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eynollah/sbb_newspapers_org_image/eynollah.py

5953 lines
271 KiB
Python

"""
tool to extract table form data from alto xml data
"""
import gc
import math
4 years ago
import os
import random
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import sys
import time
import warnings
from multiprocessing import Process, Queue, cpu_count
from sys import getsizeof
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import cv2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
stderr = sys.stderr
sys.stderr = open(os.devnull, "w")
from keras import backend as K
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from keras.models import load_model
sys.stderr = stderr
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import tensorflow as tf
tf.get_logger().setLevel("ERROR")
warnings.filterwarnings("ignore")
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from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
from shapely import geometry
from lxml import etree as ET
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from matplotlib import pyplot, transforms
import matplotlib.patches as mpatches
import imutils
from .utils import (
boosting_headers_by_longshot_region_segmentation,
contours_in_same_horizon,
crop_image_inside_box,
filter_contours_area_of_image_interiors,
filter_contours_area_of_image_tables,
filter_small_drop_capitals_from_no_patch_layout,
find_contours_mean_y_diff,
find_features_of_contours,
find_features_of_lines,
find_new_features_of_contoures,
find_num_col,
find_num_col_by_vertical_lines,
find_num_col_deskew,
find_num_col_only_image,
get_text_region_boxes_by_given_contours,
get_textregion_contours_in_org_image,
isNaN,
otsu_copy,
otsu_copy_binary,
resize_image,
return_bonding_box_of_contours,
return_contours_of_image,
return_contours_of_interested_region,
return_contours_of_interested_region_and_bounding_box,
return_contours_of_interested_region_by_min_size,
return_contours_of_interested_textline,
return_hor_spliter_by_index_for_without_verticals,
return_parent_contours,
rotate_image,
rotate_max_area,
rotate_max_area_new,
rotatedRectWithMaxArea,
rotation_image_new,
rotation_not_90_func,
rotation_not_90_func_full_layout,
seperate_lines,
seperate_lines_new_inside_teils,
seperate_lines_new_inside_teils2,
seperate_lines_vertical_cont,
delete_seperator_around,
return_regions_without_seperators,
return_deskew_slop,
put_drop_out_from_only_drop_model,
putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
check_any_text_region_in_model_one_is_main_or_header,
small_textlines_to_parent_adherence2,
return_contours_of_interested_region_by_size,
order_and_id_of_texts,
order_of_regions,
implent_law_head_main_not_parallel,
return_hor_spliter_by_index,
combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
return_points_with_boundies,
textline_contours_postprocessing,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new,
)
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SLOPE_THRESHOLD = 0.13
VERY_LARGE_NUMBER = 1000000000000000000000
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class eynollah:
def __init__(
self,
image_dir,
f_name,
dir_out,
dir_models,
dir_of_cropped_images=None,
dir_of_layout=None,
dir_of_deskewed=None,
dir_of_all=None,
allow_enhancement=False,
curved_line=False,
full_layout=False,
allow_scaling=False,
headers_off=False
):
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self.image_dir = image_dir # XXX This does not seem to be a directory as the name suggests, but a file
self.dir_out = dir_out
self.f_name = f_name
self.dir_of_cropped_images = dir_of_cropped_images
self.allow_enhancement = allow_enhancement
self.curved_line = curved_line
self.full_layout = full_layout
self.allow_scaling = allow_scaling
self.dir_of_layout = dir_of_layout
self.headers_off = headers_off
self.dir_of_deskewed = dir_of_deskewed
self.dir_of_all = dir_of_all
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if self.f_name is None:
try:
self.f_name = image_dir.split("/")[len(image_dir.split("/")) - 1]
self.f_name = self.f_name.split(".")[0]
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except:
self.f_name = self.f_name.split(".")[0]
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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'
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###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'
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def predict_enhancement(self, img):
model_enhancement, session_enhancemnet = self.start_new_session_and_model(self.model_dir_of_enhancemnet)
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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)
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if img.shape[1] < img_width_model:
img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST)
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margin = True
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if margin:
kernel = np.ones((5, 5), np.uint8)
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margin = int(0 * img_width_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
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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]))
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seg = label_p_pred[0, :, :, :]
seg = seg * 255
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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]
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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]
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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]
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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]
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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]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
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else:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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prediction_true = prediction_true.astype(int)
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del model_enhancement
del session_enhancemnet
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return prediction_true
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def check_dpi(self):
dpi = os.popen('identify -format "%x " ' + self.image_dir).read()
return int(float(dpi))
def resize_image_with_column_classifier(self, is_image_enhanced):
dpi = self.check_dpi()
img = cv2.imread(self.image_dir)
img = img.astype(np.uint8)
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_, page_coord = self.early_page_for_num_of_column_classification()
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
img_1ch = cv2.imread(self.image_dir, 0)
width_early = img_1ch.shape[1]
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
# plt.imshow(img_1ch)
# plt.show()
img_1ch = img_1ch / 255.0
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
img_in[0, :, :, 0] = img_1ch[:, :]
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
label_p_pred = model_num_classifier.predict(img_in)
num_col = np.argmax(label_p_pred[0]) + 1
print(num_col, label_p_pred, "num_col_classifier")
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session_col_classifier.close()
del model_num_classifier
del session_col_classifier
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K.clear_session()
gc.collect()
# sys.exit()
if num_col == 1 and width_early < 1100:
img_w_new = 2000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
elif num_col == 1 and width_early >= 2500:
img_w_new = 2000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
elif num_col == 1 and width_early >= 1100 and width_early < 2500:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 2 and width_early < 2000:
img_w_new = 2400
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
elif num_col == 2 and width_early >= 3500:
img_w_new = 2400
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
elif num_col == 2 and width_early >= 2000 and width_early < 3500:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 3 and width_early < 2000:
img_w_new = 3000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
elif num_col == 3 and width_early >= 4000:
img_w_new = 3000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
elif num_col == 3 and width_early >= 2000 and width_early < 4000:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 4 and width_early < 2500:
img_w_new = 4000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
elif num_col == 4 and width_early >= 5000:
img_w_new = 4000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
elif num_col == 4 and width_early >= 2500 and width_early < 5000:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 5 and width_early < 3700:
img_w_new = 5000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
elif num_col == 5 and width_early >= 7000:
img_w_new = 5000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
elif num_col == 5 and width_early >= 3700 and width_early < 7000:
img_w_new = width_early
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
elif num_col == 6 and width_early < 4500:
img_w_new = 6500 # 5400
img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500)
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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
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else:
img_new = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True
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if img_new.shape[1] > img.shape[1]:
img_new = self.predict_enhancement(img_new)
is_image_enhanced = True
return img, img_new, is_image_enhanced
def resize_and_enhance_image_with_column_classifier(self, is_image_enhanced):
dpi = self.check_dpi()
img = cv2.imread(self.image_dir)
img = img.astype(np.uint8)
_, page_coord = self.early_page_for_num_of_column_classification()
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
img_1ch = cv2.imread(self.image_dir, 0)
img_1ch = img_1ch.astype(np.uint8)
width_early = img_1ch.shape[1]
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
# plt.imshow(img_1ch)
# plt.show()
img_1ch = img_1ch / 255.0
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
img_in[0, :, :, 0] = img_1ch[:, :]
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
# plt.imshow(img_in[0,:,:,:])
# plt.show()
label_p_pred = model_num_classifier.predict(img_in)
num_col = np.argmax(label_p_pred[0]) + 1
print(num_col, label_p_pred, "num_col_classifier")
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session_col_classifier.close()
del model_num_classifier
del session_col_classifier
del img_in
del img_1ch
del page_coord
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K.clear_session()
gc.collect()
print(dpi)
if dpi < 298:
# sys.exit()
if num_col == 1 and width_early < 1100:
img_w_new = 2000
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
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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)
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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)
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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
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else:
img_new = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True
# img_new=resize_image(img,img_h_new,img_w_new)
image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
# self.image=self.image.astype(np.uint16)
# self.scale_x=1
# self.scale_y=1
# self.height_org = self.image.shape[0]
# self.width_org = self.image.shape[1]
is_image_enhanced = True
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else:
"""
if img.shape[0]<=2530 and img.shape[0]>=img.shape[1]:
img_h_new=3000
img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
img_new=resize_image(img,img_h_new,img_w_new)
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image_res=self.predict_enhancement(img_new)
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
#self.image=self.image.astype(np.uint16)
##self.scale_x=1
##self.scale_y=1
##self.height_org = self.image.shape[0]
##self.width_org = self.image.shape[1]
is_image_enhanced=True
else:
is_image_enhanced=False
image_res=np.copy(img)
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"""
is_image_enhanced = False
num_column_is_classified = True
image_res = np.copy(img)
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return is_image_enhanced, img, image_res, num_col, num_column_is_classified
def resize_and_enhance_image(self, is_image_enhanced):
dpi = self.check_dpi()
img = cv2.imread(self.image_dir)
img = img.astype(np.uint8)
# sys.exit()
print(dpi)
if dpi < 298:
if img.shape[0] < 1000:
img_h_new = int(img.shape[0] * 3)
img_w_new = int(img.shape[1] * 3)
if img_h_new < 2800:
img_h_new = 3000
img_w_new = int(img.shape[1] / float(img.shape[0]) * 3000)
elif img.shape[0] >= 1000 and img.shape[0] < 2000:
img_h_new = int(img.shape[0] * 2)
img_w_new = int(img.shape[1] * 2)
if img_h_new < 2800:
img_h_new = 3000
img_w_new = int(img.shape[1] / float(img.shape[0]) * 3000)
4 years ago
else:
img_h_new = int(img.shape[0] * 1.5)
img_w_new = int(img.shape[1] * 1.5)
img_new = resize_image(img, img_h_new, img_w_new)
image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
# self.image=self.image.astype(np.uint16)
# self.scale_x=1
# self.scale_y=1
# self.height_org = self.image.shape[0]
# self.width_org = self.image.shape[1]
is_image_enhanced = True
4 years ago
else:
is_image_enhanced = False
image_res = np.copy(img)
return is_image_enhanced, img, image_res
def resize_and_enhance_image_new(self, is_image_enhanced):
# self.check_dpi()
img = cv2.imread(self.image_dir)
img = img.astype(np.uint8)
# sys.exit()
image_res = np.copy(img)
return is_image_enhanced, img, image_res
def get_image_and_scales(self, img_org, img_res, scale):
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self.image = np.copy(img_res)
self.image_org = np.copy(img_org)
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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)
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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)
4 years ago
del img_res
del img_org
def get_image_and_scales_after_enhancing(self, img_org, img_res):
# self.image = cv2.imread(self.image_dir)
self.image = np.copy(img_res)
self.image = self.image.astype(np.uint8)
self.image_org = np.copy(img_org)
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self.height_org = self.image_org.shape[0]
self.width_org = self.image_org.shape[1]
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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])
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del img_org
del img_res
def get_image_and_scales_deskewd(self, img_deskewd):
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self.image = img_deskewd
self.image_org = np.copy(self.image)
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self.height_org = self.image.shape[0]
self.width_org = self.image.shape[1]
self.img_hight_int = int(self.image.shape[0] * 1)
self.img_width_int = int(self.image.shape[1] * 1)
4 years ago
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)
4 years ago
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):
4 years ago
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]
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if patches:
if img.shape[0] < img_height_model:
img = resize_image(img, img_height_model, img.shape[1])
if img.shape[1] < img_width_model:
img = resize_image(img, img.shape[0], img_width_model)
# print(img_height_model,img_width_model)
# margin = int(0.2 * img_width_model)
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margin = int(marginal_of_patch_percent * img_height_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
# print(sys.getsizeof(img))
# print(np.max(img))
img = img.astype(np.float16)
# print(sys.getsizeof(img))
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img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
