基于CASIA-GaitDatasetB步态图像轮廓数据库的步态周期检测与步态角度特征MATLAB源码

Posted 2022-03-23 fpga&matlab

部分核心程序：D197

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%                       步态特征提取 宽高比特征以及角度特征
 
close all;
clear;
clc;
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Trainingfilenum = 3; % 训练人数
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for filnum = 1:Trainingfilenum        % 第一部分： 从不同文件夹导入图片
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    %%%% %%%% %%%% %%%% 第一步：    导入二值化步态图片,图片来自中科院CASIA数据库B库
    %                              导入图集为一个步态周期，且以步态跨度最大时为起始时刻
    figure('NumberTitle','off','Name','数据库图集');
    %%%% %%%% 从图集中读取所有图片
    fileDir = ['gaitpic\\Trainingset\\cl',num2str(filnum),'\\']; % 文件夹路径（这里指的图片文件）
    filePattern = '*.png';       % 文件格式（图片格式）
    dis = dir([fileDir,filePattern]); % 利用dir函数，返回fileDir路径下、filePattern
                                      % 所有文件（文件名、文件路径、日期、字节等）
    infilenames = dis.name;         % 得到文件名
    infilenums = length(infilenames); % 得到文件个数

    for i=1:infilenums % 循环读取文件
        filen = [fileDir infilenamesi]; % '\\gaitpic\\Trainingset\\cl-filnum\\第i个文件'
        gpic = imread(filen);             %  imread('gaitpic\\Trainingset\\cl-filnum\\第i个文件')
        GaitMessage(filnum).GaitPicture(:,:,i) = gpic;
        imshow(GaitMessage(filnum).GaitPicture(:,:,i));title(['No.1-',num2str(i)]);
    end
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end        % 第一部分 导入图片
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for filnum = 1:Trainingfilenum        % 第二部分： 图像预处理
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    %%%% %%%% 1、形态学处理
    figure('NumberTitle','off','Name','图像预处理-形态学处理');
    % picnums获取第filnum个人的步态图片张数
    [height,width,picnums] = size( GaitMessage(filnum).GaitPicture); 
    % 选取形态学处理的结构元素 
    close_se = strel('disk',4);  % 边长为4的圆盘结构元素
    open_se = strel('disk',4);   % 边长为4的圆盘结构元素
    %%%% 进行边长为4的八边形 先闭后开运算
    for i=1:picnums % 对该人的所有步态图像进行形态学处理
        clo = imclose( GaitMessage(filnum).GaitPicture(:,:,i) ,close_se);  % 闭运算操作
        ope = imopen(clo,open_se);                                          % 开运算操作
        GaitMessage(filnum).Morphology(:,:,i) = ope;                % 形态学处理后的图片  
        imshow( GaitMessage(filnum).Morphology(:,:,i) );title(['No.2-1-',num2str(i)]);
    end

    %%%% %%%% 2、人像轮廓提取(边缘检测) - 骨骼提取(细化函数) - 生成轮廓骨骼图像 
    figure('NumberTitle','off','Name','图像预处理-边缘检测');
    for i=1:picnums
        GaitMessage(filnum).Edgepic(:,:,i) = edge( GaitMessage(filnum).Morphology(:,:,i),'sobel');  % sobel算子
        GaitMessage(filnum).Skeletonpic(:,:,i) = bwmorph( GaitMessage(filnum).Morphology(:,:,i),'thin',Inf); % 人体细化
        GaitMessage(filnum).EdgeandSkeleton(:,:,i) = GaitMessage(filnum).Edgepic(:,:,i) + GaitMessage(filnum).Skeletonpic(:,:,i); % 生成轮廓骨骼图
        % 保存下来，保存路径为：gaitpic/EdgeandSkeleton/all/filnum-es-i.png
        imwrite( GaitMessage(filnum).EdgeandSkeleton(:,:,i) , strcat(['gaitpic/EdgeandSkeleton/all/',num2str(filnum),'es',num2str(i),'.png']));
        % 画图部分
%        imshow( GaitMessage(filnum).Edgepic(:,:,i) );title(['No.2-2-',num2str(i)]); % 显示轮廓图             ;
%        imshow( GaitMessage(filnum).Skeletonpic(:,:,i) );title(['No.2-2-',num2str(i)]); % 显示骨骼图    
        imshow( GaitMessage(filnum).EdgeandSkeleton(:,:,i) );title(['No.2-2-',num2str(i)]); % 显示轮廓骨骼图
        % 画图结束
    end
      
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end        % 第二部分： 图像预处理
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for filnum = 1:Trainingfilenum        % 第三部分： 步态周期检测与提取
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    % picnums获取第filnum个人的步态图片张数
    [height,width,picnums] = size( GaitMessage(filnum).GaitPicture); 
    %%%% %%%% 获得每个轮廓的最小外接矩形 & 以其矩形的长宽作为人像高宽
    for i=1:picnums  
        imbiEdgepic = imbinarize( GaitMessage(filnum).Morphology(:,:,i) ); % 步态图二值化
        %%%% regionprops函数用于返回图片的属性，求各个轮廓的最小外接矩形
        GaitMessage(filnum).BoundBs(1,i) = regionprops(imbiEdgepic,'BoundingBox'); % 利用函数度量图像区域属性，这里度量最小外接矩形
                                                                 % 返回一个结构数据，其内是有关最小外接矩形的位置
                                                                  % 长、宽信息，调用格式：s.BoundingBox
                                                                   % s.BoundingBox = [x,y,w(△x),h(△y)]

