101 lines
3.8 KiB
Matlab
101 lines
3.8 KiB
Matlab
clc;clear all;close all;
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%**************************************************%
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% This code implements IDE baseline for the CUHK03 %
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% dataset under the new traning/testing protocol. %
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% Please modify the path to your own folder. %
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% We use the mAP and rank-1 rate as evaluation %
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%**************************************************%
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% if you find this code useful in your research, please kindly cite our
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% paper as,
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% Zhun Zhong, Liang Zheng, Donglin Cao, Shaozi Li,
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% Re-ranking Person Re-identification with k-reciprocal Encoding, CVPR, 2017.
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% Please download CUHK03 dataset and unzip it in the "dataset/CUHK03" folder.
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addpath(genpath('LOMO_XQDA/'));
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run('KISSME/toolbox/init.m');
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addpath(genpath('utils/'));
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%% re-ranking setting
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k1 = 20;
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k2 = 6;
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lambda = 0.3;
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%% network name
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netname = 'ResNet_50'; % network: CaffeNet or ResNet_50 googlenet
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detected_or_labeled = 'detected'; % detected/labeled
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load(['data/CUHK03/cuhk03_new_protocol_config_' detected_or_labeled '.mat']);
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%% load feature Deep feature
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feat = importdata(['feat/CUHK03/' netname '_IDE_' detected_or_labeled '.mat']);
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feat = double(feat);
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%% load feature LOMO feature
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% feat = importdata(['feat/CUHK03/cuhk03_' detected_or_labeled '_lomo.mat']);
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% feat = single(feat.descriptors);
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%% train info
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label_train = labels(train_idx);
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cam_train = camId(train_idx);
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train_feature = feat(:, train_idx);
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%% test info
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galFea = feat(:, gallery_idx);
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probFea = feat(:, query_idx);
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label_gallery = labels(gallery_idx);
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label_query = labels(query_idx);
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cam_gallery = camId(gallery_idx);
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cam_query = camId(query_idx);
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%% normalize
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sum_val = sqrt(sum(galFea.^2));
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for n = 1:size(galFea, 1)
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galFea(n, :) = galFea(n, :)./sum_val;
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end
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sum_val = sqrt(sum(probFea.^2));
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for n = 1:size(probFea, 1)
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probFea(n, :) = probFea(n, :)./sum_val;
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end
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sum_val = sqrt(sum(train_feature.^2));
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for n = 1:size(train_feature, 1)
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train_feature(n, :) = train_feature(n, :)./sum_val;
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end
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%% Euclidean
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%dist_eu = pdist2(galFea', probFea');
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my_pdist2 = @(A, B) sqrt( bsxfun(@plus, sum(A.^2, 2), sum(B.^2, 2)') - 2*(A*B'));
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dist_eu = my_pdist2(galFea', probFea');
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[CMC_eu, map_eu, ~, ~] = evaluation(dist_eu, label_gallery, label_query, cam_gallery, cam_query);
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fprintf(['The IDE (' netname ') + Euclidean performance:\n']);
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fprintf(' Rank1, mAP\n');
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fprintf('%5.2f%%, %5.2f%%\n\n', CMC_eu(1) * 100, map_eu(1)*100);
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%% Euclidean + re-ranking
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query_num = size(probFea, 2);
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dist_eu_re = re_ranking( [probFea galFea], 1, 1, query_num, k1, k2, lambda);
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[CMC_eu_re, map_eu_re, ~, ~] = evaluation(dist_eu_re, label_gallery, label_query, cam_gallery, cam_query);
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fprintf(['The IDE (' netname ') + Euclidean + re-ranking performance:\n']);
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fprintf(' Rank1, mAP\n');
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fprintf('%5.2f%%, %5.2f%%\n\n', CMC_eu_re(1) * 100, map_eu_re(1)*100);
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%% train and test XQDA
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[train_sample1, train_sample2, label1, label2] = gen_train_sample_xqda(label_train, cam_train, train_feature); % generate pairwise training features for XQDA
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[W, M_xqda] = XQDA(train_sample1, train_sample2, label1, label2);% train XQDA
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% Calculate distance
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dist_xqda = MahDist(M_xqda, galFea' * W, probFea' * W); % calculate MahDist between query and gallery boxes with learnt subspace. Smaller distance means larger similarity
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[CMC_xqda, map_xqda, ~, ~] = evaluation(dist_xqda, label_gallery, label_query, cam_gallery, cam_query);
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fprintf(['The IDE (' netname ') + XQDA performance:\n']);
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fprintf(' Rank1, mAP\n');
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fprintf('%5.2f%%, %5.2f%%\n\n', CMC_xqda(1) * 100, map_xqda(1)*100);
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%% XQDA + re-ranking
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query_num = size(probFea, 2);
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dist_xqda_re = re_ranking( [probFea galFea], M_xqda, W, query_num, k1, k2, lambda);
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[CMC_xqda_re, map_xqda_re, ~, ~] = evaluation(dist_xqda_re, label_gallery, label_query, cam_gallery, cam_query);
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fprintf(['The IDE (' netname ') + XQDA + re-ranking performance:\n']);
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fprintf(' Rank1, mAP\n');
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fprintf('%5.2f%%, %5.2f%%\n\n', CMC_xqda_re(1) * 100, map_xqda_re(1)*100);
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