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eynollah/sbb_newspapers_org_image/eynollah.py

2646 lines
135 KiB
Python

# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring
"""
tool to extract table form data from alto xml data
"""
import gc
import math
import os
import random
import sys
import time
import warnings
from pathlib import Path
from multiprocessing import Process, Queue, cpu_count
import cv2
import numpy as np
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
stderr = sys.stderr
sys.stderr = open(os.devnull, "w")
from keras import backend as K
from keras.models import load_model
sys.stderr = stderr
import tensorflow as tf
tf.get_logger().setLevel("ERROR")
warnings.filterwarnings("ignore")
from 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.contour import (
contours_in_same_horizon,
filter_contours_area_of_image_interiors,
filter_contours_area_of_image_tables,
filter_contours_area_of_image,
find_contours_mean_y_diff,
find_features_of_contours,
find_new_features_of_contoures,
get_text_region_boxes_by_given_contours,
get_textregion_contours_in_org_image,
return_bonding_box_of_contours,
return_contours_of_image,
return_contours_of_interested_region,
return_contours_of_interested_region_and_bounding_box,
return_contours_of_interested_region_by_min_size,
return_contours_of_interested_textline,
return_parent_contours,
return_contours_of_interested_region_by_size,
)
from .utils.rotate import (
rotate_image,
rotate_max_area,
rotate_max_area_new,
rotatedRectWithMaxArea,
rotation_image_new,
rotation_not_90_func,
rotation_not_90_func_full_layout,
rotyate_image_different,
)
from .utils.separate_lines import (
seperate_lines,
seperate_lines_new_inside_teils,
seperate_lines_new_inside_teils2,
seperate_lines_vertical,
seperate_lines_vertical_cont,
textline_contours_postprocessing,
seperate_lines_new2,
return_deskew_slop,
)
from .utils.drop_capitals import (
adhere_drop_capital_region_into_cprresponding_textline,
filter_small_drop_capitals_from_no_patch_layout
)
from .utils.marginals import get_marginals
from .utils.resize import resize_image
from .utils import (
boosting_headers_by_longshot_region_segmentation,
crop_image_inside_box,
find_features_of_lines,
find_num_col,
find_num_col_by_vertical_lines,
find_num_col_deskew,
find_num_col_only_image,
isNaN,
otsu_copy,
otsu_copy_binary,
return_hor_spliter_by_index_for_without_verticals,
delete_seperator_around,
return_regions_without_seperators,
put_drop_out_from_only_drop_model,
putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
check_any_text_region_in_model_one_is_main_or_header,
small_textlines_to_parent_adherence2,
order_and_id_of_texts,
order_of_regions,
implent_law_head_main_not_parallel,
return_hor_spliter_by_index,
combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
return_points_with_boundies,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new,
)
from .utils.xml import create_page_xml
from .plot import EynollahPlotter
SLOPE_THRESHOLD = 0.13
class eynollah:
def __init__(
self,
image_filename,
image_filename_stem,
dir_out,
dir_models,
dir_of_cropped_images=None,
dir_of_layout=None,
dir_of_deskewed=None,
dir_of_all=None,
enable_plotting=False,
allow_enhancement=False,
curved_line=False,
full_layout=False,
allow_scaling=False,
headers_off=False
):
self.image_filename = image_filename # XXX This does not seem to be a directory as the name suggests, but a file
self.cont_page = []
self.dir_out = dir_out
self.image_filename_stem = image_filename_stem
self.allow_enhancement = allow_enhancement
self.curved_line = curved_line
self.full_layout = full_layout
self.allow_scaling = allow_scaling
self.headers_off = headers_off
if not self.image_filename_stem:
self.image_filename_stem = Path(Path(image_filename).name).stem
self.plotter = None if not enable_plotting else EynollahPlotter(
dir_of_all=dir_of_all,
dir_of_deskewed=dir_of_deskewed,
dir_of_cropped_images=dir_of_cropped_images,
dir_of_layout=dir_of_layout,
image_filename=image_filename,
image_filename_stem=image_filename_stem,
)
self.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)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
else:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = label_p_pred[0, :, :, :]
seg = seg * 255
if i == 0 and j == 0:
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg
elif i == 0 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j == 0:
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
elif i == 0 and j != 0 and j != nyf - 1:
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j != 0 and j != nyf - 1:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
elif i != 0 and i != nxf - 1 and j == 0:
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
elif i != 0 and i != nxf - 1 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
else:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
prediction_true = prediction_true.astype(int)
del model_enhancement
del session_enhancemnet
return prediction_true
def check_dpi(self):
dpi = os.popen('identify -format "%x " ' + self.image_filename).read()
return int(float(dpi))
def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