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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]))
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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]
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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]
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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]
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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]
4 years ago
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]
4 years ago
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]
4 years ago
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]
4 years ago
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]
4 years ago
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
4 years ago
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]
4 years ago
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
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prediction_true = prediction_true.astype(np.uint8)
del img
del mask_true
del seg_color
del seg
del img_patch
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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)
4 years ago
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
4 years ago
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)
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prediction_true = prediction_true.astype(np.uint8)
4 years ago
del img
del seg_color
del label_p_pred
del seg
del model
gc.collect()
4 years ago
return prediction_true
4 years ago
def early_page_for_num_of_column_classification(self):
img = cv2.imread(self.image_dir)
img = img.astype(np.uint8)
patches = False
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
###img = otsu_copy(self.image)
4 years ago
for ii in range(1):
img = cv2.GaussianBlur(img, (5, 5), 0)
img_page_prediction = self.do_prediction(patches, img, model_page)
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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)
4 years ago
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
4 years ago
gc.collect()
return croped_page, page_coord
def extract_page(self):
patches = False
4 years ago
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
###img = otsu_copy(self.image)
4 years ago
for ii in range(1):
img = cv2.GaussianBlur(self.image, (5, 5), 0)
img_page_prediction = self.do_prediction(patches, img, model_page)
4 years ago
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, self.kernel, iterations=3)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
cnt = contours[np.argmax(cnt_size)]
x, y, w, h = cv2.boundingRect(cnt)
if x <= 30:
w = w + x
x = 0
if (self.image.shape[1] - (x + w)) <= 30:
w = w + (self.image.shape[1] - (x + w))
4 years ago
if y <= 30:
h = h + y
y = 0
if (self.image.shape[0] - (y + h)) <= 30:
h = h + (self.image.shape[0] - (y + h))
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box = [x, y, w, h]
croped_page, page_coord = crop_image_inside_box(box, self.image)
self.cont_page = []
self.cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
4 years ago
session_page.close()
del model_page
del session_page
del contours
del thresh
del img
del imgray
gc.collect()
return croped_page, page_coord
def extract_drop_capital_13(self, img, patches, cols):
img_height_h = img.shape[0]
img_width_h = img.shape[1]
patches = False
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
img_1 = img[: int(img.shape[0] / 3.0), :, :]
img_2 = img[int(img.shape[0] / 3.0) : int(2 * img.shape[0] / 3.0), :, :]
img_3 = img[int(2 * img.shape[0] / 3.0) :, :, :]
4 years ago
# img_1 = otsu_copy_binary(img_1)#otsu_copy(img)
# img_1 = img_1.astype(np.uint16)
4 years ago
plt.imshow(img_1)
plt.show()
# img_2 = otsu_copy_binary(img_2)#otsu_copy(img)
# img_2 = img_2.astype(np.uint16)
4 years ago
plt.imshow(img_2)
plt.show()
# img_3 = otsu_copy_binary(img_3)#otsu_copy(img)
# img_3 = img_3.astype(np.uint16)
4 years ago
plt.imshow(img_3)
plt.show()
prediction_regions_1 = self.do_prediction(patches, img_1, model_region)
4 years ago
plt.imshow(prediction_regions_1)
plt.show()
prediction_regions_2 = self.do_prediction(patches, img_2, model_region)
4 years ago
plt.imshow(prediction_regions_2)
plt.show()
prediction_regions_3 = self.do_prediction(patches, img_3, model_region)
4 years ago
plt.imshow(prediction_regions_3)
plt.show()
prediction_regions = np.zeros((img_height_h, img_width_h))
prediction_regions[: int(img.shape[0] / 3.0), :] = prediction_regions_1[:, :, 0]
prediction_regions[int(img.shape[0] / 3.0) : int(2 * img.shape[0] / 3.0), :] = prediction_regions_2[:, :, 0]
prediction_regions[int(2 * img.shape[0] / 3.0) :, :] = prediction_regions_3[:, :, 0]
4 years ago
session_region.close()
del img_1
del img_2
del img_3
del prediction_regions_1
del prediction_regions_2
del prediction_regions_3
del model_region
del session_region
del img
gc.collect()
return prediction_regions
def extract_text_regions(self, img, patches, cols):
img_height_h = img.shape[0]
img_width_h = img.shape[1]
4 years ago
###if patches and cols>=3 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
4 years ago
###if not patches:
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
4 years ago
###if patches and cols==2 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
4 years ago
###if patches and cols==1 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
4 years ago
###if patches and cols>=2:
###img = otsu_copy_binary(img)#otsu_copy(img)
###img = img.astype(np.uint8)
4 years ago
###if patches and cols==1:
###img = otsu_copy_binary(img)#otsu_copy(img)
###img = img.astype(np.uint8)
###img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
if patches:
4 years ago
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
if not patches:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
if patches and cols == 1:
img2 = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if patches and cols == 2:
img2 = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
elif patches and cols > 2:
img2 = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
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)
4 years ago
if patches and cols == 2:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
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))
4 years ago
else:
pass # img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
4 years ago
img = img.astype(np.uint8)
if patches and cols == 1:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
4 years ago
img = img.astype(np.uint8)
if patches and cols == 3:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint8)
# img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols == 4:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint8)
# img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols >= 5:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint8)
# img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
4 years ago
if not patches:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
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)
4 years ago
session_region.close()
del model_region
del session_region
del img
gc.collect()
return prediction_regions, prediction_regions2
def extract_only_text_regions(self, img, patches):
4 years ago
model_region, session_region = self.start_new_session_and_model(self.model_only_text)
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint8)
img_org = np.copy(img)
img_h = img_org.shape[0]
img_w = img_org.shape[1]
4 years ago
img = resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1))
prediction_regions1 = self.do_prediction(patches, img, model_region)
prediction_regions1 = resize_image(prediction_regions1, img_h, img_w)
4 years ago
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4)
# prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7)
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2)
img = resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1))
prediction_regions2 = self.do_prediction(patches, img, model_region)
prediction_regions2 = resize_image(prediction_regions2, img_h, img_w)
# prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
4 years ago
prediction_regions2 = cv2.erode(prediction_regions2, self.kernel, iterations=2)
prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
# prediction_regions=( (prediction_regions2[:,:,0]==1) & (prediction_regions1[:,:,0]==1) )
# prediction_regions=(prediction_regions1[:,:,0]==1)
4 years ago
session_region.close()
del model_region
del session_region
gc.collect()
return prediction_regions1[:, :, 0]
def extract_binarization(self, img, patches):
4 years ago
model_bin, session_bin = self.start_new_session_and_model(self.model_binafrization)
img_h = img.shape[0]
img_w = img.shape[1]
img = resize_image(img, int(img.shape[0] * 1), int(img.shape[1] * 1))
prediction_regions = self.do_prediction(patches, img, model_bin)
res = (prediction_regions[:, :, 0] != 0) * 1
4 years ago
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
res[:, :][res[:, :] == 0] = 2
res = res - 1
res = res * 255
img_fin[:, :, 0] = res
img_fin[:, :, 1] = res
img_fin[:, :, 2] = res
4 years ago
session_bin.close()
del model_bin
del session_bin
gc.collect()
# plt.imshow(img_fin[:,:,0])
# plt.show()
4 years ago
return img_fin
def get_slopes_and_deskew_new(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew):
num_cores = cpu_count()
4 years ago
queue_of_all_params = Queue()
4 years ago
processes = []
nh = np.linspace(0, len(boxes), num_cores + 1)
indexes_by_text_con = np.array(range(len(contours_par)))
4 years ago
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)))
4 years ago
for i in range(num_cores):
processes[i].start()
4 years ago
slopes = []
all_found_texline_polygons = []
all_found_text_regions = []
all_found_text_regions_par = []
boxes = []
all_box_coord = []
all_index_text_con = []
4 years ago
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]
4 years ago
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])
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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()
4 years ago
queue_of_all_params = Queue()
4 years ago
processes = []
nh = np.linspace(0, len(boxes), num_cores + 1)
indexes_by_text_con = np.array(range(len(contours_par)))
4 years ago
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)))
4 years ago
for i in range(num_cores):
processes[i].start()
4 years ago
slopes = []
all_found_texline_polygons = []
all_found_text_regions = []
all_found_text_regions_par = []
boxes = []
all_box_coord = []
all_index_text_con = []
4 years ago
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]
4 years ago
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])
4 years ago
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):
4 years ago
slopes_per_each_subprocess = []
bounding_box_of_textregion_per_each_subprocess = []
textlines_rectangles_per_each_subprocess = []
contours_textregion_per_each_subprocess = []
contours_textregion_par_per_each_subprocess = []
all_box_coord_per_process = []
index_by_text_region_contours = []
slope_biggest = 0
textline_cnt_seperated = np.zeros(textline_mask_tot_ea.shape)
4 years ago
for mv in range(len(boxes_text)):
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
all_text_region_raw = all_text_region_raw.astype(np.uint8)
img_int_p = all_text_region_raw[:, :] # self.all_text_region_raw[mv]
4 years ago
##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:
4 years ago
slopes_per_each_subprocess.append(0)
slope_first = 0
slope_for_all = [slope_deskew][0]
4 years ago
else:
4 years ago
try:
textline_con, hierachy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0))
4 years ago
if sigma_des < 1:
sigma_des = 1
4 years ago
img_int_p[img_int_p > 0] = 1
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if abs(slope_for_all) < 0.5:
slope_for_all = [slope_deskew][0]
# old method
# slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
# text_patch_processed=textline_contours_postprocessing(gada)
4 years ago
except:
slope_for_all = 999
##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if slope_for_all == 999:
slope_for_all = [slope_deskew][0]
4 years ago
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
4 years ago
##elif slope_for_all==999:
##slope_for_all=slope_biggest
4 years ago
slopes_per_each_subprocess.append(slope_for_all)
4 years ago
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:
4 years ago
# all_box_coord.append(crop_coor)
4 years ago
textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
cnt_o_t_max = contours_par_per_process[mv]
4 years ago
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
mask_biggest = np.zeros(mask_texts_only.shape)
mask_biggest = cv2.fillPoly(mask_biggest, pts=[cnt_o_t_max], color=(1, 1, 1))
mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
textline_biggest_region = mask_biggest * textline_mask_tot_ea
# print(slope_for_all,'slope_for_all')
textline_rotated_seperated = self.seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all)
# new line added
4 years ago
##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
4 years ago
# 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 = []
4 years ago
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)
4 years ago
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)
4 years ago
try:
# textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
4 years ago
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
except:
pass
4 years ago
else:
slope_first = 0
add_boxes_coor_into_textlines = True
textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], slope_first, add_boxes_coor_into_textlines)
add_boxes_coor_into_textlines = False
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
# textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
# index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
4 years ago
textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
# all_found_texline_polygons.append(cnt_clean_rot)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
4 years ago
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])
4 years ago
def do_work_of_slopes_new(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, indexes_r_con_per_pro, image_page_rotated, slope_deskew):
slopes_per_each_subprocess = []
bounding_box_of_textregion_per_each_subprocess = []
textlines_rectangles_per_each_subprocess = []
contours_textregion_per_each_subprocess = []
contours_textregion_par_per_each_subprocess = []
all_box_coord_per_process = []
index_by_text_region_contours = []
slope_biggest = 0
4 years ago
for mv in range(len(boxes_text)):
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
4 years ago
# all_box_coord.append(crop_coor)
denoised = None
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
all_text_region_raw = all_text_region_raw.astype(np.uint8)
img_int_p = all_text_region_raw[:, :] # self.all_text_region_raw[mv]
img_int_p = cv2.erode(img_int_p, self.kernel, iterations=2)
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
4 years ago
slopes_per_each_subprocess.append(0)
slope_for_all = [slope_deskew][0]
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
###cnt_clean_rot=textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], 0)
4 years ago
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
4 years ago
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
# all_found_texline_polygons.append(cnt_clean_rot)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
4 years ago
else:
4 years ago
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))
4 years ago
if sigma_des < 1:
sigma_des = 1
4 years ago
img_int_p[img_int_p > 0] = 1
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
if abs(slope_for_all) <= 0.5:
slope_for_all = [slope_deskew][0]
4 years ago
except:
slope_for_all = 999
##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if slope_for_all == 999:
slope_for_all = [slope_deskew][0]
4 years ago
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
4 years ago
##elif slope_for_all==999:
##slope_for_all=slope_biggest
4 years ago
slopes_per_each_subprocess.append(slope_for_all)
slope_first = 0
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
