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边缘检测时先要把其他格式图像转化为灰度图像f=imreadlbxx.bmp;a=rgb2grayf;[gt]=edgeacanny;imshowgimreadimfinfocolor__pimshowsubplotsubi__gei__ddimsubtractimmultiplyimdivideimresizeimrotate旋转imcrop剪贴a=imreadonion.png;b=imcropa
[7568130112];subplot121;imshowa;subplot122;imshowb;roipoly选择图像中的多边形区域????????、a=imreadonion.png;c=
[200250278248199172];r=
[21217512112175];b=roipolyacr;subplot121;imshowa;subplot122;imshowb;roicolor按灰度值选择的区域a=imreadonion.png;i=rgb2graya;b=roicolori128255;subplot121;imshowa;subplot122;imshowb;转化指定的多边形区域为二值掩膜poly2__skx=
[631865419063];y=
[606020920460];b=poly2__skxy256256;imshowb;holdCurrentplotheldplotxybLineWidth2roifilt2区域滤波a=imreadonion.png;i=rgb2graya;c=
[200250278248199172];r=
[21217512112175];b=roipolyicr;h=fspecialunsharp;j=roifilt2hib;subplot121imshowi;subplot122imshowj;roifill区域填充a=imreadonion.png;i=rgb2graya;c=
[200250278248199172];r=
[21217512112175];j=roifillicr;subplot211;imshowi;subplot212;imshowj;FFT变换f=zeros100100;f20:7040:60=1;imshowf;F=fft2f;F2=logabsF;imshowF2colorbar补零操作和改变图像的显示象限f=zeros100100;f20:7040:60=1;subplot121;imshowf;F=fft2f256256;F2=fftshiftF;subplot122;imshowlogabsF2离散余弦变换(dct)a=imreadonion.png;i=rgb2graya;j=dct2i;subplot131;imshowlogabsjcolorbarjabsj10=0;k=idct2j;subplot132;imshowi;subplot133;imshowk
[0255];info=imfinfotrees.tif%显示图像信息edge提取图像的边缘cannyprewittsobelradon函数用来计算指定方向上图像矩阵的投影a=imreadonion.png;i=rgb2graya;b=edgei;theta=0:179;[rxp]=radonbtheta;figurei__gescthetaxpr;color__phot;xlabel\thetadegrees;ylabelx\prime;titler_{\theta}x\prime;colorbarimhisthisteqfilter2均值滤波a=imreadonion.png;i=rgb2graya;imshowik1=filter2fspecial__erage3i/255;%3*3k2=filter2fspecial__erage5i/255;%5*5k3=filter2fspecial__erage7i/255;%7*7figureimshowk1figureimshowk2figureimshowk3wiener2滤波eg k=wienerI
[33]medfilt2中值滤波同上deconvwnr维纳滤波马赫带效应(同等差色带条)减采样a=imreadfootball.jpg;b=rgb2graya;[widhei]=sizeb;quarting=zeroswid/2+1hei/2+1;i1=1;j1=1;fori=1:2:widforj=1:2:heiquartingi1j1=bij;j1=j1+1;endi1=i1+1;j1=1;endfigureimshowuint8quartingtitle4倍减采样quarting=zeroswid/4+1hei/4+1;i1=1;j1=1;fori=1:4:widforj=1:4:heiquartingi1j1=bij;j1=j1+1;endi1=i1+1;j1=1;endfigureimshowuint8quarting;title16倍减采样结论在采用不同的减采样过程中,其图像的清晰度和尺寸均发生了变化灰度级转化a=imreadfootball.jpg;b=rgb2graya;figure;imshowb[widhei]=sizeb;img2=zeroswidhei;fori=1:widforj=1:heiimg2ij=floorbij/128;endendfigure;imshowuint8img2
[02]%2级灰度图像图像的基本运算i=imreadfootball.jpg;figure;subplot231;imshowi;title原图;j=i__djusti[.3;.6][.
