102 lines
4.3 KiB
Python
102 lines
4.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri, 25 May 2018 20:29:09
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@author: luohao
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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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probFea: all feature vectors of the query set (torch tensor)
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probFea: all feature vectors of the gallery set (torch tensor)
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k1,k2,lambda: parameters, the original paper is (k1=20,k2=6,lambda=0.3)
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MemorySave: set to 'True' when using MemorySave mode
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Minibatch: avaliable when 'MemorySave' is 'True'
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"""
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import numpy as np
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import torch
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def re_ranking(probFea, galFea, k1, k2, lambda_value, local_distmat=None, only_local=False):
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# if feature vector is numpy, you should use 'torch.tensor' transform it to tensor
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query_num = probFea.size(0)
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all_num = query_num + galFea.size(0)
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if only_local:
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original_dist = local_distmat
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else:
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feat = torch.cat([probFea,galFea])
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print('using GPU to compute original distance')
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distmat = torch.pow(feat,2).sum(dim=1, keepdim=True).expand(all_num,all_num) + \
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torch.pow(feat, 2).sum(dim=1, keepdim=True).expand(all_num, all_num).t()
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distmat.addmm_(1,-2,feat,feat.t())
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original_dist = distmat.cpu().numpy()
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del feat
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if not local_distmat is None:
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original_dist = original_dist + local_distmat
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gallery_num = original_dist.shape[0]
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original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
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V = np.zeros_like(original_dist).astype(np.float16)
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initial_rank = np.argsort(original_dist).astype(np.int32)
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print('starting re_ranking')
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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,
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: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(
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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] = 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.float16)
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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.float16)
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for i in range(query_num):
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temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
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indNonZero = np.where(V[i, :] != 0)[0]
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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]],
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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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