# plt.imshow(mask_only_con_region)
# plt.show()
all_text_region_raw = np.copy(textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]])
mask_only_con_region = mask_only_con_region[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
4 years ago
##plt.imshow(textline_mask_tot_ea)
##plt.show()
##plt.imshow(all_text_region_raw)
##plt.show()
4 years ago
##plt.imshow(mask_only_con_region)
##plt.show()
all_text_region_raw[mask_only_con_region == 0] = 0
###cnt_clean_rot=textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], slope_first)
4 years ago
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
# all_found_texline_polygons.append(cnt_clean_rot)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
4 years ago
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])
4 years ago
def get_text_region_contours_and_boxes(self, image):
rgb_class_of_texts = (1, 1, 1)
mask_texts = np.all(image == rgb_class_of_texts, axis=-1)
image = np.repeat(mask_texts[:, :, np.newaxis], 3, axis=2) * 255
image = image.astype(np.uint8)
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, self.kernel)
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, self.kernel)
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
4 years ago
self.boxes = []
4 years ago
for jj in range(len(main_contours)):
x, y, w, h = cv2.boundingRect(main_contours[jj])
self.boxes.append([x, y, w, h])
4 years ago
return main_contours
def textline_contours(self, img, patches, scaler_h, scaler_w):
4 years ago
if patches:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
if not patches:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir_np)
##img = otsu_copy(img)
4 years ago
img = img.astype(np.uint8)
img_org = np.copy(img)
img_h = img_org.shape[0]
img_w = img_org.shape[1]
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
prediction_textline = self.do_prediction(patches, img, model_textline)
prediction_textline = resize_image(prediction_textline, img_h, img_w)
patches = False
prediction_textline_longshot = self.do_prediction(patches, img, model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
# scaler_w=1.5
# scaler_h=1.5
# patches=True
# img= resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w))
# prediction_textline_streched=self.do_prediction(patches,img,model_textline)
# prediction_textline_streched= resize_image(prediction_textline_streched, img_h, img_w)
4 years ago
##plt.imshow(prediction_textline_streched[:,:,0])
##plt.show()
# sys.exit()
4 years ago
session_textline.close()
del model_textline
del session_textline
del img
del img_org
4 years ago
gc.collect()
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
4 years ago
def seperate_lines_new(self, img_path, thetha, num_col):
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
4 years ago
else:
num_patches = int(img_path.shape[1] / 100.0)
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0:
num_patches = 1
4 years ago
(h, w) = img_path.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180.0 * np.pi
4 years ago
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
x_min_cont = 0
x_max_cont = img_path.shape[1]
y_min_cont = 0
y_max_cont = img_path.shape[0]
xv = np.linspace(x_min_cont, x_max_cont, 1000)
mada_n = img_path.sum(axis=1)
4 years ago
##plt.plot(mada_n)
##plt.show()
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = mada_n[:] # [first_nonzero:last_nonzero]
y_help = np.zeros(len(y) + 40)
y_help[20 : len(y) + 20] = y
4 years ago
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if len(peaks_real) <= 2 and len(peaks_real) > 1:
sigma_gaus = 10
4 years ago
else:
sigma_gaus = 6
z = gaussian_filter1d(y_help, sigma_gaus)
zneg_rev = -y_help + np.max(y_help)
zneg = np.zeros(len(zneg_rev) + 40)
zneg[20 : len(zneg_rev) + 20] = zneg_rev
zneg = gaussian_filter1d(zneg, sigma_gaus)
4 years ago
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
4 years ago
for nn in range(len(peaks_neg)):
if peaks_neg[nn] > len(z) - 1:
peaks_neg[nn] = len(z) - 1
if peaks_neg[nn] < 0:
peaks_neg[nn] = 0
diff_peaks = np.abs(np.diff(peaks_neg))
cut_off = 20
peaks_neg_true = []
forest = []
4 years ago
for i in range(len(peaks_neg)):
if i == 0:
4 years ago
forest.append(peaks_neg[i])
if i < (len(peaks_neg) - 1):
if diff_peaks[i] <= cut_off:
forest.append(peaks_neg[i + 1])
if diff_peaks[i] > cut_off:
# print(forest[np.argmin(z[forest]) ] )
if not isNaN(forest[np.argmin(z[forest])]):
# print(len(z),forest)
peaks_neg_true.append(forest[np.argmin(z[forest])])
forest = []
forest.append(peaks_neg[i + 1])
if i == (len(peaks_neg) - 1):
# print(print(forest[np.argmin(z[forest]) ] ))
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
peaks_neg_true = np.array(peaks_neg_true)
4 years ago
"""
#plt.figure(figsize=(40,40))
#plt.subplot(1,2,1)
#plt.title('Textline segmentation von Textregion')
#plt.imshow(img_path)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.subplot(1,2,2)
#plt.title('Dichte entlang X')
#base = pyplot.gca().transData
#rot = transforms.Affine2D().rotate_deg(90)
#plt.plot(zneg,np.array(range(len(zneg))))
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
#plt.gca().invert_yaxis()
4 years ago
#plt.xlabel('Dichte')
#plt.ylabel('Y')
##plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
"""
4 years ago
peaks_neg_true = peaks_neg_true - 20 - 20
peaks = peaks - 20
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
4 years ago
##plt.imshow(img_patch_ineterst)
##plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
4 years ago
margin = int(0.04 * length_x)
4 years ago
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise = []
4 years ago
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
sigma = 2
4 years ago
try:
slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
except:
slope_xline = 0
4 years ago
slopes_tile_wise.append(slope_xline)
# print(slope_xline,'xlineeee')
img_line_rotated = rotate_image(img_xline, slope_xline)
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
4 years ago
"""
4 years ago
xline=np.linspace(0,img_path.shape[1],nx)
slopes_tile_wise=[]
4 years ago
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
##plt.imshow(img_xline)
4 years ago
##plt.show()
4 years ago
sigma=3
try:
slope_xline=return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
except:
slope_xline=0
slopes_tile_wise.append(slope_xline)
print(slope_xline,'xlineeee')
img_line_rotated=rotate_image(img_xline,slope_xline)
4 years ago
##plt.imshow(img_line_rotated)
##plt.show()
"""
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
4 years ago
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
4 years ago
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
4 years ago
##plt.imshow(img_xline)
##plt.show()
img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated, 0)
4 years ago
##plt.imshow(img_patch_seperated)
##plt.show()
img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
4 years ago
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
4 years ago
"""
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
4 years ago
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
4 years ago
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated=rotate_image(img_resized,slopes_tile_wise[ui])
#img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated_returned=rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
4 years ago
##plt.imshow(img_patch_seperated)
##plt.show()
print(img_patch_seperated_returned.shape)
#plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
#plt.show()
4 years ago
img_patch_ineterst_revised[:,int(xline[ui]):int(xline[ui+1])]=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
4 years ago
"""
# print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
4 years ago
##plt.imshow(img_patch_ineterst_revised)
##plt.show()
return img_patch_ineterst_revised
def seperate_lines_new2(self, img_path, thetha, num_col, slope_region):
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
4 years ago
else:
num_patches = int(img_path.shape[1] / 140.0)
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0:
num_patches = 1
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
# plt.imshow(img_patch_ineterst)
# plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
4 years ago
margin = int(0.04 * length_x)
# print(margin,'margin')
# if margin<=4:
# margin = int(0.08 * length_x)
# margin=0
4 years ago
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise = []
4 years ago
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
sigma = 2
4 years ago
try:
slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
except:
slope_xline = 0
if abs(slope_region) < 25 and abs(slope_xline) > 25:
slope_xline = [slope_region][0]
# if abs(slope_region)>70 and abs(slope_xline)<25:
# slope_xline=[slope_region][0]
4 years ago
slopes_tile_wise.append(slope_xline)
# print(slope_xline,'xlineeee')
img_line_rotated = rotate_image(img_xline, slope_xline)
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
4 years ago
# print(slopes_tile_wise,'slopes_tile_wise')
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
4 years ago
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
# plt.imshow(img_xline)
# plt.show()
img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
img_patch_seperated = seperate_lines_new_inside_teils2(img_line_rotated, 0)
4 years ago
img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
4 years ago
# plt.imshow(img_patch_ineterst_revised)
# plt.show()
4 years ago
return img_patch_ineterst_revised
4 years ago
def textline_contours_to_get_slope_correctly(self, textline_mask, img_patch, contour_interest):
slope_new = 0 # deskew_images(img_patch)
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
textline_mask = textline_mask.astype(np.uint8)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, self.kernel)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, self.kernel)
textline_mask = cv2.erode(textline_mask, self.kernel, iterations=1)
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, self.kernel)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.003)
4 years ago
textline_maskt = textline_mask[:, :, 0]
textline_maskt[textline_maskt != 0] = 1
peaks_point, _ = seperate_lines(textline_maskt, contour_interest, slope_new)
4 years ago
mean_dis = np.mean(np.diff(peaks_point))
len_x = thresh.shape[1]
slope_lines = []
contours_slope_new = []
for kk in range(len(main_contours)):
if len(main_contours[kk].shape) == 2:
xminh = np.min(main_contours[kk][:, 0])
xmaxh = np.max(main_contours[kk][:, 0])
4 years ago
yminh = np.min(main_contours[kk][:, 1])
ymaxh = np.max(main_contours[kk][:, 1])
elif len(main_contours[kk].shape) == 3:
xminh = np.min(main_contours[kk][:, 0, 0])
xmaxh = np.max(main_contours[kk][:, 0, 0])
4 years ago
yminh = np.min(main_contours[kk][:, 0, 1])
ymaxh = np.max(main_contours[kk][:, 0, 1])
4 years ago
if ymaxh - yminh <= mean_dis and (xmaxh - xminh) >= 0.3 * len_x: # xminh>=0.05*len_x and xminh<=0.4*len_x and xmaxh<=0.95*len_x and xmaxh>=0.6*len_x:
4 years ago
contours_slope_new.append(main_contours[kk])
rows, cols = thresh.shape[:2]
[vx, vy, x, y] = cv2.fitLine(main_contours[kk], cv2.DIST_L2, 0, 0.01, 0.01)
slope_lines.append((vy / vx) / np.pi * 180)
if len(slope_lines) >= 2:
slope = np.mean(slope_lines) # slope_true/np.pi*180
else:
slope = 999
else:
slope = 0
return slope
def return_deskew_slope_new(self, img_patch, sigma_des):
max_x_y = max(img_patch.shape[0], img_patch.shape[1])
##img_patch=resize_image(img_patch,max_x_y,max_x_y)
4 years ago
img_patch_copy = np.zeros((img_patch.shape[0], img_patch.shape[1]))
img_patch_copy[:, :] = img_patch[:, :] # img_patch_org[:,:,0]
img_patch_padded = np.zeros((int(max_x_y * (1.4)), int(max_x_y * (1.4))))
img_patch_padded_center_p = int(img_patch_padded.shape[0] / 2.0)
len_x_org_patch_half = int(img_patch_copy.shape[1] / 2.0)
len_y_org_patch_half = int(img_patch_copy.shape[0] / 2.0)
img_patch_padded[img_patch_padded_center_p - len_y_org_patch_half : img_patch_padded_center_p - len_y_org_patch_half + img_patch_copy.shape[0], img_patch_padded_center_p - len_x_org_patch_half : img_patch_padded_center_p - len_x_org_patch_half + img_patch_copy.shape[1]] = img_patch_copy[:, :]
# img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
angles = np.linspace(-25, 25, 80)
res = []
num_of_peaks = []
index_cor = []
var_res = []
# plt.imshow(img_patch)
# plt.show()
indexer = 0
4 years ago
for rot in angles:
# print(rot,'rot')
img_rotated = rotate_image(img_patch_padded, rot)
img_rotated[img_rotated != 0] = 1
# plt.imshow(img_rotated)
# plt.show()
4 years ago
try:
neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3)
res_me = np.mean(neg_peaks)
if res_me == 0:
res_me = VERY_LARGE_NUMBER
4 years ago
else:
pass
res_num = len(neg_peaks)
4 years ago
except:
res_me = VERY_LARGE_NUMBER
res_num = 0
var_spectrum = 0
if isNaN(res_me):
4 years ago
pass
else:
res.append(res_me)
4 years ago
var_res.append(var_spectrum)
num_of_peaks.append(res_num)
4 years ago
index_cor.append(indexer)
indexer = indexer + 1
4 years ago
try:
var_res = np.array(var_res)
# print(var_res)
ang_int = angles[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
4 years ago
except:
ang_int = 0
if abs(ang_int) > 15:
angles = np.linspace(-90, -50, 30)
res = []
num_of_peaks = []
index_cor = []
var_res = []
# plt.imshow(img_patch)
# plt.show()
indexer = 0
4 years ago
for rot in angles:
# print(rot,'rot')
img_rotated = rotate_image(img_patch_padded, rot)
img_rotated[img_rotated != 0] = 1
# plt.imshow(img_rotated)
# plt.show()
4 years ago
try:
neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3)
res_me = np.mean(neg_peaks)
if res_me == 0:
res_me = VERY_LARGE_NUMBER
4 years ago
else:
pass
res_num = len(neg_peaks)
4 years ago
except:
res_me = VERY_LARGE_NUMBER
res_num = 0
var_spectrum = 0
if isNaN(res_me):
4 years ago
pass
else:
res.append(res_me)
4 years ago
var_res.append(var_spectrum)
num_of_peaks.append(res_num)
4 years ago
index_cor.append(indexer)
indexer = indexer + 1
4 years ago
try:
var_res = np.array(var_res)
# print(var_res)
ang_int = angles[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
4 years ago
except:
ang_int = 0
4 years ago
return ang_int
def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
slope_biggest = 0
4 years ago
slopes_sub = []
boxes_sub_new = []
poly_sub = []
4 years ago
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)
4 years ago
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)
4 years ago
sigma_des = int(y_diff_mean * (4.0 / 40.0))
4 years ago
if sigma_des < 1:
sigma_des = 1
4 years ago
crop_img[crop_img > 0] = 1
slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
except:
slope_corresponding_textregion = 999
if slope_corresponding_textregion == 999:
slope_corresponding_textregion = slope_biggest
4 years ago
##if np.abs(slope_corresponding_textregion)>12.5 and slope_corresponding_textregion!=999:
##slope_corresponding_textregion=slope_biggest
4 years ago
##elif slope_corresponding_textregion==999:
##slope_corresponding_textregion=slope_biggest
4 years ago
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])
4 years ago
poly_sub.append(cnt_clean_rot)
boxes_sub_new.append(boxes_per_process[mv])
4 years ago
q.put(slopes_sub)
poly.put(poly_sub)
box_sub.put(boxes_sub_new)
4 years ago
def get_slopes_and_deskew(self, contours, textline_mask_tot):
4 years ago
slope_biggest = 0 # return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
num_cores = cpu_count()
q = Queue()
poly = Queue()
box_sub = Queue()
4 years ago
processes = []
nh = np.linspace(0, len(self.boxes), num_cores + 1)
4 years ago
for i in range(num_cores):
boxes_per_process = self.boxes[int(nh[i]) : int(nh[i + 1])]
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
processes.append(Process(target=self.do_work_of_slopes, args=(q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process)))
4 years ago
for i in range(num_cores):
processes[i].start()
4 years ago
self.slopes = []
self.all_found_texline_polygons = []
self.boxes = []
4 years ago
for i in range(num_cores):
slopes_for_sub_process = q.get(True)
boxes_for_sub_process = box_sub.get(True)
polys_for_sub_process = poly.get(True)
4 years ago
for j in range(len(slopes_for_sub_process)):
self.slopes.append(slopes_for_sub_process[j])
self.all_found_texline_polygons.append(polys_for_sub_process[j])
self.boxes.append(boxes_for_sub_process[j])
