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2269 lines
102 KiB
Python
2269 lines
102 KiB
Python
import math
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import matplotlib.pyplot as plt
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import numpy as np
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from shapely import geometry
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import cv2
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import imutils
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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from .is_nan import isNaN
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from .contour import (contours_in_same_horizon,
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find_new_features_of_contoures,
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return_contours_of_image,
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return_parent_contours)
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def return_x_start_end_mothers_childs_and_type_of_reading_order(x_min_hor_some,x_max_hor_some,cy_hor_some,peak_points,cy_hor_diff):
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x_start=[]
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x_end=[]
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kind=[]#if covers 2 and more than 2 columns set it to 1 otherwise 0
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len_sep=[]
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y_sep=[]
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y_diff=[]
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new_main_sep_y=[]
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indexer=0
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for i in range(len(x_min_hor_some)):
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starting=x_min_hor_some[i]-peak_points
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starting=starting[starting>=0]
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min_start=np.argmin(starting)
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ending=peak_points-x_max_hor_some[i]
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len_ending_neg=len(ending[ending<=0])
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ending=ending[ending>0]
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max_end=np.argmin(ending)+len_ending_neg
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if (max_end-min_start)>=2:
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if (max_end-min_start)==(len(peak_points)-1):
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new_main_sep_y.append(indexer)
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#print((max_end-min_start),len(peak_points),'(max_end-min_start)')
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y_sep.append(cy_hor_some[i])
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y_diff.append(cy_hor_diff[i])
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x_end.append(max_end)
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x_start.append( min_start)
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len_sep.append(max_end-min_start)
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if max_end==min_start+1:
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kind.append(0)
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else:
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kind.append(1)
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indexer+=1
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x_start_returned=np.copy(x_start)
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x_end_returned=np.copy(x_end)
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y_sep_returned=np.copy(y_sep)
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y_diff_returned=np.copy(y_diff)
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all_args_uniq=contours_in_same_horizon(y_sep_returned)
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args_to_be_unified=[]
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y_unified=[]
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y_diff_unified=[]
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x_s_unified=[]
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x_e_unified=[]
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if len(all_args_uniq)>0:
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#print('burda')
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if type(all_args_uniq[0]) is list:
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for dd in range(len(all_args_uniq)):
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if len(all_args_uniq[dd])==2:
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x_s_same_hor=np.array(x_start_returned)[all_args_uniq[dd]]
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x_e_same_hor=np.array(x_end_returned)[all_args_uniq[dd]]
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y_sep_same_hor=np.array(y_sep_returned)[all_args_uniq[dd]]
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y_diff_same_hor=np.array(y_diff_returned)[all_args_uniq[dd]]
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#print('burda2')
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if x_s_same_hor[0]==(x_e_same_hor[1]-1) or x_s_same_hor[1]==(x_e_same_hor[0]-1) and x_s_same_hor[0]!=x_s_same_hor[1] and x_e_same_hor[0]!=x_e_same_hor[1]:
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#print('burda3')
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for arg_in in all_args_uniq[dd]:
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#print(arg_in,'arg_in')
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args_to_be_unified.append(arg_in)
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y_selected=np.min(y_sep_same_hor)
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y_diff_selected=np.max(y_diff_same_hor)
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x_s_selected=np.min(x_s_same_hor)
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x_e_selected=np.max(x_e_same_hor)
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x_s_unified.append(x_s_selected)
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x_e_unified.append(x_e_selected)
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y_unified.append(y_selected)
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y_diff_unified.append(y_diff_selected)
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#print(x_s_same_hor,'x_s_same_hor')
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#print(x_e_same_hor[:]-1,'x_e_same_hor')
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#print('#############################')
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#print(x_s_unified,'y_selected')
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#print(x_e_unified,'x_s_selected')
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#print(y_unified,'x_e_same_hor')
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args_lines_not_unified=list( set(range(len(y_sep_returned)))-set(args_to_be_unified) )
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#print(args_lines_not_unified,'args_lines_not_unified')
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x_start_returned_not_unified=list( np.array(x_start_returned)[args_lines_not_unified] )
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x_end_returned_not_unified=list( np.array(x_end_returned)[args_lines_not_unified] )
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y_sep_returned_not_unified=list (np.array(y_sep_returned)[args_lines_not_unified] )
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y_diff_returned_not_unified=list (np.array(y_diff_returned)[args_lines_not_unified] )
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for dv in range(len(y_unified)):
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y_sep_returned_not_unified.append(y_unified[dv])
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y_diff_returned_not_unified.append(y_diff_unified[dv])
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x_start_returned_not_unified.append(x_s_unified[dv])
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x_end_returned_not_unified.append(x_e_unified[dv])
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#print(y_sep_returned,'y_sep_returned')
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#print(x_start_returned,'x_start_returned')
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#print(x_end_returned,'x_end_returned')
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x_start_returned=np.copy(x_start_returned_not_unified)
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x_end_returned=np.copy(x_end_returned_not_unified)
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y_sep_returned=np.copy(y_sep_returned_not_unified)
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y_diff_returned=np.copy(y_diff_returned_not_unified)
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#print(y_sep_returned,'y_sep_returned2')
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#print(x_start_returned,'x_start_returned2')
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#print(x_end_returned,'x_end_returned2')
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#print(new_main_sep_y,'new_main_sep_y')
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#print(x_start,'x_start')
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#print(x_end,'x_end')
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if len(new_main_sep_y)>0:
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min_ys=np.min(y_sep)
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max_ys=np.max(y_sep)
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y_mains=[]
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y_mains.append(min_ys)
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y_mains_sep_ohne_grenzen=[]
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for ii in range(len(new_main_sep_y)):
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y_mains.append(y_sep[new_main_sep_y[ii]])
