lecture5_for_students.rar_eastpeo_matlab_teachphv_人脸识别_机器视觉

function H = HOGFunc(I, cell_size, block_size,n_bins) % COMPUTE_HOG Computes the HOG descriptor of the given image computed % with the specified paramters. % % INPUT: % I: image to extract HOGs % cell_size: size of the cells in pixels % block_size: size of the blocks in cells % n_bins: number of bins of the histograms % % OUTPUT: % H: HoG descriptor in a column vector form. % % %$ Author Jose Marcos Rodriguez $ %$ Date: 2013/20/08 22:00:00 $ %$ Revision: 1.1 $ %% Setting gradient configuration % gradient angle range control epsilon = 0.0001; L_norm = 2; desp = 1; total_angles = 180.0; bin_width = total_angles/n_bins; %% column matrix for mapping indices to bin center values bin_centers_map = (bin_width/2:bin_width:total_angles)'; %% Compute the gradient in polar coordinates [angles, magnitudes] = compute_gradient(I); %% Split the gradient in cells cell_coords = compute_cell_coordinates(angles,cell_size,cell_size,false); %% initialize 3 dimensional matrix to hold all the histograms % number of vertical and horizontal cells [height,width] = size(I(:,:,1)); n_v_cells = floor(height/cell_size); n_h_cells = floor(width/cell_size); % init the histograms 3D matrix (7x14x9) histograms = zeros(n_v_cells,n_h_cells,n_bins); % ================================================ %% Computing histograms for all image cells % ================================================ for index=1:size(cell_coords,2) % current cell histogram initialization h = zeros(1,n_bins); % cell coords x_min = cell_coords(1,index); x_max = cell_coords(2,index); y_min = cell_coords(3,index); y_max = cell_coords(4,index); % retrieve angles and magnitudes for all the pixels in the % cell and conversion to degrees. angs = angles(y_min:y_max,x_min:x_max); angs = angs.*180/pi; mags = magnitudes(y_min:y_max,x_min:x_max); % indices for the left and right histogram bins that bound % the current angle value for all the pixels in the cell left_indices = round(angs/bin_width); right_indices = left_indices+1; % wraping contributions over the histogram boundaries. left_indices(left_indices==0) = 9; right_indices(right_indices==10) = 1; % retrieving the left bin center value. left_bin_centers = bin_centers_map(left_indices); angs(angs < left_bin_centers) = ... total_angles + angs(angs < left_bin_centers); % calculating the contribution to both bins sides (vote weight) % (matrices with same size as the cell) right_contributions = (angs-left_bin_centers)/bin_width; left_contributions = 1 - right_contributions; left_contributions = mags.*left_contributions; right_contributions = mags.*right_contributions; % computing contributions for the current histogram bin by bin. for bin=1:n_bins % pixels that contribute to the bin with its left portion pixels_to_left = (left_indices == bin); h(bin) = h(bin) + sum(left_contributions(pixels_to_left)); % pixels that contribute to the bin with its right portion pixels_to_right = (right_indices == bin); h(bin) = h(bin) + sum(right_contributions(pixels_to_right)); end % appending current hist. to the histograms matrix row_offset = floor(index/n_h_cells + 1); column_offset = mod(index-1,n_h_cells)+1; histograms(row_offset,column_offset,:) = h(1,:); end % ================================================ %% block normalization (L2 norm) % ================================================ hist_size = block_size*block_size*n_bins; descriptor_size = hist_size*(n_v_cells-block_size+desp)*(n_h_cells-block_size+desp); H = zeros(descriptor_size, 1); col = 1; row = 1; % H = []; %% Split the histogram matrix in blocks (this code assumes an 50% of overlap as desp is hard coded as 1) while row <= n_v_cells-block_size+1 while col <= n_h_cells-block_size+1 % Getting all the histograms for a block blockHists = ... histograms(row:row+block_size-1, col:col+block_size-1, :); % Getting the magnitude of the histograms of the block magnitude = norm(blockHists(:),L_norm); % Divide all of the histogram values by the magnitude to normalize % them. normalized = blockHists / (magnitude + epsilon); % H = [H; normalized(:)]; offset = (row-1)*(n_h_cells-block_size+1)+col; ini = (offset-1)*hist_size+1; fin = offset*hist_size; H(ini:fin,1) = normalized(:); col = col+desp; end row = row+desp; col = 1; end