eynollah/sbb_newspapers_org_image/eynollah.py

#! /usr/bin/env python3

import gc
import math
import os
import random
import sys
import time
import warnings
from multiprocessing import Process, Queue, cpu_count
from sys import getsizeof

import cv2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
stderr = sys.stderr
sys.stderr = open(devnull, 'w')
from keras import backend as K
from keras.models import load_model
sys.stderr = stderr
import tensorflow as tf
tf.get_logger().setLevel('ERROR')
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
from shapely import geometry
from lxml import etree as ET
import click
from matplotlib import pyplot, transforms
import matplotlib.patches as mpatches
import imutils

warnings.filterwarnings('ignore')
#
__doc__ = \
    """
    tool to extract table form data from alto xml data
    """


class eynollah:
    def __init__(self, image_dir, f_name,dir_out, dir_models,dir_of_cropped_images=None,dir_of_layout=None,dir_of_deskewed=None,dir_of_all=None,allow_enhancement='false',curved_line='false',full_layout='false',allow_scaling='false',headers_off='false'):
        self.image_dir = image_dir  # XXX This does not seem to be a directory as the name suggests, but a file
        self.dir_out = dir_out
        self.f_name = f_name
        self.dir_of_cropped_images=dir_of_cropped_images
        self.allow_enhancement=allow_enhancement
        self.curved_line=curved_line
        self.full_layout=full_layout
        self.allow_scaling=allow_scaling
        self.dir_of_layout=dir_of_layout
        self.headers_off=headers_off
        self.dir_of_deskewed=dir_of_deskewed
        self.dir_of_all=dir_of_all
        if self.f_name is None:
            try:
                self.f_name = image_dir.split('/')[len(image_dir.split('/')) - 1]
                self.f_name = self.f_name.split('.')[0]
            except:
                self.f_name = self.f_name.split('.')[0]
        self.dir_models = dir_models
        self.kernel = np.ones((5, 5), np.uint8)

        self.model_dir_of_enhancemnet=dir_models +'/model_enhancement.h5'
        self.model_dir_of_col_classifier=dir_models +'/model_scale_classifier.h5'
        self.model_region_dir_p = dir_models +'/model_main_covid19_lr5-5_scale_1_1_great.h5'#dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
        #self.model_region_dir_p_ens = dir_models +'/model_ensemble_s.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
        self.model_region_dir_p2 = dir_models +'/model_main_home_corona3_rot.h5'

        self.model_region_dir_fully_np = dir_models + '/model_no_patches_class0_30eopch.h5'
        self.model_region_dir_fully = dir_models + '/model_3up_new_good_no_augmentation.h5'#"model_3col_p_soft_10_less_aug_binarization_only.h5"

        self.model_page_dir = dir_models + '/model_page_mixed_best.h5'
        self.model_region_dir_p_ens =dir_models +'/model_ensemble_s.h5'#dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5' #dir_models +'/model_ensemble_s.h5'
        ###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
        self.model_textline_dir = dir_models + '/model_textline_newspapers.h5'#'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'


    def filter_contours_area_of_image_tables(self,image,contours,hirarchy,max_area,min_area):
        found_polygons_early = list()

        jv=0
        for c in contours:
            if len(c) < 3:  # A polygon cannot have less than 3 points
                continue

            polygon = geometry.Polygon([point[0] for point in c])
            #area = cv2.contourArea(c)
            area = polygon.area
            ##print(np.prod(thresh.shape[:2]))
            # Check that polygon has area greater than minimal area
            #print(hirarchy[0][jv][3],hirarchy )
            if area >=min_area*np.prod(image.shape[:2]) and area <=max_area*np.prod(image.shape[:2]):#and hirarchy[0][jv][3]==-1 :
                #print(c[0][0][1])
                found_polygons_early.append(
                    np.array(  [ [point] for point in polygon.exterior.coords] , dtype=np.int32) )
            jv+=1
        return found_polygons_early
    def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8):
        found_polygons_early = list()

        for c in contours:
            if len(c) < 3:  # A polygon cannot have less than 3 points
                continue

            polygon = geometry.Polygon([point[0] for point in c])
            area = polygon.area
            # Check that polygon has area greater than minimal area
            if area >= median_area * scaler_down and area <= median_area * scaler_up:
                found_polygons_early.append(
                    np.array([point for point in polygon.exterior.coords], dtype=np.uint))
        return found_polygons_early

    def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area):
        found_polygons_early = list()

        jv = 0
        for c in contours:
            if len(c) < 3:  # A polygon cannot have less than 3 points
                continue

            polygon = geometry.Polygon([point[0] for point in c])
            area = polygon.area
            if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(
                    image.shape[:2]) and hirarchy[0][jv][3] == -1 :  # and hirarchy[0][jv][3]==-1 :
                found_polygons_early.append(
                    np.array([ [point] for point in polygon.exterior.coords], dtype=np.uint))
            jv += 1
        return found_polygons_early

    def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area):
        found_polygons_early = list()

        jv = 0
        for c in contours:
            if len(c) < 3:  # A polygon cannot have less than 3 points
                continue

            polygon = geometry.Polygon([point[0] for point in c])
            area = polygon.area
            if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and \
                    hirarchy[0][jv][3] != -1:
                # print(c[0][0][1])
                found_polygons_early.append(
                    np.array([point for point in polygon.exterior.coords], dtype=np.uint))
            jv += 1
        return found_polygons_early

    def resize_image(self, img_in, input_height, input_width):
        return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

    def resize_ann(self, seg_in, input_height, input_width):
        return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

    def rotatedRectWithMaxArea(self,w, h, angle):
        if w <= 0 or h <= 0:
            return 0,0

        width_is_longer = w >= h
        side_long, side_short = (w,h) if width_is_longer else (h,w)

        # since the solutions for angle, -angle and 180-angle are all the same,
        # if suffices to look at the first quadrant and the absolute values of sin,cos:
        sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
        if side_short <= 2.*sin_a*cos_a*side_long or abs(sin_a-cos_a) < 1e-10:
            # half constrained case: two crop corners touch the longer side,
            #   the other two corners are on the mid-line parallel to the longer line
            x = 0.5*side_short
            wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a)
        else:
            # fully constrained case: crop touches all 4 sides
            cos_2a = cos_a*cos_a - sin_a*sin_a
            wr,hr = (w*cos_a - h*sin_a)/cos_2a, (h*cos_a - w*sin_a)/cos_2a

        return wr,hr

    def get_one_hot(self, seg, input_height, input_width, n_classes):
        seg = seg[:, :, 0]
        seg_f = np.zeros((input_height, input_width, n_classes))
        for j in range(n_classes):
            seg_f[:, :, j] = (seg == j).astype(int)
        return seg_f


    def color_images(self, seg, n_classes):
        ann_u = range(n_classes)
        if len(np.shape(seg)) == 3:
            seg = seg[:, :, 0]

        seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(np.uint8)
        colors = sns.color_palette("hls", n_classes)

        for c in ann_u:
            c = int(c)
            segl = (seg == c)
            seg_img[:, :, 0] = segl * c
            seg_img[:, :, 1] = segl * c
            seg_img[:, :, 2] = segl * c
        return seg_img

    def color_images_diva(self, seg, n_classes):
        ann_u = range(n_classes)
        if len(np.shape(seg)) == 3:
            seg = seg[:, :, 0]

        seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
        # colors=sns.color_palette("hls", n_classes)
        colors = [[1, 0, 0], [8, 0, 0], [2, 0, 0], [4, 0, 0]]

        for c in ann_u:
            c = int(c)
            segl = (seg == c)
            seg_img[:, :, 0][seg == c] = colors[c][0]  # segl*(colors[c][0])
            seg_img[:, :, 1][seg == c] = colors[c][1]  # seg_img[:,:,1]=segl*(colors[c][1])
            seg_img[:, :, 2][seg == c] = colors[c][2]  # seg_img[:,:,2]=segl*(colors[c][2])
        return seg_img

    def rotate_image(self, img_patch, slope):
        (h, w) = img_patch.shape[:2]
        center = (w // 2, h // 2)
        M = cv2.getRotationMatrix2D(center, slope, 1.0)
        return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

    def rotyate_image_different(self,img,slope):
        #img = cv2.imread('images/input.jpg')
        num_rows, num_cols = img.shape[:2]

        rotation_matrix = cv2.getRotationMatrix2D((num_cols/2, num_rows/2), slope, 1)
        img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows) )
        return img_rotation

    def cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray:
        # Smooth
        if sigma > 0.:
            return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma)
        elif sigma == 0.:
            return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255
        else:  # Negative sigma, do not do anything
            return probs

    def crop_image_inside_box(self, box, img_org_copy):
        image_box = img_org_copy[box[1]:box[1] + box[3], box[0]:box[0] + box[2]]
        return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]

    def otsu_copy(self, img):
        img_r = np.zeros(img.shape)
        img1 = img[:, :, 0]
        img2 = img[:, :, 1]
        img3 = img[:, :, 2]
        # print(img.min())
        # print(img[:,:,0].min())
        # blur = cv2.GaussianBlur(img,(5,5))
        # ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

        img_r[:, :, 0] = threshold1
        img_r[:, :, 1] = threshold1
        img_r[:, :, 2] = threshold1
        return img_r

    def otsu_copy_binary(self,img):
        img_r=np.zeros((img.shape[0],img.shape[1],3))
        img1=img[:,:,0]

        retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)



        img_r[:,:,0]=threshold1
        img_r[:,:,1]=threshold1
        img_r[:,:,2]=threshold1

        img_r=img_r/float(np.max(img_r))*255
        return img_r

    def predict_enhancement(self,img):
        model_enhancement,session_enhancemnet=self.start_new_session_and_model(self.model_dir_of_enhancemnet)

        img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
        img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
        #n_classes = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[3]
        if img.shape[0]<img_height_model:
            img=cv2.resize( img, ( img.shape[1],img_width_model) ,interpolation=cv2.INTER_NEAREST)

        if img.shape[1]<img_width_model:
            img=cv2.resize( img, ( img_height_model,img.shape[0]) ,interpolation=cv2.INTER_NEAREST)

        margin=True

        if margin:
            kernel = np.ones((5,5),np.uint8)


            margin = int(0 * img_width_model)

            width_mid = img_width_model - 2 * margin
            height_mid = img_height_model - 2 * margin


            img = img / float(255.0)

            img_h = img.shape[0]
            img_w = img.shape[1]

            prediction_true = np.zeros((img_h, img_w, 3))
            mask_true = np.zeros((img_h, img_w))
            nxf = img_w / float(width_mid)
            nyf = img_h / float(height_mid)

            if nxf > int(nxf):
                nxf = int(nxf) + 1
            else:
                nxf = int(nxf)

            if nyf > int(nyf):
                nyf = int(nyf) + 1
            else:
                nyf = int(nyf)

            for i in range(nxf):
                for j in range(nyf):

                    if i == 0:
                        index_x_d = i * width_mid
                        index_x_u = index_x_d + img_width_model
                    elif i > 0:
                        index_x_d = i * width_mid
                        index_x_u = index_x_d + img_width_model

                    if j == 0:
                        index_y_d = j * height_mid
                        index_y_u = index_y_d + img_height_model
                    elif j > 0:
                        index_y_d = j * height_mid
                        index_y_u = index_y_d + img_height_model

                    if index_x_u > img_w:
                        index_x_u = img_w
                        index_x_d = img_w - img_width_model
                    if index_y_u > img_h:
                        index_y_u = img_h
                        index_y_d = img_h - img_height_model



                    img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]

                    label_p_pred = model_enhancement.predict(
                        img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))

                    seg =label_p_pred[0,:,:,:]
                    seg=seg*255


                    if i==0 and j==0:
                        seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
                        prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
                        :] = seg

                    elif i==nxf-1 and j==nyf-1:
                        seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
                        prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
                        :] = seg

                    elif i==0 and j==nyf-1:
                        seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]

                        prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
                        :] = seg

                    elif i==nxf-1 and j==0:
                        seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]

                        prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
                        :] = seg

                    elif i==0 and j!=0 and j!=nyf-1:
                        seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]

                        prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
                        :] = seg

                    elif i==nxf-1 and j!=0 and j!=nyf-1:
                        seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]

                        prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
                        :] = seg

                    elif i!=0 and i!=nxf-1 and j==0:
                        seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
                        prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
                        :] = seg

                    elif i!=0 and i!=nxf-1 and j==nyf-1:
                        seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]

                        prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
                        :] = seg

                    else:
                        seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]

                        prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
                        :] = seg

            prediction_true = prediction_true.astype(int)

            del model_enhancement
            del session_enhancemnet


            return prediction_true

    def check_dpi(self):
        self.dpi=os.popen('identify -format "%x " '+self.image_dir).read()
        self.dpi=int(float(self.dpi))


    def resize_image_with_column_classifier(self,is_image_enhanced):
        self.check_dpi()
        img=cv2.imread(self.image_dir)
        img=img.astype(np.uint8)

        _,page_coord=self.early_page_for_num_of_column_classification()
        model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)

        img_1ch=cv2.imread(self.image_dir,0)

        width_early=img_1ch.shape[1]

        img_1ch=img_1ch[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3]]

        #plt.imshow(img_1ch)
        #plt.show()
        img_1ch=img_1ch/255.

        img_1ch=cv2.resize( img_1ch, ( 448,448) ,interpolation=cv2.INTER_NEAREST)

        img_in=np.zeros((1,img_1ch.shape[0],img_1ch.shape[1],3))
        img_in[0,:,:,0]=img_1ch[:,:]
        img_in[0,:,:,1]=img_1ch[:,:]
        img_in[0,:,:,2]=img_1ch[:,:]

        label_p_pred=model_num_classifier.predict(img_in)
        num_col=np.argmax(label_p_pred[0])+1

        print(num_col,label_p_pred,'num_col_classifier')

        session_col_classifier.close()
        del model_num_classifier
        del session_col_classifier

        K.clear_session()
        gc.collect()

        #sys.exit()
        if num_col==1 and width_early<1100:
            img_w_new=2000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)

        elif num_col==1 and width_early>=2500:
            img_w_new=2000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)
        elif num_col==1 and width_early>=1100 and width_early<2500:
            img_w_new=width_early
            img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)

        elif num_col==2 and width_early<2000:
            img_w_new=2400
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)

        elif num_col==2 and width_early>=3500:
            img_w_new=2400
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)

        elif num_col==2 and width_early>=2000 and width_early<3500:
            img_w_new=width_early
            img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)


        elif num_col==3 and width_early<2000:
            img_w_new=3000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)

        elif num_col==3 and width_early>=4000:
            img_w_new=3000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)

        elif num_col==3 and width_early>=2000 and width_early<4000:
            img_w_new=width_early
            img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)


        elif num_col==4 and width_early<2500:
            img_w_new=4000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)

        elif num_col==4 and width_early>=5000:
            img_w_new=4000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)

        elif num_col==4 and width_early>=2500 and width_early<5000:
            img_w_new=width_early
            img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)



        elif num_col==5 and width_early<3700:
            img_w_new=5000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)

        elif num_col==5 and width_early>=7000:
            img_w_new=5000
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)

        elif num_col==5 and width_early>=3700 and width_early<7000:
            img_w_new=width_early
            img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)



        elif num_col==6 and width_early<4500:
            img_w_new=6500#5400
            img_h_new=int(img.shape[0]/float(img.shape[1]) * 6500)

        else:
            img_w_new=width_early
            img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)



        if label_p_pred[0][int(num_col-1)]<0.9 and img_w_new<width_early:
            img_new=np.copy(img)
            num_column_is_classified=False
        else:
            img_new=self.resize_image(img,img_h_new,img_w_new)
            num_column_is_classified=True

        if img_new.shape[1]>img.shape[1]:
            img_new=self.predict_enhancement(img_new)
            is_image_enhanced=True

        return img,img_new,is_image_enhanced

    def resize_and_enhance_image_with_column_classifier(self,is_image_enhanced):
        self.check_dpi()
        img=cv2.imread(self.image_dir)

        img=img.astype(np.uint8)

        _,page_coord=self.early_page_for_num_of_column_classification()
        model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)

        img_1ch=cv2.imread(self.image_dir,0)
        img_1ch=img_1ch.astype(np.uint8)

        width_early=img_1ch.shape[1]

        img_1ch=img_1ch[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3]]

        #plt.imshow(img_1ch)
        #plt.show()
        img_1ch=img_1ch/255.

        img_1ch=cv2.resize( img_1ch, ( 448,448) ,interpolation=cv2.INTER_NEAREST)

        img_in=np.zeros((1,img_1ch.shape[0],img_1ch.shape[1],3))
        img_in[0,:,:,0]=img_1ch[:,:]
        img_in[0,:,:,1]=img_1ch[:,:]
        img_in[0,:,:,2]=img_1ch[:,:]


        #plt.imshow(img_in[0,:,:,:])
        #plt.show()

        label_p_pred=model_num_classifier.predict(img_in)
        num_col=np.argmax(label_p_pred[0])+1

        print(num_col,label_p_pred,'num_col_classifier')

        session_col_classifier.close()
        del model_num_classifier
        del session_col_classifier
        del img_in
        del img_1ch
        del page_coord

        K.clear_session()
        gc.collect()


        print(self.dpi)

        if self.dpi<298:

            #sys.exit()
            if num_col==1 and width_early<1100:
                img_w_new=2000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)

            elif num_col==1 and width_early>=2500:
                img_w_new=2000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)
            elif num_col==1 and width_early>=1100 and width_early<2500:
                img_w_new=width_early
                img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)

            elif num_col==2 and width_early<2000:
                img_w_new=2400
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)

            elif num_col==2 and width_early>=3500:
                img_w_new=2400
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)

            elif num_col==2 and width_early>=2000 and width_early<3500:
                img_w_new=width_early
                img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)


            elif num_col==3 and width_early<2000:
                img_w_new=3000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)

            elif num_col==3 and width_early>=4000:
                img_w_new=3000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)

            elif num_col==3 and width_early>=2000 and width_early<4000:
                img_w_new=width_early
                img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)


            elif num_col==4 and width_early<2500:
                img_w_new=4000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)

            elif num_col==4 and width_early>=5000:
                img_w_new=4000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)

            elif num_col==4 and width_early>=2500 and width_early<5000:
                img_w_new=width_early
                img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)



            elif num_col==5 and width_early<3700:
                img_w_new=5000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)

            elif num_col==5 and width_early>=7000:
                img_w_new=5000
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)

            elif num_col==5 and width_early>=3700 and width_early<7000:
                img_w_new=width_early
                img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)



            elif num_col==6 and width_early<4500:
                img_w_new=6500#5400
                img_h_new=int(img.shape[0]/float(img.shape[1]) * 6500)

            else:
                img_w_new=width_early
                img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)



            if label_p_pred[0][int(num_col-1)]<0.9 and img_w_new<width_early:
                img_new=np.copy(img)
                num_column_is_classified=False
            else:
                img_new=self.resize_image(img,img_h_new,img_w_new)
                num_column_is_classified=True

            #img_new=self.resize_image(img,img_h_new,img_w_new)
            image_res=self.predict_enhancement(img_new)
            #cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
            #self.image=self.image.astype(np.uint16)

            #self.scale_x=1
            #self.scale_y=1
            #self.height_org = self.image.shape[0]
            #self.width_org = self.image.shape[1]
            is_image_enhanced=True
        else:
            """
            if img.shape[0]<=2530 and img.shape[0]>=img.shape[1]:
                img_h_new=3000
                img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
                img_new=self.resize_image(img,img_h_new,img_w_new)
                image_res=self.predict_enhancement(img_new)
                #cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
                #self.image=self.image.astype(np.uint16)
                ##self.scale_x=1
                ##self.scale_y=1
                ##self.height_org = self.image.shape[0]
                ##self.width_org = self.image.shape[1]
                is_image_enhanced=True
            else:
                is_image_enhanced=False
                image_res=np.copy(img)

            """
            is_image_enhanced=False
            num_column_is_classified=True
            image_res=np.copy(img)




        return is_image_enhanced,img,image_res,num_col,num_column_is_classified
    def resize_and_enhance_image(self,is_image_enhanced):
        self.check_dpi()
        img=cv2.imread(self.image_dir)
        img=img.astype(np.uint8)
        #sys.exit()


        print(self.dpi)

        if self.dpi<298:
            if img.shape[0]<1000:
                img_h_new=int(img.shape[0]*3)
                img_w_new=int(img.shape[1]*3)
                if img_h_new<2800:
                    img_h_new=3000
                    img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
            elif img.shape[0]>=1000 and img.shape[0]<2000:
                img_h_new=int(img.shape[0]*2)
                img_w_new=int(img.shape[1]*2)
                if img_h_new<2800:
                    img_h_new=3000
                    img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
            else:
                img_h_new=int(img.shape[0]*1.5)
                img_w_new=int(img.shape[1]*1.5)
            img_new=self.resize_image(img,img_h_new,img_w_new)
            image_res=self.predict_enhancement(img_new)
            #cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
            #self.image=self.image.astype(np.uint16)

            #self.scale_x=1
            #self.scale_y=1
            #self.height_org = self.image.shape[0]
            #self.width_org = self.image.shape[1]
            is_image_enhanced=True
        else:
            is_image_enhanced=False
            image_res=np.copy(img)

        return is_image_enhanced,img,image_res


    def resize_and_enhance_image_new(self,is_image_enhanced):
        #self.check_dpi()
        img=cv2.imread(self.image_dir)
        img=img.astype(np.uint8)
        #sys.exit()

        image_res=np.copy(img)


        return is_image_enhanced,img,image_res




    def get_image_and_scales(self,img_org,img_res,scale):
        self.image = np.copy(img_res)
        self.image_org=np.copy(img_org)
        self.height_org = self.image.shape[0]
        self.width_org = self.image.shape[1]



        self.img_hight_int =int(self.image.shape[0]*scale)
        self.img_width_int = int(self.image.shape[1]*scale)
        self.scale_y = self.img_hight_int / float(self.image.shape[0])
        self.scale_x = self.img_width_int / float(self.image.shape[1])

        self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)
        del img_res
        del img_org
    def get_image_and_scales_after_enhancing(self,img_org,img_res):

        #self.image = cv2.imread(self.image_dir)

        self.image=np.copy(img_res)
        self.image=self.image.astype(np.uint8)
        self.image_org=np.copy(img_org)
        self.height_org = self.image_org.shape[0]
        self.width_org = self.image_org.shape[1]



        self.scale_y = img_res.shape[0] / float(self.image_org.shape[0])
        self.scale_x = img_res.shape[1]/ float(self.image_org.shape[1])

        del img_org
        del img_res



    def get_image_and_scales_deskewd(self,img_deskewd):

        self.image = img_deskewd
        self.image_org=np.copy(self.image)
        self.height_org = self.image.shape[0]
        self.width_org = self.image.shape[1]


        self.img_hight_int =int(self.image.shape[0]*1)
        self.img_width_int = int(self.image.shape[1]*1)
        self.scale_y = self.img_hight_int / float(self.image.shape[0])
        self.scale_x = self.img_width_int / float(self.image.shape[1])

        self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)

    def start_new_session_and_model(self, model_dir):
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True

        session = tf.InteractiveSession()
        model = load_model(model_dir, compile=False)

        return model, session

    def return_bonding_box_of_contours(self,cnts):
        boxes_tot=[]
        for i in range(len(cnts)):
            x,y,w,h = cv2.boundingRect(cnts[i])

            box=[x,y,w,h]
            boxes_tot.append(box)
        return boxes_tot

    def find_features_of_lines(self,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))])

        slope_lines=[]

        for kk in range(len(contours_main)):
            [vx,vy,x,y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2,0,0.01,0.01)
            slope_lines.append( ( (vy/vx)/np.pi*180 )[0]  )

        slope_lines_org=slope_lines
        slope_lines=np.array(slope_lines)
        slope_lines[(slope_lines<10) & (slope_lines>-10)]=0

        slope_lines[(slope_lines<-200) | (slope_lines>200)]=1
        slope_lines[ (slope_lines!=0) &  (slope_lines!=1)]=2

        dis_x=np.abs(x_max_main-x_min_main)
        return slope_lines,dis_x, x_min_main ,x_max_main ,np.array(cy_main),np.array(slope_lines_org),y_min_main ,y_max_main,np.array(cx_main)

    def return_parent_contours(self,contours,hierarchy):
        contours_parent=[ contours[i] for i in range(len(contours) ) if hierarchy[0][i][3]==-1  ]
        return contours_parent

    def isNaN(self,num):
        return num != num
    def early_deskewing_slope_calculation_based_on_lines(self,region_pre_p):
        # lines are labels by 6 in this model
        seperators_closeup=( (region_pre_p[:,:,:]==6))*1

        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=self.find_features_of_lines(contours_lines)

        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)

        if np.abs(slope_mean_hor)>1.2:
            slope_mean_hor=0

        #deskewed_new=rotate_image(image_regions_eraly_p[:,:,:],slope_mean_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

        return slope_mean_hor,cx_main_ver,dist_y_ver

    def return_contours_of_interested_region(self,region_pre_p,pixel,min_area=0.0002):

        # pixels of images are identified by 5
        if len(region_pre_p.shape)==3:
            cnts_images=(region_pre_p[:,:,0]==pixel)*1
        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)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=min_area)

        return contours_imgs


    def return_contours_of_interested_region_by_min_size(self,region_pre_p,pixel,min_size=0.00003):

        # pixels of images are identified by 5
        if len(region_pre_p.shape)==3:
            cnts_images=(region_pre_p[:,:,0]==pixel)*1
        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)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=min_size)

        return contours_imgs


    def return_contours_of_interested_textline(self,region_pre_p,pixel):

        # pixels of images are identified by 5
        if len(region_pre_p.shape)==3:
            cnts_images=(region_pre_p[:,:,0]==pixel)*1
        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)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=0.000000003)
        return contours_imgs
    def find_images_contours_and_replace_table_and_graphic_pixels_by_image(self,region_pre_p):

        # pixels of images are identified by 5
        cnts_images=(region_pre_p[:,:,0]==5)*1
        cnts_images=cnts_images.astype(np.uint8)
        cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        #print(len(contours_imgs),'contours_imgs')
        contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=0.0003)

        #print(len(contours_imgs),'contours_imgs')

        boxes_imgs=self.return_bonding_box_of_contours(contours_imgs)

        for i in range(len(boxes_imgs)):
            x1=int(boxes_imgs[i][0] )
            x2=int(boxes_imgs[i][0]+ boxes_imgs[i][2])
            y1=int(boxes_imgs[i][1] )
            y2=int(boxes_imgs[i][1]+ boxes_imgs[i][3])
            region_pre_p[y1:y2,x1:x2,0][region_pre_p[y1:y2,x1:x2,0]==8]=5
            region_pre_p[y1:y2,x1:x2,0][region_pre_p[y1:y2,x1:x2,0]==7]=5
        return region_pre_p

    def do_prediction(self,patches,img,model,marginal_of_patch_percent=0.1):

        img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
        img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
        n_classes = model.layers[len(model.layers) - 1].output_shape[3]



        if patches:
            if img.shape[0]<img_height_model:
                img=self.resize_image(img,img_height_model,img.shape[1])

            if img.shape[1]<img_width_model:
                img=self.resize_image(img,img.shape[0],img_width_model)

            #print(img_height_model,img_width_model)
            #margin = int(0.2 * img_width_model)
            margin = int(marginal_of_patch_percent * img_height_model)

            width_mid = img_width_model - 2 * margin
            height_mid = img_height_model - 2 * margin


            img = img / float(255.0)
            #print(sys.getsizeof(img))
            #print(np.max(img))

            img=img.astype(np.float16)

            #print(sys.getsizeof(img))

            img_h = img.shape[0]
            img_w = img.shape[1]

            prediction_true = np.zeros((img_h, img_w, 3))
            mask_true = np.zeros((img_h, img_w))
            nxf = img_w / float(width_mid)
            nyf = img_h / float(height_mid)

            if nxf > int(nxf):
                nxf = int(nxf) + 1
            else:
                nxf = int(nxf)

            if nyf > int(nyf):
                nyf = int(nyf) + 1
            else:
                nyf = int(nyf)

            for i in range(nxf):
                for j in range(nyf):

                    if i == 0:
                        index_x_d = i * width_mid
                        index_x_u = index_x_d + img_width_model
                    elif i > 0:
                        index_x_d = i * width_mid
                        index_x_u = index_x_d + img_width_model

                    if j == 0:
                        index_y_d = j * height_mid
                        index_y_u = index_y_d + img_height_model
                    elif j > 0:
                        index_y_d = j * height_mid
                        index_y_u = index_y_d + img_height_model

                    if index_x_u > img_w:
                        index_x_u = img_w
                        index_x_d = img_w - img_width_model
                    if index_y_u > img_h:
                        index_y_u = img_h
                        index_y_d = img_h - img_height_model



                    img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]

                    label_p_pred = model.predict(
                        img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))

                    seg = np.argmax(label_p_pred, axis=3)[0]

                    seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)

                    if i==0 and j==0:
                        seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
                        seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]

                        mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
                        prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
                        :] = seg_color

                    elif i==nxf-1 and j==nyf-1:
                        seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
                        seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]

                        mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
                        prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
                        :] = seg_color

                    elif i==0 and j==nyf-1:
                        seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
                        seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]

                        mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
                        prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
                        :] = seg_color

                    elif i==nxf-1 and j==0:
                        seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
                        seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]

                        mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
                        prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
                        :] = seg_color

                    elif i==0 and j!=0 and j!=nyf-1:
                        seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
                        seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]

                        mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
                        prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
                        :] = seg_color

                    elif i==nxf-1 and j!=0 and j!=nyf-1:
                        seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
                        seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]

                        mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
                        prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
                        :] = seg_color

                    elif i!=0 and i!=nxf-1 and j==0:
                        seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
                        seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]

                        mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
                        prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
                        :] = seg_color

                    elif i!=0 and i!=nxf-1 and j==nyf-1:
                        seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
                        seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]

                        mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
                        prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
                        :] = seg_color

                    else:
                        seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
                        seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]

                        mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
                        prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
                        :] = seg_color

            prediction_true = prediction_true.astype(np.uint8)
            del img
            del mask_true
            del seg_color
            del seg
            del img_patch

        if not patches:
            img_h_page=img.shape[0]
            img_w_page=img.shape[1]
            img = img /float( 255.0)
            img = self.resize_image(img, img_height_model, img_width_model)

            label_p_pred = model.predict(
                img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))

            seg = np.argmax(label_p_pred, axis=3)[0]
            seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2)
            prediction_true = self.resize_image(seg_color, img_h_page, img_w_page)
            prediction_true = prediction_true.astype(np.uint8)


            del img
            del seg_color
            del label_p_pred
            del seg
        del model
        gc.collect()

        return prediction_true

    def early_page_for_num_of_column_classification(self):
        img=cv2.imread(self.image_dir)
        img=img.astype(np.uint8)
        patches=False
        model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
        ###img = self.otsu_copy(self.image)
        for ii in range(1):
            img = cv2.GaussianBlur(img, (5, 5), 0)


        img_page_prediction=self.do_prediction(patches,img,model_page)

        imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
        _, thresh = cv2.threshold(imgray, 0, 255, 0)

        thresh = cv2.dilate(thresh, self.kernel, iterations=3)
        contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])

        cnt = contours[np.argmax(cnt_size)]

        x, y, w, h = cv2.boundingRect(cnt)

        box = [x, y, w, h]

        croped_page, page_coord = self.crop_image_inside_box(box, img)

        session_page.close()
        del model_page
        del session_page
        del contours
        del thresh
        del img
        del cnt_size
        del cnt
        del box
        del x
        del y
        del w
        del h
        del imgray
        del img_page_prediction


        gc.collect()
        return croped_page, page_coord

    def extract_page(self):
        patches=False
        model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
        ###img = self.otsu_copy(self.image)
        for ii in range(1):
            img = cv2.GaussianBlur(self.image, (5, 5), 0)


        img_page_prediction=self.do_prediction(patches,img,model_page)

        imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
        _, thresh = cv2.threshold(imgray, 0, 255, 0)

        thresh = cv2.dilate(thresh, self.kernel, iterations=3)
        contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])

        cnt = contours[np.argmax(cnt_size)]

        x, y, w, h = cv2.boundingRect(cnt)


        if x<=30:
            w=w+x
            x=0
        if (self.image.shape[1]-(x+w) )<=30:
            w=w+(self.image.shape[1]-(x+w) )

        if y<=30:
            h=h+y
            y=0
        if (self.image.shape[0]-(y+h) )<=30:
            h=h+(self.image.shape[0]-(y+h) )



        box = [x, y, w, h]

        croped_page, page_coord = self.crop_image_inside_box(box, self.image)

        self.cont_page=[]
        self.cont_page.append( np.array( [ [ page_coord[2] , page_coord[0] ] ,
                                                    [ page_coord[3] , page_coord[0] ] ,
                                                    [ page_coord[3] , page_coord[1] ] ,
                                                [ page_coord[2] , page_coord[1] ]] ) )

        session_page.close()
        del model_page
        del session_page
        del contours
        del thresh
        del img
        del imgray

        gc.collect()
        return croped_page, page_coord

    def extract_drop_capital_13(self, img,patches,cols):


        img_height_h=img.shape[0]
        img_width_h=img.shape[1]
        patches=False



        img = self.otsu_copy_binary(img)#self.otsu_copy(img)
        img = img.astype(np.uint16)

        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)

        img_1=img[:int(img.shape[0]/3.),:,:]
        img_2=img[int(img.shape[0]/3.):int(2*img.shape[0]/3.),:,:]
        img_3=img[int(2*img.shape[0]/3.):,:,:]



        #img_1 = self.otsu_copy_binary(img_1)#self.otsu_copy(img)
        #img_1 = img_1.astype(np.uint16)

        plt.imshow(img_1)
        plt.show()
        #img_2 = self.otsu_copy_binary(img_2)#self.otsu_copy(img)
        #img_2 = img_2.astype(np.uint16)

        plt.imshow(img_2)
        plt.show()
        #img_3 = self.otsu_copy_binary(img_3)#self.otsu_copy(img)
        #img_3 = img_3.astype(np.uint16)


        plt.imshow(img_3)
        plt.show()

        prediction_regions_1=self.do_prediction(patches,img_1,model_region)


        plt.imshow(prediction_regions_1)
        plt.show()

        prediction_regions_2=self.do_prediction(patches,img_2,model_region)

        plt.imshow(prediction_regions_2)
        plt.show()
        prediction_regions_3=self.do_prediction(patches,img_3,model_region)


        plt.imshow(prediction_regions_3)
        plt.show()
        prediction_regions=np.zeros((img_height_h,img_width_h))

        prediction_regions[:int(img.shape[0]/3.),:]=prediction_regions_1[:,:,0]
        prediction_regions[int(img.shape[0]/3.):int(2*img.shape[0]/3.),:]=prediction_regions_2[:,:,0]
        prediction_regions[int(2*img.shape[0]/3.):,:]=prediction_regions_3[:,:,0]



        session_region.close()
        del img_1
        del img_2
        del img_3
        del prediction_regions_1
        del prediction_regions_2
        del prediction_regions_3
        del model_region
        del session_region
        del img
        gc.collect()
        return prediction_regions



    def extract_text_regions(self, img,patches,cols):
        img_height_h=img.shape[0]
        img_width_h=img.shape[1]

        ###if patches and cols>=3 :
            ###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
        ###if not patches:
            ###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)

        ###if patches and cols==2 :
            ###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)

        ###if patches and cols==1 :
            ###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)



        ###if patches and cols>=2:

            ###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            ###img = img.astype(np.uint8)


        ###if patches and cols==1:

            ###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            ###img = img.astype(np.uint8)
            ###img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) )


        if patches :
            model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
        if not patches:
            model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)

        if patches and cols==1:
            img2 = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img2 = img2.astype(np.uint8)
            img2= self.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=self.resize_image(prediction_regions2, img_height_h, img_width_h )

        if patches and cols==2:
            img2 = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img2 = img2.astype(np.uint8)
            img2= self.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=self.resize_image(prediction_regions2, img_height_h, img_width_h )
        elif patches and cols>2:
            img2 = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img2 = img2.astype(np.uint8)
            img2= self.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=self.resize_image(prediction_regions2, img_height_h, img_width_h )


        if patches and cols==2:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)

            img = img.astype(np.uint8)

            if img_width_h>=2000:
                img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
            else:
                pass#img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) )
            img = img.astype(np.uint8)

        if patches and cols==1:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)

            img = img.astype(np.uint8)
            img= self.resize_image(img, int(img_height_h*0.5), int(img_width_h*0.5) )
            img = img.astype(np.uint8)

        if patches and cols==3:

            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint8)
            #img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )

        if patches and cols==4:

            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint8)
            #img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )

        if patches and cols>=5:

            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint8)
            #img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )

        if not patches:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint8)
            prediction_regions2=None

        marginal_of_patch_percent=0.1
        prediction_regions=self.do_prediction(patches,img,model_region,marginal_of_patch_percent)
        prediction_regions=self.resize_image(prediction_regions, img_height_h, img_width_h )




        session_region.close()
        del model_region
        del session_region
        del img
        gc.collect()
        return prediction_regions,prediction_regions2

    def extract_only_text_regions(self, img,patches):

        model_region, session_region = self.start_new_session_and_model(self.model_only_text)
        img = self.otsu_copy_binary(img)#self.otsu_copy(img)
        img = img.astype(np.uint8)
        img_org=np.copy(img)

        img_h=img_org.shape[0]
        img_w=img_org.shape[1]

        img= self.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= self.resize_image(prediction_regions1, img_h, img_w)


        #prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4)
        #prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7)
        #prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2)


        img= self.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= self.resize_image(prediction_regions2, img_h, img_w)


        #prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
        prediction_regions2 = cv2.erode(prediction_regions2, self.kernel, iterations=2)
        prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)


        #prediction_regions=(  (prediction_regions2[:,:,0]==1) & (prediction_regions1[:,:,0]==1) )
        #prediction_regions=(prediction_regions1[:,:,0]==1)

        session_region.close()
        del model_region
        del session_region
        gc.collect()
        return prediction_regions1[:,:,0]

    def extract_binarization(self, img,patches):

        model_bin, session_bin = self.start_new_session_and_model(self.model_binafrization)


        img_h=img.shape[0]
        img_w=img.shape[1]

        img= self.resize_image(img, int(img.shape[0]*1), int(img.shape[1]*1))


        prediction_regions=self.do_prediction(patches,img,model_bin)

        res=(prediction_regions[:,:,0]!=0)*1




        img_fin=np.zeros((res.shape[0],res.shape[1],3) )
        res[:,:][res[:,:]==0]=2
        res=res-1
        res=res*255
        img_fin[:,:,0]=res
        img_fin[:,:,1]=res
        img_fin[:,:,2]=res

        session_bin.close()
        del model_bin
        del session_bin
        gc.collect()
        #plt.imshow(img_fin[:,:,0])
        #plt.show()
        return img_fin

    def get_slopes_and_deskew_new(self, contours,contours_par,textline_mask_tot,image_page_rotated,boxes,slope_deskew):
        num_cores =cpu_count()
        queue_of_all_params = Queue()

        processes = []
        nh=np.linspace(0, len(boxes), num_cores+1)
        indexes_by_text_con=np.array(range(len(contours_par)))

        for i in range(num_cores):
            boxes_per_process=boxes[int(nh[i]):int(nh[i+1])]
            contours_per_process=contours[int(nh[i]):int(nh[i+1])]
            contours_par_per_process=contours_par[int(nh[i]):int(nh[i+1])]
            indexes_text_con_per_process=indexes_by_text_con[int(nh[i]):int(nh[i+1])]

            processes.append(Process(target=self.do_work_of_slopes_new, args=(queue_of_all_params,  boxes_per_process, textline_mask_tot, contours_per_process,contours_par_per_process,indexes_text_con_per_process,image_page_rotated,slope_deskew)))

        for i in range(num_cores):
            processes[i].start()

        slopes = []
        all_found_texline_polygons=[]
        all_found_text_regions=[]
        all_found_text_regions_par=[]
        boxes=[]
        all_box_coord=[]
        all_index_text_con=[]

        for i in range(num_cores):
            list_all_par=queue_of_all_params.get(True)

            slopes_for_sub_process=list_all_par[0]
            polys_for_sub_process=list_all_par[1]
            boxes_for_sub_process=list_all_par[2]
            contours_for_subprocess=list_all_par[3]
            contours_par_for_subprocess=list_all_par[4]
            boxes_coord_for_subprocess=list_all_par[5]
            indexes_for_subprocess=list_all_par[6]

            for j in range(len(slopes_for_sub_process)):
                slopes.append(slopes_for_sub_process[j])
                all_found_texline_polygons.append(polys_for_sub_process[j])
                boxes.append(boxes_for_sub_process[j])
                all_found_text_regions.append(contours_for_subprocess[j])
                all_found_text_regions_par.append(contours_par_for_subprocess[j])
                all_box_coord.append(boxes_coord_for_subprocess[j])
                all_index_text_con.append(indexes_for_subprocess[j])

        for i in range(num_cores):
            processes[i].join()
        #print(slopes,'slopes')
        return slopes,all_found_texline_polygons,boxes,all_found_text_regions,all_found_text_regions_par,all_box_coord,all_index_text_con


    def get_slopes_and_deskew_new_curved(self, contours,contours_par,textline_mask_tot,image_page_rotated,boxes,mask_texts_only,num_col,scale_par,slope_deskew):
        num_cores =cpu_count()
        queue_of_all_params = Queue()

        processes = []
        nh=np.linspace(0, len(boxes), num_cores+1)
        indexes_by_text_con=np.array(range(len(contours_par)))


        for i in range(num_cores):
            boxes_per_process=boxes[int(nh[i]):int(nh[i+1])]
            contours_per_process=contours[int(nh[i]):int(nh[i+1])]
            contours_par_per_process=contours_par[int(nh[i]):int(nh[i+1])]
            indexes_text_con_per_process=indexes_by_text_con[int(nh[i]):int(nh[i+1])]

            processes.append(Process(target=self.do_work_of_slopes_new_curved, args=(queue_of_all_params,  boxes_per_process, textline_mask_tot, contours_per_process,contours_par_per_process,image_page_rotated,mask_texts_only,num_col,scale_par,indexes_text_con_per_process,slope_deskew)))

        for i in range(num_cores):
            processes[i].start()

        slopes = []
        all_found_texline_polygons=[]
        all_found_text_regions=[]
        all_found_text_regions_par=[]
        boxes=[]
        all_box_coord=[]
        all_index_text_con=[]

        for i in range(num_cores):
            list_all_par=queue_of_all_params.get(True)

            polys_for_sub_process=list_all_par[0]
            boxes_for_sub_process=list_all_par[1]
            contours_for_subprocess=list_all_par[2]
            contours_par_for_subprocess=list_all_par[3]
            boxes_coord_for_subprocess=list_all_par[4]
            indexes_for_subprocess=list_all_par[5]
            slopes_for_sub_process=list_all_par[6]

            for j in range(len(polys_for_sub_process)):
                slopes.append(slopes_for_sub_process[j])
                all_found_texline_polygons.append(polys_for_sub_process[j])
                boxes.append(boxes_for_sub_process[j])
                all_found_text_regions.append(contours_for_subprocess[j])
                all_found_text_regions_par.append(contours_par_for_subprocess[j])
                all_box_coord.append(boxes_coord_for_subprocess[j])
                all_index_text_con.append(indexes_for_subprocess[j])

        for i in range(num_cores):
            processes[i].join()
        #print(slopes,'slopes')
        return all_found_texline_polygons,boxes,all_found_text_regions,all_found_text_regions_par,all_box_coord,all_index_text_con,slopes



    def do_work_of_slopes_new_curved(self,queue_of_all_params,boxes_text,textline_mask_tot_ea,contours_per_process,contours_par_per_process,image_page_rotated,mask_texts_only,num_col,scale_par,indexes_r_con_per_pro,slope_deskew):
        slopes_per_each_subprocess = []
        bounding_box_of_textregion_per_each_subprocess=[]
        textlines_rectangles_per_each_subprocess=[]
        contours_textregion_per_each_subprocess=[]
        contours_textregion_par_per_each_subprocess=[]
        all_box_coord_per_process=[]
        index_by_text_region_contours=[]
        slope_biggest=0

        textline_cnt_seperated=np.zeros(textline_mask_tot_ea.shape)


        for mv in range(len(boxes_text)):

            all_text_region_raw=textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
            all_text_region_raw=all_text_region_raw.astype(np.uint8)

            img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]

            ##img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)

            #plt.imshow(img_int_p)
            #plt.show()

            if img_int_p.shape[0]/img_int_p.shape[1]<0.1:

                slopes_per_each_subprocess.append(0)

                slope_first=0
                slope_for_all=[slope_deskew][0]

            else:


                try:
                    textline_con,hierachy=self.return_contours_of_image(img_int_p)
                    textline_con_fil=self.filter_contours_area_of_image(img_int_p,textline_con,hierachy,max_area=1,min_area=0.0008)
                    y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)

                    sigma_des=int(  y_diff_mean * (4./40.0) )

                    if sigma_des<1:
                        sigma_des=1

                    img_int_p[img_int_p>0]=1
                    #slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
                    slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)

                    if abs(slope_for_all)<0.5:
                        slope_for_all=[slope_deskew][0]
                    #old method
                    #slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
                    #text_patch_processed=textline_contours_postprocessing(gada)

                except:
                    slope_for_all=999


                ##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)

                if slope_for_all==999:
                    slope_for_all=[slope_deskew][0]
                ##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
                    ##slope_for_all=slope_biggest
                ##elif slope_for_all==999:
                    ##slope_for_all=slope_biggest
                slopes_per_each_subprocess.append(slope_for_all)



            index_by_text_region_contours.append(indexes_r_con_per_pro[mv])

            crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
            if abs(slope_for_all)<45:


                #all_box_coord.append(crop_coor)

                textline_region_in_image=np.zeros(textline_mask_tot_ea.shape)
                cnt_o_t_max=contours_par_per_process[mv]

                x, y, w, h = cv2.boundingRect(cnt_o_t_max)


                mask_biggest=np.zeros(mask_texts_only.shape)
                mask_biggest=cv2.fillPoly(mask_biggest, pts =[cnt_o_t_max], color=(1,1,1))

                mask_region_in_patch_region=mask_biggest[y:y+h,x:x+w]


                textline_biggest_region=mask_biggest*textline_mask_tot_ea

                #print(slope_for_all,'slope_for_all')
                textline_rotated_seperated=self.seperate_lines_new2(textline_biggest_region[y:y+h,x:x+w],0,num_col,slope_for_all)


                #new line added
                ##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
                textline_rotated_seperated[mask_region_in_patch_region[:,:]!=1]=0
                #till here

                textline_cnt_seperated[y:y+h,x:x+w]=textline_rotated_seperated
                textline_region_in_image[y:y+h,x:x+w]=textline_rotated_seperated

                #plt.imshow(textline_region_in_image)
                #plt.show()

                #plt.imshow(textline_cnt_seperated)
                #plt.show()




                pixel_img=1
                cnt_textlines_in_image=self.return_contours_of_interested_textline(textline_region_in_image,pixel_img)



                textlines_cnt_per_region=[]
                for jjjj in range(len(cnt_textlines_in_image)):
                    mask_biggest2=np.zeros(mask_texts_only.shape)
                    mask_biggest2=cv2.fillPoly(mask_biggest2, pts =[cnt_textlines_in_image[jjjj]], color=(1,1,1))
                    if num_col+1==1:
                        mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=5)
                    else:

                        mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=4)

                    pixel_img=1

                    mask_biggest2=self.resize_image(mask_biggest2,int(mask_biggest2.shape[0]*scale_par) ,int(mask_biggest2.shape[1]*scale_par)  )

                    cnt_textlines_in_image_ind=self.return_contours_of_interested_textline(mask_biggest2,pixel_img)

                    try:
                        #textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
                        textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
                    except:
                        pass

            else:
                slope_first=0
                add_boxes_coor_into_textlines=True
                textlines_cnt_per_region=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])


            textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
            #all_found_texline_polygons.append(cnt_clean_rot)
            bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv] )



            contours_textregion_per_each_subprocess.append(contours_per_process[mv])
            contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
            all_box_coord_per_process.append(crop_coor)

        queue_of_all_params.put([textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess,contours_textregion_par_per_each_subprocess,all_box_coord_per_process,index_by_text_region_contours,slopes_per_each_subprocess])
    def do_work_of_slopes_new(self,queue_of_all_params,boxes_text,textline_mask_tot_ea,contours_per_process,contours_par_per_process,indexes_r_con_per_pro,image_page_rotated,slope_deskew):

        slopes_per_each_subprocess = []
        bounding_box_of_textregion_per_each_subprocess=[]
        textlines_rectangles_per_each_subprocess=[]
        contours_textregion_per_each_subprocess=[]
        contours_textregion_par_per_each_subprocess=[]
        all_box_coord_per_process=[]
        index_by_text_region_contours=[]
        slope_biggest=0



        for mv in range(len(boxes_text)):

            crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)

            #all_box_coord.append(crop_coor)


            denoised=None
            all_text_region_raw=textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
            all_text_region_raw=all_text_region_raw.astype(np.uint8)

            img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]

            img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)

            if img_int_p.shape[0]/img_int_p.shape[1]<0.1:

                slopes_per_each_subprocess.append(0)

                slope_for_all=[slope_deskew][0]

                all_text_region_raw=textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
                ###cnt_clean_rot=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)

                textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)

                index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
                #all_found_texline_polygons.append(cnt_clean_rot)
                bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv] )
            else:

                try:
                    textline_con,hierachy=self.return_contours_of_image(img_int_p)
                    textline_con_fil=self.filter_contours_area_of_image(img_int_p,textline_con,hierachy,max_area=1,min_area=0.00008)

                    y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)

                    sigma_des=int(  y_diff_mean * (4./40.0) )


                    if sigma_des<1:
                        sigma_des=1

                    img_int_p[img_int_p>0]=1
                    #slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
                    slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)

                    if abs(slope_for_all)<=0.5:
                        slope_for_all=[slope_deskew][0]


                except:
                    slope_for_all=999


                ##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)

                if slope_for_all==999:
                    slope_for_all=[slope_deskew][0]
                ##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
                    ##slope_for_all=slope_biggest
                ##elif slope_for_all==999:
                    ##slope_for_all=slope_biggest
                slopes_per_each_subprocess.append(slope_for_all)


                slope_first=0

                mask_only_con_region=np.zeros(textline_mask_tot_ea.shape)
                mask_only_con_region=cv2.fillPoly(mask_only_con_region,pts=[contours_par_per_process[mv]],color=(1,1,1))


                #plt.imshow(mask_only_con_region)
                #plt.show()
                all_text_region_raw=np.copy(textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ])
                mask_only_con_region=mask_only_con_region[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]


                ##plt.imshow(textline_mask_tot_ea)
                ##plt.show()
                ##plt.imshow(all_text_region_raw)
                ##plt.show()


                ##plt.imshow(mask_only_con_region)
                ##plt.show()

                all_text_region_raw[mask_only_con_region==0]=0
                ###cnt_clean_rot=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)

                textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
                index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
                #all_found_texline_polygons.append(cnt_clean_rot)
                bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv] )




            contours_textregion_per_each_subprocess.append(contours_per_process[mv])
            contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
            all_box_coord_per_process.append(crop_coor)

        queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess,contours_textregion_par_per_each_subprocess,all_box_coord_per_process,index_by_text_region_contours])


    def get_text_region_contours_and_boxes(self, image):
        rgb_class_of_texts = (1, 1, 1)
        mask_texts = np.all(image == rgb_class_of_texts, axis=-1)

        image = np.repeat(mask_texts[:, :, np.newaxis], 3, axis=2) * 255
        image = image.astype(np.uint8)

        image = cv2.morphologyEx(image, cv2.MORPH_OPEN, self.kernel)
        image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, self.kernel)


        imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        _, thresh = cv2.threshold(imgray, 0, 255, 0)

        contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
        self.boxes = []

        for jj in range(len(main_contours)):
            x, y, w, h = cv2.boundingRect(main_contours[jj])
            self.boxes.append([x, y, w, h])


        return main_contours
    def boosting_headers_by_longshot_region_segmentation(self,textregion_pre_p,textregion_pre_np,img_only_text):
        textregion_pre_p_org=np.copy(textregion_pre_p)
        # 4 is drop capitals
        headers_in_longshot= (textregion_pre_np[:,:,0]==2)*1
        #headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1
        textregion_pre_p[:,:,0][(headers_in_longshot[:,:] ==1) & (textregion_pre_p[:,:,0] !=4)]=2
        textregion_pre_p[:,:,0][textregion_pre_p[:,:,0]==1]=0
        #textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7)  & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted
        textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7)  & (textregion_pre_p[:,:,0]!=4) & (textregion_pre_p[:,:,0]!=2)]=1
        return textregion_pre_p

    def boosting_text_only_regions_by_header(self,textregion_pre_np,img_only_text):
        result= (( img_only_text[:,:]==1) | (textregion_pre_np[:,:,0]==2) ) *1
        return result

    def get_all_image_patches_coordination(self, image_page):
        self.all_box_coord=[]
        for jk in range(len(self.boxes)):
            _,crop_coor=self.crop_image_inside_box(self.boxes[jk],image_page)
            self.all_box_coord.append(crop_coor)


    def textline_contours(self, img,patches,scaler_h,scaler_w):



        if patches:
            model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
        if not patches:
            model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir_np)

        ##img = self.otsu_copy(img)
        img = img.astype(np.uint8)

        img_org=np.copy(img)
        img_h=img_org.shape[0]
        img_w=img_org.shape[1]

        img= self.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= self.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= self.resize_image(prediction_textline_longshot, img_h, img_w)


        #scaler_w=1.5
        #scaler_h=1.5
        #patches=True
        #img= self.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= self.resize_image(prediction_textline_streched, img_h, img_w)

        ##plt.imshow(prediction_textline_streched[:,:,0])
        ##plt.show()

        #sys.exit()
        session_textline.close()

        del model_textline
        del session_textline
        del img
        del img_org

        gc.collect()
        return prediction_textline[:,:,0],prediction_textline_longshot_true_size[:,:,0]

    def get_textlines_for_each_textregions(self, textline_mask_tot, boxes):
        textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1)
        self.area_of_cropped = []
        self.all_text_region_raw = []
        for jk in range(len(boxes)):
            crop_img, crop_coor = self.crop_image_inside_box(boxes[jk],
                                                             np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
            crop_img=crop_img.astype(np.uint8)
            self.all_text_region_raw.append(crop_img[:, :, 0])
            self.area_of_cropped.append(crop_img.shape[0] * crop_img.shape[1])
    def seperate_lines_new_inside_teils2(self, img_patch, thetha):


        (h, w) = img_patch.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. * np.pi
        rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
        #contour_text_interest_copy = contour_text_interest.copy()

        #x_cont = contour_text_interest[:, 0, 0]
        #y_cont = contour_text_interest[:, 0, 1]
        #x_cont = x_cont - np.min(x_cont)
        #y_cont = y_cont - np.min(y_cont)

        x_min_cont = 0
        x_max_cont = img_patch.shape[1]
        y_min_cont = 0
        y_max_cont = img_patch.shape[0]

        xv = np.linspace(x_min_cont, x_max_cont, 1000)

        textline_patch_sum_along_width = img_patch.sum(axis=1)

        first_nonzero = 0  # (next((i for i, x in enumerate(mada_n) if x), None))

        y = textline_patch_sum_along_width[:]  # [first_nonzero:last_nonzero]
        y_padded = np.zeros(len(y) + 40)
        y_padded[20:len(y) + 20] = y
        x = np.array(range(len(y)))

        peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
        if 1>0:

            try:

                y_padded_smoothed_e= gaussian_filter1d(y_padded, 2)
                y_padded_up_to_down_e=-y_padded+np.max(y_padded)
                y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40)
                y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e
                y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2)


                peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
                peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
                neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])

                arg_neg_must_be_deleted= np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3  ]
                diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)



                arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
                arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]


                peaks_new=peaks_e[:]
                peaks_neg_new=peaks_neg_e[:]

                clusters_to_be_deleted=[]
                if len(arg_diff_cluster)>0:

                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
                    for i in range(len(arg_diff_cluster)-1):
                        clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])


                if len(clusters_to_be_deleted)>0:
                    peaks_new_extra=[]
                    for m in range(len(clusters_to_be_deleted)):
                        min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]])
                        max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]])
                        peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
                        for m1 in range(len(clusters_to_be_deleted[m])):
                            peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]]
                            peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]]

                            peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]]
                    peaks_new_tot=[]
                    for i1 in peaks_new:
                        peaks_new_tot.append(i1)
                    for i1 in peaks_new_extra:
                        peaks_new_tot.append(i1)
                    peaks_new_tot=np.sort(peaks_new_tot)


                else:
                    peaks_new_tot=peaks_e[:]


                textline_con,hierachy=self.return_contours_of_image(img_patch)
                textline_con_fil=self.filter_contours_area_of_image(img_patch,textline_con,hierachy,max_area=1,min_area=0.0008)
                y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil)

                sigma_gaus=int(  y_diff_mean * (7./40.0) )
                #print(sigma_gaus,'sigma_gaus')
            except:
                sigma_gaus=12
            if sigma_gaus<3:
                sigma_gaus=3
            #print(sigma_gaus,'sigma')


        y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus)
        y_padded_up_to_down=-y_padded+np.max(y_padded)
        y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40)
        y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down
        y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)


        peaks, _ = find_peaks(y_padded_smoothed, height=0)
        peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)


        peaks_new=peaks[:]
        peaks_neg_new=peaks_neg[:]

        try:
            neg_peaks_max=np.max(y_padded_smoothed[peaks])


            arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.24  ]


            diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)



            arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
            arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]



            clusters_to_be_deleted=[]


            if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0:

                clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
                for i in range(len(arg_diff_cluster)-1):
                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
                clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
            elif len(arg_neg_must_be_deleted)>=2 and len(arg_diff_cluster)==0:
                clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])


            if  len(arg_neg_must_be_deleted)==1:
                clusters_to_be_deleted.append(arg_neg_must_be_deleted)


            if len(clusters_to_be_deleted)>0:
                peaks_new_extra=[]
                for m in range(len(clusters_to_be_deleted)):
                    min_cluster=np.min(peaks[clusters_to_be_deleted[m]])
                    max_cluster=np.max(peaks[clusters_to_be_deleted[m]])
                    peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
                    for m1 in range(len(clusters_to_be_deleted[m])):
                        peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]]
                        peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]]

                        peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[clusters_to_be_deleted[m][m1]]]
                peaks_new_tot=[]
                for i1 in peaks_new:
                    peaks_new_tot.append(i1)
                for i1 in peaks_new_extra:
                    peaks_new_tot.append(i1)
                peaks_new_tot=np.sort(peaks_new_tot)

                #plt.plot(y_padded_up_to_down_padded)
                #plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
                #plt.show()

                #plt.plot(y_padded_up_to_down_padded)
                #plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
                #plt.show()

                #plt.plot(y_padded_smoothed)
                #plt.plot(peaks,y_padded_smoothed[peaks],'*')
                #plt.show()

                #plt.plot(y_padded_smoothed)
                #plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
                #plt.show()

                peaks=peaks_new_tot[:]
                peaks_neg=peaks_neg_new[:]
        except:
            pass








        else:
            peaks_new_tot=peaks[:]
            peaks=peaks_new_tot[:]
            peaks_neg=peaks_neg_new[:]

        mean_value_of_peaks=np.mean(y_padded_smoothed[peaks])
        std_value_of_peaks=np.std(y_padded_smoothed[peaks])
        peaks_values=y_padded_smoothed[peaks]


        ###peaks_neg = peaks_neg - 20 - 20
        ###peaks = peaks - 20
        peaks_neg_true=peaks_neg[:]
        peaks_pos_true=peaks[:]

        if len(peaks_neg_true)>0:
            peaks_neg_true=np.array(peaks_neg_true)

            peaks_neg_true = peaks_neg_true - 20 - 20


            #print(peaks_neg_true)
            for i in range(len(peaks_neg_true)):
                img_patch[peaks_neg_true[i]-6:peaks_neg_true[i]+6,:]=0


        else:
            pass

        if len(peaks_pos_true)>0:
            peaks_pos_true=np.array(peaks_pos_true)
            peaks_pos_true = peaks_pos_true - 20

            for i in range(len(peaks_pos_true)):
                ##img_patch[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
                img_patch[peaks_pos_true[i]-6:peaks_pos_true[i]+6,:]=1
        else:
            pass
        kernel = np.ones((5,5),np.uint8)

        #img_patch = cv2.erode(img_patch,kernel,iterations = 3)
        #######################img_patch = cv2.erode(img_patch,kernel,iterations = 2)
        img_patch = cv2.erode(img_patch,kernel,iterations = 1)
        return img_patch

    def seperate_lines_new_inside_teils(self, img_path, thetha):
        (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. * np.pi
        rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])


        x_min_cont = 0
        x_max_cont = img_path.shape[1]
        y_min_cont = 0
        y_max_cont = img_path.shape[0]

        xv = np.linspace(x_min_cont, x_max_cont, 1000)

        mada_n = img_path.sum(axis=1)

        ##plt.plot(mada_n)
        ##plt.show()

        first_nonzero = 0  # (next((i for i, x in enumerate(mada_n) if x), None))

        y = mada_n[:]  # [first_nonzero:last_nonzero]
        y_help = np.zeros(len(y) + 40)
        y_help[20:len(y) + 20] = y
        x = np.array(range(len(y)))

        peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
        if len(peaks_real)<=2 and len(peaks_real)>1:
            sigma_gaus=10
        else:
            sigma_gaus=5


        z= gaussian_filter1d(y_help, sigma_gaus)
        zneg_rev=-y_help+np.max(y_help)
        zneg=np.zeros(len(zneg_rev)+40)
        zneg[20:len(zneg_rev)+20]=zneg_rev
        zneg= gaussian_filter1d(zneg, sigma_gaus)

        peaks, _ = find_peaks(z, height=0)
        peaks_neg, _ = find_peaks(zneg, height=0)

        for nn in range(len(peaks_neg)):
            if peaks_neg[nn]>len(z)-1:
                peaks_neg[nn]=len(z)-1
            if peaks_neg[nn]<0:
                peaks_neg[nn]=0

        diff_peaks=np.abs( np.diff(peaks_neg) )

        cut_off=20
        peaks_neg_true=[]
        forest=[]

        for i in range(len(peaks_neg)):
            if i==0:
                forest.append(peaks_neg[i])
            if i<(len(peaks_neg)-1):
                if diff_peaks[i]<=cut_off:
                    forest.append(peaks_neg[i+1])
                if diff_peaks[i]>cut_off:
                    #print(forest[np.argmin(z[forest]) ] )
                    if not self.isNaN(forest[np.argmin(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 self.isNaN(forest[np.argmin(z[forest]) ]):
                    peaks_neg_true.append(forest[np.argmin(z[forest]) ])


        diff_peaks_pos=np.abs( np.diff(peaks) )

        cut_off=20
        peaks_pos_true=[]
        forest=[]

        for i in range(len(peaks)):
            if i==0:
                forest.append(peaks[i])
            if i<(len(peaks)-1):
                if diff_peaks_pos[i]<=cut_off:
                    forest.append(peaks[i+1])
                if diff_peaks_pos[i]>cut_off:
                    #print(forest[np.argmin(z[forest]) ] )
                    if not self.isNaN(forest[np.argmax(z[forest]) ]):
                        peaks_pos_true.append(forest[np.argmax(z[forest]) ])
                    forest=[]
                    forest.append(peaks[i+1])
            if i==(len(peaks)-1):
                #print(print(forest[np.argmin(z[forest]) ] ))
                if not self.isNaN(forest[np.argmax(z[forest]) ]):
                    peaks_pos_true.append(forest[np.argmax(z[forest]) ])

        #print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss')

        if len(peaks_neg_true)>0:
            peaks_neg_true=np.array(peaks_neg_true)
            """
            #plt.figure(figsize=(40,40))
            #plt.subplot(1,2,1)
            #plt.title('Textline segmentation von Textregion')
            #plt.imshow(img_path)
            #plt.xlabel('X')
            #plt.ylabel('Y')
            #plt.subplot(1,2,2)
            #plt.title('Dichte entlang X')
            #base = pyplot.gca().transData
            #rot = transforms.Affine2D().rotate_deg(90)
            #plt.plot(zneg,np.array(range(len(zneg))))
            #plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
            #plt.gca().invert_yaxis()

            #plt.xlabel('Dichte')
            #plt.ylabel('Y')
            ##plt.plot([0,len(y)], [grenze,grenze])
            #plt.show()
            """
            peaks_neg_true = peaks_neg_true - 20 - 20


            #print(peaks_neg_true)
            for i in range(len(peaks_neg_true)):
                img_path[peaks_neg_true[i]-6:peaks_neg_true[i]+6,:]=0


        else:
            pass

        if len(peaks_pos_true)>0:
            peaks_pos_true=np.array(peaks_pos_true)
            peaks_pos_true = peaks_pos_true - 20

            for i in range(len(peaks_pos_true)):
                img_path[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
        else:
            pass
        kernel = np.ones((5,5),np.uint8)

        #img_path = cv2.erode(img_path,kernel,iterations = 3)
        img_path = cv2.erode(img_path,kernel,iterations = 2)
        return img_path



    def seperate_lines_new(self, img_path, thetha,num_col):

        if num_col==1:
            num_patches=int(img_path.shape[1]/200.)
        else:
            num_patches=int(img_path.shape[1]/100.)
        #num_patches=int(img_path.shape[1]/200.)
        if num_patches==0:
            num_patches=1
        (h, w) = img_path.shape[:2]
        center = (w // 2, h // 2)
        M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
        x_d = M[0, 2]
        y_d = M[1, 2]

        thetha = thetha / 180. * np.pi
        rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])


        x_min_cont = 0
        x_max_cont = img_path.shape[1]
        y_min_cont = 0
        y_max_cont = img_path.shape[0]

        xv = np.linspace(x_min_cont, x_max_cont, 1000)

        mada_n = img_path.sum(axis=1)

        ##plt.plot(mada_n)
        ##plt.show()
        first_nonzero = 0  # (next((i for i, x in enumerate(mada_n) if x), None))

        y = mada_n[:]  # [first_nonzero:last_nonzero]
        y_help = np.zeros(len(y) + 40)
        y_help[20:len(y) + 20] = y
        x = np.array(range(len(y)))

        peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
        if len(peaks_real)<=2 and len(peaks_real)>1:
            sigma_gaus=10
        else:
            sigma_gaus=6


        z= gaussian_filter1d(y_help, sigma_gaus)
        zneg_rev=-y_help+np.max(y_help)
        zneg=np.zeros(len(zneg_rev)+40)
        zneg[20:len(zneg_rev)+20]=zneg_rev
        zneg= gaussian_filter1d(zneg, sigma_gaus)

        peaks, _ = find_peaks(z, height=0)
        peaks_neg, _ = find_peaks(zneg, height=0)


        for nn in range(len(peaks_neg)):
            if peaks_neg[nn]>len(z)-1:
                peaks_neg[nn]=len(z)-1
            if peaks_neg[nn]<0:
                peaks_neg[nn]=0

        diff_peaks=np.abs( np.diff(peaks_neg) )
        cut_off=20
        peaks_neg_true=[]
        forest=[]

        for i in range(len(peaks_neg)):
            if i==0:
                forest.append(peaks_neg[i])
            if i<(len(peaks_neg)-1):
                if diff_peaks[i]<=cut_off:
                    forest.append(peaks_neg[i+1])
                if diff_peaks[i]>cut_off:
                    #print(forest[np.argmin(z[forest]) ] )
                    if not self.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 self.isNaN(forest[np.argmin(z[forest]) ]):

                    peaks_neg_true.append(forest[np.argmin(z[forest]) ])



        peaks_neg_true=np.array(peaks_neg_true)

        """
        #plt.figure(figsize=(40,40))
        #plt.subplot(1,2,1)
        #plt.title('Textline segmentation von Textregion')
        #plt.imshow(img_path)
        #plt.xlabel('X')
        #plt.ylabel('Y')
        #plt.subplot(1,2,2)
        #plt.title('Dichte entlang X')
        #base = pyplot.gca().transData
        #rot = transforms.Affine2D().rotate_deg(90)
        #plt.plot(zneg,np.array(range(len(zneg))))
        #plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
        #plt.gca().invert_yaxis()

        #plt.xlabel('Dichte')
        #plt.ylabel('Y')
        ##plt.plot([0,len(y)], [grenze,grenze])
        #plt.show()
        """

        peaks_neg_true = peaks_neg_true - 20 - 20
        peaks = peaks - 20

        #dis_up=peaks_neg_true[14]-peaks_neg_true[0]
        #dis_down=peaks_neg_true[18]-peaks_neg_true[14]

        img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]

