var_spectrum = 0
if isNaN ( res_me ) :
pass
else :
res . append ( res_me )
var_res . append ( var_spectrum )
num_of_peaks . append ( res_num )
index_cor . append ( indexer )
indexer = indexer + 1
try :
var_res = np . array ( var_res )
# print(var_res)
ang_int = angles [ np . argmax ( var_res ) ] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except :
ang_int = 0
if abs ( ang_int ) > 15 :
angles = np . linspace ( - 90 , - 50 , 30 )
res = [ ]
num_of_peaks = [ ]
index_cor = [ ]
var_res = [ ]
# plt.imshow(img_patch)
# plt.show()
indexer = 0
for rot in angles :
# print(rot,'rot')
img_rotated = rotate_image ( img_patch_padded , rot )
img_rotated [ img_rotated != 0 ] = 1
# plt.imshow(img_rotated)
# plt.show()
try :
neg_peaks , var_spectrum = self . get_standard_deviation_of_summed_textline_patch_along_width ( img_rotated , sigma_des , 20.3 )
res_me = np . mean ( neg_peaks )
if res_me == 0 :
res_me = VERY_LARGE_NUMBER
else :
pass
res_num = len ( neg_peaks )
except :
res_me = VERY_LARGE_NUMBER
res_num = 0
var_spectrum = 0
if isNaN ( res_me ) :
pass
else :
res . append ( res_me )
var_res . append ( var_spectrum )
num_of_peaks . append ( res_num )
index_cor . append ( indexer )
indexer = indexer + 1
try :
var_res = np . array ( var_res )
# print(var_res)
ang_int = angles [ np . argmax ( var_res ) ] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except :
ang_int = 0
return ang_int
def do_work_of_slopes ( self , q , poly , box_sub , boxes_per_process , textline_mask_tot , contours_per_process ) :
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 , dir_of_all = self . dir_of_all , f_name = self . f_name )
except :
slope_corresponding_textregion = 999
if slope_corresponding_textregion == 999 :
slope_corresponding_textregion = slope_biggest
##if np.abs(slope_corresponding_textregion)>12.5 and slope_corresponding_textregion!=999:
##slope_corresponding_textregion=slope_biggest
##elif 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 get_slopes_and_deskew ( self , contours , textline_mask_tot ) :
slope_biggest = 0 # return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
num_cores = cpu_count ( )
q = Queue ( )
poly = Queue ( )
box_sub = Queue ( )
processes = [ ]
nh = np . linspace ( 0 , len ( self . boxes ) , num_cores + 1 )
for i in range ( num_cores ) :
boxes_per_process = self . boxes [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
contours_per_process = contours [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
processes . append ( Process ( target = self . do_work_of_slopes , args = ( q , poly , box_sub , boxes_per_process , textline_mask_tot , contours_per_process ) ) )
for i in range ( num_cores ) :
processes [ i ] . start ( )
self . slopes = [ ]
self . all_found_texline_polygons = [ ]
self . boxes = [ ]
for i in range ( num_cores ) :
slopes_for_sub_process = q . get ( True )
boxes_for_sub_process = box_sub . get ( True )
polys_for_sub_process = poly . get ( True )
for j in range ( len ( slopes_for_sub_process ) ) :
self . slopes . append ( slopes_for_sub_process [ j ] )
self . all_found_texline_polygons . append ( polys_for_sub_process [ j ] )
self . boxes . append ( boxes_for_sub_process [ j ] )
for i in range ( num_cores ) :
processes [ i ] . join ( )
def write_into_page_xml_only_textlines ( self , contours , page_coord , all_found_texline_polygons , all_box_coord , dir_of_image ) :
found_polygons_text_region = contours
# create the file structure
data = ET . Element ( " PcGts " )
data . set ( " xmlns " , " http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15 " )
data . set ( " xmlns:xsi " , " http://www.w3.org/2001/XMLSchema-instance " )
data . set ( " xsi:schemaLocation " , " http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15 " )
metadata = ET . SubElement ( data , " Metadata " )
author = ET . SubElement ( metadata , " Creator " )
author . text = " SBB_QURATOR "
created = ET . SubElement ( metadata , " Created " )
created . text = " 2019-06-17T18:15:12 "
changetime = ET . SubElement ( metadata , " LastChange " )
changetime . text = " 2019-06-17T18:15:12 "
page = ET . SubElement ( data , " Page " )
page . set ( " imageFilename " , self . image_dir )
page . set ( " imageHeight " , str ( self . height_org ) )
page . set ( " imageWidth " , str ( self . width_org ) )
page . set ( " type " , " content " )
page . set ( " readingDirection " , " left-to-right " )
page . set ( " textLineOrder " , " top-to-bottom " )
page_print_sub = ET . SubElement ( page , " PrintSpace " )
coord_page = ET . SubElement ( page_print_sub , " Coords " )
points_page_print = " "
for lmm in range ( len ( self . cont_page [ 0 ] ) ) :
if len ( self . cont_page [ 0 ] [ lmm ] ) == 2 :
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] ) / self . scale_x ) )
points_page_print = points_page_print + " , "
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 1 ] ) / self . scale_y ) )
else :
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] [ 0 ] ) / self . scale_x ) )
points_page_print = points_page_print + " , "
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 + " "
coord_page . set ( " points " , points_page_print )
if len ( contours ) > 0 :
id_indexer = 0
id_indexer_l = 0
for mm in range ( len ( found_polygons_text_region ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
textregion . set ( " id " , " r " + str ( id_indexer ) )
id_indexer + = 1
textregion . set ( " type " , " paragraph " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_text_region [ mm ] ) ) :
if len ( found_polygons_text_region [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_text_region [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
for j in range ( len ( all_found_texline_polygons [ mm ] ) ) :
textline = ET . SubElement ( textregion , " TextLine " )
textline . set ( " id " , " l " + str ( id_indexer_l ) )
id_indexer_l + = 1
coord = ET . SubElement ( textline , " Coords " )
texteq = ET . SubElement ( textline , " TextEquiv " )
uni = ET . SubElement ( texteq , " Unicode " )
uni . text = " "
# points = ET.SubElement(coord, 'Points')
points_co = " "
for l in range ( len ( all_found_texline_polygons [ mm ] [ j ] ) ) :
# point = ET.SubElement(coord, 'Point')
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
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 ) )
if l < ( len ( all_found_texline_polygons [ mm ] [ j ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord . set ( " points " , points_co )
texteqreg = ET . SubElement ( textregion , " TextEquiv " )
unireg = ET . SubElement ( texteqreg , " Unicode " )
unireg . text = " "
# print(dir_of_image)
print ( self . f_name )
# print(os.path.join(dir_of_image, self.f_name) + ".xml")
tree = ET . ElementTree ( data )
tree . write ( os . path . join ( dir_of_image , self . f_name ) + " .xml " )
def write_into_page_xml_full ( self , contours , contours_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 ) :
found_polygons_text_region = contours
found_polygons_text_region_h = contours_h
# create the file structure
data = ET . Element ( " PcGts " )
data . set ( " xmlns " , " http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15 " )
data . set ( " xmlns:xsi " , " http://www.w3.org/2001/XMLSchema-instance " )
data . set ( " xsi:schemaLocation " , " http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15 " )
metadata = ET . SubElement ( data , " Metadata " )
author = ET . SubElement ( metadata , " Creator " )
author . text = " SBB_QURATOR "
created = ET . SubElement ( metadata , " Created " )
created . text = " 2019-06-17T18:15:12 "
changetime = ET . SubElement ( metadata , " LastChange " )
changetime . text = " 2019-06-17T18:15:12 "
page = ET . SubElement ( data , " Page " )
page . set ( " imageFilename " , self . image_dir )
page . set ( " imageHeight " , str ( self . height_org ) )
page . set ( " imageWidth " , str ( self . width_org ) )
page . set ( " type " , " content " )
page . set ( " readingDirection " , " left-to-right " )
page . set ( " textLineOrder " , " top-to-bottom " )
page_print_sub = ET . SubElement ( page , " PrintSpace " )
coord_page = ET . SubElement ( page_print_sub , " Coords " )
points_page_print = " "
for lmm in range ( len ( self . cont_page [ 0 ] ) ) :
if len ( self . cont_page [ 0 ] [ lmm ] ) == 2 :
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] ) / self . scale_x ) )
points_page_print = points_page_print + " , "
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 1 ] ) / self . scale_y ) )
else :
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] [ 0 ] ) / self . scale_x ) )
points_page_print = points_page_print + " , "
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 + " "
coord_page . set ( " points " , points_page_print )
if len ( contours ) > 0 :
region_order = ET . SubElement ( page , " ReadingOrder " )
region_order_sub = ET . SubElement ( region_order , " OrderedGroup " )
region_order_sub . set ( " id " , " ro357564684568544579089 " )
# args_sort=order_of_texts
for vj in order_of_texts :
name = " coord_text_ " + str ( vj )
name = ET . SubElement ( region_order_sub , " RegionRefIndexed " )
name . set ( " index " , str ( order_of_texts [ vj ] ) )
name . set ( " regionRef " , id_of_texts [ vj ] )
id_of_marginalia = [ ]
indexer_region = len ( contours ) + len ( contours_h )
for vm in range ( len ( found_polygons_marginals ) ) :
id_of_marginalia . append ( " r " + str ( indexer_region ) )
name = " coord_text_ " + str ( indexer_region )
name = ET . SubElement ( region_order_sub , " RegionRefIndexed " )
name . set ( " index " , str ( indexer_region ) )
name . set ( " regionRef " , " r " + str ( indexer_region ) )
indexer_region + = 1
id_indexer = 0
id_indexer_l = 0
for mm in range ( len ( found_polygons_text_region ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
textregion . set ( " id " , " r " + str ( id_indexer ) )
id_indexer + = 1
textregion . set ( " type " , " paragraph " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_text_region [ mm ] ) ) :
if len ( found_polygons_text_region [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_text_region [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
for j in range ( len ( all_found_texline_polygons [ mm ] ) ) :
textline = ET . SubElement ( textregion , " TextLine " )
textline . set ( " id " , " l " + str ( id_indexer_l ) )
id_indexer_l + = 1
coord = ET . SubElement ( textline , " Coords " )
texteq = ET . SubElement ( textline , " TextEquiv " )
uni = ET . SubElement ( texteq , " Unicode " )
uni . text = " "
# points = ET.SubElement(coord, 'Points')
points_co = " "
for l in range ( len ( all_found_texline_polygons [ mm ] [ j ] ) ) :
# point = ET.SubElement(coord, 'Point')
if not self . curved_line :
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = 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 + " , "
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 = 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 + " , "
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 ( self . curved_line ) and np . abs ( slopes [ mm ] ) < = 45 :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
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 ( self . curved_line ) and np . abs ( slopes [ mm ] ) > 45 :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = 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 + " , "
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 = 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 + " , "
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 = points_co + " "
# print(points_co)
coord . set ( " points " , points_co )
texteqreg = ET . SubElement ( textregion , " TextEquiv " )
unireg = ET . SubElement ( texteqreg , " Unicode " )
unireg . text = " "
print ( len ( contours_h ) )
if len ( contours_h ) > 0 :
