%
%  High-Speed Tracking with Kernelized Correlation Filters
%
%  Joao F. Henriques, 2014
%  http://www.isr.uc.pt/~henriques/
%
%  Main interface for Kernelized/Dual Correlation Filters (KCF/DCF).
%  This function takes care of setting up parameters, loading video
%  information and computing precisions. For the actual tracking code,
%  check out the TRACKER function.
%
%  RUN_TRACKER
%    Without any parameters, will ask you to choose a video, track using
%    the Gaussian KCF on HOG, and show the results in an interactive
%    figure. Press 'Esc' to stop the tracker early. You can navigate the
%    video using the scrollbar at the bottom.
%
%  RUN_TRACKER VIDEO
%    Allows you to select a VIDEO by its name. 'all' will run all videos
%    and show average statistics. 'choose' will select one interactively.
%
%  RUN_TRACKER VIDEO KERNEL
%    Choose a KERNEL. 'gaussian'/'polynomial' to run KCF, 'linear' for DCF.
%
%  RUN_TRACKER VIDEO KERNEL FEATURE
%    Choose a FEATURE type, either 'hog' or 'gray' (raw pixels).
%
%  RUN_TRACKER(VIDEO, KERNEL, FEATURE, SHOW_VISUALIZATION, SHOW_PLOTS)
%    Decide whether to show the scrollable figure, and the precision plot.
%
%  Useful combinations:
%  >> run_tracker choose gaussian hog  %Kernelized Correlation Filter (KCF)
%  >> run_tracker choose linear hog    %Dual Correlation Filter (DCF)
%  >> run_tracker choose gaussian gray %Single-channel KCF (ECCV'12 paper)
%  >> run_tracker choose linear gray   %MOSSE filter (single channel)
%


function [precision, fps] = run_tracker(video, kernel_type, feature_type, show_visualization, show_plots)

	%path to the videos (you'll be able to choose one with the GUI).
	base_path = 'E:\matlab_code\tracker_release2\dataset\';%'./data/Benchmark/';

	%default settings
	if nargin < 1, video = 'choose'; end
	if nargin < 2, kernel_type = 'gaussian'; end
	if nargin < 3, feature_type = 'hog'; end
	if nargin < 4, show_visualization = ~strcmp(video, 'all'); end
	if nargin < 5, show_plots = ~strcmp(video, 'all'); end


	%parameters according to the paper. at this point we can override
	%parameters based on the chosen kernel or feature type
	kernel.type = kernel_type;
	
	features.gray = false;
	features.hog = false;
	
	padding = 1.5;  %extra area surrounding the target
	lambda = 1e-4;  %regularization
	output_sigma_factor = 0.1;  %spatial bandwidth (proportional to target)
%% 	
	switch feature_type
	case 'gray',
		interp_factor = 0.075;  %linear interpolation factor for adaptation

		kernel.sigma = 0.2;  %gaussian kernel bandwidth
		
		kernel.poly_a = 1;  %polynomial kernel additive term
		kernel.poly_b = 7;  %polynomial kernel exponent
	
		features.gray = true;
		cell_size = 1;
		
	case 'hog',
		interp_factor = 0.02;
		
		kernel.sigma = 0.5;
		
		kernel.poly_a = 1;
		kernel.poly_b = 9;
		
		features.hog = true;
		features.hog_orientations = 9;
		cell_size = 4;
		
	otherwise
		error('Unknown feature.')
	end


	assert(any(strcmp(kernel_type, {'linear', 'polynomial', 'gaussian'})), 'Unknown kernel.')

%% 
	switch video
	case 'choose',
		%ask the user for the video, then call self with that video name.
		video = choose_video(base_path);
		if ~isempty(video),
			[precision, fps] = run_tracker(video, kernel_type, ...
				feature_type, show_visualization, show_plots);
			
			if nargout == 0,  %don't output precision as an argument
				clear precision
			end
		end
		
		
	case 'all',
		%all videos, call self with each video name.
		
		%only keep valid directory names
		dirs = dir(base_path);
		videos = {dirs.name};
		videos(strcmp('.', videos) | strcmp('..', videos) | ...
			strcmp('anno', videos) | ~[dirs.isdir]) = [];
		
		%the 'Jogging' sequence has 2 targets, create one entry for each.
		%we could make this more general if multiple targets per video
		%becomes a common occurence.
		videos(strcmpi('Jogging', videos)) = [];
		videos(end+1:end+2) = {'Jogging.1', 'Jogging.2'};
		
		all_precisions = zeros(numel(videos),1);  %to compute averages
		all_fps = zeros(numel(videos),1);
		
		if ~exist('matlabpool', 'file'),
			%no parallel toolbox, use a simple 'for' to iterate
			for k = 1:numel(videos),
				[all_precisions(k), all_fps(k)] = run_tracker(videos{k}, ...
					kernel_type, feature_type, show_visualization, show_plots);
			end
		else
			%evaluate trackers for all videos in parallel
			if matlabpool('size') == 0,
				matlabpool open;
			end
			parfor k = 1:numel(videos),
				[all_precisions(k), all_fps(k)] = run_tracker(videos{k}, ...
					kernel_type, feature_type, show_visualization, show_plots);
			end
		end
		
		%compute average precision at 20px, and FPS
		mean_precision = mean(all_precisions);
		fps = mean(all_fps);
		fprintf('\nAverage precision (20px):% 1.3f, Average FPS:% 4.2f\n\n', mean_precision, fps)
		if nargout > 0,
			precision = mean_precision;
		end
		
		
	case 'benchmark',
		%running in benchmark mode - this is meant to interface easily
		%with the benchmark's code.
		
		%get information (image file names, initial position, etc) from
		%the benchmark's workspace variables
		seq = evalin('base', 'subS');
		target_sz = seq.init_rect(1,[4,3]);
		pos = seq.init_rect(1,[2,1]) + floor(target_sz/2);
		img_files = seq.s_frames;
		video_path = [];
		
		%call tracker function with all the relevant parameters
		positions = tracker(video_path, img_files, pos, target_sz, ...
			padding, kernel, lambda, output_sigma_factor, interp_factor, ...
			cell_size, features, false);
		
		%return results to benchmark, in a workspace variable
		rects = [positions(:,2) - target_sz(2)/2, positions(:,1) - target_sz(1)/2];
		rects(:,3) = target_sz(2);
		rects(:,4) = target_sz(1);
		res.type = 'rect';
		res.res = rects;
		assignin('base', 'res', res);
		
		
	otherwise
		%we were given the name of a single video to process.
	
		%get image file names, initial state, and ground truth for evaluation
		[img_files, pos, target_sz, ground_truth, video_path] = load_video_info(base_path, video);
		
		%call tracker function with all the relevant parameters
		[positions, time] = tracker(video_path, img_files, pos, target_sz, ...
			padding, kernel, lambda, output_sigma_factor, interp_factor, ...
			cell_size, features, show_visualization);
		
		
		%calculate and show precision plot, as well as frames-per-second
% 		precisions = precision_plot(positions, ground_truth, video, show_plots);
% 		fps = numel(img_files) / time;
% 
% 		fprintf('%12s - Precision (20px):% 1.3f, FPS:% 4.2f\n', video, precisions(20), fps)
% 
% 		if nargout > 0,
% 			%return precisions at a 20 pixels threshold
% 			precision = precisions(20);
% 		end

	end
end