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@ -2771,6 +2771,7 @@ class Eynollah:
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for ijv in range(len(y_min_tab_col1)):
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image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv]),:,:]=pixel_table
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return image_revised_last
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def do_order_of_regions(self, *args, **kwargs):
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if self.full_layout:
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return self.do_order_of_regions_full_layout(*args, **kwargs)
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@ -3380,171 +3381,35 @@ class Eynollah:
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model = load_model(model_file , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
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return model
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def do_order_of_regions_with_model(self,contours_only_text_parent, contours_only_text_parent_h, text_regions_p):
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y_len = text_regions_p.shape[0]
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x_len = text_regions_p.shape[1]
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img_poly = np.zeros((y_len,x_len), dtype='uint8')
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unique_pix = np.unique(text_regions_p)
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img_poly = np.zeros((y_len,x_len), dtype='uint8')
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img_poly[text_regions_p[:,:]==1] = 1
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img_poly[text_regions_p[:,:]==2] = 2
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img_poly[text_regions_p[:,:]==3] = 4
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img_poly[text_regions_p[:,:]==6] = 5
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if not self.dir_in:
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self.model_reading_order, _ = self.start_new_session_and_model(self.model_reading_order_dir)
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height1 =672#448
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width1 = 448#224
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height2 =672#448
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width2= 448#224
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height3 =672#448
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width3 = 448#224
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img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
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if contours_only_text_parent_h:
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_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
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for j in range(len(cy_main)):
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img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
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co_text_all = contours_only_text_parent + contours_only_text_parent_h
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else:
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co_text_all = contours_only_text_parent
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if not len(co_text_all):
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return [], []
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labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
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labels_con = np.zeros((y_len, x_len, len(co_text_all)), dtype=bool)
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for i in range(len(co_text_all)):
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img_label = np.zeros((y_len,x_len,3),dtype='uint8')
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img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1))
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labels_con[:,:,i] = img_label[:,:,0]
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img3= np.copy(img_poly)
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labels_con = resize_image(labels_con, height1, width1)
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img_header_and_sep = resize_image(img_header_and_sep, height1, width1)
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img3= resize_image (img3, height3, width3)
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img3 = img3.astype(np.uint16)
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order_matrix = np.zeros((labels_con.shape[2], labels_con.shape[2]))-1
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inference_bs = 6
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tot_counter = 1
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batch_counter = 0
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i_indexer = []
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j_indexer =[]
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input_1= np.zeros( (inference_bs, height1, width1,3))
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tot_iteration = int( ( labels_con.shape[2]*(labels_con.shape[2]-1) )/2. )
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full_bs_ite= tot_iteration//inference_bs
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last_bs = tot_iteration % inference_bs
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#print(labels_con.shape[2],"number of regions for reading order")
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for i in range(labels_con.shape[2]):
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for j in range(labels_con.shape[2]):
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if j>i:
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img1= np.repeat(labels_con[:,:,i][:, :, np.newaxis], 3, axis=2)
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img2 = np.repeat(labels_con[:,:,j][:, :, np.newaxis], 3, axis=2)
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img2[:,:,0][img3[:,:]==5] = 2
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img2[:,:,0][img_header_and_sep[:,:]==1] = 3
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img1[:,:,0][img3[:,:]==5] = 2
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img1[:,:,0][img_header_and_sep[:,:]==1] = 3
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i_indexer.append(i)
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j_indexer.append(j)
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input_1[batch_counter,:,:,0] = img1[:,:,0]/3.
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input_1[batch_counter,:,:,2] = img2[:,:,0]/3.
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input_1[batch_counter,:,:,1] = img3[:,:]/5.