        ##plt.imshow(img_patch_ineterst)
        ##plt.show()




        length_x=int(img_path.shape[1]/float(num_patches))
        margin = int(0.04 * length_x)

        width_mid = length_x - 2 * margin





        nxf = img_path.shape[1] / float(width_mid)

        if nxf > int(nxf):
            nxf = int(nxf) + 1
        else:
            nxf = int(nxf)

        slopes_tile_wise=[]
        for i in range(nxf):
            if i == 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x
            elif i > 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x


            if index_x_u > img_path.shape[1]:
                index_x_u = img_path.shape[1]
                index_x_d = img_path.shape[1] - length_x




            #img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
            img_xline=img_patch_ineterst[:,index_x_d:index_x_u]


            sigma=2
            try:
                slope_xline=self.return_deskew_slop(img_xline,sigma)
            except:
                slope_xline=0
            slopes_tile_wise.append(slope_xline)
            #print(slope_xline,'xlineeee')
            img_line_rotated=self.rotate_image(img_xline,slope_xline)
            img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1

        """

        xline=np.linspace(0,img_path.shape[1],nx)
        slopes_tile_wise=[]

        for ui in range( nx-1 ):
            img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]


            ##plt.imshow(img_xline)
            ##plt.show()

            sigma=3
            try:
                slope_xline=self.return_deskew_slop(img_xline,sigma)
            except:
                slope_xline=0
            slopes_tile_wise.append(slope_xline)
            print(slope_xline,'xlineeee')
            img_line_rotated=self.rotate_image(img_xline,slope_xline)

            ##plt.imshow(img_line_rotated)
            ##plt.show()
        """

        #dis_up=peaks_neg_true[14]-peaks_neg_true[0]
        #dis_down=peaks_neg_true[18]-peaks_neg_true[14]

        img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]

        img_patch_ineterst_revised=np.zeros(img_patch_ineterst.shape)


        for i in range(nxf):
            if i == 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x
            elif i > 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x


            if index_x_u > img_path.shape[1]:
                index_x_u = img_path.shape[1]
                index_x_d = img_path.shape[1] - length_x

            img_xline=img_patch_ineterst[:,index_x_d:index_x_u]

            img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
            img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]

            img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))

            img_resized[ int( img_int.shape[0]*(.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=self.rotate_image(img_resized,slopes_tile_wise[i])
            img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1



            img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)

            ##plt.imshow(img_patch_seperated)
            ##plt.show()
            img_patch_seperated_returned=self.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]*(.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_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin:length_x - margin]
            img_patch_ineterst_revised[:,index_x_d + margin:index_x_u - margin]=img_patch_seperated_returned_true_size


        """
        for ui in range( nx-1 ):
            img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]


            img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
            img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]

            img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))

            img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
            ##plt.imshow(img_xline)
            ##plt.show()
            img_line_rotated=self.rotate_image(img_resized,slopes_tile_wise[ui])


            #img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)

            img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)

            img_patch_seperated_returned=self.rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
            ##plt.imshow(img_patch_seperated)
            ##plt.show()
            print(img_patch_seperated_returned.shape)
            #plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
            #plt.show()

            img_patch_ineterst_revised[:,int(xline[ui]):int(xline[ui+1])]=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]


        """

        #print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
        ##plt.imshow(img_patch_ineterst_revised)
        ##plt.show()
        return img_patch_ineterst_revised

    def seperate_lines_new2(self, img_path, thetha,num_col,slope_region):

        if num_col==1:
            num_patches=int(img_path.shape[1]/200.)
        else:
            num_patches=int(img_path.shape[1]/140.)
        #num_patches=int(img_path.shape[1]/200.)
        if num_patches==0:
            num_patches=1


        img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]

        #plt.imshow(img_patch_ineterst)
        #plt.show()




        length_x=int(img_path.shape[1]/float(num_patches))
        #margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
        margin = int(0.04 * length_x)
        #print(margin,'margin')
        #if margin<=4:
            #margin = int(0.08 * length_x)

        #margin=0

        width_mid = length_x - 2 * margin





        nxf = img_path.shape[1] / float(width_mid)

        if nxf > int(nxf):
            nxf = int(nxf) + 1
        else:
            nxf = int(nxf)

        slopes_tile_wise=[]
        for i in range(nxf):
            if i == 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x
            elif i > 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x


            if index_x_u > img_path.shape[1]:
                index_x_u = img_path.shape[1]
                index_x_d = img_path.shape[1] - length_x




            #img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
            img_xline=img_patch_ineterst[:,index_x_d:index_x_u]


            sigma=2
            try:
                slope_xline=self.return_deskew_slop(img_xline,sigma)
            except:
                slope_xline=0

            if abs(slope_region)<25 and abs(slope_xline)>25:
                slope_xline=[slope_region][0]
            #if abs(slope_region)>70 and abs(slope_xline)<25:
                #slope_xline=[slope_region][0]
            slopes_tile_wise.append(slope_xline)
            #print(slope_xline,'xlineeee')
            img_line_rotated=self.rotate_image(img_xline,slope_xline)
            img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1


        #print(slopes_tile_wise,'slopes_tile_wise')
        img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]

        img_patch_ineterst_revised=np.zeros(img_patch_ineterst.shape)


        for i in range(nxf):
            if i == 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x
            elif i > 0:
                index_x_d = i * width_mid
                index_x_u = index_x_d + length_x


            if index_x_u > img_path.shape[1]:
                index_x_u = img_path.shape[1]
                index_x_d = img_path.shape[1] - length_x

            img_xline=img_patch_ineterst[:,index_x_d:index_x_u]

            img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
            img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]

            img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))

            img_resized[ int( img_int.shape[0]*(.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=self.rotate_image(img_resized,slopes_tile_wise[i])
            img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1



            img_patch_seperated=self.seperate_lines_new_inside_teils2(img_line_rotated,0)


            img_patch_seperated_returned=self.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]*(.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_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin:length_x - margin]
            img_patch_ineterst_revised[:,index_x_d + margin:index_x_u - margin]=img_patch_seperated_returned_true_size


        #plt.imshow(img_patch_ineterst_revised)
        #plt.show()
        return img_patch_ineterst_revised

    def seperate_lines(self, img_patch, contour_text_interest, thetha,x_help,y_help):


        (h, w) = img_patch.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. * np.pi
        rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
        contour_text_interest_copy = contour_text_interest.copy()

        x_cont = contour_text_interest[:, 0, 0]
        y_cont = contour_text_interest[:, 0, 1]
        x_cont = x_cont - np.min(x_cont)
        y_cont = y_cont - np.min(y_cont)

        x_min_cont = 0
        x_max_cont = img_patch.shape[1]
        y_min_cont = 0
        y_max_cont = img_patch.shape[0]

        xv = np.linspace(x_min_cont, x_max_cont, 1000)

        textline_patch_sum_along_width = img_patch.sum(axis=1)

        first_nonzero = 0  # (next((i for i, x in enumerate(mada_n) if x), None))

        y = textline_patch_sum_along_width[:]  # [first_nonzero:last_nonzero]
        y_padded = np.zeros(len(y) + 40)
        y_padded[20:len(y) + 20] = y
        x = np.array(range(len(y)))

        peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)

        if 1>0:

            try:

                y_padded_smoothed_e= gaussian_filter1d(y_padded, 2)
                y_padded_up_to_down_e=-y_padded+np.max(y_padded)
                y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40)
                y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e
                y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2)


                peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
                peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
                neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])

                arg_neg_must_be_deleted= np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3  ]
                diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)



                arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
                arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]


                peaks_new=peaks_e[:]
                peaks_neg_new=peaks_neg_e[:]

                clusters_to_be_deleted=[]
                if len(arg_diff_cluster)>0:

                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
                    for i in range(len(arg_diff_cluster)-1):
                        clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])


                if len(clusters_to_be_deleted)>0:
                    peaks_new_extra=[]
                    for m in range(len(clusters_to_be_deleted)):
                        min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]])
                        max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]])
                        peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
                        for m1 in range(len(clusters_to_be_deleted[m])):
                            peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]]
                            peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]]

                            peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]]
                    peaks_new_tot=[]
                    for i1 in peaks_new:
                        peaks_new_tot.append(i1)
                    for i1 in peaks_new_extra:
                        peaks_new_tot.append(i1)
                    peaks_new_tot=np.sort(peaks_new_tot)


                else:
                    peaks_new_tot=peaks_e[:]


                textline_con,hierachy=self.return_contours_of_image(img_patch)
                textline_con_fil=self.filter_contours_area_of_image(img_patch,textline_con,hierachy,max_area=1,min_area=0.0008)
                y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil)

                sigma_gaus=int(  y_diff_mean * (7./40.0) )
                #print(sigma_gaus,'sigma_gaus')
            except:
                sigma_gaus=12
            if sigma_gaus<3:
                sigma_gaus=3
            #print(sigma_gaus,'sigma')


        y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus)
        y_padded_up_to_down=-y_padded+np.max(y_padded)
        y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40)
        y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down
        y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)


        peaks, _ = find_peaks(y_padded_smoothed, height=0)
        peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)





        try:
            neg_peaks_max=np.max(y_padded_smoothed[peaks])


            arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42  ]


            diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)



            arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
            arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
        except:
            arg_neg_must_be_deleted=[]
            arg_diff_cluster=[]


        try:
            peaks_new=peaks[:]
            peaks_neg_new=peaks_neg[:]
            clusters_to_be_deleted=[]


            if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0:

                clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
                for i in range(len(arg_diff_cluster)-1):
                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
                clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
            elif len(arg_neg_must_be_deleted)>=2 and len(arg_diff_cluster)==0:
                clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])



            if  len(arg_neg_must_be_deleted)==1:
                clusters_to_be_deleted.append(arg_neg_must_be_deleted)


            if len(clusters_to_be_deleted)>0:
                peaks_new_extra=[]
                for m in range(len(clusters_to_be_deleted)):
                    min_cluster=np.min(peaks[clusters_to_be_deleted[m]])
                    max_cluster=np.max(peaks[clusters_to_be_deleted[m]])
                    peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
                    for m1 in range(len(clusters_to_be_deleted[m])):
                        peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]]
                        peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]]

                        peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[clusters_to_be_deleted[m][m1]]]
                peaks_new_tot=[]
                for i1 in peaks_new:
                    peaks_new_tot.append(i1)
                for i1 in peaks_new_extra:
                    peaks_new_tot.append(i1)
                peaks_new_tot=np.sort(peaks_new_tot)

                ##plt.plot(y_padded_up_to_down_padded)
                ##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
                ##plt.show()

                ##plt.plot(y_padded_up_to_down_padded)
                ##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
                ##plt.show()

                ##plt.plot(y_padded_smoothed)
                ##plt.plot(peaks,y_padded_smoothed[peaks],'*')
                ##plt.show()

                ##plt.plot(y_padded_smoothed)
                ##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
                ##plt.show()

                peaks=peaks_new_tot[:]
                peaks_neg=peaks_neg_new[:]


            else:
                peaks_new_tot=peaks[:]
                peaks=peaks_new_tot[:]
                peaks_neg=peaks_neg_new[:]
        except:
            pass


        mean_value_of_peaks=np.mean(y_padded_smoothed[peaks])
        std_value_of_peaks=np.std(y_padded_smoothed[peaks])
        peaks_values=y_padded_smoothed[peaks]


        peaks_neg = peaks_neg - 20 - 20
        peaks = peaks - 20

        for jj in range(len(peaks_neg)):
            if peaks_neg[jj] > len(x) - 1:
                peaks_neg[jj] = len(x) - 1

        for jj in range(len(peaks)):
            if peaks[jj] > len(x) - 1:
                peaks[jj] = len(x) - 1



        textline_boxes = []
        textline_boxes_rot = []

        if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
            for jj in range(len(peaks)):

                if jj==(len(peaks)-1):
                    dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
                    dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])

                    if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
                        point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)
                    else:
                        point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)

                    point_down_narrow = peaks[jj] + first_nonzero + int(
                        1.4 * dis_to_next_down)  ###-int(dis_to_next_down*1./2)
                else:
                    dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
                    dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])

                    if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
                        point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)
                    else:
                        point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)

                    point_down_narrow = peaks[jj] + first_nonzero + int(
                        1.1 * dis_to_next_down)  ###-int(dis_to_next_down*1./2)



                if point_down_narrow >= img_patch.shape[0]:
                    point_down_narrow = img_patch.shape[0] - 2

                distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
                             for mj in range(len(xv))]
                distances = np.array(distances)

                xvinside = xv[distances >= 0]

                if len(xvinside) == 0:
                    x_min = x_min_cont
                    x_max = x_max_cont
                else:
                    x_min = np.min(xvinside)  # max(x_min_interest,x_min_cont)
                    x_max = np.max(xvinside)  # min(x_max_interest,x_max_cont)

                p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
                p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
                p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
                p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])

                x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
                x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
                x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
                x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d

                if x_min_rot1<0:
                    x_min_rot1=0
                if x_min_rot4<0:
                    x_min_rot4=0
                if point_up_rot1<0:
                    point_up_rot1=0
                if point_up_rot2<0:
                    point_up_rot2=0



                x_min_rot1=x_min_rot1-x_help
                x_max_rot2=x_max_rot2-x_help
                x_max_rot3=x_max_rot3-x_help
                x_min_rot4=x_min_rot4-x_help

                point_up_rot1=point_up_rot1-y_help
                point_up_rot2=point_up_rot2-y_help
                point_down_rot3=point_down_rot3-y_help
                point_down_rot4=point_down_rot4-y_help




                textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                    [int(x_max_rot2), int(point_up_rot2)],
                                                    [int(x_max_rot3), int(point_down_rot3)],
                                                    [int(x_min_rot4), int(point_down_rot4)]]))

                textline_boxes.append(np.array([[int(x_min), int(point_up)],
                                                [int(x_max), int(point_up)],
                                                [int(x_max), int(point_down)],
                                                [int(x_min), int(point_down)]]))

        elif len(peaks) < 1:
            pass

        elif len(peaks) == 1:

            distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True)
                            for mj in range(len(xv))]
            distances = np.array(distances)

            xvinside = xv[distances >= 0]

            if len(xvinside) == 0:
                x_min = x_min_cont
                x_max = x_max_cont
            else:
                x_min = np.min(xvinside)  # max(x_min_interest,x_min_cont)
                x_max = np.max(xvinside)  # min(x_max_interest,x_max_cont)
            #x_min = x_min_cont
            #x_max = x_max_cont

            y_min = y_min_cont
            y_max = y_max_cont

            p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)])
            p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)])
            p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)])
            p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)])

            x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
            x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
            x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
            x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d


            if x_min_rot1<0:
                x_min_rot1=0
            if x_min_rot4<0:
                x_min_rot4=0
            if point_up_rot1<0:
                point_up_rot1=0
            if point_up_rot2<0:
                point_up_rot2=0


            x_min_rot1=x_min_rot1-x_help
            x_max_rot2=x_max_rot2-x_help
            x_max_rot3=x_max_rot3-x_help
            x_min_rot4=x_min_rot4-x_help

            point_up_rot1=point_up_rot1-y_help
            point_up_rot2=point_up_rot2-y_help
            point_down_rot3=point_down_rot3-y_help
            point_down_rot4=point_down_rot4-y_help




            textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                [int(x_max_rot2), int(point_up_rot2)],
                                                [int(x_max_rot3), int(point_down_rot3)],
                                                [int(x_min_rot4), int(point_down_rot4)]]))

            textline_boxes.append(np.array([[int(x_min), int(y_min)],
                                            [int(x_max), int(y_min)],
                                            [int(x_max), int(y_max)],
                                            [int(x_min), int(y_max)]]))



        elif len(peaks) == 2:
            dis_to_next = np.abs(peaks[1] - peaks[0])
            for jj in range(len(peaks)):
                if jj == 0:
                    point_up = 0#peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
                    if point_up < 0:
                        point_up = 1
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
                elif jj == 1:
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
                    if point_down >= img_patch.shape[0]:
                        point_down = img_patch.shape[0] - 2
                    point_up = peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next)

                distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
                             for mj in range(len(xv))]
                distances = np.array(distances)

                xvinside = xv[distances >= 0]

                if len(xvinside) == 0:
                    x_min = x_min_cont
                    x_max = x_max_cont
                else:
                    x_min = np.min(xvinside)
                    x_max = np.max(xvinside)

                p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
                p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
                p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
                p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])

                x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
                x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
                x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
                x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d



                if x_min_rot1<0:
                    x_min_rot1=0
                if x_min_rot4<0:
                    x_min_rot4=0
                if point_up_rot1<0:
                    point_up_rot1=0
                if point_up_rot2<0:
                    point_up_rot2=0

                x_min_rot1=x_min_rot1-x_help
                x_max_rot2=x_max_rot2-x_help
                x_max_rot3=x_max_rot3-x_help
                x_min_rot4=x_min_rot4-x_help

                point_up_rot1=point_up_rot1-y_help
                point_up_rot2=point_up_rot2-y_help
                point_down_rot3=point_down_rot3-y_help
                point_down_rot4=point_down_rot4-y_help




                textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                    [int(x_max_rot2), int(point_up_rot2)],
                                                    [int(x_max_rot3), int(point_down_rot3)],
                                                    [int(x_min_rot4), int(point_down_rot4)]]))

                textline_boxes.append(np.array([[int(x_min), int(point_up)],
                                                [int(x_max), int(point_up)],
                                                [int(x_max), int(point_down)],
                                                [int(x_min), int(point_down)]]))
        else:
            for jj in range(len(peaks)):

                if jj == 0:
                    dis_to_next = peaks[jj + 1] - peaks[jj]
                    # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
                    point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
                    if point_up < 0:
                        point_up = 1
                    # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next)
                elif jj == len(peaks) - 1:
                    dis_to_next = peaks[jj] - peaks[jj - 1]
                    # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.7 * dis_to_next)
                    if point_down >= img_patch.shape[0]:
                        point_down = img_patch.shape[0] - 2
                    # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
                    point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
                else:
                    dis_to_next_down = peaks[jj + 1] - peaks[jj]
                    dis_to_next_up = peaks[jj] - peaks[jj - 1]

                    point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next_up)
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down)

                distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
                             for mj in range(len(xv))]
                distances = np.array(distances)

                xvinside = xv[distances >= 0]

                if len(xvinside) == 0:
                    x_min = x_min_cont
                    x_max = x_max_cont
                else:
                    x_min = np.min(xvinside)  # max(x_min_interest,x_min_cont)
                    x_max = np.max(xvinside)  # min(x_max_interest,x_max_cont)

                p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
                p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
                p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
                p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])

                x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
                x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
                x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
                x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d

                if x_min_rot1<0:
                    x_min_rot1=0
                if x_min_rot4<0:
                    x_min_rot4=0
                if point_up_rot1<0:
                    point_up_rot1=0
                if point_up_rot2<0:
                    point_up_rot2=0


                x_min_rot1=x_min_rot1-x_help
                x_max_rot2=x_max_rot2-x_help
                x_max_rot3=x_max_rot3-x_help
                x_min_rot4=x_min_rot4-x_help

                point_up_rot1=point_up_rot1-y_help
                point_up_rot2=point_up_rot2-y_help
                point_down_rot3=point_down_rot3-y_help
                point_down_rot4=point_down_rot4-y_help




                textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                    [int(x_max_rot2), int(point_up_rot2)],
                                                    [int(x_max_rot3), int(point_down_rot3)],
                                                    [int(x_min_rot4), int(point_down_rot4)]]))

                textline_boxes.append(np.array([[int(x_min), int(point_up)],
                                                [int(x_max), int(point_up)],
                                                [int(x_max), int(point_down)],
                                                [int(x_min), int(point_down)]]))


        return peaks, textline_boxes_rot

    def return_rotated_contours(self,slope,img_patch):
            dst = self.rotate_image(img_patch, slope)
            dst = dst.astype(np.uint8)
            dst = dst[:, :, 0]
            dst[dst != 0] = 1

            imgray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
            _, thresh = cv2.threshold(imgray, 0, 255, 0)
            thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
            thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
            contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            return contours

    def seperate_lines_vertical_cont(self, img_patch, contour_text_interest, thetha,box_ind,add_boxes_coor_into_textlines):
        kernel = np.ones((5, 5), np.uint8)
        pixel=255
        min_area=0
        max_area=1


        if len(img_patch.shape)==3:
            cnts_images=(img_patch[:,:,0]==pixel)*1
        else:
            cnts_images=(img_patch[:,:]==pixel)*1
        cnts_images=cnts_images.astype(np.uint8)
        cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=max_area,min_area=min_area)


        cont_final=[]
        ###print(add_boxes_coor_into_textlines,'ikki')
        for i in range(len(contours_imgs)):
            img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
            img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))



            img_contour = img_contour.astype(np.uint8)

            img_contour = cv2.dilate(img_contour, kernel, iterations=4)
            imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
            _, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
            contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

            ##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
                ##0]
            ##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
            ##if add_boxes_coor_into_textlines:
                ##print(np.shape(contours_text_rot[0]),'sjppo')
                ##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0]
                ##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1]
            cont_final.append(contours_text_rot[0])

        ##print(cont_final,'nadizzzz')
        return None,cont_final

    def seperate_lines_vertical(self, img_patch, contour_text_interest, thetha):


        thetha=thetha+90

        (h, w) = img_patch.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. * np.pi
        rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
        contour_text_interest_copy = contour_text_interest.copy()

        x_cont = contour_text_interest[:, 0, 0]
        y_cont = contour_text_interest[:, 0, 1]
        x_cont = x_cont - np.min(x_cont)
        y_cont = y_cont - np.min(y_cont)

        x_min_cont = 0
        x_max_cont = img_patch.shape[1]
        y_min_cont = 0
        y_max_cont = img_patch.shape[0]

        xv = np.linspace(x_min_cont, x_max_cont, 1000)

        textline_patch_sum_along_width = img_patch.sum(axis=0)

        first_nonzero = 0  # (next((i for i, x in enumerate(mada_n) if x), None))

        y = textline_patch_sum_along_width[:]  # [first_nonzero:last_nonzero]
        y_padded = np.zeros(len(y) + 40)
        y_padded[20:len(y) + 20] = y
        x = np.array(range(len(y)))

        peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
        if 1>0:

            try:

                y_padded_smoothed_e= gaussian_filter1d(y_padded, 2)
                y_padded_up_to_down_e=-y_padded+np.max(y_padded)
                y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40)
                y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e
                y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2)


                peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
                peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
                neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])

                arg_neg_must_be_deleted= np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3  ]
                diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)



                arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
                arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]


                peaks_new=peaks_e[:]
                peaks_neg_new=peaks_neg_e[:]

                clusters_to_be_deleted=[]
                if len(arg_diff_cluster)>0:

                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
                    for i in range(len(arg_diff_cluster)-1):
                        clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
                    clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])


                if len(clusters_to_be_deleted)>0:
                    peaks_new_extra=[]
                    for m in range(len(clusters_to_be_deleted)):
                        min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]])
                        max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]])
                        peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
                        for m1 in range(len(clusters_to_be_deleted[m])):
                            peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]]
                            peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]]

                            peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]]
                    peaks_new_tot=[]
                    for i1 in peaks_new:
                        peaks_new_tot.append(i1)
                    for i1 in peaks_new_extra:
                        peaks_new_tot.append(i1)
                    peaks_new_tot=np.sort(peaks_new_tot)


                else:
                    peaks_new_tot=peaks_e[:]


                textline_con,hierachy=self.return_contours_of_image(img_patch)
                textline_con_fil=self.filter_contours_area_of_image(img_patch,textline_con,hierachy,max_area=1,min_area=0.0008)
                y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil)

                sigma_gaus=int(  y_diff_mean * (7./40.0) )
                #print(sigma_gaus,'sigma_gaus')
            except:
                sigma_gaus=12
            if sigma_gaus<3:
                sigma_gaus=3
            #print(sigma_gaus,'sigma')


        y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus)
        y_padded_up_to_down=-y_padded+np.max(y_padded)
        y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40)
        y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down
        y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)


        peaks, _ = find_peaks(y_padded_smoothed, height=0)
        peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)


        #plt.plot(y_padded_up_to_down_padded)
        #plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
        #plt.title('negs')
        #plt.show()



        #plt.plot(y_padded_smoothed)
        #plt.plot(peaks,y_padded_smoothed[peaks],'*')
        #plt.title('poss')
        #plt.show()



        neg_peaks_max=np.max(y_padded_up_to_down_padded[peaks_neg])


        arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42  ]


        diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)



        arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
        arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]


        peaks_new=peaks[:]
        peaks_neg_new=peaks_neg[:]
        clusters_to_be_deleted=[]


        if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0:

            clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
            for i in range(len(arg_diff_cluster)-1):
                clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
            clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
        elif len(arg_neg_must_be_deleted)>=2 and len(arg_diff_cluster)==0:
            clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])



        if  len(arg_neg_must_be_deleted)==1:
            clusters_to_be_deleted.append(arg_neg_must_be_deleted)


        if len(clusters_to_be_deleted)>0:
            peaks_new_extra=[]
            for m in range(len(clusters_to_be_deleted)):
                min_cluster=np.min(peaks[clusters_to_be_deleted[m]])
                max_cluster=np.max(peaks[clusters_to_be_deleted[m]])
                peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
                for m1 in range(len(clusters_to_be_deleted[m])):
                    peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]]
                    peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]]

                    peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[clusters_to_be_deleted[m][m1]]]
            peaks_new_tot=[]
            for i1 in peaks_new:
                peaks_new_tot.append(i1)
            for i1 in peaks_new_extra:
                peaks_new_tot.append(i1)
            peaks_new_tot=np.sort(peaks_new_tot)


            peaks=peaks_new_tot[:]
            peaks_neg=peaks_neg_new[:]


        else:
            peaks_new_tot=peaks[:]
            peaks=peaks_new_tot[:]
            peaks_neg=peaks_neg_new[:]

        mean_value_of_peaks=np.mean(y_padded_smoothed[peaks])
        std_value_of_peaks=np.std(y_padded_smoothed[peaks])
        peaks_values=y_padded_smoothed[peaks]


        peaks_neg = peaks_neg - 20 - 20
        peaks = peaks - 20

        for jj in range(len(peaks_neg)):
            if peaks_neg[jj] > len(x) - 1:
                peaks_neg[jj] = len(x) - 1

        for jj in range(len(peaks)):
            if peaks[jj] > len(x) - 1:
                peaks[jj] = len(x) - 1



        textline_boxes = []
        textline_boxes_rot = []

        if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
            #print('11')
            for jj in range(len(peaks)):

                if jj==(len(peaks)-1):
                    dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
                    dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])

                    if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
                        point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down =x_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)
                    else:
                        point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down =x_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)

                    point_down_narrow = peaks[jj] + first_nonzero + int(
                        1.4 * dis_to_next_down)  ###-int(dis_to_next_down*1./2)
                else:
                    dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
                    dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])

                    if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
                        point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)
                    else:
                        point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up)  ##+int(dis_to_next_up*1./4.0)
                        point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down)  ###-int(dis_to_next_down*1./4.0)

                    point_down_narrow = peaks[jj] + first_nonzero + int(
                        1.1 * dis_to_next_down)  ###-int(dis_to_next_down*1./2)



                if point_down_narrow >= img_patch.shape[0]:
                    point_down_narrow = img_patch.shape[0] - 2

                distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
                             for mj in range(len(xv))]
                distances = np.array(distances)

                xvinside = xv[distances >= 0]

                if len(xvinside) == 0:
                    x_min = x_min_cont
                    x_max = x_max_cont
                else:
                    x_min = np.min(xvinside)  # max(x_min_interest,x_min_cont)
                    x_max = np.max(xvinside)  # min(x_max_interest,x_max_cont)

                p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
                p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
                p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
                p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])

                x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
                x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
                x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
                x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d

                if x_min_rot1<0:
                    x_min_rot1=0
                if x_min_rot4<0:
                    x_min_rot4=0
                if point_up_rot1<0:
                    point_up_rot1=0
                if point_up_rot2<0:
                    point_up_rot2=0

                textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                    [int(x_max_rot2), int(point_up_rot2)],
                                                    [int(x_max_rot3), int(point_down_rot3)],
                                                    [int(x_min_rot4), int(point_down_rot4)]]))

                textline_boxes.append(np.array([[int(x_min), int(point_up)],
                                                [int(x_max), int(point_up)],
                                                [int(x_max), int(point_down)],
                                                [int(x_min), int(point_down)]]))

        elif len(peaks) < 1:
            pass

        elif len(peaks) == 1:
            x_min = x_min_cont
            x_max = x_max_cont

            y_min = y_min_cont
            y_max = y_max_cont

            p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
            p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
            p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
            p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])

            x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
            x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
            x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
            x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d


            if x_min_rot1<0:
                x_min_rot1=0
            if x_min_rot4<0:
                x_min_rot4=0
            if point_up_rot1<0:
                point_up_rot1=0
            if point_up_rot2<0:
                point_up_rot2=0

            textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                [int(x_max_rot2), int(point_up_rot2)],
                                                [int(x_max_rot3), int(point_down_rot3)],
                                                [int(x_min_rot4), int(point_down_rot4)]]))

            textline_boxes.append(np.array([[int(x_min), int(y_min)],
                                            [int(x_max), int(y_min)],
                                            [int(x_max), int(y_max)],
                                            [int(x_min), int(y_max)]]))



        elif len(peaks) == 2:
            dis_to_next = np.abs(peaks[1] - peaks[0])
            for jj in range(len(peaks)):
                if jj == 0:
                    point_up = 0#peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
                    if point_up < 0:
                        point_up = 1
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
                elif jj == 1:
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
                    if point_down >= img_patch.shape[0]:
                        point_down = img_patch.shape[0] - 2
                    point_up = peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next)

                distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
                             for mj in range(len(xv))]
                distances = np.array(distances)

                xvinside = xv[distances >= 0]

                if len(xvinside) == 0:
                    x_min = x_min_cont
                    x_max = x_max_cont
                else:
                    x_min = np.min(xvinside)
                    x_max = np.max(xvinside)

                p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
                p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
                p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
                p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])

                x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
                x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
                x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
                x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d

                if x_min_rot1<0:
                    x_min_rot1=0
                if x_min_rot4<0:
                    x_min_rot4=0
                if point_up_rot1<0:
                    point_up_rot1=0
                if point_up_rot2<0:
                    point_up_rot2=0

                textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                    [int(x_max_rot2), int(point_up_rot2)],
                                                    [int(x_max_rot3), int(point_down_rot3)],
                                                    [int(x_min_rot4), int(point_down_rot4)]]))

                textline_boxes.append(np.array([[int(x_min), int(point_up)],
                                                [int(x_max), int(point_up)],
                                                [int(x_max), int(point_down)],
                                                [int(x_min), int(point_down)]]))
        else:
            for jj in range(len(peaks)):

                if jj == 0:
                    dis_to_next = peaks[jj + 1] - peaks[jj]
                    # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
                    point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
                    if point_up < 0:
                        point_up = 1
                    # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next)
                elif jj == len(peaks) - 1:
                    dis_to_next = peaks[jj] - peaks[jj - 1]
                    # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.7 * dis_to_next)
                    if point_down >= img_patch.shape[0]:
                        point_down = img_patch.shape[0] - 2
                    # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
                    point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
                else:
                    dis_to_next_down = peaks[jj + 1] - peaks[jj]
                    dis_to_next_up = peaks[jj] - peaks[jj - 1]

                    point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next_up)
                    point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down)

                distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
                             for mj in range(len(xv))]
                distances = np.array(distances)

                xvinside = xv[distances >= 0]

                if len(xvinside) == 0:
                    x_min = x_min_cont
                    x_max = x_max_cont
                else:
                    x_min = np.min(xvinside)  # max(x_min_interest,x_min_cont)
                    x_max = np.max(xvinside)  # min(x_max_interest,x_max_cont)

                p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
                p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
                p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
                p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])

                x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
                x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
                x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
                x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d


                if x_min_rot1<0:
                    x_min_rot1=0
                if x_min_rot4<0:
                    x_min_rot4=0
                if point_up_rot1<0:
                    point_up_rot1=0
                if point_up_rot2<0:
                    point_up_rot2=0



                textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
                                                    [int(x_max_rot2), int(point_up_rot2)],
                                                    [int(x_max_rot3), int(point_down_rot3)],
                                                    [int(x_min_rot4), int(point_down_rot4)]]))

                textline_boxes.append(np.array([[int(x_min), int(point_up)],
                                                [int(x_max), int(point_up)],
                                                [int(x_max), int(point_down)],
                                                [int(x_min), int(point_down)]]))


        return peaks, textline_boxes_rot

    def textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind,slope_first,add_boxes_coor_into_textlines=False):

        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')
        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 = self.rotate_image(textline_mask_help, slope)
            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()

            contour_text_copy = contour_text_interest.copy()

            contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
                0]
            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()

            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 = self.rotate_image(img_contour_help, slope)





            #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()

            img_contour_rot = img_contour_rot.astype(np.uint8)
            #dst_help = dst_help.astype(np.uint8)
            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 = self.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)
            else:
                _, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope,x_help,y_help)



