eynollah/sbb_newspapers_org_image/utils/separate_lines.py

import matplotlib.pyplot as plt
import numpy as np
import cv2
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d

from .rotate import rotate_image
from .contour import (
    return_parent_contours,
    filter_contours_area_of_image_tables,
    return_contours_of_image,
    filter_contours_area_of_image
)
from .is_nan import isNaN

def seperate_lines(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 / 180.0 * 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 = return_contours_of_image(img_patch)
            textline_con_fil = 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 * (7.0 / 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 / 2.0:
                    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 / 2.0:
                    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(1.0 / 1.8 * dis_to_next)
            elif jj == 1:
                point_down = peaks[jj] + first_nonzero + int(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 1.9 * dis_to_next_up)
                point_down = peaks[jj] + first_nonzero + int(1.0 / 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 seperate_lines_vertical(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 / 180.0 * 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 = return_contours_of_image(img_patch)
            textline_con_fil = 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 * (7.0 / 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 / 2.0:
                    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 / 2.0:
                    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(1.0 / 1.8 * dis_to_next)
            elif jj == 1:
                point_down = peaks[jj] + first_nonzero + int(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 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(1.0 / 1.9 * dis_to_next_up)
                point_down = peaks[jj] + first_nonzero + int(1.0 / 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 seperate_lines_new_inside_teils2(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 / 180.0 * 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 = return_contours_of_image(img_patch)
            textline_con_fil = 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 * (7.0 / 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(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 / 180.0 * np.pi
    rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])

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

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

    mada_n = img_path.sum(axis=1)

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

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

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

    peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
    if len(peaks_real) <= 2 and len(peaks_real) > 1:
        sigma_gaus = 10
    else:
        sigma_gaus = 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 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 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 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 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_vertical_cont(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 = return_parent_contours(contours_imgs, hiearchy)
    contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)

    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 textline_contours_postprocessing(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 = 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 = 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 = 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 = seperate_lines(dst, contours_text_rot[ind_big_con], slope, x_help, y_help)

    except:

        contours_rotated_clean = []

    return contours_rotated_clean

def seperate_lines_new2(img_path, thetha, num_col, slope_region, dir_of_all, f_name):

    if num_col == 1:
        num_patches = int(img_path.shape[1] / 200.0)
    else:
        num_patches = int(img_path.shape[1] / 140.0)
    # num_patches=int(img_path.shape[1]/200.)
    if num_patches == 0:
        num_patches = 1

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

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

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

    # margin=0

    width_mid = length_x - 2 * margin

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

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

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

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

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

        sigma = 2
        try:
            slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=dir_of_all, f_name=f_name)
        except:
            slope_xline = 0

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

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

    img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)

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

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

        img_xline = img_patch_ineterst[:, index_x_d:index_x_u]

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

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

        img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
        # plt.imshow(img_xline)
        # plt.show()
        img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
        img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1

        img_patch_seperated = seperate_lines_new_inside_teils2(img_line_rotated, 0)

        img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
        img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1

        img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]

        img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
        img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size

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

def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None):


    if main_page and 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(dir_of_all, 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=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]



    max_shape=np.max(img_int.shape)
    img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))


    onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
    onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)


    #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[:,:]
    img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]

    #print(img_resized.shape,'img_resizedshape')
    #plt.imshow(img_resized)
    #plt.show()

    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:
                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

            var_res.append(var_spectrum)
            #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=[]


        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:
                var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )

            except:
                var_spectrum=0

            var_res.append(var_spectrum)




        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])


        var_res=[]

        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:
                var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )

            except:
                var_spectrum=0

            var_res.append(var_spectrum)


        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)

            var_res=[]

            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:
                    var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                except:
                    var_spectrum=0

                var_res.append(var_spectrum)


            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)


            var_res=[]

            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:
                    var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                    #print(indexer,'indexer')
                except:
                    var_spectrum=0

                var_res.append(var_spectrum)


            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)

        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:
                var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
            except:
                var_spectrum=0

            var_res.append(var_spectrum)


        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

        #plt.plot(var_res)
        #plt.show()

        ##plt.plot(mom3_res)
        ##plt.show()
        #print(ang_int,'ang_int111')

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

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

            var_res=[]

            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:
                    var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )

                except:
                    var_spectrum=0

                var_res.append(var_spectrum)



            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)

            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:
                    var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
                    #print(indexer,'indexer')
                except:
                    var_spectrum=0

                var_res.append(var_spectrum)



            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