text: import numpy as np
  
  
  def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
  
      # The following naming, e.g. gallery_num, is different from outer scope.
      # Don't care about it.
  
      original_dist = np.concatenate(
        [np.concatenate([q_q_dist, q_g_dist], axis=1),
         np.concatenate([q_g_dist.T, g_g_dist], axis=1)],
        axis=0)
      original_dist = np.power(original_dist, 2).astype(np.float32)
      original_dist = np.transpose(1. * original_dist/np.max(original_dist,axis = 0))
      V = np.zeros_like(original_dist).astype(np.float32)
      initial_rank = np.argsort(original_dist).astype(np.int32)
  
      query_num = q_g_dist.shape[0]
      gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
      all_num = gallery_num
  
      for i in range(all_num):
          # k-reciprocal neighbors
          forward_k_neigh_index = initial_rank[i,:k1+1]
          backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1]
          fi = np.where(backward_k_neigh_index==i)[0]
          k_reciprocal_index = forward_k_neigh_index[fi]
          k_reciprocal_expansion_index = k_reciprocal_index
          for j in range(len(k_reciprocal_index)):
              candidate = k_reciprocal_index[j]
              candidate_forward_k_neigh_index = initial_rank[candidate,:int(np.around(k1/2.))+1]
              candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,:int(np.around(k1/2.))+1]
              fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
              candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
              if len(np.intersect1d(candidate_k_reciprocal_index,k_reciprocal_index))> 2./3*len(candidate_k_reciprocal_index):
                  k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index,candidate_k_reciprocal_index)
  
          k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
          weight = np.exp(-original_dist[i,k_reciprocal_expansion_index])
          V[i,k_reciprocal_expansion_index] = 1.*weight/np.sum(weight)
      original_dist = original_dist[:query_num,]
      if k2 != 1:
          V_qe = np.zeros_like(V,dtype=np.float32)
          for i in range(all_num):
              V_qe[i,:] = np.mean(V[initial_rank[i,:k2],:],axis=0)
          V = V_qe
          del V_qe
      del initial_rank
      invIndex = []
      for i in range(gallery_num):
          invIndex.append(np.where(V[:,i] != 0)[0])
  
      jaccard_dist = np.zeros_like(original_dist,dtype = np.float32)
  
  
      for i in range(query_num):
          temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float32)
          indNonZero = np.where(V[i,:] != 0)[0]
          indImages = []
          indImages = [invIndex[ind] for ind in indNonZero]
          for j in range(len(indNonZero)):
              temp_min[0,indImages[j]] = temp_min[0,indImages[j]]+ np.minimum(V[i,indNonZero[j]],V[indImages[j],indNonZero[j]])
          jaccard_dist[i] = 1-temp_min/(2.-temp_min)
  
      final_dist = jaccard_dist*(1-lambda_value) + original_dist*lambda_value
      del original_dist
      del V
      del jaccard_dist
      final_dist = final_dist[:query_num,query_num:]
      return final_dist