for mm in range ( len ( found_polygons_text_region_h ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
try :
id_indexer = id_indexer
id_indexer_l = id_indexer_l
except :
id_indexer = 0
id_indexer_l = 0
textregion . set ( " id " , " r " + str ( id_indexer ) )
id_indexer + = 1
textregion . set ( " type " , " header " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_text_region_h [ mm ] ) ) :
if len ( found_polygons_text_region_h [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_text_region_h [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region_h [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_text_region_h [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region_h [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_text_region_h [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
for j in range ( len ( all_found_texline_polygons_h [ mm ] ) ) :
textline = ET . SubElement ( textregion , " TextLine " )
textline . set ( " id " , " l " + str ( id_indexer_l ) )
id_indexer_l + = 1
coord = ET . SubElement ( textline , " Coords " )
texteq = ET . SubElement ( textline , " TextEquiv " )
uni = ET . SubElement ( texteq , " Unicode " )
uni . text = " "
# points = ET.SubElement(coord, 'Points')
points_co = " "
for l in range ( len ( all_found_texline_polygons_h [ mm ] [ j ] ) ) :
# point = ET.SubElement(coord, 'Point')
if not self . curved_line :
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] [ 0 ] + all_box_coord_h [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] [ 1 ] + all_box_coord_h [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] [ 0 ] [ 0 ] + all_box_coord_h [ mm ] [ 2 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + all_box_coord_h [ mm ] [ 0 ] + page_coord [ 0 ] ) / self . scale_y ) )
if self . curved_line :
if len ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ mm ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( all_found_texline_polygons_h [ 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_h [ mm ] [ j ] [ l ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if l < ( len ( all_found_texline_polygons_h [ mm ] [ j ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord . set ( " points " , points_co )
texteqreg = ET . SubElement ( textregion , " TextEquiv " )
unireg = ET . SubElement ( texteqreg , " Unicode " )
unireg . text = " "
if len ( found_polygons_drop_capitals ) > 0 :
id_indexer = len ( contours_h ) + len ( contours ) + len ( found_polygons_marginals )
for mm in range ( len ( found_polygons_drop_capitals ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
# id_indexer_l=id_indexer_l
textregion . set ( " id " , " r " + str ( id_indexer ) )
id_indexer + = 1
textregion . set ( " type " , " drop-capital " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_drop_capitals [ mm ] ) ) :
if len ( found_polygons_drop_capitals [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_drop_capitals [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
##for j in range(len(all_found_texline_polygons_h[mm])):
##textline=ET.SubElement(textregion, 'TextLine')
##textline.set('id','l'+str(id_indexer_l))
##id_indexer_l+=1
##coord = ET.SubElement(textline, 'Coords')
##texteq=ET.SubElement(textline, 'TextEquiv')
##uni=ET.SubElement(texteq, 'Unicode')
##uni.text = ' '
###points = ET.SubElement(coord, 'Points')
##points_co=''
##for l in range(len(all_found_texline_polygons_h[mm][j])):
###point = ET.SubElement(coord, 'Point')
##if not curved_line:
###point.set('x',str(found_polygons[j][l][0]))
###point.set('y',str(found_polygons[j][l][1]))
##if len(all_found_texline_polygons_h[mm][j][l])==2:
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x) )
##points_co=points_co+','
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
##else:
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x ) )
##points_co=points_co+','
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
##if curved_line:
##if len(all_found_texline_polygons_h[mm][j][l])==2:
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
##+page_coord[2])/self.scale_x) )
##points_co=points_co+','
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
##+page_coord[0])/self.scale_y) )
##else:
##points_co=points_co+str( int( ( all_found_texline_polygons_h[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_h[mm][j][l][0][1]
##+page_coord[0])/self.scale_y) )
##if l<(len(all_found_texline_polygons_h[mm][j])-1):
##points_co=points_co+' '
###print(points_co)
####coord.set('points',points_co)
texteqreg = ET . SubElement ( textregion , " TextEquiv " )
unireg = ET . SubElement ( texteqreg , " Unicode " )
unireg . text = " "
try :
try :
###id_indexer=id_indexer
id_indexer_l = id_indexer_l
except :
###id_indexer=0
id_indexer_l = 0
for mm in range ( len ( found_polygons_marginals ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
textregion . set ( " id " , id_of_marginalia [ mm ] )
textregion . set ( " type " , " marginalia " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_marginals [ mm ] ) ) :
if len ( found_polygons_marginals [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_marginals [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
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 " )
texteq = ET . SubElement ( textline , " TextEquiv " )
uni = ET . SubElement ( texteq , " Unicode " )
uni . text = " "
# points = ET.SubElement(coord, 'Points')
points_co = " "
for l in range ( len ( all_found_texline_polygons_marginals [ mm ] [ j ] ) ) :
# point = ET.SubElement(coord, 'Point')
if not self . curved_line :
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co = 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 + " , "
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 = 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 + " , "
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 ) )
if self . curved_line :
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( all_found_texline_polygons_marginals [ 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_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 + " "
# print(points_co)
coord . set ( " points " , points_co )
texteqreg = ET . SubElement ( textregion , " TextEquiv " )
unireg = ET . SubElement ( texteqreg , " Unicode " )
unireg . text = " "
except :
pass
try :
id_indexer = len ( contours_h ) + len ( contours ) + 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 " + str ( id_indexer ) )
id_indexer + = 1
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_text_region_img [ mm ] ) ) :
if len ( found_polygons_text_region_img [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_text_region_img [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region_img [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_text_region_img [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
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 = points_co + " "
coord_text . set ( " points " , points_co )
except :
pass
try :
for mm in range ( len ( found_polygons_tables ) ) :
textregion = ET . SubElement ( page , " TableRegion " )
textregion . set ( " id " , " r " + str ( id_indexer ) )
id_indexer + = 1
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_tables [ mm ] ) ) :
if len ( found_polygons_tables [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_tables [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_tables [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_tables [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_tables [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_tables [ mm ] ) - 1 ) :
points_co = points_co + " "
coord_text . set ( " points " , points_co )
except :
pass
print ( dir_of_image )
print ( self . f_name )
print ( os . path . join ( dir_of_image , self . f_name ) + " .xml " )
tree = ET . ElementTree ( data )
tree . write ( os . path . join ( dir_of_image , self . f_name ) + " .xml " )
def write_into_page_xml ( self , contours , 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 ) :
found_polygons_text_region = contours
##found_polygons_text_region_h=contours_h
# create the file structure
data = ET . Element ( " PcGts " )
data . set ( " xmlns " , " http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15 " )
data . set ( " xmlns:xsi " , " http://www.w3.org/2001/XMLSchema-instance " )
data . set ( " xsi:schemaLocation " , " http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15 " )
metadata = ET . SubElement ( data , " Metadata " )
author = ET . SubElement ( metadata , " Creator " )
author . text = " SBB_QURATOR "
created = ET . SubElement ( metadata , " Created " )
created . text = " 2019-06-17T18:15:12 "
changetime = ET . SubElement ( metadata , " LastChange " )
changetime . text = " 2019-06-17T18:15:12 "
page = ET . SubElement ( data , " Page " )
page . set ( " imageFilename " , self . image_dir )
page . set ( " imageHeight " , str ( self . height_org ) )
page . set ( " imageWidth " , str ( self . width_org ) )
page . set ( " type " , " content " )
page . set ( " readingDirection " , " left-to-right " )
page . set ( " textLineOrder " , " top-to-bottom " )
page_print_sub = ET . SubElement ( page , " PrintSpace " )
coord_page = ET . SubElement ( page_print_sub , " Coords " )
points_page_print = " "
for lmm in range ( len ( self . cont_page [ 0 ] ) ) :
if len ( self . cont_page [ 0 ] [ lmm ] ) == 2 :
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] ) / self . scale_x ) )
points_page_print = points_page_print + " , "
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 1 ] ) / self . scale_y ) )
else :
points_page_print = points_page_print + str ( int ( ( self . cont_page [ 0 ] [ lmm ] [ 0 ] [ 0 ] ) / self . scale_x ) )
points_page_print = points_page_print + " , "
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 + " "
coord_page . set ( " points " , points_page_print )
if len ( contours ) > 0 :
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_ " + str ( vj )
name = ET . SubElement ( region_order_sub , " RegionRefIndexed " )
name . set ( " index " , str ( indexer_region ) )
name . set ( " regionRef " , id_of_texts [ vj ] )
indexer_region + = 1
id_of_marginalia = [ ]
for vm in range ( len ( found_polygons_marginals ) ) :
id_of_marginalia . append ( " r " + str ( indexer_region ) )
name = " coord_text_ " + str ( indexer_region )
name = ET . SubElement ( region_order_sub , " RegionRefIndexed " )
name . set ( " index " , str ( indexer_region ) )
name . set ( " regionRef " , " r " + str ( indexer_region ) )
indexer_region + = 1
id_indexer = 0
id_indexer_l = 0
for mm in range ( len ( found_polygons_text_region ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
textregion . set ( " id " , " r " + str ( id_indexer ) )
id_indexer + = 1
textregion . set ( " type " , " paragraph " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_text_region [ mm ] ) ) :
if len ( found_polygons_text_region [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_text_region [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_text_region [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
for j in range ( len ( all_found_texline_polygons [ mm ] ) ) :
textline = ET . SubElement ( textregion , " TextLine " )
textline . set ( " id " , " l " + str ( id_indexer_l ) )
id_indexer_l + = 1
coord = ET . SubElement ( textline , " Coords " )
texteq = ET . SubElement ( textline , " TextEquiv " )
uni = ET . SubElement ( texteq , " Unicode " )
uni . text = " "
# points = ET.SubElement(coord, 'Points')
points_co = " "
for l in range ( len ( all_found_texline_polygons [ mm ] [ j ] ) ) :
# point = ET.SubElement(coord, 'Point')
if not self . curved_line :
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = 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 + " , "
