@ -61,6 +61,8 @@ from .utils import (
seperate_lines_new_inside_teils2 ,
filter_small_drop_capitals_from_no_patch_layout ,
find_num_col_deskew ,
return_hor_spliter_by_index_for_without_verticals ,
find_new_features_of_contoures ,
)
@ -2263,78 +2265,6 @@ class eynollah:
cy_main = [ ( M_main [ j ] [ " m01 " ] / ( M_main [ j ] [ " m00 " ] + 1e-32 ) ) for j in range ( len ( M_main ) ) ]
return np . mean ( np . diff ( np . sort ( np . array ( cy_main ) ) ) )
def find_num_col_olddd ( 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 ]
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
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
y_help = np . zeros ( len ( y ) + 20 )
y_help [ 10 : len ( y ) + 10 ] = y
x = np . array ( range ( len ( y ) ) )
zneg_rev = - y_help + np . max ( y_help )
zneg = np . zeros ( len ( zneg_rev ) + 20 )
zneg [ 10 : len ( zneg_rev ) + 10 ] = zneg_rev
z = gaussian_filter1d ( y , sigma_ )
zneg = gaussian_filter1d ( zneg , sigma_ )
peaks_neg , _ = find_peaks ( zneg , height = 0 )
peaks , _ = find_peaks ( z , height = 0 )
peaks_neg = peaks_neg - 10 - 10
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 = peaks [ ( peaks > 0.06 * 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 ]
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
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
p_l = 0
p_u = len ( y ) - 1
p_m = int ( len ( y ) / 2.0 )
p_g_l = int ( len ( y ) / 3.0 )
p_g_u = len ( y ) - int ( len ( y ) / 3.0 )
diff_peaks = np . abs ( np . diff ( peaks_neg_fin ) )
diff_peaks_annormal = diff_peaks [ diff_peaks < 30 ]
return interest_neg_fin
def return_deskew_slop ( self , img_patch_org , sigma_des , main_page = False ) :
@ -4161,23 +4091,6 @@ class eynollah:
return regions_without_seperators
def return_regions_without_seperators_new ( self , regions_pre , regions_only_text ) :
kernel = np . ones ( ( 5 , 5 ) , np . uint8 )
regions_without_seperators = ( ( regions_pre [ : , : ] != 6 ) & ( regions_pre [ : , : ] != 0 ) & ( regions_pre [ : , : ] != 1 ) & ( regions_pre [ : , : ] != 2 ) ) * 1
# plt.imshow(regions_without_seperators)
# plt.show()
regions_without_seperators_n = ( ( regions_without_seperators [ : , : ] == 1 ) | ( regions_only_text [ : , : ] == 1 ) ) * 1
# regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
regions_without_seperators_n = regions_without_seperators_n . astype ( np . uint8 )
regions_without_seperators_n = cv2 . erode ( regions_without_seperators_n , kernel , iterations = 6 )
return regions_without_seperators_n
def image_change_background_pixels_to_zero ( self , image_page ) :
image_back_zero = np . zeros ( ( image_page . shape [ 0 ] , image_page . shape [ 1 ] ) )
@ -4373,86 +4286,6 @@ class eynollah:
return len ( peaks_fin_true ) , peaks_fin_true
def return_hor_spliter_by_index_for_without_verticals ( self , peaks_neg_fin_t , x_min_hor_some , x_max_hor_some ) :
# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
arg_min_hor_sort = np . argsort ( x_min_hor_some )
x_min_hor_some_sort = np . sort ( x_min_hor_some )
x_max_hor_some_sort = x_max_hor_some [ arg_min_hor_sort ]
arg_minmax = np . array ( range ( len ( peaks_neg_fin_t ) ) )
indexer_lines = [ ]
indexes_to_delete = [ ]
indexer_lines_deletions_len = [ ]
indexr_uniq_ind = [ ]
for i in range ( len ( x_min_hor_some_sort ) ) :
min_h = peaks_neg_fin_t - x_min_hor_some_sort [ i ]
max_h = peaks_neg_fin_t - x_max_hor_some_sort [ i ]
min_h [ 0 ] = min_h [ 0 ] # +20
max_h [ len ( max_h ) - 1 ] = max_h [ len ( max_h ) - 1 ] - 20
min_h_neg = arg_minmax [ ( min_h < 0 ) ]
min_h_neg_n = min_h [ min_h < 0 ]
try :
min_h_neg = [ min_h_neg [ np . argmax ( min_h_neg_n ) ] ]
except :
min_h_neg = [ ]
max_h_neg = arg_minmax [ ( max_h > 0 ) ]
max_h_neg_n = max_h [ max_h > 0 ]
if len ( max_h_neg_n ) > 0 :
max_h_neg = [ max_h_neg [ np . argmin ( max_h_neg_n ) ] ]
else :
max_h_neg = [ ]
if len ( min_h_neg ) > 0 and len ( max_h_neg ) > 0 :
