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https://github.com/qurator-spk/eynollah.git
synced 2025-11-09 22:24:13 +01:00
Continue commit 5a0e4c3 using find_number_of_columns_in_document from eynollah v0.5.0. Ignoring deskewing_angle issues. Fixes some reading-order problems but further debugging is needed.
This commit is contained in:
vahidrezanezhad
parent
5a0e4c3b0f
commit
7d3405928b
1 changed files with 215 additions and 2 deletions
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@ -801,13 +801,15 @@ def find_num_col_only_image(regions_without_separators, multiplier=3.8):
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return len(peaks_fin_true), peaks_fin_true
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return len(peaks_fin_true), peaks_fin_true
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def find_num_col_by_vertical_lines(regions_without_separators, multiplier=3.8):
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def find_num_col_by_vertical_lines(regions_without_separators, multiplier=3.8):
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print(regions_without_separators.shape)
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regions_without_separators_0 = regions_without_separators.sum(axis=0)
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regions_without_separators_0 = regions_without_separators.sum(axis=0)
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##plt.plot(regions_without_separators_0)
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##plt.plot(regions_without_separators_0)
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##plt.show()
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##plt.show()
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sigma_ = 35 # 70#35
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sigma_ = 35 # 70#35
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print(regions_without_separators_0.shape, 'regions_without_separators_0')
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z = gaussian_filter1d(regions_without_separators_0, sigma_)
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z = gaussian_filter1d(regions_without_separators_0, sigma_)
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print(z, z.shape, 'z')
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peaks, _ = find_peaks(z, height=0)
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peaks, _ = find_peaks(z, height=0)
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# print(peaks,'peaksnew')
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# print(peaks,'peaksnew')
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@ -1377,7 +1379,218 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
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peaks_neg_tot.append(last_point)
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peaks_neg_tot.append(last_point)
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return peaks_neg_tot
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return peaks_neg_tot
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def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, label_seps, contours_h=None):
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def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None):
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region_pre_p = np.repeat(region_pre_p[:, :, np.newaxis], 3, axis=2)
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t_ins_c0 = time.time()
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separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
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separators_closeup[0:110,:,:]=0
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separators_closeup[separators_closeup.shape[0]-150:,:,:]=0
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kernel = np.ones((5,5),np.uint8)
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separators_closeup=separators_closeup.astype(np.uint8)
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separators_closeup = cv2.dilate(separators_closeup,kernel,iterations = 1)
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separators_closeup = cv2.erode(separators_closeup,kernel,iterations = 1)
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separators_closeup_new=np.zeros((separators_closeup.shape[0] ,separators_closeup.shape[1] ))
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separators_closeup_n=np.copy(separators_closeup)
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separators_closeup_n=separators_closeup_n.astype(np.uint8)
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separators_closeup_n_binary=np.zeros(( separators_closeup_n.shape[0],separators_closeup_n.shape[1]) )
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separators_closeup_n_binary[:,:]=separators_closeup_n[:,:,0]
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separators_closeup_n_binary[:,:][separators_closeup_n_binary[:,:]!=0]=1
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gray_early=np.repeat(separators_closeup_n_binary[:, :, np.newaxis], 3, axis=2)
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gray_early=gray_early.astype(np.uint8)
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imgray_e = cv2.cvtColor(gray_early, cv2.COLOR_BGR2GRAY)
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ret_e, thresh_e = cv2.threshold(imgray_e, 0, 255, 0)
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contours_line_e,hierarchy_e=cv2.findContours(thresh_e,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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_, dist_xe, _, _, _, _, y_min_main, y_max_main, _ = \
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find_features_of_lines(contours_line_e)
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dist_ye = y_max_main - y_min_main
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args_e=np.arange(len(contours_line_e))
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args_hor_e=args_e[(dist_ye<=50) &
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(dist_xe>=3*dist_ye)]
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cnts_hor_e=[]
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for ce in args_hor_e:
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cnts_hor_e.append(contours_line_e[ce])
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figs_e=np.zeros(thresh_e.shape)
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figs_e=cv2.fillPoly(figs_e,pts=cnts_hor_e,color=(1,1,1))
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separators_closeup_n_binary=cv2.fillPoly(separators_closeup_n_binary, pts=cnts_hor_e, color=(0,0,0))
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gray = cv2.bitwise_not(separators_closeup_n_binary)
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gray=gray.astype(np.uint8)
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bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
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cv2.THRESH_BINARY, 15, -2)
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horizontal = np.copy(bw)
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vertical = np.copy(bw)