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 = 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 + " , "
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 ( self . curved_line ) and abs ( slopes [ mm ] ) < = 45 :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
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 ( self . curved_line ) and abs ( slopes [ mm ] ) > 45 :
if len ( all_found_texline_polygons [ mm ] [ j ] [ l ] ) == 2 :
points_co = 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 + " , "
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 = 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 + " , "
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 = points_co + " "
# print(points_co)
coord . set ( " points " , points_co )
texteqreg = ET . SubElement ( textregion , " TextEquiv " )
unireg = ET . SubElement ( texteqreg , " Unicode " )
unireg . text = " "
###print(len(contours_h))
###if len(contours_h)>0:
###for mm in range(len(found_polygons_text_region_h)):
###textregion=ET.SubElement(page, 'TextRegion')
###try:
###id_indexer=id_indexer
###id_indexer_l=id_indexer_l
###except:
###id_indexer=0
###id_indexer_l=0
###textregion.set('id','r'+str(id_indexer))
###id_indexer+=1
###textregion.set('type','header')
####if mm==0:
#### textregion.set('type','header')
####else:
#### textregion.set('type','paragraph')
###coord_text = ET.SubElement(textregion, 'Coords')
###points_co=''
###for lmm in range(len(found_polygons_text_region_h[mm])):
###if len(found_polygons_text_region_h[mm][lmm])==2:
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
###else:
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
###if lmm<(len(found_polygons_text_region_h[mm])-1):
###points_co=points_co+' '
####print(points_co)
###coord_text.set('points',points_co)
###for j in range(len(all_found_texline_polygons_h[mm])):
###textline=ET.SubElement(textregion, 'TextLine')
###textline.set('id','l'+str(id_indexer_l))
###id_indexer_l+=1
###coord = ET.SubElement(textline, 'Coords')
###texteq=ET.SubElement(textline, 'TextEquiv')
###uni=ET.SubElement(texteq, 'Unicode')
###uni.text = ' '
####points = ET.SubElement(coord, 'Points')
###points_co=''
###for l in range(len(all_found_texline_polygons_h[mm][j])):
####point = ET.SubElement(coord, 'Point')
####point.set('x',str(found_polygons[j][l][0]))
####point.set('y',str(found_polygons[j][l][1]))
###if len(all_found_texline_polygons_h[mm][j][l])==2:
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0] +page_coord[2]
###+all_box_coord_h[mm][2])/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1] +page_coord[0]
###+all_box_coord_h[mm][0])/self.scale_y) )
###else:
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0] +page_coord[2]
###+all_box_coord_h[mm][2])/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1] +page_coord[0]
###+all_box_coord_h[mm][0])/self.scale_y) )
###if l<(len(all_found_texline_polygons_h[mm][j])-1):
###points_co=points_co+' '
####print(points_co)
###coord.set('points',points_co)
###texteqreg=ET.SubElement(textregion, 'TextEquiv')
###unireg=ET.SubElement(texteqreg, 'Unicode')
###unireg.text = ' '
try :
# id_indexer_l=0
try :
###id_indexer=id_indexer
id_indexer_l = id_indexer_l
except :
###id_indexer=0
id_indexer_l = 0
for mm in range ( len ( found_polygons_marginals ) ) :
textregion = ET . SubElement ( page , " TextRegion " )
textregion . set ( " id " , id_of_marginalia [ mm ] )
textregion . set ( " type " , " marginalia " )
# if mm==0:
# textregion.set('type','header')
# else:
# textregion.set('type','paragraph')
coord_text = ET . SubElement ( textregion , " Coords " )
points_co = " "
for lmm in range ( len ( found_polygons_marginals [ mm ] ) ) :
if len ( found_polygons_marginals [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( found_polygons_marginals [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_marginals [ mm ] ) - 1 ) :
points_co = points_co + " "
# print(points_co)
coord_text . set ( " points " , points_co )
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 " )
texteq = ET . SubElement ( textline , " TextEquiv " )
uni = ET . SubElement ( texteq , " Unicode " )
uni . text = " "
# points = ET.SubElement(coord, 'Points')
points_co = " "
for l in range ( len ( all_found_texline_polygons_marginals [ mm ] [ j ] ) ) :
# point = ET.SubElement(coord, 'Point')
if not self . curved_line :
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co = 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 + " , "
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 = 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 + " , "
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 ) )
if self . curved_line :
if len ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] ) == 2 :
points_co = points_co + str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
points_co = points_co + str ( int ( ( all_found_texline_polygons_marginals [ mm ] [ j ] [ l ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( all_found_texline_polygons_marginals [ 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_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 + " "
# print(points_co)
coord . set ( " points " , points_co )
except :
pass
try :
for mm in range ( len ( found_polygons_text_region_img ) ) :
textregion = ET . SubElement ( page , " ImageRegion " )
textregion . set ( " id " , " r " + str ( 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 = points_co + str ( int ( ( found_polygons_text_region_img [ mm ] [ lmm , 0 , 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + " , "
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 = points_co + " "
coord_text . set ( " points " , points_co )
###for mm in range(len(found_polygons_text_region_img)):
###textregion=ET.SubElement(page, 'ImageRegion')
###textregion.set('id','r'+str(id_indexer))
###id_indexer+=1
###coord_text = ET.SubElement(textregion, 'Coords')
###print(found_polygons_text_region_img[mm])
###points_co=''
###for lmm in range(len(found_polygons_text_region_img[mm])):
###print(len(found_polygons_text_region_img[mm][lmm]))
###if len(found_polygons_text_region_img[mm][lmm])==2:
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
###else:
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
###points_co=points_co+','
###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=points_co+' '
###coord_text.set('points',points_co)
except :
pass
####try:
####for mm in range(len(found_polygons_tables)):
####textregion=ET.SubElement(page, 'TableRegion')
####textregion.set('id','r'+str(id_indexer))
####id_indexer+=1
####coord_text = ET.SubElement(textregion, 'Coords')
####points_co=''
####for lmm in range(len(found_polygons_tables[mm])):
####if len(found_polygons_tables[mm][lmm])==2:
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
####points_co=points_co+','
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
####else:
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
####points_co=points_co+','
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
####if lmm<(len(found_polygons_tables[mm])-1):
####points_co=points_co+' '
####coord_text.set('points',points_co)
####except:
####pass
"""
try :
for mm in range ( len ( found_polygons_drop_capitals ) ) :
textregion = ET . SubElement ( page , ' DropCapitals ' )
textregion . set ( ' id ' , ' r ' + str ( id_indexer ) )
id_indexer + = 1
coord_text = ET . SubElement ( textregion , ' Coords ' )
points_co = ' '
for lmm in range ( len ( found_polygons_drop_capitals [ mm ] ) ) :
if len ( found_polygons_drop_capitals [ mm ] [ lmm ] ) == 2 :
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + ' , '
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
else :
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 0 ] [ 0 ] + page_coord [ 2 ] ) / self . scale_x ) )
points_co = points_co + ' , '
points_co = points_co + str ( int ( ( found_polygons_drop_capitals [ mm ] [ lmm ] [ 0 ] [ 1 ] + page_coord [ 0 ] ) / self . scale_y ) )
if lmm < ( len ( found_polygons_drop_capitals [ mm ] ) - 1 ) :
points_co = points_co + ' '
coord_text . set ( ' points ' , points_co )
except :
pass
"""
# print(dir_of_image)
print ( self . f_name )
# print(os.path.join(dir_of_image, self.f_name) + ".xml")
tree = ET . ElementTree ( data )
tree . write ( os . path . join ( dir_of_image , self . f_name ) + " .xml " )
# cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)
def add_tables_heuristic_to_layout ( self , image_regions_eraly_p , boxes , slope_mean_hor , spliter_y , peaks_neg_tot , image_revised ) :
image_revised_1 = delete_seperator_around ( spliter_y , peaks_neg_tot , image_revised )
img_comm_e = np . zeros ( image_revised_1 . shape )
img_comm = np . repeat ( img_comm_e [ : , : , np . newaxis ] , 3 , axis = 2 )
for indiv in np . unique ( image_revised_1 ) :
# print(indiv,'indd')
image_col = ( image_revised_1 == indiv ) * 255
img_comm_in = np . repeat ( image_col [ : , : , np . newaxis ] , 3 , axis = 2 )
img_comm_in = img_comm_in . astype ( np . uint8 )
imgray = cv2 . cvtColor ( img_comm_in , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours , hirarchy = cv2 . findContours ( thresh . copy ( ) , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
main_contours = filter_contours_area_of_image_tables ( thresh , contours , hirarchy , max_area = 1 , min_area = 0.0001 )
img_comm = cv2 . fillPoly ( img_comm , pts = main_contours , color = ( indiv , indiv , indiv ) )
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
# img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
img_comm = img_comm . astype ( np . uint8 )
if not isNaN ( slope_mean_hor ) :
image_revised_last = np . zeros ( ( image_regions_eraly_p . shape [ 0 ] , image_regions_eraly_p . shape [ 1 ] , 3 ) )
for i in range ( len ( boxes ) ) :
image_box = img_comm [ int ( boxes [ i ] [ 2 ] ) : int ( boxes [ i ] [ 3 ] ) , int ( boxes [ i ] [ 0 ] ) : int ( boxes [ i ] [ 1 ] ) , : ]
image_box_tabels_1 = ( image_box [ : , : , 0 ] == 7 ) * 1
contours_tab , _ = return_contours_of_image ( image_box_tabels_1 )
contours_tab = filter_contours_area_of_image_tables ( image_box_tabels_1 , contours_tab , _ , 1 , 0.001 )
image_box_tabels_1 = ( image_box [ : , : , 0 ] == 6 ) * 1
image_box_tabels_and_m_text = ( ( image_box [ : , : , 0 ] == 7 ) | ( image_box [ : , : , 0 ] == 1 ) ) * 1
image_box_tabels_and_m_text = image_box_tabels_and_m_text . astype ( np . uint8 )
image_box_tabels_1 = image_box_tabels_1 . astype ( np . uint8 )
image_box_tabels_1 = cv2 . dilate ( image_box_tabels_1 , self . kernel , iterations = 5 )
contours_table_m_text , _ = return_contours_of_image ( image_box_tabels_and_m_text )
image_box_tabels = np . repeat ( image_box_tabels_1 [ : , : , np . newaxis ] , 3 , axis = 2 )
image_box_tabels = image_box_tabels . astype ( np . uint8 )
imgray = cv2 . cvtColor ( image_box_tabels , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_line , hierachy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
y_min_main_line , y_max_main_line , _ = find_features_of_contours ( contours_line )
# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
y_min_main_tab , y_max_main_tab , _ = find_features_of_contours ( contours_tab )
cx_tab_m_text , cy_tab_m_text , x_min_tab_m_text , x_max_tab_m_text , y_min_tab_m_text , y_max_tab_m_text = find_new_features_of_contoures ( contours_table_m_text )
cx_tabl , cy_tabl , x_min_tabl , x_max_tabl , y_min_tabl , y_max_tabl , _ = find_new_features_of_contoures ( contours_tab )
if len ( y_min_main_tab ) > 0 :
y_down_tabs = [ ]
y_up_tabs = [ ]
for i_t in range ( len ( y_min_main_tab ) ) :
y_down_tab = [ ]
y_up_tab = [ ]
for i_l in range ( len ( y_min_main_line ) ) :
if y_min_main_tab [ i_t ] > y_min_main_line [ i_l ] and y_max_main_tab [ i_t ] > y_min_main_line [ i_l ] and y_min_main_tab [ i_t ] > y_max_main_line [ i_l ] and y_max_main_tab [ i_t ] > y_min_main_line [ i_l ] :
