图像处理源代码
图像处理编码
1．"浮雕"图像"浮雕"图象效果是指图像的前景前向凸出背景。
所谓的"浮雕"概念是指标绘图像上的一个像素和它左上方的那个像素之间差值的一种处理过程，为了使图像保持一定的亮度并呈现灰色，我在处理过程中为这个差值加了一个数值为128的常量。
需要读者注意的是，当设置一个像素值的时候，它和它左上方的像素都要被用到，为了避免用到已经设置过的像素，应该从图像的右下方的像素开始处理，下面是实现的源代码：void CDibView::OnFDImage() //产生"浮雕"效果图函数{HANDLE data1handle;//用来存放图像数据的句柄；LPBITMAPINFOHEADER lpBi;//图像的信息头结构；CDibDoc *pDoc=GetDocument();//得到文挡指针；HDIB hdib;//用来存放图像数据的句柄；unsigned char *pData;//指向原始图像数据的指针；unsigned char *data;//指向处理后图像数据的指针；hdib=pDoc->m_hDIB;//拷贝存放已经读取的图像文件数据句柄；lpBi=(LPBITMAPINFOHEADER)GlobalLock((HGLOBAL)hdib);//获取图像信息头pData=(unsigned char*)FindDIBBits((LPSTR)lpBi);//FindDIBBits是我定义的一个函数、根据图像的结构得到位图的灰度值数据、pDoc->SetModifiedFlag(TRUE);//设置文档修改标志为"真"、为后续的修改存盘作准备；data1handle=GlobalAlloc(GMEM_SHARE,WIDTHBYTES(lpBi->biWidth*8)*lpBi-> biHeight); //声明一个缓冲区用来暂存处理后的图像数据；data=(unsigned char*)GlobalLock((HGLOBAL)data1handle);//得到该缓冲区的指针；AfxGetApp()->BeginWaitCursor();int i,j,buf;for( i=lpBi->biHeight; i>=2; i--)//从图像右下角开始对图像的各个像素进行"浮雕"处理；for( j=lpBi->biWidth; j>=2; j--){//浮雕处理buf=*(pData+(lpBi->biHeight-i)*WIDTHBYTES(lpBi->biWidth*8)+j)-*(pData +(lpBi->biHeight-i+1)*WIDTHBYTES(lpBi->biWidth*8)+j-1)+128;if(buf>255) buf=255;if(buf<0)buf=0;*(data+(lpBi->biHeight-i)*WIDTHBYTES(lpBi->biWidth*8)+j)=(BYTE)buf; }for( j=0; jbiHeight; j++)for( i=0; ibiWidth; i++)//重新写回原始图像的数据缓冲区;*(pData+i*WIDTHBYTES(lpBi->biWidth*8)+j)=*(data+i*WIDTHBYTES(lpBi->bi Width*8)+j); AfxGetApp()->EndWaitCursor();pDoc->m_hDIB =hdib//将处理过的图像数据写回pDoc中的图像缓冲区；GlobalUnlock((HGLOBAL)hdib);//解锁、释放缓冲区；GlobalUnlock((HGLOBAL)data1handle);GlobalFree((HGLOBAL)hdib);GlobalFree((HGLOBAL)data1handle);Invalidate(TRUE);//显示图像}2．"雕刻"图像上面讲述了通过求一个像素和它左上方像素之间的差值并加上一个常数的方法生成"浮雕"效果的灰度图像，"雕刻"图像与之相反，它是通过取一个像素和它右下方的像素之间的差值并加上一个常数，这里我也取128，经过这样处理，就可以得到"雕刻"图像，这时候图像的前景凹陷进背景之中。
VC++图形图像处理源代码
delete pData; pData=NULL;
if(m_pBMI!=NULL) delete m_pBMI; m_pBMI=NULL; if(pfi!=NULL)
delete pfi; pfi=NULL; } CAvi::CAviCreate(CString &string)//读文件初始化该类 { HRESULT hr; pfi=new AVIFILEINFO; hr = AVIFileOpen(&pfile, // returned file pointer string, // file name OF_READ, // mode to open file with NULL); hr= AVIFileInfo(pfile, file://获取 AVI 信息，放入 pfi 中 pfi, sizeof(AVIFILEINFO) ); cx=pfi>dwWidth;//图象宽、高 cy=pfi>dwHeight; hr=AVIFileGetStream(//将 AVI 变成视频流 pfile, &pavi, streamtypeVIDEO, 0//LONG lParam ); m_pBMI=new BITMAPINFO;//定义 BMP 信息头 m_pBMI>bmiHeader.biBitCount=24; m_pBMI>bmiHeader.biClrImportant=0; m_pBMI>bmiHeader.biClrUsed=0; m_pBMI>bmiHeader.biCompression=BI_RGB; m_pBMI>bmiHeader.biHeight=cy; m_pBMI>bmiHeader.biWidth=cx; m_pBMI>bmiHeader.biPlanes=1; m_pBMI>bmiHeader.biSize=sizeof(BITMAPINFOHEADER); m_pBMI>bmiHeader.biXPelsPerMeter=0; m_pBMI>bmiHeader.biYPelsPerMeter=0; m_pBMI>bmiHeader.biSizeImage=cx*cy*3; pData=(BYTE*)new char[cx*cy*3];//根据 AVI 中 BMP 图象的信息定义缓冲区 } BOOL CAvi::AviRead(int mFrame)//将 AVI 文件的 M 帧数据读入 PData 缓冲区 { HRESULT hr; hr= AVIStreamRead( pavi, mFrame, 1, pData, cx*cy*3,