if num_col == 1 and width_early < 1100:
img_w_new = 2000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
elif num_col == 1 and width_early >= 2500:
img_w_new = 2000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
elif num_col == 1 and width_early >= 1100 and width_early < 2500:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 2 and width_early < 2000:
img_w_new = 2400
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
elif num_col == 2 and width_early >= 3500:
img_w_new = 2400
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
elif num_col == 2 and width_early >= 2000 and width_early < 3500:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 3 and width_early < 2000:
img_w_new = 3000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
elif num_col == 3 and width_early >= 4000:
img_w_new = 3000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
elif num_col == 3 and width_early >= 2000 and width_early < 4000:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 4 and width_early < 2500:
img_w_new = 4000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
elif num_col == 4 and width_early >= 5000:
img_w_new = 4000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
elif num_col == 4 and width_early >= 2500 and width_early < 5000:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 5 and width_early < 3700:
img_w_new = 5000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
elif num_col == 5 and width_early >= 7000:
img_w_new = 5000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
elif num_col == 5 and width_early >= 3700 and width_early < 7000:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 6 and width_early < 4500:
img_w_new = 6500 # 5400
img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500)
else:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
img_new = np.copy(img)
num_column_is_classified = False
else:
img_new = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True
return img_new, num_column_is_classified
def resize_image_with_column_classifier(self, is_image_enhanced):
img = cv2.imread(self.image_filename)
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_filename, 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()
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
if img_new.shape[1] > img.shape[1]:
img_new = self.predict_enhancement(img_new)
is_image_enhanced = True
return img, img_new, is_image_enhanced
def resize_and_enhance_image_with_column_classifier(self, is_image_enhanced):
dpi = self.check_dpi()
img = cv2.imread(self.image_filename)
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_filename, 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:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem) + ".tif",self.image)
# self.image=self.image.astype(np.uint16)
is_image_enhanced = True
else:
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 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)
# Also set for the plotter
# XXX TODO hacky
#self.plotter.image_org = self.image_org
#self.plotter.scale_y = self.scale_y
#self.plotter.scale_x = self.scale_x
def get_image_and_scales_after_enhancing(self, img_org, img_res):
self.image = np.copy(img_res)
self.image = self.image.astype(np.uint8)
self.image_org = np.copy(img_org)
self.height_org = self.image_org.shape[0]
self.width_org = self.image_org.shape[1]
self.scale_y = img_res.shape[0] / float(self.image_org.shape[0])
self.scale_x = img_res.shape[1] / float(self.image_org.shape[1])
del img_org
del img_res
def start_new_session_and_model(self, model_dir):
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(marginal_of_patch_percent * img_height_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img = img.astype(np.float16)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
else:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i == 0 and j == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
elif i == 0 and j == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
elif i == 0 and j != 0 and j != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j != 0 and j != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
elif i != 0 and i != nxf - 1 and j == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
elif i != 0 and i != nxf - 1 and j == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
else:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
prediction_true = prediction_true.astype(np.uint8)
del img
del mask_true
del seg_color
del seg
del img_patch
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_filename)
img = img.astype(np.uint8)
patches = False
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
for ii in range(1):
img = cv2.GaussianBlur(img, (5, 5), 0)
img_page_prediction = self.do_prediction(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)
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 += x
x = 0
if (self.image.shape[1] - (x + w)) <= 30:
w = w + (self.image.shape[1] - (x + w))
if y <= 30:
h = h + y
y = 0
if (self.image.shape[0] - (y + h)) <= 30:
h = h + (self.image.shape[0] - (y + h))
box = [x, y, w, h]