pass
elif y_min_main_tab [ i_t ] < y_max_main_line [ i_l ] and y_max_main_tab [ i_t ] < y_max_main_line [ i_l ] and y_max_main_tab [ i_t ] < y_min_main_line [ i_l ] and y_min_main_tab [ i_t ] < y_min_main_line [ i_l ] :
pass
elif np . abs ( y_max_main_line [ i_l ] - y_min_main_line [ i_l ] ) < 100 :
pass
else :
y_up_tab . append ( np . min ( [ y_min_main_line [ i_l ] , y_min_main_tab [ i_t ] ] ) )
y_down_tab . append ( np . max ( [ y_max_main_line [ i_l ] , y_max_main_tab [ i_t ] ] ) )
if len ( y_up_tab ) == 0 :
for v_n in range ( len ( cx_tab_m_text ) ) :
if cx_tabl [ i_t ] < = x_max_tab_m_text [ v_n ] and cx_tabl [ i_t ] > = x_min_tab_m_text [ v_n ] and cy_tabl [ i_t ] < = y_max_tab_m_text [ v_n ] and cy_tabl [ i_t ] > = y_min_tab_m_text [ v_n ] and cx_tabl [ i_t ] != cx_tab_m_text [ v_n ] and cy_tabl [ i_t ] != cy_tab_m_text [ v_n ] :
y_up_tabs . append ( y_min_tab_m_text [ v_n ] )
y_down_tabs . append ( y_max_tab_m_text [ v_n ] )
# y_up_tabs.append(y_min_main_tab[i_t])
# y_down_tabs.append(y_max_main_tab[i_t])
else :
y_up_tabs . append ( np . min ( y_up_tab ) )
y_down_tabs . append ( np . max ( y_down_tab ) )
else :
y_down_tabs = [ ]
y_up_tabs = [ ]
pass
for ii in range ( len ( y_up_tabs ) ) :
image_box [ y_up_tabs [ ii ] : y_down_tabs [ ii ] , : , 0 ] = 7
image_revised_last [ int ( boxes [ i ] [ 2 ] ) : int ( boxes [ i ] [ 3 ] ) , int ( boxes [ i ] [ 0 ] ) : int ( boxes [ i ] [ 1 ] ) , : ] = image_box [ : , : , : ]
else :
for i in range ( len ( boxes ) ) :
image_box = img_comm [ int ( boxes [ i ] [ 2 ] ) : int ( boxes [ i ] [ 3 ] ) , int ( boxes [ i ] [ 0 ] ) : int ( boxes [ i ] [ 1 ] ) , : ]
image_revised_last [ int ( boxes [ i ] [ 2 ] ) : int ( boxes [ i ] [ 3 ] ) , int ( boxes [ i ] [ 0 ] ) : int ( boxes [ i ] [ 1 ] ) , : ] = image_box [ : , : , : ]
##plt.figure(figsize=(20,20))
##plt.imshow(image_box[:,:,0])
##plt.show()
return image_revised_last
def return_teilwiese_deskewed_lines ( self , text_regions_p , textline_rotated ) :
kernel = np . ones ( ( 5 , 5 ) , np . uint8 )
textline_rotated = cv2 . erode ( textline_rotated , kernel , iterations = 1 )
textline_rotated_new = np . zeros ( textline_rotated . shape )
rgb_m = 1
rgb_h = 2
cnt_m , boxes_m = return_contours_of_interested_region_and_bounding_box ( text_regions_p , rgb_m )
cnt_h , boxes_h = return_contours_of_interested_region_and_bounding_box ( text_regions_p , rgb_h )
areas_cnt_m = np . array ( [ cv2 . contourArea ( cnt_m [ j ] ) for j in range ( len ( cnt_m ) ) ] )
argmax = np . argmax ( areas_cnt_m )
# plt.imshow(textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]])
# plt.show()
for argmax in range ( len ( boxes_m ) ) :
textline_text_region = textline_rotated [ boxes_m [ argmax ] [ 1 ] : boxes_m [ argmax ] [ 1 ] + boxes_m [ argmax ] [ 3 ] , boxes_m [ argmax ] [ 0 ] : boxes_m [ argmax ] [ 0 ] + boxes_m [ argmax ] [ 2 ] ]
textline_text_region_revised = self . seperate_lines_new ( textline_text_region , 0 )
# except:
# textline_text_region_revised=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2] ]
textline_rotated_new [ boxes_m [ argmax ] [ 1 ] : boxes_m [ argmax ] [ 1 ] + boxes_m [ argmax ] [ 3 ] , boxes_m [ argmax ] [ 0 ] : boxes_m [ argmax ] [ 0 ] + boxes_m [ argmax ] [ 2 ] ] = textline_text_region_revised [ : , : ]
# textline_rotated_new[textline_rotated_new>0]=1
# textline_rotated_new[textline_rotated_new<0]=0
# plt.imshow(textline_rotated_new)
# plt.show()
def get_regions_from_xy_neu ( self , img ) :
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 )
gaussian_filter = False
patches = True
binary = True
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 ) # 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 ( patches , 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()
# sys.exit()
prediction_regions_org = prediction_regions_org [ : , : , 0 ]
gaussian_filter = False
patches = 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 ) # otsu_copy(img)
img = img . astype ( np . uint16 )
if median_blur :
img = cv2 . medianBlur ( img , 5 )
img = cv2 . medianBlur ( img , 5 )
if gaussian_filter :
img = cv2 . GaussianBlur ( img , ( 5 , 5 ) , 0 )
img = img . astype ( np . uint16 )
prediction_regions_orgt = self . do_prediction ( patches , img , model_region )
prediction_regions_orgt = resize_image ( prediction_regions_orgt , img_height_h , img_width_h )
# plt.imshow(prediction_regions_orgt[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_orgt = prediction_regions_orgt [ : , : , 0 ]
mask_texts_longshot = ( prediction_regions_orgt [ : , : ] == 1 ) * 1
mask_texts_longshot = np . uint8 ( mask_texts_longshot )
# mask_texts_longshot = cv2.dilate(mask_texts_longshot[:,:], self.kernel, iterations=2)
pixel_img = 1
polygons_of_only_texts_longshot = return_contours_of_interested_region ( mask_texts_longshot , pixel_img )
longshot_true = np . zeros ( mask_texts_longshot . shape )
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
longshot_true = cv2 . fillPoly ( longshot_true , pts = polygons_of_only_texts_longshot , color = ( 1 , 1 , 1 ) )
# plt.imshow(longshot_true)
# plt.show()
gaussian_filter = False
patches = 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 ) )
one_third_upper_ny = int ( img . shape [ 0 ] / 3.0 )
img = img [ 0 : one_third_upper_ny , : , : ]
if binary :
img = otsu_copy_binary ( img ) # 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_longshot_one_third = self . do_prediction ( patches , img , model_region )
prediction_regions_longshot_one_third = resize_image ( prediction_regions_longshot_one_third , one_third_upper_ny , img_width_h )
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * ratio_y ) , int ( img_org . shape [ 1 ] * ratio_x ) )
img = img [ one_third_upper_ny : int ( 2 * one_third_upper_ny ) , : , : ]
if binary :
img = otsu_copy_binary ( img ) # 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_longshot_one_third_middle = self . do_prediction ( patches , img , model_region )
prediction_regions_longshot_one_third_middle = resize_image ( prediction_regions_longshot_one_third_middle , one_third_upper_ny , img_width_h )
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * ratio_y ) , int ( img_org . shape [ 1 ] * ratio_x ) )
img = img [ int ( 2 * one_third_upper_ny ) : , : , : ]
if binary :
img = otsu_copy_binary ( img ) # 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_longshot_one_third_down = self . do_prediction ( patches , img , model_region )
prediction_regions_longshot_one_third_down = resize_image ( prediction_regions_longshot_one_third_down , img_height_h - int ( 2 * one_third_upper_ny ) , img_width_h )
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_longshot = np . zeros ( ( img_height_h , img_width_h ) )
# prediction_regions_longshot=prediction_regions_longshot[:,:,0]
# prediction_regions_longshot[0:one_third_upper_ny,:]=prediction_regions_longshot_one_third[:,:,0]
# prediction_regions_longshot[one_third_upper_ny:int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_middle[:,:,0]
# prediction_regions_longshot[int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_down[:,:,0]
prediction_regions_longshot = longshot_true [ : , : ]
# plt.imshow(prediction_regions_longshot)
# plt.show()
gaussian_filter = False
patches = True
binary = False
ratio_x = 1 # 1.1
ratio_y = 1
median_blur = False
# img= resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
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 ) # 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 = self . do_prediction ( patches , img , model_region )
text_region1 = resize_image ( prediction_regions , img_height_h , img_width_h )
# plt.imshow(text_region1[:,:,0])
# plt.show()
ratio_x = 1
ratio_y = 1.2 # 1.3
binary = False
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 ) # 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 = self . do_prediction ( patches , img , model_region )
text_region2 = resize_image ( prediction_regions , img_height_h , img_width_h )
# plt.imshow(text_region2[:,:,0])
# plt.show()
session_region . close ( )
del model_region
del session_region
gc . collect ( )
# text_region1=text_region1[:,:,0]
# text_region2=text_region2[:,:,0]
# text_region1[(text_region1[:,:]==2) & (text_region2[:,:]==1)]=1
mask_zeros_from_1 = ( text_region2 [ : , : , 0 ] == 0 ) * 1
# mask_text_from_1=(text_region1[:,:,0]==1)*1
mask_img_text_region1 = ( text_region1 [ : , : , 0 ] == 2 ) * 1
text_region2_1st_channel = text_region1 [ : , : , 0 ]
text_region2_1st_channel [ mask_zeros_from_1 == 1 ] = 0
##text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
# text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
mask_lines1 = ( text_region1 [ : , : , 0 ] == 3 ) * 1
mask_lines2 = ( text_region2 [ : , : , 0 ] == 3 ) * 1
mask_lines2 [ mask_lines1 [ : , : ] == 1 ] = 1
# plt.imshow(text_region2_1st_channel)
# plt.show()
text_region2_1st_channel = cv2 . erode ( text_region2_1st_channel [ : , : ] , self . kernel , iterations = 4 )
# plt.imshow(text_region2_1st_channel)
# plt.show()
text_region2_1st_channel = cv2 . dilate ( text_region2_1st_channel [ : , : ] , self . kernel , iterations = 4 )
text_region2_1st_channel [ mask_lines2 [ : , : ] == 1 ] = 3
# text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
# only in the case of model 3
text_region2_1st_channel [ ( prediction_regions_longshot [ : , : ] == 1 ) & ( text_region2_1st_channel [ : , : ] == 2 ) ] = 1
text_region2_1st_channel [ ( prediction_regions_org [ : , : ] == 2 ) & ( text_region2_1st_channel [ : , : ] == 0 ) ] = 2
# text_region2_1st_channel[prediction_regions_org[:,:]==0]=0
# plt.imshow(text_region2_1st_channel)
# plt.show()
# text_region2_1st_channel[:,:400]=0
mask_texts_only = ( text_region2_1st_channel [ : , : ] == 1 ) * 1
mask_images_only = ( text_region2_1st_channel [ : , : ] == 2 ) * 1
mask_lines_only = ( text_region2_1st_channel [ : , : ] == 3 ) * 1
pixel_img = 1
polygons_of_only_texts = return_contours_of_interested_region ( mask_texts_only , pixel_img )
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 )
text_regions_p_true = np . zeros ( text_region2_1st_channel . shape )
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_lines , color = ( 3 , 3 , 3 ) )
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_images , color = ( 2 , 2 , 2 ) )
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_texts , color = ( 1 , 1 , 1 ) )
##print(np.unique(text_regions_p_true))
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
return text_regions_p_true # text_region2_1st_channel
def get_regions_from_xy ( self , img ) :
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 )
gaussian_filter = False
patches = True
binary = True
ratio_x = 1
ratio_y = 1
median_blur = False
if binary :
img = otsu_copy_binary ( img ) # 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 ( patches , img , model_region )
###plt.imshow(prediction_regions_org[:,:,0])
###plt.show()
##sys.exit()
prediction_regions_org = prediction_regions_org [ : , : , 0 ]
gaussian_filter = False
patches = True
binary = False
ratio_x = 1.1
ratio_y = 1
median_blur = False
# img= resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
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 ) # 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 = self . do_prediction ( patches , img , model_region )
text_region1 = resize_image ( prediction_regions , img_height_h , img_width_h )
ratio_x = 1
ratio_y = 1.1
binary = False
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 ) # 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 = self . do_prediction ( patches , img , model_region )
text_region2 = resize_image ( prediction_regions , img_height_h , img_width_h )
session_region . close ( )
del model_region
del session_region