deletions = list ( range ( min_h_neg [ 0 ] + 1 , max_h_neg [ 0 ] ) )
unique_delets_int = [ ]
# print(deletions,len(deletions),'delii')
if len ( deletions ) > 0 :
for j in range ( len ( deletions ) ) :
indexes_to_delete . append ( deletions [ j ] )
# print(deletions,indexes_to_delete,'badiii')
unique_delets = np . unique ( indexes_to_delete )
# print(min_h_neg[0],unique_delets)
unique_delets_int = unique_delets [ unique_delets < min_h_neg [ 0 ] ]
indexer_lines_deletions_len . append ( len ( deletions ) )
indexr_uniq_ind . append ( [ deletions ] )
else :
indexer_lines_deletions_len . append ( 0 )
indexr_uniq_ind . append ( - 999 )
index_line_true = min_h_neg [ 0 ] - len ( unique_delets_int )
# print(index_line_true)
if index_line_true > 0 and min_h_neg [ 0 ] > = 2 :
index_line_true = index_line_true
else :
index_line_true = min_h_neg [ 0 ]
indexer_lines . append ( index_line_true )
if len ( unique_delets_int ) > 0 :
for dd in range ( len ( unique_delets_int ) ) :
indexes_to_delete . append ( unique_delets_int [ dd ] )
else :
indexer_lines . append ( - 999 )
indexer_lines_deletions_len . append ( - 999 )
indexr_uniq_ind . append ( - 999 )
peaks_true = [ ]
for m in range ( len ( peaks_neg_fin_t ) ) :
if m in indexes_to_delete :
pass
else :
peaks_true . append ( peaks_neg_fin_t [ m ] )
return indexer_lines , peaks_true , arg_min_hor_sort , indexer_lines_deletions_len , indexr_uniq_ind
def find_num_col_by_vertical_lines ( self , regions_without_seperators , multiplier = 3.8 ) :
regions_without_seperators_0 = regions_without_seperators [ : , : , 0 ] . sum ( axis = 0 )
@ -4660,40 +4493,6 @@ class eynollah:
##print(len(peaks_neg_true))
return len ( peaks_neg_true ) , peaks_neg_true
def find_new_features_of_contoures ( self , contours_main ) :
areas_main = np . array ( [ cv2 . contourArea ( contours_main [ j ] ) for j in range ( len ( contours_main ) ) ] )
M_main = [ cv2 . moments ( contours_main [ j ] ) for j in range ( len ( contours_main ) ) ]
cx_main = [ ( M_main [ j ] [ " m10 " ] / ( M_main [ j ] [ " m00 " ] + 1e-32 ) ) for j in range ( len ( M_main ) ) ]
cy_main = [ ( M_main [ j ] [ " m01 " ] / ( M_main [ j ] [ " m00 " ] + 1e-32 ) ) for j in range ( len ( M_main ) ) ]
try :
x_min_main = np . array ( [ np . min ( contours_main [ j ] [ : , 0 , 0 ] ) for j in range ( len ( contours_main ) ) ] )
argmin_x_main = np . array ( [ np . argmin ( contours_main [ j ] [ : , 0 , 0 ] ) for j in range ( len ( contours_main ) ) ] )
x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 0 , 0 ] for j in range ( len ( contours_main ) ) ] )
y_corr_x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 0 , 1 ] for j in range ( len ( contours_main ) ) ] )
x_max_main = np . array ( [ np . max ( contours_main [ j ] [ : , 0 , 0 ] ) for j in range ( len ( contours_main ) ) ] )
y_min_main = np . array ( [ np . min ( contours_main [ j ] [ : , 0 , 1 ] ) for j in range ( len ( contours_main ) ) ] )
y_max_main = np . array ( [ np . max ( contours_main [ j ] [ : , 0 , 1 ] ) for j in range ( len ( contours_main ) ) ] )
except :
x_min_main = np . array ( [ np . min ( contours_main [ j ] [ : , 0 ] ) for j in range ( len ( contours_main ) ) ] )
argmin_x_main = np . array ( [ np . argmin ( contours_main [ j ] [ : , 0 ] ) for j in range ( len ( contours_main ) ) ] )
x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 0 ] for j in range ( len ( contours_main ) ) ] )
y_corr_x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 1 ] for j in range ( len ( contours_main ) ) ] )
x_max_main = np . array ( [ np . max ( contours_main [ j ] [ : , 0 ] ) for j in range ( len ( contours_main ) ) ] )
y_min_main = np . array ( [ np . min ( contours_main [ j ] [ : , 1 ] ) for j in range ( len ( contours_main ) ) ] )
y_max_main = np . array ( [ np . max ( contours_main [ j ] [ : , 1 ] ) for j in range ( len ( contours_main ) ) ] )
