Face Alignment by 3000 FPS系列学习总结（一）

face alignment 流程图

train阶段

测试阶段

预处理

裁剪图片

1. tr_data = loadsamples(imgpathlistfile, 2);
   说明： 本函数用于将原始图片取ground-truth points的包围盒，然后将其向左上角放大一倍。然后截取此部分图像，同时变换ground-truth points.hou，然后为了节省内存，使用了缩放，将其缩放在150*150的大小内，作为我们后续处理的图像及ground-truth points。
   截取前示意图：
   
   截取后示意图：
   
   缩放后示意图：
   
   注意： loadsamples的第二个参数是2，表明需要进行缩放，但是测试时，并没有进行缩放，参数为1.
   To do list:本函数使用的真实特征点的包围盒截取的图像，在实际作用时，肯定不能去真实特征点截取图像了。我们应该改为人脸检测框代替这个真实特征点的包围盒，如果检测不到，再用真实特征点的包围盒替代.

function Data = loadsamples(imgpathlistfile, exc_setlabel)
%LOADSAMPLES Summary of this function goes here
% Function: load samples from dbname database
% Detailed explanation goes here
% Input: 
% dbname: the name of one database
% exc_setlabel: excluded set label
% Output:
% Data: loaded data from the database
% 基本步骤：
% 1. 载入图片，取ground_truth shape的包围盒，然后放大一倍，截取图像。
% 同时相应地变换shape.
% 2. 为了防止图片过大，我们把图像控制在150*150内.
% 3. 用matlab自带的人脸检测求解或者直接拿bbox代替bbox_facedet
% 4. 后面还为了防止train文件下夹杂了test的图片，做了排除处理.
%

imgpathlist = textread(imgpathlistfile, '%s', 'delimiter', '\n');

Data = cell(length(imgpathlist), 1);

setnames = {'train' 'test'};

% Create a cascade detector object.
% faceDetector = vision.CascadeObjectDetector();
% bboxes_facedet = zeros(length(imgpathlist), 4);
% bboxes_gt = zeros(length(imgpathlist), 4);
% isdetected = zeros(length(imgpathlist), 1);

parfor i = 1:length(imgpathlist)
    img = im2uint8(imread(imgpathlist{i}));
    Data{i}.width_orig    = size(img, 2);
    Data{i}.height_orig   = size(img, 1);

    % Data{i}.img = img
    % shapepath = strrep(imgpathlist{i}, 'png', 'pts');%这一句和下一句是一样的用途
    shapepath = strcat(imgpathlist{i}(1:end-3), 'pts');
    Data{i}.shape_gt = double(loadshape(shapepath));    
    % Data{i}.shape_gt = Data{i}.shape_gt(params.ind_usedpts, :);
    % bbox = bounding_boxes_allsamples{i}.bb_detector; %
    Data{i}.bbox_gt = getbbox(Data{i}.shape_gt); % [bbox(1) bbox(2) bbox(3)-bbox(1) bbox(4)-bbox(2)]; %shape的包围盒

    % cut original image to a region which is a bit larger than the face
    % bounding box
    region = enlargingbbox(Data{i}.bbox_gt, 2.0);  %将true_shape的包围盒放大一倍，形成更大的包围盒，以此裁剪人脸，也有通过人脸检测框放大的

    region(2) = double(max(region(2), 1)); %防止放大后的region超过图像，这样一旦超过坐标会变成负数。因此取了和1的最大值.
    region(1) = double(max(region(1), 1));

    bottom_y = double(min(region(2) + region(4) - 1, Data{i}.height_orig)); %同理防止过高，过宽
    right_x = double(min(region(1) + region(3) - 1, Data{i}.width_orig));

    img_region = img(region(2):bottom_y, region(1):right_x, :);
    Data{i}.shape_gt = bsxfun(@minus, Data{i}.shape_gt, double([region(1) region(2)])); %68*2的矩阵减去一个向量

    % to save memory cost during training
    if exc_setlabel == 2
        ratio = min(1, sqrt(single(150 * 150) / single(size(img_region, 1) * size(img_region, 2)))); %如果图像小于150*150，则不用缩放，否则缩放到150*150以内
        img_region = imresize(img_region, ratio);
        Data{i}.shape_gt = Data{i}.shape_gt .* ratio;
    end 
    Data{i}.ori_img=img;

     Data{i}.lefttop=[region(1) region(2)]; %自己补充的

    Data{i}.bbox_gt = getbbox(Data{i}.shape_gt);

