【图像处理】基于数字图像处理含Matlab源码

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     end