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cols = horizontal.shape[1]
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horizontal_size = cols // 30
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# Create structure element for extracting horizontal lines through morphology operations
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horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
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# Apply morphology operations
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horizontal = cv2.erode(horizontal, horizontalStructure)
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horizontal = cv2.dilate(horizontal, horizontalStructure)
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kernel = np.ones((5,5),np.uint8)
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horizontal = cv2.dilate(horizontal,kernel,iterations = 2)
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horizontal = cv2.erode(horizontal,kernel,iterations = 2)
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horizontal = cv2.fillPoly(horizontal, pts=cnts_hor_e, color=(255,255,255))
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rows = vertical.shape[0]
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verticalsize = rows // 30
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# Create structure element for extracting vertical lines through morphology operations
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verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
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# Apply morphology operations
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vertical = cv2.erode(vertical, verticalStructure)
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vertical = cv2.dilate(vertical, verticalStructure)
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vertical = cv2.dilate(vertical,kernel,iterations = 1)
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horizontal, special_separators = \
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combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(
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vertical, horizontal, num_col_classifier)
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separators_closeup_new[:,:][vertical[:,:]!=0]=1
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separators_closeup_new[:,:][horizontal[:,:]!=0]=1
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vertical=np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
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vertical=vertical.astype(np.uint8)
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imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_line_vers,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = \
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find_features_of_lines(contours_line_vers)
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args=np.arange(len(slope_lines))
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args_ver=args[slope_lines==1]
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dist_x_ver=dist_x[slope_lines==1]
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y_min_main_ver=y_min_main[slope_lines==1]
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y_max_main_ver=y_max_main[slope_lines==1]
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x_min_main_ver=x_min_main[slope_lines==1]
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x_max_main_ver=x_max_main[slope_lines==1]
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cx_main_ver=cx_main[slope_lines==1]
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dist_y_ver=y_max_main_ver-y_min_main_ver
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len_y=separators_closeup.shape[0]/3.0
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horizontal=np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
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horizontal=horizontal.astype(np.uint8)
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imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_line_hors,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = \
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find_features_of_lines(contours_line_hors)
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slope_lines_org_hor=slope_lines_org[slope_lines==0]
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args=np.arange(len(slope_lines))
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len_x=separators_closeup.shape[1]/5.0
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dist_y=np.abs(y_max_main-y_min_main)
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args_hor=args[slope_lines==0]
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dist_x_hor=dist_x[slope_lines==0]
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y_min_main_hor=y_min_main[slope_lines==0]
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y_max_main_hor=y_max_main[slope_lines==0]
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x_min_main_hor=x_min_main[slope_lines==0]
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x_max_main_hor=x_max_main[slope_lines==0]
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dist_y_hor=dist_y[slope_lines==0]
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cy_main_hor=cy_main[slope_lines==0]
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args_hor=args_hor[dist_x_hor>=len_x/2.0]
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x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
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x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
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cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
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y_min_main_hor=y_min_main_hor[dist_x_hor>=len_x/2.0]
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y_max_main_hor=y_max_main_hor[dist_x_hor>=len_x/2.0]
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dist_y_hor=dist_y_hor[dist_x_hor>=len_x/2.0]
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slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
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dist_x_hor=dist_x_hor[dist_x_hor>=len_x/2.0]
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matrix_of_lines_ch=np.zeros((len(cy_main_hor)+len(cx_main_ver),10))
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matrix_of_lines_ch[:len(cy_main_hor),0]=args_hor
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matrix_of_lines_ch[len(cy_main_hor):,0]=args_ver
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matrix_of_lines_ch[len(cy_main_hor):,1]=cx_main_ver
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matrix_of_lines_ch[:len(cy_main_hor),2]=x_min_main_hor+50#x_min_main_hor+150
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matrix_of_lines_ch[len(cy_main_hor):,2]=x_min_main_ver
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matrix_of_lines_ch[:len(cy_main_hor),3]=x_max_main_hor-50#x_max_main_hor-150
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matrix_of_lines_ch[len(cy_main_hor):,3]=x_max_main_ver
