SIFT四部曲之——構建關鍵點特徵描述符

最近沒空寫最後一部分的內容，先把代碼放上來

% SIFT 算法的最後一步是特徵向量生成


orient_bin_spacing = pi/4;
orient_angles = [-pi:orient_bin_spacing:(pi-orient_bin_spacing)];


grid_spacing = 4;
[x_coords y_coords] = meshgrid( [-6:grid_spacing:6] );
feat_grid = [x_coords(:) y_coords(:)]';
[x_coords y_coords] = meshgrid( [-(2*grid_spacing-0.5):(2*grid_spacing-0.5)] );
feat_samples = [x_coords(:) y_coords(:)]';
feat_window = 2*grid_spacing;


desc = [];


if interactive >= 1
   fprintf( 2, 'Computing keypoint feature descriptors for %d keypoints', size(pos,1) );
end
for k = 1:size(pos,1)
   x = pos(k,1)/subsample(scale(k,1));
   y = pos(k,2)/subsample(scale(k,1));   
   

   % 將座標軸旋轉爲關鍵點的方向，以確保旋轉不變性
   M = [cos(orient(k)) -sin(orient(k)); sin(orient(k)) cos(orient(k))];
   feat_rot_grid = M*feat_grid + repmat([x; y],1,size(feat_grid,2));
   feat_rot_samples = M*feat_samples + repmat([x; y],1,size(feat_samples,2));
   

   % 初始化特徵向量.
   feat_desc = zeros(1,128);
   

   for s = 1:size(feat_rot_samples,2)
      x_sample = feat_rot_samples(1,s);
      y_sample = feat_rot_samples(2,s);
      

      % 在採樣位置進行梯度插值
      [X Y] = meshgrid( (x_sample-1):(x_sample+1), (y_sample-1):(y_sample+1) );
      G = interp2( gauss_pyr{scale(k,1),scale(k,2)}, X, Y, '*linear' );
      G(find(isnan(G))) = 0;
      diff_x = 0.5*(G(2,3) - G(2,1));
      diff_y = 0.5*(G(3,2) - G(1,2));
      mag_sample = sqrt( diff_x^2 + diff_y^2 );
      grad_sample = atan2( diff_y, diff_x );
      if grad_sample == pi
         grad_sample = -pi;
      end      
      

      % 計算x、y方向上的權重
      x_wght = max(1 - (abs(feat_rot_grid(1,:) - x_sample)/grid_spacing), 0);
      y_wght = max(1 - (abs(feat_rot_grid(2,:) - y_sample)/grid_spacing), 0); 
      pos_wght = reshape(repmat(x_wght.*y_wght,8,1),1,128);
      

      diff = mod( grad_sample - orient(k) - orient_angles + pi, 2*pi ) - pi;
      orient_wght = max(1 - abs(diff)/orient_bin_spacing,0);
      orient_wght = repmat(orient_wght,1,16);         
      

      % 計算高斯權重
      g = exp(-((x_sample-x)^2+(y_sample-y)^2)/(2*feat_window^2))/(2*pi*feat_window^2);
      

      feat_desc = feat_desc + pos_wght.*orient_wght*g*mag_sample;
   end
   

   % 將特徵向量的長度歸一化，則可以進一步去除光照變化的影響.
   feat_desc = feat_desc / norm(feat_desc);
   

   feat_desc( find(feat_desc > 0.2) ) = 0.2;
   feat_desc = feat_desc / norm(feat_desc);
   

   % 存儲特徵向量.
   desc = [desc; feat_desc];
   if (interactive >= 1) & (mod(k,25) == 0)
      fprintf( 2, '.' );
   end
end
desc_time = toc;


% 調整採樣偏差
sample_offset = -(subsample - 1);
for k = 1:size(pos,1)
   pos(k,:) = pos(k,:) + sample_offset(scale(k,1));
end


if size(pos,1) > 0
	scale = scale(:,3);
end
   

% 在交互模式下顯示運行過程耗時.
if interactive >= 1
   fprintf( 2, '\nDescriptor processing time %.2f seconds.\n', desc_time );
   fprintf( 2, 'Processing time summary:\n' );
   fprintf( 2, '\tPreprocessing:\t%.2f s\n', pre_time );
   fprintf( 2, '\tPyramid:\t%.2f s\n', pyr_time );
   fprintf( 2, '\tKeypoints:\t%.2f s\n', keypoint_time );
   fprintf( 2, '\tGradient:\t%.2f s\n', grad_time );
   fprintf( 2, '\tOrientation:\t%.2f s\n', orient_time );
   fprintf( 2, '\tDescriptor:\t%.2f s\n', desc_time );
   fprintf( 2, 'Total processing time %.2f seconds.\n', pre_time + pyr_time + keypoint_time + grad_time + orient_time + desc_time );
end

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