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rerank_for_cluster.py
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/
rerank_for_cluster.py
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#!/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
"""
"""
Modified by L.Song and C.Wang
"""
import numpy as np
from scipy.spatial.distance import cdist
def re_ranking(input_feature_source, input_feature, k1=20, k2=6, lambda_value=0.1):
all_num = input_feature.shape[0]
feat = input_feature.astype(np.float16)
if lambda_value != 0:
print('Computing source distance...')
all_num_source = input_feature_source.shape[0]
sour_tar_dist = np.power(
cdist(input_feature, input_feature_source), 2).astype(np.float16)
sour_tar_dist = 1-np.exp(-sour_tar_dist)
source_dist_vec = np.min(sour_tar_dist, axis = 1)
source_dist_vec = source_dist_vec / np.max(source_dist_vec)
source_dist = np.zeros([all_num, all_num])
for i in range(all_num):
source_dist[i, :] = source_dist_vec + source_dist_vec[i]
del sour_tar_dist
del source_dist_vec
print('Computing original distance...')
original_dist = cdist(feat,feat).astype(np.float16)
original_dist = np.power(original_dist,2).astype(np.float16)
del feat
euclidean_dist = original_dist
gallery_num = original_dist.shape[0] #gallery_num=all_num
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) ## default axis=-1.
print('Starting re_ranking...')
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[i,:k1+1] ## k1+1 because self always ranks first. forward_k_neigh_index.shape=[k1+1]. forward_k_neigh_index[0] == i.
backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1] ##backward.shape = [k1+1, k1+1]. For each ele in forward_k_neigh_index, find its rank k1 neighbors
fi = np.where(backward_k_neigh_index==i)[0]
k_reciprocal_index = forward_k_neigh_index[fi] ## get R(p,k) in the paper
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) ## element-wise unique
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]) #len(invIndex)=all_num
jaccard_dist = np.zeros_like(original_dist,dtype = np.float16)
for i in range(all_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)
pos_bool = (jaccard_dist < 0)
jaccard_dist[pos_bool] = 0.0
if lambda_value == 0:
return jaccard_dist
else:
final_dist = jaccard_dist*(1-lambda_value) + source_dist*lambda_value
return final_dist