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eynollah/sbb_newspapers_org_image/eynollah.py

8431 lines
407 KiB
Python

"""
tool to extract table form data from alto xml data
"""
import gc
import math
4 years ago
import os
import random
4 years ago
import sys
import time
import warnings
from multiprocessing import Process, Queue, cpu_count
from sys import getsizeof
4 years ago
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,
)
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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)
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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):
4 years ago
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]
4 years ago
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))
4 years ago
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
4 years ago
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
4 years ago
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)
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)
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()
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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)))
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for i in range(num_cores):
processes[i].start()
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slopes = []
all_found_texline_polygons = []
all_found_text_regions = []
all_found_text_regions_par = []
boxes = []
all_box_coord = []
all_index_text_con = []
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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]
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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()
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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)))
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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)))
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for i in range(num_cores):
processes[i].start()
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slopes = []
all_found_texline_polygons = []
all_found_text_regions = []
all_found_text_regions_par = []
boxes = []
all_box_coord = []
all_index_text_con = []
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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]
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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])
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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)
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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]
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##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:
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slopes_per_each_subprocess.append(0)
slope_first = 0
slope_for_all = [slope_deskew][0]
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else:
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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))
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if sigma_des < 1:
sigma_des = 1
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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]
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##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
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##elif slope_for_all==999:
##slope_for_all=slope_biggest
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slopes_per_each_subprocess.append(slope_for_all)
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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:
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# all_box_coord.append(crop_coor)
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textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
cnt_o_t_max = contours_par_per_process[mv]
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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
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# 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)
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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)
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try:
# textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
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textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
except:
pass
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else:
slope_first = 0
add_boxes_coor_into_textlines = True
textlines_cnt_per_region = self.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])
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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])
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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
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for mv in range(len(boxes_text)):
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
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# 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]]
4 years ago
###cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot = self.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
4 years ago
###cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot = self.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
def textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind, slope_first, add_boxes_coor_into_textlines=False):
4 years ago
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
textline_mask = textline_mask.astype(np.uint8)
kernel = np.ones((5, 5), np.uint8)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel)
textline_mask = cv2.erode(textline_mask, kernel, iterations=2)
# textline_mask = cv2.erode(textline_mask, kernel, iterations=1)
# print(textline_mask.shape[0]/float(textline_mask.shape[1]),'miz')
4 years ago
try:
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
# plt.imshow(textline_mask)
# plt.show()
# if abs(slope)>1:
# x_help=30
# y_help=2
# else:
# x_help=2
# y_help=2
x_help = 30
y_help = 2
textline_mask_help = np.zeros((textline_mask.shape[0] + int(2 * y_help), textline_mask.shape[1] + int(2 * x_help), 3))
textline_mask_help[y_help : y_help + textline_mask.shape[0], x_help : x_help + textline_mask.shape[1], :] = np.copy(textline_mask[:, :, :])
dst = rotate_image(textline_mask_help, slope)
4 years ago
dst = dst[:, :, 0]
dst[dst != 0] = 1
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
# plt.imshow(dst)
# plt.show()
4 years ago
contour_text_copy = contour_text_interest.copy()
contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[0]
4 years ago
contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
img_contour = np.zeros((box_ind[3], box_ind[2], 3))
img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255))
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
# plt.imshow(img_contour)
# plt.show()
4 years ago
img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help), img_contour.shape[1] + int(2 * x_help), 3))
img_contour_help[y_help : y_help + img_contour.shape[0], x_help : x_help + img_contour.shape[1], :] = np.copy(img_contour[:, :, :])
img_contour_rot = rotate_image(img_contour_help, slope)
4 years ago
# plt.imshow(img_contour_rot_help)
# plt.show()
# plt.imshow(dst_help)
# plt.show()
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
# plt.imshow(img_contour_rot_help)
# plt.show()
# plt.imshow(dst_help)
# plt.show()
4 years ago
img_contour_rot = img_contour_rot.astype(np.uint8)
# dst_help = dst_help.astype(np.uint8)
4 years ago
imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))]
ind_big_con = np.argmax(len_con_text_rot)
# print('juzaa')
if abs(slope) > 45:
# print(add_boxes_coor_into_textlines,'avval')
_, contours_rotated_clean = seperate_lines_vertical_cont(textline_mask, contours_text_rot[ind_big_con], box_ind, slope, add_boxes_coor_into_textlines=add_boxes_coor_into_textlines)
4 years ago
else:
_, contours_rotated_clean = seperate_lines(dst, contours_text_rot[ind_big_con], slope, x_help, y_help)
4 years ago
except:
contours_rotated_clean = []
return contours_rotated_clean
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 = self.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 image_change_background_pixels_to_zero(self, image_page):
image_back_zero = np.zeros((image_page.shape[0], image_page.shape[1]))
image_back_zero[:, :] = image_page[:, :, 0]
image_back_zero[:, :][image_back_zero[:, :] == 0] = -255
image_back_zero[:, :][image_back_zero[:, :] == 255] = 0
image_back_zero[:, :][image_back_zero[:, :] == -255] = 255
4 years ago
return image_back_zero
def return_points_with_boundies(self, peaks_neg_fin, first_point, last_point):
peaks_neg_tot = []
4 years ago
peaks_neg_tot.append(first_point)
for ii in range(len(peaks_neg_fin)):
peaks_neg_tot.append(peaks_neg_fin[ii])
peaks_neg_tot.append(last_point)
return peaks_neg_tot
def return_boxes_of_images_by_order_of_reading_without_seperators(self, spliter_y_new, image_p_rev, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
4 years ago
boxes = []
4 years ago
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new) - 1):
# print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
4 years ago
# check to see is there any vertical seperator to find holes.
if np.abs(spliter_y_new[i + 1] - spliter_y_new[i]) > 1.0 / 3.0 * regions_without_seperators.shape[0]: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
4 years ago
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
4 years ago
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
num_col, peaks_neg_fin = find_num_col_only_image(image_p_rev[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=2.4)
4 years ago
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
4 years ago
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
4 years ago
# print(len(arg_min_hor_sort),len(arg_org_hor_some_sort),'vizzzzzz')
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
4 years ago
for kkk1 in range(len(start_index_of_hor_with_subset)):
# print(lines_indexes_deleted,'hiii')
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
4 years ago
for kkk2 in range(len(start_index_of_hor_with_subset)):
4 years ago
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
vahid_subset[kkk1, kkk2] = kkk1
4 years ago
else:
pass
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
4 years ago
# check the len of matrix if it has no length means that there is no spliter at all
if len(vahid_subset > 0):
# print('hihoo')
4 years ago
# find parenets args
line_int = np.zeros(vahid_subset.shape[0])
childs_id = []
arg_child = []
4 years ago
for li in range(vahid_subset.shape[0]):
if np.all(vahid_subset[:, li] == -1):
line_int[li] = -1
4 years ago
else:
line_int[li] = 1
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
# helpi=[]
# for nad in range(len(childs_args_in)):
4 years ago
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
arg_child.append(arg_min_hor_sort_with_subset[li])
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
4 years ago
cy_hor_some_sort = cy_hor_some[arg_parent]
newest_y_spliter_tot = []
4 years ago
for tj in range(len(newest_peaks) - 1):
newest_y_spliter = []
4 years ago
newest_y_spliter.append(spliter_y_new[i])
if tj in np.unique(start_index_of_hor_parent):
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
cy_help_sort = np.sort(cy_help)
4 years ago
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
4 years ago
for mj in range(len(cy_help_sort)):
newest_y_spliter.append(cy_help_sort[mj])
newest_y_spliter.append(spliter_y_new[i + 1])
4 years ago
newest_y_spliter_tot.append(newest_y_spliter)
else:
line_int = []
newest_y_spliter_tot = []
4 years ago
for tj in range(len(newest_peaks) - 1):
newest_y_spliter = []
4 years ago
newest_y_spliter.append(spliter_y_new[i])
newest_y_spliter.append(spliter_y_new[i + 1])
4 years ago
newest_y_spliter_tot.append(newest_y_spliter)
# if line_int is all -1 means that big spliters have no child and we can easily go through
if np.all(np.array(line_int) == -1):
for j in range(len(newest_peaks) - 1):
newest_y_spliter = newest_y_spliter_tot[j]
4 years ago
for n in range(len(newest_y_spliter) - 1):
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
4 years ago
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
##plt.show()
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
4 years ago
pass
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
# num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
num_col_sub, peaks_neg_fin_sub = find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.4)
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
4 years ago
peaks_sub.append(newest_peaks[j + 1])
4 years ago
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
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else:
for j in range(len(newest_peaks) - 1):
newest_y_spliter = newest_y_spliter_tot[j]
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if j in start_index_of_hor_parent:
x_min_ch = x_min_hor_some[arg_child]
x_max_ch = x_max_hor_some[arg_child]
cy_hor_some_sort_child = cy_hor_some[arg_child]
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
for n in range(len(newest_y_spliter) - 1):
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
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if len(cy_child_in) > 0:
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###num_col_ch, peaks_neg_ch=find_num_col( regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
num_col_ch, peaks_neg_ch = find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.3)
peaks_neg_ch = peaks_neg_ch[:] + newest_peaks[j]
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
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ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
newest_y_spliter_ch_tot = []
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for tjj in range(len(nst_p_ch) - 1):
newest_y_spliter_new = []
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newest_y_spliter_new.append(newest_y_spliter[n])
if tjj in np.unique(ss_in_ch):
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
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for mjj in range(len(cy_child_in)):
newest_y_spliter_new.append(cy_child_in[mjj])
newest_y_spliter_new.append(newest_y_spliter[n + 1])
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newest_y_spliter_ch_tot.append(newest_y_spliter_new)
for jn in range(len(nst_p_ch) - 1):
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
for nd in range(len(newest_y_spliter_h) - 1):
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
# num_col_sub_ch, peaks_neg_fin_sub_ch=find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]):int(newest_y_spliter_h[nd+1]),nst_p_ch[jn]:nst_p_ch[jn+1]],multiplier=2.3)
num_col_sub_ch, peaks_neg_fin_sub_ch = find_num_col_only_image(image_p_rev[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=2.3)
# print(peaks_neg_fin_sub_ch,'gada kutullllllll')
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else:
peaks_neg_fin_sub_ch = []
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peaks_sub_ch = []
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peaks_sub_ch.append(nst_p_ch[jn])
for kjj in range(len(peaks_neg_fin_sub_ch)):
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
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peaks_sub_ch.append(nst_p_ch[jn + 1])
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# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
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for khh in range(len(peaks_sub_ch) - 1):
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
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else:
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
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###num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
num_col_sub, peaks_neg_fin_sub = find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.3)
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else:
peaks_neg_fin_sub = []
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peaks_sub = []
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peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
peaks_sub.append(newest_peaks[j + 1])
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
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else:
for n in range(len(newest_y_spliter) - 1):
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for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
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pass
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
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###num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
num_col_sub, peaks_neg_fin_sub = find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.3)
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else:
peaks_neg_fin_sub = []
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peaks_sub = []
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peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
peaks_sub.append(newest_peaks[j + 1])
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
else:
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
return boxes
def return_boxes_of_images_by_order_of_reading_without_seperators_2cols(self, spliter_y_new, image_p_rev, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
boxes = []
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new) - 1):
# print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
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# check to see is there any vertical seperator to find holes.