# dis_x=np.abs(x_max_main-x_min_main)
return cx_main , cy_main , x_min_main , x_max_main , y_min_main , y_max_main , y_corr_x_min_from_argmin
def return_points_with_boundies ( self , peaks_neg_fin , first_point , last_point ) :
peaks_neg_tot = [ ]
@ -4753,7 +4552,7 @@ class eynollah:
peaks_neg_tot = self . return_points_with_boundies ( peaks_neg_fin , 0 , seperators_closeup_n [ : , : , 0 ] . shape [ 1 ] )
start_index_of_hor , newest_peaks , arg_min_hor_sort , lines_length_dels , lines_indexes_deleted = self . return_hor_spliter_by_index_for_without_verticals ( peaks_neg_tot , x_min_hor_some , x_max_hor_some )
start_index_of_hor , newest_peaks , arg_min_hor_sort , lines_length_dels , lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals ( peaks_neg_tot , x_min_hor_some , x_max_hor_some )
arg_org_hor_some_sort = arg_org_hor_some [ arg_min_hor_sort ]
@ -4906,7 +4705,7 @@ class eynollah:
peaks_neg_ch_tot = self . return_points_with_boundies ( peaks_neg_ch , newest_peaks [ j ] , newest_peaks [ j + 1 ] )
ss_in_ch , nst_p_ch , arg_n_ch , lines_l_del_ch , lines_in_del_ch = self . return_hor_spliter_by_index_for_without_verticals ( peaks_neg_ch_tot , x_min_ch , x_max_ch )
ss_in_ch , nst_p_ch , arg_n_ch , lines_l_del_ch , lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals ( peaks_neg_ch_tot , x_min_ch , x_max_ch )
newest_y_spliter_ch_tot = [ ]
@ -5154,7 +4953,7 @@ class eynollah:
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_cross , _ = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
cx_cross , cy_cross , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_cross )
cx_cross , cy_cross , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_cross )
for ii in range ( len ( cx_cross ) ) :
sep_ver_hor [ int ( cy_cross [ ii ] ) - 15 : int ( cy_cross [ ii ] ) + 15 , int ( cx_cross [ ii ] ) + 5 : int ( cx_cross [ ii ] ) + 40 ] = 0
@ -5341,7 +5140,7 @@ class eynollah:
ret , thresh = cv2 . threshold ( imgray , 0 , 255 , 0 )
contours_cross , _ = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
cx_cross , cy_cross , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_cross )
cx_cross , cy_cross , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_cross )
for ii in range ( len ( cx_cross ) ) :
img_p_in [ int ( cy_cross [ ii ] ) - 30 : int ( cy_cross [ ii ] ) + 30 , int ( cx_cross [ ii ] ) + 5 : int ( cx_cross [ ii ] ) + 40 , 0 ] = 0
@ -5680,7 +5479,7 @@ class eynollah:
peaks_neg_tot = self . return_points_with_boundies ( peaks_neg_fin , 0 , regions_without_seperators [ : , : ] . shape [ 1 ] )
start_index_of_hor , newest_peaks , arg_min_hor_sort , lines_length_dels , lines_indexes_deleted = self . return_hor_spliter_by_index_for_without_verticals ( peaks_neg_tot , x_min_hor_some , x_max_hor_some )
start_index_of_hor , newest_peaks , arg_min_hor_sort , lines_length_dels , lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals ( peaks_neg_tot , x_min_hor_some , x_max_hor_some )
arg_org_hor_some_sort = arg_org_hor_some [ arg_min_hor_sort ]
@ -5845,7 +5644,7 @@ class eynollah:
peaks_neg_ch_tot = self . return_points_with_boundies ( peaks_neg_ch , newest_peaks [ j ] , newest_peaks [ j + 1 ] )
ss_in_ch , nst_p_ch , arg_n_ch , lines_l_del_ch , lines_in_del_ch = self . return_hor_spliter_by_index_for_without_verticals ( peaks_neg_ch_tot , x_min_ch , x_max_ch )
ss_in_ch , nst_p_ch , arg_n_ch , lines_l_del_ch , lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals ( peaks_neg_ch_tot , x_min_ch , x_max_ch )
newest_y_spliter_ch_tot = [ ]
@ -6266,8 +6065,8 @@ class eynollah:
# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
y_min_main_tab , y_max_main_tab = self . find_features_of_contoures ( contours_tab )
cx_tab_m_text , cy_tab_m_text , x_min_tab_m_text , x_max_tab_m_text , y_min_tab_m_text , y_max_tab_m_text = self . find_new_features_of_contoures ( contours_table_m_text )
cx_tabl , cy_tabl , x_min_tabl , x_max_tabl , y_min_tabl , y_max_tabl , _ = self . find_new_features_of_contoures ( contours_tab )
