99 lines
4.2 KiB
Python
99 lines
4.2 KiB
Python
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#!/usr/bin/env python2/python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jun 26 14:46:56 2017
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@author: luohao
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Modified by Houjing Huang, 2017-12-22.
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- This version accepts distance matrix instead of raw features.
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- The difference of `/` division between python 2 and 3 is handled.
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- numpy.float16 is replaced by numpy.float32 for numerical precision.
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"""
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"""
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CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
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url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
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Matlab version: https://github.com/zhunzhong07/person-re-ranking
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"""
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"""
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API
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q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery]
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q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query]
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g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery]
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k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3)
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Returns:
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final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery]
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"""
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import numpy as np
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def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
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# The following naming, e.g. gallery_num, is different from outer scope.
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# Don't care about it.
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original_dist = np.concatenate(
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[np.concatenate([q_q_dist, q_g_dist], axis=1),
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np.concatenate([q_g_dist.T, g_g_dist], axis=1)],
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axis=0)
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original_dist = np.power(original_dist, 2).astype(np.float32)
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original_dist = np.transpose(1. * original_dist/np.max(original_dist,axis = 0))
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V = np.zeros_like(original_dist).astype(np.float32)
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initial_rank = np.argsort(original_dist).astype(np.int32)
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query_num = q_g_dist.shape[0]
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gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
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all_num = gallery_num
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for i in range(all_num):
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# k-reciprocal neighbors
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forward_k_neigh_index = initial_rank[i,:k1+1]
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backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1]
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fi = np.where(backward_k_neigh_index==i)[0]
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k_reciprocal_index = forward_k_neigh_index[fi]
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k_reciprocal_expansion_index = k_reciprocal_index
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for j in range(len(k_reciprocal_index)):
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candidate = k_reciprocal_index[j]
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candidate_forward_k_neigh_index = initial_rank[candidate,:int(np.around(k1/2.))+1]
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candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,:int(np.around(k1/2.))+1]
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fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
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candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
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if len(np.intersect1d(candidate_k_reciprocal_index,k_reciprocal_index))> 2./3*len(candidate_k_reciprocal_index):
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k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index,candidate_k_reciprocal_index)
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k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
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weight = np.exp(-original_dist[i,k_reciprocal_expansion_index])
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V[i,k_reciprocal_expansion_index] = 1.*weight/np.sum(weight)
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original_dist = original_dist[:query_num,]
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if k2 != 1:
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V_qe = np.zeros_like(V,dtype=np.float32)
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for i in range(all_num):
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V_qe[i,:] = np.mean(V[initial_rank[i,:k2],:],axis=0)
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V = V_qe
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del V_qe
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del initial_rank
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invIndex = []
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for i in range(gallery_num):
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invIndex.append(np.where(V[:,i] != 0)[0])
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jaccard_dist = np.zeros_like(original_dist,dtype = np.float32)
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for i in range(query_num):
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temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float32)
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indNonZero = np.where(V[i,:] != 0)[0]
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indImages = []
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indImages = [invIndex[ind] for ind in indNonZero]
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for j in range(len(indNonZero)):
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temp_min[0,indImages[j]] = temp_min[0,indImages[j]]+ np.minimum(V[i,indNonZero[j]],V[indImages[j],indNonZero[j]])
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jaccard_dist[i] = 1-temp_min/(2.-temp_min)
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final_dist = jaccard_dist*(1-lambda_value) + original_dist*lambda_value
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del original_dist
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del V
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del jaccard_dist
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final_dist = final_dist[:query_num,query_num:]
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return final_dist
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