        %%%% 求各个对应的宽高比
        rec_width = GaitMessage(filnum).BoundBs(i).BoundingBox(3); % 宽
        rec_heigh = GaitMessage(filnum).BoundBs(i).BoundingBox(4); % 高
        GaitFeatures(filnum).Aspectratio(1,i) =  rec_width / rec_heigh; % 宽高比特征
    end
    
    % 画图部分：
    figure('NumberTitle','off','Name','步态周期检测-宽高比');
    plot(GaitFeatures(filnum).Aspectratio); title(['No.3-',num2str(filnum),'步态集-人像宽高比变化曲线']);% 画出面积走势图
    % 画图结束
    %%%% %%%% 取连续三个局部最小值之间为一个周期（也可以取得连续三个局部最大值之间为一个周期）
    %%%% 利用寻找峰值函数找到局部极值
    [peas,locs] = findpeaks( -GaitFeatures(filnum).Aspectratio );% 由于想寻找局部极小值，而该函数是寻找局部极大值，所以对其取负
    peas = - peas; % 将值还原   % 由于本次未使用这个值，所以注释掉了；如果需要使用，则需要将注释去除，不然值是所需值的负数
    %%%% 取连续三个局部最小值之间为一个周期，第一个极小为起点，第三个极小为终点
    periodstart = 1;periodend = 3;
    GaitMessage(filnum).periodlocs = [locs(periodstart) locs(periodend)];
   
    %%%% 取宽高比周期
    GaitMessage(filnum).mPeriod = GaitFeatures(filnum).Aspectratio(GaitMessage(filnum).periodlocs(1):GaitMessage(filnum).periodlocs(2));
    % 画图部分：
    figure('NumberTitle','off','Name','步态周期检测-周期提取');
    plot(GaitMessage(filnum).mPeriod);title(['No.3-',num2str(filnum),'步态集-步态周期内人像宽高比变化曲线']);% 画出面积走势图
    % 画图结束
    %%%% 取周期内的步态图片
    GaitMessage(filnum).GaitPeriod = GaitMessage(filnum).EdgeandSkeleton(:,:,GaitMessage(filnum).periodlocs(1):GaitMessage(filnum).periodlocs(2));
   [height,width,picnums] = size( GaitMessage(filnum).GaitPeriod ); 
    for i = 1:picnums
        %保存下来，保存路径为：gaitpic/EdgeandSkeleton/period/filnum-es-i.png
        imwrite( GaitMessage(filnum).GaitPeriod(:,:,i) , strcat(['gaitpic/EdgeandSkeleton/period/',num2str(filnum),'es',num2str(i),'.png']));
    end  
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end        % 第三部分： 步态周期检测
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for filnum = 1:Trainingfilenum        % 第四部分： 角度特征提取
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    %%%% %%%% %%%% %%%% 第四步：    步态特征提取
    sectornums = 2; % 分区数，即将下半身分为两个分区，将人像以垂直轴(y轴)为分界线，左右分别为一个分区
    %%%% %%%% 整个过程角度特征距离特征       以质心为原点，对下半身采用2分区，提取各个分区信息以及轮廓像素信息，
    [GaitFeatures(filnum).nthsectorFeatures,Outlinepixelmessage,centroid] = test_Outlinepixel2Centroid_AngleDistance(GaitMessage(filnum).EdgeandSkeleton,sectornums);
    % 只获取一个步态周期内的角度特征
    for nths = 1:sectornums
        GaitFeatures(filnum).nthsectorPeriodFeatures(nths).PeriodaverageAngle = GaitFeatures(filnum).nthsectorFeatures(nths).averageAngle(GaitMessage(filnum).periodlocs(1):GaitMessage(filnum).periodlocs(2));
    end
    
    %%%% 画图部分：画出该人步态角度特征
    figure('NumberTitle','off','Name','同一人整个过程-角度特征');
    for i = 1:sectornums
        plot(GaitFeatures(filnum).nthsectorFeatures(i).averageAngle - 270);title(['No.4-',num2str(filnum),'-1','同一人的1/2分区-角度均值变化曲线']) % 画出角度走势图
        hold on
    end
    % 对应的周期角度特征曲线
    figure('NumberTitle','off','Name','同一人步态周期内-角度特征');
    for i = 1:sectornums
        plot(GaitFeatures(filnum).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270);title(['No.4-',num2str(filnum),'-2','同一人的1/2分区-周期角度均值变化曲线']) % 画出角度走势图
        hold on
    end
    %%%% 画图结束
    