1.9];%Adjusti__geintensityvaluesorcolor__p图像灰度值或color__p调整%J=I__DJUSTI[LOW_IN;HIGH_IN][LOW_OUT;HIGH_OUT]subplot232;imshowj;title线性扩展;i1=doublei;i2=i1/255;c=2;k=c*log1+i2;subplot233;imshowk;title非线性扩展;m=255-i;subplot234;imshowmtitle灰度倒置n1=im2bwi.4;n2=im2bwi.7;subplot235;imshown1;title二值化阈值
0.4subplot236;imshown2;title二值化阈值
0.7图像的代数运算加减,乘法(获取感兴趣的区域)imresize放大a=imresizeI2%比例a=imresizeI
[3324]%非比例imrotate旋转a=imrotateI45时域旋转多少度,频域也就旋转多少度i=zeros256256;i88:168124:132=1;imshowij=fft2i;f=absj;j1=fftshiftf;figure;imshowj1
[550]j=imrotatei90bilinearcrop;figure;imshowj;j1=fft2j;f=absj1;j2=fftshiftf;figure;imshowj2
[550]边缘检测a=imreadkids.tif;subplot211;imshowa;titleprib=edgeacanny;subplot212;imshowb;titlebyfigurec=edgeaprewitt;imshowc腐蚀和膨胀a=imreadfootball.jpg;subplot231;imshowa;title原灰度图像t=graythresha;bw1=im2bwat;se1=strelsquare3;se2=strelsquare5;bw2=imerodebw1se1;subplot232;imshowbw2title3*3腐蚀bw3=imdilatebw1se1;subplot233;imshowbw3;title3*3膨胀bw4=imerodebw1se2;subplot234;imshowbw4;title5*5腐蚀bw5=imdilatebw1se2;subplot235;imshowbw5;title5*5膨胀log算子x=-2:.06:2;y=-2:.06:2;sig__=.6;y=y;fori=1:4/.06+1xxi:=x;yy:i=y;endr=1/2*pi*sig__^4*xx.^2+yy.^2/sig__^2-
2.*exp-xx.^2+yy.^2/sig__^2;color__pjet16;meshxxyyr分水岭算法分割图像f=imreadlbxx.bmp;f=rgb2grayf;subplot221;imshowftitleprisubplot222;f=doublef;hv=fspecialprewitt;hh=hv.;gv=absimfilterfhvreplicate;gh=absimfilterfhhreplicate;g=sqrtgv.^2+gh.^2;subplot222;l=watershedg;wr=l==0;imshowwr;title分水岭fwr=255;subplot223;imshowuint8f;titlec分割结果rm=imregionalming;subplot224;imshowrm;title(d)局部极小值区域生长法彩色基础rgb_r=zeros512512;rgb_r1:256257:512=1;rgb_g=zeros512512;rgb_g1:2561:256=1;rgb_g257:512257:512=1;rgb_b=zeros512512;rgb_b257:5121:256=1;rgb=cat3rgb_rrgb_grgb_b;figureimshowrgbtitlergb彩色图像将上题转化到HIS空间模糊H分量,得到rgb=imreadfootball.jpg;rgb1=im2doublergb;r=rgb1::1;g=rgb1::2;b=rgb1::3;i=r+g+b/3;tmp1=minminrgb;tmp2=r+g+b;tmp2tmp2==0=eps;s=1-
3.*tmp
1./tmp2;tmp1=.5*r-g+r-b;tmp2=sqrtr-g.^2+r-b.*g-b;theta=acostmp
1./tmp2+eps;h=theta;hbg=2*pi-hbg;h=h/2*pi;hs==0=0;subplot231;imshowrgb;titlei__gesubplot232;imshowhtitlehsubplot233;imshowstitlessubplot234;imshowititleih1=filter2fspecial__erage25h/255;s1=filter2fspecial__erage25s/255;subplot235;imshow__t2grayh1titleh模糊结果hsi=cat3h1si;h=hsi::1*2*pi;s=hsi::2;i=hsi::3;R=zerossizehsi1sizehsi2;G=zerossizehsi1sizehsi2;B=zerossizehsi1sizehsi2;ind=findh=0h2*pi/3;Bind=iind.*
1.0-sind;Rind=iind.*
1.0+sind.*coshind./cospi/
3.0-hind;Gind=
1.0-Rind+Bind;ind=findh2*pi/3h4*pi/3;hind=hind-pi*2/3;rind=iind.*
1.0-sind;gind=iind.*
1.0+sind.*coshind./cospi/
3.0-hind;bind=
1.0-Rind+Gind;ind=findh4*pi/3h2*pi;hind=hind-pi*4/3;gind=iind.*
1.0-sind;bind=iind.*
1.0+sind.*coshind./cospi/
3.0-hind;rind=
1.0-Gind+Bind;RGB=cat3RGB;subplot236;imshowRGB;title处理后的图像结论图像变柔和
