qurator-spk
/
eynollah
mirror of https://github.com/qurator-spk/eynollah.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

typo: s,spliter,splitter,g

Browse Source
pull/23/head
Konstantin Baierer 3 years ago
parent e332da34f6
commit 375e9771e2
2 changed files with 72 additions and 72 deletions
Show Stats Download Patch File Download Diff File

22
qurator/eynollah/eynollah.py
Unescape Escape View File

@ -1486,10 +1486,10 @@ class Eynollah:
regions_without_seperators_d = None
pixel_lines = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
K.clear_session()
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -1503,11 +1503,11 @@ class Eynollah:
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], KERNEL, iterations=6)
t1 = time.time()
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
boxes = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes))
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
boxes_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d))
@ -1760,17 +1760,17 @@ class Eynollah:
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h)
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h)
else:
_, _, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
else:
_, _, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
# print(splitter_y_new,splitter_y_new_d,'num_col_classifier')
# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
if num_col_classifier >= 3:
@ -1790,9 +1790,9 @@ class Eynollah:
regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
boxes = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
boxes_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)

122
qurator/eynollah/utils/__init__.py
Unescape Escape View File

@ -1549,30 +1549,30 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
matrix_of_lines_ch=np.copy(matrix_l_n)
cy_main_spliters=cy_main_hor[ (x_min_main_hor<=.16*region_pre_p.shape[1]) & (x_max_main_hor>=.84*region_pre_p.shape[1] )]
cy_main_splitters=cy_main_hor[ (x_min_main_hor<=.16*region_pre_p.shape[1]) & (x_max_main_hor>=.84*region_pre_p.shape[1] )]
cy_main_spliters=np.array( list(cy_main_spliters)+list(special_seperators))
cy_main_splitters=np.array( list(cy_main_splitters)+list(special_seperators))
if contours_h is not None:
try:
cy_main_spliters_head=cy_main_head[ (x_min_main_head<=.16*region_pre_p.shape[1]) & (x_max_main_head>=.84*region_pre_p.shape[1] )]
cy_main_spliters=np.array( list(cy_main_spliters)+list(cy_main_spliters_head))
cy_main_splitters_head=cy_main_head[ (x_min_main_head<=.16*region_pre_p.shape[1]) & (x_max_main_head>=.84*region_pre_p.shape[1] )]
cy_main_splitters=np.array( list(cy_main_splitters)+list(cy_main_splitters_head))
except:
pass
args_cy_spliter=np.argsort(cy_main_spliters)
args_cy_splitter=np.argsort(cy_main_splitters)
cy_main_spliters_sort=cy_main_spliters[args_cy_spliter]
cy_main_splitters_sort=cy_main_splitters[args_cy_splitter]
spliter_y_new=[]
spliter_y_new.append(0)
for i in range(len(cy_main_spliters_sort)):
spliter_y_new.append( cy_main_spliters_sort[i] )
splitter_y_new=[]
splitter_y_new.append(0)
for i in range(len(cy_main_splitters_sort)):
splitter_y_new.append( cy_main_splitters_sort[i] )
spliter_y_new.append(region_pre_p.shape[0])
splitter_y_new.append(region_pre_p.shape[0])
spliter_y_new_diff=np.diff(spliter_y_new)/float(region_pre_p.shape[0])*100
splitter_y_new_diff=np.diff(splitter_y_new)/float(region_pre_p.shape[0])*100
args_big_parts=np.array(range(len(spliter_y_new_diff))) [ spliter_y_new_diff>22 ]
args_big_parts=np.array(range(len(splitter_y_new_diff))) [ splitter_y_new_diff>22 ]
@ -1587,8 +1587,8 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
for itiles in args_big_parts:
regions_without_seperators_tile=regions_without_seperators[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:,0]
#image_page_background_zero_tile=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:]
regions_without_seperators_tile=regions_without_seperators[int(splitter_y_new[iteils]):int(splitter_y_new[iteils+1]),:,0]
#image_page_background_zero_tile=image_page_background_zero[int(splitter_y_new[iteils]):int(splitter_y_new[iteils+1]),:]
#print(regions_without_seperators_tile.shape)
##plt.imshow(regions_without_seperators_tile)
@ -1614,25 +1614,25 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
#print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza')
return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,spliter_y_new,seperators_closeup_n
return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,splitter_y_new,seperators_closeup_n
def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier):
def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier):
boxes=[]
for i in range(len(spliter_y_new)-1):