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y_mains_sep_ohne_grenzen.append(y_sep[new_main_sep_y[ii]])
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y_mains.append(max_ys)
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y_mains_sorted=np.sort(y_mains)
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diff=np.diff(y_mains_sorted)
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argm=np.argmax(diff)
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y_min_new=y_mains_sorted[argm]
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y_max_new=y_mains_sorted[argm+1]
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#print(y_min_new,'y_min_new')
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#print(y_max_new,'y_max_new')
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#print(y_sep[new_main_sep_y[0]],y_sep,'yseps')
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x_start=np.array(x_start)
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x_end=np.array(x_end)
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kind=np.array(kind)
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y_sep=np.array(y_sep)
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if (y_min_new in y_mains_sep_ohne_grenzen) and (y_max_new in y_mains_sep_ohne_grenzen):
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x_start=x_start[(y_sep>y_min_new) & (y_sep<y_max_new)]
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x_end=x_end[(y_sep>y_min_new) & (y_sep<y_max_new)]
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kind=kind[(y_sep>y_min_new) & (y_sep<y_max_new)]
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y_sep=y_sep[(y_sep>y_min_new) & (y_sep<y_max_new)]
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elif (y_min_new in y_mains_sep_ohne_grenzen) and (y_max_new not in y_mains_sep_ohne_grenzen):
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#print('burda')
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x_start=x_start[(y_sep>y_min_new) & (y_sep<=y_max_new)]
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#print('burda1')
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x_end=x_end[(y_sep>y_min_new) & (y_sep<=y_max_new)]
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#print('burda2')
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kind=kind[(y_sep>y_min_new) & (y_sep<=y_max_new)]
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y_sep=y_sep[(y_sep>y_min_new) & (y_sep<=y_max_new)]
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elif (y_min_new not in y_mains_sep_ohne_grenzen) and (y_max_new in y_mains_sep_ohne_grenzen):
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x_start=x_start[(y_sep>=y_min_new) & (y_sep<y_max_new)]
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x_end=x_end[(y_sep>=y_min_new) & (y_sep<y_max_new)]
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kind=kind[(y_sep>=y_min_new) & (y_sep<y_max_new)]
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y_sep=y_sep[(y_sep>=y_min_new) & (y_sep<y_max_new)]
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else:
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x_start=x_start[(y_sep>=y_min_new) & (y_sep<=y_max_new)]
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x_end=x_end[(y_sep>=y_min_new) & (y_sep<=y_max_new)]
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kind=kind[(y_sep>=y_min_new) & (y_sep<=y_max_new)]
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y_sep=y_sep[(y_sep>=y_min_new) & (y_sep<=y_max_new)]
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#print(x_start,'x_start')
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#print(x_end,'x_end')
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#print(len_sep)
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deleted=[]
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for i in range(len(x_start)-1):
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nodes_i=set(range(x_start[i],x_end[i]+1))
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for j in range(i+1,len(x_start)):
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if nodes_i==set(range(x_start[j],x_end[j]+1)):
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deleted.append(j)
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#print(np.unique(deleted))
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remained_sep_indexes=set(range(len(x_start)))-set(np.unique(deleted) )
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#print(remained_sep_indexes,'remained_sep_indexes')
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mother=[]#if it has mother
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child=[]
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for index_i in remained_sep_indexes:
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have_mother=0
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have_child=0
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nodes_ind=set(range(x_start[index_i],x_end[index_i]+1))
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for index_j in remained_sep_indexes:
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nodes_ind_j=set(range(x_start[index_j],x_end[index_j]+1))
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if nodes_ind<nodes_ind_j:
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have_mother=1
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if nodes_ind>nodes_ind_j:
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have_child=1
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mother.append(have_mother)
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child.append(have_child)
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#print(mother,'mother')
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#print(len(remained_sep_indexes))
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#print(len(remained_sep_indexes),len(x_start),len(x_end),len(y_sep),'lens')
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y_lines_without_mother=[]
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x_start_without_mother=[]
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x_end_without_mother=[]
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y_lines_with_child_without_mother=[]
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x_start_with_child_without_mother=[]
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x_end_with_child_without_mother=[]
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#print(mother,'mother')
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#print(child,'child')
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if len(remained_sep_indexes)>1:
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#print(np.array(remained_sep_indexes),'np.array(remained_sep_indexes)')
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#print(np.array(mother),'mother')
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remained_sep_indexes_without_mother=np.array(list(remained_sep_indexes))[np.array(mother)==0]
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remained_sep_indexes_with_child_without_mother=np.array(list(remained_sep_indexes))[(np.array(mother)==0) & (np.array(child)==1)]
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#print(remained_sep_indexes_without_mother,'remained_sep_indexes_without_mother')
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x_end_with_child_without_mother=np.array(x_end)[np.array(remained_sep_indexes_with_child_without_mother)]
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x_start_with_child_without_mother=np.array(x_start)[np.array(remained_sep_indexes_with_child_without_mother)]
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y_lines_with_child_without_mother=np.array(y_sep)[np.array(remained_sep_indexes_with_child_without_mother)]
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reading_orther_type=0
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x_end_without_mother=np.array(x_end)[np.array(remained_sep_indexes_without_mother)]
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x_start_without_mother=np.array(x_start)[np.array(remained_sep_indexes_without_mother)]
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y_lines_without_mother=np.array(y_sep)[np.array(remained_sep_indexes_without_mother)]
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if len(remained_sep_indexes_without_mother)>=2:
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for i in range(len(remained_sep_indexes_without_mother)-1):
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##nodes_i=set(range(x_start[remained_sep_indexes_without_mother[i]],x_end[remained_sep_indexes_without_mother[i]]+1))
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nodes_i=set(range(x_start[remained_sep_indexes_without_mother[i]],x_end[remained_sep_indexes_without_mother[i]]))
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for j in range(i+1,len(remained_sep_indexes_without_mother)):
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#nodes_j=set(range(x_start[remained_sep_indexes_without_mother[j]],x_end[remained_sep_indexes_without_mother[j]]+1))
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nodes_j=set(range(x_start[remained_sep_indexes_without_mother[j]],x_end[remained_sep_indexes_without_mother[j]]))
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set_diff=nodes_i-nodes_j
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if set_diff!=nodes_i:
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reading_orther_type=1
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else:
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reading_orther_type=0
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#print(reading_orther_type,'javab')
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#print(y_lines_with_child_without_mother,'y_lines_with_child_without_mother')
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#print(x_start_with_child_without_mother,'x_start_with_child_without_mother')
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#print(x_end_with_child_without_mother,'x_end_with_hild_without_mother')