croped_page, page_coord = crop_image_inside_box(box, self.image)
self.cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
session_page.close()
del model_page
del session_page
del contours
del thresh
del img
del imgray
gc.collect()
return croped_page, page_coord
def extract_text_regions(self, img, patches, cols):
img_height_h = img.shape[0]
img_width_h = img.shape[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)
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)
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)
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)
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)
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:
if (self.scale_x == 1 and img_width_h > 3000) or (self.scale_x != 1 and img_width_h > 2800):
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 2800 / float(img_width_h)), 2800)
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
if patches and cols == 4:
#print(self.scale_x,img_width_h,'scale')
if (self.scale_x == 1 and img_width_h > 4000) or (self.scale_x != 1 and img_width_h > 3700):
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 3700 / float(img_width_h)), 3700)
else:
img = otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
if patches and cols==5:
if self.scale_x == 1 and img_width_h > 5000:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.7), int(img_width_h * 0.7))
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9) )
if patches and cols>=6:
if img_width_h > 5600:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 5600 / float(img_width_h)), 5600)
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
if not patches:
img = otsu_copy_binary(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 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 = []
textline_cnt_seperated = np.zeros(textline_mask_tot_ea.shape)
for mv in range(len(boxes_text)):
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
all_text_region_raw = all_text_region_raw.astype(np.uint8)
img_int_p = all_text_region_raw[:, :]
# img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
# plt.imshow(img_int_p)
# plt.show()
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
slopes_per_each_subprocess.append(0)
slope_for_all = [slope_deskew][0]
else:
try:
textline_con, hierachy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
if abs(slope_for_all) < 0.5:
slope_for_all = [slope_deskew][0]
# old method
# slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
# text_patch_processed=textline_contours_postprocessing(gada)
except:
slope_for_all = 999
if slope_for_all == 999:
slope_for_all = [slope_deskew][0]
slopes_per_each_subprocess.append(slope_for_all)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
if abs(slope_for_all) < 45:
# all_box_coord.append(crop_coor)
textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
cnt_o_t_max = contours_par_per_process[mv]
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
mask_biggest = np.zeros(mask_texts_only.shape)
mask_biggest = cv2.fillPoly(mask_biggest, pts=[cnt_o_t_max], color=(1, 1, 1))
mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
textline_biggest_region = mask_biggest * textline_mask_tot_ea
# print(slope_for_all,'slope_for_all')
textline_rotated_seperated = seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all, plotter=self.plotter)
# new line added
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
textline_rotated_seperated[mask_region_in_patch_region[:, :] != 1] = 0
# till here
textline_cnt_seperated[y : y + h, x : x + w] = textline_rotated_seperated
textline_region_in_image[y : y + h, x : x + w] = textline_rotated_seperated
# plt.imshow(textline_region_in_image)
# plt.show()
# plt.imshow(textline_cnt_seperated)
# plt.show()
pixel_img = 1
cnt_textlines_in_image = return_contours_of_interested_textline(textline_region_in_image, pixel_img)
textlines_cnt_per_region = []
for jjjj in range(len(cnt_textlines_in_image)):
mask_biggest2 = np.zeros(mask_texts_only.shape)
mask_biggest2 = cv2.fillPoly(mask_biggest2, pts=[cnt_textlines_in_image[jjjj]], color=(1, 1, 1))
if num_col + 1 == 1:
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=5)
else:
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=4)
pixel_img = 1
mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img)
try:
# textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
except:
pass
else:
add_boxes_coor_into_textlines = True
textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], add_boxes_coor_into_textlines)
add_boxes_coor_into_textlines = False
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
all_box_coord_per_process.append(crop_coor)
queue_of_all_params.put([textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours, slopes_per_each_subprocess])
def do_work_of_slopes_new(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, indexes_r_con_per_pro, image_page_rotated, slope_deskew):
slopes_per_each_subprocess = []
bounding_box_of_textregion_per_each_subprocess = []
textlines_rectangles_per_each_subprocess = []
contours_textregion_per_each_subprocess = []
contours_textregion_par_per_each_subprocess = []
