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KCF/fhog.m

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+function H = fhog( I, binSize, nOrients, clip, crop )
+% Efficiently compute Felzenszwalb's HOG (FHOG) features.
+%
+% A fast implementation of the HOG variant used by Felzenszwalb et al.
+% in their work on discriminatively trained deformable part models.
+%  http://www.cs.berkeley.edu/~rbg/latent/index.html
+% Gives nearly identical results to features.cc in code release version 5
+% but runs 4x faster (over 125 fps on VGA color images).
+%
+% The computed HOG features are 3*nOrients+5 dimensional. There are
+% 2*nOrients contrast sensitive orientation channels, nOrients contrast
+% insensitive orientation channels, 4 texture channels and 1 all zeros
+% channel (used as a 'truncation' feature). Using the standard value of
+% nOrients=9 gives a 32 dimensional feature vector at each cell. This
+% variant of HOG, refered to as FHOG, has been shown to achieve superior
+% performance to the original HOG features. For details please refer to
+% work by Felzenszwalb et al. (see link above).
+%
+% This function is essentially a wrapper for calls to gradientMag()
+% and gradientHist(). Specifically, it is equivalent to the following:
+%  [M,O] = gradientMag( I,0,0,0,1 ); softBin = -1; useHog = 2;
+%  H = gradientHist(M,O,binSize,nOrients,softBin,useHog,clip);
+% See gradientHist() for more general usage.
+%
+% This code requires SSE2 to compile and run (most modern Intel and AMD
+% processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2.
+%
+% USAGE
+%  H = fhog( I, [binSize], [nOrients], [clip], [crop] )
+%
+% INPUTS
+%  I        - [hxw] color or grayscale input image (must have type single)
+%  binSize  - [8] spatial bin size
+%  nOrients - [9] number of orientation bins
+%  clip     - [.2] value at which to clip histogram bins
+%  crop     - [0] if true crop boundaries
+%
+% OUTPUTS
+%  H        - [h/binSize w/binSize nOrients*3+5] computed hog features
+%
+% EXAMPLE
+%  I=imResample(single(imread('peppers.png'))/255,[480 640]);
+%  tic, for i=1:100, H=fhog(I,8,9); end; disp(100/toc) % >125 fps
+%  figure(1); im(I); V=hogDraw(H,25,1); figure(2); im(V)
+%
+% EXAMPLE
+%  % comparison to features.cc (requires DPM code release version 5)
+%  I=imResample(single(imread('peppers.png'))/255,[480 640]); Id=double(I);
+%  tic, for i=1:100, H1=features(Id,8); end; disp(100/toc)
+%  tic, for i=1:100, H2=fhog(I,8,9,.2,1); end; disp(100/toc)
+%  figure(1); montage2(H1); figure(2); montage2(H2);
+%  D=abs(H1-H2); mean(D(:))
+%
+% See also hog, hogDraw, gradientHist
+%
+% Piotr's Image&Video Toolbox      Version 3.23
+% Copyright 2013 Piotr Dollar.  [pdollar-at-caltech.edu]
+% Please email me if you find bugs, or have suggestions or questions!
+% Licensed under the Simplified BSD License [see external/bsd.txt]
+
+%Note: modified to be more self-contained
+
+if( nargin<2 ), binSize=8; end
+if( nargin<3 ), nOrients=9; end
+if( nargin<4 ), clip=.2; end
+if( nargin<5 ), crop=0; end
+
+softBin = -1; useHog = 2; b = binSize;
+
+[M,O]=gradientMex('gradientMag',I,0,1);
+
+H = gradientMex('gradientHist',M,O,binSize,nOrients,softBin,useHog,clip);
+
+if( crop ), e=mod(size(I),b)<b/2; H=H(2:end-e(1),2:end-e(2),:); end
+
+end

KCF/gaussian_correlation.m

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+function kf = gaussian_correlation(xf, yf, sigma)
+%GAUSSIAN_CORRELATION Gaussian Kernel at all shifts, i.e. kernel correlation.
+%   Evaluates a Gaussian kernel with bandwidth SIGMA for all relative
+%   shifts between input images X and Y, which must both be MxN. They must 
+%   also be periodic (ie., pre-processed with a cosine window). The result
+%   is an MxN map of responses.
+%
+%   Inputs and output are all in the Fourier domain.
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+	N = size(xf,1) * size(xf,2);
+	xx = xf(:)' * xf(:) / N;  %squared norm of x
+	yy = yf(:)' * yf(:) / N;  %squared norm of y
+
+	%cross-correlation term in Fourier domain
+	xyf = xf .* conj(yf);
+	xy = sum(real(ifft2(xyf)), 3);  %to spatial domain
+
+	%calculate gaussian response for all positions, then go back to the
+	%Fourier domain
+	kf = fft2(exp(-1 / sigma^2 * max(0, (xx + yy - 2 * xy) / numel(xf))));
+
+end
+