    Data{i}.bbox_facedet = getbbox(Data{i}.shape_gt); %应该改为人脸检测框
    % perform face detection using matlab face detector
    %{
    bbox = step(faceDetector, img_region);
    if isempty(bbox)
        % if face detection is failed 
        isdetected(i) = 1;
        Data{i}.bbox_facedet = getbbox(Data{i}.shape_gt);
    else
        int_ratios = zeros(1, size(bbox, 1));
        for b = 1:size(bbox, 1)
            area = rectint(Data{i}.bbox_gt, bbox(b, :));
            int_ratios(b) = (area)/(bbox(b, 3)*bbox(b, 4) + Data{i}.bbox_gt(3)*Data{i}.bbox_gt(4) - area);            
        end
        [max_ratio, max_ind] = max(int_ratios);

        if max_ratio < 0.4  % detection fail
            isdetected(i) = 0;
        else
            Data{i}.bbox_facedet = bbox(max_ind, 1:4);
            isdetected(i) = 1;
            % imgOut = insertObjectAnnotation(img_region,'rectangle',Data{i}.bbox_facedet,'Face');
            % imshow(imgOut);
        end   
    end
    %}
    % recalculate the location of groundtruth shape and bounding box
    % Data{i}.shape_gt = bsxfun(@minus, Data{i}.shape_gt, double([region(1) region(2)]));
    % Data{i}.bbox_gt = getbbox(Data{i}.shape_gt);

    if size(img_region, 3) == 1
        Data{i}.img_gray = img_region;
    else
        % hsv = rgb2hsv(img_region);
        Data{i}.img_gray = rgb2gray(img_region);
    end    

    Data{i}.width    = size(img_region, 2);
    Data{i}.height   = size(img_region, 1);
end

ind_valid = ones(1, length(imgpathlist));
parfor i = 1:length(imgpathlist)
    if ~isempty(exc_setlabel)
        ind = strfind(imgpathlist{i}, setnames{exc_setlabel});  %strfind是找imgpathlist{i}中含有setnames{exc_setlabel}='test'的地址
        if ~isempty(ind) % | ~isdetected(i)
            ind_valid(i) = 0;
        end
    end
end

% learn the linear transformation from detected bboxes to groundtruth bboxes
% bboxes = [bboxes_gt bboxes_facedet];
% bboxes = bboxes(ind_valid == 1, :);

Data = Data(ind_valid == 1); %找到含有test的地址，并排除出去，保证Data都是train_data

end

function shape = loadshape(path)
% function: load shape from pts file
file = fopen(path);

if ~isempty(strfind(path, 'COFW'))
    shape = textscan(file, '%d16 %d16 %d8', 'HeaderLines', 3, 'CollectOutput', 3);
else
    shape = textscan(file, '%d16 %d16', 'HeaderLines', 3, 'CollectOutput', 2);
end
fclose(file);

shape = shape{1};
end

function region = enlargingbbox(bbox, scale)

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));

% region.right_x = floor(region.left_x + region.width - 1);
% region.bottom_y = floor(region.top_y + region.height - 1);
end

示意图：

对一幅图片，实际上我们感兴趣的就是人脸区域，至于其他部分是无关紧要的。因此出于节省内存的原因，我们需要裁剪图片。前面已经讲述了裁剪的方法。从坐标系的角度来看，实际上是从原始坐标系转化到以扩充后的盒子为基础的坐标系。即如图蓝色的为原始坐标系，就是一张图片的坐标系。红色的为后来裁剪后的坐标系，而黑色框为人脸框（可以是人脸检测获得的，也可以是ground_truth points的包围盒）。那么坐标系的变化，简单而言可以写作：

shapes_gt˜=shapes_gt−[Region(1),Region(2)]

上式就是原来的shape经过裁剪后的坐标。后来还加上了缩放，即：

shapes_gt˜=shapes_gt˜.∗Ratio

左右翻转图片

train_model.m 第60行

% Augmentate data for traing: assign multiple initial shapes to each
% image(为每一张图片增加多个初始shape)
Data = Tr_Data; % (1:10:end);
Param = params;

if Param.flipflag % if conduct flipping
    Data_flip = cell(size(Data, 1), 1);
    for i = 1:length(Data_flip)
        Data_flip{i}.img_gray    = fliplr(Data{i}.img_gray);%左右翻转
        Data_flip{i}.width_orig  = Data{i}.width_orig;
        Data_flip{i}.height_orig = Data{i}.height_orig;    
        Data_flip{i}.width       = Data{i}.width;
        Data_flip{i}.height      = Data{i}.height; 

        Data_flip{i}.shape_gt    = flipshape(Data{i}.shape_gt);        
        Data_flip{i}.shape_gt(:, 1)  =  Data{i}.width - Data_flip{i}.shape_gt(:, 1);

        Data_flip{i}.bbox_gt        = Data{i}.bbox_gt;
        Data_flip{i}.bbox_gt(1)     = Data_flip{i}.width - Data_flip{i}.bbox_gt(1) - Data_flip{i}.bbox_gt(3);       

        Data_flip{i}.bbox_facedet        = Data{i}.bbox_facedet;
        Data_flip{i}.bbox_facedet(1)     = Data_flip{i}.width - Data_flip{i}.bbox_facedet(1) - Data_flip{i}.bbox_facedet(3);   
    end
    Data = [Data; Data_flip];
end

示意图: