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