if np.abs(spliter_y_new[i + 1] - spliter_y_new[i]) > 1.0 / 3.0 * regions_without_seperators.shape[0]: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
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##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
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##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
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try:
num_col, peaks_neg_fin = find_num_col_only_image(image_p_rev[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=2.4)
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except:
peaks_neg_fin = []
num_col = 0
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peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
for kh in range(len(peaks_neg_tot) - 1):
boxes.append([peaks_neg_tot[kh], peaks_neg_tot[kh + 1], spliter_y_new[i], spliter_y_new[i + 1]])
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else:
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
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return boxes
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(self, regions_pre_p):
seperators_closeup = ((regions_pre_p[:, :] == 6)) * 1
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seperators_closeup = seperators_closeup.astype(np.uint8)
kernel = np.ones((5, 5), np.uint8)
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
if len(seperators_closeup.shape) == 2:
seperators_closeup_n = np.zeros((seperators_closeup.shape[0], seperators_closeup.shape[1], 3))
seperators_closeup_n[:, :, 0] = seperators_closeup
seperators_closeup_n[:, :, 1] = seperators_closeup
seperators_closeup_n[:, :, 2] = seperators_closeup
else:
seperators_closeup_n = seperators_closeup[:, :, :]
# seperators_closeup=seperators_closeup.astype(np.uint8)
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
imgray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_lines)
dist_y = np.abs(y_max_main - y_min_main)
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
args = np.array(range(len(slope_lines)))
len_x = seperators_closeup.shape[1] * 0
len_y = seperators_closeup.shape[0] * 0.01
args_hor = args[slope_lines == 0]
dist_x_hor = dist_x[slope_lines == 0]
dist_y_hor = dist_y[slope_lines == 0]
x_min_main_hor = x_min_main[slope_lines == 0]
x_max_main_hor = x_max_main[slope_lines == 0]
cy_main_hor = cy_main[slope_lines == 0]
y_min_main_hor = y_min_main[slope_lines == 0]
y_max_main_hor = y_max_main[slope_lines == 0]
args_hor = args_hor[dist_x_hor >= len_x]
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x]
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x]
cy_main_hor = cy_main_hor[dist_x_hor >= len_x]
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x]
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x]
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x]
dist_y_hor = dist_y_hor[dist_x_hor >= len_x]
dist_x_hor = dist_x_hor[dist_x_hor >= len_x]
args_ver = args[slope_lines == 1]
dist_y_ver = dist_y[slope_lines == 1]
dist_x_ver = dist_x[slope_lines == 1]
x_min_main_ver = x_min_main[slope_lines == 1]
x_max_main_ver = x_max_main[slope_lines == 1]
y_min_main_ver = y_min_main[slope_lines == 1]
y_max_main_ver = y_max_main[slope_lines == 1]
cx_main_ver = cx_main[slope_lines == 1]
args_ver = args_ver[dist_y_ver >= len_y]
x_max_main_ver = x_max_main_ver[dist_y_ver >= len_y]
x_min_main_ver = x_min_main_ver[dist_y_ver >= len_y]
cx_main_ver = cx_main_ver[dist_y_ver >= len_y]
y_min_main_ver = y_min_main_ver[dist_y_ver >= len_y]
y_max_main_ver = y_max_main_ver[dist_y_ver >= len_y]
dist_x_ver = dist_x_ver[dist_y_ver >= len_y]
dist_y_ver = dist_y_ver[dist_y_ver >= len_y]
img_p_in_ver = np.zeros(seperators_closeup_n[:, :, 2].shape)
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for jv in range(len(args_ver)):
img_p_in_ver = cv2.fillPoly(img_p_in_ver, pts=[contours_lines[args_ver[jv]]], color=(1, 1, 1))
img_in_hor = np.zeros(seperators_closeup_n[:, :, 2].shape)
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for jv in range(len(args_hor)):
img_p_in_hor = cv2.fillPoly(img_in_hor, pts=[contours_lines[args_hor[jv]]], color=(1, 1, 1))
all_args_uniq = contours_in_same_horizon(cy_main_hor)
# print(all_args_uniq,'all_args_uniq')
if len(all_args_uniq) > 0:
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if type(all_args_uniq[0]) is list:
contours_new = []
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for dd in range(len(all_args_uniq)):
merged_all = None
some_args = args_hor[all_args_uniq[dd]]
some_cy = cy_main_hor[all_args_uniq[dd]]
some_x_min = x_min_main_hor[all_args_uniq[dd]]
some_x_max = x_max_main_hor[all_args_uniq[dd]]
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img_in = np.zeros(seperators_closeup_n[:, :, 2].shape)
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for jv in range(len(some_args)):
img_p_in = cv2.fillPoly(img_p_in_hor, pts=[contours_lines[some_args[jv]]], color=(1, 1, 1))
img_p_in[int(np.mean(some_cy)) - 5 : int(np.mean(some_cy)) + 5, int(np.min(some_x_min)) : int(np.max(some_x_max))] = 1
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else:
img_p_in = seperators_closeup
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else:
img_p_in = seperators_closeup
sep_ver_hor = img_p_in + img_p_in_ver
sep_ver_hor_cross = (sep_ver_hor == 2) * 1
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sep_ver_hor_cross = np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
sep_ver_hor_cross = sep_ver_hor_cross.astype(np.uint8)
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imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cx_cross, cy_cross, _, _, _, _, _ = find_new_features_of_contoures(contours_cross)
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for ii in range(len(cx_cross)):
sep_ver_hor[int(cy_cross[ii]) - 15 : int(cy_cross[ii]) + 15, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40] = 0
sep_ver_hor[int(cy_cross[ii]) - 15 : int(cy_cross[ii]) + 15, int(cx_cross[ii]) - 40 : int(cx_cross[ii]) - 4] = 0
img_p_in[:, :] = sep_ver_hor[:, :]
if len(img_p_in.shape) == 2:
seperators_closeup_n = np.zeros((img_p_in.shape[0], img_p_in.shape[1], 3))
seperators_closeup_n[:, :, 0] = img_p_in
seperators_closeup_n[:, :, 1] = img_p_in
seperators_closeup_n[:, :, 2] = img_p_in
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else:
seperators_closeup_n = img_p_in[:, :, :]
# seperators_closeup=seperators_closeup.astype(np.uint8)
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
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imgray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_lines)
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dist_y = np.abs(y_max_main - y_min_main)
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slope_lines_org_hor = slope_lines_org[slope_lines == 0]
args = np.array(range(len(slope_lines)))
len_x = seperators_closeup.shape[1] * 0.04
len_y = seperators_closeup.shape[0] * 0.08
args_hor = args[slope_lines == 0]
dist_x_hor = dist_x[slope_lines == 0]
dist_y_hor = dist_y[slope_lines == 0]
x_min_main_hor = x_min_main[slope_lines == 0]
x_max_main_hor = x_max_main[slope_lines == 0]
cy_main_hor = cy_main[slope_lines == 0]
y_min_main_hor = y_min_main[slope_lines == 0]
y_max_main_hor = y_max_main[slope_lines == 0]
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args_hor = args_hor[dist_x_hor >= len_x]
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x]
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x]