gc . collect ( )
mask_zeros_from_1 = ( text_region1 [ : , : , 0 ] == 0 ) * 1
# mask_text_from_1=(text_region1[:,:,0]==1)*1
mask_img_text_region1 = ( text_region1 [ : , : , 0 ] == 2 ) * 1
text_region2_1st_channel = text_region2 [ : , : , 0 ]
text_region2_1st_channel [ mask_zeros_from_1 == 1 ] = 0
text_region2_1st_channel [ mask_img_text_region1 [ : , : ] == 1 ] = 2
# text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
mask_lines1 = ( text_region1 [ : , : , 0 ] == 3 ) * 1
mask_lines2 = ( text_region2 [ : , : , 0 ] == 3 ) * 1
mask_lines2 [ mask_lines1 [ : , : ] == 1 ] = 1
##plt.imshow(text_region2_1st_channel)
##plt.show()
text_region2_1st_channel = cv2 . erode ( text_region2_1st_channel [ : , : ] , self . kernel , iterations = 5 )
##plt.imshow(text_region2_1st_channel)
##plt.show()
text_region2_1st_channel = cv2 . dilate ( text_region2_1st_channel [ : , : ] , self . kernel , iterations = 5 )
text_region2_1st_channel [ mask_lines2 [ : , : ] == 1 ] = 3
text_region2_1st_channel [ ( prediction_regions_org [ : , : ] == 1 ) & ( text_region2_1st_channel [ : , : ] == 2 ) ] = 1
text_region2_1st_channel [ prediction_regions_org [ : , : ] == 3 ] = 3
##plt.imshow(text_region2_1st_channel)
##plt.show()
return text_region2_1st_channel
def get_regions_from_xy_2models_ens ( self , img ) :
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
patches = False
binary = False
ratio_x = 1
ratio_y = 1
img = resize_image ( img_org , int ( img_org . shape [ 0 ] * ratio_y ) , int ( img_org . shape [ 1 ] * ratio_x ) )
prediction_regions_long = self . do_prediction ( patches , img , model_region )
prediction_regions_long = resize_image ( prediction_regions_long , img_height_h , img_width_h )
gaussian_filter = False
patches = True
binary = False
ratio_x = 1
ratio_y = 1.2
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 ) # 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_y = self . do_prediction ( patches , img , model_region )
prediction_regions_org_y = resize_image ( prediction_regions_org_y , img_height_h , img_width_h )
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_org_y = prediction_regions_org_y [ : , : , 0 ]
mask_zeros_y = ( prediction_regions_org_y [ : , : ] == 0 ) * 1
ratio_x = 1.2
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 ) # 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 ( patches , 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()
# sys.exit()
prediction_regions_org = prediction_regions_org [ : , : , 0 ]
prediction_regions_org [ ( prediction_regions_org [ : , : ] == 1 ) & ( mask_zeros_y [ : , : ] == 1 ) ] = 0
prediction_regions_org [ ( prediction_regions_long [ : , : , 0 ] == 1 ) & ( prediction_regions_org [ : , : ] == 2 ) ] = 1
session_region . close ( )
del model_region
del session_region
gc . collect ( )
return prediction_regions_org
def get_regions_from_xy_2models ( self , img , is_image_enhanced ) :
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
patches = True
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 ) # 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_y = self . do_prediction ( patches , 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()
# sys.exit()
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 ) # 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 ( patches , 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()
##sys.exit()
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 ( )
###K.clear_session()
model_region , session_region = self . start_new_session_and_model ( self . model_region_dir_p2 )
gaussian_filter = False
patches = True
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 ) # 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_org2 = self . do_prediction ( patches , img , model_region )
prediction_regions_org2 = resize_image ( prediction_regions_org2 , img_height_h , img_width_h )
# plt.imshow(prediction_regions_org2[:,:,0])
# plt.show()
# sys.exit()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region . close ( )
del model_region
del session_region
gc . collect ( )
###K.clear_session()
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
print ( rate_two_models , " ratio_of_two_models " )
if is_image_enhanced and rate_two_models < 95.50 : # 98.45:
pass
else :
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
# if is_image_enhanced:
# pass
# else:
# model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
# gaussian_filter=False
# patches=True
# 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)#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_org2=self.do_prediction(patches,img,model_region)
# prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
##sys.exit()
###prediction_regions_org=prediction_regions_org[:,:,0]
# session_region.close()
# del model_region
# del session_region
# gc.collect()
####K.clear_session()
# 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[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
###prediction_regions_org[mask_lines2[:,:]==1]=3
# prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
# text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
# print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
# 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[:,:]=text_regions_p_1[:,:]
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_lines , color = ( 3 , 3 , 3 ) )
##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
text_regions_p_true [ : , : ] [ mask_images_only [ : , : ] == 1 ] = 2
text_regions_p_true = cv2 . fillPoly ( text_regions_p_true , pts = polygons_of_only_texts , color = ( 1 , 1 , 1 ) )
##print(np.unique(text_regions_p_true))
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
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 ( )
return text_regions_p_true
def write_images_into_directory ( self , img_contoures , dir_of_cropped_imgs , image_page ) :
index = 0
for cont_ind in img_contoures :
# cont_ind[:,0,0]=cont_ind[:,0,0]/self.scale_x
# cont_ind[:,0,1]=cont_ind[:,0,1]/self.scale_y
x , y , w , h = cv2 . boundingRect ( cont_ind )
box = [ x , y , w , h ]
croped_page , page_coord = crop_image_inside_box ( box , image_page )
croped_page = resize_image ( croped_page , int ( croped_page . shape [ 0 ] / self . scale_y ) , int ( croped_page . shape [ 1 ] / self . scale_x ) )
path = os . path . join ( dir_of_cropped_imgs , self . f_name + " _ " + str ( index ) + " .jpg " )
cv2 . imwrite ( path , croped_page )
index + = 1
def get_marginals ( self , text_with_lines , text_regions , num_col , slope_deskew ) :
mask_marginals = np . zeros ( ( text_with_lines . shape [ 0 ] , text_with_lines . shape [ 1 ] ) )
mask_marginals = mask_marginals . astype ( np . uint8 )
text_with_lines = text_with_lines . astype ( np . uint8 )
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded = cv2 . erode ( text_with_lines , self . kernel , iterations = 5 )
if text_with_lines . shape [ 0 ] < = 1500 :
pass
elif text_with_lines . shape [ 0 ] > 1500 and text_with_lines . shape [ 0 ] < = 1800 :
text_with_lines = resize_image ( text_with_lines , int ( text_with_lines . shape [ 0 ] * 1.5 ) , text_with_lines . shape [ 1 ] )
text_with_lines = cv2 . erode ( text_with_lines , self . kernel , iterations = 5 )
text_with_lines = resize_image ( text_with_lines , text_with_lines_eroded . shape [ 0 ] , text_with_lines_eroded . shape [ 1 ] )
else :
text_with_lines = resize_image ( text_with_lines , int ( text_with_lines . shape [ 0 ] * 1.8 ) , text_with_lines . shape [ 1 ] )
text_with_lines = cv2 . erode ( text_with_lines , self . kernel , iterations = 7 )
text_with_lines = resize_image ( text_with_lines , text_with_lines_eroded . shape [ 0 ] , text_with_lines_eroded . shape [ 1 ] )
text_with_lines_y = text_with_lines . sum ( axis = 0 )
text_with_lines_y_eroded = text_with_lines_eroded . sum ( axis = 0 )
thickness_along_y_percent = text_with_lines_y_eroded . max ( ) / ( float ( text_with_lines . shape [ 0 ] ) ) * 100
# print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent < 30 :
min_textline_thickness = 8
elif thickness_along_y_percent > = 30 and thickness_along_y_percent < 50 :
min_textline_thickness = 20
else :
min_textline_thickness = 40
if thickness_along_y_percent > = 14 :
text_with_lines_y_rev = - 1 * text_with_lines_y [ : ]
# print(text_with_lines_y)
# print(text_with_lines_y_rev)
# plt.plot(text_with_lines_y)
# plt.show()
text_with_lines_y_rev = text_with_lines_y_rev - np . min ( text_with_lines_y_rev )
# plt.plot(text_with_lines_y_rev)
# plt.show()
sigma_gaus = 1
region_sum_0 = gaussian_filter1d ( text_with_lines_y , sigma_gaus )
region_sum_0_rev = gaussian_filter1d ( text_with_lines_y_rev , sigma_gaus )
# plt.plot(region_sum_0_rev)
# plt.show()
region_sum_0_updown = region_sum_0 [ len ( region_sum_0 ) : : - 1 ]
first_nonzero = next ( ( i for i , x in enumerate ( region_sum_0 ) if x ) , None )
last_nonzero = next ( ( i for i , x in enumerate ( region_sum_0_updown ) if x ) , None )
last_nonzero = len ( region_sum_0 ) - last_nonzero
##img_sum_0_smooth_rev=-region_sum_0
mid_point = ( last_nonzero + first_nonzero ) / 2.0
one_third_right = ( last_nonzero - mid_point ) / 3.0
one_third_left = ( mid_point - first_nonzero ) / 3.0
# img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks , _ = find_peaks ( text_with_lines_y_rev , height = 0 )
peaks = np . array ( peaks )
# print(region_sum_0[peaks])
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
# print(first_nonzero,last_nonzero,peaks)
peaks = peaks [ ( peaks > first_nonzero ) & ( ( peaks < last_nonzero ) ) ]
# print(first_nonzero,last_nonzero,peaks)
# print(region_sum_0[peaks]<10)
####peaks=peaks[region_sum_0[peaks]<25 ]
# print(region_sum_0[peaks])
peaks = peaks [ region_sum_0 [ peaks ] < min_textline_thickness ]
# print(peaks)
# print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col == 1 :
peaks_right = peaks [ peaks > mid_point ]
peaks_left = peaks [ peaks < mid_point ]
if num_col == 2 :
peaks_right = peaks [ peaks > ( mid_point + one_third_right ) ]
peaks_left = peaks [ peaks < ( mid_point - one_third_left ) ]
try :
point_right = np . min ( peaks_right )
except :
point_right = last_nonzero
try :
point_left = np . max ( peaks_left )
except :
point_left = first_nonzero
# print(point_left,point_right)
# print(text_regions.shape)
if point_right > = mask_marginals . shape [ 1 ] :
point_right = mask_marginals . shape [ 1 ] - 1
try :
mask_marginals [ : , point_left : point_right ] = 1
except :
mask_marginals [ : , : ] = 1
# print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated = rotate_image ( mask_marginals , - slope_deskew )
# print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum = mask_marginals_rotated . sum ( axis = 0 )
mask_marginals_rotated_sum [ mask_marginals_rotated_sum != 0 ] = 1
index_x = np . array ( range ( len ( mask_marginals_rotated_sum ) ) ) + 1
index_x_interest = index_x [ mask_marginals_rotated_sum == 1 ]
min_point_of_left_marginal = np . min ( index_x_interest ) - 16
max_point_of_right_marginal = np . max ( index_x_interest ) + 16
if min_point_of_left_marginal < 0 :
min_point_of_left_marginal = 0
if max_point_of_right_marginal > = text_regions . shape [ 1 ] :
max_point_of_right_marginal = text_regions . shape [ 1 ] - 1
# print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
# print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
# plt.imshow(mask_marginals)
# plt.show()
# plt.imshow(mask_marginals_rotated)
# plt.show()
text_regions [ ( mask_marginals_rotated [ : , : ] != 1 ) & ( text_regions [ : , : ] == 1 ) ] = 4
pixel_img = 4
min_area_text = 0.00001
polygons_of_marginals = return_contours_of_interested_region ( text_regions , pixel_img , min_area_text )
cx_text_only , cy_text_only , x_min_text_only , x_max_text_only , y_min_text_only , y_max_text_only , y_cor_x_min_main = find_new_features_of_contoures ( polygons_of_marginals )