4 years ago
for i in range(num_cores):
processes[i].join()
def write_into_page_xml_only_textlines(self, contours, page_coord, all_found_texline_polygons, all_box_coord, dir_of_image):
4 years ago
found_polygons_text_region = contours
4 years ago
# create the file structure
data = ET.Element("PcGts")
4 years ago
data.set("xmlns", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set("xmlns:xsi", "http://www.w3.org/2001/XMLSchema-instance")
data.set("xsi:schemaLocation", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
4 years ago
metadata = ET.SubElement(data, "Metadata")
4 years ago
author = ET.SubElement(metadata, "Creator")
author.text = "SBB_QURATOR"
4 years ago
created = ET.SubElement(metadata, "Created")
created.text = "2019-06-17T18:15:12"
4 years ago
changetime = ET.SubElement(metadata, "LastChange")
changetime.text = "2019-06-17T18:15:12"
4 years ago
page = ET.SubElement(data, "Page")
4 years ago
page.set("imageFilename", self.image_dir)
page.set("imageHeight", str(self.height_org))
page.set("imageWidth", str(self.width_org))
page.set("type", "content")
page.set("readingDirection", "left-to-right")
page.set("textLineOrder", "top-to-bottom")
4 years ago
page_print_sub = ET.SubElement(page, "PrintSpace")
coord_page = ET.SubElement(page_print_sub, "Coords")
points_page_print = ""
4 years ago
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm]) == 2:
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0]) / self.scale_x))
points_page_print = points_page_print + ","
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][1]) / self.scale_y))
4 years ago
else:
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
points_page_print = points_page_print + ","
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][1]) / self.scale_y))
4 years ago
if lmm < (len(self.cont_page[0]) - 1):
points_page_print = points_page_print + " "
coord_page.set("points", points_page_print)
if len(contours) > 0:
4 years ago
id_indexer = 0
id_indexer_l = 0
4 years ago
for mm in range(len(found_polygons_text_region)):
textregion = ET.SubElement(page, "TextRegion")
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
textregion.set("type", "paragraph")
# if mm==0:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
points_co = ""
4 years ago
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
for j in range(len(all_found_texline_polygons[mm])):
textline = ET.SubElement(textregion, "TextLine")
textline.set("id", "l" + str(id_indexer_l))
id_indexer_l += 1
coord = ET.SubElement(textline, "Coords")
texteq = ET.SubElement(textline, "TextEquiv")
uni = ET.SubElement(texteq, "Unicode")
uni.text = " "
# points = ET.SubElement(coord, 'Points')
points_co = ""
4 years ago
for l in range(len(all_found_texline_polygons[mm][j])):
# point = ET.SubElement(coord, 'Point')
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
if l < (len(all_found_texline_polygons[mm][j]) - 1):
points_co = points_co + " "
# print(points_co)
coord.set("points", points_co)
texteqreg = ET.SubElement(textregion, "TextEquiv")
4 years ago
unireg = ET.SubElement(texteqreg, "Unicode")
unireg.text = " "
4 years ago
# print(dir_of_image)
4 years ago
print(self.f_name)
# print(os.path.join(dir_of_image, self.f_name) + ".xml")
4 years ago
tree = ET.ElementTree(data)
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
def write_into_page_xml_full(self, contours, contours_h, page_coord, dir_of_image, order_of_texts, id_of_texts, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals):
found_polygons_text_region = contours
found_polygons_text_region_h = contours_h
4 years ago
# create the file structure
data = ET.Element("PcGts")
4 years ago
data.set("xmlns", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set("xmlns:xsi", "http://www.w3.org/2001/XMLSchema-instance")
data.set("xsi:schemaLocation", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
4 years ago
metadata = ET.SubElement(data, "Metadata")
4 years ago
author = ET.SubElement(metadata, "Creator")
author.text = "SBB_QURATOR"
4 years ago
created = ET.SubElement(metadata, "Created")
created.text = "2019-06-17T18:15:12"
4 years ago
changetime = ET.SubElement(metadata, "LastChange")
changetime.text = "2019-06-17T18:15:12"
4 years ago
page = ET.SubElement(data, "Page")
4 years ago
page.set("imageFilename", self.image_dir)
page.set("imageHeight", str(self.height_org))
page.set("imageWidth", str(self.width_org))
page.set("type", "content")
page.set("readingDirection", "left-to-right")
page.set("textLineOrder", "top-to-bottom")
4 years ago
page_print_sub = ET.SubElement(page, "PrintSpace")
coord_page = ET.SubElement(page_print_sub, "Coords")
points_page_print = ""
4 years ago
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm]) == 2:
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0]) / self.scale_x))
points_page_print = points_page_print + ","
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][1]) / self.scale_y))
4 years ago
else:
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
points_page_print = points_page_print + ","
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][1]) / self.scale_y))
4 years ago
if lmm < (len(self.cont_page[0]) - 1):
points_page_print = points_page_print + " "
coord_page.set("points", points_page_print)
4 years ago
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
4 years ago
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)
4 years ago
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
4 years ago
for mm in range(len(found_polygons_text_region)):
textregion = ET.SubElement(page, "TextRegion")
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
textregion.set("type", "paragraph")
# if mm==0:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
points_co = ""
4 years ago
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
for j in range(len(all_found_texline_polygons[mm])):
textline = ET.SubElement(textregion, "TextLine")
textline.set("id", "l" + str(id_indexer_l))
id_indexer_l += 1
coord = ET.SubElement(textline, "Coords")
texteq = ET.SubElement(textline, "TextEquiv")
uni = ET.SubElement(texteq, "Unicode")
uni.text = " "
# points = ET.SubElement(coord, 'Points')
points_co = ""
4 years ago
for l in range(len(all_found_texline_polygons[mm][j])):
# point = ET.SubElement(coord, 'Point')
if not self.curved_line:
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
if (self.curved_line) and np.abs(slopes[mm]) <= 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
elif (self.curved_line) and np.abs(slopes[mm]) > 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
if l < (len(all_found_texline_polygons[mm][j]) - 1):
points_co = points_co + " "
# print(points_co)
coord.set("points", points_co)
texteqreg = ET.SubElement(textregion, "TextEquiv")
unireg = ET.SubElement(texteqreg, "Unicode")
unireg.text = " "
4 years ago
print(len(contours_h))
if len(contours_h) > 0:
4 years ago
for mm in range(len(found_polygons_text_region_h)):
textregion = ET.SubElement(page, "TextRegion")
4 years ago
try:
id_indexer = id_indexer
id_indexer_l = id_indexer_l
4 years ago
except:
id_indexer = 0
id_indexer_l = 0
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
4 years ago
textregion.set("type", "header")
# if mm==0:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
4 years ago
points_co = ""
4 years ago
for lmm in range(len(found_polygons_text_region_h[mm])):
if len(found_polygons_text_region_h[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region_h[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
for j in range(len(all_found_texline_polygons_h[mm])):
textline = ET.SubElement(textregion, "TextLine")
textline.set("id", "l" + str(id_indexer_l))
id_indexer_l += 1
coord = ET.SubElement(textline, "Coords")
texteq = ET.SubElement(textline, "TextEquiv")
uni = ET.SubElement(texteq, "Unicode")
uni.text = " "
# points = ET.SubElement(coord, 'Points')
points_co = ""
4 years ago
for l in range(len(all_found_texline_polygons_h[mm][j])):
# point = ET.SubElement(coord, 'Point')
if not self.curved_line:
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons_h[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0] + all_box_coord_h[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][1] + all_box_coord_h[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][0] + all_box_coord_h[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][1] + all_box_coord_h[mm][0] + page_coord[0]) / self.scale_y))
if self.curved_line:
if len(all_found_texline_polygons_h[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
4 years ago
if l < (len(all_found_texline_polygons_h[mm][j]) - 1):
points_co = points_co + " "
# print(points_co)
coord.set("points", points_co)
4 years ago
texteqreg = ET.SubElement(textregion, "TextEquiv")
4 years ago
unireg = ET.SubElement(texteqreg, "Unicode")
unireg.text = " "
4 years ago
if len(found_polygons_drop_capitals) > 0:
id_indexer = len(contours_h) + len(contours) + len(found_polygons_marginals)
4 years ago
for mm in range(len(found_polygons_drop_capitals)):
textregion = ET.SubElement(page, "TextRegion")
4 years ago
# id_indexer_l=id_indexer_l
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
4 years ago
textregion.set("type", "drop-capital")
# if mm==0:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
4 years ago
points_co = ""
4 years ago
for lmm in range(len(found_polygons_drop_capitals[mm])):
if len(found_polygons_drop_capitals[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_drop_capitals[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
##for j in range(len(all_found_texline_polygons_h[mm])):
##textline=ET.SubElement(textregion, 'TextLine')
##textline.set('id','l'+str(id_indexer_l))
##id_indexer_l+=1
##coord = ET.SubElement(textline, 'Coords')
##texteq=ET.SubElement(textline, 'TextEquiv')
##uni=ET.SubElement(texteq, 'Unicode')
##uni.text = ' '
###points = ET.SubElement(coord, 'Points')
##points_co=''
##for l in range(len(all_found_texline_polygons_h[mm][j])):
###point = ET.SubElement(coord, 'Point')
##if not curved_line:
###point.set('x',str(found_polygons[j][l][0]))
###point.set('y',str(found_polygons[j][l][1]))
##if len(all_found_texline_polygons_h[mm][j][l])==2:
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x) )
##points_co=points_co+','
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
##else:
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x ) )
##points_co=points_co+','
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
##if curved_line:
##if len(all_found_texline_polygons_h[mm][j][l])==2:
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
##+page_coord[2])/self.scale_x) )
##points_co=points_co+','
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
##+page_coord[0])/self.scale_y) )
##else:
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
##+page_coord[2])/self.scale_x ) )
##points_co=points_co+','
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
##+page_coord[0])/self.scale_y) )
##if l<(len(all_found_texline_polygons_h[mm][j])-1):
##points_co=points_co+' '
###print(points_co)
####coord.set('points',points_co)
texteqreg = ET.SubElement(textregion, "TextEquiv")
unireg = ET.SubElement(texteqreg, "Unicode")
unireg.text = " "
4 years ago
try:
4 years ago
try:
###id_indexer=id_indexer
id_indexer_l = id_indexer_l
4 years ago
except:
###id_indexer=0
id_indexer_l = 0
4 years ago
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:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
points_co = ""
4 years ago
for lmm in range(len(found_polygons_marginals[mm])):
if len(found_polygons_marginals[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_marginals[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
for j in range(len(all_found_texline_polygons_marginals[mm])):
textline = ET.SubElement(textregion, "TextLine")
textline.set("id", "l" + str(id_indexer_l))
id_indexer_l += 1
coord = ET.SubElement(textline, "Coords")
texteq = ET.SubElement(textline, "TextEquiv")
uni = ET.SubElement(texteq, "Unicode")
uni.text = " "
# points = ET.SubElement(coord, 'Points')
points_co = ""
4 years ago
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
# point = ET.SubElement(coord, 'Point')
if not self.curved_line:
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
if self.curved_line:
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
4 years ago
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 = " "
4 years ago
except:
pass
4 years ago
try:
id_indexer = len(contours_h) + len(contours) + len(found_polygons_marginals) + len(found_polygons_drop_capitals)
4 years ago
for mm in range(len(found_polygons_text_region_img)):
textregion = ET.SubElement(page, "ImageRegion")
4 years ago
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
4 years ago
coord_text = ET.SubElement(textregion, "Coords")
4 years ago
points_co = ""
4 years ago
for lmm in range(len(found_polygons_text_region_img[mm])):
if len(found_polygons_text_region_img[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region_img[mm]) - 1):
points_co = points_co + " "
coord_text.set("points", points_co)
4 years ago
except:
pass
try:
for mm in range(len(found_polygons_tables)):
textregion = ET.SubElement(page, "TableRegion")
4 years ago
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
4 years ago
coord_text = ET.SubElement(textregion, "Coords")
4 years ago
points_co = ""
4 years ago
for lmm in range(len(found_polygons_tables[mm])):
if len(found_polygons_tables[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_tables[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_tables[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_tables[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_tables[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_tables[mm]) - 1):
points_co = points_co + " "
coord_text.set("points", points_co)
4 years ago
except:
pass
print(dir_of_image)
print(self.f_name)
print(os.path.join(dir_of_image, self.f_name) + ".xml")
tree = ET.ElementTree(data)
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
def write_into_page_xml(self, contours, page_coord, dir_of_image, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, curved_line, slopes, slopes_marginals):
4 years ago
found_polygons_text_region = contours
4 years ago
##found_polygons_text_region_h=contours_h
# create the file structure
data = ET.Element("PcGts")
4 years ago
data.set("xmlns", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set("xmlns:xsi", "http://www.w3.org/2001/XMLSchema-instance")
data.set("xsi:schemaLocation", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
4 years ago
metadata = ET.SubElement(data, "Metadata")
4 years ago
author = ET.SubElement(metadata, "Creator")
author.text = "SBB_QURATOR"
4 years ago
created = ET.SubElement(metadata, "Created")
created.text = "2019-06-17T18:15:12"
4 years ago
changetime = ET.SubElement(metadata, "LastChange")
changetime.text = "2019-06-17T18:15:12"
4 years ago
page = ET.SubElement(data, "Page")
4 years ago
page.set("imageFilename", self.image_dir)
page.set("imageHeight", str(self.height_org))
page.set("imageWidth", str(self.width_org))
page.set("type", "content")
page.set("readingDirection", "left-to-right")
page.set("textLineOrder", "top-to-bottom")
4 years ago
page_print_sub = ET.SubElement(page, "PrintSpace")
coord_page = ET.SubElement(page_print_sub, "Coords")
points_page_print = ""
4 years ago
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm]) == 2:
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0]) / self.scale_x))
points_page_print = points_page_print + ","
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][1]) / self.scale_y))
4 years ago
else:
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
points_page_print = points_page_print + ","
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][1]) / self.scale_y))
if lmm < (len(self.cont_page[0]) - 1):
points_page_print = points_page_print + " "
coord_page.set("points", points_page_print)
4 years ago
if len(contours) > 0:
region_order = ET.SubElement(page, "ReadingOrder")
region_order_sub = ET.SubElement(region_order, "OrderedGroup")
region_order_sub.set("id", "ro357564684568544579089")
4 years ago
indexer_region = 0
4 years ago
for vj in order_of_texts:
name = "coord_text_" + str(vj)