(2)模糊S分量将hsi=cat3h1si;给为hsi=cat3hs1i;结论图像饱和度降低边缘clearrgb_r=zeros128128;rgb_r1:641:64=1;rgb_g=zeros128128;rgb_g1:6465:128=1;rgb_b=zeros128128;rgb_b65:1281:64=1;rgb=cat3rgb_rrgb_grgb_b;sob=fspecialsobel;rx=imfilterdoublergb::1sobreplicate;ry=imfilterdoublergb::1sobreplicate;gx=imfilterdoublergb::2sobreplicate;gy=imfilterdoublergb::2sobreplicate;bx=imfilterdoublergb::3sobreplicate;by=imfilterdoublergb::3sobreplicate;r_gradiant=__t2gray__xrxry;g_gradiant=__t2gray__xgxgy;b_gradiant=__t2gray__xbxby;rgb_gradiant=rgb2graycat3r_gradiantg_gradiantb_gradiant;gxx=rx.^2+gx.^2+bx.^2;gyy=ry.^2+gy.^2+by.^2;gxy=rx.*ry+gx.*gy+bx.*by;theta=.5*atan2*gxy./gxx-gyy+eps;g1=.5*gxx+gyy+gxx-gyy.*cos2*theta+2*gxy.*sin2*theta;theta=theta+pi/2;g2=.5*gxx+gyy+gxx-gyy.*cos2*theta+2*gxy.*sin2*theta;g1=g
1.^5;g2=g
2.^5;rgb_vectorgradiant=__t2gray__xg1g2;diff=absrgb_vectorgradiant-rgb_gradiant;subplot331;imshowrgb;title彩色原图subplot332;imshowr_gradiant;titleR分量边缘subplot333;imshowg_gradiant;titleG分量边缘subplot334;imshowb_gradiant;titleB分量边缘subplot335;imshowrgb_gradiant;title分量合成边缘subplot336;imshowrgb_vectorgradiant;title向量梯度边缘subplot337;imshowdiff;title差别平滑再合成锐化rgb1=imreadfootball.jpg;rgb=im2doublergb1;fr=rgb::1;fg=rgb::2;fb=rgb::3;lap__trix=[111;1-81;111];fr_filtered=imfilterfrlap__trixreplicate;fg_filtered=imfilterfglap__trixreplicate;fb_filtered=imfilterfblap__trixreplicate;rgb_tmp=cat3fr_filteredfg_filteredfb_filtered;rgb_filtered=imsubtractrgbrgb_tmp;i1=fr+fg+fb/3;tmp1=minminfrfgfb;tmp2=fr+fg+fb;tmp2tmp2==0=eps;%s=1-
3.*tmp
1./tmp2;tmp1=.5*fr-fg+fr-fb;tmp2=sqrtfr-fg.^2+fr-fb.*fg-fb;theta=acostmp
1./tmp2+eps;h1=theta;h1fbfg=2*pi-h1fbfg;h1=h1/2*pi;h1s==0=0;lap__trix=[111;1-81;111];i=imfilteri1lap__trixreplicatei=imfilteri1lap__trixreplicate;subplot331;imshowrgb1;titleprisubplot332;imshowfr_flitered;titlefr_fliteredsubplot333;imshowfg_filtered;titlefg_filteredsubplot334;imshowfb_filtered;titlefb_filteredsubplot335;imshowh1;titleh1subplot336;imshows;titlessubplot337;imshowi1;titlei1subplot338;imshowrgb_tmp;titlemixturesubplot339;imshowcat3h1si1;titlehsimixture彩色图像向量梯度的计算rgb=imreadfootball.jpg;sob=fspecialsobel;rx=imfilterdoublergb::1sobreplicate;ry=imfilterdoublergb::1sobreplicate;gx=imfilterdoublergb::2sobreplicate;gy=imfilterdoublergb::2sobreplicate;bx=imfilterdoublergb::3sobreplicate;by=imfilterdoublergb::3sobreplicate;gxx=rx.^2+gx.^2+bx.^2;gyy=ry.^2+gy.^2+by.^2;gxy=rx.*ry+gx.*gy+bx.*by;theta=.5*atan2*gxy./gxx-gyy+eps;g1=.5*gxx+gyy+gxx-gyy.*cos2*theta+2*gxy.*sin2*theta;theta=theta+pi/2;g2=.5*gxx+gyy+gxx-gyy.*cos2*theta+2*gxy.*sin2*theta;g1=g
1.^.5;g2=g
2.^.5;rgb_gradiant=__t2gray__xg1g2;subplot121;imshowrgb;titleprisubplot122;imshowrgb_gradiant;titlergb\_gradiant得到图像边界,并计算周长、__和重心坐标a=imreadlbxx.bmp;a1=bwperima;l=0;[mn]=sizea1;fori=1:m*nifa1i==1l=l+1;endend[mn]=sizea;s=0;fori=1:m*nifai==1s=s+1;endendx=0;y=0;fori=1:mforj=1:nifaij==1x=i+x;y=j+y;endendendx=x/s;y=y/s;lsxya=imreadtrees.tif%么有加;号33333333333333193328333333333333333333193333193319333333192133333333333333333328333333193333333333213333333345575733331045333345454545333319334533334545333333195733333345333333332833333333573333333333574533454533333319194533453333453333333345453357453333333319a=imreadtrees.tif;imshowasubplot211imshowasubplot212imshow~a[x__p]=imreadtrees.tif;i__gex;%显示矩阵x图像Color__p__p%设置颜色映射表,这样才可以将tif文件显示成彩色的????i=imreadtrees.tif;j=filter2[12-1-2]i;imshowj[]i=imreadri__.png;subplot121imshowi
