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eynollah
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more outsourcing of utils

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pull/8/head
Konstantin Baierer 4 years ago
parent 8d2227b04b
commit 87ef313502
6 changed files with 979 additions and 967 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -53,7 +53,6 @@ from .utils.contour import (
return_contours_of_interested_textline,
return_parent_contours,
return_contours_of_interested_region_by_size,
textline_contours_postprocessing,
)
from .utils.rotate import (
@ -73,6 +72,9 @@ from .utils.separate_lines import (
seperate_lines_new_inside_teils2,
seperate_lines_vertical,
seperate_lines_vertical_cont,
textline_contours_postprocessing,
seperate_lines_new2,
return_deskew_slop,
)
from .utils.drop_capitals import (
@ -80,6 +82,10 @@ from .utils.drop_capitals import (
filter_small_drop_capitals_from_no_patch_layout
)
from .utils.marginals import get_marginals
from .utils.resize import resize_image
from .utils import (
boosting_headers_by_longshot_region_segmentation,
crop_image_inside_box,
@ -91,11 +97,9 @@ from .utils import (
isNaN,
otsu_copy,
otsu_copy_binary,
resize_image,
return_hor_spliter_by_index_for_without_verticals,
delete_seperator_around,
return_regions_without_seperators,
return_deskew_slop,
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,
@ -1177,7 +1181,7 @@ class eynollah:
textline_biggest_region = mask_biggest * textline_mask_tot_ea
# print(slope_for_all,'slope_for_all')
textline_rotated_seperated = self.seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all)
textline_rotated_seperated = seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all, self.dir_of_all, self.f_name)
# new line added
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
@ -1398,374 +1402,6 @@ class eynollah:
gc.collect()
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
def seperate_lines_new(self, img_path, thetha, num_col):
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
else:
num_patches = int(img_path.shape[1] / 100.0)
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0:
num_patches = 1
(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 = 6
z = gaussian_filter1d(y_help, sigma_gaus)
zneg_rev = -y_help + np.max(y_help)
zneg = np.zeros(len(zneg_rev) + 40)
zneg[20 : len(zneg_rev) + 20] = zneg_rev
zneg = gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
for nn in range(len(peaks_neg)):
if peaks_neg[nn] > len(z) - 1:
peaks_neg[nn] = len(z) - 1
if peaks_neg[nn] < 0:
peaks_neg[nn] = 0
diff_peaks = np.abs(np.diff(peaks_neg))
cut_off = 20
peaks_neg_true = []
forest = []
for i in range(len(peaks_neg)):
if i == 0:
forest.append(peaks_neg[i])
if i < (len(peaks_neg) - 1):
if diff_peaks[i] <= cut_off:
forest.append(peaks_neg[i + 1])
if diff_peaks[i] > cut_off:
# print(forest[np.argmin(z[forest]) ] )
if not isNaN(forest[np.argmin(z[forest])]):
# print(len(z),forest)
peaks_neg_true.append(forest[np.argmin(z[forest])])
forest = []
forest.append(peaks_neg[i + 1])
if i == (len(peaks_neg) - 1):
# print(print(forest[np.argmin(z[forest]) ] ))
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
peaks_neg_true = np.array(peaks_neg_true)
"""
#plt.figure(figsize=(40,40))
#plt.subplot(1,2,1)
#plt.title('Textline segmentation von Textregion')
#plt.imshow(img_path)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.subplot(1,2,2)
#plt.title('Dichte entlang X')
#base = pyplot.gca().transData
#rot = transforms.Affine2D().rotate_deg(90)
#plt.plot(zneg,np.array(range(len(zneg))))
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
#plt.gca().invert_yaxis()
#plt.xlabel('Dichte')
#plt.ylabel('Y')
##plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
"""
peaks_neg_true = peaks_neg_true - 20 - 20
peaks = peaks - 20
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
##plt.imshow(img_patch_ineterst)
##plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
margin = int(0.04 * length_x)