cx_tab_m_text , cy_tab_m_text , x_min_tab_m_text , x_max_tab_m_text , y_min_tab_m_text , y_max_tab_m_text = find_new_features_of_contoures ( contours_table_m_text )
cx_tabl , cy_tabl , x_min_tabl , x_max_tabl , y_min_tabl , y_max_tabl , _ = find_new_features_of_contoures ( contours_tab )
if len ( y_min_main_tab ) > 0 :
y_down_tabs = [ ]
@ -8012,7 +7811,7 @@ class eynollah:
min_area_text = 0.00001
polygons_of_marginals = return_contours_of_interested_region ( text_regions , pixel_img , min_area_text )
cx_text_only , cy_text_only , x_min_text_only , x_max_text_only , y_min_text_only , y_max_text_only , y_cor_x_min_main = self . find_new_features_of_contoures ( polygons_of_marginals )
cx_text_only , cy_text_only , x_min_text_only , x_max_text_only , y_min_text_only , y_max_text_only , y_cor_x_min_main = find_new_features_of_contoures ( polygons_of_marginals )
text_regions [ ( text_regions [ : , : ] == 4 ) ] = 1
@ -8293,7 +8092,7 @@ class eynollah:
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 )
cx_main , cy_main , x_min_main , x_max_main , y_min_main , y_max_main , y_corr_x_min_from_argmin = 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
@ -8401,8 +8200,8 @@ class eynollah:
def do_order_of_regions ( self , contours_only_text_parent , contours_only_text_parent_h , boxes , textline_mask_tot ) :
if self . full_layout :
cx_text_only , cy_text_only , x_min_text_only , _ , _ , _ , y_cor_x_min_main = self . find_new_features_of_contoures ( contours_only_text_parent )
cx_text_only_h , cy_text_only_h , x_min_text_only_h , _ , _ , _ , y_cor_x_min_main_h = self . find_new_features_of_contoures ( contours_only_text_parent_h )
cx_text_only , cy_text_only , x_min_text_only , _ , _ , _ , y_cor_x_min_main = find_new_features_of_contoures ( contours_only_text_parent )
cx_text_only_h , cy_text_only_h , x_min_text_only_h , _ , _ , _ , y_cor_x_min_main_h = find_new_features_of_contoures ( contours_only_text_parent_h )
try :
arg_text_con = [ ]
@ -8568,7 +8367,7 @@ class eynollah:
return order_text_new , id_of_texts_tot
else :
cx_text_only , cy_text_only , x_min_text_only , _ , _ , _ , y_cor_x_min_main = self . find_new_features_of_contoures ( contours_only_text_parent )
cx_text_only , cy_text_only , x_min_text_only , _ , _ , _ , y_cor_x_min_main = find_new_features_of_contoures ( contours_only_text_parent )
try :
arg_text_con = [ ]
@ -8684,9 +8483,9 @@ class eynollah:
def adhere_drop_capital_region_into_cprresponding_textline ( self , text_regions_p , polygons_of_drop_capitals , contours_only_text_parent , contours_only_text_parent_h , all_box_coord , all_box_coord_h , all_found_texline_polygons , all_found_texline_polygons_h ) :
# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
# print(all_found_texline_polygons[3])
cx_m , cy_m , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_only_text_parent )
cx_h , cy_h , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_only_text_parent_h )
cx_d , cy_d , _ , _ , y_min_d , y_max_d , _ = self . find_new_features_of_contoures ( polygons_of_drop_capitals )
cx_m , cy_m , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent )
cx_h , cy_h , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent_h )
cx_d , cy_d , _ , _ , y_min_d , y_max_d , _ = find_new_features_of_contoures ( polygons_of_drop_capitals )
img_con_all = np . zeros ( ( text_regions_p . shape [ 0 ] , text_regions_p . shape [ 1 ] , 3 ) )
for j_cont in range ( len ( contours_only_text_parent ) ) :
@ -8751,9 +8550,9 @@ class eynollah:
region_final = region_with_intersected_drop [ np . argmax ( sum_pixels_of_intersection ) ] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= self. find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try :
cx_t , cy_t , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -8805,9 +8604,9 @@ class eynollah:
