101 lines
3.2 KiB
Matlab
101 lines
3.2 KiB
Matlab
function [hog_data, valid_inds, vid_id] = Read_HOG_files_dynamic_pp(users, hog_data_dir)
|
|
|
|
hog_data = [];
|
|
vid_id = {};
|
|
valid_inds = [];
|
|
|
|
feats_filled = 0;
|
|
|
|
for i=1:numel(users)
|
|
|
|
start_person_ind = feats_filled + 1;
|
|
|
|
hog_files = dir([hog_data_dir, '/train/' users{i} '*.hog']);
|
|
hog_dir = [hog_data_dir, '/train/'];
|
|
if(isempty(hog_files))
|
|
hog_files = dir([hog_data_dir, '/devel/' users{i} '*.hog']);
|
|
hog_dir = [hog_data_dir, '/devel/'];
|
|
end
|
|
|
|
for h=1:numel(hog_files)
|
|
hog_file = [hog_dir, hog_files(h).name];
|
|
f = fopen(hog_file, 'r');
|
|
|
|
curr_data = [];
|
|
curr_ind = 0;
|
|
|
|
while(~feof(f))
|
|
|
|
if(curr_ind == 0)
|
|
num_cols = fread(f, 1, 'int32');
|
|
if(isempty(num_cols))
|
|
break;
|
|
end
|
|
|
|
num_rows = fread(f, 1, 'int32');
|
|
num_chan = fread(f, 1, 'int32');
|
|
|
|
curr_ind = curr_ind + 1;
|
|
|
|
% preallocate some space
|
|
if(curr_ind == 1)
|
|
curr_data = zeros(1000, 1 + num_rows * num_cols * num_chan);
|
|
num_feats = 1 + num_rows * num_cols * num_chan;
|
|
end
|
|
|
|
if(curr_ind > size(curr_data,1))
|
|
curr_data = cat(1, curr_data, zeros(1000, 1 + num_rows * num_cols * num_chan));
|
|
end
|
|
feature_vec = fread(f, [1, 1 + num_rows * num_cols * num_chan], 'float32');
|
|
curr_data(curr_ind, :) = feature_vec;
|
|
else
|
|
|
|
% Reading in batches of 5000
|
|
|
|
feature_vec = fread(f, [4 + num_rows * num_cols * num_chan, 5000], 'float32');
|
|
feature_vec = feature_vec(4:end,:)';
|
|
|
|
num_rows_read = size(feature_vec,1);
|
|
|
|
curr_data(curr_ind+1:curr_ind+num_rows_read,:) = feature_vec;
|
|
|
|
curr_ind = curr_ind + size(feature_vec,1);
|
|
|
|
end
|
|
|
|
end
|
|
|
|
fclose(f);
|
|
|
|
curr_data = curr_data(1:curr_ind,:);
|
|
vid_id_curr = cell(curr_ind,1);
|
|
vid_id_curr(:) = users(i);
|
|
|
|
vid_id = cat(1, vid_id, vid_id_curr);
|
|
|
|
% Assume same number of frames per video
|
|
if(i==1 && h == 1)
|
|
hog_data = zeros(curr_ind * numel(users) * 8, num_feats);
|
|
end
|
|
|
|
if(size(hog_data,1) < feats_filled+curr_ind)
|
|
hog_data = cat(1, hog_data, zeros(size(hog_data,1), num_feats));
|
|
end
|
|
|
|
|
|
hog_data(feats_filled+1:feats_filled+curr_ind,:) = curr_data;
|
|
|
|
feats_filled = feats_filled + curr_ind;
|
|
end
|
|
|
|
person_ids = start_person_ind:feats_filled;
|
|
% Do the median normalisation per person here
|
|
hog_data(person_ids,2:end) = bsxfun(@plus, hog_data(person_ids,2:end), -median(hog_data(person_ids,2:end)));
|
|
|
|
end
|
|
|
|
if(~isempty(hog_data))
|
|
valid_inds = hog_data(1:feats_filled,1);
|
|
hog_data = hog_data(1:feats_filled,2:end);
|
|
end
|
|
end |