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise = []
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
sigma = 2
try:
slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
except:
slope_xline = 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
"""
xline=np.linspace(0,img_path.shape[1],nx)
slopes_tile_wise=[]
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
##plt.imshow(img_xline)
##plt.show()
sigma=3
try:
slope_xline=return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
except:
slope_xline=0
slopes_tile_wise.append(slope_xline)
print(slope_xline,'xlineeee')
img_line_rotated=rotate_image(img_xline,slope_xline)
##plt.imshow(img_line_rotated)
##plt.show()
"""
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
img_resized[int(img_int.shape[0] * (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_teils(img_line_rotated, 0)
##plt.imshow(img_patch_seperated)
##plt.show()
img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
"""
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated=rotate_image(img_resized,slopes_tile_wise[ui])
#img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated_returned=rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
##plt.imshow(img_patch_seperated)
##plt.show()
print(img_patch_seperated_returned.shape)
#plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
#plt.show()
img_patch_ineterst_revised[:,int(xline[ui]):int(xline[ui+1])]=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
"""
# print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
##plt.imshow(img_patch_ineterst_revised)
##plt.show()
return img_patch_ineterst_revised
def seperate_lines_new2(self, img_path, thetha, num_col, slope_region):
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
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=self.dir_of_all, f_name=self.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 do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
slope_biggest = 0
slopes_sub = []
@ -2941,7 +2577,7 @@ class eynollah:
#ratio_y=1
#median_blur=False
#img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
#img= resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
#if binary:
#img = self.otsu_copy_binary(img)#self.otsu_copy(img)
@ -2954,7 +2590,7 @@ class eynollah:
#img = img.astype(np.uint16)
#prediction_regions_org2=self.do_prediction(patches,img,model_region)
#prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
#prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
@ -3057,249 +2693,6 @@ class eynollah:
cv2.imwrite(path, croped_page)
index += 1
def get_marginals(self,text_with_lines,text_regions,num_col,slope_deskew):
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
mask_marginals=mask_marginals.astype(np.uint8)
text_with_lines=text_with_lines.astype(np.uint8)
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded=cv2.erode(text_with_lines,self.kernel,iterations=5)
if text_with_lines.shape[0]<=1500:
pass
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=5)
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
else:
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=7)
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
text_with_lines_y=text_with_lines.sum(axis=0)
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
#print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent<30:
min_textline_thickness=8
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
min_textline_thickness=20
else:
min_textline_thickness=40
if thickness_along_y_percent>=14:
text_with_lines_y_rev=-1*text_with_lines_y[:]
#print(text_with_lines_y)
#print(text_with_lines_y_rev)
#plt.plot(text_with_lines_y)
#plt.show()
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
#plt.plot(text_with_lines_y_rev)
#plt.show()
sigma_gaus=1
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
#plt.plot(region_sum_0_rev)
#plt.show()
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
last_nonzero=len(region_sum_0)-last_nonzero
##img_sum_0_smooth_rev=-region_sum_0
mid_point=(last_nonzero+first_nonzero)/2.