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= self. find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
cx_t , cy_t , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -8855,7 +8654,7 @@ class eynollah:
# print(cx_t,'print')
try :
# print(all_found_texline_polygons[j_cont][0])
cx_t , cy_t , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -8902,7 +8701,7 @@ class eynollah:
else :
pass
##cx_t,cy_t ,_, _, _ ,_,_= self. find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
###print(all_box_coord[j_cont])
###print(cx_t)
###print(cy_t)
@ -8956,9 +8755,9 @@ class eynollah:
region_final = region_with_intersected_drop [ np . argmax ( sum_pixels_of_intersection ) ] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= self. find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try :
cx_t , cy_t , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -9010,12 +8809,12 @@ class eynollah:
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= self. find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(cx_t,'print')
try :
# print(all_found_texline_polygons[j_cont][0])
cx_t , cy_t , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
cx_t , cy_t , _ , _ , _ , _ , _ = find_new_features_of_contoures ( all_found_texline_polygons [ int ( region_final ) ] )
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -9081,7 +8880,7 @@ class eynollah:
#####try:
#####if len(contours_new_parent)==1:
######print(all_found_texline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_= self. find_new_features_of_contoures(all_found_texline_polygons[j_cont])
#####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[j_cont])
######print(all_box_coord[j_cont])
######print(cx_t)
######print(cy_t)
@ -9594,8 +9393,8 @@ class eynollah:
contours_only_text_parent = list ( np . array ( contours_only_text_parent ) [ index_con_parents ] )
areas_cnt_text_parent = list ( np . array ( areas_cnt_text_parent ) [ index_con_parents ] )
cx_bigest_big , cy_biggest_big , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( [ contours_biggest ] )
cx_bigest , cy_biggest , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_only_text_parent )
cx_bigest_big , cy_biggest_big , _ , _ , _ , _ , _ = find_new_features_of_contoures ( [ contours_biggest ] )
cx_bigest , cy_biggest , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent )
contours_only_text_d , hir_on_text_d = return_contours_of_image ( text_only_d )
contours_only_text_parent_d = return_parent_contours ( contours_only_text_d , hir_on_text_d )
@ -9606,8 +9405,8 @@ class eynollah:
contours_biggest_d = contours_only_text_parent_d [ np . argmax ( areas_cnt_text_d ) ]
cx_bigest_d_big , cy_biggest_d_big , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( [ contours_biggest_d ] )
cx_bigest_d , cy_biggest_d , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_only_text_parent_d )
cx_bigest_d_big , cy_biggest_d_big , _ , _ , _ , _ , _ = find_new_features_of_contoures ( [ contours_biggest_d ] )
cx_bigest_d , cy_biggest_d , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent_d )
( h , w ) = text_only . shape [ : 2 ]
center = ( w / / 2.0 , h / / 2.0 )
@ -9665,8 +9464,8 @@ class eynollah:
contours_only_text_parent = list ( np . array ( contours_only_text_parent ) [ index_con_parents ] )
areas_cnt_text_parent = list ( np . array ( areas_cnt_text_parent ) [ index_con_parents ] )
cx_bigest_big , cy_biggest_big , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( [ contours_biggest ] )
cx_bigest , cy_biggest , _ , _ , _ , _ , _ = self . find_new_features_of_contoures ( contours_only_text_parent )
cx_bigest_big , cy_biggest_big , _ , _ , _ , _ , _ = find_new_features_of_contoures ( [ contours_biggest ] )
cx_bigest , cy_biggest , _ , _ , _ , _ , _ = find_new_features_of_contoures ( contours_only_text_parent )
# print(areas_cnt_text_parent,'areas_cnt_text_parent')
###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)
Konstantin Baierer
4 years ago