cy_main_hor = cy_main_hor[dist_x_hor >= len_x]
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x]
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x]
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x]
dist_y_hor = dist_y_hor[dist_x_hor >= len_x]
dist_x_hor = dist_x_hor[dist_x_hor >= len_x]
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args_ver = args[slope_lines == 1]
dist_y_ver = dist_y[slope_lines == 1]
dist_x_ver = dist_x[slope_lines == 1]
x_min_main_ver = x_min_main[slope_lines == 1]
x_max_main_ver = x_max_main[slope_lines == 1]
y_min_main_ver = y_min_main[slope_lines == 1]
y_max_main_ver = y_max_main[slope_lines == 1]
cx_main_ver = cx_main[slope_lines == 1]
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args_ver = args_ver[dist_y_ver >= len_y]
x_max_main_ver = x_max_main_ver[dist_y_ver >= len_y]
x_min_main_ver = x_min_main_ver[dist_y_ver >= len_y]
cx_main_ver = cx_main_ver[dist_y_ver >= len_y]
y_min_main_ver = y_min_main_ver[dist_y_ver >= len_y]
y_max_main_ver = y_max_main_ver[dist_y_ver >= len_y]
dist_x_ver = dist_x_ver[dist_y_ver >= len_y]
dist_y_ver = dist_y_ver[dist_y_ver >= len_y]
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matrix_of_lines_ch = np.zeros((len(cy_main_hor) + len(cx_main_ver), 10))
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matrix_of_lines_ch[: len(cy_main_hor), 0] = args_hor
matrix_of_lines_ch[len(cy_main_hor) :, 0] = args_ver
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matrix_of_lines_ch[len(cy_main_hor) :, 1] = cx_main_ver
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matrix_of_lines_ch[: len(cy_main_hor), 2] = x_min_main_hor
matrix_of_lines_ch[len(cy_main_hor) :, 2] = x_min_main_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 3] = x_max_main_hor
matrix_of_lines_ch[len(cy_main_hor) :, 3] = x_max_main_ver
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matrix_of_lines_ch[: len(cy_main_hor), 4] = dist_x_hor
matrix_of_lines_ch[len(cy_main_hor) :, 4] = dist_x_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 5] = cy_main_hor
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 6] = y_min_main_hor
matrix_of_lines_ch[len(cy_main_hor) :, 6] = y_min_main_ver
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matrix_of_lines_ch[: len(cy_main_hor), 7] = y_max_main_hor
matrix_of_lines_ch[len(cy_main_hor) :, 7] = y_max_main_ver
matrix_of_lines_ch[: len(cy_main_hor), 8] = dist_y_hor
matrix_of_lines_ch[len(cy_main_hor) :, 8] = dist_y_ver
matrix_of_lines_ch[len(cy_main_hor) :, 9] = 1
return matrix_of_lines_ch, seperators_closeup_n
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(self, img_p_in_ver, img_in_hor):
# plt.imshow(img_in_hor)
# plt.show()
# img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
img_p_in_ver = img_p_in_ver.astype(np.uint8)
img_p_in_ver = np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2)
4 years ago
imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines_ver, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
slope_lines_ver, dist_x_ver, x_min_main_ver, x_max_main_ver, cy_main_ver, slope_lines_org_ver, y_min_main_ver, y_max_main_ver, cx_main_ver = find_features_of_lines(contours_lines_ver)
4 years ago
for i in range(len(x_min_main_ver)):
img_p_in_ver[int(y_min_main_ver[i]) : int(y_min_main_ver[i]) + 30, int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0
img_p_in_ver[int(y_max_main_ver[i]) - 30 : int(y_max_main_ver[i]), int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0
# plt.imshow(img_p_in_ver[:,:,0])
# plt.show()
img_in_hor = img_in_hor.astype(np.uint8)
img_in_hor = np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2)
4 years ago
imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines_hor, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
slope_lines_hor, dist_x_hor, x_min_main_hor, x_max_main_hor, cy_main_hor, slope_lines_org_hor, y_min_main_hor, y_max_main_hor, cx_main_hor = find_features_of_lines(contours_lines_hor)
args_hor = np.array(range(len(slope_lines_hor)))
all_args_uniq = contours_in_same_horizon(cy_main_hor)
# print(all_args_uniq,'all_args_uniq')
if len(all_args_uniq) > 0:
4 years ago
if type(all_args_uniq[0]) is list:
special_seperators = []
contours_new = []
4 years ago
for dd in range(len(all_args_uniq)):
merged_all = None
some_args = args_hor[all_args_uniq[dd]]
some_cy = cy_main_hor[all_args_uniq[dd]]
some_x_min = x_min_main_hor[all_args_uniq[dd]]
some_x_max = x_max_main_hor[all_args_uniq[dd]]
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# img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
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for jv in range(len(some_args)):
img_p_in = cv2.fillPoly(img_in_hor, pts=[contours_lines_hor[some_args[jv]]], color=(1, 1, 1))
img_p_in[int(np.mean(some_cy)) - 5 : int(np.mean(some_cy)) + 5, int(np.min(some_x_min)) : int(np.max(some_x_max))] = 1
sum_dis = dist_x_hor[some_args].sum()
diff_max_min_uniques = np.max(x_max_main_hor[some_args]) - np.min(x_min_main_hor[some_args])
# print( sum_dis/float(diff_max_min_uniques) ,diff_max_min_uniques/float(img_p_in_ver.shape[1]),dist_x_hor[some_args].sum(),diff_max_min_uniques,np.mean( dist_x_hor[some_args]),np.std( dist_x_hor[some_args]) )
if diff_max_min_uniques > sum_dis and ((sum_dis / float(diff_max_min_uniques)) > 0.85) and ((diff_max_min_uniques / float(img_p_in_ver.shape[1])) > 0.85) and np.std(dist_x_hor[some_args]) < (0.55 * np.mean(dist_x_hor[some_args])):
# print(dist_x_hor[some_args],dist_x_hor[some_args].sum(),np.min(x_min_main_hor[some_args]) ,np.max(x_max_main_hor[some_args]),'jalibdi')
# print(np.mean( dist_x_hor[some_args] ),np.std( dist_x_hor[some_args] ),np.var( dist_x_hor[some_args] ),'jalibdiha')
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special_seperators.append(np.mean(cy_main_hor[some_args]))
else:
img_p_in = img_in_hor
special_seperators = []
4 years ago
else:
img_p_in = img_in_hor
special_seperators = []
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img_p_in_ver[:, :, 0][img_p_in_ver[:, :, 0] == 255] = 1
# print(img_p_in_ver.shape,np.unique(img_p_in_ver[:,:,0]))
# plt.imshow(img_p_in[:,:,0])
# plt.show()
# plt.imshow(img_p_in_ver[:,:,0])
# plt.show()
sep_ver_hor = img_p_in + img_p_in_ver
# print(sep_ver_hor.shape,np.unique(sep_ver_hor[:,:,0]),'sep_ver_horsep_ver_horsep_ver_hor')
# plt.imshow(sep_ver_hor[:,:,0])
# plt.show()
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sep_ver_hor_cross = (sep_ver_hor[:, :, 0] == 2) * 1
4 years ago
sep_ver_hor_cross = np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
sep_ver_hor_cross = sep_ver_hor_cross.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cx_cross, cy_cross, _, _, _, _, _ = find_new_features_of_contoures(contours_cross)
4 years ago
for ii in range(len(cx_cross)):
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40, 0] = 0
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) - 40 : int(cx_cross[ii]) - 4, 0] = 0
# plt.imshow(img_p_in[:,:,0])
# plt.show()
return img_p_in[:, :, 0], special_seperators
4 years ago
def return_boxes_of_images_by_order_of_reading(self, spliter_y_new, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
boxes = []
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# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new) - 1):
# print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
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# check to see is there any vertical seperator to find holes.
