darkliang
65 lines
3.0 KiB
Python
65 lines
3.0 KiB
Python
import numpy as np
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def re_ranking(original_dist, query_num, k1, k2, lambda_value):
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# Modified from https://github.com/michuanhaohao/reid-strong-baseline/blob/master/utils/re_ranking.py
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all_num = original_dist.shape[0]
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original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
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V = np.zeros_like(original_dist).astype(np.float16)
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initial_rank = np.argsort(original_dist).astype(np.int32)
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for i in range(all_num):
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# k-reciprocal neighbors
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forward_k_neigh_index = initial_rank[i, :k1 + 1]
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backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
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fi = np.where(backward_k_neigh_index == i)[0]
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k_reciprocal_index = forward_k_neigh_index[fi]
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k_reciprocal_expansion_index = k_reciprocal_index
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for j in range(len(k_reciprocal_index)):
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candidate = k_reciprocal_index[j]
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candidate_forward_k_neigh_index = initial_rank[candidate, :int(
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np.around(k1 / 2)) + 1]
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candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,
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:int(np.around(k1 / 2)) + 1]
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fi_candidate = np.where(
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candidate_backward_k_neigh_index == candidate)[0]
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candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
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if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2 / 3 * len(
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candidate_k_reciprocal_index):
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k_reciprocal_expansion_index = np.append(
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k_reciprocal_expansion_index, candidate_k_reciprocal_index)
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k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
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weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
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V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
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original_dist = original_dist[:query_num, ]
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if k2 != 1:
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V_qe = np.zeros_like(V, dtype=np.float16)
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for i in range(all_num):
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V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
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V = V_qe
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del V_qe
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del initial_rank
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invIndex = []
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for i in range(all_num):
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invIndex.append(np.where(V[:, i] != 0)[0])
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jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
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for i in range(query_num):
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temp_min = np.zeros(shape=[1, all_num], dtype=np.float16)
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indNonZero = np.where(V[i, :] != 0)[0]
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indImages = [invIndex[ind] for ind in indNonZero]
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for j in range(len(indNonZero)):
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temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
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V[indImages[j], indNonZero[j]])
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jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
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final_dist = jaccard_dist * (1 - lambda_value) + \
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original_dist * lambda_value
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del original_dist
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del V
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del jaccard_dist
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final_dist = final_dist[:query_num, query_num:]
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return final_dist
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