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matrix_of_lines_ch[:len(cy_main_hor),4]=dist_x_hor
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matrix_of_lines_ch[len(cy_main_hor):,4]=dist_x_ver
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matrix_of_lines_ch[:len(cy_main_hor),5]=cy_main_hor
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matrix_of_lines_ch[:len(cy_main_hor),6]=y_min_main_hor
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matrix_of_lines_ch[len(cy_main_hor):,6]=y_min_main_ver
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matrix_of_lines_ch[:len(cy_main_hor),7]=y_max_main_hor
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matrix_of_lines_ch[len(cy_main_hor):,7]=y_max_main_ver
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matrix_of_lines_ch[:len(cy_main_hor),8]=dist_y_hor
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matrix_of_lines_ch[len(cy_main_hor):,8]=dist_y_ver
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matrix_of_lines_ch[len(cy_main_hor):,9]=1
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if contours_h is not None:
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_, dist_x_head, x_min_main_head, x_max_main_head, cy_main_head, _, y_min_main_head, y_max_main_head, _ = \
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find_features_of_lines(contours_h)
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matrix_l_n=np.zeros((matrix_of_lines_ch.shape[0]+len(cy_main_head),matrix_of_lines_ch.shape[1]))
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matrix_l_n[:matrix_of_lines_ch.shape[0],:]=np.copy(matrix_of_lines_ch[:,:])
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args_head=np.arange(len(cy_main_head)) + len(cy_main_hor)
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matrix_l_n[matrix_of_lines_ch.shape[0]:,0]=args_head
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matrix_l_n[matrix_of_lines_ch.shape[0]:,2]=x_min_main_head+30
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matrix_l_n[matrix_of_lines_ch.shape[0]:,3]=x_max_main_head-30
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matrix_l_n[matrix_of_lines_ch.shape[0]:,4]=dist_x_head
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matrix_l_n[matrix_of_lines_ch.shape[0]:,5]=y_min_main_head-3-8
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matrix_l_n[matrix_of_lines_ch.shape[0]:,6]=y_min_main_head-5-8
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matrix_l_n[matrix_of_lines_ch.shape[0]:,7]=y_max_main_head#y_min_main_head+1-8
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matrix_l_n[matrix_of_lines_ch.shape[0]:,8]=4
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matrix_of_lines_ch=np.copy(matrix_l_n)
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cy_main_splitters=cy_main_hor[(x_min_main_hor<=.16*region_pre_p.shape[1]) &
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(x_max_main_hor>=.84*region_pre_p.shape[1])]
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cy_main_splitters=np.array( list(cy_main_splitters)+list(special_separators))
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if contours_h is not None:
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try:
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cy_main_splitters_head=cy_main_head[(x_min_main_head<=.16*region_pre_p.shape[1]) &
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(x_max_main_head>=.84*region_pre_p.shape[1])]
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cy_main_splitters=np.array( list(cy_main_splitters)+list(cy_main_splitters_head))
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except:
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pass
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args_cy_splitter=np.argsort(cy_main_splitters)
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cy_main_splitters_sort=cy_main_splitters[args_cy_splitter]
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splitter_y_new=[]
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splitter_y_new.append(0)
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for i in range(len(cy_main_splitters_sort)):
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splitter_y_new.append( cy_main_splitters_sort[i] )
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splitter_y_new.append(region_pre_p.shape[0])
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splitter_y_new_diff=np.diff(splitter_y_new)/float(region_pre_p.shape[0])*100
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args_big_parts=np.arange(len(splitter_y_new_diff))[ splitter_y_new_diff>22 ]
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regions_without_separators=return_regions_without_separators(region_pre_p)
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length_y_threshold=regions_without_separators.shape[0]/4.0
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num_col_fin=0
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peaks_neg_fin_fin=[]
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for itiles in args_big_parts:
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regions_without_separators_tile=regions_without_separators[int(splitter_y_new[itiles]):
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int(splitter_y_new[itiles+1]),:,0]
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try:
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num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile,
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num_col_classifier, tables, multiplier=7.0)
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except:
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num_col = 0
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peaks_neg_fin = []
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if num_col>num_col_fin:
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num_col_fin=num_col
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peaks_neg_fin_fin=peaks_neg_fin
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if len(args_big_parts)==1 and (len(peaks_neg_fin_fin)+1)<num_col_classifier:
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peaks_neg_fin=find_num_col_by_vertical_lines(vertical[:,:,0])
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peaks_neg_fin=peaks_neg_fin[peaks_neg_fin>=500]
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peaks_neg_fin=peaks_neg_fin[peaks_neg_fin<=(vertical.shape[1]-500)]
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peaks_neg_fin_fin=peaks_neg_fin[:]
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return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,splitter_y_new,separators_closeup_n
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ncomps, ccomps = cv2.connectedComponents(region_pre_p.astype(np.uint8))
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ncomps, ccomps = cv2.connectedComponents(region_pre_p.astype(np.uint8))
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separators_closeup = 1 * (region_pre_p == label_seps)
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separators_closeup = 1 * (region_pre_p == label_seps)
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