55.MATLAB编程 基于SVM的图像分割-真彩色图像分割.rar

%% Matlab神经网络43个案例分析 % 基于SVM的图像分割-真彩色图像分割 % by 李洋(faruto) % http://www.matlabsky.com % Email:faruto@163.com % http://weibo.com/faruto % http://blog.sina.com.cn/faruto % 2013.01.01 %% A Little Clean Work tic; close all; clear; clc; format compact; %% 读取图像数据 % 读入图像，放在矩阵pic pic = imread('littleduck.jpg'); % 查看矩阵pic的大小和类型 whos pic; scrsz = get(0,'ScreenSize'); figure('Position',[scrsz(3)*1/4 scrsz(4)*1/6 scrsz(3)*4/5 scrsz(4)]*3/4); imshow(pic); %% 确定训练集 TrainData_background = zeros(20,3,'double'); TrainData_foreground = zeros(20,3,'double'); % % 背景（湖水）采样 % msgbox('Please get 20 background samples（点击OK后再按任意键继续）', ... % 'Background Samples','help'); % pause; % for run = 1:20 % [x,y] = ginput(1); % hold on; % plot(x,y,'r*'); % x = uint8(x); % y = uint8(y); % TrainData_background(run,1) = pic(x,y,1); % TrainData_background(run,2) = pic(x,y,2); % TrainData_background(run,3) = pic(x,y,3); % end % % 待分割出来的前景（鸭子）采样 % msgbox('Please get 20 foreground samples which is the part to be segmented（点击OK后再按任意键继续）', ... % 'Foreground Samples','help'); % pause; % for run = 1:20 % [x,y] = ginput(1); % hold on; % plot(x,y,'ro'); % x = uint8(x); % y = uint8(y); % TrainData_foreground(run,1) = pic(x,y,1); % TrainData_foreground(run,2) = pic(x,y,2); % TrainData_foreground(run,3) = pic(x,y,3); % end % % 背景（湖水）训练样本 10*3 TrainData_background = ... [52 74 87; 76 117 150; 19 48 62; 35 64 82; 46 58 36; 50 57 23; 110 127 135; 156 173 189; 246 242 232; 166 174 151]; % % 前景（鸭子）训练样本 8*3 TrainData_foreground = ... [211 192 107; 202 193 164; 32 25 0; 213 201 151; 115 75 16; 101 70 0; 169 131 22; 150 133 87]; %% 建立支持向量机 % let background be 0 & foreground 1 % 即 属于背景（湖水）的点为0，属于前景（鸭子）的点位1 TrainLabel = [zeros(length(TrainData_background),1); ... ones(length(TrainData_foreground),1)]; TrainData = [TrainData_background;TrainData_foreground]; model = svmtrain(TrainLabel, TrainData, '-t 1 -d 1'); %% 进行预测i.e.进行图像分割 preTrainLabel = svmpredict(TrainLabel, TrainData, model); % 求三维矩阵pic的行数m，列数n，页数k [m,n,k] = size(pic) % 将三维矩阵pic转成m*n行，k列的双精度二维矩阵 TestData = double(reshape(pic,m*n,k)); % 查看矩阵TestData的大小和类型 whos TestData; % 预测前景（鸭子）和背景（湖水）的标签 TestLabal = svmpredict(zeros(length(TestData),1), TestData, model); %% 展示分割后的图像 % 根据预测得到的前景（鸭子）和背景（湖水）标签对整个图像的像素点进行分类，进而达到图像分割目的。 ind = reshape([TestLabal,TestLabal,TestLabal],m,n,k); ind = logical(ind); pic_seg = pic; pic_seg(~ind) = 0; % 展示分割后的图像 scrsz = get(0,'ScreenSize'); figure('Position',[scrsz(3)*1/4 scrsz(4)*1/6 scrsz(3)*4/5 scrsz(4)]*3/4); imshow(pic_seg); % 分割前和分割后图像对比查看 scrsz = get(0,'ScreenSize'); figure('Position',[scrsz(3)*1/4 scrsz(4)*1/6 scrsz(3)*4/5 scrsz(4)]*3/4); subplot(1,2,1); imshow(pic); subplot(1,2,2); imshow(pic_seg); %% toc;