Haar-like矩形遍历检测窗口演示Matlab源代码
clc; clear; close all;% Haar-like特征矩形计算board = 24 % 检测窗口宽度num = 24 % 检测窗口分划数show = 1; % 1为作图time = 0.001; % 作图间隔%%if mod(board,num)~=0 error('检测窗口宽度必须是分划数的整数倍')else delta = board/num % 滑动步进值 end%% Haar特征1：左白,右黑,(s,t)=(1,2)s = 1;t = 2;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征1：左白,右黑,(s,t)=(1,2) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口宽 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 列方向移动个数 Px0 = [0 r]; % 矩形坐标初始化 Py0 = [0 c/2 c]; for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(Px,repmat(Py',1,2),'r','LineWidth',5) plot(repmat(Px,2,1),repmat([Py(1) Py(end)]',1,2),'r','LineWidth',5); hold off pause(time) end end end endendNUM%% Haar特征2：上白,下黑,(s,t)=(2,1)s = 2;t = 1;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征2：上白,下黑,(s,t)=(2,1) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口宽 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 列方向移动个数 Px0 = [0 r/2 r]; % 矩形坐标初始化 Py0 = [0 c]; for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(repmat(Px,2,1),repmat(Py',1,length(Px)),'r','LineWidth',3); plot(repmat([Px(1) Px(end)]',1,2),repmat(Py,2,1),'r','LineWidth',3); hold off pause(time) end end end endendNUM%% Haar特征3：左右白,中间黑,(s,t)=(1,3)s = 1;t = 3;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征3：左右白,中间黑,(s,t)=(1,3) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口宽 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 列方向移动个数 Px0 = [0 r]; % 矩形坐标初始化 Py0 = [0 c/3 c*2/3 c]; for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(Px,repmat(Py',1,2),'r','LineWidth',5) plot(repmat(Px,2,1),repmat([Py(1) Py(end)]',1,2),'r','LineWidth',5); hold off pause(time) end end end endendNUM%% Haar特征4：左右白,中间黑(2倍宽度),(s,t)=(1,4)s = 1;t = 4;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征4：左右白,中间黑(2倍宽度),(s,t)=(1,4) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口宽 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 列方向移动个数 Px0 = [0 r]; % 矩形坐标初始化 Py0 = [0 c/4 c*3/4 c]; for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(Px,repmat(Py',1,2),'r','LineWidth',5) plot(repmat(Px,2,1),repmat([Py(1) Py(end)]',1,2),'r','LineWidth',5); hold off pause(time) end end end endendNUM%% Haar特征5：上下白，中间黑,(s,t)=(3,1)s = 3;t = 1;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征5：上下白，中间黑,(s,t)=(3,1) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口宽 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 列方向移动个数 Px0 = [0 r/3 r*2/3 r]; % 矩形坐标初始化 Py0 = [0 c]; for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(repmat(Px,2,1),repmat(Py',1,length(Px)),'r','LineWidth',3); plot(repmat([Px(1) Px(end)]',1,2),repmat(Py,2,1),'r','LineWidth',3); hold off pause(time) end end end endendNUM%% Haar特征6：上下白，中间黑(2倍宽度),(s,t)=(4,1)s = 4;t = 1;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征6：上下白，中间黑(2倍宽度),(s,t)=(4,1) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口宽 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 列方向移动个数 Px0 = [0 r/4 r*3/4 r]; % 矩形坐标初始化 Py0 = [0 c]; for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(repmat(Px,2,1),repmat(Py',1,length(Px)),'r','LineWidth',3); plot(repmat([Px(1) Px(end)]',1,2),repmat(Py,2,1),'r','LineWidth',3); hold off pause(time) end end end endendNUM%% Haar特征7：左上右下白，其它黑,(s,s)=(2,2)s = 2;t = 2;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征7：左上右下白，其它黑,(s,s)=(2,2) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口高 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 行方向移动个数 Px0 = [0 r/2 r]; % 矩形坐标初始化 Py0 = [0 c/2 c]; % 矩形坐标初始化 for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(repmat(Px,3,1),repmat(Py',1,length(Px)),'r','LineWidth',3); plot(repmat([Px(1) Px(end)]',1,3),repmat(Py,2,1),'r','LineWidth',3); hold off pause(time) end end end endendNUM%% Haar特征8：四周白，中间黑,(s,s)=(3,3)s = 3;t = 3;R = s:s:floor(num/s)*s; % Haar窗口高C = t:t:floor(num/t)*t; % Haar窗口宽NUM = 0; % Haar特征总数'---- Haar特征8：四周白，中间黑,(s,s)=(3,3) ---'for I = 1:length(R) for J = 1:length(C) r = R(I)*delta; % Haar窗口高 c = C(J)*delta; % Haar窗口高 nr = num-R(I)+1; % 行方向移动个数 nc = num-C(J)+1; % 行方向移动个数 Px0 = [0 r/3 r*2/3 r]; % 矩形坐标初始化 Py0 = [0 c/3 c*2/3 c]; % 矩形坐标初始化 for i = 1:nr for j = 1:nc Px = Px0+(i-1)*delta; % 滑动取点 Py = Py0+(j-1)*delta; NUM = NUM+1; if show plot([0 board],repmat((0:delta:board)',1,2),'k'); hold on; plot(repmat((0:delta:board)',1,2),[0 board],'k'); axis tight; axis square; title('Haar矩形遍历演示');xlabel('x');ylabel('y'); plot(repmat(Px,4,1),repmat(Py',1,length(Px)),'r','LineWidth',3); plot(repmat([Px(1) Px(end)]',1,4),repmat(Py,2,1),'r','LineWidth',3); hold off pause(time) end end end endendNUM% 毕业院校：海军工程大学，水声工程专业，博士% 精通方向：数字信号(图像、视频)处理，人工智能与模式识别，群体智能优化，非线性与混沌，支持向量机，Matlab与VC++混编% 现任岗位：沈阳聚德视频技术有限公司，图像处理及模式识别研发工程师% 工作职责：车牌识别，视频目标跟踪等算法开发，C/C++实现，DSP植入% 兴趣爱好：金融时序的程式化交易%% 主要成果：% [1] 实现车牌识别C/C实现，DSP植入，识别率：汉字不低于99%，数字字母不低于99.5%，整牌不低于97%% [2] 精通数字信号(图像、视频)“特征提取”与“模式识别”的研究与开发，开展了“支持向量机”应用研究，原创文章有《四种支持向量机工具箱使用要点》，独立开发了“支持向量机Matlab工具箱Version1.0”。结题项目有：语音信号处理与识别，遥感图像的特征提取与分类，人脸识别，主被动声纳信号处理与识别等% [3] 精通“群体智能优化”，原创工具箱有“群体智能算法”Matlab工具箱 Version2.0”，误差精度优于现有公开发表文献，工程中解决了各种高维复杂问题的优化计算% [4] 精通“时间序列混沌建模和预测”，基于Matlab和VC 混编平台，独立开发了混沌分析和预测软件包“混沌时间序列分析与预测工具箱 Version2.9”。结题项目有：金融数据波动性分析与程式化交易，银行反洗钱异常检测系统，混沌背景弱信号检测，海洋混响背景弱目标检测等% [5] 精通Matlab与VC 混合编程：(a)以VC 为界面，核心算法采用Matlab函数，原创文章有《如何将Matlab7.0函数转换成VC 6.0动态链接库》；(b)以Matlab为界面，耗时算法在VC 环境中采用Mexfunction编译。%% 联系方式 转载来源：
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