all_box_coord_per_process = []
index_by_text_region_contours = []
for mv in range(len(boxes_text)):
crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)
mask_textline=np.zeros((textline_mask_tot_ea.shape))
mask_textline=cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
denoised=None
all_text_region_raw=(textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
all_text_region_raw=all_text_region_raw.astype(np.uint8)
img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
if img_int_p.shape[0]/img_int_p.shape[1]<0.1:
slopes_per_each_subprocess.append(0)
slope_for_all = [slope_deskew][0]
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], 0)
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
else:
try:
textline_con, hierachy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.00008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0))
if sigma_des < 1:
sigma_des = 1
img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
if abs(slope_for_all) <= 0.5:
slope_for_all = [slope_deskew][0]
except:
slope_for_all = 999
if slope_for_all == 999:
slope_for_all = [slope_deskew][0]
slopes_per_each_subprocess.append(slope_for_all)
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
# plt.imshow(mask_only_con_region)
# plt.show()
all_text_region_raw = np.copy(textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]])
mask_only_con_region = mask_only_con_region[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
##plt.imshow(textline_mask_tot_ea)
##plt.show()
##plt.imshow(all_text_region_raw)
##plt.show()
##plt.imshow(mask_only_con_region)
##plt.show()
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv])
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
all_box_coord_per_process.append(crop_coor)
queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours])
def textline_contours(self, img, patches, scaler_h, scaler_w):
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 = 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)
# 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()
session_textline.close()
del model_textline
del session_textline
del img
del img_org
gc.collect()
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
slope_biggest = 0
slopes_sub = []
boxes_sub_new = []
poly_sub = []
for mv in range(len(boxes_per_process)):
crop_img, _ = crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
crop_img = crop_img[:, :, 0]
crop_img = cv2.erode(crop_img, self.kernel, iterations=2)
try:
textline_con, hierachy = return_contours_of_image(crop_img)
textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0))
if sigma_des < 1:
sigma_des = 1
crop_img[crop_img > 0] = 1
slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, plotter=self.plotter)
except:
slope_corresponding_textregion = 999
if slope_corresponding_textregion == 999:
slope_corresponding_textregion = slope_biggest
slopes_sub.append(slope_corresponding_textregion)
cnt_clean_rot = textline_contours_postprocessing(crop_img, slope_corresponding_textregion, contours_per_process[mv], boxes_per_process[mv])
poly_sub.append(cnt_clean_rot)
boxes_sub_new.append(boxes_per_process[mv])
q.put(slopes_sub)
poly.put(poly_sub)
box_sub.put(boxes_sub_new)
def serialize_lines_in_region(self, textregion, all_found_texline_polygons, region_idx, page_coord, all_box_coord, slopes, id_indexer_l):
for j in range(len(all_found_texline_polygons[region_idx])):
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[region_idx][j])):
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[region_idx][j][l])==2:
textline_x_coord=int( (all_found_texline_polygons[region_idx][j][l][0]
+all_box_coord[region_idx][2]+page_coord[2])/self.scale_x)
textline_y_coord=int( (all_found_texline_polygons[region_idx][j][l][1]
+all_box_coord[region_idx][0]+page_coord[0])/self.scale_y)
if textline_x_coord<0:
textline_x_coord=0
if textline_y_coord<0:
textline_y_coord=0
points_co=points_co+str( textline_x_coord )
points_co=points_co+','
points_co=points_co+str( textline_y_coord )
else:
textline_x_coord=int( ( all_found_texline_polygons[region_idx][j][l][0][0]
+all_box_coord[region_idx][2]+page_coord[2])/self.scale_x )
textline_y_coord=int( ( all_found_texline_polygons[region_idx][j][l][0][1]
+all_box_coord[region_idx][0]+page_coord[0])/self.scale_y)
if textline_x_coord<0:
textline_x_coord=0
if textline_y_coord<0:
textline_y_coord=0
points_co=points_co+str( textline_x_coord )
points_co=points_co+','
points_co=points_co+str( textline_y_coord )
if (self.curved_line) and np.abs(slopes[region_idx]) <= 45 :
if len(all_found_texline_polygons[region_idx][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[region_idx][j][l][0]
+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[region_idx][j][l][1]
+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[region_idx][j][l][0][0]
+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[region_idx][j][l][0][1]
+page_coord[0])/self.scale_y) )
elif (self.curved_line) and np.abs(slopes[region_idx]) > 45 :
if len(all_found_texline_polygons[region_idx][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[region_idx][j][l][0]
+all_box_coord[region_idx][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[region_idx][j][l][1]