if len(matrix_new[:, 9][matrix_new[:, 9] == 1]) > 0 and np.max(matrix_new[:, 8][matrix_new[:, 9] == 1]) >= 0.1 * (np.abs(spliter_y_new[i + 1] - spliter_y_new[i])):
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
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##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
4 years ago
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
num_col, peaks_neg_fin = find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
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peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
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start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = self.return_hor_spliter_by_index(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
4 years ago
for kkk1 in range(len(start_index_of_hor_with_subset)):
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
4 years ago
for kkk2 in range(len(start_index_of_hor_with_subset)):
4 years ago
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
vahid_subset[kkk1, kkk2] = kkk1
4 years ago
else:
pass
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
# print(vahid_subset,'zartt222')
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# check the len of matrix if it has no length means that there is no spliter at all
if len(vahid_subset > 0):
# print('hihoo')
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# find parenets args
line_int = np.zeros(vahid_subset.shape[0])
childs_id = []
arg_child = []
4 years ago
for li in range(vahid_subset.shape[0]):
# print(vahid_subset[:,li])
if np.all(vahid_subset[:, li] == -1):
line_int[li] = -1
4 years ago
else:
line_int[li] = 1
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
# helpi=[]
# for nad in range(len(childs_args_in)):
4 years ago
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
arg_child.append(arg_min_hor_sort_with_subset[li])
# line_int=vahid_subset[0,:]
# print(arg_child,line_int[0],'zartt33333')
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
4 years ago
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
4 years ago
cy_hor_some_sort = cy_hor_some[arg_parent]
# print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
# args_indexes=np.array(range(len(start_index_of_hor) ))
newest_y_spliter_tot = []
4 years ago
for tj in range(len(newest_peaks) - 1):
newest_y_spliter = []
4 years ago
newest_y_spliter.append(spliter_y_new[i])
if tj in np.unique(start_index_of_hor_parent):
##print(cy_hor_some_sort)
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
cy_help_sort = np.sort(cy_help)
4 years ago
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
4 years ago
for mj in range(len(cy_help_sort)):
newest_y_spliter.append(cy_help_sort[mj])
newest_y_spliter.append(spliter_y_new[i + 1])
4 years ago
newest_y_spliter_tot.append(newest_y_spliter)
else:
line_int = []
newest_y_spliter_tot = []
4 years ago
for tj in range(len(newest_peaks) - 1):
newest_y_spliter = []
4 years ago
newest_y_spliter.append(spliter_y_new[i])
newest_y_spliter.append(spliter_y_new[i + 1])
4 years ago
newest_y_spliter_tot.append(newest_y_spliter)
# if line_int is all -1 means that big spliters have no child and we can easily go through
if np.all(np.array(line_int) == -1):
for j in range(len(newest_peaks) - 1):
newest_y_spliter = newest_y_spliter_tot[j]
4 years ago
for n in range(len(newest_y_spliter) - 1):
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
4 years ago
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
##plt.show()
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
4 years ago
pass
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len(matrix_new_new[:, 9][matrix_new_new[:, 9] == 1]) > 0 and np.max(matrix_new_new[:, 8][matrix_new_new[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter[n + 1] - newest_y_spliter[n])):
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
4 years ago
peaks_sub.append(newest_peaks[j + 1])
4 years ago
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
4 years ago
else:
for j in range(len(newest_peaks) - 1):
newest_y_spliter = newest_y_spliter_tot[j]
4 years ago
if j in start_index_of_hor_parent:
x_min_ch = x_min_hor_some[arg_child]
x_max_ch = x_max_hor_some[arg_child]
cy_hor_some_sort_child = cy_hor_some[arg_child]
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
# print(cy_hor_some_sort_child,'ychilds')
for n in range(len(newest_y_spliter) - 1):
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
if len(cy_child_in) > 0:
num_col_ch, peaks_neg_ch = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
# print(peaks_neg_ch,'mizzzz')
# peaks_neg_ch=[]
# for djh in range(len(peaks_neg_ch)):
4 years ago
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = self.return_hor_spliter_by_index(peaks_neg_ch_tot, x_min_ch, x_max_ch)
newest_y_spliter_ch_tot = []
for tjj in range(len(nst_p_ch) - 1):
newest_y_spliter_new = []
4 years ago
newest_y_spliter_new.append(newest_y_spliter[n])
if tjj in np.unique(ss_in_ch):
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
4 years ago
for mjj in range(len(cy_child_in)):
newest_y_spliter_new.append(cy_child_in[mjj])
newest_y_spliter_new.append(newest_y_spliter[n + 1])
4 years ago
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
for jn in range(len(nst_p_ch) - 1):
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
for nd in range(len(newest_y_spliter_h) - 1):
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len(matrix_new_new2[:, 9][matrix_new_new2[:, 9] == 1]) > 0 and np.max(matrix_new_new2[:, 8][matrix_new_new2[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter_h[nd + 1] - newest_y_spliter_h[nd])):
num_col_sub_ch, peaks_neg_fin_sub_ch = find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=5.0)
4 years ago
else:
peaks_neg_fin_sub_ch = []
4 years ago
peaks_sub_ch = []
4 years ago
peaks_sub_ch.append(nst_p_ch[jn])
for kjj in range(len(peaks_neg_fin_sub_ch)):
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
4 years ago
peaks_sub_ch.append(nst_p_ch[jn + 1])
4 years ago
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
4 years ago
for khh in range(len(peaks_sub_ch) - 1):
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
4 years ago
else:
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len(matrix_new_new[:, 9][matrix_new_new[:, 9] == 1]) > 0 and np.max(matrix_new_new[:, 8][matrix_new_new[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter[n + 1] - newest_y_spliter[n])):
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
peaks_sub.append(newest_peaks[j + 1])
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
4 years ago
else:
for n in range(len(newest_y_spliter) - 1):
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len(matrix_new_new[:, 9][matrix_new_new[:, 9] == 1]) > 0 and np.max(matrix_new_new[:, 8][matrix_new_new[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter[n + 1] - newest_y_spliter[n])):
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
peaks_sub.append(newest_peaks[j + 1])
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
4 years ago
else:
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
4 years ago
return boxes
def return_boxes_of_images_by_order_of_reading_new(self, spliter_y_new, regions_without_seperators, matrix_of_lines_ch):
boxes = []
4 years ago
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new) - 1):
# print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
4 years ago
# check to see is there any vertical seperator to find holes.
if 1 > 0: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
4 years ago
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
4 years ago
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
# print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'firssst')
# plt.imshow(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:])
# plt.show()
4 years ago
try:
num_col, peaks_neg_fin = find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
4 years ago
except:
peaks_neg_fin = []
# print(peaks_neg_fin,'peaks_neg_fin')
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
4 years ago
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, regions_without_seperators[:, :].shape[1])
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
4 years ago
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
4 years ago
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
4 years ago
for kkk1 in range(len(start_index_of_hor_with_subset)):
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
4 years ago
for kkk2 in range(len(start_index_of_hor_with_subset)):
4 years ago
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
vahid_subset[kkk1, kkk2] = kkk1
4 years ago
else:
pass
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
4 years ago
# check the len of matrix if it has no length means that there is no spliter at all
if len(vahid_subset > 0):
# print('hihoo')
4 years ago
# find parenets args
line_int = np.zeros(vahid_subset.shape[0])
childs_id = []
arg_child = []
4 years ago
for li in range(vahid_subset.shape[0]):
# print(vahid_subset[:,li])
if np.all(vahid_subset[:, li] == -1):
line_int[li] = -1
4 years ago
else:
line_int[li] = 1
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
# helpi=[]
# for nad in range(len(childs_args_in)):
4 years ago
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
arg_child.append(arg_min_hor_sort_with_subset[li])
# line_int=vahid_subset[0,:]
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
4 years ago
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
4 years ago
cy_hor_some_sort = cy_hor_some[arg_parent]
# print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
# args_indexes=np.array(range(len(start_index_of_hor) ))
newest_y_spliter_tot = []
4 years ago
for tj in range(len(newest_peaks) - 1):
newest_y_spliter = []
4 years ago
newest_y_spliter.append(spliter_y_new[i])
if tj in np.unique(start_index_of_hor_parent):
# print(cy_hor_some_sort)
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
cy_help_sort = np.sort(cy_help)
4 years ago
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
4 years ago
for mj in range(len(cy_help_sort)):
newest_y_spliter.append(cy_help_sort[mj])
newest_y_spliter.append(spliter_y_new[i + 1])
4 years ago
newest_y_spliter_tot.append(newest_y_spliter)
else:
line_int = []
newest_y_spliter_tot = []
4 years ago
for tj in range(len(newest_peaks) - 1):
newest_y_spliter = []
4 years ago
newest_y_spliter.append(spliter_y_new[i])
newest_y_spliter.append(spliter_y_new[i + 1])
4 years ago
newest_y_spliter_tot.append(newest_y_spliter)
# if line_int is all -1 means that big spliters have no child and we can easily go through
if np.all(np.array(line_int) == -1):
for j in range(len(newest_peaks) - 1):
newest_y_spliter = newest_y_spliter_tot[j]
4 years ago
for n in range(len(newest_y_spliter) - 1):
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
4 years ago
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
##plt.show()
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
4 years ago
pass
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
# print( int(newest_y_spliter[n]),int(newest_y_spliter[n+1]),newest_peaks[j],newest_peaks[j+1] )
4 years ago
try:
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
4 years ago
except:
peaks_neg_fin_sub = []
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
4 years ago
peaks_sub.append(newest_peaks[j + 1])
4 years ago
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
4 years ago
else:
for j in range(len(newest_peaks) - 1):
4 years ago
newest_y_spliter = newest_y_spliter_tot[j]
4 years ago
if j in start_index_of_hor_parent:
x_min_ch = x_min_hor_some[arg_child]
x_max_ch = x_max_hor_some[arg_child]
cy_hor_some_sort_child = cy_hor_some[arg_child]
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
for n in range(len(newest_y_spliter) - 1):
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
if len(cy_child_in) > 0:
4 years ago
try:
num_col_ch, peaks_neg_ch = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
4 years ago
except:
peaks_neg_ch = []
# print(peaks_neg_ch,'mizzzz')
# peaks_neg_ch=[]
# for djh in range(len(peaks_neg_ch)):
4 years ago
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
newest_y_spliter_ch_tot = []
for tjj in range(len(nst_p_ch) - 1):
newest_y_spliter_new = []
4 years ago
newest_y_spliter_new.append(newest_y_spliter[n])
if tjj in np.unique(ss_in_ch):
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
4 years ago
for mjj in range(len(cy_child_in)):
newest_y_spliter_new.append(cy_child_in[mjj])
newest_y_spliter_new.append(newest_y_spliter[n + 1])
4 years ago
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
for jn in range(len(nst_p_ch) - 1):
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
for nd in range(len(newest_y_spliter_h) - 1):
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
4 years ago
try:
num_col_sub_ch, peaks_neg_fin_sub_ch = find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=7.0)
4 years ago
except:
peaks_neg_fin_sub_ch = []
4 years ago
else:
peaks_neg_fin_sub_ch = []
4 years ago
peaks_sub_ch = []
4 years ago
peaks_sub_ch.append(nst_p_ch[jn])
for kjj in range(len(peaks_neg_fin_sub_ch)):
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
4 years ago
peaks_sub_ch.append(nst_p_ch[jn + 1])
4 years ago
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
4 years ago
for khh in range(len(peaks_sub_ch) - 1):
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
4 years ago
else:
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
4 years ago
try:
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
4 years ago
except:
peaks_neg_fin_sub = []
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
peaks_sub.append(newest_peaks[j + 1])
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
4 years ago
else:
for n in range(len(newest_y_spliter) - 1):
4 years ago
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
4 years ago
try:
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
4 years ago
except:
peaks_neg_fin_sub = []
4 years ago
else:
peaks_neg_fin_sub = []
4 years ago
peaks_sub = []
4 years ago
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
peaks_sub.append(newest_peaks[j + 1])
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub) - 1):
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
4 years ago
else:
boxes.append([0, regions_without_seperators[:, :].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
4 years ago
return boxes
def return_boxes_of_images_by_order_of_reading_2cols(self, spliter_y_new, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
boxes = []
4 years ago
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new) - 1):
