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faster_rcnn_ucf.m
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function faster_rcnn_ucf(varargin)
% FASTER_RCNN_UCF: detect bouding boxes for ucf
% Adapted from Faster R-CNN
% save detection results to a cell array [{frame1} ... {frameN}}
% framen is a cell array [{obj_class1} ... {obj_classK}]
% obj_class1 is a Mx5 array with the coordinates and detect prob
% of the detected objects, i.e. [x, y, w, h, prob]
ip = inputParser;
ip.addParameter('caffe_version', 'caffe', @ischar);
ip.addParameter('cache_root_dir', '.', @ischar);
ip.addParameter('save_bb', true, @islogical);
ip.addParameter('gpu_id', 0, @isscalar);
ip.addParameter('per_nms_topN', 6000, @isscalar);
ip.addParameter('nms_overlap_thres', 0.7, @isscalar);
ip.addParameter('after_nms_topN', 300, @isscalar);
ip.addParameter('test_scales', 600, @isscalar);
ip.addParameter('test_nms', 0.3, @isscalar);
ip.addParameter('thres', 0.1, @isscalar);
ip.addParameter('from_to', [], @isvector);
ip.parse(varargin{:});
opts = ip.Results;
clc;
clear mex;
clear is_valid_handle; % to clear init_key
run('startup');
%% -------------------- DATA ----------------------
dataset = get_dataset_ucf([],'/home/jsong/saiwen/ucf101_image', 'cache_root_dir', opts.cache_root_dir);
if ~isfield(dataset,'faster_rcnn_dir')
dataset.faster_rcnn_dir = '/disks/sdc/01/Yifan_sdc/feature_data/faster_rcnn';
save(dataset.cache_file, 'dataset');
end
if ~exist(dataset.faster_rcnn_dir, 'dir')
mkdir(dataset.faster_rcnn_dir);
end
%% -------------------- CONFIG --------------------
caffe_mex(opts.gpu_id, opts.caffe_version);
fprintf('Using caffe version: %s\n',opts.caffe_version);
%% -------------------- INIT_MODEL --------------------
proposal_detection_model = [];
proposal_detection_model = load_proposal_detection_model(model_dir);
proposal_detection_model.conf_proposal.test_scales = opts.test_scales;
proposal_detection_model.conf_detection.test_scales = opts.test_scales;
if proposal_detection_model.is_share_feature
proposal_detection_model.detection_net_def = strrep(...
proposal_detection_model.detection_net_def, '.prototxt', '_shared.prototxt');
end
proposal_detection_model.conf_proposal.image_means = gpuArray(proposal_detection_model.conf_proposal.image_means);
proposal_detection_model.conf_detection.image_means = gpuArray(proposal_detection_model.conf_detection.image_means);
% proposal net
rpn_net = caffe.Net(proposal_detection_model.proposal_net_def, 'test');
rpn_net.copy_from(proposal_detection_model.proposal_net);
% fast rcnn net
fast_rcnn_net = caffe.Net(proposal_detection_model.detection_net_def, 'test');
fast_rcnn_net.copy_from(proposal_detection_model.detection_net)
% set gpu/cpu
caffe.set_mode_gpu();
%% test and save
boxes_db = cell(dataset.num_video, 1);
num_classes = length(proposal_detection_model.classes);
invalid = [];
if isempty(opts.from_to)
opts.from_to = [1 dataset.num_video];
else
opts.from_to = [max(opts.from_to(1),1), min(opts.from_to(2), dataset.num_video)];
end
for j = opts.from_to(1):opts.from_to(2)
try
save_path = dataset.feat_paths(dataset.faster_rcnn_dir,j);
if exist(save_path, 'file')
continue;
end
frames = dataset.frames_of(j);
boxes_frames = cell(length(frames), 1);
tend = 0;
for f = 1:length(frames)
im = imread(frames{f});
im = gpuArray(im);
tstart = tic;
% test proposal
% boxes N*4 (x, y, w, h), scores N*1 objectiveness
[boxes, scores] = proposal_im_detect(proposal_detection_model.conf_proposal, rpn_net, im);
% non-maximum suppression N_nms*5 (x, y, w, h, score)
aboxes = boxes_filter([boxes, scores], opts.per_nms_topN, opts.nms_overlap_thres, opts.after_nms_topN, true);
% test detection
if proposal_detection_model.is_share_feature
% boxes N_nms*(num_classes*4), scores N_nms*numclasses
[boxes, scores] = fast_rcnn_conv_feat_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
rpn_net.blobs(proposal_detection_model.last_shared_output_blob_name), ...
aboxes(:, 1:4), opts.after_nms_topN);
else
[boxes, scores] = fast_rcnn_im_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
aboxes(:, 1:4), opts.after_nms_topN);
end
tend = toc(tstart) + tend;
boxes_cell = cell(num_classes, 1);
for i = 1:num_classes
% non-maximum suppression for one classes among all proposals
% each cell [x0_c, y0_c, x1_c, y1_c, prob_c]
boxes_cell{i} = [boxes(:, (1+(i-1)*4):(i*4)), scores(:, i)];
boxes_cell{i} = boxes_cell{i}(nms(boxes_cell{i}, opts.test_nms), :);
I = boxes_cell{i}(:, 5) >= opts.thres;
boxes_cell{i} = boxes_cell{i}(I, :);
end
boxes_frames{f} = boxes_cell;
end
tic_toc_print('ucf test: %d/%d. Avg time %fs/frame\n', j, opts.from_to(2), tend/length(frames));
if opts.save_bb
mkdir_if_missing(fileparts(save_path));
save(save_path, 'boxes_frames');
else
boxes_db{j} = boxes_frames;
end
catch err
fprintf(2, '%s (::%d): %s %s\n', err.stack(1).file, err.stack(1).line, dataset.video_ids{j}, err.message);
invalid(end+1) = j;
end
end
caffe.reset_all();
end
function aboxes = boxes_filter(aboxes, per_nms_topN, nms_overlap_thres, after_nms_topN, use_gpu)
% to speed up nms
if per_nms_topN > 0
aboxes = aboxes(1:min(length(aboxes), per_nms_topN), :);
end
% do nms
if nms_overlap_thres > 0 && nms_overlap_thres < 1
aboxes = aboxes(nms(aboxes, nms_overlap_thres, use_gpu), :);
end
if after_nms_topN > 0
aboxes = aboxes(1:min(length(aboxes), after_nms_topN), :);
end
end
function proposal_detection_model = load_proposal_detection_model(model_dir)
ld = load(fullfile(model_dir, 'model'));
proposal_detection_model = ld.proposal_detection_model;
clear ld;
proposal_detection_model.proposal_net_def ...
= fullfile(model_dir, proposal_detection_model.proposal_net_def);
proposal_detection_model.proposal_net ...
= fullfile(model_dir, proposal_detection_model.proposal_net);
proposal_detection_model.detection_net_def ...
= fullfile(model_dir, proposal_detection_model.detection_net_def);
proposal_detection_model.detection_net ...
= fullfile(model_dir, proposal_detection_model.detection_net);
end