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batch_counter = batch_counter+1
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if batch_counter==inference_bs or ( (tot_counter//inference_bs)==full_bs_ite and tot_counter%inference_bs==last_bs):
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y_pr = self.model_reading_order.predict(input_1 , verbose=0)
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if batch_counter==inference_bs:
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iteration_batches = inference_bs
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else:
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iteration_batches = last_bs
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for jb in range(iteration_batches):
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if y_pr[jb][0]>=0.5:
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order_class = 1
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else:
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order_class = 0
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order_matrix[i_indexer[jb],j_indexer[jb]] = y_pr[jb][0]#order_class
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order_matrix[j_indexer[jb],i_indexer[jb]] = 1-y_pr[jb][0]#int( 1 - order_class)
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batch_counter = 0
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i_indexer = []
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j_indexer = []
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tot_counter = tot_counter+1
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sum_mat = np.sum(order_matrix, axis=1)
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index_sort = np.argsort(sum_mat)
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index_sort = index_sort[::-1]
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REGION_ID_TEMPLATE = 'region_%04d'
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order_of_texts = []
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id_of_texts = []
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for order, id_text in enumerate(index_sort):
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order_of_texts.append(id_text)
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id_of_texts.append( REGION_ID_TEMPLATE % order )
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return order_of_texts, id_of_texts
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def update_list_and_return_first_with_length_bigger_than_one(self,index_element_to_be_updated, innner_index_pr_pos, pr_list, pos_list,list_inp):
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list_inp.pop(index_element_to_be_updated)
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if len(pr_list)>0:
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list_inp.insert(index_element_to_be_updated, pr_list)
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else:
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index_element_to_be_updated = index_element_to_be_updated -1
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list_inp.insert(index_element_to_be_updated+1, [innner_index_pr_pos])
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if len(pos_list)>0:
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list_inp.insert(index_element_to_be_updated+2, pos_list)
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len_all_elements = [len(i) for i in list_inp]
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list_len_bigger_1 = np.where(np.array(len_all_elements)>1)
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list_len_bigger_1 = list_len_bigger_1[0]
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if len(list_len_bigger_1)>0:
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early_list_bigger_than_one = list_len_bigger_1[0]
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else:
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early_list_bigger_than_one = -20
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return list_inp, early_list_bigger_than_one
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def do_order_of_regions_with_model_optimized_algorithm(self,contours_only_text_parent, contours_only_text_parent_h, text_regions_p):
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y_len = text_regions_p.shape[0]
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x_len = text_regions_p.shape[1]
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img_poly = np.zeros((y_len,x_len), dtype='uint8')
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unique_pix = np.unique(text_regions_p)
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img_poly[text_regions_p[:,:]==1] = 1
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img_poly[text_regions_p[:,:]==2] = 2
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img_poly[text_regions_p[:,:]==3] = 4
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img_poly[text_regions_p[:,:]==6] = 5
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if not self.dir_in:
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self.model_reading_order, _ = self.start_new_session_and_model(self.model_reading_order_dir)
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img = labels_con[:,:,i].astype(np.uint8)
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cv2.fillPoly(img, pts=[co_text_all[i]], color=(1,))
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labels_con[:,:,i] = img
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height1 =672#448
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width1 = 448#224
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@ -3554,112 +3419,67 @@ class Eynollah:
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height3 =672#448
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width3 = 448#224
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img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
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if contours_only_text_parent_h:
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_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
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for j in range(len(cy_main)):
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img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
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co_text_all = contours_only_text_parent + contours_only_text_parent_h
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else:
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co_text_all = contours_only_text_parent
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labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
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for i in range(len(co_text_all)):
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img_label = np.zeros((y_len,x_len,3),dtype='uint8')
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img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1))
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labels_con[:,:,i] = img_label[:,:,0]
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img3= np.copy(img_poly)
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labels_con = resize_image(labels_con, height1, width1)
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labels_con = resize_image(labels_con.astype(np.uint8), height1, width1).astype(bool)
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img_header_and_sep = resize_image(img_header_and_sep, height1, width1)
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img_poly = resize_image(img_poly, height3, width3)
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img3= resize_image (img3, height3, width3)
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if not self.dir_in:
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self.model_reading_order, _ = self.start_new_session_and_model(self.model_reading_order_dir)
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img3 = img3.astype(np.uint16)
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inference_bs = 3
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input_1= np.zeros( (inference_bs, height1, width1,3))
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starting_list_of_regions = []
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if len(co_text_all)<=1:
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starting_list_of_regions.append( list(range(1)) )
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else:
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starting_list_of_regions.append( list(range(labels_con.shape[2])) )
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input_1 = np.zeros((inference_bs, height1, width1, 3))
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ordered = [list(range(len(co_text_all)))]
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index_update = 0
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index_selected = starting_list_of_regions[0]
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#print(labels_con.shape[2],"number of regions for reading order")
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while index_update>=0:
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ij_list = starting_list_of_regions[index_update]
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i = ij_list[0]
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ij_list.pop(0)
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pr_list = []
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ij_list = ordered.pop(index_update)
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i = ij_list.pop(0)
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ante_list = []
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post_list = []
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batch_counter = 0
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tot_counter = 1
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tot_iteration = len(ij_list)
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full_bs_ite= tot_iteration//inference_bs
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last_bs = tot_iteration % inference_bs
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jbatch_indexer =[]
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tot_counter = 0
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batch = []
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for j in ij_list:
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img1= np.repeat(labels_con[:,:,i][:, :, np.newaxis], 3, axis=2)
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img2 = np.repeat(labels_con[:,:,j][:, :, np.newaxis], 3, axis=2)
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img2[:,:,0][img3[:,:]==5] = 2
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img2[:,:,0][img_header_and_sep[:,:]==1] = 3
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img1[:,:,0][img3[:,:]==5] = 2
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img1[:,:,0][img_header_and_sep[:,:]==1] = 3
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jbatch_indexer.append(j)
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input_1[batch_counter,:,:,0] = img1[:,:,0]/3.
