diff options
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/kmean.py | 48 | ||||
| -rw-r--r-- | lib/rerank.py | 82 |
2 files changed, 130 insertions, 0 deletions
diff --git a/lib/kmean.py b/lib/kmean.py new file mode 100644 index 0000000..2e34702 --- /dev/null +++ b/lib/kmean.py @@ -0,0 +1,48 @@ +import numpy as np +from sklearn.cluster import KMeans + +def create_kmean_clusters(feature_vectors, labels, gallery_idx, camId): + gallery = ([],[]) + gallerylab = ([],[]) + for i in range(gallery_idx.size): + cam = camId[gallery_idx[i]] - 1 + gallery[cam].append(feature_vectors[gallery_idx[i]]) + gallerylab[cam].append(labels[gallery_idx[i]]) + + + train = np.array(gallery) + tlabel = np.array(gallerylab) + + km_train_data = [] + km_idx = [] + km_labels = [] + + for i in range(2): + km_train_data.append(KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train[i])) + km_idx.append(km_train_data[i].labels_) + km_labels.append(range(np.max(labels))) + for j in range(np.max(labels)): + class_vote = np.zeros(np.max(labels)) + for q in range(km_idx[i].size): + if km_idx[i][q]==j: + class_vote[int(tlabel[i][q])-1] += 1 + km_labels[i][j] = np.argmax(class_vote) + 1 + + #MERGE CLUSTERS + cl = [] + cllab = [] + clustercam = [] + for i in range(2): + clustercam.append(np.add(np.ones(len(km_labels[i])), i)) + print(len(km_labels[i])) + for j in range(len(km_labels[i])): + cl.append(km_train_data[i].cluster_centers_[j]) + cllab.append(km_labels[i][j]) + + train_data = np.array(cl) + print(train_data.shape) + train_label = np.array(cllab) + print(train_label.shape) + train_cam = np.array([clustercam[i] for i in range(2)]).reshape(train_label.shape[0]) + print(train_cam.shape) + return train_data, train_label, train_cam diff --git a/lib/rerank.py b/lib/rerank.py new file mode 100644 index 0000000..6b20f53 --- /dev/null +++ b/lib/rerank.py @@ -0,0 +1,82 @@ +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 + |