save_extracted_features

save_extracted_features.m

function save_extracted_features()

clear;clc;close all;
cd('../')

%% caffe settings
matCaffe = fullfile(pwd, '../tools/caffe-sphereface/matlab');
addpath(genpath(matCaffe));
gpu = 1;
if gpu
   gpu_id = 0;
   caffe.set_mode_gpu();
   caffe.set_device(gpu_id);
else
   caffe.set_mode_cpu();
end
caffe.reset_all();

model   = '../train/code/sphereface_deploy.prototxt';
weights = '../train/result/sphereface_model.caffemodel';
net     = caffe.Net(model, weights, 'test');

%% recursively compute and save features

dirs = genpath('data/vggface2_test-112x96/'); % all folders recursively
dirs = regexp(dirs, pathsep, 'split'); % split into cellstr
dirs = dirs(2:end);
for i = 1:numel(dirs)
    tokens = split(dirs{i}, '/');
    personName = string(tokens(end));
    dirpath = 'extracted_features/vggface2_test-mtcnn-112x96/sphereface-20/' + personName;
    if ~exist(dirpath, 'dir')
        mkdir(dirpath); 
    end
    dirImage = dir([dirs{i} '/*.jpg']); % jpg in one sub-folder
    for j = 1:numel(dirImage)
        imgPath = [dirs{i} '/' dirImage(j).name];
        feature = extractDeepFeature(imgPath, net);
        fileID = fopen('extracted_features/vggface2_test-mtcnn-112x96/sphereface-20/' + personName + '/' + dirImage(j).name + '.txt', 'w');
        for k=1:numel(feature)
            if k ~= numel(feature)
                fprintf(fileID, '%.15f ', feature(k));
            else
                fprintf(fileID, '%.15f', feature(k));
            end
        end
        fclose(fileID);
        

    end 
end
end

function feature = extractDeepFeature(file, net)
    img     = single(imread(file));
    img     = (img - 127.5)/128;
    img     = permute(img, [2,1,3]);
    img     = img(:,:,[3,2,1]);
    res     = net.forward({img});
    res_    = net.forward({flip(img, 1)});
    feature = double([res{1}; res_{1}]);
end