# print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
4 years ago
# check to see is there any vertical seperator to find holes.
if 1 > 0: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
# print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'burayaaaa galimiirrrrrrrrrrrrrrrrrrrrrrrrrrr')
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
4 years ago
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
4 years ago
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
4 years ago
try:
num_col, peaks_neg_fin = find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
4 years ago
except:
peaks_neg_fin = []
num_col = 0
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
for kh in range(len(peaks_neg_tot) - 1):
boxes.append([peaks_neg_tot[kh], peaks_neg_tot[kh + 1], spliter_y_new[i], spliter_y_new[i + 1]])
4 years ago
else:
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
4 years ago
return boxes
def return_hor_spliter_by_index(self, peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
arg_min_hor_sort = np.argsort(x_min_hor_some)
x_min_hor_some_sort = np.sort(x_min_hor_some)
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
indexer_lines = []
indexes_to_delete = []
indexer_lines_deletions_len = []
indexr_uniq_ind = []
4 years ago
for i in range(len(x_min_hor_some_sort)):
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
min_h[0] = min_h[0] # +20
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] ##-20
4 years ago
min_h_neg = arg_minmax[(min_h < 0) & (np.abs(min_h) < 360)]
max_h_neg = arg_minmax[(max_h >= 0) & (np.abs(max_h) < 360)]
4 years ago
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
unique_delets_int = []
# print(deletions,len(deletions),'delii')
if len(deletions) > 0:
# print(deletions,len(deletions),'delii2')
4 years ago
for j in range(len(deletions)):
indexes_to_delete.append(deletions[j])
# print(deletions,indexes_to_delete,'badiii')
unique_delets = np.unique(indexes_to_delete)
# print(min_h_neg[0],unique_delets)
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
4 years ago
indexer_lines_deletions_len.append(len(deletions))
indexr_uniq_ind.append([deletions])
4 years ago
else:
indexer_lines_deletions_len.append(0)
indexr_uniq_ind.append(-999)
index_line_true = min_h_neg[0] - len(unique_delets_int)
# print(index_line_true)
if index_line_true > 0 and min_h_neg[0] >= 2:
index_line_true = index_line_true
4 years ago
else:
index_line_true = min_h_neg[0]
4 years ago
indexer_lines.append(index_line_true)
if len(unique_delets_int) > 0:
4 years ago
for dd in range(len(unique_delets_int)):
indexes_to_delete.append(unique_delets_int[dd])
else:
indexer_lines.append(-999)
indexer_lines_deletions_len.append(-999)
indexr_uniq_ind.append(-999)
peaks_true = []
4 years ago
for m in range(len(peaks_neg_fin_t)):
if m in indexes_to_delete:
pass
else:
peaks_true.append(peaks_neg_fin_t[m])
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
def return_region_segmentation_after_implementing_not_head_maintext_parallel(self, image_regions_eraly_p, boxes):
image_revised = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1]))
4 years ago
for i in range(len(boxes)):
image_box = image_regions_eraly_p[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1])]
image_box = np.array(image_box)
# plt.imshow(image_box)
# plt.show()
4 years ago
# print(int(boxes[i][2]),int(boxes[i][3]),int(boxes[i][0]),int(boxes[i][1]),'addaa')
image_box = self.implent_law_head_main_not_parallel(image_box)
image_box = self.implent_law_head_main_not_parallel(image_box)
image_box = self.implent_law_head_main_not_parallel(image_box)
image_revised[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1])] = image_box[:, :]
4 years ago
return image_revised
def tear_main_texts_on_the_boundaries_of_boxes(self, img_revised_tab, boxes):
4 years ago
for i in range(len(boxes)):
img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 0][img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 0] == 1] = 0
img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 1][img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 1] == 1] = 0
img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 2][img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 2] == 1] = 0
4 years ago
return img_revised_tab
def implent_law_head_main_not_parallel(self, text_regions):
# print(text_regions.shape)
text_indexes = [1, 2] # 1: main text , 2: header , 3: comments
4 years ago
for t_i in text_indexes:
textline_mask = text_regions[:, :] == t_i
textline_mask = textline_mask * 255.0
4 years ago
textline_mask = textline_mask.astype(np.uint8)
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2)
kernel = np.ones((5, 5), np.uint8)
4 years ago
# print(type(textline_mask),np.unique(textline_mask),textline_mask.shape)
4 years ago
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
if t_i == 1:
contours_main, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(type(contours_main))
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
# print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0])
elif t_i == 2:
contours_header, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(type(contours_header))
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
args = np.array(range(1, len(cy_header) + 1))
args_main = np.array(range(1, len(cy_main) + 1))
4 years ago
for jj in range(len(contours_main)):
headers_in_main = [(cy_header > y_min_main[jj]) & ((cy_header < y_max_main[jj]))]
mains_in_main = [(cy_main > y_min_main[jj]) & ((cy_main < y_max_main[jj]))]
args_log = args * headers_in_main
res = args_log[args_log > 0]
res_true = res - 1
args_log_main = args_main * mains_in_main
res_main = args_log_main[args_log_main > 0]
res_true_main = res_main - 1
if len(res_true) > 0:
sum_header = np.sum(areas_header[res_true])
sum_main = np.sum(areas_main[res_true_main])
if sum_main > sum_header:
cnt_int = [contours_header[j] for j in res_true]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
4 years ago
else:
cnt_int = [contours_main[j] for j in res_true_main]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
4 years ago
for jj in range(len(contours_header)):
main_in_header = [(cy_main > y_min_header[jj]) & ((cy_main < y_max_header[jj]))]
header_in_header = [(cy_header > y_min_header[jj]) & ((cy_header < y_max_header[jj]))]
args_log = args_main * main_in_header
res = args_log[args_log > 0]
res_true = res - 1
args_log_header = args * header_in_header
res_header = args_log_header[args_log_header > 0]
res_true_header = res_header - 1
if len(res_true) > 0:
sum_header = np.sum(areas_header[res_true_header])
sum_main = np.sum(areas_main[res_true])
if sum_main > sum_header:
cnt_int = [contours_header[j] for j in res_true_header]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
4 years ago
else:
cnt_int = [contours_main[j] for j in res_true]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
4 years ago
return text_regions
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 remove_headers_and_mains_intersection(self, seperators_closeup_n, img_revised_tab, boxes):
4 years ago
for ind in range(len(boxes)):
asp = np.zeros((img_revised_tab[:, :, 0].shape[0], seperators_closeup_n[:, :, 0].shape[1]))
asp[int(boxes[ind][2]) : int(boxes[ind][3]), int(boxes[ind][0]) : int(boxes[ind][1])] = img_revised_tab[int(boxes[ind][2]) : int(boxes[ind][3]), int(boxes[ind][0]) : int(boxes[ind][1]), 0]
4 years ago
head_patch_con = (asp[:, :] == 2) * 1
main_patch_con = (asp[:, :] == 1) * 1
# print(head_patch_con)
head_patch_con = head_patch_con.astype(np.uint8)
main_patch_con = main_patch_con.astype(np.uint8)
4 years ago
head_patch_con = np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
main_patch_con = np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
4 years ago
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_head_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_head_patch_con = return_parent_contours(contours_head_patch_con, hiearchy)
4 years ago
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy)
4 years ago
y_patch_head_min, y_patch_head_max, _ = find_features_of_contours(contours_head_patch_con)
y_patch_main_min, y_patch_main_max, _ = find_features_of_contours(contours_main_patch_con)
4 years ago
for i in range(len(y_patch_head_min)):
for j in range(len(y_patch_main_min)):
if y_patch_head_max[i] > y_patch_main_min[j] and y_patch_head_min[i] < y_patch_main_min[j]:
y_down = y_patch_head_max[i]
y_up = y_patch_main_min[j]
4 years ago
patch_intersection = np.zeros(asp.shape)
patch_intersection[y_up:y_down, :] = asp[y_up:y_down, :]
4 years ago
head_patch_con = (patch_intersection[:, :] == 2) * 1
main_patch_con = (patch_intersection[:, :] == 1) * 1
head_patch_con = head_patch_con.astype(np.uint8)
main_patch_con = main_patch_con.astype(np.uint8)
4 years ago
head_patch_con = np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
main_patch_con = np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
4 years ago
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_head_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_head_patch_con = return_parent_contours(contours_head_patch_con, hiearchy)
4 years ago
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy)
4 years ago
_, _, areas_head = find_features_of_contours(contours_head_patch_con)
_, _, areas_main = find_features_of_contours(contours_main_patch_con)