数字图像处理及matlab实现源代码【1】
% *-*--*-*-*-*-*-*-*-*-*-*-*图像处理*-*-*-*-*-*-*-*-*-*-*-*%{% （一）图像文件的读/写A=imread('drum.jpg'); % 读入图像imshow(A); % 显示图像imwrite(A,'drum.jpg');info=imfinfo('drum.jpg') % 查询图像文件信息% 用colorbar函数将颜色条添加到坐标轴对象中RGB=imread('drum.jpg');I=rgb2gray(RGB); % 把RGB图像转换成灰度图像h=[1 2 1;0 0 0;-1 -2 -1];I2=filter2(h,I);imshow(I2,[]);colorbar('vert') % 将颜色条添加到坐标轴对象中% wrap函数将图像作为纹理进行映射A=imread('4.jpg');imshow(A);I=rgb2gray(RGB);[x,y,z]=sphere;warp(x,y,z,I); % 用warp函数将图像作为纹理进行映射%}% subimage函数实现一个图形窗口中显示多幅图像RGB=imread('drum.jpg');I=rgb2gray(RGB);subplot(1,2,1);subimage(RGB); % subimage函数实现一个图形窗口中显示多幅图像subplot(1,2,2),subimage(I);% *-*--*-*-*-*-*-*-*-*-*-*-*图像处理*-*-*-*-*-*-*-*-*-*-*-*% （二）图像处理的基本操作% ----------------图像代数运算------------------%{% imadd函数实现两幅图像的相加或给一幅图像加上一个常数% 给图像每个像素都增加亮度I=imread('4.jpg');J=imadd(I,100); % 给图像增加亮度subplot(1,2,1),imshow(I);title('原图');subplot(1,2,2),imshow(J);title('增加亮度图');%% imsubtract函数实现将一幅图像从另一个图像中减去或减去一个常数I=imread('drum.jpg');J=imsubtract(I,100); % 给图像减去亮度subplot(1,2,1),imshow(I);%% immultiply实现两幅图像的相乘或者一幅图像的亮度缩放I=imread('drum.jpg');J=immultiply(I,2); % 进行亮度缩放subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(J);%% imdivide函数实现两幅图像的除法或一幅图像的亮度缩放I=imread('4.jpg');J=imdivide(I,0.5); % 图像的亮度缩放subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(J);%}% ----------------图像的空间域操作------------------%{% imresize函数实现图像的缩放J=imread('4.jpg');subplot(1,2,1),imshow(J);title('原图');X1=imresize(J,0.2); % 对图像进行缩放subplot(1,2,2),imshow(X1);title('缩放图');%% imrotate函数实现图像的旋转I=imread('drum.jpg');J=imrotate(I,50,'bilinear'); % 对图像进行旋转subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(J);%% imcrop函数实现图像的剪切I=imread('drum.jpg');I2=imcrop(I,[1 100 130 112]); % 对图像进行剪切subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(I2);%}% ----------------特定区域处理------------------%{% roipoly函数用于选择图像中的多边形区域I=imread('4.jpg');c=[200 250 278 248 199 172];r=[21 21 75 121 121 75];BW=roipoly(I,c,r); % roipoly函数选择图像中的多边形区域subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(BW);%% roicolor函数式对RGB图像和灰度图像实现按灰度或亮度值选择区域进行处理a=imread('4.jpg');subplot(2,2,1),imshow(a);I=rgb2gray(a);BW=roicolor(I,128,225); % 按灰度值选择的区域subplot(2,2,4),imshow(BW);%% ploy2mask 函数转化指定的多边形区域为二值掩模x=[63 186 54 190 63];y=[60 60 209 204 601];bw=poly2mask(x,y,256,256); % 转化指定的多边形区域为二值掩模imshow(bw);hold onplot(x,y,'r','LineWidth',2);hold off%% roifilt2函数实现区域滤波a=imread('4.jpg');I=rgb2gray(a);c=[200 250 278 248 199 172];r=[21 21 75 121 121 75];BW=roipoly(I,c,r); % roipoly函数选择图像中的多边形区域h=fspecial('unsharp');J=roifilt2(h,I,BW); % 