text_regions [ ( text_regions [ : , : ] == 4 ) ] = 1
marginlas_should_be_main_text = [ ]
x_min_marginals_left = [ ]
x_min_marginals_right = [ ]
for i in range ( len ( cx_text_only ) ) :
x_width_mar = abs ( x_min_text_only [ i ] - x_max_text_only [ i ] )
y_height_mar = abs ( y_min_text_only [ i ] - y_max_text_only [ i ] )
# print(x_width_mar,y_height_mar,'y_height_mar')
if x_width_mar > 16 and y_height_mar / x_width_mar < 10 :
marginlas_should_be_main_text . append ( polygons_of_marginals [ i ] )
if x_min_text_only [ i ] < ( mid_point - one_third_left ) :
x_min_marginals_left_new = x_min_text_only [ i ]
if len ( x_min_marginals_left ) == 0 :
x_min_marginals_left . append ( x_min_marginals_left_new )
else :
x_min_marginals_left [ 0 ] = min ( x_min_marginals_left [ 0 ] , x_min_marginals_left_new )
else :
x_min_marginals_right_new = x_min_text_only [ i ]
if len ( x_min_marginals_right ) == 0 :
x_min_marginals_right . append ( x_min_marginals_right_new )
else :
x_min_marginals_right [ 0 ] = min ( x_min_marginals_right [ 0 ] , x_min_marginals_right_new )
if len ( x_min_marginals_left ) == 0 :
x_min_marginals_left = [ 0 ]
if len ( x_min_marginals_right ) == 0 :
x_min_marginals_right = [ text_regions . shape [ 1 ] - 1 ]
# print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
# print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions = cv2 . fillPoly ( text_regions , pts = marginlas_should_be_main_text , color = ( 4 , 4 ) )
# print(np.unique(text_regions))
# text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
# text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions [ : , : int ( min_point_of_left_marginal ) ] [ text_regions [ : , : int ( min_point_of_left_marginal ) ] == 1 ] = 0
text_regions [ : , int ( max_point_of_right_marginal ) : ] [ text_regions [ : , int ( max_point_of_right_marginal ) : ] == 1 ] = 0
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
# plt.plot(region_sum_0)
# plt.plot(peaks,region_sum_0[peaks],'*')
# plt.show()
# plt.imshow(text_regions)
# plt.show()
# sys.exit()
else :
pass
return text_regions
def do_work_of_textline_seperation ( self , queue_of_all_params , polygons_per_process , index_polygons_per_process , con_par_org , textline_mask_tot , mask_texts_only , num_col , scale_par , boxes_text ) :
textregions_cnt_tot_per_process = [ ]
textlines_cnt_tot_per_process = [ ]
index_polygons_per_process_per_process = [ ]
polygons_per_par_process_per_process = [ ]
textline_cnt_seperated = np . zeros ( textline_mask_tot . shape )
for iiii in range ( len ( polygons_per_process ) ) :
# crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)
# arg_max=np.argmax(areas_cnt_only_text)
textregions_cnt_tot_per_process . append ( polygons_per_process [ iiii ] / scale_par )
textline_region_in_image = np . zeros ( textline_mask_tot . shape )
cnt_o_t_max = polygons_per_process [ iiii ]
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
textline_rotated_seperated = self . seperate_lines_new2 ( textline_biggest_region [ y : y + h , x : x + w ] , 0 , num_col )
# 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
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 )
except :
pass
# print(len(cnt_textlines_in_image_ind))
# plt.imshow(mask_biggest2)
# plt.show()
textlines_cnt_tot_per_process . append ( textlines_cnt_per_region )
index_polygons_per_process_per_process . append ( index_polygons_per_process [ iiii ] )
polygons_per_par_process_per_process . append ( con_par_org [ iiii ] )
queue_of_all_params . put ( [ index_polygons_per_process_per_process , polygons_per_par_process_per_process , textregions_cnt_tot_per_process , textlines_cnt_tot_per_process ] )
def do_order_of_regions ( self , contours_only_text_parent , contours_only_text_parent_h , boxes , textline_mask_tot ) :
if self . 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
arg_arg_text_con = np . argsort ( arg_text_con )
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
arg_arg_text_con = 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 ) )
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
arg_arg_text_con = np . argsort ( arg_text_con )
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
else :
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
arg_arg_text_con = np . argsort ( arg_text_con )
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 )
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
arg_arg_text_con = np . argsort ( arg_text_con )
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 adhere_drop_capital_region_into_cprresponding_textline ( self , 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 ) :
# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
# print(all_found_texline_polygons[3])
cx_m , cy_m , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent )
cx_h , cy_h , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent_h )
cx_d , cy_d , _ , _ , y_min_d , y_max_d , _ = find_new_features_of_contoures ( polygons_of_drop_capitals )
img_con_all = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
for j_cont in range ( len ( contours_only_text_parent ) ) :
img_con_all [ all_box_coord [ j_cont ] [ 0 ] : all_box_coord [ j_cont ] [ 1 ] , all_box_coord [ j_cont ] [ 2 ] : all_box_coord [ j_cont ] [ 3 ] , 0 ] = ( j_cont + 1 ) * 3
# img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))
# plt.imshow(img_con_all[:,:,0])
# plt.show()
# img_con_all=cv2.dilate(img_con_all, self.kernel, iterations=3)
# plt.imshow(img_con_all[:,:,0])
# plt.show()
# print(np.unique(img_con_all[:,:,0]))
for i_drop in range ( len ( polygons_of_drop_capitals ) ) :
# print(i_drop,'i_drop')
img_con_all_copy = np . copy ( img_con_all )
img_con = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
img_con = cv2 . fillPoly ( img_con , pts = [ polygons_of_drop_capitals [ i_drop ] ] , color = ( 1 , 1 , 1 ) )
# plt.imshow(img_con[:,:,0])
# plt.show()
##img_con=cv2.dilate(img_con, self.kernel, iterations=30)
# plt.imshow(img_con[:,:,0])
# plt.show()
# print(np.unique(img_con[:,:,0]))
img_con_all_copy [ : , : , 0 ] = img_con_all_copy [ : , : , 0 ] + img_con [ : , : , 0 ]
img_con_all_copy [ : , : , 0 ] [ img_con_all_copy [ : , : , 0 ] == 1 ] = 0
kherej_ghesmat = np . unique ( img_con_all_copy [ : , : , 0 ] ) / 3
res_summed_pixels = np . unique ( img_con_all_copy [ : , : , 0 ] ) % 3
region_with_intersected_drop = kherej_ghesmat [ res_summed_pixels == 1 ]
# region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop = region_with_intersected_drop . astype ( np . uint8 )
# print(len(region_with_intersected_drop),'region_with_intersected_drop1')
if len ( region_with_intersected_drop ) == 0 :
img_con_all_copy = np . copy ( img_con_all )
img_con = cv2 . dilate ( img_con , self . kernel , iterations = 4 )
img_con_all_copy [ : , : , 0 ] = img_con_all_copy [ : , : , 0 ] + img_con [ : , : , 0 ]
img_con_all_copy [ : , : , 0 ] [ img_con_all_copy [ : , : , 0 ] == 1 ] = 0
kherej_ghesmat = np . unique ( img_con_all_copy [ : , : , 0 ] ) / 3
res_summed_pixels = np . unique ( img_con_all_copy [ : , : , 0 ] ) % 3
region_with_intersected_drop = kherej_ghesmat [ res_summed_pixels == 1 ]
# region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop = region_with_intersected_drop . astype ( np . uint8 )
# print(np.unique(img_con_all_copy[:,:,0]))
if self . curved_line :
if len ( region_with_intersected_drop ) > 1 :
sum_pixels_of_intersection = [ ]
for i in range ( len ( region_with_intersected_drop ) ) :
# print((region_with_intersected_drop[i]*3+1))
sum_pixels_of_intersection . append ( ( ( img_con_all_copy [ : , : , 0 ] == ( region_with_intersected_drop [ i ] * 3 + 1 ) ) * 1 ) . sum ( ) )
# print(sum_pixels_of_intersection)
region_final = region_with_intersected_drop [ np . argmax ( sum_pixels_of_intersection ) ] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try :
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = np . array ( cy_t ) # all_box_coord[int(region_final)][0]+np.array(cy_t)
# print(y_lines)
y_lines [ y_lines < y_min_d [ i_drop ] ] = 0
# print(y_lines)
arg_min = np . argmin ( np . abs ( y_lines - y_min_d [ i_drop ] ) )
# print(arg_min)
cnt_nearest = np . copy ( all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] )
cnt_nearest [ : , 0 , 0 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 , 0 ] # +all_box_coord[int(region_final)][2]
cnt_nearest [ : , 0 , 1 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 , 1 ] # +all_box_coord[int(region_final)][0]
img_textlines = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ cnt_nearest ] , color = ( 255 , 255 , 255 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ polygons_of_drop_capitals [ i_drop ] ] , color = ( 255 , 255 , 255 ) )
img_textlines = img_textlines . astype ( np . uint8 )
imgray = cv2 . cvtColor ( img_textlines , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_combined , hierachy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_combined [ j ] ) for j in range ( len ( contours_combined ) ) ] )
contours_biggest = contours_combined [ np . argmax ( areas_cnt_text ) ]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] = contours_biggest
except :
# print('gordun1')
pass
elif len ( region_with_intersected_drop ) == 1 :
region_final = region_with_intersected_drop [ 0 ] - 1
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = np . array ( cy_t ) # all_box_coord[int(region_final)][0]+np.array(cy_t)
y_lines [ y_lines < y_min_d [ i_drop ] ] = 0
# print(y_lines)
arg_min = np . argmin ( np . abs ( y_lines - y_min_d [ i_drop ] ) )
# print(arg_min)
cnt_nearest = np . copy ( all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] )
cnt_nearest [ : , 0 , 0 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 , 0 ] # +all_box_coord[int(region_final)][2]
cnt_nearest [ : , 0 , 1 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 , 1 ] # +all_box_coord[int(region_final)][0]
img_textlines = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ cnt_nearest ] , color = ( 255 , 255 , 255 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ polygons_of_drop_capitals [ i_drop ] ] , color = ( 255 , 255 , 255 ) )
img_textlines = img_textlines . astype ( np . uint8 )
# plt.imshow(img_textlines)
# plt.show()
imgray = cv2 . cvtColor ( img_textlines , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_combined , hierachy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_combined [ j ] ) for j in range ( len ( contours_combined ) ) ] )
contours_biggest = contours_combined [ np . argmax ( areas_cnt_text ) ]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# print(np.shape(contours_biggest),'contours_biggest')
# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] = contours_biggest
# print(cx_t,'print')
try :
# print(all_found_texline_polygons[j_cont][0])
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = all_box_coord [ int ( region_final ) ] [ 0 ] + np . array ( cy_t )
y_lines [ y_lines < y_min_d [ i_drop ] ] = 0
# print(y_lines)
arg_min = np . argmin ( np . abs ( y_lines - y_min_d [ i_drop ] ) )
# print(arg_min)
cnt_nearest = np . copy ( all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] )
cnt_nearest [ : , 0 , 0 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 , 0 ] # +all_box_coord[int(region_final)][2]
cnt_nearest [ : , 0 , 1 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 , 1 ] # +all_box_coord[int(region_final)][0]
img_textlines = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ cnt_nearest ] , color = ( 255 , 255 , 255 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ polygons_of_drop_capitals [ i_drop ] ] , color = ( 255 , 255 , 255 ) )
img_textlines = img_textlines . astype ( np . uint8 )
