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eynollah
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🎨 remove trailing spaces

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pull/8/head
Konstantin Baierer 4 years ago
parent 6ab7abdfcd
commit a2341deab4
2 changed files with 278 additions and 278 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -1238,12 +1238,12 @@ class eynollah:
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
# 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*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] ]
@ -2758,258 +2758,258 @@ class eynollah:
def get_regions_from_xy_2models(self,img,is_image_enhanced):
img_org=np.copy(img)
img_height_h=img_org.shape[0]
img_width_h=img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
gaussian_filter=False
patches=True
binary=False
ratio_y=1.3
ratio_x=1
median_blur=False
img= self.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)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org_y=self.do_prediction(patches,img,model_region)
prediction_regions_org_y=self.resize_image(prediction_regions_org_y, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org_y[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_org_y=prediction_regions_org_y[:,:,0]
mask_zeros_y=(prediction_regions_org_y[:,:]==0)*1
if is_image_enhanced:
ratio_x=1.2
else:
ratio_x=1
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))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org=self.do_prediction(patches,img,model_region)
prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
##sys.exit()
prediction_regions_org=prediction_regions_org[:,:,0]
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
gaussian_filter=False
patches=True
binary=False
ratio_x=1
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))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
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 )
#plt.imshow(prediction_regions_org2[:,:,0])
#plt.show()
#sys.exit()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
prediction_regions_org_copy=np.copy(prediction_regions_org)
prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)]=0
text_sume_second=( (prediction_regions_org_copy[:,:]==1)*1 ).sum()
rate_two_models=text_sume_second/float(text_sume_early)*100
print(rate_two_models,'ratio_of_two_models')
if is_image_enhanced and rate_two_models<95.50:#98.45:
pass
else:
prediction_regions_org=np.copy(prediction_regions_org_copy)
##prediction_regions_org[mask_lines2[:,:]==1]=3
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
del mask_lines2
del mask_zeros2
del prediction_regions_org2
#if is_image_enhanced:
#pass
#else:
#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
#gaussian_filter=False
#patches=True
#binary=False
#ratio_x=1
#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))
#if binary:
#img = self.otsu_copy_binary(img)#self.otsu_copy(img)
#img = img.astype(np.uint16)
#if median_blur:
#img=cv2.medianBlur(img,5)
#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)
#prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
##sys.exit()
###prediction_regions_org=prediction_regions_org[:,:,0]
#session_region.close()
#del model_region
#del session_region
#gc.collect()
####K.clear_session()
#mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
#mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
#text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
#prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
###prediction_regions_org[mask_lines2[:,:]==1]=3
#prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
#text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
#print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
#del mask_lines2
#del mask_zeros2
#del prediction_regions_org2
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
pixel_img=1
min_area_text=0.00001
polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)
polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)
polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)
text_regions_p_true=np.zeros(prediction_regions_org.shape)
#text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))
##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
text_regions_p_true[:,:][mask_images_only[:,:]==1]=2
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
##print(np.unique(text_regions_p_true))
#text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
#text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
del polygons_of_only_texts
del polygons_of_only_images
del polygons_of_only_lines
@ -3017,14 +3017,14 @@ class eynollah:
del prediction_regions_org
del img
del mask_zeros_y
del prediction_regions_org_y
del img_org
gc.collect()
return text_regions_p_true
def write_images_into_directory(self, img_contoures, dir_of_cropped_imgs, image_page):
index = 0
for cont_ind in img_contoures:
@ -3044,13 +3044,13 @@ class eynollah:
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:
@ -3061,46 +3061,46 @@ class eynollah:
text_with_lines=self.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=self.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
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]
@ -3110,125 +3110,125 @@ class eynollah:
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=self.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=self.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=self.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')
@ -3246,39 +3246,39 @@ class eynollah:
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:
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(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))
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

204
sbb_newspapers_org_image/utils.py
Unescape Escape View File

@ -1526,12 +1526,12 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
###peaks, _ = find_peaks(z, height=0)
###peaks_neg=peaks_neg-10-10
####print(np.std(z),'np.std(z)np.std(z)np.std(z)')
#####plt.plot(z)
#####plt.show()
#####plt.imshow(regions_without_seperators)
#####plt.show()
###"""
@ -1539,18 +1539,18 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
###first_nonzero=first_nonzero+0#+100
###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]
###interest_pos=interest_pos[interest_pos>10]
###interest_neg=z[peaks_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
@ -1558,18 +1558,18 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
###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]
###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
@ -1586,11 +1586,11 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
###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]
#print(len(interest_neg_fin),np.mean(interest_neg_fin))
return np.std(z)#interest_neg_fin,np.std(z)
@ -2349,11 +2349,11 @@ def return_regions_without_seperators(regions_pre):
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)
@ -2364,49 +2364,49 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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)
@ -2414,8 +2414,8 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
#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:
@ -2426,7 +2426,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
#res_me=1000000000000000000000
#else:
#pass
#res_num=len(neg_peaks)
except:
#res_me=1000000000000000000000
@ -2440,7 +2440,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
#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
@ -2448,19 +2448,19 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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)
@ -2476,33 +2476,33 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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:
@ -2513,7 +2513,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
var_res.append(var_spectrum)
if self.dir_of_all is not None:
#print('galdi?')
plt.figure(figsize=(60,30))
@ -2521,7 +2521,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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'))
@ -2529,19 +2529,19 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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)
@ -2558,18 +2558,18 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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)
@ -2587,17 +2587,17 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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)
@ -2605,39 +2605,39 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
#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)
@ -2649,24 +2649,24 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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)
@ -2686,11 +2686,11 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
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
@ -2749,55 +2749,55 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
def check_any_text_region_in_model_one_is_main_or_header(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)
"""
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))])
###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1])
###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002]
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
"""
cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=self.find_new_features_of_contoures(contours_only_text_parent)
length_con=x_max_main-x_min_main
height_con=y_max_main-y_min_main
all_found_texline_polygons_main=[]
all_found_texline_polygons_head=[]
all_box_coord_main=[]
all_box_coord_head=[]
slopes_main=[]
slopes_head=[]
contours_only_text_parent_main=[]
contours_only_text_parent_head=[]
contours_only_text_parent_main_d=[]
contours_only_text_parent_head_d=[]
for ii in range(len(contours_only_text_parent)):
con=contours_only_text_parent[ii]
img=np.zeros((regions_model_1.shape[0],regions_model_1.shape[1],3))
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
all_pixels=((img[:,:,0]==255)*1).sum()
pixels_header=( ( (img[:,:,0]==255) & (regions_model_full[:,:,0]==2) )*1 ).sum()
pixels_main=all_pixels-pixels_header
if (pixels_header>=pixels_main) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
contours_only_text_parent_head.append(con)
@ -2814,11 +2814,11 @@ def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions
all_box_coord_main.append(all_box_coord[ii])
slopes_main.append(slopes[ii])
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
#print(all_pixels,pixels_main,pixels_header)
#plt.imshow(img[:,:,0])
#plt.show()
return regions_model_1,contours_only_text_parent_main,contours_only_text_parent_head,all_box_coord_main,all_box_coord_head,all_found_texline_polygons_main,all_found_texline_polygons_head,slopes_main,slopes_head,contours_only_text_parent_main_d,contours_only_text_parent_head_d

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