KCF/gaussian_shaped_labels.m

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+function labels = gaussian_shaped_labels(sigma, sz)
+%GAUSSIAN_SHAPED_LABELS
+%   Gaussian-shaped labels for all shifts of a sample.
+%
+%   LABELS = GAUSSIAN_SHAPED_LABELS(SIGMA, SZ)
+%   Creates an array of labels (regression targets) for all shifts of a
+%   sample of dimensions SZ. The output will have size SZ, representing
+%   one label for each possible shift. The labels will be Gaussian-shaped,
+%   with the peak at 0-shift (top-left element of the array), decaying
+%   as the distance increases, and wrapping around at the borders.
+%   The Gaussian function has spatial bandwidth SIGMA.
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+
+% 	%as a simple example, the limit sigma = 0 would be a Dirac delta,
+% 	%instead of a Gaussian:
+% 	labels = zeros(sz(1:2));  %labels for all shifted samples
+% 	labels(1,1) = magnitude;  %label for 0-shift (original sample)
+
+
+	%evaluate a Gaussian with the peak at the center element
+	[rs, cs] = ndgrid((1:sz(1)) - floor(sz(1)/2), (1:sz(2)) - floor(sz(2)/2));
+	labels = exp(-0.5 / sigma^2 * (rs.^2 + cs.^2));
+
+	%move the peak to the top-left, with wrap-around
+	labels = circshift(labels, -floor(sz(1:2) / 2) + 1);
+
+	%sanity check: make sure it's really at top-left
+	assert(labels(1,1) == 1)
+
+end
+

KCF/get_features.m

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+function x = get_features(im, features, cell_size, cos_window)
+%GET_FEATURES
+%   Extracts dense features from image.
+%
+%   X = GET_FEATURES(IM, FEATURES, CELL_SIZE)
+%   Extracts features specified in struct FEATURES, from image IM. The
+%   features should be densely sampled, in cells or intervals of CELL_SIZE.
+%   The output has size [height in cells, width in cells, features].
+%
+%   To specify HOG features, set field 'hog' to true, and
+%   'hog_orientations' to the number of bins.
+%
+%   To experiment with other features simply add them to this function
+%   and include any needed parameters in the FEATURES struct. To allow
+%   combinations of features, stack them with x = cat(3, x, new_feat).
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+
+	if features.hog,
+		%HOG features, from Piotr's Toolbox
+		x = double(fhog(single(im) / 255, cell_size, features.hog_orientations));
+		x(:,:,end) = [];  %remove all-zeros channel ("truncation feature")
+	end
+
+	if features.gray,
+		%gray-level (scalar feature)
+		x = double(im) / 255;
+
+		x = x - mean(x(:));
+	end
+
+	%process with cosine window if needed
+	if ~isempty(cos_window),
+		x = bsxfun(@times, x, cos_window);
+	end
+
+end

KCF/get_subwindow.m

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+function out = get_subwindow(im, pos, sz)
+%GET_SUBWINDOW Obtain sub-window from image, with replication-padding.
+%   Returns sub-window of image IM centered at POS ([y, x] coordinates),
+%   with size SZ ([height, width]). If any pixels are outside of the image,
+%   they will replicate the values at the borders.
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+	if isscalar(sz),  %square sub-window
+		sz = [sz, sz];
+	end
+
+	xs = floor(pos(2)) + (1:sz(2)) - floor(sz(2)/2);
+	ys = floor(pos(1)) + (1:sz(1)) - floor(sz(1)/2);
+
+	%check for out-of-bounds coordinates, and set them to the values at
+	%the borders
+	xs(xs < 1) = 1;
+	ys(ys < 1) = 1;
+	xs(xs > size(im,2)) = size(im,2);
+	ys(ys > size(im,1)) = size(im,1);
+
+	%extract image
+	out = im(ys, xs, :);
+
+end
+

KCF/linear_correlation.m

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+function kf = linear_correlation(xf, yf)
+%LINEAR_CORRELATION Linear Kernel at all shifts, i.e. correlation.
+%   Computes the dot-product for all relative shifts between input images
+%   X and Y, which must both be MxN. They must also be periodic (ie.,
+%   pre-processed with a cosine window). The result is an MxN map of
+%   responses.
+%
+%   Inputs and output are all in the Fourier domain.
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+	%cross-correlation term in Fourier domain
+	kf = sum(xf .* conj(yf), 3) / numel(xf);
+
+end
+

