diff options
Diffstat (limited to 'rerank.py')
| -rw-r--r-- | rerank.py | 82 |
1 files changed, 0 insertions, 82 deletions
diff --git a/rerank.py b/rerank.py deleted file mode 100644 index 6b20f53..0000000 --- a/rerank.py +++ /dev/null @@ -1,82 +0,0 @@ -from scipy.spatial.distance import cdist -import numpy as np - -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 - |