1 简介
基于数字图像处理含Matlab源码
2 完整代码
function varargout = image_processing(varargin)
% IMAGE_PROCESSING MATLAB code for image_processing.fig
% IMAGE_PROCESSING, by itself, creates a new IMAGE_PROCESSING or raises the existing
% singleton*.
%
% H = IMAGE_PROCESSING returns the handle to a new IMAGE_PROCESSING or the handle to
% the existing singleton*.
%
% IMAGE_PROCESSING('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in IMAGE_PROCESSING.M with the given input arguments.
%
% IMAGE_PROCESSING('Property','Value',...) creates a new IMAGE_PROCESSING or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before image_processing_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to image_processing_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 image_processing
% Last Modified by GUIDE v2.5 22-Apr-2021 15:16:04
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @image_processing_OpeningFcn, ...
'gui_OutputFcn', @image_processing_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
% --- Executes just before image_processing is made visible.
function image_processing_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 image_processing (see VARARGIN)
% Choose default command line output for image_processing
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes image_processing wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = image_processing_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
axis off;
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
global M;
ima_gray=image_gray(M);
imshow(ima_gray);
% 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)
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
global M;
ima_gray=image_gray(M);
[i,j]=size(ima_gray);
prompt = {'请输入阈值:'};
dlg_title = '提示';
num_lines = 1;
def = {'5'};
value_i = inputdlg(prompt,dlg_title,num_lines);
threshold_value=str2double(value_i);
for a=1:i
for b=1:j
if ima_gray(a,b)<threshold_value
ima_gray(a,b)=0;
else
ima_gray(a,b)=1;
end
end
end
ima_gray=mat2gray(ima_gray);
imshow(ima_gray);
% 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)
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
global M;
ima_red=M(:,:,1);
ima_green=M(:,:,2);
ima_blue=M(:,:,3);
img_red=mid_filter(ima_red,6);
img_green=mid_filter(ima_green,6);
img_blue=mid_filter(ima_blue,6);
image(:,:,1)=img_red;
image(:,:,2)=img_green;
image(:,:,3)=img_blue;
imshow(image);
% 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)
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
global M;
if size(M,1)<2;
msgbox('请先打开图片');
end
ima_red=M(:,:,1);
ima_green=M(:,:,2);
ima_blue=M(:,:,3);
processing_red=low_pass_filter(ima_red);
processing_green=low_pass_filter(ima_green);
processing_blue=low_pass_filter(ima_blue);
image(:,:,1)=processing_red;
image(:,:,2)=processing_green;
image(:,:,3)=processing_blue;
imshow(image);
% 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)
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
global M;
prompt = {'请输入滤波核大小:'};
dlg_title = '提示';
num_lines = 1;
def = {'5'};
value_i = inputdlg(prompt,dlg_title,num_lines);
N=str2double(value_i);
d=avg_filter(M,N);
imshow(d);
% 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)
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
global M;
%滤波核大小
ima_red=M(:,:,1);
ima_green=M(:,:,2);
ima_blue=M(:,:,3);
prompt = {'请输入滤波器大小:'};
dlg_title = '提示';
num_lines = 1;
value_i = inputdlg(prompt,dlg_title,num_lines);
N=str2double(value_i);
sigma=1.7;
img_red=image_gaussian(ima_red,sigma,N);
img_green=image_gaussian(ima_green,sigma,N);
img_blue=image_gaussian(ima_blue,sigma,N);
img(:,:,1)=img_red;
img(:,:,2)=img_green;
img(:,:,3)=img_blue;
imshow(img);
% 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)
% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
global M;
ima_red=M(:,:,1);
ima_green=M(:,:,2);
ima_blue=M(:,:,3);
histogram_red=histogram(ima_red);
histogram_green=histogram(ima_green);
histogram_blue=histogram(ima_blue);
figure,
subplot(1,3,1);plot(histogram_red),title('红色通道');
xlim([0 255])
subplot(1,3,2),plot(histogram_green),title('绿色通道');
xlim([0 255])
subplot(1,3,3),plot(histogram_blue),title('蓝色通道');
xlim([0 255])
% ima=imread('1.jpg');
% ima_gaussian=image_gaussian(ima,2,500);
% 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)
% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)%每个通道不一样，不能按照一个通道来
global M;
ima_gray=image_gray(M);
[i,j]=size(ima_gray);
threshold_value=150;
for a=1:i
for b=1:j
if ima_gray(a,b)<threshold_value
ima_gray(a,b)=0;
else
ima_gray(a,b)=1;
end
end
end
ima_gray=mat2gray(ima_gray);
IMG=ima_gray;
[row,col]=size(IMG);
figure,imshow(IMG);title('二值化');
for i=1:row-1
for j=1:col-1
if(IMG(i,j+1)&&IMG(i+1,j)) %若S中为1的位置全为1则为1
IMG(i,j)=1; %正向判断1
else
IMG(i,j)=0;
end
end
end
figure,imshow(IMG);title('腐蚀');
% figure,
% subplot(1,3,1);plot(histogram_red),title('红色通道');
% xlim([0 255])
% subplot(1,3,2),plot(histogram_green),title('绿色通道');
% xlim([0 255])
% subplot(1,3,3),plot(histogram_blue),title('蓝色通道');
% xlim([0 255])
% 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)
% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
global M;
[m,n,q]=size(M);
img=double(M);
%%canny边缘检测的前两步相对不复杂，所以我就直接调用系统函数了
%%高斯滤波
w=fspecial('gaussian',[5 5]);
img=imfilter(img,w,'replicate');
%%sobel边缘检测
w=fspecial('sobel');
img_w=imfilter(img,w,'replicate'); %求横边缘
w=w';
img_h=imfilter(img,w,'replicate'); %求竖边缘
img=sqrt(img_w.^2+img_h.^2); %注意这里不是简单的求平均，而是平方和在开方。我曾经好长一段时间都搞错了
%%下面是非极大抑制
new_edge=zeros(m,n);
for i=2:m-1
for j=2:n-1
Mx=img_w(i,j);
My=img_h(i,j);
if My~=0
o=atan(Mx/My); %边缘的法线弧度
elseif My==0 && Mx>0
o=pi/2;
else
o=-pi/2;
end
%Mx处用My和img进行插值
adds=get_coords(o); %边缘像素法线一侧求得的两点坐标，插值需要
M1=My*img(i+adds(2),j+adds(1))+(Mx-My)*img(i+adds(4),j+adds(3)); %插值后得到的像素，用此像素和当前像素比较
adds=get_coords(o+pi);%边缘法线另一侧求得的两点坐标，插值需要
M2=My*img(i+adds(2),j+adds(1))+(Mx-My)*img(i+adds(4),j+adds(3)); %另一侧插值得到的像素，同样和当前像素比较
isbigger=(Mx*img(i,j)>M1)*(Mx*img(i,j)>=M2)+(Mx*img(i,j)<M1)*(Mx*img(i,j)<=M2); %如果当前点比两边点都大置1
if isbigger
new_edge(i,j)=img(i,j);
end
end
end
%%下面是滞后阈值处理
up=120; %上阈值
low=100; %下阈值
set(0,'RecursionLimit',10000); %设置最大递归深度
for i=1:m
for j=1:n
if new_edge(i,j)>up &&new_edge(i,j)~=255 %判断上阈值
new_edge(i,j)=255;
new_edge=connect(new_edge,i,j,low);
end
end
end
imshow(new_edge==255);
% 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)
% --- Executes on button press in pushbutton10.
function pushbutton10_Callback(hObject, eventdata, handles)
global M;
u=image_gray(M);
F=double(M);
U=double(u);
[H,W]=size(u);
uSobel=u;
% ms=0;
% ns=0;
for i=2:H-1
for j=2:W-1
Gx=(U(i+1,j-1)+2*U(i+1,j)+F(i+1,j+1))-(U(i-1,j-1)+2*U(i-1,j)+F(i-1,j+1));
Gy=(U(i-1,j+1)+2*U(i,j+1)+F(i+1,j+1))-(U(i-1,j-1)+2*U(i,j-1)+F(i+1,j-1));
uSobel(i,j)=sqrt(Gx^2+Gy^2);
% ms=ms+uSobel(i,j);
% ns=ns+(uSobel(i,j)-ms)^2;
end
end
% ms=ms/(H*W);
% ns=ns/(H*W);
imshow(M);
figure;
imshow(im2uint8(uSobel));title('Sobel处理后');
for i=1:H
for j=1:W
if(uSobel(i,j)<150)
uSobel(i,j)=0;
else
uSobel(i,j)=1;
end
end
end
uSobel=mat2gray(uSobel);
figure;imshow(uSobel);title('阈值细化');
% 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)
% --- Executes on button press in pushbutton12.
function pushbutton12_Callback(hObject, eventdata, handles)
[filename, pathname] = uigetfile('*.jpg', '读取图片文件'); %选择图片文件
if isequal(filename,0) %判断是否选择
msgbox('没有选择任何图片');
else
pathfile=fullfile(pathname, filename); %获得图片路径
global M;
M=imread(pathfile); %将图片读入矩阵
imshow(M);
end
% 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)
% --- Executes on button press in pushbutton13.