+all_box_coord[region_idx][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[region_idx][j][l][0][0]
+all_box_coord[region_idx][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[region_idx][j][l][0][1]
+all_box_coord[region_idx][0]+page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons[region_idx][j])-1):
points_co=points_co+' '
coord.set('points',points_co)
return id_indexer_l
def calculate_polygon_coords(self, contour_list, i, page_coord):
coords = ''
for j in range(len(contour_list[i])):
if len(contour_list[i][j]) == 2:
coords += str(int((contour_list[i][j][0] + page_coord[2]) / self.scale_x))
coords += ','
coords += str(int((contour_list[i][j][1] + page_coord[0]) / self.scale_y))
else:
coords += str(int((contour_list[i][j][0][0] + page_coord[2]) / self.scale_x))
coords += ','
coords += str(int((contour_list[i][j][0][1] + page_coord[0]) / self.scale_y))
if j < len(contour_list[i]) - 1:
coords=coords+' '
#print(coords)
return coords
def 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
pcgts, page = create_page_xml(self.image_filename, self.height_org, self.width_org)
page_print_sub = ET.SubElement(page, "PrintSpace")
coord_page = ET.SubElement(page_print_sub, "Coords")
coord_page.set('points', self.calculate_page_coords())
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')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region, mm, page_coord))
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons, mm, page_coord, all_box_coord, slopes, id_indexer_l)
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')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region_h, mm, page_coord))
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons_h, mm, page_coord, all_box_coord_h, slopes, id_indexer_l)
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')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_drop_capitals, mm, page_coord))
texteqreg = ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
try:
try:
id_indexer_l=id_indexer_l
except:
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')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord))
for j in range(len(all_found_texline_polygons_marginals[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
points_co=''
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
if not self.curved_line:
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
points_co=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) )
else:
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')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region_img, mm, page_coord))
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')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_tables, mm, page_coord))
except:
pass
##print(dir_of_image)
##print(self.f_name)
##print(os.path.join(dir_of_image, self.f_name) + ".xml")
##tree = ET.ElementTree(pcgts)
##tree.write(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
print(self.image_filename_stem)
# print(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
tree = ET.ElementTree(pcgts)
tree.write(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
def calculate_page_coords(self):
points_page_print = ""
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm]) == 2:
points_page_print += str(int((self.cont_page[0][lmm][0] ) / self.scale_x))
points_page_print += ','
points_page_print += str(int((self.cont_page[0][lmm][1] ) / self.scale_y))
else:
points_page_print += str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
points_page_print += ','
points_page_print += str(int((self.cont_page[0][lmm][0][1] ) / self.scale_y))
if lmm < (len( self.cont_page[0] ) - 1):
points_page_print = points_page_print + ' '
return points_page_print
def 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
pcgts, page = create_page_xml(self.image_filename, self.height_org, self.width_org)
page_print_sub = ET.SubElement(page, "PrintSpace")
coord_page = ET.SubElement(page_print_sub, "Coords")
coord_page.set('points', self.calculate_page_coords())
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')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region, mm, page_coord))
for j in range(len(all_found_texline_polygons[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id', 'l' + 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_co=''
for l in range(len(all_found_texline_polygons[mm][j])):
#point = ET.SubElement(coord, 'Point')
if not curved_line:
if len(all_found_texline_polygons[mm][j][l]) == 2:
textline_x_coord = max(0, int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
points_co += str(textline_x_coord) + ',' + str(textline_y_coord)
else:
textline_x_coord = max(0, int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2]+page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0]+page_coord[0]) / self.scale_y))
points_co += str(textline_x_coord) + ',' + str(textline_y_coord)
if curved_line and abs(slopes[mm]) <= 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co += str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ','
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
