尺度不变的额特征提取代码

function [ pos, scale, orient, desc ] = SIFT( im, octaves, intervals, object_mask, contrast_threshold, curvature_threshold, interactive )% 功能：提取灰度图像的尺度不变特征（SIFT特征）% 输入:% im - 灰度图像，该图像的灰度值在0到1之间（注意：应首先对输入图像的灰度值进行归一化处理）% octaves - 金字塔的组数：octaves (默认值为4).% intervals - 该输入参数决定每组金字塔的层数(默认值为2).% object_mask - 确定图像中尺度不变特征点的搜索区域，如果没有特别指出，则算法将搜索整个图像% contrast_threshold - 对比度阈值(默认值为0.03).% curvature_threshold - 曲率阈值（默认值为10.0）.% interactive - 函数运行显示标志，将其设定为1,则显示算法运行时间和过程的相关信息；% 如果将其设定为2，则仅显示最终运行记过(default = 1).% 输出:% pos - Nx2 矩阵，每一行包括尺度不变特征点的坐标(x,y)% scale - Nx3 矩阵，每一行包括尺度不变特征点的尺度信息(第一列是尺度不变特征点所在的组，% 第二列是其所在的层, 第三列是尺度不变特征点的sigma).% orient - Nx1 向量，每个元素是特征点的主方向，其范围在[-pi,pi)之间.% desc - Nx128 矩阵，每一行包含特征点的特征向量.% 参考文献:% [1] David G. Lowe, "Distinctive Image Features from Sacle-Invariant Keypoints",% accepted for publicatoin in the International Journal of Computer% Vision, 2004.% [2] David G. Lowe, "Object Recognition from Local Scale-Invariant Features",% Proc. of the International Conference on Computer Vision, Corfu,% September 1999.%% Xiaochuan ZHAO;zhaoxch@% 设定输入量的默认值if ~exist('octaves')octaves = 4;endif ~exist('intervals')intervals = 2;endif ~exist('object_mask')object_mask = ones(size(im));endif size(object_mask) ~= size(im)object_mask = ones(size(im));endif ~exist('contrast_threshold')contrast_threshold = 0.02; %0.03吧endif ~exist('curvature_threshold')curvature_threshold = 10.0;endif ~exist('interactive')interactive = 1;end% 检验输入灰度图像的像素灰度值是否已归一化到[0,1]if( (min(im(:)) < 0) | (max(im(:)) > 1) )fprintf( 2, 'Warning: image not normalized to [0,1].\n' );end% 将输入图像经过高斯平滑处理，采用双线性差值将其扩大一倍.if interactive >= 1fprintf( 2, 'Doubling image size for first octave...\n' );endtic;antialias_sigma = 0.5;if antialias_sigma == 0signal = im;elseg = gaussian_filter( antialias_sigma );if exist('corrsep') == 3signal = corrsep( g, g, im );elsesignal = conv2( g, g, im, 'same' );endendsignal = im;[X Y] = meshgrid( 1:0.5:size(signal,2), 1:0.5:size(signal,1) );signal = interp2( signal, X, Y, '*linear' );subsample = [0.5]; % 降采样率；%下一步是生成高斯和差分高斯(DOG)金字塔，这两个金字塔的数据分别存储在名为gauss_pyr{orient,interval}% 和DOG_pyr{orient,interval}的元胞数字中。

高斯金字塔含有s+3层，差分高斯金字塔含有s+2层。

if interactive >= 1fprintf( 2, 'Prebluring image...\n' );endpreblur_sigma = sqrt(sqrt(2)^2 - (2*antialias_sigma)^2);if preblur_sigma == 0gauss_pyr{1,1} = signal;elseg = gaussian_filter( preblur_sigma );if exist('corrsep') == 3gauss_pyr{1,1} = corrsep( g, g, signal );elsegauss_pyr{1,1} = conv2( g, g, signal, 'same' );endendclear signalpre_time = toc;if interactive >= 1fprintf( 2, 'Preprocessing time %.2f seconds.\n', pre_time );end% 第一组第一层的sigmainitial_sigma = sqrt( (2*antialias_sigma)^2 + preblur_sigma^2 );% 记录每一层和每一个尺度的sigmaabsolute_sigma = zeros(octaves,intervals+3);absolute_sigma(1,1) = initial_sigma * subsample(1);% 记录产生金字塔的滤波器的尺寸和标准差filter_size = zeros(octaves,intervals+3);filter_sigma = zeros(octaves,intervals+3);% 生成高斯和差分高斯金字塔if interactive >= 1fprintf( 2, 'Expanding the Gaussian and DOG pyramids...\n' );endtic;for octave = 1:octavesif interactive >= 1fprintf( 2, '\tProcessing octave %d: image size %d x %d subsample %.1f\n', octave,size(gauss_pyr{octave,1},2), size(gauss_pyr{octave,1},1), subsample(octave) );fprintf( 2, '\t\tInterval 1 sigma %f\n', absolute_sigma(octave,1) );endsigma = initial_sigma;g = gaussian_filter( sigma );filter_size( octave, 1 ) = length(g);filter_sigma( octave, 1 ) = sigma;DOG_pyr{octave} = zeros(size(gauss_pyr{octave,1},1),size(gauss_pyr{octave,1},2),intervals+2);for interval = 2:(intervals+3)% 计算生成下一层几何采样金字塔的标准差% 其中，sigma_i+1 = k*sigma.% sigma_i+1^2 = sigma_f,i^2 + sigma_i^2% (k*sigma_i)^2 = sigma_f,i^2 + sigma_i^2% 因此:% sigma_f,i = sqrt(k^2 - 1)sigma_i% 对于扩展的组（span the octave），k = 2^(1/intervals)% 所以% sigma_f,i = sqrt(2^(2/intervals) - 1)sigma_isigma_f = sqrt(2^(2/intervals) - 1)*sigma;g = gaussian_filter( sigma_f );sigma = (2^(1/intervals))*sigma;% 记录sigma的值absolute_sigma(octave,interval) = sigma * subsample(octave);% 记录滤波器的尺寸和标准差filter_size(octave,interval) = length(g);filter_sigma(octave,interval) = sigma;if exist('corrsep') == 3gauss_pyr{octave,interval} = corrsep( g, g, gauss_pyr{octave,interval-1} );elsegauss_pyr{octave,interval} = conv2( g, g, gauss_pyr{octave,interval-1}, 'same' );endDOG_pyr{octave}(:,:,interval-1) = gauss_pyr{octave,interval} - gauss_pyr{octave,interval-1};if interactive >= 1fprintf( 2, '\t\tInterval %d sigma %f\n', interval, absolute_sigma(octave,interval) );endendif octave < octavessz = size(gauss_pyr{octave,intervals+1});[X Y] = meshgrid( 1:2:sz(2), 1:2:sz(1) );gauss_pyr{octave+1,1} = interp2(gauss_pyr{octave,intervals+1},X,Y,'*nearest');absolute_sigma(octave+1,1) = absolute_sigma(octave,intervals+1);subsample = [subsample subsample(end)*2];endendpyr_time = toc;if interactive >= 1fprintf( 2, 'Pryamid processing time %.2f seconds.\n', pyr_time );end% 在交互模式下显示高斯金字塔if interactive >= 2sz = zeros(1,2);sz(2) = (intervals+3)*size(gauss_pyr{1,1},2);for octave = 1:octavessz(1) = sz(1) + size(gauss_pyr{octave,1},1);endpic = zeros(sz);y = 1;for octave = 1:octavesx = 1;sz = size(gauss_pyr{octave,1});for interval = 1:(intervals + 3)pic(y:(y+sz(1)-1),x:(x+sz(2)-1)) = gauss_pyr{octave,interval};x = x + sz(2);endy = y + sz(1);endfig = figure;clf;imshow(pic);resizeImageFig( fig, size(pic), 0.25 );fprintf( 2, 'The gaussian pyramid (0.25 scale).\nPress any key to continue.