[100200];%灰度范围为
[100200]subplot122imshowi20%灰度等级为20loadtreesa=
[1060];i__gescXa;Color__pgrayimshowri__.png%直接从磁盘显示,无需单独读入a=imreadonion.png;i=rgb2graya;h=[121;000;-1-2-1];i2=filter2hi;%使用二维滤波器h进行滤波imshowi2[]colorbar显示单帧图像mri=uint8zeros128128127;forframe=1:27[mri:::frame__p]=imreadmri.tifframe;endimshowmri:::3__p;%显示第三帧problem如何知道一幅图像有几帧????????显示多帧图像montagemri__pmov=immoviemri__p;moviemov%播放动画get0ScreenDepth%获取计算机系统支持的像素点的位数ans=32同时显示多幅图像,除了使用subplot-imshow外,也可以使用下面的方法[x1__p1]=imreadforest.tif;[x2__p2]=imreadtrees.tif;subplot121subi__gex1__p1subplot122subi__gex2__p2纹理映射2维转化为3维[xyz]=cylinder;%新建一个柱形面a=imreadpeppers.png;subplot211warpxyza;%插值映射subplot212;imshowpeppers.pngtitle2维subplot211;title3维i=imreadri__.png;subplot121;imshowi;title原来的灰度图像subplot122;b=ditheri;%转化成二值图像imshowb;title转换后的二值图像索引图像转化为灰度图像loadtreesi=ind2grayX__p;figure1imshowX__pholdCurrentplotheldfigure2imshowi注意不能再一个窗口的不同子窗口中既绘制灰度图像,又要绘制彩色图像i=imreadri__.png;figure1imshowititle原灰度图像figure[X__p]=gray2indi6;imshowX__ptitle转换后的索引图像graysli__函数通过设定阈值将灰度图像转化为索引图像i=imreadsnowflakes.png;x=graysli__i16;imshowifigureimshowxjet16__t2gray函数将一个数据矩阵转化为一个灰度图像i=imreadri__.png;j=filter2fspecialsobeli;k=__t2grayj;imshowifigureimshowkrgb2gary函数将真彩色图像转化为灰度图像[x__p]=imreadri__.png;np=rgb2gray__p;Errorusing==rgb2grayparse_inputs__Pmustbeamx3array.Errorin==rgb2grayat35X=parse_inputsvarargin{:};????????????rgb=imreadpeppers.png;[X_nodither__p]=rgb2indrgb8nodither;[X_dither__p]=rgb2ind8dither;subplot131imshowrgb;subplot132imshowX_nodither__p;%无抖动subplot133imshowX_dither__p%有抖动im2bw函数通过设定阈值将真彩、索引和灰度图像转化为二值图像loadtreesb=im2bwX__p.4;imshowX__pfigureimshowbrgb=reshapeones641*reshapejet641192
[64643];hsv=rgb2hsvrgb;h=hsv::1;s=hsv::2;v=hsv::3;subplot221imshowhtitle色调subplot222imshowstitle饱和度subplot223imshowvtitle亮度subplot224imshowhtitle原真彩色调色板rgb=imreadpeppers.png;yiq=rgb2ntscrgb;subplot121imshowrgbtitle原始rgb图像subplot122imshowyiq::1title变换后的ntsc图像rgb=imreadpeppers.png;hsv=rgb2hsvrgb;subplot121imshowrgbtitle原始rgb图像subplot122imshowhsvtitle变换后的hsv图像rgb=imreadpeppers.png;ycbcr=rgb2ycbcrrgb;subplot121imshowrgbtitle原始rgb图像subplot122imshowycbcrtitle变换后的ycbcr图像色彩重排[x__p]=imreadcanoe.tif;[ynew__p]=cmpermutex__p;__pnew__p褪色前后[x__p]=imreadcanoe.tif;[ynew__p]=i__pproxx__p20;imshowx__pfigureimshowynew__p[x__p]=imreadcanoe.tif;[ynew__p]=cmuniquex__p;%由相同图像产生索引图像y和调色板new__psize__pans=2563sizenew__pans=2483绘制调色板[x__p]=imreadcanoe.tif;rgbplot__p图像轮廓线及直方图计算i=imreadri__.png;subplot221imshowi;title原始图像subplot223imcontourititle图像的轮廓线subplot122imhisti64title图像的直方图灰度倒置线性变换d=imreadtrees.tif;color__p;j=i__djustd
[01]
[10]
1.5;subplot121imshowdsubplot122subi__gej灰度直方图i=imreadcamera__n.tif;subplot121imshowisubplot122imhistiaxissquarei__djust示例i=imreadcamera__n.tif;j=i__djusti[.