name = ET.SubElement(region_order_sub, "RegionRefIndexed")
4 years ago
name.set("index", str(indexer_region))
name.set("regionRef", id_of_texts[vj])
indexer_region += 1
id_of_marginalia = []
4 years ago
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
4 years ago
for mm in range(len(found_polygons_text_region)):
textregion = ET.SubElement(page, "TextRegion")
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
textregion.set("type", "paragraph")
# if mm==0:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
points_co = ""
4 years ago
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
for j in range(len(all_found_texline_polygons[mm])):
textline = ET.SubElement(textregion, "TextLine")
textline.set("id", "l" + str(id_indexer_l))
id_indexer_l += 1
coord = ET.SubElement(textline, "Coords")
texteq = ET.SubElement(textline, "TextEquiv")
uni = ET.SubElement(texteq, "Unicode")
uni.text = " "
# points = ET.SubElement(coord, 'Points')
points_co = ""
4 years ago
for l in range(len(all_found_texline_polygons[mm][j])):
# point = ET.SubElement(coord, 'Point')
if not self.curved_line:
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
if (self.curved_line) and abs(slopes[mm]) <= 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
elif (self.curved_line) and abs(slopes[mm]) > 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
if l < (len(all_found_texline_polygons[mm][j]) - 1):
points_co = points_co + " "
# print(points_co)
coord.set("points", points_co)
texteqreg = ET.SubElement(textregion, "TextEquiv")
unireg = ET.SubElement(texteqreg, "Unicode")
unireg.text = " "
4 years ago
###print(len(contours_h))
###if len(contours_h)>0:
###for mm in range(len(found_polygons_text_region_h)):
###textregion=ET.SubElement(page, 'TextRegion')
###try:
###id_indexer=id_indexer
###id_indexer_l=id_indexer_l
###except:
###id_indexer=0
###id_indexer_l=0
###textregion.set('id','r'+str(id_indexer))
###id_indexer+=1
###textregion.set('type','header')
####if mm==0:
#### textregion.set('type','header')
####else:
#### textregion.set('type','paragraph')
###coord_text = ET.SubElement(textregion, 'Coords')
###points_co=''
###for lmm in range(len(found_polygons_text_region_h[mm])):
###if len(found_polygons_text_region_h[mm][lmm])==2:
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
###else:
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
###if lmm<(len(found_polygons_text_region_h[mm])-1):
###points_co=points_co+' '
####print(points_co)
###coord_text.set('points',points_co)
###for j in range(len(all_found_texline_polygons_h[mm])):
###textline=ET.SubElement(textregion, 'TextLine')
###textline.set('id','l'+str(id_indexer_l))
###id_indexer_l+=1
###coord = ET.SubElement(textline, 'Coords')
###texteq=ET.SubElement(textline, 'TextEquiv')
###uni=ET.SubElement(texteq, 'Unicode')
###uni.text = ' '
####points = ET.SubElement(coord, 'Points')
###points_co=''
###for l in range(len(all_found_texline_polygons_h[mm][j])):
####point = ET.SubElement(coord, 'Point')
####point.set('x',str(found_polygons[j][l][0]))
####point.set('y',str(found_polygons[j][l][1]))
###if len(all_found_texline_polygons_h[mm][j][l])==2:
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0] +page_coord[2]
###+all_box_coord_h[mm][2])/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1] +page_coord[0]
###+all_box_coord_h[mm][0])/self.scale_y) )
###else:
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0] +page_coord[2]
###+all_box_coord_h[mm][2])/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1] +page_coord[0]
###+all_box_coord_h[mm][0])/self.scale_y) )
###if l<(len(all_found_texline_polygons_h[mm][j])-1):
###points_co=points_co+' '
####print(points_co)
###coord.set('points',points_co)
###texteqreg=ET.SubElement(textregion, 'TextEquiv')
###unireg=ET.SubElement(texteqreg, 'Unicode')
###unireg.text = ' '
4 years ago
try:
# id_indexer_l=0
4 years ago
try:
###id_indexer=id_indexer
id_indexer_l = id_indexer_l
4 years ago
except:
###id_indexer=0
id_indexer_l = 0
4 years ago
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:
4 years ago
# textregion.set('type','header')
# else:
4 years ago
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, "Coords")
points_co = ""
4 years ago
for lmm in range(len(found_polygons_marginals[mm])):
if len(found_polygons_marginals[mm][lmm]) == 2:
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][1] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_marginals[mm]) - 1):
points_co = points_co + " "
# print(points_co)
coord_text.set("points", points_co)
4 years ago
for j in range(len(all_found_texline_polygons_marginals[mm])):
textline = ET.SubElement(textregion, "TextLine")
textline.set("id", "l" + str(id_indexer_l))
id_indexer_l += 1
coord = ET.SubElement(textline, "Coords")
texteq = ET.SubElement(textline, "TextEquiv")
uni = ET.SubElement(texteq, "Unicode")
uni.text = " "
# points = ET.SubElement(coord, 'Points')
points_co = ""
4 years ago
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
# point = ET.SubElement(coord, 'Point')
if not self.curved_line:
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
4 years ago
else:
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
if self.curved_line:
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
4 years ago
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)
4 years ago
except:
pass
4 years ago
try:
4 years ago
for mm in range(len(found_polygons_text_region_img)):
textregion = ET.SubElement(page, "ImageRegion")
4 years ago
textregion.set("id", "r" + str(id_indexer))
id_indexer += 1
4 years ago
coord_text = ET.SubElement(textregion, "Coords")
points_co = ""
4 years ago
for lmm in range(len(found_polygons_text_region_img[mm])):
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm, 0, 0] + page_coord[2]) / self.scale_x))
points_co = points_co + ","
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm, 0, 1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region_img[mm]) - 1):
points_co = points_co + " "
coord_text.set("points", points_co)
4 years ago
###for mm in range(len(found_polygons_text_region_img)):
###textregion=ET.SubElement(page, 'ImageRegion')
###textregion.set('id','r'+str(id_indexer))
###id_indexer+=1
###coord_text = ET.SubElement(textregion, 'Coords')
###print(found_polygons_text_region_img[mm])
###points_co=''
###for lmm in range(len(found_polygons_text_region_img[mm])):
###print(len(found_polygons_text_region_img[mm][lmm]))
###if len(found_polygons_text_region_img[mm][lmm])==2:
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
###else:
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][1]+page_coord[0] )/self.scale_y) )
###if lmm<(len(found_polygons_text_region_img[mm])-1):
###points_co=points_co+' '
###coord_text.set('points',points_co)
4 years ago
except:
pass
####try:
####for mm in range(len(found_polygons_tables)):
####textregion=ET.SubElement(page, 'TableRegion')
4 years ago
####textregion.set('id','r'+str(id_indexer))
####id_indexer+=1
4 years ago
####coord_text = ET.SubElement(textregion, 'Coords')
4 years ago
####points_co=''
####for lmm in range(len(found_polygons_tables[mm])):
4 years ago
####if len(found_polygons_tables[mm][lmm])==2:
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
####points_co=points_co+','
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
####else:
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
####points_co=points_co+','
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
####if lmm<(len(found_polygons_tables[mm])-1):
####points_co=points_co+' '
####coord_text.set('points',points_co)
4 years ago
####except:
####pass
4 years ago
"""
4 years ago
try:
for mm in range(len(found_polygons_drop_capitals)):
textregion=ET.SubElement(page, 'DropCapitals')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_drop_capitals[mm])):
if len(found_polygons_drop_capitals[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_drop_capitals[mm])-1):
points_co=points_co+' '
4 years ago
coord_text.set('points',points_co)
except:
pass
"""
# print(dir_of_image)
4 years ago
print(self.f_name)
# print(os.path.join(dir_of_image, self.f_name) + ".xml")
4 years ago
tree = ET.ElementTree(data)
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
# cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)
def add_tables_heuristic_to_layout(self, image_regions_eraly_p, boxes, slope_mean_hor, spliter_y, peaks_neg_tot, image_revised):
4 years ago
image_revised_1 = delete_seperator_around(spliter_y, peaks_neg_tot, image_revised)
img_comm_e = np.zeros(image_revised_1.shape)
img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
4 years ago
for indiv in np.unique(image_revised_1):
# print(indiv,'indd')
image_col = (image_revised_1 == indiv) * 255
img_comm_in = np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
img_comm_in = img_comm_in.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
4 years ago
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
4 years ago
img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv))
4 years ago
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
# img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
img_comm = img_comm.astype(np.uint8)
if not isNaN(slope_mean_hor):
image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1], 3))
4 years ago
for i in range(len(boxes)):
image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
4 years ago
image_box_tabels_1 = (image_box[:, :, 0] == 7) * 1
4 years ago
contours_tab, _ = return_contours_of_image(image_box_tabels_1)
4 years ago
contours_tab = filter_contours_area_of_image_tables(image_box_tabels_1, contours_tab, _, 1, 0.001)
4 years ago
image_box_tabels_1 = (image_box[:, :, 0] == 6) * 1
image_box_tabels_and_m_text = ((image_box[:, :, 0] == 7) | (image_box[:, :, 0] == 1)) * 1
image_box_tabels_and_m_text = image_box_tabels_and_m_text.astype(np.uint8)
4 years ago
image_box_tabels_1 = image_box_tabels_1.astype(np.uint8)
image_box_tabels_1 = cv2.dilate(image_box_tabels_1, self.kernel, iterations=5)
4 years ago
contours_table_m_text, _ = return_contours_of_image(image_box_tabels_and_m_text)
4 years ago
image_box_tabels = np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
4 years ago
image_box_tabels = image_box_tabels.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
4 years ago
y_min_main_line, y_max_main_line, _ = find_features_of_contours(contours_line)
# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
y_min_main_tab, y_max_main_tab, _ = find_features_of_contours(contours_tab)
4 years ago
cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = find_new_features_of_contoures(contours_table_m_text)
cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contoures(contours_tab)
4 years ago
if len(y_min_main_tab) > 0:
y_down_tabs = []
y_up_tabs = []
4 years ago
for i_t in range(len(y_min_main_tab)):
y_down_tab = []
y_up_tab = []
4 years ago
for i_l in range(len(y_min_main_line)):
if y_min_main_tab[i_t] > y_min_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l] and y_min_main_tab[i_t] > y_max_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l]:
4 years ago
pass
elif y_min_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_min_main_line[i_l] and y_min_main_tab[i_t] < y_min_main_line[i_l]:
4 years ago
pass
elif np.abs(y_max_main_line[i_l] - y_min_main_line[i_l]) < 100:
4 years ago
pass
else:
y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t]]))
y_down_tab.append(np.max([y_max_main_line[i_l], y_max_main_tab[i_t]]))
4 years ago
if len(y_up_tab) == 0:
4 years ago
for v_n in range(len(cx_tab_m_text)):
if cx_tabl[i_t] <= x_max_tab_m_text[v_n] and cx_tabl[i_t] >= x_min_tab_m_text[v_n] and cy_tabl[i_t] <= y_max_tab_m_text[v_n] and cy_tabl[i_t] >= y_min_tab_m_text[v_n] and cx_tabl[i_t] != cx_tab_m_text[v_n] and cy_tabl[i_t] != cy_tab_m_text[v_n]:
4 years ago
y_up_tabs.append(y_min_tab_m_text[v_n])
y_down_tabs.append(y_max_tab_m_text[v_n])
# y_up_tabs.append(y_min_main_tab[i_t])
# y_down_tabs.append(y_max_main_tab[i_t])
4 years ago
else:
y_up_tabs.append(np.min(y_up_tab))
y_down_tabs.append(np.max(y_down_tab))
else:
y_down_tabs = []
y_up_tabs = []
4 years ago
pass
for ii in range(len(y_up_tabs)):
image_box[y_up_tabs[ii] : y_down_tabs[ii], :, 0] = 7
4 years ago
image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
4 years ago
else:
for i in range(len(boxes)):
image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
4 years ago
##plt.figure(figsize=(20,20))
##plt.imshow(image_box[:,:,0])
##plt.show()
return image_revised_last
def return_teilwiese_deskewed_lines(self, text_regions_p, textline_rotated):
4 years ago
kernel = np.ones((5, 5), np.uint8)
textline_rotated = cv2.erode(textline_rotated, kernel, iterations=1)
textline_rotated_new = np.zeros(textline_rotated.shape)
rgb_m = 1
rgb_h = 2
cnt_m, boxes_m = return_contours_of_interested_region_and_bounding_box(text_regions_p, rgb_m)
cnt_h, boxes_h = return_contours_of_interested_region_and_bounding_box(text_regions_p, rgb_h)
areas_cnt_m = np.array([cv2.contourArea(cnt_m[j]) for j in range(len(cnt_m))])
argmax = np.argmax(areas_cnt_m)
# plt.imshow(textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]])
# plt.show()
4 years ago
for argmax in range(len(boxes_m)):
textline_text_region = textline_rotated[boxes_m[argmax][1] : boxes_m[argmax][1] + boxes_m[argmax][3], boxes_m[argmax][0] : boxes_m[argmax][0] + boxes_m[argmax][2]]
textline_text_region_revised = self.seperate_lines_new(textline_text_region, 0)
# except:
4 years ago
# textline_text_region_revised=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2] ]
textline_rotated_new[boxes_m[argmax][1] : boxes_m[argmax][1] + boxes_m[argmax][3], boxes_m[argmax][0] : boxes_m[argmax][0] + boxes_m[argmax][2]] = textline_text_region_revised[:, :]
# textline_rotated_new[textline_rotated_new>0]=1
# textline_rotated_new[textline_rotated_new<0]=0
# plt.imshow(textline_rotated_new)
# plt.show()
def get_regions_from_xy_neu(self, img):
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
4 years ago
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)
gaussian_filter = False
patches = True
binary = True
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_org = prediction_regions_org[:, :, 0]
gaussian_filter = False
patches = False
binary = False
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_orgt = self.do_prediction(patches, img, model_region)
prediction_regions_orgt = resize_image(prediction_regions_orgt, img_height_h, img_width_h)
# plt.imshow(prediction_regions_orgt[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_orgt = prediction_regions_orgt[:, :, 0]
mask_texts_longshot = (prediction_regions_orgt[:, :] == 1) * 1
mask_texts_longshot = np.uint8(mask_texts_longshot)
# mask_texts_longshot = cv2.dilate(mask_texts_longshot[:,:], self.kernel, iterations=2)
pixel_img = 1
polygons_of_only_texts_longshot = return_contours_of_interested_region(mask_texts_longshot, pixel_img)
longshot_true = np.zeros(mask_texts_longshot.shape)
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
longshot_true = cv2.fillPoly(longshot_true, pts=polygons_of_only_texts_longshot, color=(1, 1, 1))
# plt.imshow(longshot_true)
# plt.show()
gaussian_filter = False
patches = False
binary = False
4 years ago
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
one_third_upper_ny = int(img.shape[0] / 3.0)
img = img[0:one_third_upper_ny, :, :]
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_longshot_one_third = self.do_prediction(patches, img, model_region)
prediction_regions_longshot_one_third = resize_image(prediction_regions_longshot_one_third, one_third_upper_ny, img_width_h)
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
img = img[one_third_upper_ny : int(2 * one_third_upper_ny), :, :]
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_longshot_one_third_middle = self.do_prediction(patches, img, model_region)
prediction_regions_longshot_one_third_middle = resize_image(prediction_regions_longshot_one_third_middle, one_third_upper_ny, img_width_h)
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
img = img[int(2 * one_third_upper_ny) :, :, :]
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_longshot_one_third_down = self.do_prediction(patches, img, model_region)
