sw_detect_face.asv

function [ patches,bbox_location ] = sw_detect_face(img, window_size, scale, stride)
% sw_multiscale_detect_face
% - This is a function to proposed the potential face images via moving the
% sliding window. 
%==========================================================================
% Output:
%   - patches: a cell to store every window_size proposed images. The size
%               of save images are H*W*N, where N is the number of sliding
%   - bbox_location: bounding box [x,y,height,width]
%--------------------------------------------------------------------------
% Input:
%   - real_image : The original images without resize
%   - window_size: The proposed sliding window size
%   - scale      : The scale of for each original image
%   - stride     : The steps between each save images
%==========================================================================

real_image = img;
img = imresize(img, scale);

% single-scale sliding window
[irow, icol] = size(img);
% window_size = int16(window_size / scale);
% stride = int16(stride / scale);

window_y = window_size(1);
window_x = window_size(2);

number_of_windows = floor(((irow-window_y)/stride) + 1) * floor(((icol-window_x)/stride) + 1);

single_patches = zeros(window_y, window_x, number_of_windows, 'uint8');
single_bbox_location = zeros(number_of_windows, 4, 'uint8');

count = 1;

% Iteratively save the patches.
for y = 1:stride:irow+1-window_y
    for x = 1:stride:icol+1-window_x
        single_patches(:,:,count) = img(y:y+window_y-1, x:x+window_x-1); % Image patch at bbox location
        single_bbox_location(count, :) = [y * scale, x * scale, window_y, window_x];     % Bbox cords, top-left y, x, height, width
        count = count + 1;
    end
end

% Pass through output
patches{1} = single_patches;
bbox_location{1} = single_bbox_location;

end