text: #!/usr/bin/env python2/python3
  # -*- coding: utf-8 -*-
  """
  Source: https://github.com/zhunzhong07/person-re-ranking
  
  Created on Mon Jun 26 14:46:56 2017
  @author: luohao
  Modified by Houjing Huang, 2017-12-22.
  - This version accepts distance matrix instead of raw features.
  - The difference of `/` division between python 2 and 3 is handled.
  - numpy.float16 is replaced by numpy.float32 for numerical precision.
  
  CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
  url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
  Matlab version: https://github.com/zhunzhong07/person-re-ranking
  
  API
  q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery]
  q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query]
  g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery]
  k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3)
  Returns:
    final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery]
  """
  from __future__ import division, print_function, absolute_import
  import numpy as np
  
  __all__ = ['re_ranking']
  
  
  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(1.0 * 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 / 2.0)) + 1
              ]
              candidate_backward_k_neigh_index = initial_rank[
                  candidate_forward_k_neigh_index, : int(np.around(k1 / 2.0)) + 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)
              ) > 2.0 / 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] = 1.0 * 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 / (2.0 - 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
  