        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 = self.filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.003)

        textline_maskt = textline_mask[:, :, 0]
        textline_maskt[textline_maskt != 0] = 1

        peaks_point, _ = self.seperate_lines(textline_maskt, contour_interest, slope_new)

        mean_dis = np.mean(np.diff(peaks_point))

        len_x = thresh.shape[1]

        slope_lines = []
        contours_slope_new = []

        for kk in range(len(main_contours)):

            if len(main_contours[kk].shape)==2:
                xminh=np.min(main_contours[kk][:,0])
                xmaxh=np.max(main_contours[kk][:,0])

                yminh=np.min(main_contours[kk][:,1])
                ymaxh=np.max(main_contours[kk][:,1])
            elif len(main_contours[kk].shape)==3:
                xminh=np.min(main_contours[kk][:,0,0])
                xmaxh=np.max(main_contours[kk][:,0,0])

                yminh=np.min(main_contours[kk][:,0,1])
                ymaxh=np.max(main_contours[kk][:,0,1])


            if ymaxh - yminh <= mean_dis and (
                    xmaxh - xminh) >= 0.3 * len_x:  # xminh>=0.05*len_x and xminh<=0.4*len_x and xmaxh<=0.95*len_x and xmaxh>=0.6*len_x:
                contours_slope_new.append(main_contours[kk])

                rows, cols = thresh.shape[:2]
                [vx, vy, x, y] = cv2.fitLine(main_contours[kk], cv2.DIST_L2, 0, 0.01, 0.01)

                slope_lines.append((vy / vx) / np.pi * 180)

            if len(slope_lines) >= 2:

                slope = np.mean(slope_lines)  # slope_true/np.pi*180
            else:
                slope = 999

        else:
            slope = 0

        return slope
    def return_contours_of_image(self,image):

        if len(image.shape)==2:
            image=np.repeat(image[:, :, np.newaxis], 3, axis=2)
            image=image.astype(np.uint8)
        else:
            image=image.astype(np.uint8)
        imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        ret, thresh = cv2.threshold(imgray, 0, 255, 0)
        contours,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        return contours,hierachy

    def find_contours_mean_y_diff(self,contours_main):
        M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
        cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
        return np.mean( np.diff( np.sort( np.array(cy_main) ) ) )


    def isNaN(self,num):
        return num != num

    def find_num_col_olddd(self,regions_without_seperators,sigma_,multiplier=3.8 ):
        regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)

        meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]

        first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
        last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))

        last_nonzero=len(regions_without_seperators_0)-last_nonzero


        y=regions_without_seperators_0#[first_nonzero:last_nonzero]

        y_help=np.zeros(len(y)+20)

        y_help[10:len(y)+10]=y

        x=np.array( range(len(y)) )




        zneg_rev=-y_help+np.max(y_help)

        zneg=np.zeros(len(zneg_rev)+20)

        zneg[10:len(zneg_rev)+10]=zneg_rev

        z=gaussian_filter1d(y, sigma_)
        zneg= gaussian_filter1d(zneg, sigma_)


        peaks_neg, _ = find_peaks(zneg, height=0)
        peaks, _ = find_peaks(z, height=0)

        peaks_neg=peaks_neg-10-10



        last_nonzero=last_nonzero-0#100
        first_nonzero=first_nonzero+0#+100

        peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]

        peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]

        interest_pos=z[peaks]

        interest_pos=interest_pos[interest_pos>10]

        interest_neg=z[peaks_neg]


        if interest_neg[0]<0.1:
            interest_neg=interest_neg[1:]
        if interest_neg[len(interest_neg)-1]<0.1:
            interest_neg=interest_neg[:len(interest_neg)-1]



        min_peaks_pos=np.min(interest_pos)
        min_peaks_neg=0#np.min(interest_neg)


        dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
        grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0

        interest_neg_fin=interest_neg#[(interest_neg<grenze)]
        peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
        interest_neg_fin=interest_neg#[(interest_neg<grenze)]

        num_col=(len(interest_neg_fin))+1


        p_l=0
        p_u=len(y)-1
        p_m=int(len(y)/2.)
        p_g_l=int(len(y)/3.)
        p_g_u=len(y)-int(len(y)/3.)


        diff_peaks=np.abs( np.diff(peaks_neg_fin) )
        diff_peaks_annormal=diff_peaks[diff_peaks<30]


        return interest_neg_fin

    def find_num_col_deskew(self,regions_without_seperators,sigma_,multiplier=3.8 ):
        regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)

        meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]

        first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
        last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))

        last_nonzero=len(regions_without_seperators_0)-last_nonzero


        y=regions_without_seperators_0#[first_nonzero:last_nonzero]

        y_help=np.zeros(len(y)+20)

        y_help[10:len(y)+10]=y

        x=np.array( range(len(y)) )




        zneg_rev=-y_help+np.max(y_help)

        zneg=np.zeros(len(zneg_rev)+20)

        zneg[10:len(zneg_rev)+10]=zneg_rev

        z=gaussian_filter1d(y, sigma_)
        zneg= gaussian_filter1d(zneg, sigma_)


        peaks_neg, _ = find_peaks(zneg, height=0)
        peaks, _ = find_peaks(z, height=0)

        peaks_neg=peaks_neg-10-10

        #print(np.std(z),'np.std(z)np.std(z)np.std(z)')

        ##plt.plot(z)
        ##plt.show()

        ##plt.imshow(regions_without_seperators)
        ##plt.show()
        """
        last_nonzero=last_nonzero-0#100
        first_nonzero=first_nonzero+0#+100

        peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]

        peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
        """
        interest_pos=z[peaks]

        interest_pos=interest_pos[interest_pos>10]

        interest_neg=z[peaks_neg]

        min_peaks_pos=np.mean(interest_pos)
        min_peaks_neg=0#np.min(interest_neg)

        dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
        #print(interest_pos)
        grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0

        interest_neg_fin=interest_neg[(interest_neg<grenze)]
        peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
        interest_neg_fin=interest_neg[(interest_neg<grenze)]

        """
        if interest_neg[0]<0.1:
            interest_neg=interest_neg[1:]
        if interest_neg[len(interest_neg)-1]<0.1:
            interest_neg=interest_neg[:len(interest_neg)-1]



        min_peaks_pos=np.min(interest_pos)
        min_peaks_neg=0#np.min(interest_neg)


        dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
        grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
        """
        #interest_neg_fin=interest_neg#[(interest_neg<grenze)]
        #peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
        #interest_neg_fin=interest_neg#[(interest_neg<grenze)]

        num_col=(len(interest_neg_fin))+1


        p_l=0
        p_u=len(y)-1
        p_m=int(len(y)/2.)
        p_g_l=int(len(y)/3.)
        p_g_u=len(y)-int(len(y)/3.)


        diff_peaks=np.abs( np.diff(peaks_neg_fin) )
        diff_peaks_annormal=diff_peaks[diff_peaks<30]

        #print(len(interest_neg_fin),np.mean(interest_neg_fin))
        return interest_neg_fin,np.std(z)
    def return_deskew_slop(self,img_patch_org,sigma_des,main_page=False):


        if main_page and self.dir_of_all is not None:


            plt.figure(figsize=(70,40))
            plt.rcParams['font.size']='50'
            plt.subplot(1,2,1)
            plt.imshow(img_patch_org)
            plt.subplot(1,2,2)
            plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
            plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
            plt.ylabel('Height',fontsize=60)
            plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
            plt.gca().invert_yaxis()

            plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_density_of_textline.png'))
        #print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')

        #img_patch_org=self.resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))

        #print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')

        img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
        img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]

        img_resized=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) ))



        img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]


        if main_page and img_patch_org.shape[1]>img_patch_org.shape[0]:

            #plt.imshow(img_resized)
            #plt.show()
            angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])

            res=[]
            num_of_peaks=[]
            index_cor=[]
            var_res=[]

            indexer=0
            for rot in angels:
                img_rot=self.rotate_image(img_resized,rot)
                #plt.imshow(img_rot)
                #plt.show()
                img_rot[img_rot!=0]=1
                #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))


                #neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                #print(var_spectrum,'var_spectrum')
                try:
                    neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                    #print(rot,var_spectrum,'var_spectrum')
                    res_me=np.mean(neg_peaks)
                    if res_me==0:
                        res_me=1000000000000000000000
                    else:
                        pass

                    res_num=len(neg_peaks)
                except:
                    res_me=1000000000000000000000
                    res_num=0
                    var_spectrum=0
                if self.isNaN(res_me):
                    pass
                else:
                    res.append( res_me )
                    var_res.append(var_spectrum)
                    num_of_peaks.append( res_num )
                    index_cor.append(indexer)
                indexer=indexer+1


            try:
                var_res=np.array(var_res)

                ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
            except:
                ang_int=0


            angels=np.linspace(ang_int-22.5,ang_int+22.5,100)

            res=[]
            num_of_peaks=[]
            index_cor=[]
            var_res=[]

            indexer=0
            for rot in angels:
                img_rot=self.rotate_image(img_resized,rot)
                ##plt.imshow(img_rot)
                ##plt.show()
                img_rot[img_rot!=0]=1
                #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
                try:
                    neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                    #print(indexer,'indexer')
                    res_me=np.mean(neg_peaks)
                    if res_me==0:
                        res_me=1000000000000000000000
                    else:
                        pass

                    res_num=len(neg_peaks)
                except:
                    res_me=1000000000000000000000
                    res_num=0
                    var_spectrum=0
                if self.isNaN(res_me):
                    pass
                else:
                    res.append( res_me )
                    var_res.append(var_spectrum)
                    num_of_peaks.append( res_num )
                    index_cor.append(indexer)
                indexer=indexer+1



            try:
                var_res=np.array(var_res)

                ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
            except:
                ang_int=0

        elif main_page and img_patch_org.shape[1]<=img_patch_org.shape[0]:

            #plt.imshow(img_resized)
            #plt.show()
            angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])

            res=[]
            num_of_peaks=[]
            index_cor=[]
            var_res=[]

            indexer=0
            for rot in angels:
                img_rot=self.rotate_image(img_resized,rot)
                #plt.imshow(img_rot)
                #plt.show()
                img_rot[img_rot!=0]=1
                #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))


                #neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                #print(var_spectrum,'var_spectrum')
                try:
                    neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                    #print(rot,var_spectrum,'var_spectrum')
                    res_me=np.mean(neg_peaks)
                    if res_me==0:
                        res_me=1000000000000000000000
                    else:
                        pass

                    res_num=len(neg_peaks)
                except:
                    res_me=1000000000000000000000
                    res_num=0
                    var_spectrum=0
                if self.isNaN(res_me):
                    pass
                else:
                    res.append( res_me )
                    var_res.append(var_spectrum)
                    num_of_peaks.append( res_num )
                    index_cor.append(indexer)
                indexer=indexer+1

            if self.dir_of_all is not None:
                print('galdi?')
                plt.figure(figsize=(60,30))
                plt.rcParams['font.size']='50'
                plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
                plt.xlabel('angle',fontsize=50)
                plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)

                plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))]  ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
                plt.legend(loc='best')
                plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_rotation_angle.png'))


            try:
                var_res=np.array(var_res)

                ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
            except:
                ang_int=0


            early_slope_edge=11
            if abs(ang_int)>early_slope_edge and ang_int<0:

                angels=np.linspace(-90,-12,100)

                res=[]
                num_of_peaks=[]
                index_cor=[]
                var_res=[]

                indexer=0
                for rot in angels:
                    img_rot=self.rotate_image(img_resized,rot)
                    ##plt.imshow(img_rot)
                    ##plt.show()
                    img_rot[img_rot!=0]=1
                    #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
                    try:
                        neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                        #print(indexer,'indexer')
                        res_me=np.mean(neg_peaks)
                        if res_me==0:
                            res_me=1000000000000000000000
                        else:
                            pass

                        res_num=len(neg_peaks)
                    except:
                        res_me=1000000000000000000000
                        res_num=0
                        var_spectrum=0
                    if self.isNaN(res_me):
                        pass
                    else:
                        res.append( res_me )
                        var_res.append(var_spectrum)
                        num_of_peaks.append( res_num )
                        index_cor.append(indexer)
                    indexer=indexer+1


                try:
                    var_res=np.array(var_res)

                    ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
                except:
                    ang_int=0

            elif abs(ang_int)>early_slope_edge and ang_int>0:

                angels=np.linspace(90,12,100)

                res=[]
                num_of_peaks=[]
                index_cor=[]
                var_res=[]

                indexer=0
                for rot in angels:
                    img_rot=self.rotate_image(img_resized,rot)
                    ##plt.imshow(img_rot)
                    ##plt.show()
                    img_rot[img_rot!=0]=1
                    #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
                    try:
                        neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                        #print(indexer,'indexer')
                        res_me=np.mean(neg_peaks)
                        if res_me==0:
                            res_me=1000000000000000000000
                        else:
                            pass

                        res_num=len(neg_peaks)
                    except:
                        res_me=1000000000000000000000
                        res_num=0
                        var_spectrum=0
                    if self.isNaN(res_me):
                        pass
                    else:
                        res.append( res_me )
                        var_res.append(var_spectrum)
                        num_of_peaks.append( res_num )
                        index_cor.append(indexer)
                    indexer=indexer+1


                try:
                    var_res=np.array(var_res)

                    ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
                except:
                    ang_int=0
        else:


            angels=np.linspace(-25,25,60)

            res=[]
            num_of_peaks=[]
            index_cor=[]
            var_res=[]

            indexer=0
            for rot in angels:
                img_rot=self.rotate_image(img_resized,rot)
                #plt.imshow(img_rot)
                #plt.show()
                img_rot[img_rot!=0]=1
                #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))


                #neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                #print(var_spectrum,'var_spectrum')
                try:
                    neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                    #print(rot,var_spectrum,'var_spectrum')
                    res_me=np.mean(neg_peaks)
                    if res_me==0:
                        res_me=1000000000000000000000
                    else:
                        pass

                    res_num=len(neg_peaks)
                except:
                    res_me=1000000000000000000000
                    res_num=0
                    var_spectrum=0
                if self.isNaN(res_me):
                    pass
                else:
                    res.append( res_me )
                    var_res.append(var_spectrum)
                    num_of_peaks.append( res_num )
                    index_cor.append(indexer)
                indexer=indexer+1


            try:
                var_res=np.array(var_res)

                ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
            except:
                ang_int=0

            #print(ang_int,'ang_int')

            early_slope_edge=22
            if abs(ang_int)>early_slope_edge and ang_int<0:

                angels=np.linspace(-90,-25,60)

                res=[]
                num_of_peaks=[]
                index_cor=[]
                var_res=[]

                indexer=0
                for rot in angels:
                    img_rot=self.rotate_image(img_resized,rot)
                    ##plt.imshow(img_rot)
                    ##plt.show()
                    img_rot[img_rot!=0]=1
                    #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
                    try:
                        neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                        #print(indexer,'indexer')
                        res_me=np.mean(neg_peaks)
                        if res_me==0:
                            res_me=1000000000000000000000
                        else:
                            pass

                        res_num=len(neg_peaks)
                    except:
                        res_me=1000000000000000000000
                        res_num=0
                        var_spectrum=0
                    if self.isNaN(res_me):
                        pass
                    else:
                        res.append( res_me )
                        var_res.append(var_spectrum)
                        num_of_peaks.append( res_num )
                        index_cor.append(indexer)
                    indexer=indexer+1


                try:
                    var_res=np.array(var_res)

                    ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
                except:
                    ang_int=0

            elif abs(ang_int)>early_slope_edge and ang_int>0:

                angels=np.linspace(90,25,60)

                res=[]
                num_of_peaks=[]
                index_cor=[]
                var_res=[]

                indexer=0
                for rot in angels:
                    img_rot=self.rotate_image(img_resized,rot)
                    ##plt.imshow(img_rot)
                    ##plt.show()
                    img_rot[img_rot!=0]=1
                    #res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
                    try:
                        neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                        #print(indexer,'indexer')
                        res_me=np.mean(neg_peaks)
                        if res_me==0:
                            res_me=1000000000000000000000
                        else:
                            pass

                        res_num=len(neg_peaks)
                    except:
                        res_me=1000000000000000000000
                        res_num=0
                        var_spectrum=0
                    if self.isNaN(res_me):
                        pass
                    else:
                        res.append( res_me )
                        var_res.append(var_spectrum)
                        num_of_peaks.append( res_num )
                        index_cor.append(indexer)
                    indexer=indexer+1


                try:
                    var_res=np.array(var_res)

                    ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
                except:
                    ang_int=0

        return ang_int

    def return_deskew_slope_new(self,img_patch,sigma_des):
        max_x_y=max(img_patch.shape[0],img_patch.shape[1])

        ##img_patch=self.resize_image(img_patch,max_x_y,max_x_y)



        img_patch_copy=np.zeros((img_patch.shape[0],img_patch.shape[1]))
        img_patch_copy[:,:]=img_patch[:,:]#img_patch_org[:,:,0]


        img_patch_padded=np.zeros((int( max_x_y*(1.4) ) , int( max_x_y*(1.4) ) ))

        img_patch_padded_center_p=int(img_patch_padded.shape[0]/2.)
        len_x_org_patch_half=int(img_patch_copy.shape[1]/2.)
        len_y_org_patch_half=int(img_patch_copy.shape[0]/2.)

        img_patch_padded[img_patch_padded_center_p-len_y_org_patch_half:img_patch_padded_center_p-len_y_org_patch_half+img_patch_copy.shape[0],img_patch_padded_center_p-len_x_org_patch_half:img_patch_padded_center_p-len_x_org_patch_half+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
        #img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
        angles=np.linspace(-25,25,80)

        res=[]
        num_of_peaks=[]
        index_cor=[]
        var_res=[]

        #plt.imshow(img_patch)
        #plt.show()
        indexer=0
        for rot in angles:
            #print(rot,'rot')
            img_rotated=self.rotate_image(img_patch_padded,rot)
            img_rotated[img_rotated!=0]=1

            #plt.imshow(img_rotated)
            #plt.show()

            try:
                neg_peaks,var_spectrum=self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated,sigma_des,20.3  )
                res_me=np.mean(neg_peaks)
                if res_me==0:
                    res_me=1000000000000000000000
                else:
                    pass

                res_num=len(neg_peaks)
            except:
                res_me=1000000000000000000000
                res_num=0
                var_spectrum=0
            if self.isNaN(res_me):
                pass
            else:
                res.append( res_me )
                var_res.append(var_spectrum)
                num_of_peaks.append( res_num )
                index_cor.append(indexer)
            indexer=indexer+1


        try:
            var_res=np.array(var_res)
            #print(var_res)

            ang_int=angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
        except:
            ang_int=0

        if abs(ang_int)>15:
            angles=np.linspace(-90,-50,30)
            res=[]
            num_of_peaks=[]
            index_cor=[]
            var_res=[]

            #plt.imshow(img_patch)
            #plt.show()
            indexer=0
            for rot in angles:
                #print(rot,'rot')
                img_rotated=self.rotate_image(img_patch_padded,rot)
                img_rotated[img_rotated!=0]=1

                #plt.imshow(img_rotated)
                #plt.show()

                try:
                    neg_peaks,var_spectrum=self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated,sigma_des,20.3  )
                    res_me=np.mean(neg_peaks)
                    if res_me==0:
                        res_me=1000000000000000000000
                    else:
                        pass

                    res_num=len(neg_peaks)
                except:
                    res_me=1000000000000000000000
                    res_num=0
                    var_spectrum=0
                if self.isNaN(res_me):
                    pass
                else:
                    res.append( res_me )
                    var_res.append(var_spectrum)
                    num_of_peaks.append( res_num )
                    index_cor.append(indexer)
                indexer=indexer+1


            try:
                var_res=np.array(var_res)
                #print(var_res)

                ang_int=angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
            except:
                ang_int=0

        return ang_int


    def do_work_of_slopes(self,q,poly,box_sub,boxes_per_process,textline_mask_tot,contours_per_process):
        slope_biggest=0
        slopes_sub = []
        boxes_sub_new=[]
        poly_sub=[]
        for mv in range(len(boxes_per_process)):


            crop_img, _ = self.crop_image_inside_box(boxes_per_process[mv],
                                                                        np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
            crop_img=crop_img[:,:,0]
            crop_img=cv2.erode(crop_img,self.kernel,iterations = 2)

            try:
                textline_con,hierachy=self.return_contours_of_image(crop_img)
                textline_con_fil=self.filter_contours_area_of_image(crop_img,textline_con,hierachy,max_area=1,min_area=0.0008)
                y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)

                sigma_des=int(  y_diff_mean * (4./40.0) )

                if sigma_des<1:
                    sigma_des=1

                crop_img[crop_img>0]=1
                slope_corresponding_textregion=self.return_deskew_slop(crop_img,sigma_des)


            except:
                slope_corresponding_textregion=999

            if slope_corresponding_textregion==999:
                slope_corresponding_textregion=slope_biggest
            ##if np.abs(slope_corresponding_textregion)>12.5 and slope_corresponding_textregion!=999:
                ##slope_corresponding_textregion=slope_biggest
            ##elif slope_corresponding_textregion==999:
                ##slope_corresponding_textregion=slope_biggest
            slopes_sub.append(slope_corresponding_textregion)

            cnt_clean_rot = self.textline_contours_postprocessing(crop_img
                                                                                        , slope_corresponding_textregion,
                                                                                        contours_per_process[mv], boxes_per_process[mv])

            poly_sub.append(cnt_clean_rot)
            boxes_sub_new.append(boxes_per_process[mv] )


        q.put(slopes_sub)
        poly.put(poly_sub)
        box_sub.put(boxes_sub_new )

    def get_slopes_and_deskew(self, contours,textline_mask_tot):

        slope_biggest=0#self.return_deskew_slop(img_int_p,sigma_des)

        num_cores = cpu_count()
        q = Queue()
        poly=Queue()
        box_sub=Queue()

        processes = []
        nh=np.linspace(0, len(self.boxes), num_cores+1)


        for i in range(num_cores):
            boxes_per_process=self.boxes[int(nh[i]):int(nh[i+1])]
            contours_per_process=contours[int(nh[i]):int(nh[i+1])]
            processes.append(Process(target=self.do_work_of_slopes, args=(q,poly,box_sub,  boxes_per_process, textline_mask_tot, contours_per_process)))

        for i in range(num_cores):
            processes[i].start()

        self.slopes = []
        self.all_found_texline_polygons=[]
        self.boxes=[]

        for i in range(num_cores):
            slopes_for_sub_process=q.get(True)
            boxes_for_sub_process=box_sub.get(True)
            polys_for_sub_process=poly.get(True)

            for j in range(len(slopes_for_sub_process)):
                self.slopes.append(slopes_for_sub_process[j])
                self.all_found_texline_polygons.append(polys_for_sub_process[j])
                self.boxes.append(boxes_for_sub_process[j])

        for i in range(num_cores):
            processes[i].join()


    def order_of_regions_old(self, textline_mask,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)


        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



        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))])

            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))])



        if contours_main!=None:
            indexer_main=np.array(range(len(contours_main)))


        if contours_main!=None:
            len_main=len(contours_main)
        else:
            len_main=0


        matrix_of_orders=np.zeros((len_main,5))

        matrix_of_orders[:,0]=np.array( range( len_main ) )

        matrix_of_orders[:len_main,1]=1
        matrix_of_orders[len_main:,1]=2

        matrix_of_orders[:len_main,2]=cx_main
        matrix_of_orders[:len_main,3]=cy_main

        matrix_of_orders[:len_main,4]=np.array( range( len_main ) )

        peaks_neg_new=[]
        peaks_neg_new.append(0)
        for iii in range(len(peaks_neg)):
            peaks_neg_new.append(peaks_neg[iii])
        peaks_neg_new.append(textline_mask.shape[0])

        final_indexers_sorted=[]
        for i in range(len(peaks_neg_new)-1):
            top=peaks_neg_new[i]
            down=peaks_neg_new[i+1]

            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))]

            sorted_inside=np.argsort(cxs_in)

            ind_in_int=indexes_in[sorted_inside]

            for j in range(len(ind_in_int)):
                final_indexers_sorted.append(int(ind_in_int[j]) )


        return final_indexers_sorted, matrix_of_orders




    def order_and_id_of_texts_old(self, found_polygons_text_region ,matrix_of_orders ,indexes_sorted ):
        id_of_texts=[]
        order_of_texts=[]
        index_b=0
        for mm in range(len(found_polygons_text_region)):
            id_of_texts.append('r'+str(index_b) )
            index_matrix=matrix_of_orders[:,0][( matrix_of_orders[:,1]==1 ) & ( matrix_of_orders[:,4]==mm ) ]
            order_of_texts.append(np.where(indexes_sorted == index_matrix)[0][0])

            index_b+=1

        order_of_texts
        return order_of_texts, id_of_texts

    def write_into_page_xml_only_textlines(self,contours,page_coord ,all_found_texline_polygons,all_box_coord,dir_of_image):

        found_polygons_text_region=contours



        # create the file structure
        data = ET.Element('PcGts')

        data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
        data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
        data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")



        metadata=ET.SubElement(data,'Metadata')

        author=ET.SubElement(metadata, 'Creator')
        author.text = 'SBB_QURATOR'


        created=ET.SubElement(metadata, 'Created')
        created.text = '2019-06-17T18:15:12'

        changetime=ET.SubElement(metadata, 'LastChange')
        changetime.text = '2019-06-17T18:15:12'



        page=ET.SubElement(data,'Page')

        page.set('imageFilename', self.image_dir)
        page.set('imageHeight',str(self.height_org) )
        page.set('imageWidth',str(self.width_org) )
        page.set('type',"content")
        page.set('readingDirection',"left-to-right")
        page.set('textLineOrder',"top-to-bottom" )



        page_print_sub=ET.SubElement(page, 'PrintSpace')
        coord_page = ET.SubElement(page_print_sub, 'Coords')
        points_page_print=''

        for lmm in range(len(self.cont_page[0])):
            if len(self.cont_page[0][lmm])==2:
                points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
                points_page_print=points_page_print+','
                points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
            else:
                points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
                points_page_print=points_page_print+','
                points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )

            if lmm<(len(self.cont_page[0])-1):
                points_page_print=points_page_print+' '
        coord_page.set('points',points_page_print)


        if len(contours)>0:

            id_indexer=0
            id_indexer_l=0

            for mm in range(len(found_polygons_text_region)):
                textregion=ET.SubElement(page, 'TextRegion')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1

                textregion.set('type','paragraph')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_text_region[mm])):
                    if len(found_polygons_text_region[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_text_region[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)



                for j in range(len(all_found_texline_polygons[mm])):

                    textline=ET.SubElement(textregion, 'TextLine')

                    textline.set('id','l'+str(id_indexer_l))

                    id_indexer_l+=1


                    coord = ET.SubElement(textline, 'Coords')

                    texteq=ET.SubElement(textline, 'TextEquiv')

                    uni=ET.SubElement(texteq, 'Unicode')
                    uni.text = ' '

                    #points = ET.SubElement(coord, 'Points')

                    points_co=''
                    for l in range(len(all_found_texline_polygons[mm][j])):
                        #point = ET.SubElement(coord, 'Point')



                        #point.set('x',str(found_polygons[j][l][0]))
                        #point.set('y',str(found_polygons[j][l][1]))
                        if len(all_found_texline_polygons[mm][j][l])==2:
                            points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0] +page_coord[2])/self.scale_x) )
                            points_co=points_co+','
                            points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1] +page_coord[0])/self.scale_y) )
                        else:
                            points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0] +page_coord[2])/self.scale_x ) )
                            points_co=points_co+','
                            points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1] +page_coord[0])/self.scale_y) )

                        if l<(len(all_found_texline_polygons[mm][j])-1):
                            points_co=points_co+' '
                    #print(points_co)
                    coord.set('points',points_co)

                texteqreg=ET.SubElement(textregion, 'TextEquiv')

                unireg=ET.SubElement(texteqreg, 'Unicode')
                unireg.text = ' '





        #print(dir_of_image)
        print(self.f_name)
        #print(os.path.join(dir_of_image, self.f_name) + ".xml")
        tree = ET.ElementTree(data)
        tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")



    def write_into_page_xml_full(self,contours,contours_h,page_coord,dir_of_image,order_of_texts , id_of_texts,all_found_texline_polygons,all_found_texline_polygons_h,all_box_coord,all_box_coord_h,found_polygons_text_region_img,found_polygons_tables,found_polygons_drop_capitals,found_polygons_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,slopes,slopes_marginals):

        found_polygons_text_region=contours
        found_polygons_text_region_h=contours_h


        # create the file structure
        data = ET.Element('PcGts')

        data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
        data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
        data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")



        metadata=ET.SubElement(data,'Metadata')

        author=ET.SubElement(metadata, 'Creator')
        author.text = 'SBB_QURATOR'


        created=ET.SubElement(metadata, 'Created')
        created.text = '2019-06-17T18:15:12'

        changetime=ET.SubElement(metadata, 'LastChange')
        changetime.text = '2019-06-17T18:15:12'



        page=ET.SubElement(data,'Page')

        page.set('imageFilename', self.image_dir)
        page.set('imageHeight',str(self.height_org) )
        page.set('imageWidth',str(self.width_org) )
        page.set('type',"content")
        page.set('readingDirection',"left-to-right")
        page.set('textLineOrder',"top-to-bottom" )



        page_print_sub=ET.SubElement(page, 'PrintSpace')
        coord_page = ET.SubElement(page_print_sub, 'Coords')
        points_page_print=''

        for lmm in range(len(self.cont_page[0])):
            if len(self.cont_page[0][lmm])==2:
                points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
                points_page_print=points_page_print+','
                points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
            else:
                points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
                points_page_print=points_page_print+','
                points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )

            if lmm<(len(self.cont_page[0])-1):
                points_page_print=points_page_print+' '
        coord_page.set('points',points_page_print)




        if len(contours)>0:
            region_order=ET.SubElement(page, 'ReadingOrder')
            region_order_sub = ET.SubElement(region_order, 'OrderedGroup')

            region_order_sub.set('id',"ro357564684568544579089")

            #args_sort=order_of_texts
            for vj in order_of_texts:
                name="coord_text_"+str(vj)
                name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
                name.set('index',str(order_of_texts[vj]) )
                name.set('regionRef',id_of_texts[vj])

            id_of_marginalia=[]
            indexer_region=len(contours)+len(contours_h)
            for vm in range(len(found_polygons_marginals)):
                id_of_marginalia.append('r'+str(indexer_region))

                name="coord_text_"+str(indexer_region)
                name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
                name.set('index',str(indexer_region) )
                name.set('regionRef','r'+str(indexer_region))
                indexer_region+=1


            id_indexer=0
            id_indexer_l=0

            for mm in range(len(found_polygons_text_region)):
                textregion=ET.SubElement(page, 'TextRegion')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1

                textregion.set('type','paragraph')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_text_region[mm])):
                    if len(found_polygons_text_region[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_text_region[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)





                for j in range(len(all_found_texline_polygons[mm])):

                    textline=ET.SubElement(textregion, 'TextLine')

                    textline.set('id','l'+str(id_indexer_l))

                    id_indexer_l+=1


                    coord = ET.SubElement(textline, 'Coords')

                    texteq=ET.SubElement(textline, 'TextEquiv')

                    uni=ET.SubElement(texteq, 'Unicode')
                    uni.text = ' '

                    #points = ET.SubElement(coord, 'Points')

                    points_co=''
                    for l in range(len(all_found_texline_polygons[mm][j])):
                        #point = ET.SubElement(coord, 'Point')


                        if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
                            #point.set('x',str(found_polygons[j][l][0]))
                            #point.set('y',str(found_polygons[j][l][1]))
                            if len(all_found_texline_polygons[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )

                        if (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and np.abs(slopes[mm])<=45 :
                            if len(all_found_texline_polygons[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
                                                        +page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
                                                        +page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
                                                        +page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
                                                        +page_coord[0])/self.scale_y) )
                        elif (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and np.abs(slopes[mm])>45 :
                            if len(all_found_texline_polygons[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )

                        if l<(len(all_found_texline_polygons[mm][j])-1):
                            points_co=points_co+' '
                    #print(points_co)
                    coord.set('points',points_co)

                texteqreg=ET.SubElement(textregion, 'TextEquiv')

                unireg=ET.SubElement(texteqreg, 'Unicode')
                unireg.text = ' '


        print(len(contours_h))
        if len(contours_h)>0:
            for mm in range(len(found_polygons_text_region_h)):
                textregion=ET.SubElement(page, 'TextRegion')
                try:
                    id_indexer=id_indexer
                    id_indexer_l=id_indexer_l
                except:
                    id_indexer=0
                    id_indexer_l=0
                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1

                textregion.set('type','header')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_text_region_h[mm])):

                    if len(found_polygons_text_region_h[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_text_region_h[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)


                for j in range(len(all_found_texline_polygons_h[mm])):

                    textline=ET.SubElement(textregion, 'TextLine')

                    textline.set('id','l'+str(id_indexer_l))

                    id_indexer_l+=1


                    coord = ET.SubElement(textline, 'Coords')

                    texteq=ET.SubElement(textline, 'TextEquiv')

                    uni=ET.SubElement(texteq, 'Unicode')
                    uni.text = ' '

                    #points = ET.SubElement(coord, 'Points')

                    points_co=''
                    for l in range(len(all_found_texline_polygons_h[mm][j])):
                        #point = ET.SubElement(coord, 'Point')


                        if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
                            #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=='true' or curved_line=='True' or curved_line=='TRUE' :
                            if len(all_found_texline_polygons_h[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
                                                        +page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
                                                        +page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
                                                        +page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
                                                        +page_coord[0])/self.scale_y) )

                        if l<(len(all_found_texline_polygons_h[mm][j])-1):
                            points_co=points_co+' '
                    #print(points_co)
                    coord.set('points',points_co)

                texteqreg=ET.SubElement(textregion, 'TextEquiv')

                unireg=ET.SubElement(texteqreg, 'Unicode')
                unireg.text = ' '






        if len(found_polygons_drop_capitals)>0:
            id_indexer=len(contours_h)+len(contours)+len(found_polygons_marginals)
            for mm in range(len(found_polygons_drop_capitals)):
                textregion=ET.SubElement(page, 'TextRegion')


                #id_indexer_l=id_indexer_l

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1

                textregion.set('type','drop-capital')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_drop_capitals[mm])):

                    if len(found_polygons_drop_capitals[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_drop_capitals[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)


                ##for j in range(len(all_found_texline_polygons_h[mm])):

                    ##textline=ET.SubElement(textregion, 'TextLine')

                    ##textline.set('id','l'+str(id_indexer_l))

                    ##id_indexer_l+=1


                    ##coord = ET.SubElement(textline, 'Coords')

                    ##texteq=ET.SubElement(textline, 'TextEquiv')

                    ##uni=ET.SubElement(texteq, 'Unicode')
                    ##uni.text = ' '

                    ###points = ET.SubElement(coord, 'Points')

                    ##points_co=''
                    ##for l in range(len(all_found_texline_polygons_h[mm][j])):
                        ###point = ET.SubElement(coord, 'Point')


                        ##if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
                            ###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=='true' or curved_line=='True' or curved_line=='TRUE' :
                            ##if len(all_found_texline_polygons_h[mm][j][l])==2:
                                ##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
                                                        ##+page_coord[2])/self.scale_x) )
                                ##points_co=points_co+','
                                ##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
                                                        ##+page_coord[0])/self.scale_y) )
                            ##else:
                                ##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
                                                        ##+page_coord[2])/self.scale_x ) )
                                ##points_co=points_co+','
                                ##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
                                                        ##+page_coord[0])/self.scale_y) )

                        ##if l<(len(all_found_texline_polygons_h[mm][j])-1):
                            ##points_co=points_co+' '
                    ###print(points_co)
                    ####coord.set('points',points_co)

                texteqreg=ET.SubElement(textregion, 'TextEquiv')

                unireg=ET.SubElement(texteqreg, 'Unicode')
                unireg.text = ' '





        try:

            try:
                ###id_indexer=id_indexer
                id_indexer_l=id_indexer_l
            except:
                ###id_indexer=0
                id_indexer_l=0
            for mm in range(len(found_polygons_marginals)):
                textregion=ET.SubElement(page, 'TextRegion')

                textregion.set('id',id_of_marginalia[mm])

                textregion.set('type','marginalia')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_marginals[mm])):
                    if len(found_polygons_marginals[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_marginals[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)


                for j in range(len(all_found_texline_polygons_marginals[mm])):

                    textline=ET.SubElement(textregion, 'TextLine')

                    textline.set('id','l'+str(id_indexer_l))

                    id_indexer_l+=1


                    coord = ET.SubElement(textline, 'Coords')

                    texteq=ET.SubElement(textline, 'TextEquiv')

                    uni=ET.SubElement(texteq, 'Unicode')
                    uni.text = ' '

                    #points = ET.SubElement(coord, 'Points')

                    points_co=''
                    for l in range(len(all_found_texline_polygons_marginals[mm][j])):
                        #point = ET.SubElement(coord, 'Point')


                        if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
                            #point.set('x',str(found_polygons[j][l][0]))
                            #point.set('y',str(found_polygons[j][l][1]))
                            if len(all_found_texline_polygons_marginals[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
                                                        +all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
                                                        +all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
                                                        +all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
                                                        +all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )

                        if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
                            if len(all_found_texline_polygons_marginals[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
                                                        +page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
                                                        +page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
                                                        +page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
                                                        +page_coord[0])/self.scale_y) )

                        if l<(len(all_found_texline_polygons_marginals[mm][j])-1):
                            points_co=points_co+' '
                    #print(points_co)
                    coord.set('points',points_co)


                texteqreg=ET.SubElement(textregion, 'TextEquiv')

                unireg=ET.SubElement(texteqreg, 'Unicode')
                unireg.text = ' '
        except:
            pass

        try:
            id_indexer=len(contours_h)+len(contours)+len(found_polygons_marginals)+len(found_polygons_drop_capitals)
            for mm in range(len(found_polygons_text_region_img)):
                textregion=ET.SubElement(page, 'ImageRegion')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1


                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_text_region_img[mm])):

                    if len(found_polygons_text_region_img[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_text_region_img[mm])-1):
                        points_co=points_co+' '


                coord_text.set('points',points_co)
        except:
            pass


        try:
            for mm in range(len(found_polygons_tables)):
                textregion=ET.SubElement(page, 'TableRegion')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1


                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_tables[mm])):

                    if len(found_polygons_tables[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_tables[mm])-1):
                        points_co=points_co+' '


                coord_text.set('points',points_co)
        except:
            pass




        print(dir_of_image)
        print(self.f_name)
        print(os.path.join(dir_of_image, self.f_name) + ".xml")
        tree = ET.ElementTree(data)
        tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")

    def write_into_page_xml(self,contours,page_coord,dir_of_image,order_of_texts , id_of_texts,all_found_texline_polygons,all_box_coord,found_polygons_text_region_img,found_polygons_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,curved_line,slopes,slopes_marginals):

        found_polygons_text_region=contours
        ##found_polygons_text_region_h=contours_h


        # create the file structure
        data = ET.Element('PcGts')

        data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
        data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
        data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")



        metadata=ET.SubElement(data,'Metadata')

        author=ET.SubElement(metadata, 'Creator')
        author.text = 'SBB_QURATOR'


        created=ET.SubElement(metadata, 'Created')
        created.text = '2019-06-17T18:15:12'

        changetime=ET.SubElement(metadata, 'LastChange')
        changetime.text = '2019-06-17T18:15:12'



        page=ET.SubElement(data,'Page')

        page.set('imageFilename', self.image_dir)
        page.set('imageHeight',str(self.height_org) )
        page.set('imageWidth',str(self.width_org) )
        page.set('type',"content")
        page.set('readingDirection',"left-to-right")
        page.set('textLineOrder',"top-to-bottom" )



        page_print_sub=ET.SubElement(page, 'PrintSpace')
        coord_page = ET.SubElement(page_print_sub, 'Coords')
        points_page_print=''

        for lmm in range(len(self.cont_page[0])):
            if len(self.cont_page[0][lmm])==2:
                points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
                points_page_print=points_page_print+','
                points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
            else:
                points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
                points_page_print=points_page_print+','
                points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )

            if lmm<(len(self.cont_page[0])-1):
                points_page_print=points_page_print+' '
        coord_page.set('points',points_page_print)


        if len(contours)>0:
            region_order=ET.SubElement(page, 'ReadingOrder')
            region_order_sub = ET.SubElement(region_order, 'OrderedGroup')

            region_order_sub.set('id',"ro357564684568544579089")

            indexer_region=0


            for vj in order_of_texts:
                name="coord_text_"+str(vj)
                name = ET.SubElement(region_order_sub, 'RegionRefIndexed')

                name.set('index',str(indexer_region) )
                name.set('regionRef',id_of_texts[vj])
                indexer_region+=1

            id_of_marginalia=[]
            for vm in range(len(found_polygons_marginals)):
                id_of_marginalia.append('r'+str(indexer_region))

                name="coord_text_"+str(indexer_region)
                name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
                name.set('index',str(indexer_region) )
                name.set('regionRef','r'+str(indexer_region))
                indexer_region+=1





            id_indexer=0
            id_indexer_l=0

            for mm in range(len(found_polygons_text_region)):
                textregion=ET.SubElement(page, 'TextRegion')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1

                textregion.set('type','paragraph')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_text_region[mm])):
                    if len(found_polygons_text_region[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_text_region[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)



                for j in range(len(all_found_texline_polygons[mm])):

                    textline=ET.SubElement(textregion, 'TextLine')

                    textline.set('id','l'+str(id_indexer_l))

                    id_indexer_l+=1


                    coord = ET.SubElement(textline, 'Coords')

                    texteq=ET.SubElement(textline, 'TextEquiv')

                    uni=ET.SubElement(texteq, 'Unicode')
                    uni.text = ' '

                    #points = ET.SubElement(coord, 'Points')

                    points_co=''
                    for l in range(len(all_found_texline_polygons[mm][j])):
                        #point = ET.SubElement(coord, 'Point')


                        if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
                            #point.set('x',str(found_polygons[j][l][0]))
                            #point.set('y',str(found_polygons[j][l][1]))
                            if len(all_found_texline_polygons[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )

                        if (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and abs(slopes[mm])<=45 :
                            if len(all_found_texline_polygons[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
                                                        +page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
                                                        +page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
                                                        +page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
                                                        +page_coord[0])/self.scale_y) )

                        elif (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and abs(slopes[mm])>45:
                            if len(all_found_texline_polygons[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
                                                        +all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
                                                        +all_box_coord[mm][0]+page_coord[0])/self.scale_y) )


                        if l<(len(all_found_texline_polygons[mm][j])-1):
                            points_co=points_co+' '
                    #print(points_co)
                    coord.set('points',points_co)

                texteqreg=ET.SubElement(textregion, 'TextEquiv')

                unireg=ET.SubElement(texteqreg, 'Unicode')
                unireg.text = ' '

        ###print(len(contours_h))
        ###if len(contours_h)>0:
            ###for mm in range(len(found_polygons_text_region_h)):
                ###textregion=ET.SubElement(page, 'TextRegion')
                ###try:
                    ###id_indexer=id_indexer
                    ###id_indexer_l=id_indexer_l
                ###except:
                    ###id_indexer=0
                    ###id_indexer_l=0
                ###textregion.set('id','r'+str(id_indexer))
                ###id_indexer+=1

                ###textregion.set('type','header')
                ####if mm==0:
                ####    textregion.set('type','header')
                ####else:
                ####    textregion.set('type','paragraph')
                ###coord_text = ET.SubElement(textregion, 'Coords')

                ###points_co=''
                ###for lmm in range(len(found_polygons_text_region_h[mm])):

                    ###if len(found_polygons_text_region_h[mm][lmm])==2:
                        ###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        ###points_co=points_co+','
                        ###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    ###else:
                        ###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        ###points_co=points_co+','
                        ###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    ###if lmm<(len(found_polygons_text_region_h[mm])-1):
                        ###points_co=points_co+' '
                ####print(points_co)
                ###coord_text.set('points',points_co)

                ###for j in range(len(all_found_texline_polygons_h[mm])):

                    ###textline=ET.SubElement(textregion, 'TextLine')

                    ###textline.set('id','l'+str(id_indexer_l))

                    ###id_indexer_l+=1


                    ###coord = ET.SubElement(textline, 'Coords')

                    ###texteq=ET.SubElement(textline, 'TextEquiv')

                    ###uni=ET.SubElement(texteq, 'Unicode')
                    ###uni.text = ' '

                    ####points = ET.SubElement(coord, 'Points')

                    ###points_co=''
                    ###for l in range(len(all_found_texline_polygons_h[mm][j])):
                        ####point = ET.SubElement(coord, 'Point')



                        ####point.set('x',str(found_polygons[j][l][0]))
                        ####point.set('y',str(found_polygons[j][l][1]))
                        ###if len(all_found_texline_polygons_h[mm][j][l])==2:
                            ###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0] +page_coord[2]
                                                    ###+all_box_coord_h[mm][2])/self.scale_x) )
                            ###points_co=points_co+','
                            ###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1] +page_coord[0]
                                                    ###+all_box_coord_h[mm][0])/self.scale_y) )
                        ###else:
                            ###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0] +page_coord[2]
                                                    ###+all_box_coord_h[mm][2])/self.scale_x ) )
                            ###points_co=points_co+','
                            ###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1] +page_coord[0]
                                                    ###+all_box_coord_h[mm][0])/self.scale_y) )

                        ###if l<(len(all_found_texline_polygons_h[mm][j])-1):
                            ###points_co=points_co+' '
                    ####print(points_co)
                    ###coord.set('points',points_co)

                ###texteqreg=ET.SubElement(textregion, 'TextEquiv')

                ###unireg=ET.SubElement(texteqreg, 'Unicode')
                ###unireg.text = ' '
        try:
            #id_indexer_l=0

            try:
                ###id_indexer=id_indexer
                id_indexer_l=id_indexer_l
            except:
                ###id_indexer=0
                id_indexer_l=0

            for mm in range(len(found_polygons_marginals)):
                textregion=ET.SubElement(page, 'TextRegion')

                textregion.set('id',id_of_marginalia[mm])

                textregion.set('type','marginalia')
                #if mm==0:
                #    textregion.set('type','header')
                #else:
                #    textregion.set('type','paragraph')
                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_marginals[mm])):
                    if len(found_polygons_marginals[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_marginals[mm])-1):
                        points_co=points_co+' '
                #print(points_co)
                coord_text.set('points',points_co)

                for j in range(len(all_found_texline_polygons_marginals[mm])):

                    textline=ET.SubElement(textregion, 'TextLine')

                    textline.set('id','l'+str(id_indexer_l))

                    id_indexer_l+=1


                    coord = ET.SubElement(textline, 'Coords')

                    texteq=ET.SubElement(textline, 'TextEquiv')

                    uni=ET.SubElement(texteq, 'Unicode')
                    uni.text = ' '

                    #points = ET.SubElement(coord, 'Points')

                    points_co=''
                    for l in range(len(all_found_texline_polygons_marginals[mm][j])):
                        #point = ET.SubElement(coord, 'Point')


                        if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
                            #point.set('x',str(found_polygons[j][l][0]))
                            #point.set('y',str(found_polygons[j][l][1]))
                            if len(all_found_texline_polygons_marginals[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
                                                        +all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
                                                        +all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
                                                        +all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
                                                        +all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )

                        if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
                            if len(all_found_texline_polygons_marginals[mm][j][l])==2:
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
                                                        +page_coord[2])/self.scale_x) )
                                points_co=points_co+','
                                points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
                                                        +page_coord[0])/self.scale_y) )
                            else:
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
                                                        +page_coord[2])/self.scale_x ) )
                                points_co=points_co+','
                                points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
                                                        +page_coord[0])/self.scale_y) )

                        if l<(len(all_found_texline_polygons_marginals[mm][j])-1):
                            points_co=points_co+' '
                    #print(points_co)
                    coord.set('points',points_co)
        except:
            pass

        try:

            for mm in range(len(found_polygons_text_region_img)):
                textregion=ET.SubElement(page, 'ImageRegion')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1


                coord_text = ET.SubElement(textregion, 'Coords')
                points_co=''
                for lmm in range(len(found_polygons_text_region_img[mm])):
                    points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm,0,0]+page_coord[2] )/self.scale_x ) )
                    points_co=points_co+','
                    points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm,0,1]+page_coord[0] )/self.scale_y ) )

                    if lmm<(len(found_polygons_text_region_img[mm])-1):
                        points_co=points_co+' '


                coord_text.set('points',points_co)
            ###for mm in range(len(found_polygons_text_region_img)):
                ###textregion=ET.SubElement(page, 'ImageRegion')

                ###textregion.set('id','r'+str(id_indexer))
                ###id_indexer+=1


                ###coord_text = ET.SubElement(textregion, 'Coords')
                ###print(found_polygons_text_region_img[mm])
                ###points_co=''
                ###for lmm in range(len(found_polygons_text_region_img[mm])):
                    ###print(len(found_polygons_text_region_img[mm][lmm]))

                    ###if len(found_polygons_text_region_img[mm][lmm])==2:
                        ###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
                        ###points_co=points_co+','
                        ###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
                    ###else:
                        ###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
                        ###points_co=points_co+','
                        ###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][1]+page_coord[0] )/self.scale_y) )

                    ###if lmm<(len(found_polygons_text_region_img[mm])-1):
                        ###points_co=points_co+' '


                ###coord_text.set('points',points_co)
        except:
            pass


        ####try:
            ####for mm in range(len(found_polygons_tables)):
                ####textregion=ET.SubElement(page, 'TableRegion')

                ####textregion.set('id','r'+str(id_indexer))
                ####id_indexer+=1


                ####coord_text = ET.SubElement(textregion, 'Coords')

                ####points_co=''
                ####for lmm in range(len(found_polygons_tables[mm])):

                    ####if len(found_polygons_tables[mm][lmm])==2:
                        ####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        ####points_co=points_co+','
                        ####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    ####else:
                        ####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        ####points_co=points_co+','
                        ####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    ####if lmm<(len(found_polygons_tables[mm])-1):
                        ####points_co=points_co+' '


                ####coord_text.set('points',points_co)
        ####except:
            ####pass
        """

        try:
            for mm in range(len(found_polygons_drop_capitals)):
                textregion=ET.SubElement(page, 'DropCapitals')

                textregion.set('id','r'+str(id_indexer))
                id_indexer+=1


                coord_text = ET.SubElement(textregion, 'Coords')

                points_co=''
                for lmm in range(len(found_polygons_drop_capitals[mm])):

                    if len(found_polygons_drop_capitals[mm][lmm])==2:
                        points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][0] +page_coord[2])/self.scale_x ) )
                        points_co=points_co+','
                        points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][1] +page_coord[0])/self.scale_y ) )
                    else:
                        points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
                        points_co=points_co+','
                        points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )

                    if lmm<(len(found_polygons_drop_capitals[mm])-1):
                        points_co=points_co+' '


                coord_text.set('points',points_co)
        except:
            pass
        """


        #print(dir_of_image)
        print(self.f_name)
        #print(os.path.join(dir_of_image, self.f_name) + ".xml")
        tree = ET.ElementTree(data)
        tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
        #cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)

    def deskew_region_prediction(self,regions_prediction, slope):
        image_regions_deskewd=np.zeros(regions_prediction[:,:].shape)
        for ind in np.unique(regions_prediction[:,:]):
            interest_reg=(regions_prediction[:,:]==ind)*1
            interest_reg=interest_reg.astype(np.uint8)
            deskewed_new=self.rotate_image(interest_reg,slope)
            deskewed_new=deskewed_new[:,:]
            deskewed_new[deskewed_new!=0]=ind

            image_regions_deskewd=image_regions_deskewd+deskewed_new
        return image_regions_deskewd


    def deskew_erarly(self,textline_mask):
        textline_mask_org=np.copy(textline_mask)
        #print(textline_mask.shape,np.unique(textline_mask),'hizzzzz')
        #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)
        kernel = np.ones((5,5),np.uint8)


        imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)


        ret, thresh = cv2.threshold(imgray, 0, 255, 0)


        contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

        #print(hirarchy)


        commenst_contours=self.filter_contours_area_of_image(thresh,contours,hirarchy,max_area=0.01,min_area=0.003)
        main_contours=self.filter_contours_area_of_image(thresh,contours,hirarchy,max_area=1,min_area=0.003)
        interior_contours=self.filter_contours_area_of_image_interiors(thresh,contours,hirarchy,max_area=1,min_area=0)

        img_comm=np.zeros(thresh.shape)
        img_comm_in=cv2.fillPoly(img_comm, pts =main_contours, color=(255,255,255))
        ###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))


        img_comm_in=np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
        img_comm_in=img_comm_in.astype(np.uint8)

        imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
        ##imgray = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)


        ##mask = cv2.inRange(imgray, lower_blue, upper_blue)
        ret, thresh = cv2.threshold(imgray, 0, 255, 0)
        #print(np.unique(mask))
        ##ret, thresh = cv2.threshold(imgray, 0, 255, 0)

        ##plt.imshow(thresh)
        ##plt.show()

        contours,hirarchy=cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


        areas=[cv2.contourArea(contours[jj]) for jj in range(len(contours))]

        median_area=np.mean(areas)
        contours_slope=contours#self.find_polugons_size_filter(contours,median_area=median_area,scaler_up=100,scaler_down=0.5)

        if len(contours_slope)>0:
            for jv in range(len(contours_slope)):
                new_poly=list(contours_slope[jv])
                if jv==0:
                    merged_all=new_poly
                else:
                    merged_all=merged_all+new_poly


            merge=np.array(merged_all)


            img_in=np.zeros(textline_mask.shape)
            img_p_in=cv2.fillPoly(img_in, pts =[merge], color=(255,255,255))

            ##plt.imshow(img_p_in)
            ##plt.show()



            rect = cv2.minAreaRect(merge)

            box = cv2.boxPoints(rect)

            box = np.int0(box)


            indexes=[0,1,2,3]
            x_list=box[:,0]
            y_list=box[:,1]



            index_y_sort=np.argsort(y_list)

            index_upper_left=index_y_sort[np.argmin(x_list[index_y_sort[0:2]])]
            index_upper_right=index_y_sort[np.argmax(x_list[index_y_sort[0:2]])]

            index_lower_left=index_y_sort[np.argmin(x_list[index_y_sort[2:]]) +2]
            index_lower_right=index_y_sort[np.argmax(x_list[index_y_sort[2:]])+2]


            alpha1=float(box[index_upper_right][1]-
                        box[index_upper_left][1])/(float(box[index_upper_right][0]-box[index_upper_left][0]))
            alpha2=float(box[index_lower_right][1]-
                        box[index_lower_left][1])/(float(box[index_lower_right][0]-box[index_lower_left][0]))

            slope_true=(alpha1+alpha2)/2.0

            #slope=0#slope_true/np.pi*180


            #if abs(slope)>=1:
                #slope=0

            #dst=self.rotate_image(textline_mask,slope_true)
            #dst=dst[:,:,0]
            #dst[dst!=0]=1
        image_regions_deskewd=np.zeros(textline_mask_org[:,:].shape)
        for ind in np.unique(textline_mask_org[:,:]):
            interest_reg=(textline_mask_org[:,:]==ind)*1
            interest_reg=interest_reg.astype(np.uint8)
            deskewed_new=self.rotate_image(interest_reg,slope_true)
            deskewed_new=deskewed_new[:,:]
            deskewed_new[deskewed_new!=0]=ind

            image_regions_deskewd=image_regions_deskewd+deskewed_new
        return image_regions_deskewd,slope_true

    def return_regions_without_seperators(self,regions_pre):
        kernel = np.ones((5,5),np.uint8)
        regions_without_seperators=( (regions_pre[:,:]!=6) & (regions_pre[:,:]!=0) )*1
        #regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1

        regions_without_seperators=regions_without_seperators.astype(np.uint8)

        regions_without_seperators = cv2.erode(regions_without_seperators,kernel,iterations = 6)

        return regions_without_seperators

    def return_regions_without_seperators_new(self,regions_pre,regions_only_text):
        kernel = np.ones((5,5),np.uint8)

        regions_without_seperators=( (regions_pre[:,:]!=6) & (regions_pre[:,:]!=0)  & (regions_pre[:,:]!=1) & (regions_pre[:,:]!=2))*1

        #plt.imshow(regions_without_seperators)
        #plt.show()

        regions_without_seperators_n=( (regions_without_seperators[:,:]==1) | (regions_only_text[:,:]==1) )*1

        #regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1

        regions_without_seperators_n=regions_without_seperators_n.astype(np.uint8)

        regions_without_seperators_n = cv2.erode(regions_without_seperators_n,kernel,iterations = 6)

        return regions_without_seperators_n
    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
        return image_back_zero

    def find_num_col_only_image(self,regions_without_seperators,multiplier=3.8):
        regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=0)

        ##plt.plot(regions_without_seperators_0)
        ##plt.show()

        sigma_=15


        meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]

        first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
        last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))

        last_nonzero=len(regions_without_seperators_0)-last_nonzero


        y=regions_without_seperators_0#[first_nonzero:last_nonzero]

        y_help=np.zeros(len(y)+20)

        y_help[10:len(y)+10]=y

        x=np.array( range(len(y)) )




        zneg_rev=-y_help+np.max(y_help)

        zneg=np.zeros(len(zneg_rev)+20)

        zneg[10:len(zneg_rev)+10]=zneg_rev

        z=gaussian_filter1d(y, sigma_)
        zneg= gaussian_filter1d(zneg, sigma_)


        peaks_neg, _ = find_peaks(zneg, height=0)
        peaks, _ = find_peaks(z, height=0)


        peaks_neg=peaks_neg-10-10

        peaks_neg_org=np.copy(peaks_neg)


        peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]

        peaks=peaks[(peaks>.09*regions_without_seperators.shape[1]) & (peaks<0.91*regions_without_seperators.shape[1])]

        peaks_neg=peaks_neg[ (peaks_neg>500) & (peaks_neg< (regions_without_seperators.shape[1]-500) ) ]
        #print(peaks)
        interest_pos=z[peaks]

        interest_pos=interest_pos[interest_pos>10]

        interest_neg=z[peaks_neg]
        min_peaks_pos=np.mean(interest_pos)#np.min(interest_pos)
        min_peaks_neg=0#np.min(interest_neg)

        #$print(min_peaks_pos)
        dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
        #print(interest_pos)
        grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0

        interest_neg_fin=interest_neg[(interest_neg<grenze)]
        peaks_neg_fin=peaks_neg[(interest_neg<grenze)]

        num_col=(len(interest_neg_fin))+1

        p_l=0
        p_u=len(y)-1
        p_m=int(len(y)/2.)
        p_g_l=int(len(y)/3.)
        p_g_u=len(y)-int(len(y)/3.)

        if num_col==3:
            if (peaks_neg_fin[0]>p_g_u and peaks_neg_fin[1]>p_g_u) or (peaks_neg_fin[0]<p_g_l and peaks_neg_fin[1]<p_g_l ) or (peaks_neg_fin[0]<p_m and peaks_neg_fin[1]<p_m ) or (peaks_neg_fin[0]>p_m and peaks_neg_fin[1]>p_m ):
                num_col=1
            else:
                pass

        if num_col==2:
            if (peaks_neg_fin[0]>p_g_u) or (peaks_neg_fin[0]<p_g_l):
                num_col=1
            else:
                pass

        diff_peaks=np.abs( np.diff(peaks_neg_fin) )


        cut_off=400
        peaks_neg_true=[]
        forest=[]

        for i in range(len(peaks_neg_fin)):
            if i==0:
                forest.append(peaks_neg_fin[i])
            if i<(len(peaks_neg_fin)-1):
                if diff_peaks[i]<=cut_off:
                    forest.append(peaks_neg_fin[i+1])
                if diff_peaks[i]>cut_off:
                    #print(forest[np.argmin(z[forest]) ] )
                    if not self.isNaN(forest[np.argmin(z[forest]) ]):
                        peaks_neg_true.append(forest[np.argmin(z[forest]) ])
                    forest=[]
                    forest.append(peaks_neg_fin[i+1])
            if i==(len(peaks_neg_fin)-1):
                #print(print(forest[np.argmin(z[forest]) ] ))
                if not self.isNaN(forest[np.argmin(z[forest]) ]):
                    peaks_neg_true.append(forest[np.argmin(z[forest]) ])




        num_col=(len(peaks_neg_true))+1
        p_l=0
        p_u=len(y)-1
        p_m=int(len(y)/2.)
        p_quarter=int(len(y)/4.)
        p_g_l=int(len(y)/3.)
        p_g_u=len(y)-int(len(y)/3.)

        p_u_quarter=len(y)-p_quarter

        if num_col==3:
            if (peaks_neg_true[0]>p_g_u and peaks_neg_true[1]>p_g_u) or (peaks_neg_true[0]<p_g_l and peaks_neg_true[1]<p_g_l ) or (peaks_neg_true[0]<p_m and peaks_neg_true[1]<p_m ) or (peaks_neg_true[0]>p_m and peaks_neg_true[1]>p_m ):
                num_col=1
                peaks_neg_true=[]
            elif (peaks_neg_true[0]<p_g_u and peaks_neg_true[0]>p_g_l) and (peaks_neg_true[1]>p_u_quarter):
                peaks_neg_true=[ peaks_neg_true[0] ]
            elif (peaks_neg_true[1]<p_g_u and peaks_neg_true[1]>p_g_l) and (peaks_neg_true[0]<p_quarter):
                peaks_neg_true=[ peaks_neg_true[1] ]
            else:
                pass

        if num_col==2:
            if (peaks_neg_true[0]>p_g_u) or (peaks_neg_true[0]<p_g_l):
                num_col=1
                peaks_neg_true=[]

        if num_col==4:
            if len(np.array(peaks_neg_true)[np.array(peaks_neg_true)<p_g_l])==2 or len( np.array(peaks_neg_true)[np.array(peaks_neg_true)>(len(y)-p_g_l)] )==2:
                num_col=1
                peaks_neg_true=[]
            else:
                pass


        #no deeper hill around found hills

        peaks_fin_true=[]
        for i in range(len(peaks_neg_true)):
            hill_main=peaks_neg_true[i]
            #deep_depth=z[peaks_neg]
            hills_around=peaks_neg_org[( (peaks_neg_org>hill_main) & (peaks_neg_org<=hill_main+400) ) | ( (peaks_neg_org<hill_main) & (peaks_neg_org>=hill_main-400) )]
            deep_depth_around=z[hills_around]

            #print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo')
            try:
                if np.min(deep_depth_around)<z[hill_main]:
                    pass
                else:
                    peaks_fin_true.append(hill_main)
            except:
                pass


        diff_peaks_annormal=diff_peaks[diff_peaks<360]



        if len(diff_peaks_annormal)>0:
            arg_help=np.array(range(len(diff_peaks)))
            arg_help_ann=arg_help[diff_peaks<360]

            peaks_neg_fin_new=[]

            for ii in range(len(peaks_neg_fin)):
                if ii in arg_help_ann:
                    arg_min=np.argmin([interest_neg_fin[ii],interest_neg_fin[ii+1] ] )
                    if arg_min==0:
                        peaks_neg_fin_new.append( peaks_neg_fin[ii])
                    else:
                        peaks_neg_fin_new.append( peaks_neg_fin[ii+1])


                elif (ii-1) in arg_help_ann:
                    pass
                else:
                    peaks_neg_fin_new.append(peaks_neg_fin[ii] )
        else:
            peaks_neg_fin_new=peaks_neg_fin

        # sometime pages with one columns gives also some negative peaks. delete those peaks
        param=z[peaks_neg_true]/float(min_peaks_pos)*100

        if len(param[param<=41])==0:
            peaks_neg_true=[]






        return len(peaks_fin_true), peaks_fin_true

    def return_hor_spliter_by_index_for_without_verticals(self,peaks_neg_fin_t,x_min_hor_some,x_max_hor_some):
        #print(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=[]
        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


            min_h_neg=arg_minmax[(min_h<0)]
            min_h_neg_n=min_h[min_h<0]

            try:
                min_h_neg=[ min_h_neg[np.argmax(min_h_neg_n)] ]
            except:
                min_h_neg=[]


            max_h_neg=arg_minmax[(max_h>0)]
            max_h_neg_n=max_h[max_h>0]

            if len(max_h_neg_n)>0:
                max_h_neg=[ max_h_neg[np.argmin(max_h_neg_n)] ]
            else:
                max_h_neg=[]



            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:

                    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]]

                    indexer_lines_deletions_len.append(len(deletions))
                    indexr_uniq_ind.append([deletions])

                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
                else:
                    index_line_true=min_h_neg[0]

                indexer_lines.append(index_line_true)

                if len(unique_delets_int)>0:
                    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=[]
        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 find_num_col_by_vertical_lines(self,regions_without_seperators,multiplier=3.8):
        regions_without_seperators_0=regions_without_seperators[:,:,0].sum(axis=0)

        ##plt.plot(regions_without_seperators_0)
        ##plt.show()

        sigma_=35#70#35



        z=gaussian_filter1d(regions_without_seperators_0, sigma_)

        peaks, _ = find_peaks(z, height=0)

        #print(peaks,'peaksnew')
        return peaks


    def find_num_col(self,regions_without_seperators,multiplier=3.8):
        regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=0)

        ##plt.plot(regions_without_seperators_0)
        ##plt.show()

        sigma_=35#70#35


        meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]

        first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
        last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))

        #print(last_nonzero)
        #print(isNaN(last_nonzero))
        #last_nonzero=0#halalikh
        last_nonzero=len(regions_without_seperators_0)-last_nonzero


        y=regions_without_seperators_0#[first_nonzero:last_nonzero]

        y_help=np.zeros(len(y)+20)

        y_help[10:len(y)+10]=y

        x=np.array( range(len(y)) )




        zneg_rev=-y_help+np.max(y_help)

        zneg=np.zeros(len(zneg_rev)+20)

        zneg[10:len(zneg_rev)+10]=zneg_rev

        z=gaussian_filter1d(y, sigma_)
        zneg= gaussian_filter1d(zneg, sigma_)


        peaks_neg, _ = find_peaks(zneg, height=0)
        peaks, _ = find_peaks(z, height=0)

        peaks_neg=peaks_neg-10-10


        last_nonzero=last_nonzero-100
        first_nonzero=first_nonzero+200

        peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]

        peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
        peaks_neg=peaks_neg[ (peaks_neg>370) & (peaks_neg< (regions_without_seperators.shape[1]-370) ) ]

        #print(peaks)
        interest_pos=z[peaks]

        interest_pos=interest_pos[interest_pos>10]



        #plt.plot(z)
        #plt.show()
        interest_neg=z[peaks_neg]




        min_peaks_pos=np.min(interest_pos)
        max_peaks_pos=np.max(interest_pos)

        if max_peaks_pos/min_peaks_pos>=35:
            min_peaks_pos=np.mean(interest_pos)

        min_peaks_neg=0#np.min(interest_neg)

        #print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
        #$print(min_peaks_pos)
        dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
        #print(interest_pos)
        grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0

        #print(interest_neg,'interest_neg')
        #print(grenze,'grenze')
        #print(min_peaks_pos,'min_peaks_pos')
        #print(dis_talaei,'dis_talaei')
        #print(peaks_neg,'peaks_neg')

        interest_neg_fin=interest_neg[(interest_neg<grenze)]
        peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
        #interest_neg_fin=interest_neg[(interest_neg<grenze)]

        num_col=(len(interest_neg_fin))+1

        #print(peaks_neg_fin,'peaks_neg_fin')
        #print(num_col,'diz')
        p_l=0
        p_u=len(y)-1
        p_m=int(len(y)/2.)
        p_g_l=int(len(y)/4.)
        p_g_u=len(y)-int(len(y)/4.)

        if num_col==3:
            if (peaks_neg_fin[0]>p_g_u and peaks_neg_fin[1]>p_g_u) or (peaks_neg_fin[0]<p_g_l and peaks_neg_fin[1]<p_g_l ) or ((peaks_neg_fin[0]+200)<p_m and peaks_neg_fin[1]<p_m ) or ((peaks_neg_fin[0]-200)>p_m and peaks_neg_fin[1]>p_m ):
                num_col=1
                peaks_neg_fin=[]
            else:
                pass

        if num_col==2:
            if (peaks_neg_fin[0]>p_g_u) or (peaks_neg_fin[0]<p_g_l):
                num_col=1
                peaks_neg_fin=[]
            else:
                pass


        ##print(len(peaks_neg_fin))




        diff_peaks=np.abs( np.diff(peaks_neg_fin) )

        cut_off=400
        peaks_neg_true=[]
        forest=[]

        #print(len(peaks_neg_fin),'len_')

        for i in range(len(peaks_neg_fin)):
            if i==0:
                forest.append(peaks_neg_fin[i])
            if i<(len(peaks_neg_fin)-1):
                if diff_peaks[i]<=cut_off:
                    forest.append(peaks_neg_fin[i+1])
                if diff_peaks[i]>cut_off:
                    #print(forest[np.argmin(z[forest]) ] )
                    if not self.isNaN(forest[np.argmin(z[forest]) ]):
                        peaks_neg_true.append(forest[np.argmin(z[forest]) ])
                    forest=[]
                    forest.append(peaks_neg_fin[i+1])
            if i==(len(peaks_neg_fin)-1):
                #print(print(forest[np.argmin(z[forest]) ] ))
                if not self.isNaN(forest[np.argmin(z[forest]) ]):
                    peaks_neg_true.append(forest[np.argmin(z[forest]) ])


        num_col=(len(peaks_neg_true))+1
        p_l=0
        p_u=len(y)-1
        p_m=int(len(y)/2.)
        p_quarter=int(len(y)/5.)
        p_g_l=int(len(y)/4.)
        p_g_u=len(y)-int(len(y)/4.)