one_third_right=(last_nonzero-mid_point)/3.0
one_third_left=(mid_point-first_nonzero)/3.0
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
peaks=np.array(peaks)
#print(region_sum_0[peaks])
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
#print(first_nonzero,last_nonzero,peaks)
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
#print(first_nonzero,last_nonzero,peaks)
#print(region_sum_0[peaks]<10)
####peaks=peaks[region_sum_0[peaks]<25 ]
#print(region_sum_0[peaks])
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
#print(peaks)
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col==1:
peaks_right=peaks[peaks>mid_point]
peaks_left=peaks[peaks<mid_point]
if num_col==2:
peaks_right=peaks[peaks>(mid_point+one_third_right)]
peaks_left=peaks[peaks<(mid_point-one_third_left)]
try:
point_right=np.min(peaks_right)
except:
point_right=last_nonzero
try:
point_left=np.max(peaks_left)
except:
point_left=first_nonzero
#print(point_left,point_right)
#print(text_regions.shape)
if point_right>=mask_marginals.shape[1]:
point_right=mask_marginals.shape[1]-1
try:
mask_marginals[:,point_left:point_right]=1
except:
mask_marginals[:,:]=1
#print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew)
#print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
index_x_interest=index_x[mask_marginals_rotated_sum==1]
min_point_of_left_marginal=np.min(index_x_interest)-16
max_point_of_right_marginal=np.max(index_x_interest)+16
if min_point_of_left_marginal<0:
min_point_of_left_marginal=0
if max_point_of_right_marginal>=text_regions.shape[1]:
max_point_of_right_marginal=text_regions.shape[1]-1
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
#plt.imshow(mask_marginals)
#plt.show()
#plt.imshow(mask_marginals_rotated)
#plt.show()
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
#plt.imshow(text_regions)
#plt.show()
pixel_img=4
min_area_text=0.00001
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
for i in range(len(cx_text_only)):
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
if x_width_mar>16 and y_height_mar/x_width_mar<18:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i]<(mid_point-one_third_left):
x_min_marginals_left_new=x_min_text_only[i]
if len(x_min_marginals_left)==0:
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
else:
x_min_marginals_right_new=x_min_text_only[i]
if len(x_min_marginals_right)==0:
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
if len(x_min_marginals_left)==0:
x_min_marginals_left=[0]
if len(x_min_marginals_right)==0:
x_min_marginals_right=[text_regions.shape[1]-1]
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
#print(np.unique(text_regions))
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
#plt.plot(region_sum_0)
#plt.plot(peaks,region_sum_0[peaks],'*')
#plt.show()
#plt.imshow(text_regions)
#plt.show()
#sys.exit()
else:
pass
return text_regions
def do_order_of_regions(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
if self.full_layout:
@ -3838,7 +3231,7 @@ class eynollah:
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
regions_without_seperators = regions_without_seperators.astype(np.uint8)
text_regions_p = self.get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew)
text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel)
except:
pass

258
sbb_newspapers_org_image/unused.py
Unescape Escape View File

@ -2769,3 +2769,261 @@ def do_work_of_textline_seperation(self, queue_of_all_params, polygons_per_proce
queue_of_all_params.put([index_polygons_per_process_per_process, polygons_per_par_process_per_process, textregions_cnt_tot_per_process, textlines_cnt_tot_per_process])
def seperate_lines_new(img_path, thetha, num_col, 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] / 100.0)
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0:
num_patches = 1
(h, w) = img_path.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180.0 * 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 = 6
z = gaussian_filter1d(y_help, sigma_gaus)
zneg_rev = -y_help + np.max(y_help)
zneg = np.zeros(len(zneg_rev) + 40)
zneg[20 : len(zneg_rev) + 20] = zneg_rev
zneg = gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
for nn in range(len(peaks_neg)):
if peaks_neg[nn] > len(z) - 1:
peaks_neg[nn] = len(z) - 1
if peaks_neg[nn] < 0:
peaks_neg[nn] = 0
diff_peaks = np.abs(np.diff(peaks_neg))
cut_off = 20
peaks_neg_true = []
forest = []
for i in range(len(peaks_neg)):
if i == 0:
forest.append(peaks_neg[i])
if i < (len(peaks_neg) - 1):
if diff_peaks[i] <= cut_off:
forest.append(peaks_neg[i + 1])
if diff_peaks[i] > cut_off:
# print(forest[np.argmin(z[forest]) ] )
if not isNaN(forest[np.argmin(z[forest])]):
# print(len(z),forest)
peaks_neg_true.append(forest[np.argmin(z[forest])])
forest = []
forest.append(peaks_neg[i + 1])
if i == (len(peaks_neg) - 1):