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len_sep_with_child=len(np.array(child)[np.array(child)==1])
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#print(len_sep_with_child,'len_sep_with_child')
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there_is_sep_with_child=0
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if len_sep_with_child>=1:
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there_is_sep_with_child=1
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#print(all_args_uniq,'all_args_uniq')
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#print(args_to_be_unified,'args_to_be_unified')
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return reading_orther_type,x_start_returned, x_end_returned ,y_sep_returned,y_diff_returned,y_lines_without_mother,x_start_without_mother,x_end_without_mother,there_is_sep_with_child,y_lines_with_child_without_mother,x_start_with_child_without_mother,x_end_with_child_without_mother
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def crop_image_inside_box(box, img_org_copy):
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image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
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return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
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def otsu_copy_binary(img):
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img_r = np.zeros((img.shape[0], img.shape[1], 3))
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img1 = img[:, :, 0]
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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img_r = img_r / float(np.max(img_r)) * 255
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return img_r
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def find_features_of_lines(contours_main):
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areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
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cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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slope_lines = []
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for kk in range(len(contours_main)):
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[vx, vy, x, y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2, 0, 0.01, 0.01)
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slope_lines.append(((vy / vx) / np.pi * 180)[0])
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slope_lines_org = slope_lines
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slope_lines = np.array(slope_lines)
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slope_lines[(slope_lines < 10) & (slope_lines > -10)] = 0
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slope_lines[(slope_lines < -200) | (slope_lines > 200)] = 1
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slope_lines[(slope_lines != 0) & (slope_lines != 1)] = 2
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dis_x = np.abs(x_max_main - x_min_main)
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return slope_lines, dis_x, x_min_main, x_max_main, np.array(cy_main), np.array(slope_lines_org), y_min_main, y_max_main, np.array(cx_main)
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def boosting_headers_by_longshot_region_segmentation(textregion_pre_p, textregion_pre_np, img_only_text):
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textregion_pre_p_org = np.copy(textregion_pre_p)
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# 4 is drop capitals
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headers_in_longshot = (textregion_pre_np[:, :, 0] == 2) * 1
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# headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1
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textregion_pre_p[:, :, 0][(headers_in_longshot[:, :] == 1) & (textregion_pre_p[:, :, 0] != 4)] = 2
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textregion_pre_p[:, :, 0][textregion_pre_p[:, :, 0] == 1] = 0
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# textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted
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textregion_pre_p[:, :, 0][(img_only_text[:, :] == 1) & (textregion_pre_p[:, :, 0] != 7) & (textregion_pre_p[:, :, 0] != 4) & (textregion_pre_p[:, :, 0] != 2)] = 1
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return textregion_pre_p
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def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
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regions_without_seperators_0 = regions_without_seperators[:,:].sum(axis=1)
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z = gaussian_filter1d(regions_without_seperators_0, sigma_)
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return np.std(z)
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def find_num_col(regions_without_seperators, multiplier=3.8):
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regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
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##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
sigma_ = 35 # 70#35
|
|
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 - 100
|
|
first_nonzero = first_nonzero + 200
|
|
|
|
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])]
|
|
peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))]
|
|
interest_pos = z[peaks]
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
# plt.plot(z)
|
|
# plt.show()
|
|
interest_neg = z[peaks_neg]
|
|
|
|
min_peaks_pos = np.min(interest_pos)
|
|
max_peaks_pos = np.max(interest_pos)
|
|
|
|
if max_peaks_pos / min_peaks_pos >= 35:
|
|
min_peaks_pos = np.mean(interest_pos)
|
|
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
# print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
|
|
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
|
|
|
|
# print(interest_neg,'interest_neg')
|
|
# print(grenze,'grenze')
|
|
# print(min_peaks_pos,'min_peaks_pos')
|
|
# print(dis_talaei,'dis_talaei')
|
|
# print(peaks_neg,'peaks_neg')
|
|
|
|
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
|
|
|
|
# print(peaks_neg_fin,'peaks_neg_fin')
|
|
# print(num_col,'diz')
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 4.0)
|
|
p_g_u = len(y) - int(len(y) / 4.0)
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_fin = []
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_fin = []
|
|
|
|
##print(len(peaks_neg_fin))
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
|
|
cut_off = 400
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
# print(len(peaks_neg_fin),'len_')
|
|
|
|
for i in range(len(peaks_neg_fin)):
|
|
if i == 0:
|
|
forest.append(peaks_neg_fin[i])
|
|
if i < len(peaks_neg_fin) - 1:
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if i == (len(peaks_neg_fin) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
num_col = len(peaks_neg_true) + 1
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_quarter = int(len(y) / 5.0)
|
|
p_g_l = int(len(y) / 4.0)
|
|
p_g_u = len(y) - int(len(y) / 4.0)
|
|
|
|
p_u_quarter = len(y) - p_quarter
|
|
|
|
##print(num_col,'early')
|
|
if num_col == 3:
|
|
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and (peaks_neg_true[1] + 200) < p_m) or ((peaks_neg_true[0] - 200) > p_m and peaks_neg_true[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
|
|
peaks_neg_true = [peaks_neg_true[0]]
|
|
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
|
|
peaks_neg_true = [peaks_neg_true[1]]
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
|
|
|
|
if len(diff_peaks_annormal) > 0:
|
|
arg_help = np.array(range(len(diff_peaks)))
|
|
arg_help_ann = arg_help[diff_peaks < 360]
|
|
|
|
peaks_neg_fin_new = []
|
|
|
|
for ii in range(len(peaks_neg_fin)):
|
|
if ii in arg_help_ann:
|
|
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
|
|
if arg_min == 0:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
|
|
|
|
elif (ii - 1) not in arg_help_ann:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new = peaks_neg_fin
|
|
|
|
# plt.plot(gaussian_filter1d(y, sigma_))
|
|
# plt.plot(peaks_neg_true,z[peaks_neg_true],'*')
|
|
# plt.plot([0,len(y)], [grenze,grenze])
|
|
# plt.show()
|
|
|
|
##print(len(peaks_neg_true))
|
|
return len(peaks_neg_true), peaks_neg_true
|
|
|
|
def find_num_col_only_image(regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 15
|
|
|
|
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
|
|
|
|
peaks_neg_org = np.copy(peaks_neg)
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
|
|
|
|
peaks = peaks[(peaks > 0.09 * regions_without_seperators.shape[1]) & (peaks < 0.91 * regions_without_seperators.shape[1])]
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > 500) & (peaks_neg < (regions_without_seperators.shape[1] - 500))]
|
|
# print(peaks)
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
min_peaks_pos = np.mean(interest_pos) # np.min(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
# $print(min_peaks_pos)
|
|
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)]
|
|
|
|
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)
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or (peaks_neg_fin[0] < p_m and peaks_neg_fin[1] < p_m) or (peaks_neg_fin[0] > p_m and peaks_neg_fin[1] > p_m):
|
|
num_col = 1
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
|
|
num_col = 1
|
|
else:
|
|
pass
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
|
|
cut_off = 400
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks_neg_fin)):
|
|
if i == 0:
|
|
forest.append(peaks_neg_fin[i])
|
|
if i < (len(peaks_neg_fin) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if i == (len(peaks_neg_fin) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
num_col = (len(peaks_neg_true)) + 1
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_quarter = int(len(y) / 4.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
p_u_quarter = len(y) - p_quarter
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and peaks_neg_true[1] < p_m) or (peaks_neg_true[0] > p_m and peaks_neg_true[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