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input_1[batch_counter,:,:,2] = img2[:,:,0]/3.
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input_1[batch_counter,:,:,1] = img3[:,:]/5.
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batch_counter = batch_counter+1
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if batch_counter==inference_bs or ( (tot_counter//inference_bs)==full_bs_ite and tot_counter%inference_bs==last_bs):
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img1 = labels_con[:,:,i].astype(float)
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img2 = labels_con[:,:,j].astype(float)
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img1[img_poly==5] = 2
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img2[img_poly==5] = 2
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img1[img_header_and_sep==1] = 3
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img2[img_header_and_sep==1] = 3
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input_1[len(batch), :, :, 0] = img1 / 3.
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input_1[len(batch), :, :, 2] = img2 / 3.
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input_1[len(batch), :, :, 1] = img_poly / 5.
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tot_counter += 1
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batch.append(j)
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if tot_counter % inference_bs == 0 or tot_counter == len(ij_list):
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y_pr = self.model_reading_order.predict(input_1 , verbose=0)
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if batch_counter==inference_bs:
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iteration_batches = inference_bs
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else:
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iteration_batches = last_bs
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for jb in range(iteration_batches):
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for jb, j in enumerate(batch):
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if y_pr[jb][0]>=0.5:
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post_list.append(jbatch_indexer[jb])
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post_list.append(j)
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else:
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pr_list.append(jbatch_indexer[jb])
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batch_counter = 0
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jbatch_indexer = []
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tot_counter = tot_counter+1
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starting_list_of_regions, index_update = self.update_list_and_return_first_with_length_bigger_than_one(index_update, i, pr_list, post_list,starting_list_of_regions)
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ante_list.append(j)
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batch = []
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if len(ante_list):
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ordered.insert(index_update, ante_list)
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index_update += 1
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ordered.insert(index_update, [i])
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if len(post_list):
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ordered.insert(index_update + 1, post_list)
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index_update = -1
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for index_next, ij_list in enumerate(ordered):
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if len(ij_list) > 1:
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index_update = index_next
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break
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ordered = [i[0] for i in ordered]
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region_ids = ['region_%04d' % i for i in range(len(co_text_all))]
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return ordered, region_ids
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index_sort = [i[0] for i in starting_list_of_regions ]
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REGION_ID_TEMPLATE = 'region_%04d'
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order_of_texts = []
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id_of_texts = []
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for order, id_text in enumerate(index_sort):
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order_of_texts.append(id_text)
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id_of_texts.append( REGION_ID_TEMPLATE % order )
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return order_of_texts, id_of_texts
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def return_start_and_end_of_common_text_of_textline_ocr(self,textline_image, ind_tot):
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width = np.shape(textline_image)[1]
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height = np.shape(textline_image)[0]
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@ -4980,7 +4800,7 @@ class Eynollah:
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if self.full_layout:
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if self.reading_order_machine_based:
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|
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order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model_optimized_algorithm(contours_only_text_parent, contours_only_text_parent_h, text_regions_p)
|
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|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model(contours_only_text_parent, contours_only_text_parent_h, text_regions_p)
|
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|
else:
|
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|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
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|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
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|
@ -5007,7 +4827,7 @@ class Eynollah:
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|
|
else:
|
|
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|
|
contours_only_text_parent_h = None
|
|
|
|
|
if self.reading_order_machine_based:
|
|
|
|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model_optimized_algorithm(contours_only_text_parent, contours_only_text_parent_h, text_regions_p)
|
|
|
|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model(contours_only_text_parent, contours_only_text_parent_h, text_regions_p)
|
|
|
|
|
else:
|
|
|
|
|
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
|
|
|
|
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
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Robert Sachunsky
4 months ago