4 years ago
if np.sum(areas_head) > np.sum(areas_main):
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2
4 years ago
else:
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 2] = 1
4 years ago
elif y_patch_head_min[i] < y_patch_main_max[j] and y_patch_head_max[i] > y_patch_main_max[j]:
y_down = y_patch_main_max[j]
y_up = y_patch_head_min[i]
4 years ago
patch_intersection = np.zeros(asp.shape)
patch_intersection[y_up:y_down, :] = asp[y_up:y_down, :]
head_patch_con = (patch_intersection[:, :] == 2) * 1
main_patch_con = (patch_intersection[:, :] == 1) * 1
head_patch_con = head_patch_con.astype(np.uint8)
main_patch_con = main_patch_con.astype(np.uint8)
head_patch_con = np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
main_patch_con = np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
4 years ago
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_head_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_head_patch_con = return_parent_contours(contours_head_patch_con, hiearchy)
4 years ago
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy)
4 years ago
_, _, areas_head = find_features_of_contours(contours_head_patch_con)
_, _, areas_main = find_features_of_contours(contours_main_patch_con)
4 years ago
if np.sum(areas_head) > np.sum(areas_main):
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2
4 years ago
else:
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 2] = 1
4 years ago
# print(np.unique(patch_intersection) )
4 years ago
##plt.figure(figsize=(20,20))
##plt.imshow(patch_intersection)
##plt.show()
else:
pass
return img_revised_tab
def order_of_regions(self, textline_mask, contours_main, contours_header, y_ref):
4 years ago
##plt.imshow(textline_mask)
##plt.show()
"""
print(len(contours_main),'contours_main')
mada_n=textline_mask.sum(axis=1)
y=mada_n[:]
y_help=np.zeros(len(y)+40)
y_help[20:len(y)+20]=y
x=np.array( range(len(y)) )
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
4 years ago
##plt.imshow(textline_mask[:,:])
##plt.show()
4 years ago
sigma_gaus=8
z= gaussian_filter1d(y_help, sigma_gaus)
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+40)
zneg[20:len(zneg_rev)+20]=zneg_rev
zneg= gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks_neg=peaks_neg-20-20
peaks=peaks-20
"""
textline_sum_along_width = textline_mask.sum(axis=1)
4 years ago
y = textline_sum_along_width[:]
y_padded = np.zeros(len(y) + 40)
y_padded[20 : len(y) + 20] = y
x = np.array(range(len(y)))
4 years ago
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
sigma_gaus = 8
4 years ago
z = gaussian_filter1d(y_padded, sigma_gaus)
zneg_rev = -y_padded + np.max(y_padded)
4 years ago
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)
peaks_neg = peaks_neg - 20 - 20
peaks = peaks - 20
4 years ago
##plt.plot(z)
##plt.show()
if contours_main != None:
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
4 years ago
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
4 years ago
if len(contours_header) != None:
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
4 years ago
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
4 years ago
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
# print(cy_main,'mainy')
4 years ago
peaks_neg_new = []
4 years ago
peaks_neg_new.append(0 + y_ref)
4 years ago
for iii in range(len(peaks_neg)):
peaks_neg_new.append(peaks_neg[iii] + y_ref)
4 years ago
peaks_neg_new.append(textline_mask.shape[0] + y_ref)
if len(cy_main) > 0 and np.max(cy_main) > np.max(peaks_neg_new):
cy_main = np.array(cy_main) * (np.max(peaks_neg_new) / np.max(cy_main)) - 10
if contours_main != None:
indexer_main = np.array(range(len(contours_main)))
4 years ago
if contours_main != None:
len_main = len(contours_main)
4 years ago
else:
len_main = 0
4 years ago
matrix_of_orders = np.zeros((len(contours_main) + len(contours_header), 5))
4 years ago
matrix_of_orders[:, 0] = np.array(range(len(contours_main) + len(contours_header)))
4 years ago
matrix_of_orders[: len(contours_main), 1] = 1
matrix_of_orders[len(contours_main) :, 1] = 2
4 years ago
matrix_of_orders[: len(contours_main), 2] = cx_main
matrix_of_orders[len(contours_main) :, 2] = cx_header
4 years ago
matrix_of_orders[: len(contours_main), 3] = cy_main
matrix_of_orders[len(contours_main) :, 3] = cy_header
4 years ago
matrix_of_orders[: len(contours_main), 4] = np.array(range(len(contours_main)))
matrix_of_orders[len(contours_main) :, 4] = np.array(range(len(contours_header)))
4 years ago
# print(peaks_neg_new,'peaks_neg_new')
4 years ago
# print(matrix_of_orders,'matrix_of_orders')
# print(peaks_neg_new,np.max(peaks_neg_new))
final_indexers_sorted = []
final_types = []
final_index_type = []
for i in range(len(peaks_neg_new) - 1):
top = peaks_neg_new[i]
down = peaks_neg_new[i + 1]
# print(top,down,'topdown')
4 years ago
indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
# print(top,down)
# print(cys_in,'cyyyins')
# print(indexes_in,'indexes')
sorted_inside = np.argsort(cxs_in)
4 years ago
ind_in_int = indexes_in[sorted_inside]
ind_in_type = types_of_text[sorted_inside]
ind_ind_type = index_types_of_text[sorted_inside]
4 years ago
for j in range(len(ind_in_int)):
final_indexers_sorted.append(int(ind_in_int[j]))
4 years ago
final_types.append(int(ind_in_type[j]))
final_index_type.append(int(ind_ind_type[j]))
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
4 years ago
# print(peaks_neg_new,'peaks')
# print(final_indexers_sorted,'indexsorted')
# print(final_types,'types')
# print(final_index_type,'final_index_type')
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
4 years ago
def order_and_id_of_texts(self, found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
indexes_sorted = np.array(indexes_sorted)
index_of_types = np.array(index_of_types)
kind_of_texts = np.array(kind_of_texts)
4 years ago
id_of_texts = []
order_of_texts = []
index_of_types_1 = index_of_types[kind_of_texts == 1]
indexes_sorted_1 = indexes_sorted[kind_of_texts == 1]
index_of_types_2 = index_of_types[kind_of_texts == 2]
indexes_sorted_2 = indexes_sorted[kind_of_texts == 2]
4 years ago
##print(index_of_types,'index_of_types')
##print(kind_of_texts,'kind_of_texts')
##print(len(found_polygons_text_region),'found_polygons_text_region')
##print(index_of_types_1,'index_of_types_1')
##print(indexes_sorted_1,'indexes_sorted_1')
index_b = 0 + ref_point
4 years ago
for mm in range(len(found_polygons_text_region)):
id_of_texts.append("r" + str(index_b))
interest = indexes_sorted_1[indexes_sorted_1 == index_of_types_1[mm]]
if len(interest) > 0:
4 years ago
order_of_texts.append(interest[0])
index_b += 1
4 years ago
else:
pass
4 years ago
for mm in range(len(found_polygons_text_region_h)):
id_of_texts.append("r" + str(index_b))
interest = indexes_sorted_2[index_of_types_2[mm]]
4 years ago
order_of_texts.append(interest)
index_b += 1
4 years ago
return order_of_texts, id_of_texts
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 find_number_of_columns_in_document(self, region_pre_p, num_col_classifier, pixel_lines, contours_h=None):
seperators_closeup = ((region_pre_p[:, :, :] == pixel_lines)) * 1
seperators_closeup[0:110, :, :] = 0
seperators_closeup[seperators_closeup.shape[0] - 150 :, :, :] = 0
4 years ago
kernel = np.ones((5, 5), np.uint8)
seperators_closeup = seperators_closeup.astype(np.uint8)
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
4 years ago
##plt.imshow(seperators_closeup[:,:,0])
##plt.show()
seperators_closeup_new = np.zeros((seperators_closeup.shape[0], seperators_closeup.shape[1]))
4 years ago
##_,seperators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0])
seperators_closeup_n = np.copy(seperators_closeup)
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
4 years ago
##plt.imshow(seperators_closeup_n[:,:,0])
##plt.show()
seperators_closeup_n_binary = np.zeros((seperators_closeup_n.shape[0], seperators_closeup_n.shape[1]))
seperators_closeup_n_binary[:, :] = seperators_closeup_n[:, :, 0]
seperators_closeup_n_binary[:, :][seperators_closeup_n_binary[:, :] != 0] = 1
# seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==0]=255
# seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==-255]=0
# seperators_closeup_n_binary=(seperators_closeup_n_binary[:,:]==2)*1
# gray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
# print(np.unique(seperators_closeup_n_binary))
4 years ago
##plt.imshow(seperators_closeup_n_binary)
##plt.show()
# print( np.unique(gray),np.unique(seperators_closeup_n[:,:,1]) )
4 years ago
gray = cv2.bitwise_not(seperators_closeup_n_binary)
gray = gray.astype(np.uint8)
4 years ago
##plt.imshow(gray)
##plt.show()
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2)
4 years ago
##plt.imshow(bw[:,:])
##plt.show()
4 years ago
horizontal = np.copy(bw)
vertical = np.copy(bw)
4 years ago
cols = horizontal.shape[1]
horizontal_size = cols // 30
# Create structure element for extracting horizontal lines through morphology operations
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
# Apply morphology operations
horizontal = cv2.erode(horizontal, horizontalStructure)
horizontal = cv2.dilate(horizontal, horizontalStructure)
kernel = np.ones((5, 5), np.uint8)
4 years ago
horizontal = cv2.dilate(horizontal, kernel, iterations=2)
horizontal = cv2.erode(horizontal, kernel, iterations=2)
# plt.imshow(horizontal)
# plt.show()
4 years ago
rows = vertical.shape[0]