|
|
peaks_neg_true = [peaks_neg_true[0]]
|
|
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
|
|
peaks_neg_true = [peaks_neg_true[1]]
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
|
|
if num_col == 4:
|
|
if len(np.array(peaks_neg_true)[np.array(peaks_neg_true) < p_g_l]) == 2 or len(np.array(peaks_neg_true)[np.array(peaks_neg_true) > (len(y) - p_g_l)]) == 2:
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
else:
|
|
pass
|
|
|
|
# no deeper hill around found hills
|
|
|
|
peaks_fin_true = []
|
|
for i in range(len(peaks_neg_true)):
|
|
hill_main = peaks_neg_true[i]
|
|
# deep_depth=z[peaks_neg]
|
|
hills_around = peaks_neg_org[((peaks_neg_org > hill_main) & (peaks_neg_org <= hill_main + 400)) | ((peaks_neg_org < hill_main) & (peaks_neg_org >= hill_main - 400))]
|
|
deep_depth_around = z[hills_around]
|
|
|
|
# print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo')
|
|
try:
|
|
if np.min(deep_depth_around) < z[hill_main]:
|
|
pass
|
|
else:
|
|
peaks_fin_true.append(hill_main)
|
|
except:
|
|
pass
|
|
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
|
|
|
|
if len(diff_peaks_annormal) > 0:
|
|
arg_help = np.array(range(len(diff_peaks)))
|
|
arg_help_ann = arg_help[diff_peaks < 360]
|
|
|
|
peaks_neg_fin_new = []
|
|
|
|
for ii in range(len(peaks_neg_fin)):
|
|
if ii in arg_help_ann:
|
|
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
|
|
if arg_min == 0:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
|
|
|
|
elif (ii - 1) in arg_help_ann:
|
|
pass
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new = peaks_neg_fin
|
|
|
|
# sometime pages with one columns gives also some negative peaks. delete those peaks
|
|
param = z[peaks_neg_true] / float(min_peaks_pos) * 100
|
|
|
|
if len(param[param <= 41]) == 0:
|
|
peaks_neg_true = []
|
|
|
|
return len(peaks_fin_true), peaks_fin_true
|
|
|
|
def find_num_col_by_vertical_lines(regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 35 # 70#35
|
|
|
|
z = gaussian_filter1d(regions_without_seperators_0, sigma_)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
# print(peaks,'peaksnew')
|
|
return peaks
|
|
|
|
def return_regions_without_seperators(regions_pre):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0)) * 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 = regions_without_seperators.astype(np.uint8)
|
|
|
|
regions_without_seperators = cv2.erode(regions_without_seperators, kernel, iterations=6)
|
|
|
|
return regions_without_seperators
|
|
|
|
|
|
def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
|
|
|
|
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
|
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
# boxes.append([int(x), int(y), int(w), int(h)])
|
|
|
|
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
|
|
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
|
|
|
|
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
|
|
contours_drop_parent_final.append(contours_drop_parent[jj])
|
|
|
|
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
|
|
|
|
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
|
|
|
|
return layout_no_patch
|
|
|
|
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
|
|
|
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
|
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
layout_in_patch[y : y + h, x : x + w, 0] = 4
|
|
|
|
return 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):
|
|
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
|
|
|
|
|
|
|
|
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)
|
|
if contours_only_text_parent_d_ordered is not None:
|
|
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
|
|
all_box_coord_head.append(all_box_coord[ii])
|
|
slopes_head.append(slopes[ii])
|
|
all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
|
|
else:
|
|
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1
|
|
contours_only_text_parent_main.append(con)
|
|
if contours_only_text_parent_d_ordered is not None:
|
|
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
|
|
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
|
|
|
|
def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col):
|
|
# print(textlines_con)
|
|
# textlines_con=textlines_con.astype(np.uint32)
|
|
|
|
textlines_con_changed = []
|
|
for m1 in range(len(textlines_con)):
|
|
|
|
# textlines_tot=textlines_con[m1]
|
|
# textlines_tot=textlines_tot.astype()
|
|
textlines_tot = []
|
|
textlines_tot_org_form = []
|
|
# print(textlines_tot)
|
|
|
|
for nn in range(len(textlines_con[m1])):
|
|
textlines_tot.append(np.array(textlines_con[m1][nn], dtype=np.int32))
|
|
textlines_tot_org_form.append(textlines_con[m1][nn])
|
|
|
|
##img_text_all=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
|
|
##img_text_all=cv2.fillPoly(img_text_all, pts =textlines_tot , color=(1,1,1))
|
|
|
|
##plt.imshow(img_text_all)
|
|
##plt.show()
|
|
areas_cnt_text = np.array([cv2.contourArea(textlines_tot[j]) for j in range(len(textlines_tot))])
|
|
areas_cnt_text = areas_cnt_text / float(textline_iamge.shape[0] * textline_iamge.shape[1])
|
|
indexes_textlines = np.array(range(len(textlines_tot)))
|
|
|
|
# print(areas_cnt_text,np.min(areas_cnt_text),np.max(areas_cnt_text))
|
|
if num_col == 0:
|
|
min_area = 0.0004
|
|
elif num_col == 1:
|
|
min_area = 0.0003
|
|
else:
|
|
min_area = 0.0001
|
|
indexes_textlines_small = indexes_textlines[areas_cnt_text < min_area]
|
|
|
|
# print(indexes_textlines)
|
|
|
|
textlines_small = []
|
|
textlines_small_org_form = []
|
|
for i in indexes_textlines_small:
|
|
textlines_small.append(textlines_tot[i])
|
|
textlines_small_org_form.append(textlines_tot_org_form[i])
|
|
|
|
textlines_big = []
|
|
textlines_big_org_form = []
|
|
for i in list(set(indexes_textlines) - set(indexes_textlines_small)):
|
|
textlines_big.append(textlines_tot[i])
|
|
textlines_big_org_form.append(textlines_tot_org_form[i])
|
|
|
|
img_textline_s = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=(1, 1, 1))
|
|
|
|
img_textline_b = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=(1, 1, 1))
|
|
|
|
sum_small_big_all = img_textline_s + img_textline_b
|
|
sum_small_big_all2 = (sum_small_big_all[:, :] == 2) * 1
|
|
|
|
sum_intersection_sb = sum_small_big_all2.sum(axis=1).sum()
|
|
|
|
if sum_intersection_sb > 0:
|
|
|
|
dis_small_from_bigs_tot = []
|
|
for z1 in range(len(textlines_small)):
|
|
# print(len(textlines_small),'small')
|
|
intersections = []
|
|
for z2 in range(len(textlines_big)):
|
|
img_text = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=(1, 1, 1))
|
|
|
|
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=(1, 1, 1))
|
|
|
|
sum_small_big = img_text2 + img_text
|
|
sum_small_big_2 = (sum_small_big[:, :] == 2) * 1
|
|
|
|
sum_intersection = sum_small_big_2.sum(axis=1).sum()
|
|
|
|
# print(sum_intersection)
|
|
|
|
intersections.append(sum_intersection)
|
|
|
|
if len(np.array(intersections)[np.array(intersections) > 0]) == 0:
|
|
intersections = []
|
|
|
|
try:
|
|
dis_small_from_bigs_tot.append(np.argmax(intersections))
|
|
except:
|
|
dis_small_from_bigs_tot.append(-1)
|
|
|
|
smalls_list = np.array(dis_small_from_bigs_tot)[np.array(dis_small_from_bigs_tot) >= 0]
|
|
|
|
# index_small_textlines_rest=list( set(indexes_textlines_small)-set(smalls_list) )
|
|
|
|
textlines_big_with_change = []
|
|
textlines_big_with_change_con = []
|
|
textlines_small_with_change = []
|
|
|
|
for z in list(set(smalls_list)):
|
|
index_small_textlines = list(np.where(np.array(dis_small_from_bigs_tot) == z)[0])
|
|
# print(z,index_small_textlines)
|
|
|
|
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1], 3))
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=(255, 255, 255))
|
|
|
|
textlines_big_with_change.append(z)
|
|
|
|
for k in index_small_textlines:
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=(255, 255, 255))
|
|
textlines_small_with_change.append(k)
|
|
|
|
img_text2 = img_text2.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
cont, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(cont[0],type(cont))
|
|
|
|
textlines_big_with_change_con.append(cont)
|
|
textlines_big_org_form[z] = cont[0]
|
|
|
|
# plt.imshow(img_text2)
|
|
# plt.show()
|
|
|
|
# print(textlines_big_with_change,'textlines_big_with_change')
|
|
# print(textlines_small_with_change,'textlines_small_with_change')
|
|
# print(textlines_big)
|
|
textlines_con_changed.append(textlines_big_org_form)
|
|
|
|
else:
|
|
textlines_con_changed.append(textlines_big_org_form)
|
|
return textlines_con_changed
|
|
|
|
def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
|
|
indexes_sorted = np.array(indexes_sorted)
|
|
index_of_types = np.array(index_of_types)
|
|
kind_of_texts = np.array(kind_of_texts)
|
|
|
|
id_of_texts = []
|
|
order_of_texts = []
|
|
|
|
index_of_types_1 = index_of_types[kind_of_texts == 1]
|
|
indexes_sorted_1 = indexes_sorted[kind_of_texts == 1]
|
|
|
|
index_of_types_2 = index_of_types[kind_of_texts == 2]
|
|
indexes_sorted_2 = indexes_sorted[kind_of_texts == 2]
|
|
|
|
##print(index_of_types,'index_of_types')
|
|
##print(kind_of_texts,'kind_of_texts')
|
|
##print(len(found_polygons_text_region),'found_polygons_text_region')
|
|
##print(index_of_types_1,'index_of_types_1')
|
|
##print(indexes_sorted_1,'indexes_sorted_1')
|
|
index_b = 0 + ref_point
|
|
for mm in range(len(found_polygons_text_region)):
|
|
|
|
id_of_texts.append("r" + str(index_b))
|
|
interest = indexes_sorted_1[indexes_sorted_1 == index_of_types_1[mm]]
|
|
|
|
if len(interest) > 0:
|
|
order_of_texts.append(interest[0])
|
|
index_b += 1
|
|
else:
|
|
pass
|
|
|
|
for mm in range(len(found_polygons_text_region_h)):
|
|
id_of_texts.append("r" + str(index_b))
|
|
interest = indexes_sorted_2[index_of_types_2[mm]]
|
|
order_of_texts.append(interest)
|
|
index_b += 1
|
|
|
|
return order_of_texts, id_of_texts
|
|
|
|
def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
|
|
|
|
##plt.imshow(textline_mask)
|
|
##plt.show()
|
|
"""
|
|
print(len(contours_main),'contours_main')
|
|
mada_n=textline_mask.sum(axis=1)
|
|
y=mada_n[:]
|
|
|
|
y_help=np.zeros(len(y)+40)
|
|
y_help[20:len(y)+20]=y
|
|
x=np.array( range(len(y)) )
|
|
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