imgray = cv2 . cvtColor ( img_textlines , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_combined , hierachy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_combined [ j ] ) for j in range ( len ( contours_combined ) ) ] )
contours_biggest = contours_combined [ np . argmax ( areas_cnt_text ) ]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
contours_biggest [ : , 0 , 0 ] = contours_biggest [ : , 0 , 0 ] # -all_box_coord[int(region_final)][2]
contours_biggest [ : , 0 , 1 ] = contours_biggest [ : , 0 , 1 ] # -all_box_coord[int(region_final)][0]
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] = contours_biggest
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
except :
pass
else :
pass
##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
###print(all_box_coord[j_cont])
###print(cx_t)
###print(cy_t)
###print(cx_d[i_drop])
###print(cy_d[i_drop])
##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
##y_lines[y_lines<y_min_d[i_drop]]=0
###print(y_lines)
##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
###print(arg_min)
##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
##cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
##cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
##img_textlines=img_textlines.astype(np.uint8)
##plt.imshow(img_textlines)
##plt.show()
##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
##print(len(contours_combined),'len textlines mixed')
##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
###print(np.shape(contours_biggest))
###print(contours_biggest[:])
##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
else :
if len ( region_with_intersected_drop ) > 1 :
sum_pixels_of_intersection = [ ]
for i in range ( len ( region_with_intersected_drop ) ) :
# print((region_with_intersected_drop[i]*3+1))
sum_pixels_of_intersection . append ( ( ( img_con_all_copy [ : , : , 0 ] == ( region_with_intersected_drop [ i ] * 3 + 1 ) ) * 1 ) . sum ( ) )
# print(sum_pixels_of_intersection)
region_final = region_with_intersected_drop [ np . argmax ( sum_pixels_of_intersection ) ] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try :
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = all_box_coord [ int ( region_final ) ] [ 0 ] + np . array ( cy_t )
# print(y_lines)
y_lines [ y_lines < y_min_d [ i_drop ] ] = 0
# print(y_lines)
arg_min = np . argmin ( np . abs ( y_lines - y_min_d [ i_drop ] ) )
# print(arg_min)
cnt_nearest = np . copy ( all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] )
cnt_nearest [ : , 0 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 ] + all_box_coord [ int ( region_final ) ] [ 2 ]
cnt_nearest [ : , 1 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 1 ] + all_box_coord [ int ( region_final ) ] [ 0 ]
img_textlines = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ cnt_nearest ] , color = ( 255 , 255 , 255 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ polygons_of_drop_capitals [ i_drop ] ] , color = ( 255 , 255 , 255 ) )
img_textlines = img_textlines . astype ( np . uint8 )
imgray = cv2 . cvtColor ( img_textlines , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_combined , hierachy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_combined [ j ] ) for j in range ( len ( contours_combined ) ) ] )
contours_biggest = contours_combined [ np . argmax ( areas_cnt_text ) ]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
contours_biggest [ : , 0 , 0 ] = contours_biggest [ : , 0 , 0 ] - all_box_coord [ int ( region_final ) ] [ 2 ]
contours_biggest [ : , 0 , 1 ] = contours_biggest [ : , 0 , 1 ] - all_box_coord [ int ( region_final ) ] [ 0 ]
contours_biggest = contours_biggest . reshape ( np . shape ( contours_biggest ) [ 0 ] , np . shape ( contours_biggest ) [ 2 ] )
all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] = contours_biggest
except :
# print('gordun1')
pass
elif len ( region_with_intersected_drop ) == 1 :
region_final = region_with_intersected_drop [ 0 ] - 1
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(cx_t,'print')
try :
# print(all_found_texline_polygons[j_cont][0])
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = all_box_coord [ int ( region_final ) ] [ 0 ] + np . array ( cy_t )
y_lines [ y_lines < y_min_d [ i_drop ] ] = 0
# print(y_lines)
arg_min = np . argmin ( np . abs ( y_lines - y_min_d [ i_drop ] ) )
# print(arg_min)
cnt_nearest = np . copy ( all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] )
cnt_nearest [ : , 0 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 0 ] + all_box_coord [ int ( region_final ) ] [ 2 ]
cnt_nearest [ : , 1 ] = all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] [ : , 1 ] + all_box_coord [ int ( region_final ) ] [ 0 ]
img_textlines = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ cnt_nearest ] , color = ( 255 , 255 , 255 ) )
img_textlines = cv2 . fillPoly ( img_textlines , pts = [ polygons_of_drop_capitals [ i_drop ] ] , color = ( 255 , 255 , 255 ) )
img_textlines = img_textlines . astype ( np . uint8 )
imgray = cv2 . cvtColor ( img_textlines , cv2 . COLOR_BGR2GRAY )
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_combined , hierachy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np . array ( [ cv2 . contourArea ( contours_combined [ j ] ) for j in range ( len ( contours_combined ) ) ] )
contours_biggest = contours_combined [ np . argmax ( areas_cnt_text ) ]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
contours_biggest [ : , 0 , 0 ] = contours_biggest [ : , 0 , 0 ] - all_box_coord [ int ( region_final ) ] [ 2 ]
contours_biggest [ : , 0 , 1 ] = contours_biggest [ : , 0 , 1 ] - all_box_coord [ int ( region_final ) ] [ 0 ]
contours_biggest = contours_biggest . reshape ( np . shape ( contours_biggest ) [ 0 ] , np . shape ( contours_biggest ) [ 2 ] )
all_found_texline_polygons [ int ( region_final ) ] [ arg_min ] = contours_biggest
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
except :
pass
else :
pass
#####for i_drop in range(len(polygons_of_drop_capitals)):
#####for j_cont in range(len(contours_only_text_parent)):
#####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
#####img_con=img_con.astype(np.uint8)
######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
######ret, thresh = cv2.threshold(imgray, 0, 255, 0)
######contours_new,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####contours_new,hir_new=return_contours_of_image(img_con)
#####contours_new_parent=return_parent_contours( contours_new,hir_new)
######plt.imshow(img_con)
######plt.show()
#####try:
#####if len(contours_new_parent)==1:
######print(all_found_texline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[j_cont])
######print(all_box_coord[j_cont])
######print(cx_t)
######print(cy_t)
######print(cx_d[i_drop])
######print(cy_d[i_drop])
#####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
######print(y_lines)
#####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
######print(arg_min)
#####cnt_nearest=np.copy(all_found_texline_polygons[j_cont][arg_min])
#####cnt_nearest[:,0]=all_found_texline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
#####cnt_nearest[:,1]=all_found_texline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
#####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_textlines=img_textlines.astype(np.uint8)
#####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
#####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
#####contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
#####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
######print(np.shape(contours_biggest))
######print(contours_biggest[:])
#####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
#####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
#####all_found_texline_polygons[j_cont][arg_min]=contours_biggest
######print(contours_biggest)
######plt.imshow(img_textlines[:,:,0])
######plt.show()
#####else:
#####pass
#####except:
#####pass
return all_found_texline_polygons
def save_plot_of_layout_main ( self , text_regions_p , image_page ) :
values = np . unique ( text_regions_p [ : , : ] )
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = [ " Background " , " Main text " , " Images " , " Seperators " , " Marginalia " ]
values_indexes = [ 0 , 1 , 2 , 3 , 4 ]
plt . figure ( figsize = ( 40 , 40 ) )
plt . rcParams [ " font.size " ] = " 40 "
im = plt . imshow ( text_regions_p [ : , : ] )
colors = [ im . cmap ( im . norm ( value ) ) for value in values ]
patches = [ mpatches . Patch ( color = colors [ np . where ( values == i ) [ 0 ] [ 0 ] ] , label = " {l} " . format ( l = pixels [ int ( np . where ( values_indexes == i ) [ 0 ] [ 0 ] ) ] ) ) for i in values ]
plt . legend ( handles = patches , bbox_to_anchor = ( 1.05 , 1 ) , loc = 2 , borderaxespad = 0.0 , fontsize = 40 )
plt . savefig ( os . path . join ( self . dir_of_layout , self . f_name + " _layout_main.png " ) )
def save_plot_of_layout_main_all ( self , text_regions_p , image_page ) :
values = np . unique ( text_regions_p [ : , : ] )
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = [ " Background " , " Main text " , " Images " , " Seperators " , " Marginalia " ]
values_indexes = [ 0 , 1 , 2 , 3 , 4 ]
plt . figure ( figsize = ( 70 , 40 ) )
plt . rcParams [ " font.size " ] = " 40 "
plt . subplot ( 1 , 2 , 1 )
plt . imshow ( image_page )
plt . subplot ( 1 , 2 , 2 )
im = plt . imshow ( text_regions_p [ : , : ] )
colors = [ im . cmap ( im . norm ( value ) ) for value in values ]
patches = [ mpatches . Patch ( color = colors [ np . where ( values == i ) [ 0 ] [ 0 ] ] , label = " {l} " . format ( l = pixels [ int ( np . where ( values_indexes == i ) [ 0 ] [ 0 ] ) ] ) ) for i in values ]
plt . legend ( handles = patches , bbox_to_anchor = ( 1.05 , 1 ) , loc = 2 , borderaxespad = 0.0 , fontsize = 60 )
plt . savefig ( os . path . join ( self . dir_of_all , self . f_name + " _layout_main_and_page.png " ) )
def save_plot_of_layout ( self , text_regions_p , image_page ) :
values = np . unique ( text_regions_p [ : , : ] )
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = [ " Background " , " Main text " , " Header " , " Marginalia " , " Drop capitals " , " Images " , " Seperators " ]
values_indexes = [ 0 , 1 , 2 , 8 , 4 , 5 , 6 ]
plt . figure ( figsize = ( 40 , 40 ) )
plt . rcParams [ " font.size " ] = " 40 "
im = plt . imshow ( text_regions_p [ : , : ] )
colors = [ im . cmap ( im . norm ( value ) ) for value in values ]
patches = [ mpatches . Patch ( color = colors [ np . where ( values == i ) [ 0 ] [ 0 ] ] , label = " {l} " . format ( l = pixels [ int ( np . where ( values_indexes == i ) [ 0 ] [ 0 ] ) ] ) ) for i in values ]
plt . legend ( handles = patches , bbox_to_anchor = ( 1.05 , 1 ) , loc = 2 , borderaxespad = 0.0 , fontsize = 40 )
plt . savefig ( os . path . join ( self . dir_of_layout , self . f_name + " _layout.png " ) )
def save_plot_of_layout_all ( self , text_regions_p , image_page ) :
values = np . unique ( text_regions_p [ : , : ] )
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = [ " Background " , " Main text " , " Header " , " Marginalia " , " Drop capitals " , " Images " , " Seperators " ]
values_indexes = [ 0 , 1 , 2 , 8 , 4 , 5 , 6 ]
plt . figure ( figsize = ( 70 , 40 ) )
plt . rcParams [ " font.size " ] = " 40 "
plt . subplot ( 1 , 2 , 1 )
plt . imshow ( image_page )
plt . subplot ( 1 , 2 , 2 )
im = plt . imshow ( text_regions_p [ : , : ] )
colors = [ im . cmap ( im . norm ( value ) ) for value in values ]
patches = [ mpatches . Patch ( color = colors [ np . where ( values == i ) [ 0 ] [ 0 ] ] , label = " {l} " . format ( l = pixels [ int ( np . where ( values_indexes == i ) [ 0 ] [ 0 ] ) ] ) ) for i in values ]
plt . legend ( handles = patches , bbox_to_anchor = ( 1.05 , 1 ) , loc = 2 , borderaxespad = 0.0 , fontsize = 60 )