KCF/load_video_info.m

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+function [img_files, pos, target_sz, ground_truth, video_path] = load_video_info(base_path)
+%LOAD_VIDEO_INFO
+%   Loads all the relevant information for the video in the given path:
+%   the list of image files (cell array of strings), initial position
+%   (1x2), target size (1x2), the ground truth information for precision
+%   calculations (Nx2, for N frames), and the path where the images are
+%   located. The ordering of coordinates and sizes is always [y, x].
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+
+	%full path to the video's files
+	if base_path(end) ~= '/' && base_path(end) ~= '\',
+		base_path(end+1) = '/';
+	end
+	video_path = base_path;
+
+	%try to load ground truth from text file (Benchmark's format)
+	filename = [video_path 'groundtruth_rect.txt'];
+	f = fopen(filename);
+	assert(f ~= -1, ['No initial position or ground truth to load ("' filename '").'])
+
+	%the format is [x, y, width, height]
+	try
+		ground_truth = textscan(f, '%f,%f,%f,%f', 'ReturnOnError',false);  
+	catch  %#ok, try different format (no commas)
+		frewind(f);
+		ground_truth = textscan(f, '%f %f %f %f');  
+	end
+	ground_truth = cat(2, ground_truth{:});
+	fclose(f);
+
+	%set initial position and size
+	target_sz = [ground_truth(1,4), ground_truth(1,3)];
+	pos = [ground_truth(1,2), ground_truth(1,1)] + floor(target_sz/2);
+
+	if size(ground_truth,1) == 1,
+		%we have ground truth for the first frame only (initial position)
+		ground_truth = [];
+	else
+		%store positions instead of boxes
+		ground_truth = ground_truth(:,[2,1]) + ground_truth(:,[4,3]) / 2;
+	end
+
+
+	%from now on, work in the subfolder where all the images are
+	video_path = [video_path 'img/'];
+
+    %general case, just list all images
+    img_files = dir([video_path '*.png']);
+    if isempty(img_files),
+        img_files = dir([video_path '*.jpg']);
+        assert(~isempty(img_files), 'No image files to load.')
+    end
+    img_files = sort({img_files.name});
+
+
+end
+

KCF/polynomial_correlation.m

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+function kf = polynomial_correlation(xf, yf, a, b)
+%POLYNOMIAL_CORRELATION Polynomial Kernel at all shifts, i.e. kernel correlation.
+%   Evaluates a polynomial kernel with constant A and exponent B, for all
+%   relative shifts between input images XF and YF, which must both be MxN.
+%   They must also be periodic (ie., pre-processed with a cosine window).
+%   The result is an MxN map of responses.
+%
+%   Inputs and output are all in the Fourier domain.
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+	%cross-correlation term in Fourier domain
+	xyf = xf .* conj(yf);
+	xy = sum(real(ifft2(xyf)), 3);  %to spatial domain
+
+	%calculate polynomial response for all positions, then go back to the
+	%Fourier domain
+	kf = fft2((xy / numel(xf) + a) .^ b);
+
+end
+

KCF/show_video.m

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+function update_visualization_func = show_video(img_files, video_path, resize_image)
+%SHOW_VIDEO
+%   Visualizes a tracker in an interactive figure, given a cell array of
+%   image file names, their path, and whether to resize the images to
+%   half size or not.
+%
+%   This function returns an UPDATE_VISUALIZATION function handle, that
+%   can be called with a frame number and a bounding box [x, y, width,
+%   height], as soon as the results for a new frame have been calculated.
+%   This way, your results are shown in real-time, but they are also
+%   remembered so you can navigate and inspect the video afterwards.
+%   Press 'Esc' to send a stop signal (returned by UPDATE_VISUALIZATION).
+%
+%   Joao F. Henriques, 2014
+%   http://www.isr.uc.pt/~henriques/
+
+
+	%store one instance per frame
+	num_frames = numel(img_files);
+	boxes = cell(num_frames,1);
+
+	%create window
+	[fig_h, axes_h, unused, scroll] = videofig(num_frames, @redraw, [], [], @on_key_press);  %#ok, unused outputs
+% 	set(fig_h, 'number','off', 'name', ['Tracker - ' video_path])
+	axis off;
+
+	%image and rectangle handles start empty, they are initialized later
+	im_h = [];
+	rect_h = [];
+
+	update_visualization_func = @update_visualization;
+	stop_tracker = false;
+
+
+	function stop = update_visualization(frame, box)
+		%store the tracker instance for one frame, and show it. returns
+		%true if processing should stop (user pressed 'Esc').
+		boxes{frame} = box;
+		scroll(frame);
+		stop = stop_tracker;
+	end
+
+	function redraw(frame)
+		%render main image
+		im = imread([video_path img_files{frame}]);
+% 		if size(im,3) > 1,
+% 			im = rgb2gray(im);
+% 		end
+		if resize_image,
+			im = imresize(im, 0.5);
+		end
+
+		if isempty(im_h),  %create image
+			im_h = imshow(im, 'Border','tight', 'InitialMag',200, 'Parent',axes_h);
+		else  %just update it
+			set(im_h, 'CData', im)
+		end
+
+		%render target bounding box for this frame
+		if isempty(rect_h),  %create it for the first time
+			rect_h = rectangle('Position',[0,0,1,1], 'EdgeColor','r', 'LineWidth', 3, 'Parent',axes_h);
+		end
+		if ~isempty(boxes{frame}),
+			set(rect_h, 'Visible', 'on', 'Position', boxes{frame});
+		else
+			set(rect_h, 'Visible', 'off');
+		end
+	end
+
+	function on_key_press(key)
+		if strcmp(key, 'escape'),  %stop on 'Esc'
+			stop_tracker = true;
+		end
+	end
+
+end
+