##plt.imshow(textline_mask[:,:])
|
|
##plt.show()
|
|
|
|
|
|
sigma_gaus=8
|
|
|
|
z= gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev=-y_help+np.max(y_help)
|
|
|
|
zneg=np.zeros(len(zneg_rev)+40)
|
|
zneg[20:len(zneg_rev)+20]=zneg_rev
|
|
zneg= gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg=peaks_neg-20-20
|
|
peaks=peaks-20
|
|
"""
|
|
|
|
textline_sum_along_width = textline_mask.sum(axis=1)
|
|
|
|
y = textline_sum_along_width[:]
|
|
y_padded = np.zeros(len(y) + 40)
|
|
y_padded[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
sigma_gaus = 8
|
|
|
|
z = gaussian_filter1d(y_padded, sigma_gaus)
|
|
zneg_rev = -y_padded + np.max(y_padded)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
##plt.plot(z)
|
|
##plt.show()
|
|
|
|
if contours_main != None:
|
|
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))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) 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))])
|
|
|
|
if len(contours_header) != None:
|
|
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
|
|
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
|
|
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
|
|
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
|
|
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
# print(cy_main,'mainy')
|
|
|
|
peaks_neg_new = []
|
|
|
|
peaks_neg_new.append(0 + y_ref)
|
|
for iii in range(len(peaks_neg)):
|
|
peaks_neg_new.append(peaks_neg[iii] + y_ref)
|
|
|
|
peaks_neg_new.append(textline_mask.shape[0] + y_ref)
|
|
|
|
if len(cy_main) > 0 and np.max(cy_main) > np.max(peaks_neg_new):
|
|
cy_main = np.array(cy_main) * (np.max(peaks_neg_new) / np.max(cy_main)) - 10
|
|
|
|
if contours_main != None:
|
|
indexer_main = np.array(range(len(contours_main)))
|
|
|
|
if contours_main != None:
|
|
len_main = len(contours_main)
|
|
else:
|
|
len_main = 0
|
|
|
|
matrix_of_orders = np.zeros((len(contours_main) + len(contours_header), 5))
|
|
|
|
matrix_of_orders[:, 0] = np.array(range(len(contours_main) + len(contours_header)))
|
|
|
|
matrix_of_orders[: len(contours_main), 1] = 1
|
|
matrix_of_orders[len(contours_main) :, 1] = 2
|
|
|
|
matrix_of_orders[: len(contours_main), 2] = cx_main
|
|
matrix_of_orders[len(contours_main) :, 2] = cx_header
|
|
|
|
matrix_of_orders[: len(contours_main), 3] = cy_main
|
|
matrix_of_orders[len(contours_main) :, 3] = cy_header
|
|
|
|
matrix_of_orders[: len(contours_main), 4] = np.array(range(len(contours_main)))
|
|
matrix_of_orders[len(contours_main) :, 4] = np.array(range(len(contours_header)))
|
|
|
|
# print(peaks_neg_new,'peaks_neg_new')
|
|
|
|
# print(matrix_of_orders,'matrix_of_orders')
|
|
# print(peaks_neg_new,np.max(peaks_neg_new))
|
|
final_indexers_sorted = []
|
|
final_types = []
|
|
final_index_type = []
|
|
for i in range(len(peaks_neg_new) - 1):
|
|
top = peaks_neg_new[i]
|
|
down = peaks_neg_new[i + 1]
|
|
|
|
# print(top,down,'topdown')
|
|
|
|
indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
|
|
# print(top,down)
|
|
# print(cys_in,'cyyyins')
|
|
# print(indexes_in,'indexes')
|
|
sorted_inside = np.argsort(cxs_in)
|
|
|
|
ind_in_int = indexes_in[sorted_inside]
|
|
ind_in_type = types_of_text[sorted_inside]
|
|
ind_ind_type = index_types_of_text[sorted_inside]
|
|
|
|
for j in range(len(ind_in_int)):
|
|
final_indexers_sorted.append(int(ind_in_int[j]))
|
|
final_types.append(int(ind_in_type[j]))
|
|
final_index_type.append(int(ind_ind_type[j]))
|
|
|
|
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
|
|
|
|
# print(peaks_neg_new,'peaks')
|
|
# print(final_indexers_sorted,'indexsorted')
|
|
# print(final_types,'types')
|
|
# print(final_index_type,'final_index_type')
|
|
|
|
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
|
|
|
|
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor,num_col_classifier):
|
|
#img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
|
|
img_p_in_ver=img_p_in_ver.astype(np.uint8)
|
|
img_p_in_ver=np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines_ver,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines_ver,dist_x_ver, x_min_main_ver ,x_max_main_ver ,cy_main_ver,slope_lines_org_ver,y_min_main_ver, y_max_main_ver, cx_main_ver=find_features_of_lines(contours_lines_ver)
|
|
|
|
for i in range(len(x_min_main_ver)):
|
|
img_p_in_ver[int(y_min_main_ver[i]):int(y_min_main_ver[i])+30,int(cx_main_ver[i])-25:int(cx_main_ver[i])+25,0]=0
|
|
img_p_in_ver[int(y_max_main_ver[i])-30:int(y_max_main_ver[i]),int(cx_main_ver[i])-25:int(cx_main_ver[i])+25,0]=0
|
|
|
|
|
|
img_in_hor=img_in_hor.astype(np.uint8)
|
|
img_in_hor=np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines_hor,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines_hor,dist_x_hor, x_min_main_hor ,x_max_main_hor ,cy_main_hor,slope_lines_org_hor,y_min_main_hor, y_max_main_hor, cx_main_hor=find_features_of_lines(contours_lines_hor)
|
|
|
|
|
|
x_width_smaller_than_acolumn_width=img_in_hor.shape[1]/float(num_col_classifier+1.)
|
|
|
|
len_lines_bigger_than_x_width_smaller_than_acolumn_width=len( dist_x_hor[dist_x_hor>=x_width_smaller_than_acolumn_width] )
|
|
|
|
len_lines_bigger_than_x_width_smaller_than_acolumn_width_per_column=int( len_lines_bigger_than_x_width_smaller_than_acolumn_width/float(num_col_classifier) )
|
|
|
|
|
|
if len_lines_bigger_than_x_width_smaller_than_acolumn_width_per_column<10:
|
|
args_hor=np.array( range(len(slope_lines_hor) ))
|
|
all_args_uniq=contours_in_same_horizon(cy_main_hor)
|
|
#print(all_args_uniq,'all_args_uniq')
|
|
if len(all_args_uniq)>0:
|
|
if type(all_args_uniq[0]) is list:
|
|
special_seperators=[]
|
|
contours_new=[]
|
|
for dd in range(len(all_args_uniq)):
|
|
merged_all=None
|
|
some_args=args_hor[all_args_uniq[dd]]
|
|
some_cy=cy_main_hor[all_args_uniq[dd]]
|
|
some_x_min=x_min_main_hor[all_args_uniq[dd]]
|
|
some_x_max=x_max_main_hor[all_args_uniq[dd]]
|
|
|
|
#img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
|
|
#print(img_p_in_ver.shape[1],some_x_max-some_x_min,'xdiff')
|
|
diff_x_some=some_x_max-some_x_min
|
|
for jv in range(len(some_args)):
|
|
|
|
img_p_in=cv2.fillPoly(img_in_hor, pts =[contours_lines_hor[some_args[jv]]], color=(1,1,1))
|
|
|
|
if any(i_diff>(img_p_in_ver.shape[1]/float(3.3)) for i_diff in diff_x_some):
|
|
img_p_in[int(np.mean(some_cy))-5:int(np.mean(some_cy))+5, int(np.min(some_x_min)):int(np.max(some_x_max)) ]=1
|
|
|
|
sum_dis=dist_x_hor[some_args].sum()
|
|
diff_max_min_uniques=np.max(x_max_main_hor[some_args])-np.min(x_min_main_hor[some_args])
|
|
|
|
|
|
if diff_max_min_uniques>sum_dis and ( (sum_dis/float(diff_max_min_uniques) ) >0.85 ) and ( (diff_max_min_uniques/float(img_p_in_ver.shape[1]))>0.85 ) and np.std( dist_x_hor[some_args] )<(0.55*np.mean( dist_x_hor[some_args] )):
|
|
#print(dist_x_hor[some_args],dist_x_hor[some_args].sum(),np.min(x_min_main_hor[some_args]) ,np.max(x_max_main_hor[some_args]),'jalibdi')
|
|
#print(np.mean( dist_x_hor[some_args] ),np.std( dist_x_hor[some_args] ),np.var( dist_x_hor[some_args] ),'jalibdiha')
|
|
special_seperators.append(np.mean(cy_main_hor[some_args]))
|
|
|
|
else:
|
|
img_p_in=img_in_hor
|
|
special_seperators=[]
|
|
else:
|
|
img_p_in=img_in_hor
|
|
special_seperators=[]
|
|
|
|
|
|
img_p_in_ver[:,:,0][img_p_in_ver[:,:,0]==255]=1
|
|
sep_ver_hor=img_p_in+img_p_in_ver
|
|
|
|
|
|
sep_ver_hor_cross=(sep_ver_hor[:,:,0]==2)*1
|
|
|
|
sep_ver_hor_cross=np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
|
|
sep_ver_hor_cross=sep_ver_hor_cross.astype(np.uint8)
|
|
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_cross,_=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
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
|
|
img_p_in[int(cy_cross[ii])-30:int(cy_cross[ii])+30,int(cx_cross[ii])-40:int(cx_cross[ii])-4,0]=0
|
|
|
|
else:
|
|
img_p_in=np.copy(img_in_hor)
|
|
special_seperators=[]
|
|
return img_p_in[:,:,0],special_seperators
|
|
|
|
def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
|
|
peaks_neg_tot = []
|
|
peaks_neg_tot.append(first_point)
|
|
for ii in range(len(peaks_neg_fin)):
|
|
peaks_neg_tot.append(peaks_neg_fin[ii])
|
|
peaks_neg_tot.append(last_point)
|
|
return peaks_neg_tot
|
|
|
|
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_lines, contours_h=None):
|
|
|
|
seperators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
|
|
|
|
seperators_closeup[0:110,:,:]=0
|
|
seperators_closeup[seperators_closeup.shape[0]-150:,:,:]=0
|
|
|
|
kernel = np.ones((5,5),np.uint8)
|
|
|
|
seperators_closeup=seperators_closeup.astype(np.uint8)
|
|
seperators_closeup = cv2.dilate(seperators_closeup,kernel,iterations = 1)
|
|
seperators_closeup = cv2.erode(seperators_closeup,kernel,iterations = 1)
|
|
|
|
|
|
seperators_closeup_new=np.zeros((seperators_closeup.shape[0] ,seperators_closeup.shape[1] ))
|
|
|
|
|
|
|
|
##_,seperators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0])
|
|
seperators_closeup_n=np.copy(seperators_closeup)
|
|
|
|
seperators_closeup_n=seperators_closeup_n.astype(np.uint8)
|
|
##plt.imshow(seperators_closeup_n[:,:,0])
|
|
##plt.show()
|
|
|
|
seperators_closeup_n_binary=np.zeros(( seperators_closeup_n.shape[0],seperators_closeup_n.shape[1]) )
|
|
seperators_closeup_n_binary[:,:]=seperators_closeup_n[:,:,0]
|
|
|
|
seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]!=0]=1
|
|
#seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==0]=255
|
|
#seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==-255]=0
|
|
|
|
|
|
#seperators_closeup_n_binary=(seperators_closeup_n_binary[:,:]==2)*1
|
|
|
|
#gray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
|
|
|
|
###
|
|
|
|
#print(seperators_closeup_n_binary.shape)
|
|
gray_early=np.repeat(seperators_closeup_n_binary[:, :, np.newaxis], 3, axis=2)
|
|
gray_early=gray_early.astype(np.uint8)
|
|
|
|
#print(gray_early.shape,'burda')
|
|
imgray_e = cv2.cvtColor(gray_early, cv2.COLOR_BGR2GRAY)
|
|
#print('burda2')
|
|
ret_e, thresh_e = cv2.threshold(imgray_e, 0, 255, 0)
|
|
|
|
#print('burda3')
|
|
contours_line_e,hierachy_e=cv2.findContours(thresh_e,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
#slope_lines_e,dist_x_e, x_min_main_e ,x_max_main_e ,cy_main_e,slope_lines_org_e,y_min_main_e, y_max_main_e, cx_main_e=self.find_features_of_lines(contours_line_e)