15.9]
[01];subplot121imshowjsubplot122imhistjaxissquarei=imreadcamera__n.tif;j=i__djusti[
0.2][
0.51];subplot221imshowisubplot222imshowjsubplot223imhistisubplot224imhistj[x__p]=imreadforest.tif;j=ind2grayx__p;j=i__djustx[][].5;imshowxholdCurrentplotheldfigureimshowji=imreadtire.tif;j=histeqi;subplot221imshowititle原始图像subplot222imshowjtitle直方图均衡化处理以后的图像subplot223imhisti64title原始图像直方图subplot224imhistj64i=imreadpout.tif;j=adapthisteqi;subplot221imshowisubplot222imshowjsubplot223imhistisubplot224imhistji=imreadri__.png;subplot221imshowititle原图i1=imnoiseigaussian
0.02;subplot222imshowi1title加入高斯噪声i2=imnoiseispeckle.02;subplot223imshowi2title加入乘性噪声i3=imnoiseisalt.02;subplot224imshowi3title加入椒盐噪声i=imreadri__.png;i1=imnoiseigaussian
0.02;subplot121imshowi1title含噪__h=[111;111;111];H=h/9;j=conv2i1H;Warning:CONV2onvaluesofclassUINT8isobsolete.UseCONV2DOUBLEADOUBLEBorCONV2SINGLEASINGLEBinstead.Inuint
8.conv2at11i=uint16j;subplot122imshowj[]title3*3均值滤波结果i=imreadeight.tif;j=imnoiseisalt.02;k=medfilt2j;subplot121imshowjtitle含噪__subplot122imshowktitle3*3中值滤波结果i=imreadeight.tif;j=imnoiseisalt.02;do__in=[111;111;111];k=ordfilt2j1do__in;subplot121imshowjtitle含噪__subplot122imshowktitle3*3邻域的最小值滤波__注意找不到wiener函数i=imreadeight.tif;i=doublei;imshowi[]h=[010;1-40;010];j=conv2ihsame;k=i-j;holdCurrentplotheldfigureimshowk[]i=imreadeight.tif;subplot211imshowititlepriH=fspeciallaplacian;%应用laplacian滤波进行图像锐化laplacianH=filter2Hi;%在5*5邻域内进行维纳滤波subplot223imshowlaplacianHtitlelaplacian算子锐化图像H=fspecialprewitt;prewittH=filter2Hi;subplot224imshowprewittHtitleprewitt模板锐化图像i=imreadsaturn.png;j=imnoiseisalt.02;subplot121imshowjtitle含噪__j=doublej;f=fft2j;g=fftshiftf;[mn]=sizef;n=3;d0=20;n1=floorm/2;n2=floorn/2;fori=1:mforj=1:nd=sqrti-n1^2+j-n2^2;h=1/1+.414*d/d0^2*n;gij=h*gij;endendg=ifftshiftg;g=uint8realifft2g;subplot122imshowgtitle三阶巴特沃斯滤波结果rgb=imreadpeppers.png;subplot221imshowrgbtitleprisubplot222imshowrgb::1titleredpartsubplot223imshowrgb::2titlegreenpartsubplot224imshowrgb::3titlebluepart。