\n' );pause;close(fig)end% 在交互模式下显示差分高斯金字塔if interactive >= 2sz = zeros(1,2);sz(2) = (intervals+2)*size(DOG_pyr{1}(:,:,1),2);for octave = 1:octavessz(1) = sz(1) + size(DOG_pyr{octave}(:,:,1),1);endpic = zeros(sz);y = 1;for octave = 1:octavesx = 1;sz = size(DOG_pyr{octave}(:,:,1));for interval = 1:(intervals + 2)pic(y:(y+sz(1)-1),x:(x+sz(2)-1)) = DOG_pyr{octave}(:,:,interval);x = x + sz(2);endy = y + sz(1);endfig = figure;clf;imagesc(pic);resizeImageFig( fig, size(pic), 0.25 );fprintf( 2, 'The DOG pyramid (0.25 scale).\nPress any key to continue.\n' );pause;close(fig)end% 下一步是查找差分高斯金字塔中的局部极值，并通过曲率和照度进行检验curvature_threshold = ((curvature_threshold + 1)^2)/curvature_threshold;% 二阶微分核xx = [ 1 -2 1 ];yy = xx';xy = [ 1 0 -1; 0 0 0; -1 0 1 ]/4;raw_keypoints = [];contrast_keypoints = [];curve_keypoints = [];% 在高斯金字塔中查找局部极值if interactive >= 1fprintf( 2, 'Locating keypoints...\n' );endtic;loc = cell(size(DOG_pyr));for octave = 1:octavesif interactive >= 1fprintf( 2, '\tProcessing octave %d\n', octave );endfor interval = 2:(intervals+1)keypoint_count = 0;contrast_mask = abs(DOG_pyr{octave}(:,:,interval)) >= contrast_threshold;loc{octave,interval} = zeros(size(DOG_pyr{octave}(:,:,interval)));if exist('corrsep') == 3edge = 1;elseedge = ceil(filter_size(octave,interval)/2);endfor y=(1+edge):(size(DOG_pyr{octave}(:,:,interval),1)-edge)for x=(1+edge):(size(DOG_pyr{octave}(:,:,interval),2)-edge)if object_mask(round(y*subsample(octave)),round(x*subsample(octave))) == 1 if( (interactive >= 2) | (contrast_mask(y,x) == 1) )% 通过空间核尺度检测最大值和最小值tmp = DOG_pyr{octave}((y-1):(y+1),(x-1):(x+1),(interval-1):(interval+1));pt_val = tmp(2,2,2);if( (pt_val == min(tmp(:))) | (pt_val == max(tmp(:))) )% 存储对灰度大于对比度阈值的点的坐标raw_keypoints = [raw_keypoints; x*subsample(octave) y*subsample(octave)];if abs(DOG_pyr{octave}(y,x,interval)) >= contrast_thresholdcontrast_keypoints = [contrast_keypoints; raw_keypoints(end,:)];% 计算局部极值的Hessian矩阵Dxx = sum(DOG_pyr{octave}(y,x-1:x+1,interval) .* xx);Dyy = sum(DOG_pyr{octave}(y-1:y+1,x,interval) .* yy);Dxy = sum(sum(DOG_pyr{octave}(y-1:y+1,x-1:x+1,interval) .* xy));% 计算Hessian矩阵的直迹和行列式.Tr_H = Dxx + Dyy;Det_H = Dxx*Dyy - Dxy^2;% 计算主曲率.curvature_ratio = (Tr_H^2)/Det_H;if ((Det_H >= 0) & (curvature_ratio < curvature_threshold))% 存储主曲率小于阈值的的极值点的坐标（非边缘点）curve_keypoints = [curve_keypoints; raw_keypoints(end,:)];% 将该点的位置的坐标设为1，并计算点的数量.loc{octave,interval}(y,x) = 1;keypoint_count = keypoint_count + 1;endendendendendendendif interactive >= 1fprintf( 2, '\t\t%d keypoints found on interval %d\n', keypoint_count, interval );endendendkeypoint_time = toc;if interactive >= 1fprintf( 2, 'Keypoint location time %.2f seconds.