function varargout = ImageSegmentation(varargin)
% IMAGESEGMENTATION MATLAB code for ImageSegmentation.fig
% IMAGESEGMENTATION, by itself, creates a new IMAGESEGMENTATION or raises the existing
% singleton*.
%
% H = IMAGESEGMENTATION returns the handle to a new IMAGESEGMENTATION or the handle to
% the existing singleton*.
%
% IMAGESEGMENTATION('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in IMAGESEGMENTATION.M with the given input arguments.
%
% IMAGESEGMENTATION('Property','Value',...) creates a new IMAGESEGMENTATION or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before ImageSegmentation_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to ImageSegmentation_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help ImageSegmentation
% Last Modified by GUIDE v2.5 20-Apr-2016 13:59:55
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @ImageSegmentation_OpeningFcn, ...
'gui_OutputFcn', @ImageSegmentation_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
function InitAxes(handles)
% 清理
clc;
% 坐标系设置
axes(handles.axes1); cla reset;
set(handles.axes1, 'XTick', [], 'YTick', [], ...
'XTickLabel', '', 'YTickLabel', '', 'Color', [0.7020 0.7804 1.0000], 'Box', 'On');
% 坐标系设置
axes(handles.axes2); cla reset;
set(handles.axes2, 'XTick', [], 'YTick', [], ...
'XTickLabel', '', 'YTickLabel', '', 'Color', [0.7020 0.7804 1.0000], 'Box', 'On');
function filePath = OpenFile(imgfilePath)
% 打开文件
% 输出参数：
% filePath——文件路径
if nargin < 1
% 设置默认参数
imgfilePath = fullfile(pwd, 'images/lena.bmp');
end
% 选择文件对话框
[filename, pathname, filterindex] = uigetfile( ...
{ '*.bmp','All bmp Files';...
'*.jpg','All jpg Files';...
'*.*', '所有文件 (*.*)'}, ...
'选择文件', ...
'MultiSelect', 'off', ...
imgfilePath);
% 初始化
filePath = 0;
if isequal(filename, 0) || isequal(pathname, 0)
% 如果选择无效
return;
end
% 整合路径
filePath = fullfile(pathname, filename);
% --- Executes just before ImageSegmentation is made visible.
function ImageSegmentation_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to ImageSegmentation (see VARARGIN)
% Choose default command line output for ImageSegmentation
handles.output = hObject;
% 初始化
InitAxes(handles);
handles.I_origin = 0;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes ImageSegmentation wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = ImageSegmentation_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% 选择文件
filePath = OpenFile();
if isequal(filePath, 0)
% 如果选择文件无效
return;
end
% 读取
I_origin = imread(filePath);
% 显示
axes(handles.axes1);
imshow(I_origin, []);
title('原图像');
% 存储
handles.I_origin = I_origin;
guidata(hObject, handles);
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I1 = handles.I_origin;
for i = 1 : size(I1, 3)
% 逐层处理
I2(:,:,i) = edge(I1(:,:,i),'roberts');
end
% 归一化
I2 = mat2gray(I2);
% 显示
axes(handles.axes2);
imshow(I2, []);
title('Robert边缘检测');
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I1 = handles.I_origin;
for i = 1 : size(I1, 3)
% 逐层处理
I2(:,:,i) = edge(I1(:,:,i),'sobel');
end
% 归一化
I2 = mat2gray(I2);
% 显示
axes(handles.axes2);
imshow(I2, []);
title('Sobel边缘检测');
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I1 = handles.I_origin;
for i = 1 : size(I1, 3)
% 逐层处理
I2(:,:,i) = edge(I1(:,:,i),'prewitt');
end
% 归一化
I2 = mat2gray(I2);
% 显示
axes(handles.axes2);
imshow(I2, []);
title('Prewitt边缘检测');
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I1 = handles.I_origin;