|
|
|
|
slope_linese,dist_xe, x_min_maine ,x_max_maine ,cy_maine,slope_lines_orge,y_min_maine, y_max_maine, cx_maine=find_features_of_lines(contours_line_e)
|
|
|
|
dist_ye=y_max_maine-y_min_maine
|
|
#print(y_max_maine-y_min_maine,'y')
|
|
#print(dist_xe,'x')
|
|
|
|
|
|
args_e=np.array(range(len(contours_line_e)))
|
|
args_hor_e=args_e[(dist_ye<=50) & (dist_xe>=3*dist_ye)]
|
|
|
|
#print(args_hor_e,'jidi',len(args_hor_e),'jilva')
|
|
|
|
cnts_hor_e=[]
|
|
for ce in args_hor_e:
|
|
cnts_hor_e.append(contours_line_e[ce])
|
|
#print(len(slope_linese),'lieee')
|
|
|
|
figs_e=np.zeros(thresh_e.shape)
|
|
figs_e=cv2.fillPoly(figs_e,pts=cnts_hor_e,color=(1,1,1))
|
|
|
|
#plt.imshow(figs_e)
|
|
#plt.show()
|
|
|
|
###
|
|
|
|
seperators_closeup_n_binary=cv2.fillPoly(seperators_closeup_n_binary,pts=cnts_hor_e,color=(0,0,0))
|
|
|
|
gray = cv2.bitwise_not(seperators_closeup_n_binary)
|
|
gray=gray.astype(np.uint8)
|
|
|
|
|
|
#plt.imshow(gray)
|
|
#plt.show()
|
|
|
|
|
|
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
|
|
cv2.THRESH_BINARY, 15, -2)
|
|
##plt.imshow(bw[:,:])
|
|
##plt.show()
|
|
|
|
horizontal = np.copy(bw)
|
|
vertical = np.copy(bw)
|
|
|
|
cols = horizontal.shape[1]
|
|
horizontal_size = cols // 30
|
|
# Create structure element for extracting horizontal lines through morphology operations
|
|
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
|
|
# Apply morphology operations
|
|
horizontal = cv2.erode(horizontal, horizontalStructure)
|
|
horizontal = cv2.dilate(horizontal, horizontalStructure)
|
|
|
|
kernel = np.ones((5,5),np.uint8)
|
|
|
|
|
|
horizontal = cv2.dilate(horizontal,kernel,iterations = 2)
|
|
horizontal = cv2.erode(horizontal,kernel,iterations = 2)
|
|
|
|
|
|
###
|
|
#print(np.unique(horizontal),'uni')
|
|
horizontal=cv2.fillPoly(horizontal,pts=cnts_hor_e,color=(255,255,255))
|
|
###
|
|
|
|
|
|
|
|
#plt.imshow(horizontal)
|
|
#plt.show()
|
|
|
|
rows = vertical.shape[0]
|
|
verticalsize = rows // 30
|
|
# Create structure element for extracting vertical lines through morphology operations
|
|
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
|
|
# Apply morphology operations
|
|
vertical = cv2.erode(vertical, verticalStructure)
|
|
vertical = cv2.dilate(vertical, verticalStructure)
|
|
|
|
vertical = cv2.dilate(vertical,kernel,iterations = 1)
|
|
# Show extracted vertical lines
|
|
|
|
horizontal,special_seperators=combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical,horizontal,num_col_classifier)
|
|
|
|
|
|
#plt.imshow(horizontal)
|
|
#plt.show()
|
|
#print(vertical.shape,np.unique(vertical),'verticalvertical')
|
|
seperators_closeup_new[:,:][vertical[:,:]!=0]=1
|
|
seperators_closeup_new[:,:][horizontal[:,:]!=0]=1
|
|
|
|
##plt.imshow(seperators_closeup_new)
|
|
##plt.show()
|
|
##seperators_closeup_n
|
|
vertical=np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
|
|
vertical=vertical.astype(np.uint8)
|
|
|
|
##plt.plot(vertical[:,:,0].sum(axis=0))
|
|
##plt.show()
|
|
|
|
#plt.plot(vertical[:,:,0].sum(axis=1))
|
|
#plt.show()
|
|
|
|
imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line_vers,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=find_features_of_lines(contours_line_vers)
|
|
#print(slope_lines,'vertical')
|
|
args=np.array( range(len(slope_lines) ))
|
|
args_ver=args[slope_lines==1]
|
|
dist_x_ver=dist_x[slope_lines==1]
|
|
y_min_main_ver=y_min_main[slope_lines==1]
|
|
y_max_main_ver=y_max_main[slope_lines==1]
|
|
x_min_main_ver=x_min_main[slope_lines==1]
|
|
x_max_main_ver=x_max_main[slope_lines==1]
|
|
cx_main_ver=cx_main[slope_lines==1]
|
|
dist_y_ver=y_max_main_ver-y_min_main_ver
|
|
len_y=seperators_closeup.shape[0]/3.0
|
|
|
|
|
|
#plt.imshow(horizontal)
|
|
#plt.show()
|
|
|
|
horizontal=np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
|
|
horizontal=horizontal.astype(np.uint8)
|
|
imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line_hors,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=find_features_of_lines(contours_line_hors)
|
|
|
|
slope_lines_org_hor=slope_lines_org[slope_lines==0]
|
|
args=np.array( range(len(slope_lines) ))
|
|
len_x=seperators_closeup.shape[1]/5.0
|
|
|
|
dist_y=np.abs(y_max_main-y_min_main)
|
|
|
|
args_hor=args[slope_lines==0]
|
|
dist_x_hor=dist_x[slope_lines==0]
|
|
y_min_main_hor=y_min_main[slope_lines==0]
|
|
y_max_main_hor=y_max_main[slope_lines==0]
|
|
x_min_main_hor=x_min_main[slope_lines==0]
|
|
x_max_main_hor=x_max_main[slope_lines==0]
|
|
dist_y_hor=dist_y[slope_lines==0]
|
|
cy_main_hor=cy_main[slope_lines==0]
|
|
|
|
args_hor=args_hor[dist_x_hor>=len_x/2.0]
|
|
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
|
|
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
|
|
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
|
|
y_min_main_hor=y_min_main_hor[dist_x_hor>=len_x/2.0]
|
|
y_max_main_hor=y_max_main_hor[dist_x_hor>=len_x/2.0]
|
|
dist_y_hor=dist_y_hor[dist_x_hor>=len_x/2.0]
|
|
|
|
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
|
|
dist_x_hor=dist_x_hor[dist_x_hor>=len_x/2.0]
|
|
|
|
|
|
matrix_of_lines_ch=np.zeros((len(cy_main_hor)+len(cx_main_ver),10))
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),0]=args_hor
|
|
matrix_of_lines_ch[len(cy_main_hor):,0]=args_ver
|
|
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor):,1]=cx_main_ver
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),2]=x_min_main_hor+50#x_min_main_hor+150
|
|
matrix_of_lines_ch[len(cy_main_hor):,2]=x_min_main_ver
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),3]=x_max_main_hor-50#x_max_main_hor-150
|
|
matrix_of_lines_ch[len(cy_main_hor):,3]=x_max_main_ver
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),4]=dist_x_hor
|
|
matrix_of_lines_ch[len(cy_main_hor):,4]=dist_x_ver
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),5]=cy_main_hor
|
|
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),6]=y_min_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor):,6]=y_min_main_ver
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),7]=y_max_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor):,7]=y_max_main_ver
|
|
|
|
matrix_of_lines_ch[:len(cy_main_hor),8]=dist_y_hor
|
|
matrix_of_lines_ch[len(cy_main_hor):,8]=dist_y_ver
|
|
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor):,9]=1
|
|
|
|
|
|
|
|
if contours_h is not None:
|
|
slope_lines_head,dist_x_head, x_min_main_head ,x_max_main_head ,cy_main_head,slope_lines_org_head,y_min_main_head, y_max_main_head, cx_main_head=find_features_of_lines(contours_h)
|
|
matrix_l_n=np.zeros((matrix_of_lines_ch.shape[0]+len(cy_main_head),matrix_of_lines_ch.shape[1]))
|
|
matrix_l_n[:matrix_of_lines_ch.shape[0],:]=np.copy(matrix_of_lines_ch[:,:])
|
|
args_head=np.array(range(len(cy_main_head)))+len(cy_main_hor)
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,0]=args_head
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,2]=x_min_main_head+30
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,3]=x_max_main_head-30
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,4]=dist_x_head
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,5]=y_min_main_head-3-8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,6]=y_min_main_head-5-8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,7]=y_max_main_head#y_min_main_head+1-8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0]:,8]=4
|
|
|
|
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_spliters=np.array( list(cy_main_spliters)+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))
|
|
except:
|
|
pass
|
|
args_cy_spliter=np.argsort(cy_main_spliters)
|
|
|
|
cy_main_spliters_sort=cy_main_spliters[args_cy_spliter]
|
|
|
|
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] )
|
|
|
|
spliter_y_new.append(region_pre_p.shape[0])
|
|
|
|
spliter_y_new_diff=np.diff(spliter_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 ]
|
|
|
|
|
|
|
|
regions_without_seperators=return_regions_without_seperators(region_pre_p)
|
|
|
|
|
|
length_y_threshold=regions_without_seperators.shape[0]/4.0
|
|
|
|
num_col_fin=0
|
|
peaks_neg_fin_fin=[]
|
|
|
|
for iteils in args_big_parts:
|
|
|
|
|
|
regions_without_seperators_teil=regions_without_seperators[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:,0]
|
|
#image_page_background_zero_teil=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:]
|
|
|
|
#print(regions_without_seperators_teil.shape)
|
|
##plt.imshow(regions_without_seperators_teil)
|
|
##plt.show()
|
|
|
|
#num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)
|
|
|
|
#regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3)
|
|
#
|
|
num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=7.0)
|
|
|
|
if num_col>num_col_fin:
|
|
num_col_fin=num_col
|
|
peaks_neg_fin_fin=peaks_neg_fin
|
|
|
|
|
|
if len(args_big_parts)==1 and (len(peaks_neg_fin_fin)+1)<num_col_classifier:
|
|
peaks_neg_fin=find_num_col_by_vertical_lines(vertical)
|
|
peaks_neg_fin=peaks_neg_fin[peaks_neg_fin>=500]
|
|
peaks_neg_fin=peaks_neg_fin[peaks_neg_fin<=(vertical.shape[1]-500)]
|
|
peaks_neg_fin_fin=peaks_neg_fin[:]
|
|
|
|
#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
|
|
|
|
|
|
def return_boxes_of_images_by_order_of_reading_new(spliter_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] ) ]
|
|
#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] )):
|
|
|
|
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.)
|
|
except:
|
|
peaks_neg_fin=[]
|
|
|
|
#print(peaks_neg_fin,'peaks_neg_fin0')
|
|
|
|
try:
|
|
peaks_neg_fin_org=np.copy(peaks_neg_fin)
|
|
if (len(peaks_neg_fin)+1)<num_col_classifier:
|
|
#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.)
|
|
peaks_neg_fin_early=[]
|
|
peaks_neg_fin_early.append(0)
|
|
#print(peaks_neg_fin,'peaks_neg_fin')
|
|
for p_n in peaks_neg_fin:
|
|
peaks_neg_fin_early.append(p_n)
|
|
peaks_neg_fin_early.append(regions_without_seperators.shape[1]-1)
|
|
|
|
#print(peaks_neg_fin_early,'burda2')
|
|
peaks_neg_fin_rev=[]
|
|
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.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.)
|
|
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.)
|
|
except:
|
|
peaks_neg_fin2=[]
|
|
|
|
|
|
if len(peaks_neg_fin1)>=len(peaks_neg_fin2):
|
|
