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
from .utils import (
boosting_headers_by_longshot_region_segmentation,
crop_image_inside_box,
find_features_of_lines,
find_num_col,
find_num_col_by_vertical_lines,
find_num_col_deskew,
find_num_col_only_image,
isNaN,
otsu_copy,
otsu_copy_binary,
return_hor_spliter_by_index_for_without_verticals,
delete_seperator_around,
return_regions_without_seperators,
put_drop_out_from_only_drop_model,
putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
check_any_text_region_in_model_one_is_main_or_header,
small_textlines_to_parent_adherence2,
order_and_id_of_texts,
order_of_regions,
implent_law_head_main_not_parallel,
return_hor_spliter_by_index,
combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
return_points_with_boundies,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new,
)
def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
(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)]])
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=axis)
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)
return x, y, x_d, y_d, xv, x_min_cont, y_min_cont, x_max_cont, y_max_cont, first_nonzero, y_padded_up_to_down_padded, y_padded_smoothed, peaks, peaks_neg, rotation_matrix
def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
contour_text_interest_copy = contour_text_interest.copy()
x, y, x_d, y_d, xv, x_min_cont, y_min_cont, x_max_cont, y_max_cont, first_nonzero, y_padded_up_to_down_padded, y_padded_smoothed, peaks, peaks_neg, rotation_matrix = dedup_separate_lines(img_patch, contour_text_interest, thetha, 1)
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
contour_text_interest_copy = contour_text_interest.copy()
x, y, x_d, y_d, xv, x_min_cont, y_min_cont, x_max_cont, y_max_cont, first_nonzero, y_padded_up_to_down_padded, y_padded_smoothed, peaks, peaks_neg, rotation_matrix = dedup_separate_lines(img_patch, contour_text_interest, thetha, 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=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=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=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=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=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=find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
if 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(dir_of_all,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=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=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=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=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=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=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=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=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=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=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