Create re_ranking_ranklist.py

add re_ranking for initial ranking list

python-version/re_ranking_ranklist.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python2/python3
+# -*- coding: utf-8 -*-
+"""
+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]
+"""
+
+
+import numpy as np
+
+
+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