# print(print(forest[np.argmin(z[forest]) ] ))
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
peaks_neg_true = np.array(peaks_neg_true)
"""
#plt.figure(figsize=(40,40))
#plt.subplot(1,2,1)
#plt.title('Textline segmentation von Textregion')
#plt.imshow(img_path)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.subplot(1,2,2)
#plt.title('Dichte entlang X')
#base = pyplot.gca().transData
#rot = transforms.Affine2D().rotate_deg(90)
#plt.plot(zneg,np.array(range(len(zneg))))
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
#plt.gca().invert_yaxis()
#plt.xlabel('Dichte')
#plt.ylabel('Y')
##plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
"""
peaks_neg_true = peaks_neg_true - 20 - 20
peaks = peaks - 20
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
##plt.imshow(img_patch_ineterst)
##plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
margin = int(0.04 * length_x)
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise = []
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
sigma = 2
try:
slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=dir_of_all, f_name=f_name)
except:
slope_xline = 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
"""
xline=np.linspace(0,img_path.shape[1],nx)
slopes_tile_wise=[]
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
##plt.imshow(img_xline)
##plt.show()
sigma=3
try:
slope_xline=return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
except:
slope_xline=0
slopes_tile_wise.append(slope_xline)
print(slope_xline,'xlineeee')
img_line_rotated=rotate_image(img_xline,slope_xline)
##plt.imshow(img_line_rotated)
##plt.show()
"""
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
img_resized[int(img_int.shape[0] * (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_teils(img_line_rotated, 0)
##plt.imshow(img_patch_seperated)
##plt.show()
img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
"""
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated=rotate_image(img_resized,slopes_tile_wise[ui])
#img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated = seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated_returned=rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
##plt.imshow(img_patch_seperated)
##plt.show()
print(img_patch_seperated_returned.shape)
#plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
#plt.show()
img_patch_ineterst_revised[:,int(xline[ui]):int(xline[ui+1])]=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
"""
# print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
##plt.imshow(img_patch_ineterst_revised)
##plt.show()
return img_patch_ineterst_revised

349
sbb_newspapers_org_image/utils/__init__.py
Unescape Escape View File

@ -10,9 +10,6 @@ from scipy.ndimage import gaussian_filter1d
from .is_nan import isNaN
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def crop_image_inside_box(box, img_org_copy):
image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
@ -701,352 +698,6 @@ def return_regions_without_seperators(regions_pre):
return regions_without_seperators
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None):
if main_page and dir_of_all is not None:
plt.figure(figsize=(70,40))
plt.rcParams['font.size']='50'
plt.subplot(1,2,1)
plt.imshow(img_patch_org)
plt.subplot(1,2,2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
plt.ylabel('Height',fontsize=60)
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.gca().invert_yaxis()
plt.savefig(os.path.join(dir_of_all, f_name+'_density_of_textline.png'))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
#img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
max_shape=np.max(img_int.shape)
img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
#img_resized=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) ))
#img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
#print(img_resized.shape,'img_resizedshape')
#plt.imshow(img_resized)
#plt.show()
if main_page and img_patch_org.shape[1]>img_patch_org.shape[0]:
#plt.imshow(img_resized)
#plt.show()
angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[]
#indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
##print(rot,var_spectrum,'var_spectrum')
#res_me=np.mean(neg_peaks)
#if res_me==0:
#res_me=1000000000000000000000
#else:
#pass
#res_num=len(neg_peaks)
except:
#res_me=1000000000000000000000
#res_num=0
var_spectrum=0
#if self.isNaN(res_me):
#pass
#else:
#res.append( res_me )
#var_res.append(var_spectrum)
#num_of_peaks.append( res_num )
#index_cor.append(indexer)
#indexer=indexer+1
var_res.append(var_spectrum)
#index_cor.append(indexer)
#indexer=indexer+1
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif main_page and img_patch_org.shape[1]<=img_patch_org.shape[0]:
#plt.imshow(img_resized)
#plt.show()
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
if self.dir_of_all is not None:
#print('galdi?')