for i = 1 : size(I1, 3)
% 逐层处理
I2(:,:,i) = edge(I1(:,:,i),'log');
end
% 归一化
I2 = mat2gray(I2);
% 显示
axes(handles.axes2);
imshow(I2, []);
title('LoG边缘检测');
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I1 = handles.I_origin;
for i = 1 : size(I1, 3)
% 逐层处理
I2(:,:,i) = edge(I1(:,:,i),'canny');
end
% 归一化
I2 = mat2gray(I2);
% 显示
axes(handles.axes2);
imshow(I2, []);
title('Canny边缘检测');
% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton7 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
I1 = I_origin(:,:,ik);
% 直方图统计
[count, x] = imhist(I1);
% 对直方图平滑,平滑1次
z = medfilt1(count, 10);
% 对直方图平滑,平滑2次
z = medfilt1(z, 10);
% 对直方图插值
xi = 0:0.1:length(x)-1;
% 样条插值
yi = spline(x,z,xi);
% 显示
axes(handles.axes1);
stem(x,count);
title('直方图');
hold on;
plot(xi,yi,'r','LineWidth',3);
hold off;
% 初始化
peakfound = 0;
j = 1;
K = [];
for i = 201 : length(xi) - 201
if yi(i)>yi(i-200) && yi(i)>yi(i+200) && yi(i)==max(yi(i-200:i+200))
% 条件更新
peakfound = 1;
K(j) = i;
j = j+1;
end
end
% 长度
n = length(K);
% 初始化
flag=0;
if n == 2
% 最小位置
[~, t1] = min(yi(K(1):K(2)));
KK = t1+K(1);
elseif n > 2
for i = 1:n-1
% 最小位置
[~, t] = min(yi(K(i):K(i+1)));
KK(i) = t+K(i);
end
else
% 更新标记
flag = 1;
end
% 计算阈值,输出阈值已经归一化;
if flag == 0
% 阈值
K_T = KK/length(xi);
% 二值化
I2 = im2bw(I1, K_T(1));
% 显示
axes(handles.axes2);
imshow(I2, []);
% 存储
I_out(:,:,ik) = I2;
title('极小值点阈值');
elseif flag == 1
disp('不是双峰或者多峰图');
end
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('极小值点阈值');
end
% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton8 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
% 显示
axes(handles.axes1);
imshow(I_origin, []);
title('原图像');
for ik = 1 : size(I_origin, 3)
% 逐层处理
I = I_origin(:,:,ik);
% 求出图象大小
[R,C]=size(I);
% 将图像矩阵浮点化以便运算
I = double(I);
% 求出最大的图象强度
maxI = max(max(I));
% 求出最小的图象强
minI = min(min(I));
% 浮点化以便运算
maxI = double(maxI);
minI = double(minI);
% 将初始阈值设置为最大与最小图像强度的均值
th = (maxI(1,1)+minI(1,1))/2;
% 辅助阈值初始化
th2 = 0;
% 初始化大于阈值的元素的个数及其灰度总值
p = 0.0;
q = 0.0;
% 初始化小于阈值的元素的个数及其灰度总值
p2 = 0.0;
q2 = 0.0;
% 迭代式最佳阈值选择算法主体
while abs(th2-th) > 0
% 循环条件
th2 = th;
for i=1:R
for j=1:C
if I(i,j)>=th2
% 记录大于阈值的元素个数及其灰度总值
p=p+I(i,j);
q=q+1;
end
if I(i,j)
% 记录小于阈值的元素个数及其灰度总值
p2=p2+I(i,j);
q2=q2+1;
end
end
end
% 生成新阈值
T0=p/q;
T1=p2/q2;
th=(T0+T1)/2;
% 重置初值
p=0.0;
q=0.0;
p2=0.0;
q2=0.0;
end
% 计算阈值
K_T = th/255;
% 二值化
I2 = im2bw(handles.I_origin, K_T);
% 显示
axes(handles.axes2);
imshow(I2, []);
title('最优阈值');
I_out(:,:,ik) = I2;
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('最优阈值');
end
% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton9 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
I = I_origin(:,:,ik);
% 计算直方图
[count, x]=imhist(I);
% 显示
axes(handles.axes1);
stem(x,count);
title('直方图');
% 初始化阈值
p = max(max(I));
q = min(min(I));
th = (p+q)/2;
flag = 1;
sz = size(I);
while(flag)
% 循环更新
fg = 0;
gb = 0;
fs = 0;
bs = 0;
for i = 1:sz(1)
for j = 1:sz(2)
tmp = I(i,j);
if tmp >= th
% 达到阈值
fg = fg + 1;
fs = fs + double(tmp);
else
% 迭代
gb = gb+1;
bs = bs + double(tmp);
end
end
end
% 计算中间结果
t0 = fs/fg;
t1 = bs/gb;
tk = uint8(t0+t1)/2;
if tk == th
% 达到平衡
flag = 0;
else
% 继续
th = tk;
end
end
% 计算阈值
K_T = double(th)/255;
% 二值化
I2 = im2bw(I, K_T);
% 存储
I_out(:,:,ik) = I2;
% 显示
axes(handles.axes2);
imshow(I2, []);
title('迭代阈值');
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('迭代阈值');
end
% --- Executes on button press in pushbutton10.
