How to spearate the output of vl_hog() into 6*6*31 blocks and use reshape() to convert every block to a row vector?

How to spearate the output of vl_hog() into 6*6*31 blocks,

use reshape() to convert every block to a row vector,

save each vector to features_neg, and increase idx by 1

in the following code: check if do that correctly :

% Starter code prepared by James Hays, Brown University
% This function should return negative training examples (non-faces) from
% any images in 'non_face_scn_path'. Images should be converted to
% grayscale, because the positive training data is only available in
% grayscale. For best performance, you should sample random negative
% examples at multiple scales.

function features_neg = get_random_negative_features(non_face_scn_path, feature_params, num_samples)
% 'non_face_scn_path' is a string. This directory contains many images
%   which have no faces in them.
% 'feature_params' is a struct, with fields
%   feature_params.template_size (probably 36), the number of pixels
%      spanned by each train / test template and
%   feature_params.hog_cell_size (default 6), the number of pixels in each
%      HoG cell. template size should be evenly divisible by hog_cell_size.
%      Smaller HoG cell sizes tend to work better, but they make things
%      slower because the feature dimensionality increases and more
%      importantly the step size of the classifier decreases at test time.
% 'num_samples' is the number of random negatives to be mined, it's not
%   important for the function to find exactly 'num_samples' non-face
%   features, e.g. you might try to sample some number from each image, but
%   some images might be too small to find enough.

% 'features_neg' is N by D matrix where N is the number of non-faces and D
% is the template dimensionality, which would be
%   (feature_params.template_size / feature_params.hog_cell_size)^2 * 31
% if you're using the default vl_hog parameters

% Useful functions:
% vl_hog, HOG = VL_HOG(IM, CELLSIZE)
%  http://www.vlfeat.org/matlab/vl_hog.html  (API)
%  http://www.vlfeat.org/overview/hog.html   (Tutorial)
% rgb2gray

image_files = dir(fullfile(non_face_scn_path, '*.jpg'));
num_images = length(image_files);

% placeholder to be deleted
features_neg = rand(100, (feature_params.template_size / feature_params.hog_cell_size)^2 * 31);

step=feature_params.hog_cell_size;


idx=0;
for i=1:num_images
    im=imread([non_face_scn_path '/' image_files(i).name]);
    %%%%%  please follow the steps below to compute HOG features for each image.
    %%%%% 1. use single() function to convert input image to SINGLE class;
    %%%%% 2. call vl_hog() function, two parameters: one is the image, the
    %%%%% other is the feature_parames.hog_cell_size;
    %%%%%  Your code here!
        im=single(im);
    hog = vl_hog(im, feature_parames.hog_cell_size, 'verbose');

    
    for m=1:step:size(im,1)
        for n=1:step:size(im,2)
            %%%%%  spearate the output of vl_hog() into 6*6*31 blocks,
            %%%%% use reshape() to convert every block to a row vector,
            %%%%% save each vector to features_neg, and increase idx by 1
            %%%%% Your code here!
            
            features_neg(idx,:)=reshape(hog, [1, 6*6*31]);
            idx=idx+1

        end
    end
    disp(['idex is: ' num2str(idx)]);
    disp(['case is: ' num2str(i)]);   
end
idx