verticalsize = rows // 30
# Create structure element for extracting vertical lines through morphology operations
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
# Apply morphology operations
vertical = cv2.erode(vertical, verticalStructure)
vertical = cv2.dilate(vertical, verticalStructure)
vertical = cv2.dilate(vertical, kernel, iterations=1)
4 years ago
# Show extracted vertical lines
horizontal, special_seperators = self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical, horizontal)
4 years ago
##plt.imshow(vertical)
##plt.show()
# print(vertical.shape,np.unique(vertical),'verticalvertical')
seperators_closeup_new[:, :][vertical[:, :] != 0] = 1
seperators_closeup_new[:, :][horizontal[:, :] != 0] = 1
4 years ago
##plt.imshow(seperators_closeup_new)
##plt.show()
##seperators_closeup_n
vertical = np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
vertical = vertical.astype(np.uint8)
4 years ago
##plt.plot(vertical[:,:,0].sum(axis=0))
##plt.show()
# plt.plot(vertical[:,:,0].sum(axis=1))
# plt.show()
4 years ago
imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line_vers, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_line_vers)
# print(slope_lines,'vertical')
args = np.array(range(len(slope_lines)))
args_ver = args[slope_lines == 1]
dist_x_ver = dist_x[slope_lines == 1]
y_min_main_ver = y_min_main[slope_lines == 1]
y_max_main_ver = y_max_main[slope_lines == 1]
x_min_main_ver = x_min_main[slope_lines == 1]
x_max_main_ver = x_max_main[slope_lines == 1]
cx_main_ver = cx_main[slope_lines == 1]
dist_y_ver = y_max_main_ver - y_min_main_ver
len_y = seperators_closeup.shape[0] / 3.0
# plt.imshow(horizontal)
# plt.show()
horizontal = np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
horizontal = horizontal.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line_hors, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_line_hors)
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
args = np.array(range(len(slope_lines)))
len_x = seperators_closeup.shape[1] / 5.0
dist_y = np.abs(y_max_main - y_min_main)
args_hor = args[slope_lines == 0]
dist_x_hor = dist_x[slope_lines == 0]
y_min_main_hor = y_min_main[slope_lines == 0]
y_max_main_hor = y_max_main[slope_lines == 0]
x_min_main_hor = x_min_main[slope_lines == 0]
x_max_main_hor = x_max_main[slope_lines == 0]
dist_y_hor = dist_y[slope_lines == 0]
cy_main_hor = cy_main[slope_lines == 0]
4 years ago
args_hor = args_hor[dist_x_hor >= len_x / 2.0]
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x / 2.0]
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x / 2.0]
cy_main_hor = cy_main_hor[dist_x_hor >= len_x / 2.0]
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x / 2.0]
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x / 2.0]
dist_y_hor = dist_y_hor[dist_x_hor >= len_x / 2.0]
4 years ago
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x / 2.0]
dist_x_hor = dist_x_hor[dist_x_hor >= len_x / 2.0]
4 years ago
matrix_of_lines_ch = np.zeros((len(cy_main_hor) + len(cx_main_ver), 10))
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 0] = args_hor
matrix_of_lines_ch[len(cy_main_hor) :, 0] = args_ver
4 years ago
matrix_of_lines_ch[len(cy_main_hor) :, 1] = cx_main_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 2] = x_min_main_hor + 50 # x_min_main_hor+150
matrix_of_lines_ch[len(cy_main_hor) :, 2] = x_min_main_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 3] = x_max_main_hor - 50 # x_max_main_hor-150
matrix_of_lines_ch[len(cy_main_hor) :, 3] = x_max_main_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 4] = dist_x_hor
matrix_of_lines_ch[len(cy_main_hor) :, 4] = dist_x_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 5] = cy_main_hor
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 6] = y_min_main_hor
matrix_of_lines_ch[len(cy_main_hor) :, 6] = y_min_main_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 7] = y_max_main_hor
matrix_of_lines_ch[len(cy_main_hor) :, 7] = y_max_main_ver
4 years ago
matrix_of_lines_ch[: len(cy_main_hor), 8] = dist_y_hor
matrix_of_lines_ch[len(cy_main_hor) :, 8] = dist_y_ver
matrix_of_lines_ch[len(cy_main_hor) :, 9] = 1
4 years ago
if contours_h is not None:
slope_lines_head, dist_x_head, x_min_main_head, x_max_main_head, cy_main_head, slope_lines_org_head, y_min_main_head, y_max_main_head, cx_main_head = find_features_of_lines(contours_h)
matrix_l_n = np.zeros((matrix_of_lines_ch.shape[0] + len(cy_main_head), matrix_of_lines_ch.shape[1]))
matrix_l_n[: matrix_of_lines_ch.shape[0], :] = np.copy(matrix_of_lines_ch[:, :])
args_head = np.array(range(len(cy_main_head))) + len(cy_main_hor)
matrix_l_n[matrix_of_lines_ch.shape[0] :, 0] = args_head
matrix_l_n[matrix_of_lines_ch.shape[0] :, 2] = x_min_main_head + 30
matrix_l_n[matrix_of_lines_ch.shape[0] :, 3] = x_max_main_head - 30
matrix_l_n[matrix_of_lines_ch.shape[0] :, 4] = dist_x_head
matrix_l_n[matrix_of_lines_ch.shape[0] :, 5] = y_min_main_head - 3 - 8
matrix_l_n[matrix_of_lines_ch.shape[0] :, 6] = y_min_main_head - 5 - 8
matrix_l_n[matrix_of_lines_ch.shape[0] :, 7] = y_min_main_head + 1 - 8
matrix_l_n[matrix_of_lines_ch.shape[0] :, 8] = 4
matrix_of_lines_ch = np.copy(matrix_l_n)
# print(matrix_of_lines_ch)
4 years ago
"""
4 years ago
seperators_closeup=seperators_closeup.astype(np.uint8)
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=find_features_of_lines(contours_lines)
4 years ago
slope_lines_org_hor=slope_lines_org[slope_lines==0]
args=np.array( range(len(slope_lines) ))
len_x=seperators_closeup.shape[1]/4.0
args_hor=args[slope_lines==0]
dist_x_hor=dist_x[slope_lines==0]
x_min_main_hor=x_min_main[slope_lines==0]
x_max_main_hor=x_max_main[slope_lines==0]
cy_main_hor=cy_main[slope_lines==0]
args_hor=args_hor[dist_x_hor>=len_x/2.0]
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[np.abs(slope_lines_org_hor)<1.2]
slope_mean_hor=np.mean(slope_lines_org_hor)
args_ver=args[slope_lines==1]
y_min_main_ver=y_min_main[slope_lines==1]
y_max_main_ver=y_max_main[slope_lines==1]
x_min_main_ver=x_min_main[slope_lines==1]
x_max_main_ver=x_max_main[slope_lines==1]
cx_main_ver=cx_main[slope_lines==1]
dist_y_ver=y_max_main_ver-y_min_main_ver
len_y=seperators_closeup.shape[0]/3.0
4 years ago
print(matrix_of_lines_ch[:,8][matrix_of_lines_ch[:,9]==0],'khatlarrrr')
args_main_spliters=matrix_of_lines_ch[:,0][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
cy_main_spliters=matrix_of_lines_ch[:,5][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
"""
cy_main_spliters = cy_main_hor[(x_min_main_hor <= 0.16 * region_pre_p.shape[1]) & (x_max_main_hor >= 0.84 * region_pre_p.shape[1])]
cy_main_spliters = np.array(list(cy_main_spliters) + list(special_seperators))
4 years ago
if contours_h is not None:
try:
cy_main_spliters_head = cy_main_head[(x_min_main_head <= 0.16 * region_pre_p.shape[1]) & (x_max_main_head >= 0.84 * region_pre_p.shape[1])]
cy_main_spliters = np.array(list(cy_main_spliters) + list(cy_main_spliters_head))
4 years ago
except:
pass
args_cy_spliter = np.argsort(cy_main_spliters)
cy_main_spliters_sort = cy_main_spliters[args_cy_spliter]
spliter_y_new = []
4 years ago
spliter_y_new.append(0)
for i in range(len(cy_main_spliters_sort)):
spliter_y_new.append(cy_main_spliters_sort[i])
4 years ago
spliter_y_new.append(region_pre_p.shape[0])
spliter_y_new_diff = np.diff(spliter_y_new) / float(region_pre_p.shape[0]) * 100
args_big_parts = np.array(range(len(spliter_y_new_diff)))[spliter_y_new_diff > 22]
regions_without_seperators = return_regions_without_seperators(region_pre_p)
4 years ago
##print(args_big_parts,'args_big_parts')
# image_page_otsu=otsu_copy(image_page_deskewd)
# print(np.unique(image_page_otsu[:,:,0]))
# image_page_background_zero=self.image_change_background_pixels_to_zero(image_page_otsu)
length_y_threshold = regions_without_seperators.shape[0] / 4.0
num_col_fin = 0
peaks_neg_fin_fin = []
4 years ago
for iteils in args_big_parts:
regions_without_seperators_teil = regions_without_seperators[int(spliter_y_new[iteils]) : int(spliter_y_new[iteils + 1]), :, 0]
# image_page_background_zero_teil=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:]
# print(regions_without_seperators_teil.shape)
4 years ago
##plt.imshow(regions_without_seperators_teil)
##plt.show()
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=6.0)
# regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3)
4 years ago
#
num_col, peaks_neg_fin = find_num_col(regions_without_seperators_teil, multiplier=7.0)
if num_col > num_col_fin:
num_col_fin = num_col
peaks_neg_fin_fin = peaks_neg_fin
4 years ago
"""
#print(length_y_vertical_lines,length_y_threshold,'x_center_of_ver_linesx_center_of_ver_linesx_center_of_ver_lines')
if len(cx_main_ver)>0 and len( dist_y_ver[dist_y_ver>=length_y_threshold] ) >=1:
num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=6.0)
4 years ago
else:
#plt.imshow(image_page_background_zero_teil)
#plt.show()
#num_col, peaks_neg_fin=find_num_col_only_image(image_page_background_zero,multiplier=2.4)#2.3)
num_col, peaks_neg_fin=find_num_col_only_image(image_page_background_zero_teil,multiplier=3.4)#2.3)
4 years ago
print(num_col,'birda')
if num_col>0:
pass
elif num_col==0:
print(num_col,'birda2222')
num_col_regions, peaks_neg_fin_regions=find_num_col(regions_without_seperators_teil,multiplier=10.0)
4 years ago
if num_col_regions==0:
pass
else:
4 years ago