#print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> spliter_y_new[i] ) & (matrix_of_lines_ch[:,7]< spliter_y_new[i+1] ) ]
for i in range(len(splitter_y_new)-1):
#print(splitter_y_new[i],splitter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> splitter_y_new[i] ) & (matrix_of_lines_ch[:,7]< splitter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical seperator to find holes.
if 1>0:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
if 1>0:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(splitter_y_new[i+1]-splitter_y_new[i] )):
try:
num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.)
num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=7.)
except:
peaks_neg_fin=[]
@ -1644,7 +1644,7 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
#print('burda')
if len(peaks_neg_fin)==0:
num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=3.)
num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=3.)
peaks_neg_fin_early=[]
peaks_neg_fin_early.append(0)
#print(peaks_neg_fin,'peaks_neg_fin')
@ -1657,15 +1657,15 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
for i_n in range(len(peaks_neg_fin_early)-1):
#print(i_n,'i_n')
#plt.plot(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]].sum(axis=0) )
#plt.plot(regions_without_seperators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]].sum(axis=0) )
#plt.show()
try:
num_col, peaks_neg_fin1=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=7.)
num_col, peaks_neg_fin1=find_num_col(regions_without_seperators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=7.)
except:
peaks_neg_fin1=[]
try:
num_col, peaks_neg_fin2=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=5.)
num_col, peaks_neg_fin2=find_num_col(regions_without_seperators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=5.)
except:
peaks_neg_fin2=[]
@ -1698,7 +1698,7 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
#print(peaks_neg_fin,'peaks_neg_fin')
except:
pass
#num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
#num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some=matrix_new[:,2][ (matrix_new[:,9]==0) ]
x_max_hor_some=matrix_new[:,3][ (matrix_new[:,9]==0) ]
cy_hor_some=matrix_new[:,5][ (matrix_new[:,9]==0) ]
@ -1719,7 +1719,7 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
try:
y_grenze=int(spliter_y_new[i])+300
y_grenze=int(splitter_y_new[i])+300
@ -1728,13 +1728,13 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
args_early_ys=np.array(range(len(y_type_2)))
#print(args_early_ys,'args_early_ys')
#print(int(spliter_y_new[i]),int(spliter_y_new[i+1]))
#print(int(splitter_y_new[i]),int(splitter_y_new[i+1]))
y_type_2_up=np.array(y_type_2)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_starting_up=np.array(x_starting)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_ending_up=np.array(x_ending)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
y_diff_type_2_up=np.array(y_diff_type_2)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
args_up=args_early_ys[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
y_type_2_up=np.array(y_type_2)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_starting_up=np.array(x_starting)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_ending_up=np.array(x_ending)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
y_diff_type_2_up=np.array(y_diff_type_2)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
args_up=args_early_ys[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
@ -1747,25 +1747,25 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
if len(y_diff_main_separator_up)>0:
args_to_be_kept=np.array( list( set(args_early_ys)-set(args_main_to_deleted) ) )
#print(args_to_be_kept,'args_to_be_kept')
boxes.append([0,peaks_neg_tot[len(peaks_neg_tot)-1],int(spliter_y_new[i]),int( np.max(y_diff_main_separator_up))])
spliter_y_new[i]=[ np.max(y_diff_main_separator_up) ][0]
boxes.append([0,peaks_neg_tot[len(peaks_neg_tot)-1],int(splitter_y_new[i]),int( np.max(y_diff_main_separator_up))])
splitter_y_new[i]=[ np.max(y_diff_main_separator_up) ][0]
#print(spliter_y_new[i],'spliter_y_new[i]')
#print(splitter_y_new[i],'splitter_y_new[i]')
y_type_2=np.array(y_type_2)[args_to_be_kept]
x_starting=np.array(x_starting)[args_to_be_kept]
x_ending=np.array(x_ending)[args_to_be_kept]
y_diff_type_2=np.array(y_diff_type_2)[args_to_be_kept]
#print('galdiha')
y_grenze=int(spliter_y_new[i])+200
y_grenze=int(splitter_y_new[i])+200
args_early_ys2=np.array(range(len(y_type_2)))
y_type_2_up=np.array(y_type_2)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_starting_up=np.array(x_starting)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_ending_up=np.array(x_ending)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
