109 lines
4.3 KiB
Python
109 lines
4.3 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Mon Jun 26 14:46:56 2017
|
|
|
|
@author: luohao
|
|
"""
|
|
|
|
"""
|
|
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
|
|
|
|
probFea: all feature vectors of the query set, shape = (image_size, feature_dim)
|
|
galFea: all feature vectors of the gallery set, shape = (image_size, feature_dim)
|
|
k1,k2,lambda: parameters, the original paper is (k1=20,k2=6,lambda=0.3)
|
|
MemorySave: set to 'True' when using MemorySave mode
|
|
Minibatch: avaliable when 'MemorySave' is 'True'
|
|
"""
|
|
|
|
|
|
import numpy as np
|
|
from scipy.spatial.distance import cdist
|
|
|
|
def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch = 2000):
|
|
|
|
query_num = probFea.shape[0]
|
|
all_num = query_num + galFea.shape[0]
|
|
feat = np.append(probFea,galFea,axis = 0)
|
|
feat = feat.astype(np.float16)
|
|
print('computing original distance')
|
|
if MemorySave:
|
|
original_dist = np.zeros(shape = [all_num,all_num],dtype = np.float16)
|
|
i = 0
|
|
while True:
|
|
it = i + Minibatch
|
|
if it < np.shape(feat)[0]:
|
|
original_dist[i:it,] = np.power(cdist(feat[i:it,],feat),2).astype(np.float16)
|
|
else:
|
|
original_dist[i:,:] = np.power(cdist(feat[i:,],feat),2).astype(np.float16)
|
|
break
|
|
i = it
|
|
else:
|
|
original_dist = cdist(feat,feat).astype(np.float16)
|
|
original_dist = np.power(original_dist,2).astype(np.float16)
|
|
del feat
|
|
gallery_num = original_dist.shape[0]
|
|
original_dist = np.transpose(original_dist/np.max(original_dist,axis = 0))
|
|
V = np.zeros_like(original_dist).astype(np.float16)
|
|
initial_rank = np.argsort(original_dist).astype(np.int32)
|
|
|
|
|
|
print('starting re_ranking')
|
|
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] = weight/np.sum(weight)
|
|
original_dist = original_dist[:query_num,]
|
|
if k2 != 1:
|
|
V_qe = np.zeros_like(V,dtype=np.float16)
|
|
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.float16)
|
|
|
|
|
|
for i in range(query_num):
|
|
temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float16)
|
|
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
|
|
|
|
|