import numpy as np import cv2 from scipy.signal import find_peaks from scipy.ndimage import gaussian_filter1d from .contour import find_new_features_of_contoures, return_contours_of_interested_region from .resize import resize_image from .rotate import rotate_image def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None): mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1])) mask_marginals=mask_marginals.astype(np.uint8) text_with_lines=text_with_lines.astype(np.uint8) ##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3) text_with_lines_eroded=cv2.erode(text_with_lines,kernel,iterations=5) if text_with_lines.shape[0]<=1500: pass elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800: text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1]) text_with_lines=cv2.erode(text_with_lines,kernel,iterations=5) text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1]) else: text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1]) text_with_lines=cv2.erode(text_with_lines,kernel,iterations=7) text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1]) text_with_lines_y=text_with_lines.sum(axis=0) text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0) thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100 #print(thickness_along_y_percent,'thickness_along_y_percent') if thickness_along_y_percent<30: min_textline_thickness=8 elif thickness_along_y_percent>=30 and thickness_along_y_percent<50: min_textline_thickness=20 else: min_textline_thickness=40 if thickness_along_y_percent>=14: text_with_lines_y_rev=-1*text_with_lines_y[:] #print(text_with_lines_y) #print(text_with_lines_y_rev) #plt.plot(text_with_lines_y) #plt.show() text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev) #plt.plot(text_with_lines_y_rev) #plt.show() sigma_gaus=1 region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus) region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus) #plt.plot(region_sum_0_rev) #plt.show() region_sum_0_updown=region_sum_0[len(region_sum_0)::-1] first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None)) last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None)) last_nonzero=len(region_sum_0)-last_nonzero ##img_sum_0_smooth_rev=-region_sum_0 mid_point=(last_nonzero+first_nonzero)/2. one_third_right=(last_nonzero-mid_point)/3.0 one_third_left=(mid_point-first_nonzero)/3.0 #img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev) peaks, _ = find_peaks(text_with_lines_y_rev, height=0) peaks=np.array(peaks) #print(region_sum_0[peaks]) ##plt.plot(region_sum_0) ##plt.plot(peaks,region_sum_0[peaks],'*') ##plt.show() #print(first_nonzero,last_nonzero,peaks) peaks=peaks[(peaks>first_nonzero) & ((peaksmid_point] peaks_left=peaks[peaks(mid_point+one_third_right)] peaks_left=peaks[peaks<(mid_point-one_third_left)] try: point_right=np.min(peaks_right) except: point_right=last_nonzero try: point_left=np.max(peaks_left) except: point_left=first_nonzero #print(point_left,point_right) #print(text_regions.shape) if point_right>=mask_marginals.shape[1]: point_right=mask_marginals.shape[1]-1 try: mask_marginals[:,point_left:point_right]=1 except: mask_marginals[:,:]=1 #print(mask_marginals.shape,point_left,point_right,'nadosh') mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew) #print(mask_marginals_rotated.shape,'nadosh') mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0) mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1 index_x=np.array(range(len(mask_marginals_rotated_sum)))+1 index_x_interest=index_x[mask_marginals_rotated_sum==1] min_point_of_left_marginal=np.min(index_x_interest)-16 max_point_of_right_marginal=np.max(index_x_interest)+16 if min_point_of_left_marginal<0: min_point_of_left_marginal=0 if max_point_of_right_marginal>=text_regions.shape[1]: max_point_of_right_marginal=text_regions.shape[1]-1 #print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew') #print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated') #plt.imshow(mask_marginals) #plt.show() #plt.imshow(mask_marginals_rotated) #plt.show() text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4 #plt.imshow(text_regions) #plt.show() pixel_img=4 min_area_text=0.00001 polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text) cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contoures(polygons_of_marginals) text_regions[(text_regions[:,:]==4)]=1 marginlas_should_be_main_text=[] x_min_marginals_left=[] x_min_marginals_right=[] for i in range(len(cx_text_only)): x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i]) y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i]) #print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar') if x_width_mar>16 and y_height_mar/x_width_mar<18: marginlas_should_be_main_text.append(polygons_of_marginals[i]) if x_min_text_only[i]<(mid_point-one_third_left): x_min_marginals_left_new=x_min_text_only[i] if len(x_min_marginals_left)==0: x_min_marginals_left.append(x_min_marginals_left_new) else: x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new) else: x_min_marginals_right_new=x_min_text_only[i] if len(x_min_marginals_right)==0: x_min_marginals_right.append(x_min_marginals_right_new) else: x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new) if len(x_min_marginals_left)==0: x_min_marginals_left=[0] if len(x_min_marginals_right)==0: x_min_marginals_right=[text_regions.shape[1]-1] #print(x_min_marginals_left[0],x_min_marginals_right[0],'margo') #print(marginlas_should_be_main_text,'marginlas_should_be_main_text') text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4)) #print(np.unique(text_regions)) #text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0 #text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0 text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0 text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0 ###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4 ###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4 #plt.plot(region_sum_0) #plt.plot(peaks,region_sum_0[peaks],'*') #plt.show() #plt.imshow(text_regions) #plt.show() #sys.exit() else: pass return text_regions