function pushbutton13_Callback(hObject, eventdata, handles)
button13=questdlg('你确定退出吗？','退出程序','Yes','No','Yes');
if strcmp(button13,'Yes')
close all;
end;
% 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)
function d=image_gray(src)
ima_red=src(:,:,1);
ima_green=src(:,:,2);
ima_blue=src(:,:,3);
d=0.299*ima_red+0.587*ima_green+0.114*ima_blue;
function desimg=image_gaussian(originimg,sigma,N)
[ori_row,ori_col]=size(originimg);
N_row = 2*N+1;
H = [];%求高斯模板H
for i=1:N_row
for j=1:N_row
fenzi=double((i-N-1)^2+(j-N-1)^2);
H(i,j)=exp(-fenzi/(2*sigma*sigma))/(2*pi*sigma);
end
end
H=H/sum(H(:));%归一化
temp=[]; %模板与图像卷积实现
desimg=[ori_row,ori_col];
for ai=N+1:ori_row-N-1
for aj=N+1:ori_col-N-1
temp=0;
for bi=1:N_row
for bj=1:N_row
temp= temp+(originimg(ai+bi-N,aj+bj-N)*H(bi,bj));
end
end
desimg(ai,aj)=temp;
end
end
desimg=uint8(desimg);
function re=get_coords(angle) %angle是边缘法线角度，返回法线前后两点
sigma=0.000000001;
x1=ceil(cos(angle+pi/8)*sqrt(2)-0.5-sigma);
y1=ceil(-sin(angle-pi/8)*sqrt(2)-0.5-sigma);
x2=ceil(cos(angle-pi/8)*sqrt(2)-0.5-sigma);
y2=ceil(-sin(angle-pi/8)*sqrt(2)-0.5-sigma);
re=[x1 y1 x2 y2];
function nedge=connect(nedge,y,x,low) %种子定位后的连通分析
neighbour=[-1 -1;-1 0;-1 1;0 -1;0 1;1 -1;1 0;1 1]; %八连通搜寻
[m n]=size(nedge);
for k=1:8
yy=y+neighbour(k,1);
xx=x+neighbour(k,2);
if yy>=1 &&yy<=m &&xx>=1 && xx<=n
if nedge(yy,xx)>=low && nedge(yy,xx)~=255 %判断下阈值
nedge(yy,xx)=255;
nedge=connect(nedge,yy,xx,low);
end
end
end
function d=mid_filter(ima,N)
[height, width]=size(ima); %输入图像是p×q的,且p>n,q>n
x1=double(ima);
x2=x1;
for i=1:height-N+1
for j=1:height-N+1
c=x1(i:i+(N-1),j:j+(N-1)); %取出x1中从(i,j)开始的n行n列元素,即模板(n×n的)
e=c(1,:); %是c矩阵的第一行
for u=2:N
e=[e,c(u,:)]; %将c矩阵变为一个行矩阵
end
mm=median(e); %mm是中值
x2(i+round((N-1)/2),j+round((N-1)/2))=mm; %将模板各元素的中值赋给模板中心位置的元素
end
end
d=uint8(x2); %未被赋值的元素取原值
function d=avg_filter(image,n)
a(1:n,1:n)=1; %a即n×n模板,元素全是1
[height, width]=size(image); %输入图像是hightxwidth的,且hight>n,width>n
x1=double(image);
x2=x1;
for i=1:height-n+1
for j=1:width-n+1
c=x1(i:i+(n-1),j:j+(n-1)).*a; %取出x1中从(i,j)开始的n行n列元素与模板相乘
s=sum(sum(c)); %求c矩阵中各元素之和
x2(i+(n-1)/2,j+(n-1)/2)=s/(n*n); %将与模板运算后的各元素的均值赋给模板中心位置的元素
end
end
%未被赋值的元素取原值
d=uint8(x2);
function B=low_pass_filter(image)
m=double(image);
f=fft2(m);
f=fftshift(f);
[N1,N2]=size(f); %返回矩阵的行和列
n1=round(N1/2);
n2=round(N2/2);
n=2;d0=50; %滤波器截止频率,滤波半径
for i=1:N1
for j=1:N2
d=sqrt((i-n1)^2+(j-n2)^2); %计算低通滤波转换函数
if d<=d0
h=1;
else
h=0;
end
y(i,j)=h*f(i,j);
end
end
y=ifftshift(y);
A=ifft2(y);
B=uint8(real(A));
function nk=histogram(image)
L=256; %灰度级
nk=zeros(L,1);%出现次数
[row,col]=size(image);
n=row*col; %总像素个数
for i = 1:row
for j = 1:col
num = double(image(i,j))+1; %获取像素点灰度级0到255所以要加上1
nk(num) = nk(num)+1; %统计nk
end
end3 仿真结果
4 参考文献
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
