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eynollah
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patch in changes from 37c0338 by @vahidrezanezhad

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pull/1/head^2
Konstantin Baierer 4 years ago
parent 59b2dcb619
commit 58ecb5708e
1 changed files with 168 additions and 253 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -1866,9 +1866,14 @@ class eynollah:
#all_box_coord.append(crop_coor)
mask_textline=np.zeros((textline_mask_tot_ea.shape))
mask_textline=cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
denoised=None
all_text_region_raw=textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
all_text_region_raw=(textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
all_text_region_raw=all_text_region_raw.astype(np.uint8)
img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
@ -4232,106 +4237,106 @@ class eynollah:
def find_num_col_deskew(self,regions_without_seperators,sigma_,multiplier=3.8 ):
regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)
meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
##meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
##first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
##last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
last_nonzero=len(regions_without_seperators_0)-last_nonzero
##last_nonzero=len(regions_without_seperators_0)-last_nonzero
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
y_help=np.zeros(len(y)+20)
##y_help=np.zeros(len(y)+20)
y_help[10:len(y)+10]=y
##y_help[10:len(y)+10]=y
x=np.array( range(len(y)) )
##x=np.array( range(len(y)) )
zneg_rev=-y_help+np.max(y_help)
##zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+20)
##zneg=np.zeros(len(zneg_rev)+20)
zneg[10:len(zneg_rev)+10]=zneg_rev
##zneg[10:len(zneg_rev)+10]=zneg_rev
z=gaussian_filter1d(y, sigma_)
zneg= gaussian_filter1d(zneg, sigma_)
###zneg= gaussian_filter1d(zneg, sigma_)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks, _ = find_peaks(z, height=0)
###peaks_neg, _ = find_peaks(zneg, height=0)
###peaks, _ = find_peaks(z, height=0)
peaks_neg=peaks_neg-10-10
###peaks_neg=peaks_neg-10-10
#print(np.std(z),'np.std(z)np.std(z)np.std(z)')
####print(np.std(z),'np.std(z)np.std(z)np.std(z)')
##plt.plot(z)
##plt.show()
#####plt.plot(z)
#####plt.show()
##plt.imshow(regions_without_seperators)
##plt.show()
"""
last_nonzero=last_nonzero-0#100
first_nonzero=first_nonzero+0#+100
#####plt.imshow(regions_without_seperators)
#####plt.show()
###"""
###last_nonzero=last_nonzero-0#100
###first_nonzero=first_nonzero+0#+100
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
###peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
"""
interest_pos=z[peaks]
###peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
###"""
###interest_pos=z[peaks]
interest_pos=interest_pos[interest_pos>10]
###interest_pos=interest_pos[interest_pos>10]
interest_neg=z[peaks_neg]
###interest_neg=z[peaks_neg]
min_peaks_pos=np.mean(interest_pos)
min_peaks_neg=0#np.min(interest_neg)
###min_peaks_pos=np.mean(interest_pos)
###min_peaks_neg=0#np.min(interest_neg)
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
#print(interest_pos)
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
####print(interest_pos)
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
interest_neg_fin=interest_neg[(interest_neg<grenze)]
peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
interest_neg_fin=interest_neg[(interest_neg<grenze)]
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
###peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
"""
if interest_neg[0]<0.1:
interest_neg=interest_neg[1:]
if interest_neg[len(interest_neg)-1]<0.1:
interest_neg=interest_neg[:len(interest_neg)-1]
###"""
###if interest_neg[0]<0.1:
###interest_neg=interest_neg[1:]
###if interest_neg[len(interest_neg)-1]<0.1:
###interest_neg=interest_neg[:len(interest_neg)-1]
min_peaks_pos=np.min(interest_pos)
min_peaks_neg=0#np.min(interest_neg)
###min_peaks_pos=np.min(interest_pos)
###min_peaks_neg=0#np.min(interest_neg)
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
"""
#interest_neg_fin=interest_neg#[(interest_neg<grenze)]
#peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
#interest_neg_fin=interest_neg#[(interest_neg<grenze)]
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
###"""
####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
####peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
num_col=(len(interest_neg_fin))+1
###num_col=(len(interest_neg_fin))+1
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_g_l=int(len(y)/3.)
p_g_u=len(y)-int(len(y)/3.)
###p_l=0
###p_u=len(y)-1
###p_m=int(len(y)/2.)
###p_g_l=int(len(y)/3.)
###p_g_u=len(y)-int(len(y)/3.)
