实例介绍
【实例截图】
【核心代码】
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 | 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-2017 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 |
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