plt . savefig ( os . path . join ( self . dir_of_all , self . f_name + " _layout_and_page.png " ) )
def save_deskewed_image ( self , slope_deskew ) :
img_rotated = self . rotyate_image_different ( self . image_org , slope_deskew )
if self . dir_of_all is not None :
cv2 . imwrite ( os . path . join ( self . dir_of_all , self . f_name + " _org.png " ) , self . image_org )
cv2 . imwrite ( os . path . join ( self . dir_of_deskewed , self . f_name + " _deskewed.png " ) , img_rotated )
del img_rotated
def run ( self ) :
is_image_enhanced = False
# get image and sclaes, then extract the page of scanned image
t1 = time . time ( )
##########
###is_image_enhanced,img_org,img_res=self.resize_and_enhance_image(is_image_enhanced)
is_image_enhanced , img_org , img_res , num_col_classifier , num_column_is_classified = self . resize_and_enhance_image_with_column_classifier ( is_image_enhanced )
print ( is_image_enhanced , " is_image_enhanced " )
K . clear_session ( )
scale = 1
if ( self . allow_enhancement ) and is_image_enhanced :
cv2 . imwrite ( os . path . join ( self . dir_out , self . f_name ) + " .tif " , img_res )
img_res = img_res . astype ( np . uint8 )
self . get_image_and_scales ( img_org , img_res , scale )
if ( not self . allow_enhancement ) and is_image_enhanced :
self . get_image_and_scales_after_enhancing ( img_org , img_res )
if ( self . allow_enhancement ) and not is_image_enhanced :
self . get_image_and_scales ( img_org , img_res , scale )
if ( not self . allow_enhancement ) and not is_image_enhanced :
self . get_image_and_scales ( img_org , img_res , scale )
if ( self . allow_scaling ) and not is_image_enhanced :
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 )
# print(self.scale_x)
print ( " enhancing: " + str ( time . time ( ) - t1 ) )
text_regions_p_1 = self . get_regions_from_xy_2models ( img_res , is_image_enhanced )
K . clear_session ( )
gc . collect ( )
print ( " textregion: " + str ( time . time ( ) - t1 ) )
img_g = cv2 . imread ( self . image_dir , 0 )
img_g = img_g . astype ( np . uint8 )
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 [ : , : ]
###self.produce_groundtruth_for_textline()
image_page , page_coord = self . extract_page ( )
# print(image_page.shape,'page')
if self . dir_of_all is not None :
cv2 . imwrite ( os . path . join ( self . dir_of_all , self . f_name + " _page.png " ) , image_page )
##########
K . clear_session ( )
gc . collect ( )
img_g3_page = img_g3 [ page_coord [ 0 ] : page_coord [ 1 ] , page_coord [ 2 ] : page_coord [ 3 ] , : ]
del img_g3
del img_g
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_lines = ( text_regions_p_1 [ : , : ] == 3 ) * 1
mask_images = mask_images . astype ( np . uint8 )
mask_lines = mask_lines . astype ( np . uint8 )
mask_images = cv2 . erode ( mask_images [ : , : ] , self . kernel , iterations = 10 )
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 )
if not num_column_is_classified :
num_col_classifier = num_col + 1
except :
num_col = None
peaks_neg_fin = [ ]
print ( num_col , " num_colnum_col " )
if num_col is None :
txt_con_org = [ ]
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 = [ ]
slopes = [ ]
slopes_marginals = [ ]
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 )
else :
# pass
try :
patches = True
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 , patches , scaler_h_textline , scaler_w_textline )
K . clear_session ( )
gc . collect ( )
print ( np . unique ( textline_mask_tot_ea [ : , : ] ) , " textline " )
if self . dir_of_all is not None :
values = np . unique ( textline_mask_tot_ea [ : , : ] )
pixels = [ " Background " , " Textlines " ]
values_indexes = [ 0 , 1 ]
plt . figure ( figsize = ( 70 , 40 ) )
plt . rcParams [ " font.size " ] = " 40 "
plt . subplot ( 1 , 2 , 1 )
plt . imshow ( image_page )
plt . subplot ( 1 , 2 , 2 )
im = plt . imshow ( textline_mask_tot_ea [ : , : ] )
colors = [ im . cmap ( im . norm ( value ) ) for value in values ]
patches = [ mpatches . Patch ( color = colors [ np . where ( values == i ) [ 0 ] [ 0 ] ] , label = " {l} " . format ( l = pixels [ int ( np . where ( values_indexes == i ) [ 0 ] [ 0 ] ) ] ) ) for i in values ]
plt . legend ( handles = patches , bbox_to_anchor = ( 1.05 , 1 ) , loc = 2 , borderaxespad = 0.0 , fontsize = 60 )
plt . savefig ( os . path . join ( self . dir_of_all , self . f_name + " _textline_and_page.png " ) )
print ( " textline: " + str ( time . time ( ) - t1 ) )
# plt.imshow(textline_mask_tot_ea)
# plt.show()
# sys.exit()
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 , dir_of_all = self . dir_of_all , f_name = self . f_name )
slope_first = 0 # return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
if self . dir_of_deskewed is not None :
self . save_deskewed_image ( slope_deskew )
# img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)
print ( slope_deskew , " slope_deskew " )
##plt.imshow(img_rotated)
##plt.show()
##sys.exit()
print ( " deskewing: " + str ( time . time ( ) - t1 ) )
image_page_rotated , textline_mask_tot = image_page [ : , : ] , textline_mask_tot_ea [ : , : ] # rotation_not_90_func(image_page,textline_mask_tot_ea,slope_first)
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[(textline_mask_tot[:,:]==1) & (text_regions_p_1[:,:]==2)]=1
text_regions_p_1 [ mask_lines [ : , : ] == 1 ] = 3
##text_regions_p_1[textline_mask_tot_small_size[:,:]==1]=1
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 = self . get_marginals ( rotate_image ( regions_without_seperators , slope_deskew ) , text_regions_p , num_col_classifier , slope_deskew )
except :
pass
else :
pass
# plt.imshow(text_regions_p)
# plt.show()
if self . dir_of_all is not None :
self . save_plot_of_layout_main_all ( text_regions_p , image_page )
if self . dir_of_layout is not None :
self . save_plot_of_layout_main ( text_regions_p , image_page )
print ( " marginals: " + str ( time . time ( ) - t1 ) )
if not self . 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)
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 ( )
# print(peaks_neg_fin,num_col,'num_col2')
print ( num_col_classifier , " 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
if np . abs ( slope_deskew ) > = SLOPE_THRESHOLD :
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
else :
pass
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 )
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 )
# print(len(boxes),'boxes')
# sys.exit()
print ( " boxes in: " + str ( time . time ( ) - t1 ) )
img_revised_tab = text_regions_p [ : , : ]
# plt.imshow(img_revised_tab)
# plt.show()
K . clear_session ( )
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 :
# 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()
patches = True
image_page = image_page . astype ( np . uint8 )
# print(type(image_page))
regions_fully , regions_fully_only_drop = self . extract_text_regions ( image_page , patches , cols = num_col_classifier )
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 ( )
patches = False
regions_fully_np , _ = self . extract_text_regions ( image_page , patches , 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 )
# 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[:,:,0]==7) & (text_regions_p[:,:]!=0)]=7
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
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 [ : , : ] )
print ( " full layout in: " + str ( time . time ( ) - t1 ) )
# sys.exit()
pixel_img = 5
polygons_of_images = return_contours_of_interested_region ( img_revised_tab , pixel_img )
# 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 ] )
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 ) ]
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 )
( 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 )
# print(areas_cnt_text_parent,'areas_cnt_text_parent')
###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)
##contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d])
###areas_cnt_text_parent_d=list(np.array(areas_cnt_text_parent_d)[index_con_parents_d])
##print(areas_cnt_text_parent_d,'areas_cnt_text_parent_d')
# print(len(contours_only_text_parent),len(contours_only_text_parent_d),'vizzz')
txt_con_org = get_textregion_contours_in_org_image ( contours_only_text_parent , self . image , slope_first )
###boxes_text,_= get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_text , _ = get_text_region_boxes_by_given_contours ( contours_only_text_parent )
boxes_marginals , _ = get_text_region_boxes_by_given_contours ( polygons_of_marginals )
####boxes_text_h,_= get_text_region_boxes_by_given_contours(text_only_h,contours_only_text_parent_h,image_page)
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 )
if self . curved_line :
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,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
all_found_texline_polygons = small_textlines_to_parent_adherence2 ( all_found_texline_polygons , textline_mask_tot_ea , num_col_classifier )
# slopes=list(np.zeros(len(contours_only_text_parent)))
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,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
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 ( )
# contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
###print(index_by_text_par_con,'index_by_text_par_con')
if self . full_layout :
##for iii 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[iii]] ,color=(1,1,1))
##plt.imshow(img1[:,:,0])
##plt.show()
##img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
##img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d_ordered[iii]] ,color=(1,1,1))
##plt.imshow(img2[:,:,0])
##plt.show()
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 )
###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=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)
# text_regions_p=self.return_region_segmentation_after_implementing_not_head_maintext_parallel(text_regions_p,boxes)
# if you want to save the layout result just uncommet following plot
if self . dir_of_layout is not None :
self . save_plot_of_layout ( text_regions_p , image_page )
if self . dir_of_all is not None :
self . save_plot_of_layout_all ( text_regions_p , image_page )
K . clear_session ( )
gc . collect ( )
##print('Job done in: '+str(time.time()-t1))
polygons_of_tabels = [ ]
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size ( text_regions_p , pixel_img )
# polygons_of_drop_capitals=[]
all_found_texline_polygons = self . 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 )
# 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 , seperators_closeup_n = 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 , 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 , 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 , seperators_closeup_n = 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 , 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 )
# 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
else :
pass
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 )
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 )
# print(slopes)
if self . dir_of_cropped_images is not None :
self . write_images_into_directory ( polygons_of_images , self . dir_of_cropped_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
# print('bura galmir?')
if np . abs ( slope_deskew ) < SLOPE_THRESHOLD :
contours_only_text_parent = list ( np . array ( contours_only_text_parent ) [ index_by_text_par_con ] )
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 )
# 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)
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 )
except :
txt_con_org = [ ]
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 = [ ]
slopes = [ ]
slopes_marginals = [ ]
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 )
print ( " Job done in: " + str ( time . time ( ) - t1 ) )
