最近沒空寫最后一部分的內(nèi)容，先把代碼放上來

% 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 >= 1fprintf( 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)); % 將坐標(biāo)軸旋轉(zhuǎn)為關(guān)鍵點(diǎn)的方向，以確保旋轉(zhuǎn)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);% 在采樣位置進(jìn)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 == pigrad_sample = -pi;end % 計(jì)算x、y方向上的權(quán)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(jì)算高斯權(quán)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% 將特征向量的長(zhǎng)度歸一化，則可以進(jìn)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(chǔ)特征向量.desc = [desc; feat_desc];if (interactive >= 1) & (mod(k,25) == 0)fprintf( 2, '.' );end end desc_time = toc;% 調(diào)整采樣偏差 sample_offset = -(subsample - 1); for k = 1:size(pos,1)pos(k,:) = pos(k,:) + sample_offset(scale(k,1)); endif size(pos,1) > 0scale = scale(:,3); end% 在交互模式下顯示運(yùn)行過程耗時(shí). if interactive >= 1fprintf( 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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