diff_peaks=np.abs( np.diff(peaks_neg_fin) )
diff_peaks_annormal=diff_peaks[diff_peaks<30]
###diff_peaks=np.abs( np.diff(peaks_neg_fin) )
###diff_peaks_annormal=diff_peaks[diff_peaks<30]
#print(len(interest_neg_fin),np.mean(interest_neg_fin))
return interest_neg_fin,np.std(z)
return np.std(z)#interest_neg_fin,np.std(z)
def return_deskew_slop(self,img_patch_org,sigma_des,main_page=False):
@ -4358,13 +4363,27 @@ class eynollah:
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=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[ 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]:
@ -4372,12 +4391,12 @@ class eynollah:
#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=[]
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[]
indexer=0
#indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
@ -4389,27 +4408,31 @@ class eynollah:
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(rot,var_spectrum,'var_spectrum')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
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
#res_me=1000000000000000000000
#res_num=0
var_spectrum=0
if self.isNaN(res_me):
pass
else:
res.append( res_me )
var_res.append(var_spectrum)
num_of_peaks.append( res_num )
index_cor.append(indexer)
indexer=indexer+1
#if self.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:
@ -4422,12 +4445,12 @@ class eynollah:
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
res=[]
num_of_peaks=[]
index_cor=[]
#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)
@ -4435,27 +4458,13 @@ class eynollah:
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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)
@ -4472,12 +4481,9 @@ class eynollah:
#plt.show()
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
@ -4489,30 +4495,16 @@ class eynollah:
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(rot,var_spectrum,'var_spectrum')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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)
if self.dir_of_all is not None:
print('galdi?')
#print('galdi?')
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
@ -4537,12 +4529,8 @@ class eynollah:
angels=np.linspace(-90,-12,100)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
@ -4550,27 +4538,11 @@ class eynollah:
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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)
try:
@ -4584,12 +4556,9 @@ class eynollah:
angels=np.linspace(90,12,100)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
@ -4597,27 +4566,12 @@ class eynollah:
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
except:
res_me=1000000000000000000000
res_num=0
var_spectrum=0
if self.isNaN(res_me):
pass
else:
res.append( res_me )
var_res.append(var_spectrum)
num_of_peaks.append( res_num )
index_cor.append(indexer)
indexer=indexer+1
var_res.append(var_spectrum)
try:
@ -4631,9 +4585,6 @@ class eynollah:
angels=np.linspace(-25,25,60)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
indexer=0
@ -4648,27 +4599,11 @@ class eynollah:
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(rot,var_spectrum,'var_spectrum')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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)
try:
@ -4677,20 +4612,21 @@ class eynollah:
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
#print(ang_int,'ang_int')
#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)
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)
@ -4698,27 +4634,13 @@ class eynollah:
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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)
try:
@ -4732,9 +4654,6 @@ class eynollah:
angels=np.linspace(90,25,60)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
indexer=0
@ -4745,27 +4664,13 @@ class eynollah:
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer')
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
except:
res_me=1000000000000000000000
res_num=0
var_spectrum=0
if self.isNaN(res_me):
pass
else:
res.append( res_me )
var_res.append(var_spectrum)
num_of_peaks.append( res_num )
index_cor.append(indexer)
indexer=indexer+1
var_res.append(var_spectrum)
try:
@ -10443,7 +10348,9 @@ class eynollah:
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org2=self.do_prediction(patches,img,model_region)
marginal_patch=0.2
prediction_regions_org2=self.do_prediction(patches,img,model_region,marginal_patch)
prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
@ -10825,6 +10732,8 @@ class eynollah:
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
#plt.imshow(text_regions)
#plt.show()
pixel_img=4
min_area_text=0.00001
@ -10843,8 +10752,8 @@ class eynollah:
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
#print(x_width_mar,y_height_mar,'y_height_mar')
if x_width_mar>16 and y_height_mar/x_width_mar<10:
#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]
@ -11115,8 +11024,8 @@ class eynollah:
textlines_con_changed.append(textlines_big_org_form)
return textlines_con_changed
def check_any_text_region_in_model_one_is_main_or_header(self,regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
text_only=(regions_model_1[:,:]==1)*1
contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
#text_only=(regions_model_1[:,:]==1)*1
#contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
"""
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
@ -12290,7 +12199,7 @@ class eynollah:
num_col=None
peaks_neg_fin=[]
print(num_col,'num_colnum_col')
#print(num_col,'num_colnum_col')
if num_col is None:
txt_con_org=[]
order_text_new=[]
@ -12316,7 +12225,7 @@ class eynollah:
K.clear_session()
gc.collect()
print(np.unique(textline_mask_tot_ea[:,:]),'textline')
#print(np.unique(textline_mask_tot_ea[:,:]),'textline')
if self.dir_of_all is not None:
@ -12771,7 +12680,7 @@ class eynollah:
slopes_marginals,all_found_texline_polygons_marginals,boxes_marginals,_,polygons_of_marginals,all_box_coord_marginals,index_by_text_par_con_marginal=self.get_slopes_and_deskew_new(polygons_of_marginals,polygons_of_marginals,textline_mask_tot_ea,image_page_rotated,boxes_marginals,slope_deskew)
if self.curved_line=='true' or self.curved_line=='True' or self.curved_line=='TRUE' :
scale_param=1
@ -12911,9 +12820,15 @@ class eynollah:
boxes=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new,regions_without_seperators,matrix_of_lines_ch)
else:
boxes_d=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d,regions_without_seperators_d,matrix_of_lines_ch_d)
#print(boxes_d,len(boxes_d),'boxes_d')
#for mv in range(len(boxes_d)):
#img_box=image_page[boxes_d[mv][1]:boxes_d[mv][1]+boxes_d[mv][3] , boxes_d[mv][0]:boxes_d[mv][0]+boxes_d[mv][2],: ]
#plt.imshow(img_box)
#plt.show()
#print(slopes)
if self.dir_of_cropped_images is not None:
self.write_images_into_directory(polygons_of_images,self.dir_of_cropped_images,image_page)

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