        p_u_quarter=len(y)-p_quarter

        ##print(num_col,'early')
        if num_col==3:
            if (peaks_neg_true[0]>p_g_u and peaks_neg_true[1]>p_g_u) or (peaks_neg_true[0]<p_g_l and peaks_neg_true[1]<p_g_l ) or (peaks_neg_true[0]<p_m and (peaks_neg_true[1]+200)<p_m ) or ( (peaks_neg_true[0]-200)>p_m and peaks_neg_true[1]>p_m ):
                num_col=1
                peaks_neg_true=[]
            elif (peaks_neg_true[0]<p_g_u and peaks_neg_true[0]>p_g_l) and (peaks_neg_true[1]>p_u_quarter):
                peaks_neg_true=[ peaks_neg_true[0] ]
            elif (peaks_neg_true[1]<p_g_u and peaks_neg_true[1]>p_g_l) and (peaks_neg_true[0]<p_quarter):
                peaks_neg_true=[ peaks_neg_true[1] ]
            else:
                pass

        if num_col==2:
            if (peaks_neg_true[0]>p_g_u) or (peaks_neg_true[0]<p_g_l):
                num_col=1
                peaks_neg_true=[]
            else:
                pass

        diff_peaks_annormal=diff_peaks[diff_peaks<360]



        if len(diff_peaks_annormal)>0:
            arg_help=np.array(range(len(diff_peaks)))
            arg_help_ann=arg_help[diff_peaks<360]

            peaks_neg_fin_new=[]

            for ii in range(len(peaks_neg_fin)):
                if ii in arg_help_ann:
                    arg_min=np.argmin([interest_neg_fin[ii],interest_neg_fin[ii+1] ] )
                    if arg_min==0:
                        peaks_neg_fin_new.append( peaks_neg_fin[ii])
                    else:
                        peaks_neg_fin_new.append( peaks_neg_fin[ii+1])


                elif (ii-1) in arg_help_ann:
                    pass
                else:
                    peaks_neg_fin_new.append(peaks_neg_fin[ii] )
        else:
            peaks_neg_fin_new=peaks_neg_fin


        #plt.plot(gaussian_filter1d(y, sigma_))
        #plt.plot(peaks_neg_true,z[peaks_neg_true],'*')
        #plt.plot([0,len(y)], [grenze,grenze])
        #plt.show()

        ##print(len(peaks_neg_true))
        return len(peaks_neg_true), peaks_neg_true

    def find_new_features_of_contoures(self,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))]
        try:
            x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])

            argmin_x_main=np.array([np.argmin(contours_main[j][:,0,0]) for j in range(len(contours_main))])

            x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],0,0] for j in range(len(contours_main))])
            y_corr_x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],0,1] 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))])
        except:
            x_min_main=np.array([np.min(contours_main[j][:,0]) for j in range(len(contours_main))])

            argmin_x_main=np.array([np.argmin(contours_main[j][:,0]) for j in range(len(contours_main))])


            x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],0] for j in range(len(contours_main))])
            y_corr_x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],1] for j in range(len(contours_main))])

            x_max_main=np.array([np.max(contours_main[j][:,0]) for j in range(len(contours_main))])

            y_min_main=np.array([np.min(contours_main[j][:,1]) for j in range(len(contours_main))])
            y_max_main=np.array([np.max(contours_main[j][:,1]) for j in range(len(contours_main))])


        #dis_x=np.abs(x_max_main-x_min_main)

        return cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin
    def return_points_with_boundies(self,peaks_neg_fin,first_point, last_point):
        peaks_neg_tot=[]
        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 contours_in_same_horizon(self,cy_main_hor):
        X1=np.zeros((len(cy_main_hor),len(cy_main_hor)))
        X2=np.zeros((len(cy_main_hor),len(cy_main_hor)))

        X1[0::1,:]=cy_main_hor[:]
        X2=X1.T

        X_dif=np.abs(X2-X1)
        args_help=np.array(range(len(cy_main_hor)))
        all_args=[]
        for i in range(len(cy_main_hor)):
            list_h=list(args_help[X_dif[i,:]<=20] )
            list_h.append(i)
            if len(list_h)>1:
                all_args.append(list( set(list_h)  ))
        return np.unique(all_args)

    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):

        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')

            # check to see is there any vertical seperator to find holes.
            if np.abs(spliter_y_new[i+1]-spliter_y_new[i])>1./3.*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)
                ##plt.figure(figsize=(20,20))
                ##plt.plot(org_img_dichte)
                ##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=self.find_num_col_only_image(image_p_rev[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=2.4)

                #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) ]



                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=self.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]




                #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
                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])

                    for kkk2 in range(len(start_index_of_hor_with_subset)):

                        if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
                            vahid_subset[kkk1,kkk2]=kkk1
                        else:
                            pass
                    #print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))



                # 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')


                    # find parenets args
                    line_int=np.zeros(vahid_subset.shape[0])

                    childs_id=[]
                    arg_child=[]
                    for li in range(vahid_subset.shape[0]):
                        if np.all(vahid_subset[:,li]==-1):
                            line_int[li]=-1
                        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)):
                            #    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]


                    cy_hor_some_sort=cy_hor_some[arg_parent]


                    newest_y_spliter_tot=[]

                    for tj in range(len(newest_peaks)-1):
                        newest_y_spliter=[]
                        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)

                            #print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
                            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])

                        newest_y_spliter_tot.append(newest_y_spliter)




                else:
                    line_int=[]
                    newest_y_spliter_tot=[]

                    for tj in range(len(newest_peaks)-1):
                        newest_y_spliter=[]
                        newest_y_spliter.append(spliter_y_new[i])

                        newest_y_spliter.append(spliter_y_new[i+1])

                        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]


                        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')
                            ##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] ) ] :
                                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=self.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)
                            else:
                                peaks_neg_fin_sub=[]

                            peaks_sub=[]
                            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:
                    for j in range(len(newest_peaks)-1):
                        newest_y_spliter=newest_y_spliter_tot[j]

                        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:
                                    ###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=self.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])


                                    ss_in_ch,nst_p_ch,arg_n_ch,lines_l_del_ch,lines_in_del_ch=self.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=[]
                                        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')
                                            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])

                                        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=self.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')
                                            else:
                                                peaks_neg_fin_sub_ch=[]

                                            peaks_sub_ch=[]
                                            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])

                                            peaks_sub_ch.append(nst_p_ch[jn+1])

                                            #peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])

                                            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]])



                                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] )):
                                        ###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=self.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)
                                    else:
                                        peaks_neg_fin_sub=[]

                                    peaks_sub=[]
                                    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:
                            for n in range(len(newest_y_spliter)-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] ) ] :
                                    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=5.0)
                                    num_col_sub, peaks_neg_fin_sub=self.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)
                                else:
                                    peaks_neg_fin_sub=[]

                                peaks_sub=[]
                                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')

            # check to see is there any vertical seperator to find holes.
            if np.abs(spliter_y_new[i+1]-spliter_y_new[i])>1./3.*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)
                ##plt.figure(figsize=(20,20))
                ##plt.plot(org_img_dichte)
                ##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.)

                try:
                    num_col, peaks_neg_fin=self.find_num_col_only_image(image_p_rev[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=2.4)
                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]])
            else:
                boxes.append([ 0, seperators_closeup_n[:,:,0].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])






        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

        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=self.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]*.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)
        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)
        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=self.contours_in_same_horizon(cy_main_hor)
        #print(all_args_uniq,'all_args_uniq')
        if len(all_args_uniq)>0:
            if type(all_args_uniq[0]) is list:
                contours_new=[]
                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]]

                    img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
                    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

            else:
                img_p_in=seperators_closeup
        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

        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)
        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 ,_ , _, _ ,_,_=self.find_new_features_of_contoures(contours_cross)

        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
        else:
            seperators_closeup_n=img_p_in[:,:,:]
        #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=self.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]*.04
        len_y=seperators_closeup.shape[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]

        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]

        matrix_of_lines_ch=np.zeros((len(cy_main_hor)+len(cx_main_ver),10))

        matrix_of_lines_ch[:len(cy_main_hor),0]=args_hor
        matrix_of_lines_ch[len(cy_main_hor):,0]=args_ver


        matrix_of_lines_ch[len(cy_main_hor):,1]=cx_main_ver

        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

        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

        matrix_of_lines_ch[:len(cy_main_hor),4]=dist_x_hor
        matrix_of_lines_ch[len(cy_main_hor):,4]=dist_x_ver

        matrix_of_lines_ch[:len(cy_main_hor),5]=cy_main_hor


        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

        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)
        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=self.find_features_of_lines(contours_lines_ver)

        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)
        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=self.find_features_of_lines(contours_lines_hor)

        args_hor=np.array( range(len(slope_lines_hor) ))
        all_args_uniq=self.contours_in_same_horizon(cy_main_hor)
        #print(all_args_uniq,'all_args_uniq')
        if len(all_args_uniq)>0:
            if type(all_args_uniq[0]) is list:
                special_seperators=[]
                contours_new=[]
                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]]

                    #img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
                    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')
                        special_seperators.append(np.mean(cy_main_hor[some_args]))

            else:
                img_p_in=img_in_hor
                special_seperators=[]
        else:
            img_p_in=img_in_hor
            special_seperators=[]


        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()

        sep_ver_hor_cross=(sep_ver_hor[:,:,0]==2)*1

        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)
        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 ,_ , _, _ ,_,_=self.find_new_features_of_contoures(contours_cross)

        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

    def return_boxes_of_images_by_order_of_reading(self,spliter_y_new,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')

            # 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)
                ##plt.figure(figsize=(20,20))
                ##plt.plot(org_img_dichte)
                ##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=self.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) ]



                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=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
                for kkk1 in range(len(start_index_of_hor_with_subset)):


                    index_del_sub=np.unique(lines_indexes_deleted_with_subset[kkk1])

                    for kkk2 in range(len(start_index_of_hor_with_subset)):

                        if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
                            vahid_subset[kkk1,kkk2]=kkk1
                        else:
                            pass
                    #print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))

                #print(vahid_subset,'zartt222')


                # 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')


                    # find parenets args
                    line_int=np.zeros(vahid_subset.shape[0])

                    childs_id=[]
                    arg_child=[]
                    for li in range(vahid_subset.shape[0]):
                        #print(vahid_subset[:,li])
                        if np.all(vahid_subset[:,li]==-1):
                            line_int[li]=-1
                        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)):
                            #    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]


                    #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]




                    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=[]

                    for tj in range(len(newest_peaks)-1):
                        newest_y_spliter=[]
                        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)

                            #print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
                            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])

                        newest_y_spliter_tot.append(newest_y_spliter)




                else:
                    line_int=[]
                    newest_y_spliter_tot=[]

                    for tj in range(len(newest_peaks)-1):
                        newest_y_spliter=[]
                        newest_y_spliter.append(spliter_y_new[i])

                        newest_y_spliter.append(spliter_y_new[i+1])

                        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]


                        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')
                            ##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] ) ] :
                                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=self.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.)
                            else:
                                peaks_neg_fin_sub=[]

                            peaks_sub=[]
                            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:
                    for j in range(len(newest_peaks)-1):
                        newest_y_spliter=newest_y_spliter_tot[j]

                        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=self.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)):
                                    #    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=[]
                                        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')
                                            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])

                                        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=self.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)

                                            else:
                                                peaks_neg_fin_sub_ch=[]

                                            peaks_sub_ch=[]
                                            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])

                                            peaks_sub_ch.append(nst_p_ch[jn+1])

                                            #peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])

                                            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]])



                                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=self.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)
                                    else:
                                        peaks_neg_fin_sub=[]

                                    peaks_sub=[]
                                    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:
                            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=self.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)
                                else:
                                    peaks_neg_fin_sub=[]

                                peaks_sub=[]
                                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_new(self,spliter_y_new,regions_without_seperators,matrix_of_lines_ch):
        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')

            # 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)
                ##plt.figure(figsize=(20,20))
                ##plt.plot(org_img_dichte)
                ##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()
                try:
                    num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.)
                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) ]


                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=self.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]







                vahid_subset=np.zeros((len(start_index_of_hor_with_subset),len(start_index_of_hor_with_subset)))-1
                for kkk1 in range(len(start_index_of_hor_with_subset)):


                    index_del_sub=np.unique(lines_indexes_deleted_with_subset[kkk1])

                    for kkk2 in range(len(start_index_of_hor_with_subset)):

                        if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
                            vahid_subset[kkk1,kkk2]=kkk1
                        else:
                            pass
                    #print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))



                # 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')


                    # find parenets args
                    line_int=np.zeros(vahid_subset.shape[0])

                    childs_id=[]
                    arg_child=[]
                    for li in range(vahid_subset.shape[0]):
                        #print(vahid_subset[:,li])
                        if np.all(vahid_subset[:,li]==-1):
                            line_int[li]=-1
                        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)):
                            #    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]


                    #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]




                    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=[]

                    for tj in range(len(newest_peaks)-1):
                        newest_y_spliter=[]
                        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)

                            #print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
                            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])

                        newest_y_spliter_tot.append(newest_y_spliter)




                else:
                    line_int=[]
                    newest_y_spliter_tot=[]

                    for tj in range(len(newest_peaks)-1):
                        newest_y_spliter=[]
                        newest_y_spliter.append(spliter_y_new[i])

                        newest_y_spliter.append(spliter_y_new[i+1])

                        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]


                        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')
                            ##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] ) ] :
                                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] )
                                try:
                                    num_col_sub, peaks_neg_fin_sub=self.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.)
                                except:
                                    peaks_neg_fin_sub=[]
                            else:
                                peaks_neg_fin_sub=[]

                            peaks_sub=[]
                            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:
                    for j in range(len(newest_peaks)-1):


                        newest_y_spliter=newest_y_spliter_tot[j]

                        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:
                                    try:
                                        num_col_ch, peaks_neg_ch=self.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)
                                    except:
                                        peaks_neg_ch=[]
                                    #print(peaks_neg_ch,'mizzzz')
                                    #peaks_neg_ch=[]
                                    #for djh in range(len(peaks_neg_ch)):
                                    #    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_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=[]
                                        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')
                                            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])

                                        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] )):
                                                try:
                                                    num_col_sub_ch, peaks_neg_fin_sub_ch=self.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)
                                                except:
                                                    peaks_neg_fin_sub_ch=[]

                                            else:
                                                peaks_neg_fin_sub_ch=[]

                                            peaks_sub_ch=[]
                                            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])

                                            peaks_sub_ch.append(nst_p_ch[jn+1])

                                            #peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])

                                            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]])



                                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] )):
                                        try:
                                            num_col_sub, peaks_neg_fin_sub=self.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)
                                        except:
                                            peaks_neg_fin_sub=[]
                                    else:
                                        peaks_neg_fin_sub=[]

                                    peaks_sub=[]
                                    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:
                            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 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] )):
                                    try:
                                        num_col_sub, peaks_neg_fin_sub=self.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)
                                    except:
                                        peaks_neg_fin_sub=[]
                                else:
                                    peaks_neg_fin_sub=[]

                                peaks_sub=[]
                                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, regions_without_seperators[:,:].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])


        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=[]


        # 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')

            # 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)
                ##plt.figure(figsize=(20,20))
                ##plt.plot(org_img_dichte)
                ##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.)

                try:
                    num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)


                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]])

            else:
                boxes.append([ 0, seperators_closeup_n[:,:,0].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])



        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=[]
        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

            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) ]

            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')

                    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]]

                    indexer_lines_deletions_len.append(len(deletions))
                    indexr_uniq_ind.append([deletions])

                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
                else:
                    index_line_true=min_h_neg[0]

                indexer_lines.append(index_line_true)

                if len(unique_delets_int)>0:
                    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=[]
        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]))
        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()

            #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[:,:]
        return image_revised

    def tear_main_texts_on_the_boundaries_of_boxes(self,img_revised_tab,boxes):
        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
        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


        for t_i in text_indexes:
            textline_mask=(text_regions[:,:]==t_i)
            textline_mask=textline_mask*255.0

            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)

            #print(type(textline_mask),np.unique(textline_mask),textline_mask.shape)
            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))
        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))
                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))

        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))
                else:
                    cnt_int=[contours_main[j] for j in res_true]
                    text_regions=cv2.fillPoly(text_regions, pts =cnt_int, color=(2,2,2))




        return text_regions

    def delete_seperator_around(self,spliter_y,peaks_neg,image_by_region):
        # format of subboxes box=[x1, x2 , y1, y2]

        if len(image_by_region.shape)==3:
            for i in range(len(spliter_y)-1):
                for j in range(1,len(peaks_neg[i])-1):
                    image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0]==6 ]=0
                    image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),1]==6 ]=0
                    image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),2]==6 ]=0

                    image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0]==7 ]=0
                    image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),1]==7 ]=0
                    image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),2]==7 ]=0
        else:
            for i in range(len(spliter_y)-1):
                for j in range(1,len(peaks_neg[i])-1):
                    image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])]==6 ]=0

                    image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])]==7 ]=0
        return image_by_region

    def find_features_of_contoures(self,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))])


        return y_min_main ,y_max_main


    def add_tables_heuristic_to_layout(self,image_regions_eraly_p,boxes,slope_mean_hor,spliter_y,peaks_neg_tot,image_revised):

        image_revised_1=self.delete_seperator_around(spliter_y,peaks_neg_tot,image_revised)
        img_comm_e=np.zeros(image_revised_1.shape)
        img_comm=np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)

        for indiv in np.unique(image_revised_1):

            #print(indiv,'indd')
            image_col=(image_revised_1==indiv)*255
            img_comm_in=np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
            img_comm_in=img_comm_in.astype(np.uint8)

            imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)


            ret, thresh = cv2.threshold(imgray, 0, 255, 0)

            contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


            main_contours=self.filter_contours_area_of_image_tables(thresh,contours,hirarchy,max_area=1,min_area=0.0001)


            img_comm=cv2.fillPoly(img_comm, pts =main_contours, color=(indiv,indiv,indiv))
            ###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))


            #img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
            img_comm=img_comm.astype(np.uint8)





        if not self.isNaN(slope_mean_hor):
            image_revised_last=np.zeros((image_regions_eraly_p.shape[0] , image_regions_eraly_p.shape[1],3))
            for i in range(len(boxes)):


                image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]

                image_box_tabels_1=(image_box[:,:,0]==7)*1





                contours_tab,_=self.return_contours_of_image(image_box_tabels_1)

                contours_tab=self.filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.001)

                image_box_tabels_1=(image_box[:,:,0]==6)*1


                image_box_tabels_and_m_text=( (image_box[:,:,0]==7) | (image_box[:,:,0]==1) )*1
                image_box_tabels_and_m_text=image_box_tabels_and_m_text.astype(np.uint8)


                image_box_tabels_1=image_box_tabels_1.astype(np.uint8)
                image_box_tabels_1 = cv2.dilate(image_box_tabels_1,self.kernel,iterations = 5)




                contours_table_m_text,_=self.return_contours_of_image(image_box_tabels_and_m_text)


                image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)

                image_box_tabels=image_box_tabels.astype(np.uint8)
                imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
                ret, thresh = cv2.threshold(imgray, 0, 255, 0)


                contours_line,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


                y_min_main_line ,y_max_main_line=self.find_features_of_contoures(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=self.find_features_of_contoures(contours_tab)

                cx_tab_m_text,cy_tab_m_text ,x_min_tab_m_text , x_max_tab_m_text, y_min_tab_m_text ,y_max_tab_m_text=self.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,_=self.find_new_features_of_contoures(contours_tab)


                if len(y_min_main_tab )>0:
                    y_down_tabs=[]
                    y_up_tabs=[]

                    for i_t in range(len(y_min_main_tab )):
                        y_down_tab=[]
                        y_up_tab=[]
                        for i_l in range(len(y_min_main_line)):
                            if y_min_main_tab[i_t]>y_min_main_line[i_l] and  y_max_main_tab[i_t]>y_min_main_line[i_l] and y_min_main_tab[i_t]>y_max_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l]:
                                pass
                            elif y_min_main_tab[i_t]<y_max_main_line[i_l] and y_max_main_tab[i_t]<y_max_main_line[i_l] and y_max_main_tab[i_t]<y_min_main_line[i_l] and y_min_main_tab[i_t]<y_min_main_line[i_l]:
                                pass
                            elif np.abs(y_max_main_line[i_l]-y_min_main_line[i_l])<100:
                                pass

                            else:
                                y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t] ])  )
                                y_down_tab.append( np.max([ y_max_main_line[i_l],y_max_main_tab[i_t] ]) )

                        if len(y_up_tab)==0:
                            for v_n in range(len(cx_tab_m_text)):
                                if cx_tabl[i_t]<= x_max_tab_m_text[v_n] and cx_tabl[i_t]>= x_min_tab_m_text[v_n] and cy_tabl[i_t]<= y_max_tab_m_text[v_n] and cy_tabl[i_t]>= y_min_tab_m_text[v_n] and cx_tabl[i_t]!=cx_tab_m_text[v_n] and cy_tabl[i_t]!=cy_tab_m_text[v_n]:
                                    y_up_tabs.append(y_min_tab_m_text[v_n])
                                    y_down_tabs.append(y_max_tab_m_text[v_n])
                            #y_up_tabs.append(y_min_main_tab[i_t])
                            #y_down_tabs.append(y_max_main_tab[i_t])
                        else:
                            y_up_tabs.append(np.min(y_up_tab))
                            y_down_tabs.append(np.max(y_down_tab))


                else:
                    y_down_tabs=[]
                    y_up_tabs=[]
                    pass

                for ii in range(len(y_up_tabs)):
                    image_box[y_up_tabs[ii]:y_down_tabs[ii],:,0]=7






                image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]


        else:
            for i in range(len(boxes)):

                image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
                image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]

                ##plt.figure(figsize=(20,20))
                ##plt.imshow(image_box[:,:,0])
                ##plt.show()
        return image_revised_last
    def find_features_of_contours(self,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))])



        return y_min_main ,y_max_main,areas_main
    def remove_headers_and_mains_intersection(self,seperators_closeup_n,img_revised_tab,boxes):
        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]

            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)

            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)

            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=self.return_parent_contours(contours_head_patch_con,hiearchy)

            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=self.return_parent_contours(contours_main_patch_con,hiearchy)


            y_patch_head_min, y_patch_head_max,_= self.find_features_of_contours(contours_head_patch_con)
            y_patch_main_min, y_patch_main_max,_= self.find_features_of_contours(contours_main_patch_con)


            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]

                        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)

                        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=self.return_parent_contours(contours_head_patch_con,hiearchy)

                        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=self.return_parent_contours(contours_main_patch_con,hiearchy)

                        _,_,areas_head= self.find_features_of_contours(contours_head_patch_con)
                        _,_,areas_main= self.find_features_of_contours(contours_main_patch_con)

                        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
                        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



                    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]

                        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)

                        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=self.return_parent_contours(contours_head_patch_con,hiearchy)

                        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=self.return_parent_contours(contours_main_patch_con,hiearchy)

                        _,_,areas_head= self.find_features_of_contours(contours_head_patch_con)
                        _,_,areas_main= self.find_features_of_contours(contours_main_patch_con)

                        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
                        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



                        #print(np.unique(patch_intersection) )
                        ##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):

        ##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)

        ##plt.imshow(textline_mask[:,:])
        ##plt.show()


        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)

        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)) )


        peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)


        sigma_gaus=8

        z= gaussian_filter1d(y_padded, sigma_gaus)
        zneg_rev=-y_padded+np.max(y_padded)

        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


        ##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))])

            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))])

        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))]

            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))])
            #print(cy_main,'mainy')

        peaks_neg_new=[]

        peaks_neg_new.append(0+y_ref)
        for iii in range(len(peaks_neg)):
            peaks_neg_new.append(peaks_neg[iii]+y_ref)

        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)))


        if contours_main!=None:
            len_main=len(contours_main)
        else:
            len_main=0


        matrix_of_orders=np.zeros((len(contours_main)+len(contours_header),5))

        matrix_of_orders[:,0]=np.array( range( len(contours_main)+len(contours_header) ) )

        matrix_of_orders[:len(contours_main),1]=1
        matrix_of_orders[len(contours_main):,1]=2

        matrix_of_orders[:len(contours_main),2]=cx_main
        matrix_of_orders[len(contours_main):,2]=cx_header

        matrix_of_orders[:len(contours_main),3]=cy_main
        matrix_of_orders[len(contours_main):,3]=cy_header


        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) ) )




        #print(peaks_neg_new,'peaks_neg_new')

        #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')

            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)

            ind_in_int=indexes_in[sorted_inside]
            ind_in_type=types_of_text[sorted_inside]
            ind_ind_type=index_types_of_text[sorted_inside]

            for j in range(len(ind_in_int)):
                final_indexers_sorted.append(int(ind_in_int[j]) )
                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[:]

        #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




    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)

        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]

        ##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
        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:
                order_of_texts.append(interest[0])
                index_b+=1
            else:
                pass

        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]]
            order_of_texts.append(interest)
            index_b+=1

        return order_of_texts, id_of_texts


    def get_text_region_boxes_by_given_contours(self,contours):


        kernel = np.ones((5,5),np.uint8)
        boxes=[]
        contours_new=[]
        for jj in range(len(contours)):
            x,y,w,h = cv2.boundingRect(contours[jj])

            boxes.append([x,y,w,h])
            contours_new.append(contours[jj])

        del contours
        return boxes,contours_new


    def return_teilwiese_deskewed_lines(self,text_regions_p,textline_rotated):

        kernel = np.ones((5, 5), np.uint8)
        textline_rotated = cv2.erode(textline_rotated, kernel, iterations=1)

        textline_rotated_new=np.zeros(textline_rotated.shape)
        rgb_m=1
        rgb_h=2

        cnt_m,boxes_m=self.return_contours_of_interested_region_and_bounding_box(text_regions_p,rgb_m)
        cnt_h,boxes_h=self.return_contours_of_interested_region_and_bounding_box(text_regions_p,rgb_h)

        areas_cnt_m=np.array([cv2.contourArea(cnt_m[j]) for j in range(len(cnt_m))])

        argmax=np.argmax(areas_cnt_m)

        #plt.imshow(textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]])
        #plt.show()



        for argmax in range(len(boxes_m)):

            textline_text_region=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]  ]

            textline_text_region_revised=self.seperate_lines_new(textline_text_region,0)
            #except:
            #    textline_text_region_revised=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]  ]
            textline_rotated_new[boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]]=textline_text_region_revised[:,:]

        #textline_rotated_new[textline_rotated_new>0]=1
        #textline_rotated_new[textline_rotated_new<0]=0
        #plt.imshow(textline_rotated_new)
        #plt.show()
    def return_contours_of_interested_region_and_bounding_box(self,region_pre_p,pixel):

        # pixels of images are identified by 5
        cnts_images=(region_pre_p[:,:,0]==pixel)*1
        cnts_images=cnts_images.astype(np.uint8)
        cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=0.0003)

        boxes = []

        for jj in range(len(contours_imgs)):
            x, y, w, h = cv2.boundingRect(contours_imgs[jj])
            boxes.append([int(x), int(y), int(w), int(h)])
        return contours_imgs,boxes
    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

        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)

        ##plt.imshow(seperators_closeup[:,:,0])
        ##plt.show()
        seperators_closeup_new=np.zeros((seperators_closeup.shape[0] ,seperators_closeup.shape[1] ))



        ##_,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)
        ##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))

        ##plt.imshow(seperators_closeup_n_binary)
        ##plt.show()


        #print( np.unique(gray),np.unique(seperators_closeup_n[:,:,1]) )

        gray = cv2.bitwise_not(seperators_closeup_n_binary)
        gray=gray.astype(np.uint8)


        ##plt.imshow(gray)
        ##plt.show()
        bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
                                    cv2.THRESH_BINARY, 15, -2)
        ##plt.imshow(bw[:,:])
        ##plt.show()

        horizontal = np.copy(bw)
        vertical = np.copy(bw)

        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)


        horizontal = cv2.dilate(horizontal,kernel,iterations = 2)
        horizontal = cv2.erode(horizontal,kernel,iterations = 2)
        #plt.imshow(horizontal)
        #plt.show()

        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)
        # Show extracted vertical lines

        horizontal,special_seperators=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical,horizontal)


        ##plt.imshow(vertical)
        ##plt.show()
        #print(vertical.shape,np.unique(vertical),'verticalvertical')
        seperators_closeup_new[:,:][vertical[:,:]!=0]=1
        seperators_closeup_new[:,:][horizontal[:,:]!=0]=1

        ##plt.imshow(seperators_closeup_new)
        ##plt.show()
        ##seperators_closeup_n
        vertical=np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
        vertical=vertical.astype(np.uint8)

        ##plt.plot(vertical[:,:,0].sum(axis=0))
        ##plt.show()

        #plt.plot(vertical[:,:,0].sum(axis=1))
        #plt.show()

        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=self.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)
        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=self.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]

        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]

        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]


        matrix_of_lines_ch=np.zeros((len(cy_main_hor)+len(cx_main_ver),10))

        matrix_of_lines_ch[:len(cy_main_hor),0]=args_hor
        matrix_of_lines_ch[len(cy_main_hor):,0]=args_ver


        matrix_of_lines_ch[len(cy_main_hor):,1]=cx_main_ver

        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

        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

        matrix_of_lines_ch[:len(cy_main_hor),4]=dist_x_hor
        matrix_of_lines_ch[len(cy_main_hor):,4]=dist_x_ver

        matrix_of_lines_ch[:len(cy_main_hor),5]=cy_main_hor


        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

        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



        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=self.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)






        """



        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=self.find_features_of_lines(contours_lines)

        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



        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<=.16*region_pre_p.shape[1]) & (x_max_main_hor>=.84*region_pre_p.shape[1] )]

        cy_main_spliters=np.array( list(cy_main_spliters)+list(special_seperators))

        if contours_h is not None:
            try:
                cy_main_spliters_head=cy_main_head[ (x_min_main_head<=.16*region_pre_p.shape[1]) & (x_max_main_head>=.84*region_pre_p.shape[1] )]
                cy_main_spliters=np.array( list(cy_main_spliters)+list(cy_main_spliters_head))
            except:
                pass
        args_cy_spliter=np.argsort(cy_main_spliters)

        cy_main_spliters_sort=cy_main_spliters[args_cy_spliter]

        spliter_y_new=[]
        spliter_y_new.append(0)
        for i in range(len(cy_main_spliters_sort)):
            spliter_y_new.append(  cy_main_spliters_sort[i] )

        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=self.return_regions_without_seperators(region_pre_p)

        ##print(args_big_parts,'args_big_parts')
        #image_page_otsu=self.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=[]

        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)
            ##plt.imshow(regions_without_seperators_teil)
            ##plt.show()

            #num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)

            #regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3)
            #
            num_col, peaks_neg_fin=self.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
            """
            #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=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)
            else:
                #plt.imshow(image_page_background_zero_teil)
                #plt.show()
                #num_col, peaks_neg_fin=self.find_num_col_only_image(image_page_background_zero,multiplier=2.4)#2.3)
                num_col, peaks_neg_fin=self.find_num_col_only_image(image_page_background_zero_teil,multiplier=3.4)#2.3)

                print(num_col,'birda')
                if num_col>0:
                    pass
                elif num_col==0:
                    print(num_col,'birda2222')
                    num_col_regions, peaks_neg_fin_regions=self.find_num_col(regions_without_seperators_teil,multiplier=10.0)
                    if num_col_regions==0:
                        pass
                    else:

                        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=self.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):

        # pixels of images are identified by 5
        if len(region_pre_p.shape)==3:
            cnts_images=(region_pre_p[:,:,0]==pixel)*1
        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)
        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=self.return_parent_contours(contours_imgs,hiearchy)
        contours_imgs=self.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]

        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= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org=self.do_prediction(patches,img,model_region)

        prediction_regions_org=self.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= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_orgt=self.do_prediction(patches,img,model_region)

        prediction_regions_orgt=self.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=self.return_contours_of_interested_region(mask_texts_longshot,pixel_img)

        longshot_true=np.zeros(mask_texts_longshot.shape)
        #text_regions_p_true[:,:]=text_regions_p_1[:,:]

        longshot_true=cv2.fillPoly(longshot_true,pts=polygons_of_only_texts_longshot, color=(1,1,1))

        #plt.imshow(longshot_true)
        #plt.show()

        gaussian_filter=False
        patches=False
        binary=False




        ratio_x=1
        ratio_y=1
        median_blur=False

        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        one_third_upper_ny=int(img.shape[0]/3.0)

        img=img[0:one_third_upper_ny,:,:]

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)

        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_longshot_one_third=self.do_prediction(patches,img,model_region)

        prediction_regions_longshot_one_third=self.resize_image(prediction_regions_longshot_one_third, one_third_upper_ny, img_width_h )


        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
        img=img[one_third_upper_ny:int(2*one_third_upper_ny),:,:]

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)

        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_longshot_one_third_middle=self.do_prediction(patches,img,model_region)

        prediction_regions_longshot_one_third_middle=self.resize_image(prediction_regions_longshot_one_third_middle, one_third_upper_ny, img_width_h )


        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
        img=img[int(2*one_third_upper_ny):,:,:]

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)

        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_longshot_one_third_down=self.do_prediction(patches,img,model_region)

        prediction_regions_longshot_one_third_down=self.resize_image(prediction_regions_longshot_one_third_down, img_height_h-int(2*one_third_upper_ny), img_width_h )

        #plt.imshow(prediction_regions_org[:,:,0])
        #plt.show()
        #sys.exit()
        prediction_regions_longshot=np.zeros((img_height_h,img_width_h))

        #prediction_regions_longshot=prediction_regions_longshot[:,:,0]

        #prediction_regions_longshot[0:one_third_upper_ny,:]=prediction_regions_longshot_one_third[:,:,0]
        #prediction_regions_longshot[one_third_upper_ny:int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_middle[:,:,0]
        #prediction_regions_longshot[int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_down[:,:,0]

        prediction_regions_longshot=longshot_true[:,:]
        #plt.imshow(prediction_regions_longshot)
        #plt.show()

        gaussian_filter=False
        patches=True
        binary=False




        ratio_x=1#1.1
        ratio_y=1
        median_blur=False

        #img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)

        prediction_regions=self.do_prediction(patches,img,model_region)
        text_region1=self.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= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)

        prediction_regions=self.do_prediction(patches,img,model_region)
        text_region2=self.resize_image(prediction_regions, img_height_h, img_width_h )


        #plt.imshow(text_region2[:,:,0])
        #plt.show()
        session_region.close()
        del model_region
        del session_region
        gc.collect()

        #text_region1=text_region1[:,:,0]
        #text_region2=text_region2[:,:,0]