num_col=num_col_regions
peaks_neg_fin=peaks_neg_fin_regions[:]
"""
# print(num_col+1,'num colmsssssssss')
if len(args_big_parts) == 1 and (len(peaks_neg_fin_fin) + 1) < num_col_classifier:
peaks_neg_fin = find_num_col_by_vertical_lines(vertical)
peaks_neg_fin = peaks_neg_fin[peaks_neg_fin >= 500]
peaks_neg_fin = peaks_neg_fin[peaks_neg_fin <= (vertical.shape[1] - 500)]
peaks_neg_fin_fin = peaks_neg_fin[:]
# print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza')
return num_col_fin, peaks_neg_fin_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n
def return_contours_of_interested_region_by_size(self, region_pre_p, pixel, min_area, max_area):
4 years ago
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
4 years ago
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
4 years ago
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))
return img_ret[:, :, 0]
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 small_textlines_to_parent_adherence2(self, textlines_con, textline_iamge, num_col):
# print(textlines_con)
# textlines_con=textlines_con.astype(np.uint32)
textlines_con_changed = []
4 years ago
for m1 in range(len(textlines_con)):
# textlines_tot=textlines_con[m1]
# textlines_tot=textlines_tot.astype()
textlines_tot = []
textlines_tot_org_form = []
# print(textlines_tot)
4 years ago
for nn in range(len(textlines_con[m1])):
textlines_tot.append(np.array(textlines_con[m1][nn], dtype=np.int32))
4 years ago
textlines_tot_org_form.append(textlines_con[m1][nn])
4 years ago
##img_text_all=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
##img_text_all=cv2.fillPoly(img_text_all, pts =textlines_tot , color=(1,1,1))
4 years ago
##plt.imshow(img_text_all)
##plt.show()
areas_cnt_text = np.array([cv2.contourArea(textlines_tot[j]) for j in range(len(textlines_tot))])
areas_cnt_text = areas_cnt_text / float(textline_iamge.shape[0] * textline_iamge.shape[1])
indexes_textlines = np.array(range(len(textlines_tot)))
# print(areas_cnt_text,np.min(areas_cnt_text),np.max(areas_cnt_text))
if num_col == 0:
min_area = 0.0004
elif num_col == 1:
min_area = 0.0003
4 years ago
else:
min_area = 0.0001
indexes_textlines_small = indexes_textlines[areas_cnt_text < min_area]
# print(indexes_textlines)
textlines_small = []
textlines_small_org_form = []
4 years ago
for i in indexes_textlines_small:
textlines_small.append(textlines_tot[i])
textlines_small_org_form.append(textlines_tot_org_form[i])
textlines_big = []
textlines_big_org_form = []
for i in list(set(indexes_textlines) - set(indexes_textlines_small)):
4 years ago
textlines_big.append(textlines_tot[i])
textlines_big_org_form.append(textlines_tot_org_form[i])
img_textline_s = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=(1, 1, 1))
img_textline_b = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=(1, 1, 1))
sum_small_big_all = img_textline_s + img_textline_b
sum_small_big_all2 = (sum_small_big_all[:, :] == 2) * 1
sum_intersection_sb = sum_small_big_all2.sum(axis=1).sum()
if sum_intersection_sb > 0:
dis_small_from_bigs_tot = []
4 years ago
for z1 in range(len(textlines_small)):
# print(len(textlines_small),'small')
intersections = []
4 years ago
for z2 in range(len(textlines_big)):
img_text = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=(1, 1, 1))
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=(1, 1, 1))
sum_small_big = img_text2 + img_text
sum_small_big_2 = (sum_small_big[:, :] == 2) * 1
sum_intersection = sum_small_big_2.sum(axis=1).sum()
# print(sum_intersection)
4 years ago
intersections.append(sum_intersection)
if len(np.array(intersections)[np.array(intersections) > 0]) == 0:
intersections = []
4 years ago
try:
dis_small_from_bigs_tot.append(np.argmax(intersections))
except:
dis_small_from_bigs_tot.append(-1)
smalls_list = np.array(dis_small_from_bigs_tot)[np.array(dis_small_from_bigs_tot) >= 0]
# index_small_textlines_rest=list( set(indexes_textlines_small)-set(smalls_list) )
textlines_big_with_change = []
textlines_big_with_change_con = []
textlines_small_with_change = []
4 years ago
for z in list(set(smalls_list)):
index_small_textlines = list(np.where(np.array(dis_small_from_bigs_tot) == z)[0])
# print(z,index_small_textlines)
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1], 3))
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=(255, 255, 255))
4 years ago
textlines_big_with_change.append(z)
4 years ago
for k in index_small_textlines:
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=(255, 255, 255))
4 years ago
textlines_small_with_change.append(k)
img_text2 = img_text2.astype(np.uint8)
4 years ago
imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(cont[0],type(cont))
4 years ago
textlines_big_with_change_con.append(cont)
textlines_big_org_form[z] = cont[0]
4 years ago
# plt.imshow(img_text2)
# plt.show()
4 years ago
# print(textlines_big_with_change,'textlines_big_with_change')
# print(textlines_small_with_change,'textlines_small_with_change')
# print(textlines_big)
4 years ago
textlines_con_changed.append(textlines_big_org_form)
4 years ago
else:
textlines_con_changed.append(textlines_big_org_form)
return textlines_con_changed
def check_any_text_region_in_model_one_is_main_or_header(self, regions_model_1, regions_model_full, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered):
text_only = (regions_model_1[:, :] == 1) * 1
contours_only_text, hir_on_text = return_contours_of_image(text_only)
4 years ago
"""
contours_only_text_parent=return_parent_contours( contours_only_text,hir_on_text)
4 years ago
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])
4 years ago
###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))])
###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1])
###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002]
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
"""
cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = find_new_features_of_contoures(contours_only_text_parent)
length_con = x_max_main - x_min_main
height_con = y_max_main - y_min_main
4 years ago
all_found_texline_polygons_main = []
all_found_texline_polygons_head = []
all_box_coord_main = []
all_box_coord_head = []
slopes_main = []
slopes_head = []
contours_only_text_parent_main = []
contours_only_text_parent_head = []
contours_only_text_parent_main_d = []
contours_only_text_parent_head_d = []
4 years ago
for ii in range(len(contours_only_text_parent)):
con = contours_only_text_parent[ii]
img = np.zeros((regions_model_1.shape[0], regions_model_1.shape[1], 3))
4 years ago
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
all_pixels = ((img[:, :, 0] == 255) * 1).sum()
pixels_header = (((img[:, :, 0] == 255) & (regions_model_full[:, :, 0] == 2)) * 1).sum()
pixels_main = all_pixels - pixels_header
if (pixels_header >= pixels_main) and ((length_con[ii] / float(height_con[ii])) >= 1.3):
regions_model_1[:, :][(regions_model_1[:, :] == 1) & (img[:, :, 0] == 255)] = 2
4 years ago
contours_only_text_parent_head.append(con)
if contours_only_text_parent_d_ordered is not None:
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
all_box_coord_head.append(all_box_coord[ii])
slopes_head.append(slopes[ii])
all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
else:
regions_model_1[:, :][(regions_model_1[:, :] == 1) & (img[:, :, 0] == 255)] = 1
4 years ago
contours_only_text_parent_main.append(con)
if contours_only_text_parent_d_ordered is not None:
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
all_box_coord_main.append(all_box_coord[ii])
slopes_main.append(slopes[ii])
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
# print(all_pixels,pixels_main,pixels_header)
# plt.imshow(img[:,:,0])
# plt.show()
return regions_model_1, contours_only_text_parent_main, contours_only_text_parent_head, all_box_coord_main, all_box_coord_head, all_found_texline_polygons_main, all_found_texline_polygons_head, slopes_main, slopes_head, contours_only_text_parent_main_d, contours_only_text_parent_head_d
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(self, layout_in_patch):
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
contours_drop_parent_final = []
4 years ago
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
layout_in_patch[y : y + h, x : x + w, 0] = 4
4 years ago
return layout_in_patch
def put_drop_out_from_only_drop_model(self, layout_no_patch, layout1):
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001]
contours_drop_parent_final = []
4 years ago
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
# boxes.append([int(x), int(y), int(w), int(h)])
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
4 years ago
contours_drop_parent_final.append(contours_drop_parent[jj])
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
4 years ago
return layout_no_patch
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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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)
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##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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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
###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=self.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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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))