prediction_regions_longshot_one_third_down = resize_image(prediction_regions_longshot_one_third_down, img_height_h - int(2 * one_third_upper_ny), img_width_h)
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_longshot = np.zeros((img_height_h, img_width_h))
# prediction_regions_longshot=prediction_regions_longshot[:,:,0]
# prediction_regions_longshot[0:one_third_upper_ny,:]=prediction_regions_longshot_one_third[:,:,0]
# prediction_regions_longshot[one_third_upper_ny:int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_middle[:,:,0]
# prediction_regions_longshot[int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_down[:,:,0]
prediction_regions_longshot = longshot_true[:, :]
# plt.imshow(prediction_regions_longshot)
# plt.show()
gaussian_filter = False
patches = True
binary = False
4 years ago
ratio_x = 1 # 1.1
ratio_y = 1
median_blur = False
# img= resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region)
text_region1 = resize_image(prediction_regions, img_height_h, img_width_h)
# plt.imshow(text_region1[:,:,0])
# plt.show()
ratio_x = 1
ratio_y = 1.2 # 1.3
binary = False
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region)
text_region2 = resize_image(prediction_regions, img_height_h, img_width_h)
# plt.imshow(text_region2[:,:,0])
# plt.show()
4 years ago
session_region.close()
del model_region
del session_region
gc.collect()
# text_region1=text_region1[:,:,0]
# text_region2=text_region2[:,:,0]
# text_region1[(text_region1[:,:]==2) & (text_region2[:,:]==1)]=1
mask_zeros_from_1 = (text_region2[:, :, 0] == 0) * 1
# mask_text_from_1=(text_region1[:,:,0]==1)*1
4 years ago
mask_img_text_region1 = (text_region1[:, :, 0] == 2) * 1
text_region2_1st_channel = text_region1[:, :, 0]
4 years ago
text_region2_1st_channel[mask_zeros_from_1 == 1] = 0
4 years ago
##text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
# text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
mask_lines1 = (text_region1[:, :, 0] == 3) * 1
mask_lines2 = (text_region2[:, :, 0] == 3) * 1
mask_lines2[mask_lines1[:, :] == 1] = 1
# plt.imshow(text_region2_1st_channel)
# plt.show()
text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:, :], self.kernel, iterations=4)
# plt.imshow(text_region2_1st_channel)
# plt.show()
text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:, :], self.kernel, iterations=4)
text_region2_1st_channel[mask_lines2[:, :] == 1] = 3
# text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
4 years ago
# only in the case of model 3
text_region2_1st_channel[(prediction_regions_longshot[:, :] == 1) & (text_region2_1st_channel[:, :] == 2)] = 1
text_region2_1st_channel[(prediction_regions_org[:, :] == 2) & (text_region2_1st_channel[:, :] == 0)] = 2
# text_region2_1st_channel[prediction_regions_org[:,:]==0]=0
# plt.imshow(text_region2_1st_channel)
# plt.show()
# text_region2_1st_channel[:,:400]=0
mask_texts_only = (text_region2_1st_channel[:, :] == 1) * 1
mask_images_only = (text_region2_1st_channel[:, :] == 2) * 1
mask_lines_only = (text_region2_1st_channel[:, :] == 3) * 1
pixel_img = 1
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, pixel_img)
polygons_of_only_images = return_contours_of_interested_region(mask_images_only, pixel_img)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, pixel_img)
text_regions_p_true = np.zeros(text_region2_1st_channel.shape)
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3, 3, 3))
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_images, color=(2, 2, 2))
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1, 1, 1))
4 years ago
##print(np.unique(text_regions_p_true))
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
return text_regions_p_true # text_region2_1st_channel
4 years ago
def get_regions_from_xy(self, img):
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
4 years ago
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)
gaussian_filter = False
patches = True
binary = True
4 years ago
ratio_x = 1
ratio_y = 1
median_blur = False
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region)
4 years ago
###plt.imshow(prediction_regions_org[:,:,0])
###plt.show()
##sys.exit()
prediction_regions_org = prediction_regions_org[:, :, 0]
gaussian_filter = False
patches = True
binary = False
ratio_x = 1.1
ratio_y = 1
median_blur = False
# img= resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region)
text_region1 = resize_image(prediction_regions, img_height_h, img_width_h)
ratio_x = 1
ratio_y = 1.1
binary = False
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region)
text_region2 = resize_image(prediction_regions, img_height_h, img_width_h)
4 years ago
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros_from_1 = (text_region1[:, :, 0] == 0) * 1
# mask_text_from_1=(text_region1[:,:,0]==1)*1
mask_img_text_region1 = (text_region1[:, :, 0] == 2) * 1
text_region2_1st_channel = text_region2[:, :, 0]
4 years ago
text_region2_1st_channel[mask_zeros_from_1 == 1] = 0
text_region2_1st_channel[mask_img_text_region1[:, :] == 1] = 2
# text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
mask_lines1 = (text_region1[:, :, 0] == 3) * 1
mask_lines2 = (text_region2[:, :, 0] == 3) * 1
mask_lines2[mask_lines1[:, :] == 1] = 1
4 years ago
##plt.imshow(text_region2_1st_channel)
##plt.show()
text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:, :], self.kernel, iterations=5)
4 years ago
##plt.imshow(text_region2_1st_channel)
##plt.show()
text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:, :], self.kernel, iterations=5)
text_region2_1st_channel[mask_lines2[:, :] == 1] = 3
text_region2_1st_channel[(prediction_regions_org[:, :] == 1) & (text_region2_1st_channel[:, :] == 2)] = 1
text_region2_1st_channel[prediction_regions_org[:, :] == 3] = 3
4 years ago
##plt.imshow(text_region2_1st_channel)
##plt.show()
return text_region2_1st_channel
def get_regions_from_xy_2models_ens(self, img):
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
4 years ago
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
gaussian_filter = False
patches = False
binary = False
ratio_x = 1
ratio_y = 1
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
prediction_regions_long = self.do_prediction(patches, img, model_region)
prediction_regions_long = resize_image(prediction_regions_long, img_height_h, img_width_h)
gaussian_filter = False
patches = True
binary = False
ratio_x = 1
ratio_y = 1.2
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_org_y = self.do_prediction(patches, img, model_region)
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_org_y = prediction_regions_org_y[:, :, 0]
mask_zeros_y = (prediction_regions_org_y[:, :] == 0) * 1
ratio_x = 1.2
ratio_y = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_org = prediction_regions_org[:, :, 0]
prediction_regions_org[(prediction_regions_org[:, :] == 1) & (mask_zeros_y[:, :] == 1)] = 0
prediction_regions_org[(prediction_regions_long[:, :, 0] == 1) & (prediction_regions_org[:, :] == 2)] = 1
4 years ago
session_region.close()
del model_region
del session_region
gc.collect()
4 years ago
return prediction_regions_org
def get_regions_from_xy_2models(self, img, is_image_enhanced):
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
4 years ago
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))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
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
4 years ago
if is_image_enhanced:
ratio_x = 1.2
4 years ago
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))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
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)
4 years ago
##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
4 years ago
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
4 years ago
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))
4 years ago
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
4 years ago
img = img.astype(np.uint16)
4 years ago
if median_blur:
img = cv2.medianBlur(img, 5)
4 years ago
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
4 years ago
img = img.astype(np.uint16)
prediction_regions_org2 = self.do_prediction(patches, img, model_region)
prediction_regions_org2 = resize_image(prediction_regions_org2, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org2[:,:,0])
# plt.show()
# sys.exit()
4 years ago
##prediction_regions_org=prediction_regions_org[:,:,0]
4 years ago
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
mask_zeros2 = (prediction_regions_org2[:, :, 0] == 0) * 1
mask_lines2 = (prediction_regions_org2[:, :, 0] == 3) * 1
text_sume_early = ((prediction_regions_org[:, :] == 1) * 1).sum()
prediction_regions_org_copy = np.copy(prediction_regions_org)
4 years ago
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:
4 years ago
pass
else:
prediction_regions_org = np.copy(prediction_regions_org_copy)
4 years ago
##prediction_regions_org[mask_lines2[:,:]==1]=3
prediction_regions_org[(mask_lines2[:, :] == 1) & (prediction_regions_org[:, :] == 0)] = 3
4 years ago
del mask_lines2
del mask_zeros2
del prediction_regions_org2
# if is_image_enhanced:
# pass
# else:
# model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
4 years ago
# gaussian_filter=False
# patches=True
# binary=False
# ratio_x=1
# ratio_y=1
# median_blur=False
# img= resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
# if binary:
# img = otsu_copy_binary(img)#otsu_copy(img)
# img = img.astype(np.uint16)
# if median_blur:
# img=cv2.medianBlur(img,5)
# if gaussian_filter:
# img= cv2.GaussianBlur(img,(5,5),0)
# img = img.astype(np.uint16)
# prediction_regions_org2=self.do_prediction(patches,img,model_region)
# prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
4 years ago
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
##sys.exit()
###prediction_regions_org=prediction_regions_org[:,:,0]
# session_region.close()
# del model_region
# del session_region
# gc.collect()
####K.clear_session()
# mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
# mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
# text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
# prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
###prediction_regions_org[mask_lines2[:,:]==1]=3
# prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
# text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
# print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
# del mask_lines2
# del mask_zeros2
# del prediction_regions_org2
mask_lines_only = (prediction_regions_org[:, :] == 3) * 1
prediction_regions_org = cv2.erode(prediction_regions_org[:, :], self.kernel, iterations=2)
# plt.imshow(text_region2_1st_channel)
# plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:, :], self.kernel, iterations=2)
mask_texts_only = (prediction_regions_org[:, :] == 1) * 1
mask_images_only = (prediction_regions_org[:, :] == 2) * 1
pixel_img = 1
min_area_text = 0.00001
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, pixel_img, min_area_text)
polygons_of_only_images = return_contours_of_interested_region(mask_images_only, pixel_img)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, pixel_img, min_area_text)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3, 3, 3))
4 years ago
##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
text_regions_p_true[:, :][mask_images_only[:, :] == 1] = 2
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1, 1, 1))
4 years ago
##print(np.unique(text_regions_p_true))
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
4 years ago
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
4 years ago
del prediction_regions_org_y
del img_org
gc.collect()
4 years ago
return text_regions_p_true
def write_images_into_directory(self, img_contoures, dir_of_cropped_imgs, image_page):
index = 0
4 years ago
for cont_ind in img_contoures:
# cont_ind[:,0,0]=cont_ind[:,0,0]/self.scale_x
# cont_ind[:,0,1]=cont_ind[:,0,1]/self.scale_y
4 years ago
x, y, w, h = cv2.boundingRect(cont_ind)
box = [x, y, w, h]
croped_page, page_coord = crop_image_inside_box(box, image_page)
croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
path = os.path.join(dir_of_cropped_imgs, self.f_name + "_" + str(index) + ".jpg")
cv2.imwrite(path, croped_page)
index += 1
def get_marginals(self, text_with_lines, text_regions, num_col, slope_deskew):
mask_marginals = np.zeros((text_with_lines.shape[0], text_with_lines.shape[1]))
mask_marginals = mask_marginals.astype(np.uint8)
text_with_lines = text_with_lines.astype(np.uint8)
4 years ago
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded = cv2.erode(text_with_lines, self.kernel, iterations=5)
if text_with_lines.shape[0] <= 1500:
4 years ago
pass
elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800:
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=5)
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
4 years ago
else:
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=7)
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
4 years ago
text_with_lines_y = text_with_lines.sum(axis=0)
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent = text_with_lines_y_eroded.max() / (float(text_with_lines.shape[0])) * 100
# print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent < 30:
min_textline_thickness = 8
elif thickness_along_y_percent >= 30 and thickness_along_y_percent < 50:
min_textline_thickness = 20
4 years ago
else:
min_textline_thickness = 40
if thickness_along_y_percent >= 14:
text_with_lines_y_rev = -1 * text_with_lines_y[:]
# print(text_with_lines_y)
# print(text_with_lines_y_rev)
# plt.plot(text_with_lines_y)
# plt.show()
text_with_lines_y_rev = text_with_lines_y_rev - np.min(text_with_lines_y_rev)
4 years ago
# plt.plot(text_with_lines_y_rev)
# plt.show()
sigma_gaus = 1
region_sum_0 = gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev = gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
# plt.plot(region_sum_0_rev)
# plt.show()
region_sum_0_updown = region_sum_0[len(region_sum_0) :: -1]
first_nonzero = next((i for i, x in enumerate(region_sum_0) if x), None)
last_nonzero = next((i for i, x in enumerate(region_sum_0_updown) if x), None)
4 years ago
last_nonzero = len(region_sum_0) - last_nonzero
4 years ago
##img_sum_0_smooth_rev=-region_sum_0
mid_point = (last_nonzero + first_nonzero) / 2.0
one_third_right = (last_nonzero - mid_point) / 3.0
one_third_left = (mid_point - first_nonzero) / 3.0
# img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
peaks = np.array(peaks)
4 years ago
# print(region_sum_0[peaks])
4 years ago
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
# print(first_nonzero,last_nonzero,peaks)
peaks = peaks[(peaks > first_nonzero) & ((peaks < last_nonzero))]
# print(first_nonzero,last_nonzero,peaks)
# print(region_sum_0[peaks]<10)
4 years ago
####peaks=peaks[region_sum_0[peaks]<25 ]
# print(region_sum_0[peaks])
peaks = peaks[region_sum_0[peaks] < min_textline_thickness]
# print(peaks)
# print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col == 1:
peaks_right = peaks[peaks > mid_point]
peaks_left = peaks[peaks < mid_point]
if num_col == 2:
peaks_right = peaks[peaks > (mid_point + one_third_right)]
peaks_left = peaks[peaks < (mid_point - one_third_left)]
4 years ago
try:
point_right = np.min(peaks_right)
4 years ago
except:
point_right = last_nonzero
4 years ago
try:
point_left = np.max(peaks_left)
4 years ago
except:
point_left = first_nonzero
4 years ago
# print(point_left,point_right)
# print(text_regions.shape)
if point_right >= mask_marginals.shape[1]:
point_right = mask_marginals.shape[1] - 1
4 years ago
try:
mask_marginals[:, point_left:point_right] = 1
4 years ago
except:
mask_marginals[:, :] = 1
# print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew)
# print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum = mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum != 0] = 1
index_x = np.array(range(len(mask_marginals_rotated_sum))) + 1
index_x_interest = index_x[mask_marginals_rotated_sum == 1]
min_point_of_left_marginal = np.min(index_x_interest) - 16
max_point_of_right_marginal = np.max(index_x_interest) + 16
if min_point_of_left_marginal < 0:
min_point_of_left_marginal = 0
if max_point_of_right_marginal >= text_regions.shape[1]:
max_point_of_right_marginal = text_regions.shape[1] - 1
