|
|
|
|
|
""" |
|
Created on Fri, 25 May 2018 20:29:09 |
|
|
|
|
|
""" |
|
|
|
""" |
|
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 (torch tensor) |
|
probFea: all feature vectors of the gallery set (torch tensor) |
|
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 |
|
import torch |
|
|
|
|
|
def re_ranking(probFea, galFea, k1, k2, lambda_value, local_distmat=None, only_local=False): |
|
|
|
query_num = probFea.size(0) |
|
all_num = query_num + galFea.size(0) |
|
if only_local: |
|
original_dist = local_distmat |
|
else: |
|
feat = torch.cat([probFea, galFea]) |
|
|
|
distmat = torch.pow(feat, 2).sum(dim=1, keepdim=True).expand(all_num, all_num) + \ |
|
torch.pow(feat, 2).sum(dim=1, keepdim=True).expand(all_num, all_num).t() |
|
distmat.addmm_(1, -2, feat, feat.t()) |
|
original_dist = distmat.cpu().numpy() |
|
del feat |
|
if not local_distmat is None: |
|
original_dist = original_dist + local_distmat |
|
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) |
|
|
|
|
|
for i in range(all_num): |
|
|
|
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 = [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 |
|
|
|
|