function pushbutton10_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton10 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
% 显示
axes(handles.axes1);
imshow(I_origin, []);
title('原图像');
for ik = 1 : size(I_origin, 3)
% 逐层处理
I = I_origin(:,:,ik);
% OTSU方法计算归一化阈值
level = graythresh(I);
% 二值化图像
I2 = im2bw(I, level);
% 存储
I_out(:,:,ik) = I2;
% 显示
axes(handles.axes2);
imshow(I2, []);
title('OSTU阈值');
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('OSTU阈值');
end
% --- Executes on button press in pushbutton11.
function pushbutton11_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton11 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
I_gray = I_origin(:,:,ik);
% 维数
[m,n] = size(I_gray);
% 初始化
T = zeros(m,n);
% 参数
M = 3;
N = 3;
% 数据类型转换
I_gray = double(I_gray);
for i = M+1:m-M
for j = N+1:n-N
% 初始化
max = 1;
min = 255;
%当前像素周围的3*3区域中的最大值和最小值
for k = i-M:i+M
for l = j-N:j+N
if I_gray(k,l)>max
% 更新
max=I_gray(k,l);
end
if I_gray(k,l)
% 更新
min=I_gray(k,l);
end
end
end
%T矩阵用来记录每点像素的阈值
T(i,j)=(max+min)/2;
end
end
% 初始化
I_bw=zeros(m,n);
for i=1:m
for j=1:n
if I_gray(i,j) > T(i,j)
% 达到阈值
I_bw(i,j) = 255;
else
% 背景
I_bw(i,j)=0;
end
end
end
% 存储
I_out(:,:,ik) = I_bw;
% 显示
axes(handles.axes2);
imshow(I_bw, []);
title('Bernsen阈值');
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('Bernsen阈值');
end
% --- Executes on button press in pushbutton12.
function pushbutton12_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton12 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
I_gray = I_origin(:,:,ik);
% 窗口参数
K = [5, 5];
% 维数
mysize = size(I_gray);
% 个数
m = fix(mysize(1)/K(1));
n = fix(mysize(2)/K(2));
% 初始化
z = zeros(mysize(1),mysize(2));
for i = 1 : K(1)
for j = 1 : K(2)
% 方块处理
Idiv{i,j} = I_gray((i-1)*m+1:i*m,(j-1)*n+1:j*n);
I_th(i,j) = graythresh(Idiv{i,j})*255;
Image_C{i,j} = ones(m,n)*I_th(i,j);
end
end
% 第一次平滑过渡
for i = 1:K(1)
I_M = [];
for j = 1:K(2)-1
% 平滑过渡
r1 = linspace(I_th(i,j),I_th(i,j+1),n);
Image_C{i,j} = ones(m,1)*r1;
I_M = [I_M,Image_C{i,j}];
end
% 存储
Image_C1{i,:}=[I_M,Image_C{i,j+1}];
end
% 第二次平滑过渡
Image_C3=[];
for i = 1 : K(1)-1
% 读取
I1 = Image_C1{i};
I2 = Image_C1{i+1};
L1 = I1(1,1:end);
L2 = I2(1,1:end);
% 初始化
Image_C2 = [];
for j = 1:length(L1)
% 平滑过渡
Lj = linspace(L1(j),L2(j),m);
Lj = Lj';
Image_C2 = [Image_C2,Lj];
end
% 存储
Image_C3 = [Image_C3;Image_C2];
end
% 整合
Image_C3 = [Image_C3;Image_C1{i+1}];
% 初始化
I_Image = I_gray(1:size(Image_C3,1),1:size(Image_C3,2));
for i = 1:size(Image_C3,1)
for j = 1:size(Image_C3,2)
if I_Image(i,j) >= Image_C3(i,j)
% 设置背景
I_Image(i,j) = 0;
else
% 设备前景
I_Image(i,j) = 255;
end
end
end
% 显示
axes(handles.axes2);
imshow(I_Image, []);
title('阈值插值法分割');
% 存储
I_out(:,:,ik) = I_Image;
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('阈值插值法分割');
end
% --- Executes on button press in pushbutton13.
