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Scenes/Objects classification toolbox

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21 Dec 2010 (Updated )

Scenes recognition toolbox for vision systems

sp_find_sift_grid(I, grid_x, grid_y, patch_size, sigma_edge)
function sift_arr = sp_find_sift_grid(I, grid_x, grid_y, patch_size, sigma_edge)

% I = imread('image_0174.jpg');
% sift_arr = sp_find_sift_grid(I, 4, 9, 9, 1);

% parameters
num_angles = 8;
num_bins = 4;
num_samples = num_bins * num_bins;
alpha = 9;

if nargin < 5
    sigma_edge = 1;
end

angle_step = 2 * pi / num_angles;
angles = 0:angle_step:2*pi;
angles(num_angles+1) = []; % bin centers

[hgt wid] = size(I);
num_patches = numel(grid_x);

sift_arr = zeros(num_patches, num_samples * num_angles);


[G_X,G_Y]=gen_dgauss(sigma_edge);
I_X = filter2(G_X, I, 'same'); % vertical edges
I_Y = filter2(G_Y, I, 'same'); % horizontal edges
I_mag = sqrt(I_X.^2 + I_Y.^2); % gradient magnitude
%I_theta = atan2(I_Y,I_X);
I_theta = atan2(-I_X,-I_Y);

I_theta(find(isnan(I_theta))) = 0; % necessary????

% make default grid of samples (centered at zero, width 2)
interval = 2/num_bins:2/num_bins:2;
interval = interval - (1/num_bins + 1);
[sample_x sample_y] = meshgrid(interval, interval);
sample_x = reshape(sample_x, [1 num_samples]);
sample_y = reshape(sample_y, [1 num_samples]);

% make orientation images
I_orientation = zeros(hgt, wid, num_angles);
% for each histogram angle
for a=1:num_angles    
    % compute each orientation channel
    tmp = cos(I_theta - angles(a)).^alpha;
    tmp = tmp .* (tmp > 0);
    
    % weight by magnitude
    I_orientation(:,:,a) = tmp .* I_mag;
end

% for all patches
for i=1:num_patches
    r = patch_size/2;
    cx = grid_x(i) + r - 0.5;
    cy = grid_y(i) + r - 0.5;

    % find coordinates of sample points (bin centers)
    sample_x_t = sample_x * r + cx;
    sample_y_t = sample_y * r + cy;
    sample_res = sample_y_t(2) - sample_y_t(1);
    
    % find window of pixels that contributes to this descriptor
    x_lo = grid_x(i);
    x_hi = grid_x(i) + patch_size - 1;
    y_lo = grid_y(i);
    y_hi = grid_y(i) + patch_size - 1;
    
    % find coordinates of pixels
    [sample_px, sample_py] = meshgrid(x_lo:x_hi,y_lo:y_hi);
    num_pix = numel(sample_px);
    sample_px = reshape(sample_px, [num_pix 1]);
    sample_py = reshape(sample_py, [num_pix 1]);
        
    % find (horiz, vert) distance between each pixel and each grid sample
    dist_px = abs(repmat(sample_px, [1 num_samples]) - repmat(sample_x_t, [num_pix 1])); 
    dist_py = abs(repmat(sample_py, [1 num_samples]) - repmat(sample_y_t, [num_pix 1])); 
    
    % find weight of contribution of each pixel to each bin
    weights_x = dist_px/sample_res;
    weights_x = (1 - weights_x) .* (weights_x <= 1);
    weights_y = dist_py/sample_res;
    weights_y = (1 - weights_y) .* (weights_y <= 1);
    weights = weights_x .* weights_y;
%     % make sure that the weights for each pixel sum to one?
%     tmp = sum(weights,2);
%     tmp = tmp + (tmp == 0);
%     weights = weights ./ repmat(tmp, [1 num_samples]);
        
    % make sift descriptor
    curr_sift = zeros(num_angles, num_samples);
    for a = 1:num_angles
        tmp = reshape(I_orientation(y_lo:y_hi,x_lo:x_hi,a),[num_pix 1]);        
        tmp = repmat(tmp, [1 num_samples]);
        curr_sift(a,:) = sum(tmp .* weights);
    end
    sift_arr(i,:) = reshape(curr_sift, [1 num_samples * num_angles]);    
        
%     % visualization
%     if sigma_edge >= 3
%         subplot(1,2,1);
%         rescale_and_imshow(I(y_lo:y_hi,x_lo:x_hi) .* reshape(sum(weights,2), [y_hi-y_lo+1,x_hi-x_lo+1]));
%         subplot(1,2,2);
%         rescale_and_imshow(curr_sift);
%         pause;
%     end
end

function G=gen_gauss(sigma)

if all(size(sigma)==[1, 1])
    % isotropic gaussian
	f_wid = 4 * ceil(sigma) + 1;
    G = fspecial('gaussian', f_wid, sigma);
%	G = normpdf(-f_wid:f_wid,0,sigma);
%	G = G' * G;
else
    % anisotropic gaussian
    f_wid_x = 2 * ceil(sigma(1)) + 1;
    f_wid_y = 2 * ceil(sigma(2)) + 1;
    G_x = normpdf(-f_wid_x:f_wid_x,0,sigma(1));
    G_y = normpdf(-f_wid_y:f_wid_y,0,sigma(2));
    G = G_y' * G_x;
end

function [GX,GY]=gen_dgauss(sigma)

% laplacian of size sigma
%f_wid = 4 * floor(sigma);
%G = normpdf(-f_wid:f_wid,0,sigma);
%G = G' * G;
G = gen_gauss(sigma);
[GX,GY] = gradient(G); 

GX = GX * 2 ./ sum(sum(abs(GX)));
GY = GY * 2 ./ sum(sum(abs(GY)));

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