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end        % 第四部分： 角度特征提取
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for filnum = 1:Trainingfilenum        % 画图部分-画出当前所有测试人员的角度特征曲线
%  注：这里给出最多画10人不同曲线的代码，由于曲线线条形状、颜色不同，采用switch语句来画         
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    %%%% %%%% 画图部分： 画出当前所有测试人员的角度特征曲线
    switch filnum
        case 1 
             figure('NumberTitle','off','Name','所有人整个过程-角度特征');
             title('不同行人左右分区角度特征比较');% 画出走势图
             for i = 1:sectornums
                plot(GaitFeatures(1).nthsectorFeatures(i).averageAngle - 270,'b-o','MarkerFaceColor','b');
                hold on
             end
        case 2 
             for i = 1:sectornums
                plot(GaitFeatures(2).nthsectorFeatures(i).averageAngle - 270,'g-x','MarkerFaceColor','g');
                hold on
             end
        case 3 
             for i = 1:sectornums
                plot(GaitFeatures(3).nthsectorFeatures(i).averageAngle - 270,'r-*','MarkerFaceColor','r');
                hold on
             end
        case 4 
             for i = 1:sectornums
                plot(GaitFeatures(4).nthsectorFeatures(i).averageAngle - 270,'c-p','MarkerFaceColor','c');
                hold on
             end
        case 5 
             for i = 1:sectornums
                title('不同行人左右分区角度特征比较');% 画出走势图
                plot(GaitFeatures(5).nthsectorFeatures(i).averageAngle - 270,'k-s','MarkerFaceColor','k');
                hold on
             end
        case 6 
             figure('NumberTitle','off','Name','步态角度特征');
             title('不同行人左右分区角度特征比较');% 画出走势图
             for i = 1:sectornums
                plot(GaitFeatures(6).nthsectorFeatures(i).averageAngle - 270,'b-o','MarkerFaceColor','b');
                hold on
             end
        case 7 
             for i = 1:sectornums
                plot(GaitFeatures(7).nthsectorFeatures(i).averageAngle - 270,'g-x','MarkerFaceColor','g');
                hold on
             end
        case 8 
             for i = 1:sectornums
                plot(GaitFeatures(8).nthsectorFeatures(i).averageAngle - 270,'r-*','MarkerFaceColor','r');
                hold on
             end
        case 9 
             for i = 1:sectornums
                plot(GaitFeatures(9).nthsectorFeatures(i).averageAngle - 270,'c-p','MarkerFaceColor','c');
                hold on
             end
        case 10 
             for i = 1:sectornums
                title('不同行人左右分区角度特征比较');% 画出走势图
                plot(GaitFeatures(10).nthsectorFeatures(i).averageAngle - 270,'k-s','MarkerFaceColor','k');
                hold on
             end
    end 
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end        % 画图部分结束-画出当前所有测试人员的角度特征曲线
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for filnum = 1:Trainingfilenum        % 画图部分-画出当前所有测试人员的角度特征曲线
%  注：这里给出最多画10人不同曲线的代码，由于曲线线条形状、颜色不同，采用switch语句来画         
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    %%%% %%%% 对应的周期角度特征曲线
    switch filnum
        case 1 
             figure('NumberTitle','off','Name','不同行人步态周期内角度特征');
             title('不同行人左右分区角度特征比较');% 画出走势图
             for i = 1:sectornums
                plot(GaitFeatures(1).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'b-o','MarkerFaceColor','b');
                hold on
             end
        case 2 
             for i = 1:sectornums
                plot(GaitFeatures(2).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'g-x','MarkerFaceColor','g');
                hold on
             end
        case 3 
             for i = 1:sectornums
                plot(GaitFeatures(3).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'r-*','MarkerFaceColor','r');
                hold on
             end
        case 4 
             for i = 1:sectornums
                plot(GaitFeatures(4).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'c-p','MarkerFaceColor','c');
                hold on
             end
        case 5 
             for i = 1:sectornums
                title('不同行人左右分区角度特征比较');% 画出走势图
                plot(GaitFeatures(5).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'k-s','MarkerFaceColor','k');
                hold on
             end
        case 6 
             figure('NumberTitle','off','Name','步态角度特征');
             title('不同行人左右分区角度特征比较');% 画出走势图
             for i = 1:sectornums
                plot(GaitFeatures(6).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'b-o','MarkerFaceColor','b');
                hold on
             end
        case 7 
             for i = 1:sectornums
                plot(GaitFeatures(7).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'g-x','MarkerFaceColor','g');
                hold on
             end
        case 8 
             for i = 1:sectornums
                plot(GaitFeatures(8).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'r-*','MarkerFaceColor','r');
                hold on
             end
        case 9 
             for i = 1:sectornums
                plot(GaitFeatures(9).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'c-p','MarkerFaceColor','c');
                hold on
             end
        case 10 
             for i = 1:sectornums
                title('不同行人左右分区角度特征比较');% 画出走势图
                plot(GaitFeatures(10).nthsectorPeriodFeatures(i).PeriodaverageAngle - 270,'k-s','MarkerFaceColor','k');
                hold on
             end
    end 
    %%%% %%%% 画图结束
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end        % 画图部分结束-画出当前所有测试人员的角度特征曲线
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