peaks_neg_fin=list(np.copy(peaks_neg_fin1))
|
|
else:
|
|
peaks_neg_fin=list(np.copy(peaks_neg_fin2))
|
|
|
|
|
|
|
|
peaks_neg_fin=list(np.array(peaks_neg_fin)+peaks_neg_fin_early[i_n])
|
|
|
|
if i_n!=(len(peaks_neg_fin_early)-2):
|
|
peaks_neg_fin_rev.append(peaks_neg_fin_early[i_n+1])
|
|
#print(peaks_neg_fin,'peaks_neg_fin')
|
|
peaks_neg_fin_rev=peaks_neg_fin_rev+peaks_neg_fin
|
|
|
|
|
|
|
|
|
|
|
|
if len(peaks_neg_fin_rev)>=len(peaks_neg_fin_org):
|
|
peaks_neg_fin=list(np.sort(peaks_neg_fin_rev))
|
|
num_col=len(peaks_neg_fin)
|
|
else:
|
|
peaks_neg_fin=list(np.copy(peaks_neg_fin_org))
|
|
num_col=len(peaks_neg_fin)
|
|
|
|
#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)
|
|
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) ]
|
|
cy_hor_diff=matrix_new[:,7][ (matrix_new[:,9]==0) ]
|
|
arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
|
|
|
|
|
|
|
|
|
|
|
|
peaks_neg_tot=return_points_with_boundies(peaks_neg_fin,0, regions_without_seperators[:,:].shape[1])
|
|
|
|
reading_order_type,x_starting,x_ending,y_type_2,y_diff_type_2,y_lines_without_mother,x_start_without_mother,x_end_without_mother,there_is_sep_with_child,y_lines_with_child_without_mother,x_start_with_child_without_mother,x_end_with_child_without_mother=return_x_start_end_mothers_childs_and_type_of_reading_order(x_min_hor_some,x_max_hor_some,cy_hor_some,peaks_neg_tot,cy_hor_diff)
|
|
|
|
|
|
|
|
if (reading_order_type==1) or (reading_order_type==0 and (len(y_lines_without_mother)>=2 or there_is_sep_with_child==1)):
|
|
|
|
|
|
try:
|
|
y_grenze=int(spliter_y_new[i])+300
|
|
|
|
|
|
|
|
#check if there is a big seperater in this y_mains_sep_ohne_grenzen
|
|
|
|
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]))
|
|
|
|
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)]
|
|
|
|
|
|
|
|
if len(y_type_2_up)>0:
|
|
y_main_separator_up=y_type_2_up[(x_starting_up==0) & (x_ending_up==(len(peaks_neg_tot)-1) )]
|
|
y_diff_main_separator_up=y_diff_type_2_up[(x_starting_up==0) & (x_ending_up==(len(peaks_neg_tot)-1) )]
|
|
args_main_to_deleted=args_up[(x_starting_up==0) & (x_ending_up==(len(peaks_neg_tot)-1) )]
|
|
#print(y_main_separator_up,y_diff_main_separator_up,args_main_to_deleted,'fffffjammmm')
|
|
|
|
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]
|
|
|
|
#print(spliter_y_new[i],'spliter_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
|
|
|
|
|
|
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)]
|
|
|
|
|
|
#print(y_type_2_up,x_starting_up,x_ending_up,'didid')
|
|
|
|
nodes_in=[]
|
|
for ij in range(len(x_starting_up)):
|
|
nodes_in=nodes_in+list(np.array(range(x_starting_up[ij],x_ending_up[ij])))
|
|
|
|
#print(np.unique(nodes_in),'nodes_in')
|
|
|
|
if set(np.unique(nodes_in))==set(np.array(range(len(peaks_neg_tot)-1)) ):
|
|
pass
|
|
elif set( np.unique(nodes_in) )==set( np.array(range(1,len(peaks_neg_tot)-1)) ):
|
|
pass
|
|
else:
|
|
#print('burdaydikh')
|
|
args_to_be_kept2=np.array( list( set(args_early_ys2)-set(args_up2) ) )
|
|
|
|
if len(args_to_be_kept2)>0:
|
|
y_type_2=np.array(y_type_2)[args_to_be_kept2]
|
|
x_starting=np.array(x_starting)[args_to_be_kept2]
|
|
x_ending=np.array(x_ending)[args_to_be_kept2]
|
|
y_diff_type_2=np.array(y_diff_type_2)[args_to_be_kept2]
|
|
else:
|
|
pass
|
|
|
|
#print('burdaydikh2')
|
|
|
|
|
|
|
|
elif len(y_diff_main_separator_up)==0:
|
|
nodes_in=[]
|
|
for ij in range(len(x_starting_up)):
|
|
nodes_in=nodes_in+list(np.array(range(x_starting_up[ij],x_ending_up[ij])))
|
|
|
|
#print(np.unique(nodes_in),'nodes_in2')
|
|
#print(np.array(range(len(peaks_neg_tot)-1)),'np.array(range(len(peaks_neg_tot)-1))')
|
|
|
|
|
|
|
|
if set(np.unique(nodes_in))==set(np.array(range(len(peaks_neg_tot)-1)) ):
|
|
pass
|
|
elif set(np.unique(nodes_in) )==set( np.array(range(1,len(peaks_neg_tot)-1)) ):
|
|
pass
|
|
else:
|
|
#print('burdaydikh')
|
|
#print(args_early_ys,'args_early_ys')
|
|
#print(args_up,'args_up')
|
|
args_to_be_kept2=np.array( list( set(args_early_ys)-set(args_up) ) )
|
|
|
|
#print(args_to_be_kept2,'args_to_be_kept2')
|
|
|
|
#print(len(y_type_2),len(x_starting),len(x_ending),len(y_diff_type_2))
|
|
|
|
if len(args_to_be_kept2)>0:
|
|
y_type_2=np.array(y_type_2)[args_to_be_kept2]
|
|
x_starting=np.array(x_starting)[args_to_be_kept2]
|
|
x_ending=np.array(x_ending)[args_to_be_kept2]
|
|
y_diff_type_2=np.array(y_diff_type_2)[args_to_be_kept2]
|
|
else:
|
|
pass
|
|
|
|
#print('burdaydikh2')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
x_starting=np.array(x_starting)
|
|
x_ending=np.array(x_ending)
|
|
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])
|
|
|
|
y_lines_by_order=[]
|
|
x_start_by_order=[]
|
|
x_end_by_order=[]
|
|
|
|
if (len(x_end_with_child_without_mother)==0 and reading_order_type==0) or reading_order_type==1:
|
|
|
|
|
|
if reading_order_type==1:
|
|
y_lines_by_order.append(int(spliter_y_new[i]))
|
|
x_start_by_order.append(0)
|
|
x_end_by_order.append(len(peaks_neg_tot)-2)
|
|
else:
|
|
#print(x_start_without_mother,x_end_without_mother,peaks_neg_tot,'dodo')
|
|
|
|
columns_covered_by_mothers=[]
|
|
|
|
for dj in range(len(x_start_without_mother)):
|
|
columns_covered_by_mothers=columns_covered_by_mothers+list(np.array(range(x_start_without_mother[dj],x_end_without_mother[dj])) )
|
|
columns_covered_by_mothers=list(set(columns_covered_by_mothers))
|
|
|
|
all_columns=np.array(range(len(peaks_neg_tot)-1))
|
|
|
|
columns_not_covered=list( set(all_columns)-set(columns_covered_by_mothers) )
|
|
|
|
|
|
y_type_2=list(y_type_2)
|
|
x_starting=list(x_starting)
|
|
x_ending=list(x_ending)
|
|
|
|
for lj in columns_not_covered:
|
|
y_type_2.append(int(spliter_y_new[i]))
|
|
x_starting.append(lj)
|
|
x_ending.append(lj+1)
|
|
##y_lines_by_order.append(int(spliter_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]))
|
|
x_starting.append(x_start_without_mother[lk])
|
|
x_ending.append(x_end_without_mother[lk])
|
|
|
|
|
|
y_type_2=np.array(y_type_2)
|
|
x_starting=np.array(x_starting)
|
|
x_ending=np.array(x_ending)
|
|
|
|
|
|
|
|
|
|
ind_args=np.array(range(len(y_type_2)))
|
|
#ind_args=np.array(ind_args)
|
|
#print(ind_args,'ind_args')
|
|
for column in range(len(peaks_neg_tot)-1):
|
|
#print(column,'column')
|
|
ind_args_in_col=ind_args[x_starting==column]
|
|
#print('babali2')
|
|
#print(ind_args_in_col,'ind_args_in_col')
|
|
ind_args_in_col=np.array(ind_args_in_col)
|
|
#print(len(y_type_2))
|
|
y_column=y_type_2[ind_args_in_col]
|
|
x_start_column=x_starting[ind_args_in_col]
|
|
x_end_column=x_ending[ind_args_in_col]
|
|
#print('babali3')
|
|
ind_args_col_sorted=np.argsort(y_column)
|
|
y_col_sort=y_column[ind_args_col_sorted]
|
|
x_start_column_sort=x_start_column[ind_args_col_sorted]
|
|
x_end_column_sort=x_end_column[ind_args_col_sorted]
|
|
#print('babali4')
|
|
for ii in range(len(y_col_sort)):
|
|
#print('babali5')
|
|
y_lines_by_order.append(y_col_sort[ii])
|
|
x_start_by_order.append(x_start_column_sort[ii])
|
|
x_end_by_order.append(x_end_column_sort[ii]-1)
|
|
|
|
else:
|
|
|
|
#print(x_start_without_mother,x_end_without_mother,peaks_neg_tot,'dodo')
|
|
|
|
columns_covered_by_mothers=[]
|
|
|
|
for dj in range(len(x_start_without_mother)):
|
|
columns_covered_by_mothers=columns_covered_by_mothers+list(np.array(range(x_start_without_mother[dj],x_end_without_mother[dj])) )
|
|
columns_covered_by_mothers=list(set(columns_covered_by_mothers))
|
|
|
|
all_columns=np.array(range(len(peaks_neg_tot)-1))
|
|
|
|
columns_not_covered=list( set(all_columns)-set(columns_covered_by_mothers) )
|
|
|
|
|
|
y_type_2=list(y_type_2)
|
|
x_starting=list(x_starting)
|
|
x_ending=list(x_ending)
|
|
|
|
for lj in columns_not_covered:
|
|
y_type_2.append(int(spliter_y_new[i]))
|
|
x_starting.append(lj)
|
|
x_ending.append(lj+1)
|
|
##y_lines_by_order.append(int(spliter_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]))
|
|
x_starting.append(x_start_without_mother[lk])
|
|
x_ending.append(x_end_without_mother[lk])
|
|
|
|
|
|
y_type_2=np.array(y_type_2)
|
|
x_starting=np.array(x_starting)
|
|
x_ending=np.array(x_ending)
|
|
|
|
columns_covered_by_with_child_no_mothers=[]
|
|
|
|
for dj in range(len(x_end_with_child_without_mother)):
|
|
columns_covered_by_with_child_no_mothers=columns_covered_by_with_child_no_mothers+list(np.array(range(x_start_with_child_without_mother[dj],x_end_with_child_without_mother[dj])) )
|
|
columns_covered_by_with_child_no_mothers=list(set(columns_covered_by_with_child_no_mothers))
|
|
|
|
all_columns=np.array(range(len(peaks_neg_tot)-1))
|
|
|
|
columns_not_covered_child_no_mother=list( set(all_columns)-set(columns_covered_by_with_child_no_mothers) )
|
|
#indexes_to_be_spanned=[]
|
|
for i_s in range( len(x_end_with_child_without_mother) ):
|
|
columns_not_covered_child_no_mother.append(x_start_with_child_without_mother[i_s])
|
|
|
|
|
|
|
|
columns_not_covered_child_no_mother=np.sort(columns_not_covered_child_no_mother)
|
|
|
|
|
|
ind_args=np.array(range(len(y_type_2)))
|
|
|
|
|
|
|
|
for i_s_nc in columns_not_covered_child_no_mother:
|
|
if i_s_nc in x_start_with_child_without_mother:
|
|
x_end_biggest_column=np.array(x_end_with_child_without_mother)[np.array(x_start_with_child_without_mother)==i_s_nc][0]
|
|
args_all_biggest_lines=ind_args[(x_starting==i_s_nc) & (x_ending==x_end_biggest_column)]
|
|
|
|
args_all_biggest_lines=np.array(args_all_biggest_lines)
|
|
y_column_nc=y_type_2[args_all_biggest_lines]
|
|
x_start_column_nc=x_starting[args_all_biggest_lines]
|
|
x_end_column_nc=x_ending[args_all_biggest_lines]
|
|
|
|
y_column_nc=np.sort(y_column_nc)
|
|
|
|
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)]
|
|
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)]
|
|
|
|
y_all_between_nm_wc=y_type_2[ind_all_lines_betweeen_nm_wc]
|
|
x_starting_all_between_nm_wc=x_starting[ind_all_lines_betweeen_nm_wc]
|
|
x_ending_all_between_nm_wc=x_ending[ind_all_lines_betweeen_nm_wc]
|
|
|
|
x_diff_all_between_nm_wc=x_ending_all_between_nm_wc-x_starting_all_between_nm_wc
|
|
|
|
|
|
if len(x_diff_all_between_nm_wc)>0:
|
|
biggest=np.argmax(x_diff_all_between_nm_wc)
|
|
|
|
|
|
columns_covered_by_mothers=[]
|
|
|
|
for dj in range(len(x_starting_all_between_nm_wc)):
|
|
