最近没空写最后一部分的内容,先把代码放上来
% SIFT 算法的最后一步是特征向量生成
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));
% 将座标轴旋转为关键点的方向,以确保旋转不变性
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));
% 初始化特征向量.
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);
% 在采样位置进行梯度插值
[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
% 计算x、y方向上的权重
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);
% 计算高斯权重
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
% 将特征向量的长度归一化,则可以进一步去除光照变化的影响.
feat_desc = feat_desc / norm(feat_desc);
feat_desc( find(feat_desc > 0.2) ) = 0.2;
feat_desc = feat_desc / norm(feat_desc);
% 存储特征向量.
desc = [desc; feat_desc];
if (interactive >= 1) & (mod(k,25) == 0)
fprintf( 2, '.' );
end
end
desc_time = toc;
% 调整采样偏差
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
% 在交互模式下显示运行过程耗时.
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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