\n', keypoint_time );end% 在交互模式下显示特征点检测的结果.if interactive >= 2fig = figure;clf;imshow(im);hold on;plot(raw_keypoints(:,1),raw_keypoints(:,2),'y+');resizeImageFig( fig, size(im), 2 );fprintf( 2, 'DOG extrema (2x scale).\nPress any key to continue.\n' );pause;close(fig);fig = figure;clf;imshow(im);hold on;plot(contrast_keypoints(:,1),contrast_keypoints(:,2),'y+');resizeImageFig( fig, size(im), 2 );fprintf( 2, 'Keypoints after removing low contrast extrema (2x scale).\nPress any key to continue.\n' );pause;close(fig);fig = figure;clf;imshow(im);hold on;plot(curve_keypoints(:,1),curve_keypoints(:,2),'y+');resizeImageFig( fig, size(im), 2 );fprintf( 2, 'Keypoints after removing edge points using principal curvature filtering (2x scale).\nPress any key to continue.\n' );pause;close(fig);endclear raw_keypoints contrast_keypoints curve_keypoints% 下一步是计算特征点的主方向.% 在特征点的一个区域内计算其梯度直方图g = gaussian_filter( 1.5 * absolute_sigma(1,intervals+3) / subsample(1) );zero_pad = ceil( length(g) / 2 );% 计算高斯金字塔图像的梯度方向和幅值if interactive >= 1fprintf( 2, 'Computing gradient magnitude and orientation...\n' );endtic;mag_thresh = zeros(size(gauss_pyr));mag_pyr = cell(size(gauss_pyr));grad_pyr = cell(size(gauss_pyr));for octave = 1:octavesfor interval = 2:(intervals+1)% 计算x,y的差分diff_x = 0.5*(gauss_pyr{octave,interval}(2:(end-1),3:(end))-gauss_pyr{octave,interval}(2:(end-1),1:(end-2 )));diff_y = 0.5*(gauss_pyr{octave,interval}(3:(end),2:(end-1))-gauss_pyr{octave,interval}(1:(end-2),2:(end-1 )));% 计算梯度幅值mag = zeros(size(gauss_pyr{octave,interval}));mag(2:(end-1),2:(end-1)) = sqrt( diff_x .^ 2 + diff_y .^ 2 );% 存储高斯金字塔梯度幅值mag_pyr{octave,interval} = zeros(size(mag)+2*zero_pad);mag_pyr{octave,interval}((zero_pad+1):(end-zero_pad),(zero_pad+1):(end-zero_pad)) = mag;% 计算梯度主方向grad = zeros(size(gauss_pyr{octave,interval}));grad(2:(end-1),2:(end-1)) = atan2( diff_y, diff_x );grad(find(grad == pi)) = -pi;% 存储高斯金字塔梯度主方向grad_pyr{octave,interval} = zeros(size(grad)+2*zero_pad);grad_pyr{octave,interval}((zero_pad+1):(end-zero_pad),(zero_pad+1):(end-zero_pad)) = grad;endendclear mag gradgrad_time = toc;if interactive >= 1fprintf( 2, 'Gradient calculation time %.2f seconds.\n', grad_time );end% 下一步是确定特征点的主方向% 方法：通过寻找每个关键点的子区域内梯度直方图的峰值（注：每个关键点的主方向可以有不止一个）% 将灰度直方图分为36等分，每隔10度一份num_bins = 36;hist_step = 2*pi/num_bins;hist_orient = [-pi:hist_step:(pi-hist_step)];% 初始化关键点的位置、方向和尺度信息pos = [];orient = [];scale = [];% 给关键点确定主方向if interactive >= 1fprintf( 2, 'Assigining keypoint orientations...