columns_covered_by_mothers=columns_covered_by_mothers+list(np.array(range(x_starting_all_between_nm_wc[dj],x_ending_all_between_nm_wc[dj])) )
|
|
columns_covered_by_mothers=list(set(columns_covered_by_mothers))
|
|
|
|
|
|
all_columns=np.array(range(i_s_nc,x_end_biggest_column))
|
|
|
|
columns_not_covered=list( set(all_columns)-set(columns_covered_by_mothers) )
|
|
|
|
should_longest_line_be_extended=0
|
|
if len(x_diff_all_between_nm_wc)>0 and set( list( np.array(range(x_starting_all_between_nm_wc[biggest],x_ending_all_between_nm_wc[biggest])) )+list(columns_not_covered) ) !=set(all_columns):
|
|
should_longest_line_be_extended=1
|
|
|
|
index_lines_so_close_to_top_separator=np.array(range(len(y_all_between_nm_wc)))[(y_all_between_nm_wc>y_column_nc[i_c]) & (y_all_between_nm_wc<=(y_column_nc[i_c]+500))]
|
|
|
|
|
|
if len(index_lines_so_close_to_top_separator)>0:
|
|
indexes_remained_after_deleting_closed_lines= np.array( list ( set( list( np.array(range(len(y_all_between_nm_wc))) ) ) -set(list( index_lines_so_close_to_top_separator) ) ) )
|
|
|
|
if len(indexes_remained_after_deleting_closed_lines)>0:
|
|
y_all_between_nm_wc=y_all_between_nm_wc[indexes_remained_after_deleting_closed_lines]
|
|
x_starting_all_between_nm_wc=x_starting_all_between_nm_wc[indexes_remained_after_deleting_closed_lines]
|
|
x_ending_all_between_nm_wc=x_ending_all_between_nm_wc[indexes_remained_after_deleting_closed_lines]
|
|
|
|
|
|
y_all_between_nm_wc=list(y_all_between_nm_wc)
|
|
x_starting_all_between_nm_wc=list(x_starting_all_between_nm_wc)
|
|
x_ending_all_between_nm_wc=list(x_ending_all_between_nm_wc)
|
|
|
|
|
|
y_all_between_nm_wc.append(y_column_nc[i_c] )
|
|
x_starting_all_between_nm_wc.append(i_s_nc)
|
|
x_ending_all_between_nm_wc.append(x_end_biggest_column)
|
|
|
|
|
|
|
|
|
|
y_all_between_nm_wc=list(y_all_between_nm_wc)
|
|
x_starting_all_between_nm_wc=list(x_starting_all_between_nm_wc)
|
|
x_ending_all_between_nm_wc=list(x_ending_all_between_nm_wc)
|
|
|
|
if len(x_diff_all_between_nm_wc)>0:
|
|
try:
|
|
x_starting_all_between_nm_wc.append(x_starting_all_between_nm_wc[biggest])
|
|
x_ending_all_between_nm_wc.append(x_ending_all_between_nm_wc[biggest])
|
|
y_all_between_nm_wc.append(y_column_nc[i_c])
|
|
except:
|
|
pass
|
|
|
|
|
|
|
|
for c_n_c in columns_not_covered:
|
|
y_all_between_nm_wc.append(y_column_nc[i_c])
|
|
x_starting_all_between_nm_wc.append(c_n_c)
|
|
x_ending_all_between_nm_wc.append(c_n_c+1)
|
|
|
|
y_all_between_nm_wc=np.array(y_all_between_nm_wc)
|
|
x_starting_all_between_nm_wc=np.array(x_starting_all_between_nm_wc)
|
|
x_ending_all_between_nm_wc=np.array(x_ending_all_between_nm_wc)
|
|
|
|
ind_args_between=np.array(range(len(x_ending_all_between_nm_wc)))
|
|
|
|
for column in range(i_s_nc,x_end_biggest_column):
|
|
ind_args_in_col=ind_args_between[x_starting_all_between_nm_wc==column]
|
|
#print('babali2')
|
|
#print(ind_args_in_col,'ind_args_in_col')
|
|
ind_args_in_col=np.array(ind_args_in_col)
|
|
#print(len(y_type_2))
|
|
y_column=y_all_between_nm_wc[ind_args_in_col]
|
|
x_start_column=x_starting_all_between_nm_wc[ind_args_in_col]
|
|
x_end_column=x_ending_all_between_nm_wc[ind_args_in_col]
|
|
#print('babali3')
|
|
ind_args_col_sorted=np.argsort(y_column)
|
|
y_col_sort=y_column[ind_args_col_sorted]
|
|
x_start_column_sort=x_start_column[ind_args_col_sorted]
|
|
x_end_column_sort=x_end_column[ind_args_col_sorted]
|
|
#print('babali4')
|
|
for ii in range(len(y_col_sort)):
|
|
#print('babali5')
|
|
y_lines_by_order.append(y_col_sort[ii])
|
|
x_start_by_order.append(x_start_column_sort[ii])
|
|
x_end_by_order.append(x_end_column_sort[ii]-1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
else:
|
|
|
|
#print(column,'column')
|
|
ind_args_in_col=ind_args[x_starting==i_s_nc]
|
|
#print('babali2')
|
|
#print(ind_args_in_col,'ind_args_in_col')
|
|
ind_args_in_col=np.array(ind_args_in_col)
|
|
#print(len(y_type_2))
|
|
y_column=y_type_2[ind_args_in_col]
|
|
x_start_column=x_starting[ind_args_in_col]
|
|
x_end_column=x_ending[ind_args_in_col]
|
|
#print('babali3')
|
|
ind_args_col_sorted=np.argsort(y_column)
|
|
y_col_sort=y_column[ind_args_col_sorted]
|
|
x_start_column_sort=x_start_column[ind_args_col_sorted]
|
|
x_end_column_sort=x_end_column[ind_args_col_sorted]
|
|
#print('babali4')
|
|
for ii in range(len(y_col_sort)):
|
|
y_lines_by_order.append(y_col_sort[ii])
|
|
x_start_by_order.append(x_start_column_sort[ii])
|
|
x_end_by_order.append(x_end_column_sort[ii]-1)
|
|
|
|
|
|
|
|
for il in range(len(y_lines_by_order)):
|
|
|
|
|
|
y_copy=list( np.copy(y_lines_by_order) )
|
|
x_start_copy=list( np.copy(x_start_by_order) )
|
|
x_end_copy=list ( np.copy(x_end_by_order) )
|
|
|
|
#print(y_copy,'y_copy')
|
|
y_itself=y_copy.pop(il)
|
|
x_start_itself=x_start_copy.pop(il)
|
|
x_end_itself=x_end_copy.pop(il)
|
|
|
|
#print(y_copy,'y_copy2')
|
|
|
|
for column in range(x_start_itself,x_end_itself+1):
|
|
#print(column,'cols')
|
|
y_in_cols=[]
|
|
for yic in range(len(y_copy)):
|
|
#print('burda')
|
|
if y_copy[yic]>y_itself and column>=x_start_copy[yic] and column<=x_end_copy[yic]:
|
|
y_in_cols.append(y_copy[yic])
|
|
#print('burda2')
|
|
#print(y_in_cols,'y_in_cols')
|
|
if len(y_in_cols)>0:
|
|
y_down=np.min(y_in_cols)
|
|
else:
|
|
y_down=[int(spliter_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])])
|
|
|
|
|
|
|
|
else:
|
|
y_lines_by_order=[]
|
|
x_start_by_order=[]
|
|
x_end_by_order=[]
|
|
if len(x_starting)>0:
|
|
columns_covered_by_lines_covered_more_than_2col=[]
|
|
|
|
for dj in range(len(x_starting)):
|
|
columns_covered_by_lines_covered_more_than_2col=columns_covered_by_lines_covered_more_than_2col+list(np.array(range(x_starting[dj],x_ending[dj])) )
|
|
columns_covered_by_lines_covered_more_than_2col=list(set(columns_covered_by_lines_covered_more_than_2col))
|
|
|
|
all_columns=np.array(range(len(peaks_neg_tot)-1))
|
|
|
|
columns_not_covered=list( set(all_columns)-set(columns_covered_by_lines_covered_more_than_2col) )
|
|
|
|
|
|
y_type_2=list(y_type_2)
|
|
x_starting=list(x_starting)
|
|
x_ending=list(x_ending)
|
|
|
|
for lj in columns_not_covered:
|
|
y_type_2.append(int(spliter_y_new[i]))
|
|
x_starting.append(lj)
|
|
x_ending.append(lj+1)
|
|
##y_lines_by_order.append(int(spliter_y_new[i]))
|
|
##x_start_by_order.append(0)
|
|
|
|
y_type_2.append(int(spliter_y_new[i]))
|
|
x_starting.append(x_starting[0])
|
|
x_ending.append(x_ending[0])
|
|
|
|
|
|
y_type_2=np.array(y_type_2)
|
|
x_starting=np.array(x_starting)
|
|
x_ending=np.array(x_ending)
|
|
else:
|
|
all_columns=np.array(range(len(peaks_neg_tot)-1))
|
|
columns_not_covered=list( set(all_columns) )
|
|
|
|
|
|
y_type_2=list(y_type_2)
|
|
x_starting=list(x_starting)
|
|
x_ending=list(x_ending)
|
|
|
|
for lj in columns_not_covered:
|
|
y_type_2.append(int(spliter_y_new[i]))
|
|
x_starting.append(lj)
|
|
x_ending.append(lj+1)
|
|
##y_lines_by_order.append(int(spliter_y_new[i]))
|
|
##x_start_by_order.append(0)
|
|
|
|
|
|
|
|
y_type_2=np.array(y_type_2)
|
|
x_starting=np.array(x_starting)
|
|
x_ending=np.array(x_ending)
|
|
|
|
ind_args=np.array(range(len(y_type_2)))
|
|
#ind_args=np.array(ind_args)
|
|
#print(ind_args,'ind_args')
|
|
for column in range(len(peaks_neg_tot)-1):
|
|
#print(column,'column')
|
|
ind_args_in_col=ind_args[x_starting==column]
|
|
ind_args_in_col=np.array(ind_args_in_col)
|
|
#print(len(y_type_2))
|
|
y_column=y_type_2[ind_args_in_col]
|
|
x_start_column=x_starting[ind_args_in_col]
|
|
x_end_column=x_ending[ind_args_in_col]
|
|
|
|
ind_args_col_sorted=np.argsort(y_column)
|
|
y_col_sort=y_column[ind_args_col_sorted]
|
|
x_start_column_sort=x_start_column[ind_args_col_sorted]
|
|
x_end_column_sort=x_end_column[ind_args_col_sorted]
|
|
#print('babali4')
|
|
for ii in range(len(y_col_sort)):
|
|
#print('babali5')
|
|
y_lines_by_order.append(y_col_sort[ii])
|
|
x_start_by_order.append(x_start_column_sort[ii])
|
|
x_end_by_order.append(x_end_column_sort[ii]-1)
|
|
|
|
|
|
for il in range(len(y_lines_by_order)):
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y_copy=list( np.copy(y_lines_by_order) )
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x_start_copy=list( np.copy(x_start_by_order) )
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x_end_copy=list ( np.copy(x_end_by_order) )
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#print(y_copy,'y_copy')
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y_itself=y_copy.pop(il)
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x_start_itself=x_start_copy.pop(il)
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x_end_itself=x_end_copy.pop(il)
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#print(y_copy,'y_copy2')
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for column in range(x_start_itself,x_end_itself+1):
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#print(column,'cols')
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y_in_cols=[]
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for yic in range(len(y_copy)):
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#print('burda')
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if y_copy[yic]>y_itself and column>=x_start_copy[yic] and column<=x_end_copy[yic]:
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y_in_cols.append(y_copy[yic])
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#print('burda2')
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#print(y_in_cols,'y_in_cols')
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if len(y_in_cols)>0:
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y_down=np.min(y_in_cols)
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else:
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y_down=[int(spliter_y_new[i+1])][0]
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#print(y_itself,'y_itself')
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boxes.append([peaks_neg_tot[column],peaks_neg_tot[column+1],y_itself,y_down])
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#else:
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#boxes.append([ 0, regions_without_seperators[:,:].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
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return boxes
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