SDM 训练阶段的任务如下：
下面分别来说明：
载入数据
载入811个训练数据，按照上一章预备篇关于第一幅图片的裁剪方法裁剪这811张图片。 matlab代码如下：
function [Data] = load_single_data2 ( dbpath_img, dbpath_pts,image_index, options )%% output format%{DATA.- width_orig: the width of the original image.- height_orig: the height of the original image.- img_gray: the crop image.- height: the height of crop image.- wdith: the width of crop image.- shape_gt: ground-truth landmark.- bbox_gt: bounding box of ground-truth.%}slash = options.slash;dbname = options.datasetName;imlist = dir([dbpath_img slash ‘*.*g’]);%% load imagesimg = im2uint8(imread([dbpath_img slash imlist(image_index).name]));Data.width_orig = size(img,2);Data.height_orig = size(img,1);%% load shapeData.shape_gt = double(annotation_load(…[dbpath_pts slash imlist(image_index).name(1:end-3) ‘pts’] , dbname));if 0figure(1); imshow(img); hold on;draw_shape(Data.shape_gt(:,1),…Data.shape_gt(:,2),’y’);hold off;pause;end%% get bounding boxData.bbox_gt = getbbox(Data.shape_gt);%% enlarge region of faceregion= enlargingbbox(Data.bbox_gt, 2.0);region(2) = double(max(region(2), 1));%这里主要是为了防止求出的包围盒超过图像，因此一旦超过，则region(2)必然小于0，因此此时取1即可。region(1) = double(max(region(1), 1));bottom_y = double(min(region(2) + region(4) – 1, …Data.height_orig));right_x = double(min(region(1) + region(3) – 1, …Data.width_orig));%防止长和宽超过图片大小，因此取二者最小值img_region = img(region(2):bottom_y, region(1):right_x, :);%取人脸区域%% recalculate(重新计算) the location of groundtruth shape and bounding boxData.shape_gt = bsxfun(@minus, Data.shape_gt,…double([region(1) region(2)]));%等价于Data{iimgs}.shape_gt-repeat( double([region(1) region(2)]),size(Data{iimgs}.shape_gt,1),1)%将图像的坐标原点移到人脸包围盒的左上角,并因此得以重新计算新的特征点Data.bbox_gt = getbbox(Data.shape_gt);%新的特征点的包围盒的左上角坐标发生了改变，但是宽和高没有变化if size(img_region, 3) == 1Data.img_gray = img_region;elseData.img_gray = rgb2gray(img_region);endData.width = size(img_region, 2);Data.height = size(img_region, 1);if 0figure(2); imshow(Data.img_gray); hold on;draw_shape(Data.shape_gt(:,1),…Data.shape_gt(:,2),’y’);hold off;pause;end%% normalized the image to the mean-shapesr = options.canvasSize(1)/Data.width;sc = options.canvasSize(2)/Data.height;Data.img_gray = imresize(Data.img_gray,options.canvasSize);Data.width = options.canvasSize(1);Data.height = options.canvasSize(2);Data.shape_gt = bsxfun(@times, Data.shape_gt, [sr sc]);Data.bbox_gt(1:2) = bsxfun(@times, Data.bbox_gt(1:2), [sr sc]);%补充Data.bbox_gt(3:4) = bsxfun(@times, Data.bbox_gt(3:4), [sr sc]);%补充if 0figure(3); imshow(Data.img_gray); hold on;draw_shape(Data.shape_gt(:,1),…Data.shape_gt(:,2),’r’);hold on;rectangle(‘Position’, Data.bbox_gt, ‘EdgeColor’, ‘k’);pause;endendfunction region = enlargingbbox(bbox, scale)%同前面一样，，初始时刻这里得到仅仅是特征点盒子，而我们如果想要包住整个人脸，就必须先将原始盒子的左上角平移一半的宽高，然后再放大两倍。这个在前面求解%rect = get_correct_region( boxes, shape,Dataa(i).img, 1 );中也用到过%因此这里得到的盒子是包住全部人脸的盒子。region(1) = floor(bbox(1) – (scale – 1)/2*bbox(3));region(2) = floor(bbox(2) – (scale – 1)/2*bbox(4));region(3) = floor(scale*bbox(3));region(4) = floor(scale*bbox(4));end模拟人脸检测，产生10个初始值
事实上，每张图片都有一个ground-truth poins,因此可以求出它的包围盒，也可以通过opencv或其他的检测器可以检测出这样的框来。但两者有点不一样。如下： ，我们可以对opencv的检测盒做一些变换就可以得到近似的box gt了。 我们需要对包围盒扰动，以产生更多的盒子。怎么扰动呢？
拿望远镜看别人，拿放大镜看自己。
