From 58ecb5708ef89542bb9b0de685c693a64cc52fae Mon Sep 17 00:00:00 2001
From: Konstantin Baierer <unixprog@gmail.com>
Date: Wed, 25 Nov 2020 17:05:26 +0100
Subject: [PATCH] patch in changes from 37c0338 by @vahidrezanezhad

---
 sbb_newspapers_org_image/eynollah.py | 421 +++++++++++----------------
 1 file changed, 168 insertions(+), 253 deletions(-)

diff --git a/sbb_newspapers_org_image/eynollah.py b/sbb_newspapers_org_image/eynollah.py
index 3231794..79f5993 100644
--- a/sbb_newspapers_org_image/eynollah.py
+++ b/sbb_newspapers_org_image/eynollah.py
@@ -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)