# print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
# print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
# plt.imshow(mask_marginals)
# plt.show()
4 years ago
# plt.imshow(mask_marginals_rotated)
# plt.show()
text_regions[(mask_marginals_rotated[:, :] != 1) & (text_regions[:, :] == 1)] = 4
pixel_img = 4
min_area_text = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:, :] == 4)] = 1
marginlas_should_be_main_text = []
x_min_marginals_left = []
x_min_marginals_right = []
4 years ago
for i in range(len(cx_text_only)):
x_width_mar = abs(x_min_text_only[i] - x_max_text_only[i])
y_height_mar = abs(y_min_text_only[i] - y_max_text_only[i])
# print(x_width_mar,y_height_mar,'y_height_mar')
if x_width_mar > 16 and y_height_mar / x_width_mar < 10:
4 years ago
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i] < (mid_point - one_third_left):
x_min_marginals_left_new = x_min_text_only[i]
if len(x_min_marginals_left) == 0:
4 years ago
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0] = min(x_min_marginals_left[0], x_min_marginals_left_new)
4 years ago
else:
x_min_marginals_right_new = x_min_text_only[i]
if len(x_min_marginals_right) == 0:
4 years ago
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0] = min(x_min_marginals_right[0], x_min_marginals_right_new)
if len(x_min_marginals_left) == 0:
x_min_marginals_left = [0]
if len(x_min_marginals_right) == 0:
x_min_marginals_right = [text_regions.shape[1] - 1]
# print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
# print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions = cv2.fillPoly(text_regions, pts=marginlas_should_be_main_text, color=(4, 4))
# print(np.unique(text_regions))
# text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
# text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:, : int(min_point_of_left_marginal)][text_regions[:, : int(min_point_of_left_marginal)] == 1] = 0
text_regions[:, int(max_point_of_right_marginal) :][text_regions[:, int(max_point_of_right_marginal) :] == 1] = 0
4 years ago
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
4 years ago
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
# plt.plot(region_sum_0)
# plt.plot(peaks,region_sum_0[peaks],'*')
# plt.show()
# plt.imshow(text_regions)
# plt.show()
# sys.exit()
4 years ago
else:
pass
return text_regions
def do_work_of_textline_seperation(self, queue_of_all_params, polygons_per_process, index_polygons_per_process, con_par_org, textline_mask_tot, mask_texts_only, num_col, scale_par, boxes_text):
textregions_cnt_tot_per_process = []
textlines_cnt_tot_per_process = []
index_polygons_per_process_per_process = []
polygons_per_par_process_per_process = []
textline_cnt_seperated = np.zeros(textline_mask_tot.shape)
4 years ago
for iiii in range(len(polygons_per_process)):
# crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)
# arg_max=np.argmax(areas_cnt_only_text)
textregions_cnt_tot_per_process.append(polygons_per_process[iiii] / scale_par)
textline_region_in_image = np.zeros(textline_mask_tot.shape)
cnt_o_t_max = polygons_per_process[iiii]
4 years ago
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
mask_biggest = np.zeros(mask_texts_only.shape)
mask_biggest = cv2.fillPoly(mask_biggest, pts=[cnt_o_t_max], color=(1, 1, 1))
mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
textline_biggest_region = mask_biggest * textline_mask_tot
textline_rotated_seperated = self.seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col)
4 years ago
# new line added
4 years ago
##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 = []
4 years ago
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)
4 years ago
else:
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=4)
pixel_img = 1
cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img)
4 years ago
try:
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0] / scale_par)
4 years ago
except:
pass
# print(len(cnt_textlines_in_image_ind))
# plt.imshow(mask_biggest2)
# plt.show()
4 years ago
textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
polygons_per_par_process_per_process.append(con_par_org[iiii])
queue_of_all_params.put([index_polygons_per_process_per_process, polygons_per_par_process_per_process, textregions_cnt_tot_per_process, textlines_cnt_tot_per_process])
def do_order_of_regions(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
4 years ago
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)
4 years ago
try:
arg_text_con = []
4 years ago
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]:
4 years ago
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 = []
4 years ago
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]:
4 years ago
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))
4 years ago
ref_point = 0
order_of_texts_tot = []
id_of_texts_tot = []
4 years ago
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 = []
4 years ago
for i in range(len(args_contours_box)):
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
4 years ago
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)
4 years ago
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
4 years ago
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
4 years ago
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
4 years ago
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
4 years ago
id_of_texts_tot.append(id_of_texts[jji])
ref_point = ref_point + len(id_of_texts)
order_of_texts_tot = []
4 years ago
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1]))
4 years ago
for tj1 in range(len(contours_only_text_parent_h)):
order_of_texts_tot.append(int(order_by_con_head[tj1]))
order_text_new = []
4 years ago
for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
4 years ago
order_text_new.append(tartib_new)
except:
arg_text_con = []
4 years ago
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
4 years ago
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))
4 years ago
############################# head
arg_text_con_h = []
4 years ago
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
4 years ago
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)))
4 years ago
order_by_con_head = np.zeros(len(arg_text_con_h))
4 years ago
#####
ref_point = 0
order_of_texts_tot = []
id_of_texts_tot = []
4 years ago
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 = []
4 years ago
for i in range(len(args_contours_box)):
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
4 years ago
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)
4 years ago
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
4 years ago
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
4 years ago
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
4 years ago
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
4 years ago
id_of_texts_tot.append(id_of_texts[jji])
ref_point = ref_point + len(id_of_texts)
4 years ago
order_of_texts_tot = []
4 years ago
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1]))
4 years ago
for tj1 in range(len(contours_only_text_parent_h)):
order_of_texts_tot.append(int(order_by_con_head[tj1]))
4 years ago
order_text_new = []
4 years ago
for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
4 years ago
order_text_new.append(tartib_new)
return order_text_new, id_of_texts_tot
4 years ago
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)
4 years ago
try:
arg_text_con = []
4 years ago
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]:
4 years ago
arg_text_con.append(jj)
break
arg_arg_text_con = np.argsort(arg_text_con)
args_contours = np.array(range(len(arg_text_con)))
4 years ago
order_by_con_main = np.zeros(len(arg_text_con))
ref_point = 0
order_of_texts_tot = []
id_of_texts_tot = []
4 years ago
for iij in range(len(boxes)):
args_contours_box = args_contours[np.array(arg_text_con) == iij]
4 years ago
con_inter_box = []
con_inter_box_h = []
4 years ago
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)
4 years ago
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
4 years ago
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
4 years ago
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
4 years ago
id_of_texts_tot.append(id_of_texts[jji])
ref_point = ref_point + len(id_of_texts)
order_of_texts_tot = []
4 years ago
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1]))
4 years ago
order_text_new = []
4 years ago
for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
4 years ago
order_text_new.append(tartib_new)
except:
arg_text_con = []
4 years ago
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
4 years ago
arg_text_con.append(jj)
break
arg_arg_text_con = np.argsort(arg_text_con)
args_contours = np.array(range(len(arg_text_con)))
4 years ago
order_by_con_main = np.zeros(len(arg_text_con))
ref_point = 0
order_of_texts_tot = []
id_of_texts_tot = []
4 years ago
for iij in range(len(boxes)):
args_contours_box = args_contours[np.array(arg_text_con) == iij]
con_inter_box = []
con_inter_box_h = []
4 years ago
for i in range(len(args_contours_box)):
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
4 years ago
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]
4 years ago
zahler = 0
4 years ago
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
4 years ago
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
4 years ago
id_of_texts_tot.append(id_of_texts[jji])
ref_point = ref_point + len(id_of_texts)
order_of_texts_tot = []
4 years ago
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1]))
4 years ago
order_text_new = []
4 years ago
for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
4 years ago
order_text_new.append(tartib_new)
return order_text_new, id_of_texts_tot
def adhere_drop_capital_region_into_cprresponding_textline(self, text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h):
# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
# print(all_found_texline_polygons[3])
cx_m, cy_m, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
cx_h, cy_h, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_h)
cx_d, cy_d, _, _, y_min_d, y_max_d, _ = find_new_features_of_contoures(polygons_of_drop_capitals)
img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
4 years ago
for j_cont in range(len(contours_only_text_parent)):
img_con_all[all_box_coord[j_cont][0] : all_box_coord[j_cont][1], all_box_coord[j_cont][2] : all_box_coord[j_cont][3], 0] = (j_cont + 1) * 3
# img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))
# plt.imshow(img_con_all[:,:,0])
# plt.show()
# img_con_all=cv2.dilate(img_con_all, self.kernel, iterations=3)
# plt.imshow(img_con_all[:,:,0])
# plt.show()
# print(np.unique(img_con_all[:,:,0]))
4 years ago
for i_drop in range(len(polygons_of_drop_capitals)):
# print(i_drop,'i_drop')
img_con_all_copy = np.copy(img_con_all)
img_con = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_con = cv2.fillPoly(img_con, pts=[polygons_of_drop_capitals[i_drop]], color=(1, 1, 1))
# plt.imshow(img_con[:,:,0])
# plt.show()
4 years ago
##img_con=cv2.dilate(img_con, self.kernel, iterations=30)
# plt.imshow(img_con[:,:,0])
# plt.show()
# print(np.unique(img_con[:,:,0]))
img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
# region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
# print(len(region_with_intersected_drop),'region_with_intersected_drop1')
if len(region_with_intersected_drop) == 0:
img_con_all_copy = np.copy(img_con_all)
img_con = cv2.dilate(img_con, self.kernel, iterations=4)
img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
# region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
# print(np.unique(img_con_all_copy[:,:,0]))
if self.curved_line:
if len(region_with_intersected_drop) > 1:
sum_pixels_of_intersection = []
4 years ago
for i in range(len(region_with_intersected_drop)):
# print((region_with_intersected_drop[i]*3+1))
sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
# print(sum_pixels_of_intersection)
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
4 years ago
try:
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
# print(y_lines)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
4 years ago
# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
4 years ago
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
4 years ago
except:
# print('gordun1')
4 years ago
pass
elif len(region_with_intersected_drop) == 1:
region_final = region_with_intersected_drop[0] - 1
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
# plt.imshow(img_textlines)
# plt.show()
4 years ago
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# print(np.shape(contours_biggest),'contours_biggest')
# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
4 years ago
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
4 years ago
# print(cx_t,'print')
4 years ago
try:
# print(all_found_texline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] # -all_box_coord[int(region_final)][2]
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] # -all_box_coord[int(region_final)][0]
4 years ago
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
4 years ago
except:
pass
else:
pass
##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
4 years ago
###print(all_box_coord[j_cont])
###print(cx_t)
###print(cy_t)
###print(cx_d[i_drop])
###print(cy_d[i_drop])
##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
4 years ago
##y_lines[y_lines<y_min_d[i_drop]]=0
###print(y_lines)
4 years ago
##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
###print(arg_min)
4 years ago
##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
##cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
##cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
4 years ago
##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
4 years ago
##img_textlines=img_textlines.astype(np.uint8)
4 years ago
##plt.imshow(img_textlines)
##plt.show()
##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
4 years ago
##print(len(contours_combined),'len textlines mixed')
##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
4 years ago
##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
4 years ago
###print(np.shape(contours_biggest))
###print(contours_biggest[:])
##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
4 years ago
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
else:
if len(region_with_intersected_drop) > 1:
sum_pixels_of_intersection = []
4 years ago
for i in range(len(region_with_intersected_drop)):
# print((region_with_intersected_drop[i]*3+1))
sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
# print(sum_pixels_of_intersection)
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
4 years ago
try:
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
# print(y_lines)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
cnt_nearest[:, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] - all_box_coord[int(region_final)][2]
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] - all_box_coord[int(region_final)][0]
contours_biggest = contours_biggest.reshape(np.shape(contours_biggest)[0], np.shape(contours_biggest)[2])
4 years ago
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
4 years ago
except:
# print('gordun1')
4 years ago
pass
elif len(region_with_intersected_drop) == 1:
region_final = region_with_intersected_drop[0] - 1
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(cx_t,'print')
4 years ago
try:
# print(all_found_texline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
cnt_nearest[:, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] - all_box_coord[int(region_final)][2]
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] - all_box_coord[int(region_final)][0]
contours_biggest = contours_biggest.reshape(np.shape(contours_biggest)[0], np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
4 years ago
except:
pass
else:
pass
4 years ago
#####for i_drop in range(len(polygons_of_drop_capitals)):
#####for j_cont in range(len(contours_only_text_parent)):
#####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
#####img_con=img_con.astype(np.uint8)
######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
######ret, thresh = cv2.threshold(imgray, 0, 255, 0)
######contours_new,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####contours_new,hir_new=return_contours_of_image(img_con)
#####contours_new_parent=return_parent_contours( contours_new,hir_new)
