function cascade_data(model, data_year, pca_dim)
% cascade_data(model, data_year, pca)
%
% Compute score statistics for filters and deformation models
% using the model and training data associated with the model
% and dataset year 'data_year'.  If PCA is given a value > 0, then
% the score statistics are computed using a PCA projection of the
% model and training data.
%
% The score statistics are written in to the file 
% class_year_cascade_data_{no_pca,pca_<pca_dim>}_data_year.mat, 
% which is used by cascade_model.m.
%
% model      Object model
% data_year  Dataset year as a string (e.g., '2007')
% pca_dim    Number of PCA components to use

% AUTORIGHTS
% -------------------------------------------------------
% Copyright (C) 2009-2012 Ross Girshick
% 
% This file is part of the voc-releaseX code
% (http://people.cs.uchicago.edu/~rbg/latent/)
% and is available under the terms of an MIT-like license
% provided in COPYING. Please retain this notice and
% COPYING if you use this file (or a portion of it) in
% your project.
% -------------------------------------------------------

conf = voc_config();
cscdir = conf.cascade.data_dir;

model.interval = conf.training.interval_fg;

% get training data
pos = pascal_data(model.class, model.year);

%[model, pca_model] = project_model_adapt(model, pca_dim);
% Loads <coeff>
load('pca.mat');
[model, pca_model] = project_model(model, coeff, pca_dim);

numpos  = length(pos);
pixels  = model.minsize * model.sbin / 2;
minsize = prod(pixels);
pars    = cell(1, numpos);

% compute latent filter locations and record target bounding boxes
parfor i = 1:numpos
  pars{i}.scores           = [];
  pars{i}.pca_scores       = [];
  pars{i}.total_scores     = [];
  pars{i}.total_pca_scores = [];
  fprintf('%s %s: cascade data: %d/%d\n', procid(), model.class, i, numpos);
  bbox = pos(i).boxes;
  % skip small examples
  if (bbox(3)-bbox(1)+1)*(bbox(4)-bbox(2)+1) < minsize
    continue;
  end
  % get example
  im = imreadx(pos(i));
  [im, bbox] = croppos(im, bbox);
  [pyra, model_dp] = gdetect_pos_prepare(im, model, bbox, 0.5);
  [ds, bs, trees] = gdetect_pos(pyra, model_dp, 1, 1, 0.5);
  if ~isempty(ds)
    % collect cascade score statistics
    pars{i}.scores = get_block_scores(pyra, model, trees);
    pars{i}.total_scores = ds(1,end);

    % Valid root location for the PCA model
    t = tree_mat_to_struct(trees{1});
    valid = [];
    valid.c = t(1).rule_index;
    valid.y = t(1).y;
    valid.x = t(1).x;
    valid.l = t(1).l;
    
    % Get detection with PCA model restricted to the root filter being placed
    % exactly where the original model's root filter was placed
    [pca_pyra, pca_model_dp] = gdetect_pos_prepare_c(im, pca_model, valid);
    [ds, bs, trees] = gdetect_pos_c(pca_pyra, pca_model_dp, valid);

    % Sanity check
    t = tree_mat_to_struct(trees{1});
    assert(valid.c == t(1).rule_index);
    assert(valid.y == t(1).y);
    assert(valid.x == t(1).x);
    assert(valid.l == t(1).l);

    pars{i}.pca_scores = get_block_scores(pca_pyra, pca_model, trees);
    pars{i}.total_pca_scores = ds(1,end);
  end
end
% Collate
pars = cell2mat(pars);
scores = cat(1, pars(:).scores);
pca_scores = cat(1, pars(:).pca_scores);

% Sanity check that detection scores match sum of block scores
% computed in get_block_scores
total_scores = cat(1, pars(:).total_scores);
total_pca_scores = cat(1, pars(:).total_pca_scores);
assert(max(abs(scores(:,1) - total_scores)) < 1e-5);
assert(max(abs(pca_scores(:,1) - total_pca_scores)) < 1e-5);

% Save cascade score statistics
class_year = [model.class '_' model.year];
file     = [cscdir class_year '_cascade_data_no_pca_' model.year];
pca_file = [cscdir class_year '_cascade_data_pca_' num2str(pca_dim) ...
            '_' model.year];
save(file, 'scores');
save(pca_file, 'pca_scores');
