#!/usr/bin/env python2/python3
# -*- coding: utf-8 -*-
"""
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.
Modified by Zhedong Zheng, 2018-1-12.
- replace sort with topK, which save about 30s.
"""

"""
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]
"""


import numpy as np

def k_reciprocal_neigh( initial_rank, i, k1):
    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]
    return forward_k_neigh_index[fi]

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 = 2. - 2 * original_dist   # change the cosine similarity metric to euclidean similarity metric
    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)
    # top K1+1
    initial_rank = np.argpartition( original_dist, range(1,k1+1) )

    query_num = q_g_dist.shape[0]
    all_num = original_dist.shape[0]

    for i in range(all_num):
        # k-reciprocal neighbors
        k_reciprocal_index = k_reciprocal_neigh( initial_rank, i, k1)
        k_reciprocal_expansion_index = k_reciprocal_index
        for j in range(len(k_reciprocal_index)):
            candidate = k_reciprocal_index[j]
            candidate_k_reciprocal_index = k_reciprocal_neigh( initial_rank, candidate, int(np.around(k1/2)))
            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(all_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,all_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