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+function [ features ] = caffe_features_multiple_images( s_filelist, f_mean, net, s_layer)
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+% function [ features ] = caffe_features_multiple_images( s_filelist, f_mean, net, s_layer)
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+%
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+% BRIEF:
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+% Run a forward pass of a given net on a set of images which are
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+% listed in an external file and grep features of a specified layer.
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+% Requires Caffe version from 17-07-2015 (hash: 6d92d8fcfe0eea9495ffbc)
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+%
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+% INPUT
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+% s_filelist -- string, filename to an external list which contains
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+% image names in each line. Alternatively, the variable is
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+% given as cell array where each entry contains a loaded
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+% image.
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+% f_mean -- The average image of your dataset. This should be the same that was used during training of the CNN model.
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+% Required to be cropped to the input size of your
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+% network! See caffe_load_network.m
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+% net -- a previously loaded network, see caffe_load_network.m
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+% s_layer -- optional (default: 'relu7'), string, specifies the layer used for feature exatraction
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+%
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+
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+
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+ %% parse inputs
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+ if (nargin<2)
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+ error ( 'no mean passed');
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+ end
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+ if (nargin<3)
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+ error ( 'no network passed');
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+ end
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+ if (nargin<4)
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+ s_layer = 'relu7';
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+ end
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+
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+ %% prepare list of filenames
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+ b_filelistmode = ischar( s_filelist );
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+
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+ if (b_filelistmode)
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+ % load the file list
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+ fid = fopen( s_filelist );
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+ s_filelist_to_use = textscan(fid,'%s');
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+ s_filelist_to_use = s_filelist_to_use{1};
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+ fclose(fid);
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+ else
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+ % use the passed filelist
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+ s_filelist_to_use = s_filelist;
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+ end
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+
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+ %% new caffe layout
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+ net_input_shape = net.blobs('data').shape;
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+ i_batch_size = net_input_shape(4);
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+
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+ % create tmp for batch
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+ batch_data = {zeros(net_input_shape(1),... %height
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+ net_input_shape(2),... %width
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+ net_input_shape(3),... %width, ...%RGB
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+ i_batch_size,...
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+ 'single')};
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+
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+ % Calculate the starting indices of every batch
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+ slices = 1:i_batch_size:size(s_filelist_to_use,1);
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+ slices(end+1)=size(s_filelist_to_use,1)+1;
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+
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+ % crop the list of files into batches of adequate size
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+ % then run over every batch
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+ for i=1:numel(slices)-1
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+
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+ % debug information for progress
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+ if ( ( i > 1 ) && ( mod(i,10) == 0 ) )
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+ fprintf('Running batch number %i of %i\n',i, numel(slices)-1);
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+ end
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+
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+ % load the images of the next slice
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+ for j=slices(i):slices(i+1)-1;
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+ if (b_filelistmode)
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+ batch_data{1}(:,:,:,j-slices(i)+1) = caffe_prepare_image(imread( s_filelist_to_use{j} ), f_mean );
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+ else
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+ batch_data{1}(:,:,:,j-slices(i)+1) = caffe_prepare_image(s_filelist_to_use{j}, f_mean );
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+ end
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+ end
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+
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+ % run a single forward pass
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+ [~] = net.forward( batch_data );
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+
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+ % fetch activations from specified layer
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+ tmp_feat = net.blobs( s_layer ).get_data();
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+
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+ % vectorize and concatenate activation maps
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+ if ( ndims( tmp_feat ) > 2 )
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+ tmp_feat = reshape( tmp_feat, ...
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+ size(tmp_feat,1)*size(tmp_feat,2)*size(tmp_feat,3), ...
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+ size(tmp_feat,4)...
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+ );
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+ end
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+
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+ % allocate enough space in first run
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+ if ( ~exist('features','var') )
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+ features = zeros( size(tmp_feat,1), size(s_filelist_to_use,1), 'single');
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+ end
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+
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+ % store computed feature accordingly
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+ features( :, slices(i):(slices(i+1)-1) ) = tmp_feat( :, 1:(slices(i+1)-slices(i)) );
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+ end
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+
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+ % convert output to double precision
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+ features = double(features);
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+end
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+
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Alexander Freytag