elif curved_line and abs(slopes[mm]) > 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co += str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
if l < len(all_found_texline_polygons[mm][j]) - 1:
points_co += ' '
coord.set('points', points_co)
texteqreg = ET.SubElement(textregion, 'TextEquiv')
unireg = ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
try:
#id_indexer_l=0
try:
id_indexer_l = id_indexer_l
except:
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')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord))
for j in range(len(all_found_texline_polygons_marginals[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq = ET.SubElement(textline, 'TextEquiv')
uni = ET.SubElement(texteq, 'Unicode')
uni.text = ' '
points_co=''
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
if not curved_line:
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0])/self.scale_y))
else:
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
if l < len(all_found_texline_polygons_marginals[mm][j]) - 1:
points_co += ' '
coord.set('points',points_co)
except:
pass
try:
id_indexer=len(contours)+len(found_polygons_marginals)
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 += ' '
coord_text.set('points', points_co)
except:
pass
print(self.image_filename_stem)
# print(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
tree = ET.ElementTree(pcgts)
tree.write(os.path.join(dir_of_image, self.image_filename_stem) + ".xml")
# cv2.imwrite(os.path.join(dir_of_image, self.image_filename_stem) + ".tif",self.image_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)
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)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img = cv2.medianBlur(img, 5)
if gaussian_filter:
img = cv2.GaussianBlur(img, (5,5 ), 0)
img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(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()
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)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
marginal_patch=0.2
prediction_regions_org2=self.do_prediction(patches,img,model_region,marginal_patch)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org2[:,:,0])
#plt.show()
#sys.exit()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
prediction_regions_org_copy=np.copy(prediction_regions_org)
prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)]=0
text_sume_second=( (prediction_regions_org_copy[:,:]==1)*1 ).sum()
rate_two_models=text_sume_second/float(text_sume_early)*100
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
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
pixel_img=1
min_area_text=0.00001
polygons_of_only_texts=return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)
polygons_of_only_images=return_contours_of_interested_region(mask_images_only,pixel_img)
polygons_of_only_lines=return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)
text_regions_p_true=np.zeros(prediction_regions_org.shape)
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))
text_regions_p_true[:,:][mask_images_only[:,:]==1]=2
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
del polygons_of_only_texts
del polygons_of_only_images
del polygons_of_only_lines
del mask_images_only
del prediction_regions_org
del img
del mask_zeros_y
del prediction_regions_org_y
del img_org
gc.collect()
return text_regions_p_true
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 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.image_filename_stem) + ".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("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_filename, 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[:, :]
image_page, page_coord = self.extract_page()
# print(image_page.shape,'page')
if self.plotter:
self.plotter.save_page_image(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_images = mask_images.astype(np.uint8)
mask_images = cv2.erode(mask_images[:, :], self.kernel, iterations=10)
mask_lines = (text_regions_p_1[:, :] == 3) * 1
mask_lines = mask_lines.astype(np.uint8)
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], self.kernel, iterations=6)
try:
num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0)
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
if 1>0:#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.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
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, plotter=self.plotter)
slope_first = 0 # return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, plotter=self.plotter)
if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
# img_rotated=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[:, :]
textline_mask_tot[mask_images[:, :] == 1] = 0
pixel_img = 1
min_area = 0.00001
max_area = 0.0006
textline_mask_tot_small_size = return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
text_regions_p_1[mask_lines[:, :] == 1] = 3
text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
text_regions_p = np.array(text_regions_p)
if num_col_classifier == 1 or num_col_classifier == 2:
try:
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