######plt.imshow(img_con)
######plt.show()
#####try:
#####if len(contours_new_parent)==1:
######print(all_found_texline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[j_cont])
######print(all_box_coord[j_cont])
######print(cx_t)
######print(cy_t)
######print(cx_d[i_drop])
######print(cy_d[i_drop])
#####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
######print(y_lines)
#####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
######print(arg_min)
#####cnt_nearest=np.copy(all_found_texline_polygons[j_cont][arg_min])
#####cnt_nearest[:,0]=all_found_texline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
#####cnt_nearest[:,1]=all_found_texline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
#####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_textlines=img_textlines.astype(np.uint8)
#####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
#####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
#####contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
#####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
######print(np.shape(contours_biggest))
######print(contours_biggest[:])
#####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
#####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
#####all_found_texline_polygons[j_cont][arg_min]=contours_biggest
######print(contours_biggest)
######plt.imshow(img_textlines[:,:,0])
######plt.show()
#####else:
#####pass
#####except:
#####pass
4 years ago
return all_found_texline_polygons
def save_plot_of_layout_main(self, text_regions_p, image_page):
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
4 years ago
pixels = ["Background", "Main text", "Images", "Seperators", "Marginalia"]
values_indexes = [0, 1, 2, 3, 4]
plt.figure(figsize=(40, 40))
plt.rcParams["font.size"] = "40"
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout, self.f_name + "_layout_main.png"))
def save_plot_of_layout_main_all(self, text_regions_p, image_page):
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = ["Background", "Main text", "Images", "Seperators", "Marginalia"]
values_indexes = [0, 1, 2, 3, 4]
4 years ago
plt.figure(figsize=(70, 40))
plt.rcParams["font.size"] = "40"
plt.subplot(1, 2, 1)
4 years ago
plt.imshow(image_page)
plt.subplot(1, 2, 2)
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_layout_main_and_page.png"))
4 years ago
def save_plot_of_layout(self, text_regions_p, image_page):
values = np.unique(text_regions_p[:, :])
4 years ago
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capitals", "Images", "Seperators"]
values_indexes = [0, 1, 2, 8, 4, 5, 6]
plt.figure(figsize=(40, 40))
plt.rcParams["font.size"] = "40"
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout, self.f_name + "_layout.png"))
def save_plot_of_layout_all(self, text_regions_p, image_page):
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
4 years ago
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capitals", "Images", "Seperators"]
values_indexes = [0, 1, 2, 8, 4, 5, 6]
4 years ago
plt.figure(figsize=(70, 40))
plt.rcParams["font.size"] = "40"
plt.subplot(1, 2, 1)
4 years ago
plt.imshow(image_page)
plt.subplot(1, 2, 2)
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
4 years ago
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_layout_and_page.png"))
def save_deskewed_image(self, slope_deskew):
img_rotated = self.rotyate_image_different(self.image_org, slope_deskew)
4 years ago
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all, self.f_name + "_org.png"), self.image_org)
cv2.imwrite(os.path.join(self.dir_of_deskewed, self.f_name + "_deskewed.png"), img_rotated)
4 years ago
del img_rotated
def run(self):
is_image_enhanced = False
# get image and sclaes, then extract the page of scanned image
t1 = time.time()
##########
4 years ago
###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")
4 years ago
K.clear_session()
scale = 1
if (self.allow_enhancement) and is_image_enhanced:
cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif", img_res)
img_res = img_res.astype(np.uint8)
self.get_image_and_scales(img_org, img_res, scale)
if (not self.allow_enhancement) and is_image_enhanced:
self.get_image_and_scales_after_enhancing(img_org, img_res)
4 years ago
if (self.allow_enhancement) and not is_image_enhanced:
self.get_image_and_scales(img_org, img_res, scale)
if (not self.allow_enhancement) and not is_image_enhanced:
self.get_image_and_scales(img_org, img_res, scale)
if (self.allow_scaling) and not is_image_enhanced:
img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced)
self.get_image_and_scales_after_enhancing(img_org, img_res)
# print(self.scale_x)
print("enhancing: " + str(time.time() - t1))
text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced)
4 years ago
K.clear_session()
gc.collect()
print("textregion: " + str(time.time() - t1))
img_g = cv2.imread(self.image_dir, 0)
img_g = img_g.astype(np.uint8)
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
4 years ago
img_g3 = img_g3.astype(np.uint8)
img_g3[:, :, 0] = img_g[:, :]
img_g3[:, :, 1] = img_g[:, :]
img_g3[:, :, 2] = img_g[:, :]
4 years ago
###self.produce_groundtruth_for_textline()
image_page, page_coord = self.extract_page()
# print(image_page.shape,'page')
4 years ago
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all, self.f_name + "_page.png"), image_page)
##########
4 years ago
K.clear_session()
gc.collect()
img_g3_page = img_g3[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
4 years ago
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]]
4 years ago
mask_images = (text_regions_p_1[:, :] == 2) * 1
mask_lines = (text_regions_p_1[:, :] == 3) * 1
4 years ago
mask_images = mask_images.astype(np.uint8)
mask_lines = mask_lines.astype(np.uint8)
mask_images = cv2.erode(mask_images[:, :], self.kernel, iterations=10)
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], self.kernel, iterations=6)
4 years ago
try:
num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0)
4 years ago
if not num_column_is_classified:
num_col_classifier = num_col + 1
4 years ago
except:
num_col = None
peaks_neg_fin = []
print(num_col, "num_colnum_col")
4 years ago
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)
4 years ago
else:
# pass
4 years ago
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)
4 years ago
K.clear_session()
gc.collect()
print(np.unique(textline_mask_tot_ea[:, :]), "textline")
4 years ago
if self.dir_of_all is not None:
values = np.unique(textline_mask_tot_ea[:, :])
pixels = ["Background", "Textlines"]
values_indexes = [0, 1]
plt.figure(figsize=(70, 40))
plt.rcParams["font.size"] = "40"
plt.subplot(1, 2, 1)
4 years ago
plt.imshow(image_page)
plt.subplot(1, 2, 2)
im = plt.imshow(textline_mask_tot_ea[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_textline_and_page.png"))
print("textline: " + str(time.time() - t1))
# plt.imshow(textline_mask_tot_ea)
# plt.show()
# sys.exit()
sigma = 2
main_page_deskew = True
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2), sigma, main_page_deskew, dir_of_all=self.dir_of_all, f_name=self.f_name)
slope_first = 0 # return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
4 years ago
if self.dir_of_deskewed is not None:
self.save_deskewed_image(slope_deskew)
# img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)
print(slope_deskew, "slope_deskew")
4 years ago
##plt.imshow(img_rotated)
##plt.show()
##sys.exit()
print("deskewing: " + str(time.time() - t1))
4 years ago
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :] # rotation_not_90_func(image_page,textline_mask_tot_ea,slope_first)
textline_mask_tot[mask_images[:, :] == 1] = 0
pixel_img = 1
min_area = 0.00001
max_area = 0.0006
textline_mask_tot_small_size = return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
# text_regions_p_1[(textline_mask_tot[:,:]==1) & (text_regions_p_1[:,:]==2)]=1
text_regions_p_1[mask_lines[:, :] == 1] = 3
4 years ago
##text_regions_p_1[textline_mask_tot_small_size[:,:]==1]=1
text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
text_regions_p = np.array(text_regions_p)
if num_col_classifier == 1 or num_col_classifier == 2:
4 years ago
try:
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
regions_without_seperators = regions_without_seperators.astype(np.uint8)
4 years ago
text_regions_p = self.get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew)
4 years ago
except:
pass
else:
pass
# plt.imshow(text_regions_p)
# plt.show()
4 years ago
if self.dir_of_all is not None:
self.save_plot_of_layout_main_all(text_regions_p, image_page)
4 years ago
if self.dir_of_layout is not None:
self.save_plot_of_layout_main(text_regions_p, image_page)
print("marginals: " + str(time.time() - t1))
if not self.full_layout:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
4 years ago
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)
4 years ago
K.clear_session()
gc.collect()
# print(peaks_neg_fin,num_col,'num_col2')
print(num_col_classifier, "num_col_classifier")
if num_col_classifier >= 3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
random_pixels_for_image[random_pixels_for_image != 0] = 1
regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
random_pixels_for_image[random_pixels_for_image != 0] = 1
regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
4 years ago
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)
4 years ago
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d)
# print(len(boxes),'boxes')
# sys.exit()
print("boxes in: " + str(time.time() - t1))
img_revised_tab = text_regions_p[:, :]
# plt.imshow(img_revised_tab)
# plt.show()
4 years ago
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
4 years ago
K.clear_session()
# gc.collect()
patches = True
image_page = image_page.astype(np.uint8)
# print(type(image_page))
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
4 years ago
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()
4 years ago
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
4 years ago
else:
regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
# regions_fully_np=filter_small_drop_capitals_from_no_patch_layout(regions_fully_np,text_regions_p)
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
4 years ago
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
4 years ago
##text_regions_p[:,:][(regions_fully[:,:,0]==7) & (text_regions_p[:,:]!=0)]=7
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
4 years ago
# 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)
4 years ago
K.clear_session()
gc.collect()
img_revised_tab = np.copy(text_regions_p[:, :])
print("full layout in: " + str(time.time() - t1))
# sys.exit()
pixel_img = 5
polygons_of_images = return_contours_of_interested_region(img_revised_tab, pixel_img)
# plt.imshow(img_revised_tab)
# plt.show()
# print(img_revised_tab.shape,text_regions_p_1_n.shape)
# text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
# print(np.unique(text_regions_p_1_n),'uni')
text_only = ((img_revised_tab[:, :] == 1)) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
4 years ago
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
# print(text_only.shape,text_only_d.shape)
# plt.imshow(text_only)
# plt.show()
# plt.imshow(text_only_d)
# plt.show()
min_con_area = 0.000005
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text, hir_on_text = return_contours_of_image(text_only)
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_d)
4 years ago
(h, w) = text_only.shape[:2]
center = (w // 2.0, h // 2.0)
4 years ago
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 = []
4 years ago
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))
4 years ago
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()
4 years ago
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')
4 years ago
###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)
##contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d])
###areas_cnt_text_parent_d=list(np.array(areas_cnt_text_parent_d)[index_con_parents_d])
4 years ago
##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)
4 years ago
###boxes_text,_= get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
####boxes_text_h,_= get_text_region_boxes_by_given_contours(text_only_h,contours_only_text_parent_h,image_page)
4 years ago
if not self.curved_line:
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
if self.curved_line:
scale_param = 1
all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_curved(txt_con_org, contours_only_text_parent, cv2.erode(textline_mask_tot_ea, kernel=self.kernel, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
4 years ago
# all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
# slopes=list(np.zeros(len(contours_only_text_parent)))
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=self.kernel, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
# all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
index_of_vertical_text_contours = np.array(range(len(slopes)))[(abs(np.array(slopes)) > 60)]
contours_text_vertical = [contours_only_text_parent[i] for i in index_of_vertical_text_contours]
4 years ago
K.clear_session()
gc.collect()
# contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
4 years ago
###print(index_by_text_par_con,'index_by_text_par_con')
if self.full_layout:
4 years ago
##for iii in range(len(contours_only_text_parent)):
##img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
##img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[iii]] ,color=(1,1,1))
##plt.imshow(img1[:,:,0])
##plt.show()
##img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
##img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d_ordered[iii]] ,color=(1,1,1))
##plt.imshow(img2[:,:,0])
##plt.show()
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
4 years ago
else:
contours_only_text_parent_d_ordered = None
4 years ago
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
###text_regions_p,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h=check_any_text_region_in_model_one_is_main_or_header(text_regions_p,regions_fully,contours_only_text_parent,all_box_coord,all_found_texline_polygons)
# text_regions_p=self.return_region_segmentation_after_implementing_not_head_maintext_parallel(text_regions_p,boxes)
4 years ago
# if you want to save the layout result just uncommet following plot
4 years ago
if self.dir_of_layout is not None:
self.save_plot_of_layout(text_regions_p, image_page)
4 years ago
if self.dir_of_all is not None:
self.save_plot_of_layout_all(text_regions_p, image_page)
4 years ago
K.clear_session()
gc.collect()
4 years ago
##print('Job done in: '+str(time.time()-t1))
polygons_of_tabels = []
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
# polygons_of_drop_capitals=[]
all_found_texline_polygons = self.adhere_drop_capital_region_into_cprresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h)
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines = 6
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h)
4 years ago
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)
4 years ago
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)
4 years ago
# 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
4 years ago
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
4 years ago
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)
4 years ago
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)
4 years ago
# print(slopes)
4 years ago
if self.dir_of_cropped_images is not None:
self.write_images_into_directory(polygons_of_images, self.dir_of_cropped_images, image_page)
4 years ago
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)
4 years ago
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)
4 years ago
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)
4 years ago
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)
4 years ago
except:
txt_con_org = []
order_text_new = []
id_of_texts_tot = []
all_found_texline_polygons = []
all_box_coord = []
polygons_of_images = []
polygons_of_marginals = []
all_found_texline_polygons_marginals = []
all_box_coord_marginals = []
slopes = []
slopes_marginals = []
self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, self.curved_line, slopes, slopes_marginals)
print("Job done in: " + str(time.time() - t1))