        #text_region1[(text_region1[:,:]==2) & (text_region2[:,:]==1)]=1


        mask_zeros_from_1=(text_region2[:,:,0]==0)*1
        #mask_text_from_1=(text_region1[:,:,0]==1)*1



        mask_img_text_region1=(text_region1[:,:,0]==2)*1
        text_region2_1st_channel=text_region1[:,:,0]

        text_region2_1st_channel[mask_zeros_from_1==1]=0

        ##text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
        #text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1

        mask_lines1=(text_region1[:,:,0]==3)*1
        mask_lines2=(text_region2[:,:,0]==3)*1

        mask_lines2[mask_lines1[:,:]==1]=1

        #plt.imshow(text_region2_1st_channel)
        #plt.show()

        text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:,:], self.kernel, iterations=4)

        #plt.imshow(text_region2_1st_channel)
        #plt.show()

        text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:,:], self.kernel, iterations=4)


        text_region2_1st_channel[mask_lines2[:,:]==1]=3

        #text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1


        # only in the case of model 3

        text_region2_1st_channel[(prediction_regions_longshot[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1

        text_region2_1st_channel[ (prediction_regions_org[:,:]==2) & (text_region2_1st_channel[:,:]==0)]=2

        #text_region2_1st_channel[prediction_regions_org[:,:]==0]=0

        #plt.imshow(text_region2_1st_channel)
        #plt.show()

        #text_region2_1st_channel[:,:400]=0

        mask_texts_only=(text_region2_1st_channel[:,:]==1)*1

        mask_images_only=(text_region2_1st_channel[:,:]==2)*1

        mask_lines_only=(text_region2_1st_channel[:,:]==3)*1

        pixel_img=1
        polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img)

        polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)

        polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img)


        text_regions_p_true=np.zeros(text_region2_1st_channel.shape)
        #text_regions_p_true[:,:]=text_regions_p_1[:,:]

        text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))

        text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))

        text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))

        ##print(np.unique(text_regions_p_true))


        #text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
        #text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)


        return text_regions_p_true#text_region2_1st_channel

    def get_regions_from_xy(self,img):
        img_org=np.copy(img)

        img_height_h=img_org.shape[0]
        img_width_h=img_org.shape[1]

        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)

        gaussian_filter=False
        patches=True
        binary=True




        ratio_x=1
        ratio_y=1
        median_blur=False

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)

        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org=self.do_prediction(patches,img,model_region)

        ###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= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)

        prediction_regions=self.do_prediction(patches,img,model_region)
        text_region1=self.resize_image(prediction_regions, img_height_h, img_width_h )


        ratio_x=1
        ratio_y=1.1
        binary=False
        median_blur=False


        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)

        prediction_regions=self.do_prediction(patches,img,model_region)
        text_region2=self.resize_image(prediction_regions, img_height_h, img_width_h )

        session_region.close()
        del model_region
        del session_region
        gc.collect()


        mask_zeros_from_1=(text_region1[:,:,0]==0)*1
        #mask_text_from_1=(text_region1[:,:,0]==1)*1



        mask_img_text_region1=(text_region1[:,:,0]==2)*1
        text_region2_1st_channel=text_region2[:,:,0]

        text_region2_1st_channel[mask_zeros_from_1==1]=0

        text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
        #text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1

        mask_lines1=(text_region1[:,:,0]==3)*1
        mask_lines2=(text_region2[:,:,0]==3)*1

        mask_lines2[mask_lines1[:,:]==1]=1

        ##plt.imshow(text_region2_1st_channel)
        ##plt.show()

        text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:,:], self.kernel, iterations=5)

        ##plt.imshow(text_region2_1st_channel)
        ##plt.show()

        text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:,:], self.kernel, iterations=5)


        text_region2_1st_channel[mask_lines2[:,:]==1]=3

        text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
        text_region2_1st_channel[prediction_regions_org[:,:]==3]=3

        ##plt.imshow(text_region2_1st_channel)
        ##plt.show()
        return text_region2_1st_channel


    def rotation_image_new(self,img,thetha):
        rotated=imutils.rotate(img,thetha)
        return self.rotate_max_area_new(img, rotated,thetha)

    def rotate_max_area_new(self,image,rotated,angle):
        wr, hr =self.rotatedRectWithMaxArea(image.shape[1], image.shape[0],math.radians(angle))
        h, w, _ = rotated.shape
        y1 = h//2 - int(hr/2)
        y2 = y1 + int(hr)
        x1 = w//2 - int(wr/2)
        x2 = x1 + int(wr)
        return rotated[y1:y2, x1:x2]


    def rotation_not_90_func(self,img,textline,text_regions_p_1,thetha):
        rotated=imutils.rotate(img,thetha)
        rotated_textline=imutils.rotate(textline,thetha)
        rotated_layout=imutils.rotate(text_regions_p_1,thetha)
        return self.rotate_max_area(img, rotated,rotated_textline,rotated_layout,thetha)

    def rotate_max_area(self,image,rotated,rotated_textline,rotated_layout,angle):
        wr, hr =self.rotatedRectWithMaxArea(image.shape[1], image.shape[0],math.radians(angle))
        h, w, _ = rotated.shape
        y1 = h//2 - int(hr/2)
        y2 = y1 + int(hr)
        x1 = w//2 - int(wr/2)
        x2 = x1 + int(wr)
        return rotated[y1:y2, x1:x2],rotated_textline[y1:y2, x1:x2],rotated_layout[y1:y2, x1:x2]


    def rotation_not_90_func_full_layout(self,img,textline,text_regions_p_1,text_regions_p_fully,thetha):
        rotated=imutils.rotate(img,thetha)
        rotated_textline=imutils.rotate(textline,thetha)
        rotated_layout=imutils.rotate(text_regions_p_1,thetha)
        rotated_layout_full=imutils.rotate(text_regions_p_fully,thetha)
        return self.rotate_max_area_full_layout(img, rotated,rotated_textline,rotated_layout,rotated_layout_full,thetha)

    def rotate_max_area_full_layout(self,image,rotated,rotated_textline,rotated_layout,rotated_layout_full,angle):
        wr, hr =self.rotatedRectWithMaxArea(image.shape[1], image.shape[0],math.radians(angle))
        h, w, _ = rotated.shape
        y1 = h//2 - int(hr/2)
        y2 = y1 + int(hr)
        x1 = w//2 - int(wr/2)
        x2 = x1 + int(wr)
        return rotated[y1:y2, x1:x2],rotated_textline[y1:y2, x1:x2],rotated_layout[y1:y2, x1:x2],rotated_layout_full[y1:y2, x1:x2]

    def get_regions_from_xy_2models_ens(self,img):
        img_org=np.copy(img)

        img_height_h=img_org.shape[0]
        img_width_h=img_org.shape[1]

        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)


        gaussian_filter=False
        patches=False
        binary=False

        ratio_x=1
        ratio_y=1
        img= self.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=self.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= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org_y=self.do_prediction(patches,img,model_region)

        prediction_regions_org_y=self.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= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org=self.do_prediction(patches,img,model_region)

        prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )

        #plt.imshow(prediction_regions_org[:,:,0])
        #plt.show()
        #sys.exit()
        prediction_regions_org=prediction_regions_org[:,:,0]

        prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0


        prediction_regions_org[( prediction_regions_long[:,:,0]==1 ) & (prediction_regions_org[:,:]==2) ]=1

        session_region.close()
        del model_region
        del session_region
        gc.collect()




        return prediction_regions_org

    def get_regions_from_xy_2models(self,img,is_image_enhanced):
        img_org=np.copy(img)

        img_height_h=img_org.shape[0]
        img_width_h=img_org.shape[1]

        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)

        gaussian_filter=False
        patches=True
        binary=False





        ratio_y=1.3
        ratio_x=1

        median_blur=False

        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org_y=self.do_prediction(patches,img,model_region)

        prediction_regions_org_y=self.resize_image(prediction_regions_org_y, img_height_h, img_width_h )

        #plt.imshow(prediction_regions_org_y[:,:,0])
        #plt.show()
        #sys.exit()
        prediction_regions_org_y=prediction_regions_org_y[:,:,0]


        mask_zeros_y=(prediction_regions_org_y[:,:]==0)*1





        if is_image_enhanced:
            ratio_x=1.2
        else:
            ratio_x=1

        ratio_y=1
        median_blur=False

        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org=self.do_prediction(patches,img,model_region)

        prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )

        ##plt.imshow(prediction_regions_org[:,:,0])
        ##plt.show()
        ##sys.exit()
        prediction_regions_org=prediction_regions_org[:,:,0]

        prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
        session_region.close()
        del model_region
        del session_region
        gc.collect()
        ###K.clear_session()

        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)

        gaussian_filter=False
        patches=True
        binary=False




        ratio_x=1
        ratio_y=1
        median_blur=False

        img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

        if binary:
            img = self.otsu_copy_binary(img)#self.otsu_copy(img)
            img = img.astype(np.uint16)

        if median_blur:
            img=cv2.medianBlur(img,5)
        if gaussian_filter:
            img= cv2.GaussianBlur(img,(5,5),0)
            img = img.astype(np.uint16)
        prediction_regions_org2=self.do_prediction(patches,img,model_region)

        prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )

        #plt.imshow(prediction_regions_org2[:,:,0])
        #plt.show()
        #sys.exit()
        ##prediction_regions_org=prediction_regions_org[:,:,0]

        session_region.close()
        del model_region
        del session_region
        gc.collect()
        ###K.clear_session()

        mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
        mask_lines2=(prediction_regions_org2[:,:,0]==3)*1

        text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()


        prediction_regions_org_copy=np.copy(prediction_regions_org)


        prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)]=0

        text_sume_second=( (prediction_regions_org_copy[:,:]==1)*1 ).sum()

        rate_two_models=text_sume_second/float(text_sume_early)*100

        print(rate_two_models,'ratio_of_two_models')
        if is_image_enhanced and rate_two_models<95.50:#98.45:
            pass
        else:
            prediction_regions_org=np.copy(prediction_regions_org_copy)

        ##prediction_regions_org[mask_lines2[:,:]==1]=3
        prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3


        del mask_lines2
        del mask_zeros2
        del prediction_regions_org2

        #if is_image_enhanced:
            #pass
        #else:
            #model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)

            #gaussian_filter=False
            #patches=True
            #binary=False




            #ratio_x=1
            #ratio_y=1
            #median_blur=False

            #img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))

            #if binary:
                #img = self.otsu_copy_binary(img)#self.otsu_copy(img)
                #img = img.astype(np.uint16)

            #if median_blur:
                #img=cv2.medianBlur(img,5)
            #if gaussian_filter:
                #img= cv2.GaussianBlur(img,(5,5),0)
                #img = img.astype(np.uint16)
            #prediction_regions_org2=self.do_prediction(patches,img,model_region)

            #prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )

            ##plt.imshow(prediction_regions_org2[:,:,0])
            ##plt.show()
            ##sys.exit()
            ###prediction_regions_org=prediction_regions_org[:,:,0]

            #session_region.close()
            #del model_region
            #del session_region
            #gc.collect()
            ####K.clear_session()

            #mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
            #mask_lines2=(prediction_regions_org2[:,:,0]==3)*1

            #text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()


            #prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0

            ###prediction_regions_org[mask_lines2[:,:]==1]=3
            #prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3

            #text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()

            #print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')

            #del mask_lines2
            #del mask_zeros2
            #del prediction_regions_org2

        mask_lines_only=(prediction_regions_org[:,:]==3)*1

        prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)

        #plt.imshow(text_region2_1st_channel)
        #plt.show()

        prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)

        mask_texts_only=(prediction_regions_org[:,:]==1)*1

        mask_images_only=(prediction_regions_org[:,:]==2)*1



        pixel_img=1
        min_area_text=0.00001
        polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)

        polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)

        polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)


        text_regions_p_true=np.zeros(prediction_regions_org.shape)
        #text_regions_p_true[:,:]=text_regions_p_1[:,:]

        text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))

        ##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
        text_regions_p_true[:,:][mask_images_only[:,:]==1]=2

        text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))

        ##print(np.unique(text_regions_p_true))


        #text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
        #text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)

        del polygons_of_only_texts
        del polygons_of_only_images
        del polygons_of_only_lines
        del mask_images_only
        del prediction_regions_org
        del img
        del mask_zeros_y

        del prediction_regions_org_y
        del img_org
        gc.collect()

        return text_regions_p_true


    def get_textregion_contours_in_org_image(self,cnts,img,slope_first):

        cnts_org=[]
        #print(cnts,'cnts')
        for i in range(len(cnts)):
            img_copy=np.zeros(img.shape)
            img_copy=cv2.fillPoly(img_copy,pts=[cnts[i]], color=(1,1,1))

            #plt.imshow(img_copy)
            #plt.show()

            #print(img.shape,'img')
            img_copy=self.rotation_image_new(img_copy,-slope_first)
            ##print(img_copy.shape,'img_copy')
            #plt.imshow(img_copy)
            #plt.show()

            img_copy=img_copy.astype(np.uint8)
            imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
            ret, thresh = cv2.threshold(imgray, 0, 255, 0)

            cont_int,_=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

            cont_int[0][:,0,0]=cont_int[0][:,0,0]+np.abs(img_copy.shape[1]-img.shape[1])
            cont_int[0][:,0,1]=cont_int[0][:,0,1]+np.abs(img_copy.shape[0]-img.shape[0])
            #print(np.shape(cont_int[0]))
            cnts_org.append(cont_int[0])



        #print(cnts_org,'cnts_org')

        #sys.exit()
        self.y_shift=np.abs(img_copy.shape[0]-img.shape[0])
        self.x_shift=np.abs(img_copy.shape[1]-img.shape[1])
        return cnts_org
    def write_images_into_directory(self,img_contoures,dir_of_cropped_imgs,image_page):
        index=0
        for cont_ind in img_contoures:
            #cont_ind[:,0,0]=cont_ind[:,0,0]/self.scale_x
            #cont_ind[:,0,1]=cont_ind[:,0,1]/self.scale_y

            x, y, w, h = cv2.boundingRect(cont_ind)
            box = [x, y, w, h]
            croped_page, page_coord = self.crop_image_inside_box(box, image_page)

            croped_page = self.resize_image(croped_page, int(croped_page.shape[0]/self.scale_y), int(croped_page.shape[1]/self.scale_x))

            path=os.path.join(dir_of_cropped_imgs, self.f_name+'_'+str(index)+'.jpg')
            cv2.imwrite(path,croped_page)
            index+=1

    def get_marginals(self,text_with_lines,text_regions,num_col,slope_deskew):
        mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
        mask_marginals=mask_marginals.astype(np.uint8)


        text_with_lines=text_with_lines.astype(np.uint8)
        ##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)

        text_with_lines_eroded=cv2.erode(text_with_lines,self.kernel,iterations=5)

        if text_with_lines.shape[0]<=1500:
            pass
        elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
            text_with_lines=self.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=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
        else:
            text_with_lines=self.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=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])


        text_with_lines_y=text_with_lines.sum(axis=0)
        text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)

        thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100

        #print(thickness_along_y_percent,'thickness_along_y_percent')

        if thickness_along_y_percent<30:
            min_textline_thickness=8
        elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
            min_textline_thickness=20
        else:
            min_textline_thickness=40



        if thickness_along_y_percent>=14:

            text_with_lines_y_rev=-1*text_with_lines_y[:]
            #print(text_with_lines_y)
            #print(text_with_lines_y_rev)




            #plt.plot(text_with_lines_y)
            #plt.show()


            text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)

            #plt.plot(text_with_lines_y_rev)
            #plt.show()
            sigma_gaus=1
            region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)

            region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)

            #plt.plot(region_sum_0_rev)
            #plt.show()
            region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]

            first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
            last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))


            last_nonzero=len(region_sum_0)-last_nonzero

            ##img_sum_0_smooth_rev=-region_sum_0


            mid_point=(last_nonzero+first_nonzero)/2.


            one_third_right=(last_nonzero-mid_point)/3.0
            one_third_left=(mid_point-first_nonzero)/3.0

            #img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)




            peaks, _ = find_peaks(text_with_lines_y_rev, height=0)


            peaks=np.array(peaks)


            #print(region_sum_0[peaks])
            ##plt.plot(region_sum_0)
            ##plt.plot(peaks,region_sum_0[peaks],'*')
            ##plt.show()
            #print(first_nonzero,last_nonzero,peaks)
            peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]

            #print(first_nonzero,last_nonzero,peaks)


            #print(region_sum_0[peaks]<10)
            ####peaks=peaks[region_sum_0[peaks]<25 ]

            #print(region_sum_0[peaks])
            peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
            #print(peaks)
            #print(first_nonzero,last_nonzero,one_third_right,one_third_left)

            if num_col==1:
                peaks_right=peaks[peaks>mid_point]
                peaks_left=peaks[peaks<mid_point]
            if num_col==2:
                peaks_right=peaks[peaks>(mid_point+one_third_right)]
                peaks_left=peaks[peaks<(mid_point-one_third_left)]


            try:
                point_right=np.min(peaks_right)
            except:
                point_right=last_nonzero


            try:
                point_left=np.max(peaks_left)
            except:
                point_left=first_nonzero




            #print(point_left,point_right)
            #print(text_regions.shape)
            if point_right>=mask_marginals.shape[1]:
                point_right=mask_marginals.shape[1]-1

            try:
                mask_marginals[:,point_left:point_right]=1
            except:
                mask_marginals[:,:]=1

            #print(mask_marginals.shape,point_left,point_right,'nadosh')
            mask_marginals_rotated=self.rotate_image(mask_marginals,-slope_deskew)

            #print(mask_marginals_rotated.shape,'nadosh')
            mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)

            mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
            index_x=np.array(range(len(mask_marginals_rotated_sum)))+1

            index_x_interest=index_x[mask_marginals_rotated_sum==1]

            min_point_of_left_marginal=np.min(index_x_interest)-16
            max_point_of_right_marginal=np.max(index_x_interest)+16

            if min_point_of_left_marginal<0:
                min_point_of_left_marginal=0
            if max_point_of_right_marginal>=text_regions.shape[1]:
                max_point_of_right_marginal=text_regions.shape[1]-1


            #print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
            #print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
            #plt.imshow(mask_marginals)
            #plt.show()

            #plt.imshow(mask_marginals_rotated)
            #plt.show()

            text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4


            pixel_img=4
            min_area_text=0.00001
            polygons_of_marginals=self.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=self.find_new_features_of_contoures(polygons_of_marginals)

            text_regions[(text_regions[:,:]==4)]=1

            marginlas_should_be_main_text=[]

            x_min_marginals_left=[]
            x_min_marginals_right=[]

            for i in range(len(cx_text_only)):

                x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
                y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
                #print(x_width_mar,y_height_mar,'y_height_mar')
                if x_width_mar>16 and y_height_mar/x_width_mar<10:
                    marginlas_should_be_main_text.append(polygons_of_marginals[i])
                    if x_min_text_only[i]<(mid_point-one_third_left):
                        x_min_marginals_left_new=x_min_text_only[i]
                        if len(x_min_marginals_left)==0:
                            x_min_marginals_left.append(x_min_marginals_left_new)
                        else:
                            x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
                    else:
                        x_min_marginals_right_new=x_min_text_only[i]
                        if len(x_min_marginals_right)==0:
                            x_min_marginals_right.append(x_min_marginals_right_new)
                        else:
                            x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)

            if len(x_min_marginals_left)==0:
                x_min_marginals_left=[0]
            if len(x_min_marginals_right)==0:
                x_min_marginals_right=[text_regions.shape[1]-1]




            #print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')

            #print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
            text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))

            #print(np.unique(text_regions))

            #text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
            #text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0

            text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
            text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0

            ###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4

            ###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
            #plt.plot(region_sum_0)
            #plt.plot(peaks,region_sum_0[peaks],'*')
            #plt.show()


            #plt.imshow(text_regions)
            #plt.show()

            #sys.exit()
        else:
            pass
        return text_regions

    def do_work_of_textline_seperation(self,queue_of_all_params,polygons_per_process, index_polygons_per_process,con_par_org,textline_mask_tot,mask_texts_only,num_col,scale_par,boxes_text):


        textregions_cnt_tot_per_process=[]
        textlines_cnt_tot_per_process=[]
        index_polygons_per_process_per_process=[]
        polygons_per_par_process_per_process=[]
        textline_cnt_seperated=np.zeros(textline_mask_tot.shape)
        for iiii in range(len(polygons_per_process)):
            #crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
            #arg_max=np.argmax(areas_cnt_only_text)
            textregions_cnt_tot_per_process.append(polygons_per_process[iiii]/scale_par)
            textline_region_in_image=np.zeros(textline_mask_tot.shape)
            cnt_o_t_max=polygons_per_process[iiii]

            x, y, w, h = cv2.boundingRect(cnt_o_t_max)


            mask_biggest=np.zeros(mask_texts_only.shape)
            mask_biggest=cv2.fillPoly(mask_biggest, pts =[cnt_o_t_max], color=(1,1,1))

            mask_region_in_patch_region=mask_biggest[y:y+h,x:x+w]


            textline_biggest_region=mask_biggest*textline_mask_tot


            textline_rotated_seperated=self.seperate_lines_new2(textline_biggest_region[y:y+h,x:x+w],0,num_col)


            #new line added
            ##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
            textline_rotated_seperated[mask_region_in_patch_region[:,:]!=1]=0
            #till here

            textline_cnt_seperated[y:y+h,x:x+w]=textline_rotated_seperated
            textline_region_in_image[y:y+h,x:x+w]=textline_rotated_seperated

            #plt.imshow(textline_region_in_image)
            #plt.show()

            #plt.imshow(textline_cnt_seperated)
            #plt.show()




            pixel_img=1
            cnt_textlines_in_image=self.return_contours_of_interested_textline(textline_region_in_image,pixel_img)



            textlines_cnt_per_region=[]
            for jjjj in range(len(cnt_textlines_in_image)):
                mask_biggest2=np.zeros(mask_texts_only.shape)
                mask_biggest2=cv2.fillPoly(mask_biggest2, pts =[cnt_textlines_in_image[jjjj]], color=(1,1,1))
                if num_col+1==1:
                    mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=5)
                else:

                    mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=4)

                pixel_img=1
                cnt_textlines_in_image_ind=self.return_contours_of_interested_textline(mask_biggest2,pixel_img)

                try:
                    textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
                except:
                    pass
                #print(len(cnt_textlines_in_image_ind))



                #plt.imshow(mask_biggest2)
                #plt.show()
            textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
            index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
            polygons_per_par_process_per_process.append(con_par_org[iiii])



        queue_of_all_params.put([index_polygons_per_process_per_process,polygons_per_par_process_per_process,textregions_cnt_tot_per_process, textlines_cnt_tot_per_process ])

    def 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=[]
        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)

            for nn in range(len(textlines_con[m1])):
                textlines_tot.append(np.array( textlines_con[m1][nn],dtype=np.int32)  )
                textlines_tot_org_form.append(textlines_con[m1][nn])

            ##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))

            ##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
            else:
                min_area=0.0001
            indexes_textlines_small=indexes_textlines[areas_cnt_text<min_area]

            #print(indexes_textlines)

            textlines_small=[]
            textlines_small_org_form=[]
            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) ):
                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=[]
                for z1 in range(len(textlines_small)):
                    #print(len(textlines_small),'small')
                    intersections=[]
                    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)

                        intersections.append(sum_intersection)

                    if len(np.array(intersections)[np.array(intersections)>0] )==0:
                        intersections=[]

                    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=[]


                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))

                    textlines_big_with_change.append(z)

                    for k in index_small_textlines:
                        img_text2=cv2.fillPoly(img_text2, pts =[textlines_small[k]] , color=(255,255,255))
                        textlines_small_with_change.append(k)


                    img_text2=img_text2.astype(np.uint8)
                    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))

                    textlines_big_with_change_con.append(cont)
                    textlines_big_org_form[z]=cont[0]

                    #plt.imshow(img_text2)
                    #plt.show()

                #print(textlines_big_with_change,'textlines_big_with_change')
                #print(textlines_small_with_change,'textlines_small_with_change')
                #print(textlines_big)
                textlines_con_changed.append(textlines_big_org_form)

            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=self.return_contours_of_image(text_only)

        """
        contours_only_text_parent=self.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])

        ###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=self.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



        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=[]

        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))
            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
                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
                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=self.return_contours_of_image(drop_only)
        contours_drop_parent=self.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=[]

        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

        return layout_in_patch

    def filter_small_drop_capitals_from_no_patch_layout(self,layout_no_patch,layout1):

        drop_only=(layout_no_patch[:,:,0]==4)*1
        contours_drop,hir_on_drop=self.return_contours_of_image(drop_only)
        contours_drop_parent=self.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]>0.001]

        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=[]

        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)])

            iou_of_box_and_contoure=float(drop_only.shape[0]*drop_only.shape[1])*areas_cnt_text[jj]/float(w*h)*100
            height_to_weight_ratio=h/float(w)
            weigh_to_height_ratio=w/float(h)

            if iou_of_box_and_contoure>60 and weigh_to_height_ratio<1.2 and height_to_weight_ratio<2:
                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:
                    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))

        return layout_no_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=self.return_contours_of_image(drop_only)
        contours_drop_parent=self.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=[]

        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:
                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))

        return layout_no_patch

    def do_order_of_regions(self,contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot):

        if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
            cx_text_only,cy_text_only ,x_min_text_only, _, _ ,_,y_cor_x_min_main=self.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=self.find_new_features_of_contoures(contours_only_text_parent_h)


            try:
                arg_text_con=[]
                for ii in range(len(cx_text_only)):
                    for jj in range(len(boxes)):
                        if (x_min_text_only[ii]+80) >=boxes[jj][0] and (x_min_text_only[ii]+80) < boxes[jj][1] and y_cor_x_min_main[ii] >=boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
                            arg_text_con.append(jj)
                            break
                arg_arg_text_con=np.argsort(arg_text_con)
                args_contours=np.array(range( len(arg_text_con)) )

                arg_text_con_h=[]
                for ii in range(len(cx_text_only_h)):
                    for jj in range(len(boxes)):
                        if (x_min_text_only_h[ii]+80) >=boxes[jj][0] and (x_min_text_only_h[ii]+80) < boxes[jj][1] and y_cor_x_min_main_h[ii] >=boxes[jj][2] and y_cor_x_min_main_h[ii] < boxes[jj][3]:
                            arg_text_con_h.append(jj)
                            break
                arg_arg_text_con=np.argsort(arg_text_con_h)
                args_contours_h=np.array(range( len(arg_text_con_h)) )


                order_by_con_head=np.zeros(len(arg_text_con_h))
                order_by_con_main=np.zeros(len(arg_text_con))


                ref_point=0
                order_of_texts_tot=[]
                id_of_texts_tot=[]
                for iij in range(len(boxes)):

                    args_contours_box=args_contours[np.array(arg_text_con)==iij]
                    args_contours_box_h=args_contours_h[np.array(arg_text_con_h)==iij]
                    con_inter_box=[]
                    con_inter_box_h=[]

                    for i in range(len(args_contours_box)):
                        con_inter_box.append( contours_only_text_parent[args_contours_box[i] ]  )

                    for i in range(len(args_contours_box_h)):
                        con_inter_box_h.append( contours_only_text_parent_h[args_contours_box_h[i] ]  )

                    indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=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]



                    zahler=0
                    for mtv in args_contours_box:
                        arg_order_v=indexes_sorted_main[zahler]
                        tartib=np.where(indexes_sorted==arg_order_v )[0][0]
                        order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
                        zahler=zahler+1

                    zahler=0
                    for mtv in args_contours_box_h:
                        arg_order_v=indexes_sorted_head[zahler]
                        tartib=np.where(indexes_sorted==arg_order_v )[0][0]
                        #print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
                        order_by_con_head[ args_contours_box_h[indexes_by_type_head[zahler] ]]=tartib+ref_point
                        zahler=zahler+1



                    for jji in range(len(id_of_texts)):
                        order_of_texts_tot.append(order_of_texts[jji]+ref_point)
                        id_of_texts_tot.append(id_of_texts[jji])
                    ref_point=ref_point+len(id_of_texts)


                order_of_texts_tot=[]
                for tj1 in range(len(contours_only_text_parent)):
                    order_of_texts_tot.append(int(order_by_con_main[tj1] ))

                for tj1 in range(len(contours_only_text_parent_h)):
                    order_of_texts_tot.append(int(order_by_con_head[tj1]) )

                order_text_new=[]
                for iii in range(len(order_of_texts_tot)):
                    tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
                    order_text_new.append(tartib_new)


            except:
                arg_text_con=[]
                for ii in range(len(cx_text_only)):
                    for jj in range(len(boxes)):
                        if cx_text_only[ii] >=boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >=boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]:#this is valid if the center of region identify in which box it is located
                            arg_text_con.append(jj)
                            break
                arg_arg_text_con=np.argsort(arg_text_con)
                args_contours=np.array(range( len(arg_text_con)) )


                order_by_con_main=np.zeros(len(arg_text_con))



                ############################# head

                arg_text_con_h=[]
                for ii in range(len(cx_text_only_h)):
                    for jj in range(len(boxes)):
                        if cx_text_only_h[ii] >=boxes[jj][0] and cx_text_only_h[ii] < boxes[jj][1] and cy_text_only_h[ii] >=boxes[jj][2] and cy_text_only_h[ii] < boxes[jj][3]:#this is valid if the center of region identify in which box it is located
                            arg_text_con_h.append(jj)
                            break
                arg_arg_text_con_h=np.argsort(arg_text_con_h)
                args_contours_h=np.array(range( len(arg_text_con_h)) )

                order_by_con_head=np.zeros(len(arg_text_con_h))
                #####

                ref_point=0
                order_of_texts_tot=[]
                id_of_texts_tot=[]
                for iij in range(len(boxes)):
                    args_contours_box=args_contours[np.array(arg_text_con)==iij]
                    args_contours_box_h=args_contours_h[np.array(arg_text_con_h)==iij]
                    con_inter_box=[]
                    con_inter_box_h=[]

                    for i in range(len(args_contours_box)):

                        con_inter_box.append( contours_only_text_parent[args_contours_box[i] ]  )
                    for i in range(len(args_contours_box_h)):

                        con_inter_box_h.append( contours_only_text_parent_h[args_contours_box_h[i] ]  )

                    indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=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]



                    zahler=0
                    for mtv in args_contours_box:
                        arg_order_v=indexes_sorted_main[zahler]
                        tartib=np.where(indexes_sorted==arg_order_v )[0][0]
                        order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
                        zahler=zahler+1


                    zahler=0
                    for mtv in args_contours_box_h:
                        arg_order_v=indexes_sorted_head[zahler]
                        tartib=np.where(indexes_sorted==arg_order_v )[0][0]
                        #print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
                        order_by_con_head[ args_contours_box_h[indexes_by_type_head[zahler] ]]=tartib+ref_point
                        zahler=zahler+1



                    for jji in range(len(id_of_texts)):
                        order_of_texts_tot.append(order_of_texts[jji]+ref_point)
                        id_of_texts_tot.append(id_of_texts[jji])
                    ref_point=ref_point+len(id_of_texts)




                order_of_texts_tot=[]
                for tj1 in range(len(contours_only_text_parent)):
                    order_of_texts_tot.append(int(order_by_con_main[tj1] ))

                for tj1 in range(len(contours_only_text_parent_h)):
                    order_of_texts_tot.append(int(order_by_con_head[tj1]) )


                order_text_new=[]
                for iii in range(len(order_of_texts_tot)):
                    tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
                    order_text_new.append(tartib_new)
            return order_text_new,id_of_texts_tot

        else:
            cx_text_only,cy_text_only ,x_min_text_only, _, _ ,_,y_cor_x_min_main=self.find_new_features_of_contoures(contours_only_text_parent)


            try:
                arg_text_con=[]
                for ii in range(len(cx_text_only)):
                    for jj in range(len(boxes)):
                        if (x_min_text_only[ii]+80) >=boxes[jj][0] and (x_min_text_only[ii]+80) < boxes[jj][1] and y_cor_x_min_main[ii] >=boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
                            arg_text_con.append(jj)
                            break
                arg_arg_text_con=np.argsort(arg_text_con)
                args_contours=np.array(range( len(arg_text_con)) )



                order_by_con_main=np.zeros(len(arg_text_con))


                ref_point=0
                order_of_texts_tot=[]
                id_of_texts_tot=[]
                for iij in range(len(boxes)):

                    args_contours_box=args_contours[np.array(arg_text_con)==iij]

                    con_inter_box=[]
                    con_inter_box_h=[]

                    for i in range(len(args_contours_box)):
                        con_inter_box.append( contours_only_text_parent[args_contours_box[i] ]  )


                    indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=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]



                    zahler=0
                    for mtv in args_contours_box:
                        arg_order_v=indexes_sorted_main[zahler]
                        tartib=np.where(indexes_sorted==arg_order_v )[0][0]
                        order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
                        zahler=zahler+1




                    for jji in range(len(id_of_texts)):
                        order_of_texts_tot.append(order_of_texts[jji]+ref_point)
                        id_of_texts_tot.append(id_of_texts[jji])
                    ref_point=ref_point+len(id_of_texts)


                order_of_texts_tot=[]
                for tj1 in range(len(contours_only_text_parent)):
                    order_of_texts_tot.append(int(order_by_con_main[tj1] ))


                order_text_new=[]
                for iii in range(len(order_of_texts_tot)):
                    tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
                    order_text_new.append(tartib_new)


            except:
                arg_text_con=[]
                for ii in range(len(cx_text_only)):
                    for jj in range(len(boxes)):
                        if cx_text_only[ii] >=boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >=boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]:#this is valid if the center of region identify in which box it is located
                            arg_text_con.append(jj)
                            break
                arg_arg_text_con=np.argsort(arg_text_con)
                args_contours=np.array(range( len(arg_text_con)) )


                order_by_con_main=np.zeros(len(arg_text_con))



                ref_point=0
                order_of_texts_tot=[]
                id_of_texts_tot=[]
                for iij in range(len(boxes)):
                    args_contours_box=args_contours[np.array(arg_text_con)==iij]
                    con_inter_box=[]
                    con_inter_box_h=[]

                    for i in range(len(args_contours_box)):