regions_without_seperators = regions_without_seperators.astype(np.uint8)
text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel)
except:
pass
# plt.imshow(text_regions_p)
# plt.show()
if self.plotter:
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
self.plotter.save_plot_of_layout_main(text_regions_p, image_page)
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
else:
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
#random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
#regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
# print(len(boxes),'boxes')
# sys.exit()
print("boxes in: " + str(time.time() - t1))
img_revised_tab = text_regions_p[:, :]
pixel_img = 2
polygons_of_images = return_contours_of_interested_region(img_revised_tab, pixel_img)
# 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)
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
K.clear_session()
gc.collect()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
# plt.imshow(regions_fully[:,:,0])
# plt.show()
K.clear_session()
gc.collect()
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)
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
K.clear_session()
gc.collect()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
# plt.imshow(regions_fully[:,:,0])
# plt.show()
text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
#plt.imshow(text_regions_p)
#plt.show()
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
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))
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)]
index_con_parents_d=np.argsort(areas_cnt_text_d)
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_d)
try:
cx_bigest_d_last5=cx_bigest_d[-5:]
cy_biggest_d_last5=cy_biggest_d[-5:]
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))]
ind_largest=len(cx_bigest_d)-5+np.argmin(dists_d)
cx_bigest_d_big[0]=cx_bigest_d[ind_largest]
cy_biggest_d_big[0]=cy_biggest_d[ind_largest]
except:
pass
(h, w) = text_only.shape[:2]
center = (w // 2.0, h // 2.0)
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
M_22 = np.array(M)[:2, :2]
p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
x_diff = p_big[0] - cx_bigest_d_big
y_diff = p_big[1] - cy_biggest_d_big
# print(p_big)
# print(cx_bigest_d_big,cy_biggest_d_big)
# print(x_diff,y_diff)
contours_only_text_parent_d_ordered = []
for i in range(len(contours_only_text_parent)):
# img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
# plt.imshow(img1[:,:,0])
# plt.show()
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
# print(p)
p[0] = p[0] - x_diff[0]
p[1] = p[1] - y_diff[0]
# print(p)
# print(cx_bigest_d)
# print(cy_biggest_d)
dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
# print(np.argmin(dists))
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
# plt.imshow(img2[:,:,0])
# plt.show()
else:
contours_only_text, hir_on_text = return_contours_of_image(text_only)
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
# print(areas_cnt_text_parent,'areas_cnt_text_parent')
# 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_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
if not self.curved_line:
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
else:
scale_param = 1
all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_curved(txt_con_org, contours_only_text_parent, cv2.erode(textline_mask_tot_ea, kernel=self.kernel, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=self.kernel, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
index_of_vertical_text_contours = np.array(range(len(slopes)))[(abs(np.array(slopes)) > 60)]
contours_text_vertical = [contours_only_text_parent[i] for i in index_of_vertical_text_contours]
K.clear_session()
gc.collect()
# print(index_by_text_par_con,'index_by_text_par_con')
if self.full_layout:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
else:
contours_only_text_parent_d_ordered = None
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
if self.plotter:
self.plotter.save_plot_of_layout(text_regions_p, image_page)
self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
K.clear_session()
gc.collect()
##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)
all_found_texline_polygons = adhere_drop_capital_region_into_cprresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, kernel=self.kernel, curved_line=self.curved_line)
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines = 6
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, 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, num_col_classifier)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
# print(slopes)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
if self.full_layout:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
else:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d)
self.write_into_page_xml_full(contours_only_text_parent, contours_only_text_parent_h, page_coord, self.dir_out, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals)
else:
contours_only_text_parent_h = None
# 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)
print("Job done in: " + str(time.time() - t1))