114 lines
4.6 KiB
Python
114 lines
4.6 KiB
Python
#!/usr/bin/env python2/python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Source: https://github.com/zhunzhong07/person-re-ranking
|
|
|
|
Created on Mon Jun 26 14:46:56 2017
|
|
@author: luohao
|
|
Modified by Houjing Huang, 2017-12-22.
|
|
- This version accepts distance matrix instead of raw features.
|
|
- The difference of `/` division between python 2 and 3 is handled.
|
|
- numpy.float16 is replaced by numpy.float32 for numerical precision.
|
|
|
|
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
|
|
q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery]
|
|
q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query]
|
|
g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery]
|
|
k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3)
|
|
Returns:
|
|
final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery]
|
|
"""
|
|
from __future__ import division, print_function, absolute_import
|
|
import numpy as np
|
|
|
|
__all__ = ['re_ranking']
|
|
|
|
|
|
def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
|
|
|
|
# The following naming, e.g. gallery_num, is different from outer scope.
|
|
# Don't care about it.
|
|
|
|
original_dist = np.concatenate(
|
|
[
|
|
np.concatenate([q_q_dist, q_g_dist], axis=1),
|
|
np.concatenate([q_g_dist.T, g_g_dist], axis=1)
|
|
],
|
|
axis=0
|
|
)
|
|
original_dist = np.power(original_dist, 2).astype(np.float32)
|
|
original_dist = np.transpose(
|
|
1. * original_dist / np.max(original_dist, axis=0)
|
|
)
|
|
V = np.zeros_like(original_dist).astype(np.float32)
|
|
initial_rank = np.argsort(original_dist).astype(np.int32)
|
|
|
|
query_num = q_g_dist.shape[0]
|
|
gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
|
|
all_num = gallery_num
|
|
|
|
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] = 1. * weight / np.sum(weight)
|
|
original_dist = original_dist[:query_num, ]
|
|
if k2 != 1:
|
|
V_qe = np.zeros_like(V, dtype=np.float32)
|
|
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.float32)
|
|
|
|
for i in range(query_num):
|
|
temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)
|
|
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
|