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
plt.xlabel('angle',fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
plt.legend(loc='best')
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_rotation_angle.png'))
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
early_slope_edge=11
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-12,100)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,12,100)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
else:
angels=np.linspace(-25,25,60)
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
#plt.plot(var_res)
#plt.show()
##plt.plot(mom3_res)
##plt.show()
#print(ang_int,'ang_int111')
early_slope_edge=22
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-25,60)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,25,60)
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
return ang_int
def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
drop_only = (layout_no_patch[:, :, 0] == 4) * 1

252
sbb_newspapers_org_image/utils/marginals.py
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@ -0,0 +1,252 @@
import numpy as np
import cv2
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
from .contour import find_new_features_of_contoures, return_contours_of_interested_region
from .resize import resize_image
from .rotate import rotate_image
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
mask_marginals=mask_marginals.astype(np.uint8)
text_with_lines=text_with_lines.astype(np.uint8)
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded=cv2.erode(text_with_lines,kernel,iterations=5)
if text_with_lines.shape[0]<=1500:
pass
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=5)
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
else:
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=7)
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
text_with_lines_y=text_with_lines.sum(axis=0)
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
#print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent<30:
min_textline_thickness=8
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
min_textline_thickness=20
else:
min_textline_thickness=40
if thickness_along_y_percent>=14:
text_with_lines_y_rev=-1*text_with_lines_y[:]
#print(text_with_lines_y)
#print(text_with_lines_y_rev)
#plt.plot(text_with_lines_y)
#plt.show()
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
#plt.plot(text_with_lines_y_rev)
#plt.show()
sigma_gaus=1
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
#plt.plot(region_sum_0_rev)
#plt.show()
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
last_nonzero=len(region_sum_0)-last_nonzero
##img_sum_0_smooth_rev=-region_sum_0
mid_point=(last_nonzero+first_nonzero)/2.
one_third_right=(last_nonzero-mid_point)/3.0
one_third_left=(mid_point-first_nonzero)/3.0
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
peaks=np.array(peaks)
#print(region_sum_0[peaks])
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
#print(first_nonzero,last_nonzero,peaks)
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
#print(first_nonzero,last_nonzero,peaks)
#print(region_sum_0[peaks]<10)
####peaks=peaks[region_sum_0[peaks]<25 ]
#print(region_sum_0[peaks])
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
#print(peaks)
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col==1:
peaks_right=peaks[peaks>mid_point]
peaks_left=peaks[peaks<mid_point]
if num_col==2:
peaks_right=peaks[peaks>(mid_point+one_third_right)]
peaks_left=peaks[peaks<(mid_point-one_third_left)]
try:
point_right=np.min(peaks_right)
except:
point_right=last_nonzero
try:
point_left=np.max(peaks_left)
except:
point_left=first_nonzero
#print(point_left,point_right)
#print(text_regions.shape)
if point_right>=mask_marginals.shape[1]:
point_right=mask_marginals.shape[1]-1
try:
mask_marginals[:,point_left:point_right]=1
except:
mask_marginals[:,:]=1
#print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew)
#print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
index_x_interest=index_x[mask_marginals_rotated_sum==1]
min_point_of_left_marginal=np.min(index_x_interest)-16
max_point_of_right_marginal=np.max(index_x_interest)+16
if min_point_of_left_marginal<0:
min_point_of_left_marginal=0
if max_point_of_right_marginal>=text_regions.shape[1]:
max_point_of_right_marginal=text_regions.shape[1]-1
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
#plt.imshow(mask_marginals)
#plt.show()
#plt.imshow(mask_marginals_rotated)
#plt.show()
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
#plt.imshow(text_regions)
#plt.show()
pixel_img=4
min_area_text=0.00001
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
for i in range(len(cx_text_only)):
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
if x_width_mar>16 and y_height_mar/x_width_mar<18:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i]<(mid_point-one_third_left):
x_min_marginals_left_new=x_min_text_only[i]
if len(x_min_marginals_left)==0:
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
else:
x_min_marginals_right_new=x_min_text_only[i]
if len(x_min_marginals_right)==0:
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
if len(x_min_marginals_left)==0:
x_min_marginals_left=[0]
if len(x_min_marginals_right)==0:
x_min_marginals_right=[text_regions.shape[1]-1]
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
#print(np.unique(text_regions))
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
#plt.plot(region_sum_0)
#plt.plot(peaks,region_sum_0[peaks],'*')
#plt.show()
#plt.imshow(text_regions)
#plt.show()
#sys.exit()
else:
pass
return text_regions

4
sbb_newspapers_org_image/utils/resize.py
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import cv2
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

454
sbb_newspapers_org_image/utils/separate_lines.py
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@ -1,3 +1,4 @@
import matplotlib.pyplot as plt
import numpy as np
import cv2
from scipy.signal import find_peaks
@ -1366,3 +1367,456 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest
return contours_rotated_clean
def seperate_lines_new2(img_path, thetha, num_col, slope_region, dir_of_all, f_name):
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
else:
num_patches = int(img_path.shape[1] / 140.0)