\n' );endtic;for octave = 1:octavesif interactive >= 1fprintf( 2, '\tProcessing octave %d\n', octave );endfor interval = 2:(intervals + 1)if interactive >= 1fprintf( 2, '\t\tProcessing interval %d ', interval );endkeypoint_count = 0;% 构造高斯加权掩模g = gaussian_filter( 1.5 * absolute_sigma(octave,interval)/subsample(octave) );hf_sz = floor(length(g)/2);g = g'*g;loc_pad = zeros(size(loc{octave,interval})+2*zero_pad);loc_pad((zero_pad+1):(end-zero_pad),(zero_pad+1):(end-zero_pad)) = loc{octave,interval};[iy ix]=find(loc_pad==1);for k = 1:length(iy)x = ix(k);y = iy(k);wght = g.*mag_pyr{octave,interval}((y-hf_sz):(y+hf_sz),(x-hf_sz):(x+hf_sz));grad_window = grad_pyr{octave,interval}((y-hf_sz):(y+hf_sz),(x-hf_sz):(x+hf_sz));orient_hist=zeros(length(hist_orient),1);for bin=1:length(hist_orient)diff = mod( grad_window - hist_orient(bin) + pi, 2*pi ) - pi;orient_hist(bin)=orient_hist(bin)+sum(sum(wght.*max(1 - abs(diff)/hist_step,0)));end% 运用非极大抑制法查找主方向直方图的峰值peaks = orient_hist;rot_right = [ peaks(end); peaks(1:end-1) ];rot_left = [ peaks(2:end); peaks(1) ];peaks( find(peaks < rot_right) ) = 0;peaks( find(peaks < rot_left) ) = 0;% 提取最大峰值的值和其索引位置[max_peak_val ipeak] = max(peaks);% 将大于等于最大峰值80% 的直方图的也确定为特征点的主方向peak_val = max_peak_val;while( peak_val > 0.8*max_peak_val )% 最高峰值最近的三个柱值通过抛物线插值精确得到A = [];b = [];for j = -1:1A = [A; (hist_orient(ipeak)+hist_step*j).^2 (hist_orient(ipeak)+hist_step*j) 1];bin = mod( ipeak + j + num_bins - 1, num_bins ) + 1;b = [b; orient_hist(bin)];endc = pinv(A)*b;max_orient = -c(2)/(2*c(1));while( max_orient < -pi )max_orient = max_orient + 2*pi;endwhile( max_orient >= pi )max_orient = max_orient - 2*pi;end% 存储关键点的位置、主方向和尺度信息pos = [pos; [(x-zero_pad) (y-zero_pad)]*subsample(octave) ];orient = [orient; max_orient];scale = [scale; octave interval absolute_sigma(octave,interval)];keypoint_count = keypoint_count + 1;peaks(ipeak) = 0;[peak_val ipeak] = max(peaks);endendif interactive >= 1fprintf( 2, '(%d keypoints)\n', keypoint_count );endendendclear loc loc_padorient_time = toc;if interactive >= 1fprintf( 2, 'Orientation assignment time %.2f seconds.\n', orient_time );end% 在交互模式下显示关键点的尺度和主方向信息if interactive >= 2fig = figure;clf;imshow(im);hold on;display_keypoints( pos, scale(:,3), orient, 'y' );resizeImageFig( fig, size(im), 2 );fprintf( 2, 'Final keypoints with scale and orientation (2x scale).\nPress any key to continue.\n' );pause;close(fig);end% 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) ); endfor k = 1:size(pos,1)x = pos(k,1)/subsample(scale(k,1));y = pos(k,2)/subsample(scale(k,1));% 将坐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 == pigrad_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, '.' );endenddesc_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));endif size(pos,1) > 0scale = scale(:,3);end% 在交互模式下显示运行过程耗时.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。