text: from .text_connect_cfg import Config as TextLineCfg
  from .other import Graph
  import numpy as np
  
  
  class TextProposalGraphBuilder:
      """
          Build Text proposals into a graph.
      """
      def get_successions(self, index):
              box=self.text_proposals[index]
              results=[]
              for left in range(int(box[0])+1, min(int(box[0])+TextLineCfg.MAX_HORIZONTAL_GAP+1, self.im_size[1])):
                  adj_box_indices=self.boxes_table[left]
                  for adj_box_index in adj_box_indices:
                      if self.meet_v_iou(adj_box_index, index):
                          results.append(adj_box_index)
                  if len(results)!=0:
                      return results
              return results
  
      def get_precursors(self, index):
          box=self.text_proposals[index]
          results=[]
          for left in range(int(box[0])-1, max(int(box[0]-TextLineCfg.MAX_HORIZONTAL_GAP), 0)-1, -1):
              adj_box_indices=self.boxes_table[left]
              for adj_box_index in adj_box_indices:
                  if self.meet_v_iou(adj_box_index, index):
                      results.append(adj_box_index)
              if len(results)!=0:
                  return results
          return results
  
      def is_succession_node(self, index, succession_index):
          precursors=self.get_precursors(succession_index)
          if self.scores[index]>=np.max(self.scores[precursors]):
              return True
          return False
  
      def meet_v_iou(self, index1, index2):
          def overlaps_v(index1, index2):
              h1=self.heights[index1]
              h2=self.heights[index2]
              y0=max(self.text_proposals[index2][1], self.text_proposals[index1][1])
              y1=min(self.text_proposals[index2][3], self.text_proposals[index1][3])
              return max(0, y1-y0+1)/min(h1, h2)
  
          def size_similarity(index1, index2):
              h1=self.heights[index1]
              h2=self.heights[index2]
              return min(h1, h2)/max(h1, h2)
  
          return overlaps_v(index1, index2)>=TextLineCfg.MIN_V_OVERLAPS and \
                 size_similarity(index1, index2)>=TextLineCfg.MIN_SIZE_SIM
  
      def build_graph(self, text_proposals, scores, im_size):
          self.text_proposals=text_proposals
          self.scores=scores
          self.im_size=im_size
          self.heights=text_proposals[:, 3]-text_proposals[:, 1]+1
  
          boxes_table=[[] for _ in range(self.im_size[1])]
          for index, box in enumerate(text_proposals):
              boxes_table[int(box[0])].append(index)
          self.boxes_table=boxes_table
  
          graph=np.zeros((text_proposals.shape[0], text_proposals.shape[0]), np.bool)
  
          for index, box in enumerate(text_proposals):
              successions=self.get_successions(index)
              if len(successions)==0:
                  continue
              succession_index=successions[np.argmax(scores[successions])]
              if self.is_succession_node(index, succession_index):
                  # NOTE: a box can have multiple successions(precursors) if multiple successions(precursors)
                  # have equal scores.
                  graph[index, succession_index]=True
          return Graph(graph)
  