# num_patches=int(img_path.shape[1]/200.)
if num_patches == 0:
num_patches = 1
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
# plt.imshow(img_patch_ineterst)
# plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
margin = int(0.04 * length_x)
# print(margin,'margin')
# if margin<=4:
# margin = int(0.08 * length_x)
# margin=0
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise = []
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
sigma = 2
try:
slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=dir_of_all, f_name=f_name)
except:
slope_xline = 0
if abs(slope_region) < 25 and abs(slope_xline) > 25:
slope_xline = [slope_region][0]
# if abs(slope_region)>70 and abs(slope_xline)<25:
# slope_xline=[slope_region][0]
slopes_tile_wise.append(slope_xline)
# print(slope_xline,'xlineeee')
img_line_rotated = rotate_image(img_xline, slope_xline)
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
# print(slopes_tile_wise,'slopes_tile_wise')
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
# plt.imshow(img_xline)
# plt.show()
img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
img_patch_seperated = seperate_lines_new_inside_teils2(img_line_rotated, 0)
img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
# plt.imshow(img_patch_ineterst_revised)
# plt.show()
return img_patch_ineterst_revised
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None):
if main_page and dir_of_all is not None:
plt.figure(figsize=(70,40))
plt.rcParams['font.size']='50'
plt.subplot(1,2,1)
plt.imshow(img_patch_org)
plt.subplot(1,2,2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
plt.ylabel('Height',fontsize=60)
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.gca().invert_yaxis()
plt.savefig(os.path.join(dir_of_all, f_name+'_density_of_textline.png'))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
#img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
max_shape=np.max(img_int.shape)
img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
#img_resized=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) ))
#img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
#print(img_resized.shape,'img_resizedshape')
#plt.imshow(img_resized)
#plt.show()
if main_page and img_patch_org.shape[1]>img_patch_org.shape[0]:
#plt.imshow(img_resized)
#plt.show()
angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[]
#indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
##print(rot,var_spectrum,'var_spectrum')
#res_me=np.mean(neg_peaks)
#if res_me==0:
#res_me=1000000000000000000000
#else:
#pass
#res_num=len(neg_peaks)
except:
#res_me=1000000000000000000000
#res_num=0
var_spectrum=0
#if self.isNaN(res_me):
#pass
#else:
#res.append( res_me )
#var_res.append(var_spectrum)
#num_of_peaks.append( res_num )
#index_cor.append(indexer)
#indexer=indexer+1
var_res.append(var_spectrum)
#index_cor.append(indexer)
#indexer=indexer+1
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif main_page and img_patch_org.shape[1]<=img_patch_org.shape[0]:
#plt.imshow(img_resized)
#plt.show()
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
if self.dir_of_all is not None:
#print('galdi?')
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
plt.xlabel('angle',fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
plt.legend(loc='best')
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_rotation_angle.png'))
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
early_slope_edge=11
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-12,100)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,12,100)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
else:
angels=np.linspace(-25,25,60)
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
#plt.plot(var_res)
#plt.show()
##plt.plot(mom3_res)
##plt.show()
#print(ang_int,'ang_int111')
early_slope_edge=22
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-25,60)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,25,60)
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
except:
var_spectrum=0
var_res.append(var_spectrum)
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
return ang_int

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