function pushbutton13_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton13 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
afm = I_origin(:,:,ik);
% 算子
se = strel('disk', 15);
% 高低帽变换
Itop = imtophat(afm,se);
Ibot = imbothat(afm,se);
% 增强
Ienhance =imsubtract(imadd(Itop,afm), Ibot);
% 取反
Iec = imcomplement(Ienhance);
% 分水岭变换
wat = watershed(Iec);
% 去除边缘
wat = mat2gray(afm) - mat2gray(wat);
% 归一化
wat = mat2gray(wat);
% 显示
axes(handles.axes2);
imshow(wat, []);
title('水线阈值法分割');
% 存储
I_out(:,:,ik) = wat;
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('水线阈值法分割');
end
% --- Executes on button press in pushbutton14.
function pushbutton14_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton14 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
I_in = I_origin(:,:,ik);
% 数据类型转换
I_in = im2double(I_in);
% 维数
[M,N] = size(I_in);
% 初始化位置
Position = [123 35];
% 横坐标取整
x = round(Position(2));
% 纵坐标取整
y = round(Position(1));
% 将生长起始点灰度值存入seed中
seed = I_in(x,y);
% 作一个全零与原图像等大的图像矩阵，作为输出图像矩阵
I_out = zeros(M,N);
% 将I_out中与所取种子点相对应的位置设置为1，即种子区域设置为白色
I_out(x,y) = 1;
% 储存符合区域生长条件的点的灰度值的和
sm = seed;
% 储存符合区域生长条件的点的个数
suit = 1;
% 记录每次判断一点周围八点符合条件的新点的数目
count = 1;
% 阈值，注意需要和double类型存储的图像相符合
threshold = 0.2;
while count > 0
% 记录判断一点周围八点时，符合条件的新点的灰度值之和
s = 0;
count = 0;
for i = 1:M
for j = 1:N
if I_out(i,j) == 1
% 判断此点是否为图像边界上的点
if (i-1)>0 && (i+1)<(M+1) && (j-1)>0 && (j+1)<(N+1)
% 判断点周围八点是否符合阈值条件
for u = -1:1
for v = -1:1
if I_out(i+u,j+v)==0 && abs(I_in(i+u,j+v)-seed)<=threshold && 1/(1+1/15*abs(I_in(i+u,j+v)-seed))>0.8
I_out(i+u,j+v) = 1;
% 判断是否尚未标记，并且为符合阈值条件的点
% 符合以上两条件即将其在J中与之位置对应的点设置为白
count = count+1;
% 此点的灰度之加入s中
s = s+I_in(i+u,j+v);
end
end
end
end
end
end
end
% 将n加入符合点数计数器中
suit=suit+count;
% 将s加入符合点的灰度值总合中
sm=sm+s;
% 计算新的灰度平均值
seed=sm/suit;
end
% 显示
axes(handles.axes2);
imshow(I_out, []);
title('区域生长分割');
% 存储
I_outs(:,:,ik) = I_out;
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_outs = mat2gray(I_outs);
imshow(I_outs, []);
title('区域生长分割');
end
% --- Executes on button press in pushbutton15.
function pushbutton15_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton15 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if isequal(handles.I_origin, 0)
% 如果没有图像
return;
end
% 初始化
I_origin = handles.I_origin;
for ik = 1 : size(I_origin, 3)
% 逐层处理
I = I_origin(:,:,ik);
% 分解
S = qtdecomp(I, 0.27);
% 初始化
blocks = repmat(uint8(0),size(S));
for dim = [512 256 128 64 32 16 8 4 2 1];
% 处理每一个分块
numblocks = length(find(S==dim));
if (numblocks > 0)
% 迭代赋值
values = repmat(uint8(1),[dim dim numblocks]);
values(2:dim,2:dim,:) = 0;
blocks = qtsetblk(blocks,S,dim,values);
end
end
% 处理边界
blocks(end,1:end) = 1;
blocks(1:end,end) = 1;
% 显示
axes(handles.axes2);
imshow(blocks, []);
title('四叉树分解');
% 存储
I_out(:,:,ik) = blocks;
end
if ndims(I_origin) == 3
% 显示RGB图像
axes(handles.axes2);
% 归一化
I_out = mat2gray(I_out);
imshow(I_out, []);
title('四叉树分解');
end
% --- Executes on button press in pushbutton16.
function pushbutton16_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton16 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% 退出对话框
choice = questdlg('确定要退出系统?', ...
'退出', ...
'确定','取消','取消');
switch choice
case '确定'
close;
case '取消'
return;
end
|