y_diff_type_2_up=np.array(y_diff_type_2)[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
args_up2=args_early_ys2[( np.array(y_type_2)>int(spliter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
y_type_2_up=np.array(y_type_2)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_starting_up=np.array(x_starting)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
x_ending_up=np.array(x_ending)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
y_diff_type_2_up=np.array(y_diff_type_2)[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
args_up2=args_early_ys2[( np.array(y_type_2)>int(splitter_y_new[i]) ) & (np.array(y_type_2)<=y_grenze)]
#print(y_type_2_up,x_starting_up,x_ending_up,'didid')
@ -1840,7 +1840,7 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
y_type_2=np.array(y_type_2)
y_diff_type_2_up=np.array(y_diff_type_2_up)
#int(spliter_y_new[i])
#int(splitter_y_new[i])
y_lines_by_order=[]
x_start_by_order=[]
@ -1850,7 +1850,7 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
if reading_order_type==1:
y_lines_by_order.append(int(spliter_y_new[i]))
y_lines_by_order.append(int(splitter_y_new[i]))
x_start_by_order.append(0)
x_end_by_order.append(len(peaks_neg_tot)-2)
else:
@ -1872,13 +1872,13 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
x_ending=list(x_ending)
for lj in columns_not_covered:
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(lj)
x_ending.append(lj+1)
##y_lines_by_order.append(int(spliter_y_new[i]))
##y_lines_by_order.append(int(splitter_y_new[i]))
##x_start_by_order.append(0)
for lk in range(len(x_start_without_mother)):
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(x_start_without_mother[lk])
x_ending.append(x_end_without_mother[lk])
@ -1935,13 +1935,13 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
x_ending=list(x_ending)
for lj in columns_not_covered:
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(lj)
x_ending.append(lj+1)
##y_lines_by_order.append(int(spliter_y_new[i]))
##y_lines_by_order.append(int(splitter_y_new[i]))
##x_start_by_order.append(0)
for lk in range(len(x_start_without_mother)):
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(x_start_without_mother[lk])
x_ending.append(x_end_without_mother[lk])
@ -1986,7 +1986,7 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
for i_c in range(len(y_column_nc)):
if i_c==(len(y_column_nc)-1):
ind_all_lines_betweeen_nm_wc=ind_args[(y_type_2>y_column_nc[i_c]) & (y_type_2<int(spliter_y_new[i+1])) & (x_starting>=i_s_nc) & (x_ending<=x_end_biggest_column)]
ind_all_lines_betweeen_nm_wc=ind_args[(y_type_2>y_column_nc[i_c]) & (y_type_2<int(splitter_y_new[i+1])) & (x_starting>=i_s_nc) & (x_ending<=x_end_biggest_column)]
else:
ind_all_lines_betweeen_nm_wc=ind_args[(y_type_2>y_column_nc[i_c]) & (y_type_2<y_column_nc[i_c+1]) & (x_starting>=i_s_nc) & (x_ending<=x_end_biggest_column)]
@ -2141,11 +2141,11 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
if len(y_in_cols)>0:
y_down=np.min(y_in_cols)
else:
y_down=[int(spliter_y_new[i+1])][0]
y_down=[int(splitter_y_new[i+1])][0]
#print(y_itself,'y_itself')
boxes.append([peaks_neg_tot[column],peaks_neg_tot[column+1],y_itself,y_down])
except:
boxes.append([0,peaks_neg_tot[len(peaks_neg_tot)-1],int(spliter_y_new[i]),int(spliter_y_new[i+1])])
boxes.append([0,peaks_neg_tot[len(peaks_neg_tot)-1],int(splitter_y_new[i]),int(splitter_y_new[i+1])])
@ -2170,13 +2170,13 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
x_ending=list(x_ending)
for lj in columns_not_covered:
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(lj)
x_ending.append(lj+1)
##y_lines_by_order.append(int(spliter_y_new[i]))
##y_lines_by_order.append(int(splitter_y_new[i]))
##x_start_by_order.append(0)
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(x_starting[0])
x_ending.append(x_ending[0])
@ -2194,10 +2194,10 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
x_ending=list(x_ending)
for lj in columns_not_covered:
y_type_2.append(int(spliter_y_new[i]))
y_type_2.append(int(splitter_y_new[i]))
x_starting.append(lj)
x_ending.append(lj+1)
##y_lines_by_order.append(int(spliter_y_new[i]))
##y_lines_by_order.append(int(splitter_y_new[i]))
##x_start_by_order.append(0)
@ -2256,13 +2256,13 @@ def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_withou
if len(y_in_cols)>0:
y_down=np.min(y_in_cols)
else:
y_down=[int(spliter_y_new[i+1])][0]
y_down=[int(splitter_y_new[i+1])][0]
#print(y_itself,'y_itself')
boxes.append([peaks_neg_tot[column],peaks_neg_tot[column+1],y_itself,y_down])
#else:
#boxes.append([ 0, regions_without_seperators[:,:].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
#boxes.append([ 0, regions_without_seperators[:,:].shape[1] ,splitter_y_new[i],splitter_y_new[i+1]])
return boxes

Write Preview
Loading…
Cancel
Save