                        con_inter_box.append( contours_only_text_parent[args_contours_box[i] ]  )


                    indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=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]



                    zahler=0
                    for mtv in args_contours_box:
                        arg_order_v=indexes_sorted_main[zahler]
                        tartib=np.where(indexes_sorted==arg_order_v )[0][0]
                        order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
                        zahler=zahler+1




                    for jji in range(len(id_of_texts)):
                        order_of_texts_tot.append(order_of_texts[jji]+ref_point)
                        id_of_texts_tot.append(id_of_texts[jji])
                    ref_point=ref_point+len(id_of_texts)




                order_of_texts_tot=[]
                for tj1 in range(len(contours_only_text_parent)):
                    order_of_texts_tot.append(int(order_by_con_main[tj1] ))


                order_text_new=[]
                for iii in range(len(order_of_texts_tot)):
                    tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
                    order_text_new.append(tartib_new)

            return order_text_new,id_of_texts_tot
    def adhere_drop_capital_region_into_cprresponding_textline(self,text_regions_p,polygons_of_drop_capitals,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h):
        #print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
        #print(all_found_texline_polygons[3])
        cx_m,cy_m ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent)
        cx_h,cy_h ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent_h)
        cx_d,cy_d ,_ , _, y_min_d ,y_max_d,_=self.find_new_features_of_contoures(polygons_of_drop_capitals)

        img_con_all=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
        for j_cont in range(len(contours_only_text_parent)):
            img_con_all[all_box_coord[j_cont][0]:all_box_coord[j_cont][1],all_box_coord[j_cont][2]:all_box_coord[j_cont][3],0]=(j_cont+1)*3
            #img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))

        #plt.imshow(img_con_all[:,:,0])
        #plt.show()
        #img_con_all=cv2.dilate(img_con_all, self.kernel, iterations=3)

        #plt.imshow(img_con_all[:,:,0])
        #plt.show()
        #print(np.unique(img_con_all[:,:,0]))
        for i_drop in range(len(polygons_of_drop_capitals)):
            #print(i_drop,'i_drop')
            img_con_all_copy=np.copy(img_con_all)
            img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
            img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(1,1,1))


            #plt.imshow(img_con[:,:,0])
            #plt.show()
            ##img_con=cv2.dilate(img_con, self.kernel, iterations=30)

            #plt.imshow(img_con[:,:,0])
            #plt.show()

            #print(np.unique(img_con[:,:,0]))

            img_con_all_copy[:,:,0]=img_con_all_copy[:,:,0]+img_con[:,:,0]

            img_con_all_copy[:,:,0][img_con_all_copy[:,:,0]==1]=0

            kherej_ghesmat=np.unique(img_con_all_copy[:,:,0])/3
            res_summed_pixels=np.unique(img_con_all_copy[:,:,0])%3
            region_with_intersected_drop=kherej_ghesmat[res_summed_pixels==1]
            #region_with_intersected_drop=region_with_intersected_drop/3
            region_with_intersected_drop=region_with_intersected_drop.astype(np.uint8)

            #print(len(region_with_intersected_drop),'region_with_intersected_drop1')
            if len(region_with_intersected_drop)==0:
                img_con_all_copy=np.copy(img_con_all)
                img_con=cv2.dilate(img_con, self.kernel, iterations=4)

                img_con_all_copy[:,:,0]=img_con_all_copy[:,:,0]+img_con[:,:,0]

                img_con_all_copy[:,:,0][img_con_all_copy[:,:,0]==1]=0

                kherej_ghesmat=np.unique(img_con_all_copy[:,:,0])/3
                res_summed_pixels=np.unique(img_con_all_copy[:,:,0])%3
                region_with_intersected_drop=kherej_ghesmat[res_summed_pixels==1]
                #region_with_intersected_drop=region_with_intersected_drop/3
                region_with_intersected_drop=region_with_intersected_drop.astype(np.uint8)
            #print(np.unique(img_con_all_copy[:,:,0]))
            if self.curved_line=='True' or self.curved_line=='true':


                if len(region_with_intersected_drop)>1:
                    sum_pixels_of_intersection=[]
                    for i in range(len(region_with_intersected_drop)):
                        #print((region_with_intersected_drop[i]*3+1))
                        sum_pixels_of_intersection.append( ((img_con_all_copy[:,:,0]==(region_with_intersected_drop[i]*3+1))*1).sum() )
                    #print(sum_pixels_of_intersection)
                    region_final=region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)]-1

                    #print(region_final,'region_final')
                    #cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                    try:
                        cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                        #print(all_box_coord[j_cont])
                        #print(cx_t)
                        #print(cy_t)
                        #print(cx_d[i_drop])
                        #print(cy_d[i_drop])
                        y_lines=np.array(cy_t)#all_box_coord[int(region_final)][0]+np.array(cy_t)


                        #print(y_lines)

                        y_lines[y_lines<y_min_d[i_drop]]=0
                        #print(y_lines)

                        arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                        #print(arg_min)

                        cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
                        cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
                        cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]

                        img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                        img_textlines=img_textlines.astype(np.uint8)
                        imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                        ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                        contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

                        #print(len(contours_combined),'len textlines mixed')
                        areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                        contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                        #print(np.shape(contours_biggest))
                        #print(contours_biggest[:])
                        #contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
                        #contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]

                        #contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])


                        all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest


                    except:
                        #print('gordun1')
                        pass
                elif len(region_with_intersected_drop)==1:
                    region_final=region_with_intersected_drop[0]-1

                    #areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])


                    #cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])


                    cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                    #print(all_box_coord[j_cont])
                    #print(cx_t)
                    #print(cy_t)
                    #print(cx_d[i_drop])
                    #print(cy_d[i_drop])
                    y_lines=np.array(cy_t)#all_box_coord[int(region_final)][0]+np.array(cy_t)

                    y_lines[y_lines<y_min_d[i_drop]]=0
                    #print(y_lines)

                    arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                    #print(arg_min)

                    cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
                    cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
                    cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]

                    img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                    img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                    img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                    img_textlines=img_textlines.astype(np.uint8)

                    #plt.imshow(img_textlines)
                    #plt.show()
                    imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                    ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                    contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


                    #print(len(contours_combined),'len textlines mixed')
                    areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                    contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                    #print(np.shape(contours_biggest))
                    #print(contours_biggest[:])
                    #contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
                    #contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
                    #print(np.shape(contours_biggest),'contours_biggest')
                    #print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
                    ##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
                    all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest

                    #print(cx_t,'print')
                    try:
                        #print(all_found_texline_polygons[j_cont][0])
                        cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                        #print(all_box_coord[j_cont])
                        #print(cx_t)
                        #print(cy_t)
                        #print(cx_d[i_drop])
                        #print(cy_d[i_drop])
                        y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)

                        y_lines[y_lines<y_min_d[i_drop]]=0
                        #print(y_lines)

                        arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                        #print(arg_min)

                        cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
                        cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
                        cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]

                        img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                        img_textlines=img_textlines.astype(np.uint8)
                        imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                        ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                        contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


                        #print(len(contours_combined),'len textlines mixed')
                        areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                        contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                        #print(np.shape(contours_biggest))
                        #print(contours_biggest[:])
                        contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
                        contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]

                        ##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
                        all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
                        #all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest

                    except:
                        pass
                else:
                    pass











                ##cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                ###print(all_box_coord[j_cont])
                ###print(cx_t)
                ###print(cy_t)
                ###print(cx_d[i_drop])
                ###print(cy_d[i_drop])
                ##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)

                ##y_lines[y_lines<y_min_d[i_drop]]=0
                ###print(y_lines)

                ##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                ###print(arg_min)

                ##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
                ##cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
                ##cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]

                ##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                ##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                ##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                ##img_textlines=img_textlines.astype(np.uint8)

                ##plt.imshow(img_textlines)
                ##plt.show()
                ##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                ##ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                ##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


                ##print(len(contours_combined),'len textlines mixed')
                ##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                ##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                ###print(np.shape(contours_biggest))
                ###print(contours_biggest[:])
                ##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
                ##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]

                ##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
                ##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest

            else:
                if len(region_with_intersected_drop)>1:
                    sum_pixels_of_intersection=[]
                    for i in range(len(region_with_intersected_drop)):
                        #print((region_with_intersected_drop[i]*3+1))
                        sum_pixels_of_intersection.append( ((img_con_all_copy[:,:,0]==(region_with_intersected_drop[i]*3+1))*1).sum() )
                    #print(sum_pixels_of_intersection)
                    region_final=region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)]-1

                    #print(region_final,'region_final')
                    #cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                    try:
                        cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                        #print(all_box_coord[j_cont])
                        #print(cx_t)
                        #print(cy_t)
                        #print(cx_d[i_drop])
                        #print(cy_d[i_drop])
                        y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)


                        #print(y_lines)

                        y_lines[y_lines<y_min_d[i_drop]]=0
                        #print(y_lines)

                        arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                        #print(arg_min)

                        cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
                        cnt_nearest[:,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0]+all_box_coord[int(region_final)][2]
                        cnt_nearest[:,1]=all_found_texline_polygons[int(region_final)][arg_min][:,1]+all_box_coord[int(region_final)][0]

                        img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                        img_textlines=img_textlines.astype(np.uint8)
                        imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                        ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                        contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

                        #print(len(contours_combined),'len textlines mixed')
                        areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                        contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                        #print(np.shape(contours_biggest))
                        #print(contours_biggest[:])
                        contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[int(region_final)][2]
                        contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[int(region_final)][0]

                        contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])


                        all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest


                    except:
                        #print('gordun1')
                        pass
                elif len(region_with_intersected_drop)==1:
                    region_final=region_with_intersected_drop[0]-1


                    #areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])


                    #cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])


                    #print(cx_t,'print')
                    try:
                        #print(all_found_texline_polygons[j_cont][0])
                        cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
                        #print(all_box_coord[j_cont])
                        #print(cx_t)
                        #print(cy_t)
                        #print(cx_d[i_drop])
                        #print(cy_d[i_drop])
                        y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)

                        y_lines[y_lines<y_min_d[i_drop]]=0
                        #print(y_lines)

                        arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                        #print(arg_min)

                        cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
                        cnt_nearest[:,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0]+all_box_coord[int(region_final)][2]
                        cnt_nearest[:,1]=all_found_texline_polygons[int(region_final)][arg_min][:,1]+all_box_coord[int(region_final)][0]

                        img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                        img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                        img_textlines=img_textlines.astype(np.uint8)
                        imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                        ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                        contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


                        #print(len(contours_combined),'len textlines mixed')
                        areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                        contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                        #print(np.shape(contours_biggest))
                        #print(contours_biggest[:])
                        contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[int(region_final)][2]
                        contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[int(region_final)][0]

                        contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
                        all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
                        #all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest

                    except:
                        pass
                else:
                    pass


        #####for i_drop in range(len(polygons_of_drop_capitals)):
            #####for j_cont in range(len(contours_only_text_parent)):
                #####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                #####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
                #####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))

                #####img_con=img_con.astype(np.uint8)
                ######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
                ######ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                ######contours_new,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

                #####contours_new,hir_new=self.return_contours_of_image(img_con)
                #####contours_new_parent=self.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 ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[j_cont])
                        ######print(all_box_coord[j_cont])
                        ######print(cx_t)
                        ######print(cy_t)
                        ######print(cx_d[i_drop])
                        ######print(cy_d[i_drop])
                        #####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)


                        ######print(y_lines)

                        #####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop])  )
                        ######print(arg_min)

                        #####cnt_nearest=np.copy(all_found_texline_polygons[j_cont][arg_min])
                        #####cnt_nearest[:,0]=all_found_texline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
                        #####cnt_nearest[:,1]=all_found_texline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]

                        #####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
                        #####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
                        #####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))


                        #####img_textlines=img_textlines.astype(np.uint8)
                        #####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
                        #####ret, thresh = cv2.threshold(imgray, 0, 255, 0)

                        #####contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

                        #####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])

                        #####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]

                        ######print(np.shape(contours_biggest))
                        ######print(contours_biggest[:])
                        #####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
                        #####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]

                        #####all_found_texline_polygons[j_cont][arg_min]=contours_biggest
                        ######print(contours_biggest)
                        ######plt.imshow(img_textlines[:,:,0])
                        ######plt.show()
                    #####else:
                        #####pass
                #####except:
                    #####pass
        return all_found_texline_polygons


    def save_plot_of_layout_main(self,text_regions_p,image_page):
        values=np.unique(text_regions_p[:,:])

        #pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']

        pixels=['Background' , 'Main text'  , 'Images' , 'Seperators','Marginalia']
        values_indexes=[0 , 1 , 2 , 3,4]
        plt.figure(figsize=(40,40))
        plt.rcParams['font.size']='40'

        im=plt.imshow(text_regions_p[:,:])
        colors=[im.cmap(im.norm(value)) for value in values]
        patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int(  np.where(values_indexes==i)[0][0]  )])) for i in values]
        plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=40)
        plt.savefig(os.path.join(self.dir_of_layout,self.f_name+'_layout_main.png') )




    def save_plot_of_layout_main_all(self,text_regions_p,image_page):
        values=np.unique(text_regions_p[:,:])

        #pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']

        pixels=['Background' , 'Main text'  , 'Images' , 'Seperators','Marginalia']
        values_indexes=[0 , 1 , 2 , 3,4]

        plt.figure(figsize=(70,40))
        plt.rcParams['font.size']='40'
        plt.subplot(1,2,1)
        plt.imshow(image_page)
        plt.subplot(1,2,2)
        im=plt.imshow(text_regions_p[:,:])
        colors=[im.cmap(im.norm(value)) for value in values]
        patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int(  np.where(values_indexes==i)[0][0]  )])) for i in values]
        plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=60)


        plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_layout_main_and_page.png') )

    def save_plot_of_layout(self,text_regions_p,image_page):
        values=np.unique(text_regions_p[:,:])

        #pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']

        pixels=['Background' , 'Main text' , 'Header' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators']
        values_indexes=[0 , 1 , 2 , 8, 4, 5, 6]
        plt.figure(figsize=(40,40))
        plt.rcParams['font.size']='40'
        im=plt.imshow(text_regions_p[:,:])
        colors=[im.cmap(im.norm(value)) for value in values]
        patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int(  np.where(values_indexes==i)[0][0]  )])) for i in values]
        plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=40)
        plt.savefig(os.path.join(self.dir_of_layout,self.f_name+'_layout.png') )



    def save_plot_of_layout_all(self,text_regions_p,image_page):
        values=np.unique(text_regions_p[:,:])

        #pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']

        pixels=['Background' , 'Main text' , 'Header' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators']
        values_indexes=[0 , 1 , 2 , 8, 4, 5, 6]

        plt.figure(figsize=(70,40))
        plt.rcParams['font.size']='40'
        plt.subplot(1,2,1)
        plt.imshow(image_page)
        plt.subplot(1,2,2)
        im=plt.imshow(text_regions_p[:,:])
        colors=[im.cmap(im.norm(value)) for value in values]
        patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int(  np.where(values_indexes==i)[0][0]  )])) for i in values]
        plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=60)


        plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_layout_and_page.png') )

    def save_deskewed_image(self,slope_deskew):
        img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)

        if self.dir_of_all is not None:
            cv2.imwrite(os.path.join(self.dir_of_all,self.f_name+'_org.png'),self.image_org)

        cv2.imwrite(os.path.join(self.dir_of_deskewed,self.f_name+'_deskewed.png'),img_rotated)
        del img_rotated

    def run(self):
        is_image_enhanced=False
        #get image and sclaes, then extract the page of scanned image
        t1=time.time()


        ##########

        ###is_image_enhanced,img_org,img_res=self.resize_and_enhance_image(is_image_enhanced)
        is_image_enhanced,img_org,img_res,num_col_classifier,num_column_is_classified=self.resize_and_enhance_image_with_column_classifier(is_image_enhanced)

        print(is_image_enhanced,'is_image_enhanced')
        K.clear_session()
        scale=1
        if (self.allow_enhancement=='true' or self.allow_enhancement=='True' or self.allow_enhancement=='TRUE') 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 (self.allow_enhancement=='False' or self.allow_enhancement=='false' or self.allow_enhancement=='FALSE' or self.allow_enhancement==None) and  is_image_enhanced:
            self.get_image_and_scales_after_enhancing(img_org,img_res)

        if (self.allow_enhancement=='true' or self.allow_enhancement=='True' or self.allow_enhancement=='TRUE') and not is_image_enhanced:
            self.get_image_and_scales(img_org,img_res,scale)


        if (self.allow_enhancement=='False' or self.allow_enhancement=='false' or self.allow_enhancement=='FALSE' or self.allow_enhancement==None) and  not is_image_enhanced:
            self.get_image_and_scales(img_org,img_res,scale)

        if (self.allow_scaling=='true' or self.allow_scaling=='True' or self.allow_scaling=='TRUE') and not is_image_enhanced :
            img_org,img_res,is_image_enhanced=self.resize_image_with_column_classifier(is_image_enhanced)
            self.get_image_and_scales_after_enhancing(img_org,img_res)


        #print(self.scale_x)

        print('enhancing: '+str(time.time()-t1))
        text_regions_p_1=self.get_regions_from_xy_2models(img_res,is_image_enhanced)
        K.clear_session()
        gc.collect()

        print('textregion: '+str(time.time()-t1))

        img_g=cv2.imread(self.image_dir,0)
        img_g=img_g.astype(np.uint8)

        img_g3=np.zeros((img_g.shape[0],img_g.shape[1],3))

        img_g3 = img_g3.astype(np.uint8)

        img_g3[:,:,0]=img_g[:,:]
        img_g3[:,:,1]=img_g[:,:]
        img_g3[:,:,2]=img_g[:,:]

        ###self.produce_groundtruth_for_textline()
        image_page,page_coord=self.extract_page()

        #print(image_page.shape,'page')


        if self.dir_of_all is not None:
            cv2.imwrite(os.path.join(self.dir_of_all,self.f_name+'_page.png'),image_page)
        ##########
        K.clear_session()
        gc.collect()

        img_g3_page=img_g3[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3],:]
        del img_g3
        del img_g

        text_regions_p_1=text_regions_p_1[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3]]



        mask_images=(text_regions_p_1[:,:]==2)*1
        mask_lines=(text_regions_p_1[:,:]==3)*1

        mask_images=mask_images.astype(np.uint8)
        mask_lines=mask_lines.astype(np.uint8)

        mask_images=cv2.erode(mask_images[:,:], self.kernel, iterations=10)



        img_only_regions_with_sep=( (text_regions_p_1[:,:]!=3) & (text_regions_p_1[:,:]!=0) )*1
        img_only_regions_with_sep=img_only_regions_with_sep.astype(np.uint8)
        img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], self.kernel, iterations=6)




        try:
            num_col, peaks_neg_fin=self.find_num_col(img_only_regions,multiplier=6.0)
            if not num_column_is_classified:
                num_col_classifier=num_col+1
        except:
            num_col=None
            peaks_neg_fin=[]

        print(num_col,'num_colnum_col')
        if num_col is None:
            txt_con_org=[]
            order_text_new=[]
            id_of_texts_tot=[]
            all_found_texline_polygons=[]
            all_box_coord=[]
            polygons_of_images=[]
            polygons_of_marginals=[]
            all_found_texline_polygons_marginals=[]
            all_box_coord_marginals=[]
            slopes=[]
            slopes_marginals=[]
            self.write_into_page_xml(txt_con_org,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,
                                    all_box_coord,polygons_of_images,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,self.curved_line,slopes,slopes_marginals)
        else:
            #pass
            try:
                patches=True
                scaler_h_textline=1#1.2#1.2
                scaler_w_textline=1#0.9#1
                textline_mask_tot_ea,textline_mask_tot_long_shot=self.textline_contours(image_page,patches,scaler_h_textline,scaler_w_textline)

                K.clear_session()
                gc.collect()

                print(np.unique(textline_mask_tot_ea[:,:]),'textline')

                if self.dir_of_all is not None:

                    values=np.unique(textline_mask_tot_ea[:,:])
                    pixels=['Background' , 'Textlines']
                    values_indexes=[0 , 1]
                    plt.figure(figsize=(70,40))
                    plt.rcParams['font.size']='40'
                    plt.subplot(1,2,1)
                    plt.imshow(image_page)
                    plt.subplot(1,2,2)
                    im=plt.imshow(textline_mask_tot_ea[:,:])
                    colors=[im.cmap(im.norm(value)) for value in values]
                    patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int(  np.where(values_indexes==i)[0][0]  )])) for i in values]
                    plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,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=self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma,main_page_deskew)
                slope_first=0#self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma)

                if self.dir_of_deskewed is not None:
                    self.save_deskewed_image(slope_deskew)
                #img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)
                print(slope_deskew,'slope_deskew')

                ##plt.imshow(img_rotated)
                ##plt.show()
                ##sys.exit()
                print('deskewing: '+str(time.time()-t1))

                image_page_rotated,textline_mask_tot=image_page[:,:],textline_mask_tot_ea[:,:]#self.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


                ##text_regions_p_1[textline_mask_tot_small_size[:,:]==1]=1


                text_regions_p=text_regions_p_1[:,:]#long_short_region[:,:]#self.get_regions_from_2_models(image_page)

                text_regions_p=np.array(text_regions_p)


                if num_col_classifier==1 or num_col_classifier==2:

                    try:
                        regions_without_seperators=(text_regions_p[:,:]==1)*1
                        regions_without_seperators=regions_without_seperators.astype(np.uint8)


                        text_regions_p=self.get_marginals(self.rotate_image(regions_without_seperators,slope_deskew),text_regions_p,num_col_classifier,slope_deskew)

                    except:
                        pass
                else:
                    pass


                #plt.imshow(text_regions_p)
                #plt.show()

                if self.dir_of_all is not None:
                    self.save_plot_of_layout_main_all(text_regions_p,image_page)
                if self.dir_of_layout is not None:
                    self.save_plot_of_layout_main(text_regions_p,image_page)

                print('marginals: '+str(time.time()-t1))

                if self.full_layout is None or self.full_layout=='False' or self.full_layout=='false' or self.full_layout=='FALSE':


                    if np.abs(slope_deskew)>=.13:
                        image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n=self.rotation_not_90_func(image_page,textline_mask_tot,text_regions_p,slope_deskew)

                        text_regions_p_1_n=self.resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1])
                        textline_mask_tot_d=self.resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])

                        regions_without_seperators_d=(text_regions_p_1_n[:,:]==1)*1






                    regions_without_seperators=(text_regions_p[:,:]==1)*1#( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)



                    pixel_lines=3
                    if np.abs(slope_deskew)<0.13:
                        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)>=.13:
                        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)
                    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)<0.13:
                            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<-.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)>=.13:
                            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<-.5]=0
                            random_pixels_for_image[random_pixels_for_image!=0]=1

                            regions_without_seperators_d[(random_pixels_for_image[:,:]==1) & (text_regions_p_1_n[:,:]==2)]=1
                    else:
                        pass

                    if np.abs(slope_deskew)<0.13:
                        boxes=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new,regions_without_seperators,matrix_of_lines_ch)
                    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()
                    K.clear_session()




                pixel_img=4
                min_area_mar=0.00001
                polygons_of_marginals=self.return_contours_of_interested_region(text_regions_p,pixel_img,min_area_mar)



                if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
                    # set first model with second model
                    text_regions_p[:,:][text_regions_p[:,:]==2]=5
                    text_regions_p[:,:][text_regions_p[:,:]==3]=6
                    text_regions_p[:,:][text_regions_p[:,:]==4]=8


                    K.clear_session()
                    #gc.collect()

                    patches=True


                    image_page=image_page.astype(np.uint8)

                    #print(type(image_page))
                    regions_fully,regions_fully_only_drop=self.extract_text_regions(image_page,patches,cols=num_col_classifier)




                    regions_fully_only_drop=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
                    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()

                    K.clear_session()
                    gc.collect()
                    patches=False
                    regions_fully_np,_=self.extract_text_regions(image_page,patches,cols=num_col_classifier)





                    #plt.imshow(regions_fully_np[:,:,0])
                    #plt.show()

                    if num_col_classifier>2:
                        regions_fully_np[:,:,0][regions_fully_np[:,:,0]==4]=0
                    else:
                        regions_fully_np=self.filter_small_drop_capitals_from_no_patch_layout(regions_fully_np,text_regions_p)

                    #regions_fully_np=self.filter_small_drop_capitals_from_no_patch_layout(regions_fully_np,text_regions_p)
                    #plt.imshow(regions_fully_np[:,:,0])
                    #plt.show()

                    K.clear_session()
                    gc.collect()

                    #plt.imshow(regions_fully[:,:,0])
                    #plt.show()

                    regions_fully=self.boosting_headers_by_longshot_region_segmentation(regions_fully,regions_fully_np,img_only_regions)


                    #plt.imshow(regions_fully[:,:,0])
                    #plt.show()

                    text_regions_p[:,:][regions_fully[:,:,0]==4]=4
                    ##text_regions_p[:,:][(regions_fully[:,:,0]==7) & (text_regions_p[:,:]!=0)]=7

                    text_regions_p[:,:][regions_fully_np[:,:,0]==4]=4


                    #plt.imshow(text_regions_p)
                    #plt.show()


                    if np.abs(slope_deskew)>=0.13:
                        image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n,regions_fully_n=self.rotation_not_90_func_full_layout(image_page,textline_mask_tot,text_regions_p,regions_fully,slope_deskew)

                        text_regions_p_1_n=self.resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1])
                        textline_mask_tot_d=self.resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])
                        regions_fully_n=self.resize_image(regions_fully_n,text_regions_p.shape[0],text_regions_p.shape[1])

                        regions_without_seperators_d=(text_regions_p_1_n[:,:]==1)*1







                    regions_without_seperators=(text_regions_p[:,:]==1)*1#( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)





                    K.clear_session()
                    gc.collect()

                    img_revised_tab=np.copy(text_regions_p[:,:])

                    print('full layout in: '+str(time.time()-t1))

                #sys.exit()



                pixel_img=5
                polygons_of_images=self.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=self.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)>=0.13:
                    text_only_d=( (text_regions_p_1_n[:,:]==1) )*1
                ##text_only_h=( (img_revised_tab[:,:,0]==2) )*1




                #print(text_only.shape,text_only_d.shape)
                #plt.imshow(text_only)
                #plt.show()

                #plt.imshow(text_only_d)
                #plt.show()

                min_con_area=0.000005

                if np.abs(slope_deskew)>=0.13:

                    contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
                    contours_only_text_parent=self.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 ,_, _, _ ,_,_=self.find_new_features_of_contoures([contours_biggest])
                    cx_bigest,cy_biggest ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent)



                    contours_only_text_d,hir_on_text_d=self.return_contours_of_image(text_only_d)
                    contours_only_text_parent_d=self.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 ,_, _, _ ,_,_=self.find_new_features_of_contoures([contours_biggest_d])
                    cx_bigest_d,cy_biggest_d ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent_d)


                    (h, w) = text_only.shape[:2]
                    center = (w // 2., h // 2.)
                    M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)


                    M_22=np.array(M)[:2,:2]

                    p_big=np.dot(M_22,[cx_bigest_big,cy_biggest_big])

                    x_diff=p_big[0]-cx_bigest_d_big
                    y_diff=p_big[1]-cy_biggest_d_big


                    #print(p_big)
                    #print(cx_bigest_d_big,cy_biggest_d_big)
                    #print(x_diff,y_diff)

                    contours_only_text_parent_d_ordered=[]
                    for i in range(len(contours_only_text_parent)):
                        #img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
                        #img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))

                        #plt.imshow(img1[:,:,0])
                        #plt.show()

                        p=np.dot(M_22,[cx_bigest[i],cy_biggest[i]])
                        #print(p)
                        p[0]=p[0]-x_diff[0]
                        p[1]=p[1]-y_diff[0]
                        #print(p)
                        #print(cx_bigest_d)
                        #print(cy_biggest_d)
                        dists = [math.sqrt((p[0]-cx_bigest_d[j])**2 + (p[1]-cy_biggest_d[j])**2) for j in range(len(cx_bigest_d))]
                        #print(np.argmin(dists))
                        contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])

                        #img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
                        #img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))

                        #plt.imshow(img2[:,:,0])
                        #plt.show()

                else:
                    contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
                    contours_only_text_parent=self.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 ,_, _, _ ,_,_=self.find_new_features_of_contoures([contours_biggest])
                    cx_bigest,cy_biggest ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent)
                    #print(areas_cnt_text_parent,'areas_cnt_text_parent')

                    ###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)
                    ##contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d])
                    ###areas_cnt_text_parent_d=list(np.array(areas_cnt_text_parent_d)[index_con_parents_d])

                    ##print(areas_cnt_text_parent_d,'areas_cnt_text_parent_d')

                    #print(len(contours_only_text_parent),len(contours_only_text_parent_d),'vizzz')













                txt_con_org=self.get_textregion_contours_in_org_image(contours_only_text_parent,self.image,slope_first)

                ###boxes_text,_=self.get_text_region_boxes_by_given_contours(contours_only_text_parent)
                boxes_text,_=self.get_text_region_boxes_by_given_contours(contours_only_text_parent)
                boxes_marginals,_=self.get_text_region_boxes_by_given_contours(polygons_of_marginals)
                ####boxes_text_h,_=self.get_text_region_boxes_by_given_contours(text_only_h,contours_only_text_parent_h,image_page)




                if self.curved_line is None or self.curved_line=='false' or self.curved_line=='False' or self.curved_line=='FALSE' :
                    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=='true' or self.curved_line=='True' or self.curved_line=='TRUE' :
                    scale_param=1
                    all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord,index_by_text_par_con,slopes=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,cv2.erode(textline_mask_tot_ea,kernel=self.kernel,iterations=1),image_page_rotated,boxes_text,text_only,num_col_classifier,scale_param,slope_deskew)

                    #all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
                    all_found_texline_polygons=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]

                K.clear_session()
                gc.collect()


                #contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
                ###print(index_by_text_par_con,'index_by_text_par_con')

                if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
                    ##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)>=0.13:
                        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)
                    else:
                        contours_only_text_parent_d_ordered=None


                        text_regions_p,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h,slopes,slopes_h,contours_only_text_parent_d_ordered,contours_only_text_parent_h_d_ordered=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)


                    ###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)

                    # if you want to save the layout result just uncommet following plot


                    if self.dir_of_layout is not None:
                        self.save_plot_of_layout(text_regions_p,image_page)
                    if self.dir_of_all is not None:
                        self.save_plot_of_layout_all(text_regions_p,image_page)



                    K.clear_session()
                    gc.collect()

                    ##print('Job done in: '+str(time.time()-t1))


                    polygons_of_tabels=[]


                    pixel_img=4
                    polygons_of_drop_capitals=self.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 self.headers_off is None or self.headers_off=='false' or self.headers_off=='False' or self.headers_off=='FALSE':
                        if np.abs(slope_deskew)<0.13:
                            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)
                        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=='true' or self.headers_off=='True' or self.headers_off=='TRUE':
                        if np.abs(slope_deskew)<0.13:
                            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)
                        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)

                    #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)<0.13:
                            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<-.5]=0
                            random_pixels_for_image[random_pixels_for_image!=0]=1

                            regions_without_seperators[(random_pixels_for_image[:,:]==1) & (text_regions_p[:,:]==5)]=1

                        else:


                            regions_without_seperators_d=regions_without_seperators_d.astype(np.uint8)
                            regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:,:], self.kernel, iterations=6)


                            random_pixels_for_image=np.random.randn(regions_without_seperators_d.shape[0],regions_without_seperators_d.shape[1])
                            random_pixels_for_image[random_pixels_for_image<-.5]=0
                            random_pixels_for_image[random_pixels_for_image!=0]=1



                            regions_without_seperators_d[(random_pixels_for_image[:,:]==1) & (text_regions_p_1_n[:,:]==5)]=1
                    else:
                        pass



                    if np.abs(slope_deskew)<0.13:
                        boxes=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new,regions_without_seperators,matrix_of_lines_ch)
                    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(slopes)
                if self.dir_of_cropped_images is not None:
                    self.write_images_into_directory(polygons_of_images,self.dir_of_cropped_images,image_page)


                if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
                    if np.abs(slope_deskew)<0.13:
                        order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
                    else:
                        order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent_d_ordered,contours_only_text_parent_h_d_ordered,boxes_d,textline_mask_tot_d)




                    self.write_into_page_xml_full(contours_only_text_parent,contours_only_text_parent_h,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,all_found_texline_polygons_h,
                                            all_box_coord,all_box_coord_h,polygons_of_images ,polygons_of_tabels,polygons_of_drop_capitals,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,slopes,slopes_marginals)
                else:
                    contours_only_text_parent_h=None
                    #print('bura galmir?')
                    if np.abs(slope_deskew)<0.13:
                        contours_only_text_parent=list(np.array(contours_only_text_parent)[index_by_text_par_con])
                        order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
                    else:
                        contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
                        order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent_d_ordered,contours_only_text_parent_h,boxes_d,textline_mask_tot_d)
                    #order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
                    self.write_into_page_xml(txt_con_org,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,
                                            all_box_coord,polygons_of_images,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,self.curved_line,slopes,slopes_marginals)

            except:
                txt_con_org=[]
                order_text_new=[]
                id_of_texts_tot=[]
                all_found_texline_polygons=[]
                all_box_coord=[]
                polygons_of_images=[]
                polygons_of_marginals=[]
                all_found_texline_polygons_marginals=[]
                all_box_coord_marginals=[]
                slopes=[]
                slopes_marginals=[]
                self.write_into_page_xml(txt_con_org,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,
                                        all_box_coord,polygons_of_images,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,self.curved_line,slopes,slopes_marginals)


        print('Job done in: '+str(time.time()-t1))





@click.command()
@click.option('--image', '-i', help='image filename', type=click.Path(exists=True, dir_okay=False))
@click.option('--out', '-o', help='directory to write output xml data', type=click.Path(exists=True, file_okay=False))
@click.option('--model', '-m', help='directory of models', type=click.Path(exists=True, file_okay=False))
@click.option('--save_images', '-si', help='if a directory is given, images in documents will be cropped and saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--save_layout', '-sl', help='if a directory is given, plot of layout will be saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--save_deskewed', '-sd', help='if a directory is given, deskewed image will be saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--save_all', '-sa', help='if a directory is given, all plots needed for documentation will be saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--allow_enhancement', '-ae', help='if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory')
@click.option('--curved_line', '-cl', help='if this parameter set to true, this tool will try to return contoure of textlines instead of rectabgle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.')

@click.option('--full_layout', '-fl', help='if this parameter set to true, this tool will try to return all elements of layout.')
@click.option('--allow_scaling', '-as', help='if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection')
@click.option('--headers_off', '-ho', help='if this parameter set to true, this tool would ignore headers role in reading order')
def main(image,out, model,save_images,save_layout,save_deskewed,save_all,allow_enhancement,curved_line,full_layout,allow_scaling,headers_off):
    x = eynollah(image, None, out, model, save_images, save_layout, save_deskewed, save_all, allow_enhancement, curved_line, full_layout, allow_scaling, headers_off)
    x.run()


if __name__ == "__main__":
    main()