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from scipy.spatial.distance import cdist
import numpy as np
from logging import debug
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)
debug('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)
debug('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
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