区域滤波subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(J);%% roifill函数实现对特定区域进行填充a=imread('4.jpg');I=rgb2gray(a);c=[200 250 278 248 199 172];r=[21 21 75 121 121 75];J=roifill(I,c,r); % 对特定区域进行填充subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(J);%}% ----------------图像变换------------------%{% fft2 和ifft2函数分别是计算二维的快速傅里叶变换和反变换f=zeros(100,100);subplot(1,2,1);imshow(f);f(20:70,40:60)=1;subplot(1,2,2);imshow(f);F=fft2(f); % 计算二维的快速傅里叶变换F2=log(abs(F));% 对幅值对对数figure;subplot(1,2,1),imshow(F),colorbar;subplot(1,2,2),imshow(F2),colorbar;%% fftsshift 函数实现了补零操作和改变图像显示象限f=zeros(100,100);subplot(2,2,1),imshow(f);title('f')f(10:70,40:60)=1;subplot(2,2,2),imshow(f);title('f取后')F=fft2(f,256,256);subplot(2,2,3),imshow(F);title('F')F2=fftshift(F); % 实现补零操作subplot(2,2,4),imshow(F2);title('F2')figure,imshow(log(abs(F2)));title('log(|F2|)')%% dct2 函数采用基于快速傅里叶变换的算法，用于实现较大输入矩阵的离散余弦变换% idct2 函数实现图像的二维逆离散余弦变换RGB=imread('drum.jpg');I=rgb2gray(RGB);J=dct2(I); % 对I进行离散余弦变换imshow(log(abs(J))),title('对原图离散后取对数'),colorbar;J(abs(J)<10)=0;K=idct2(J); % 图像的二维逆离散余弦变换figure,imshow(I),title('原灰度图')figure,imshow(K,[0,255]);title('逆离散变换');%% dctmtx 函数用于实现较小输入矩阵的离散余弦变figure;RGB=imread('4.jpg');I=rgb2gray(RGB);subplot(3,2,1),imshow(I),title('原灰度图');I=im2double(I);subplot(3,2,2),imshow(I),title('取双精度后');T=dctmtx(8); % 离散余弦变换subplot(3,2,3),imshow(I),title('离散余弦变换后');B=blkproc(I,[8,8],'P1*x*P2',T,T');subplot(3,2,4),imshow(B),title('blkproc作用I后的B');mask=[ 1 1 1 1 0 0 0 01 1 1 0 0 0 0 01 1 0 0 0 0 0 01 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0 ];B2=blkproc(B,[8,8],'P1.*x',mask);subplot(3,2,5),imshow(B2),title('blkproc作用B后的B2');I2=blkproc(B2,[8,8],'P1*x*P2',T',T);subplot(3,2,6),imshow(I2),title('blkproc作用B2后的I2');%% edge函数用于提取图像的边缘RGB=imread('4.jpg');I=rgb2gray(RGB);BW=edge(I);imshow(I);figure,imshow(BW);%% radon 函数用来计算指定方向上图像矩阵的投影RGB=imread('4.jpg');I=rgb2gray(RGB);BW=edge(I);theta=0:179;[R,XP]=radon(BW,theta); % 图像矩阵的投影figure,imagesc(theta,XP,R);colormap(hot);xlabel('\theta(degrees)');ylabel('x\prime');title('R_{\theta}(x\prime)');colorbar;%}% ----------------图像增强、分割和编码------------------%{% imhist 函数产生图像的直方图A=imread('4.jpg');B=rgb2gray(A);subplot(2,1,1),imshow(B);subplot(2,1,2),imhist(B);%% histeq 函数用于对图像的直方图均衡化A=imread('4.jpg');B=rgb2gray(A);subplot(2,1,1),imshow(B);subplot(2,1,2),imhist(B);C=histeq(B); % 对图像B进行均衡化figure;subplot(2,1,1),imshow(C);subplot(2,1,2),imhist(C);%% filter2 函数实现均值滤波a=imread('4.jpg');I=rgb2gray(a);subplot(2,2,1),imshow(I);K1=filter2(fspecial('average',3),I)/255; % 3*3的均值滤波K2=filter2(fspecial('average',5),I)/255; % 5*5的均值滤波K3=filter2(fspecial('average',7),I)/255; % 7*7的均值滤波subplot(2,2,2),imshow(K1);subplot(2,2,3),imshow(K2);subplot(2,2,4),imshow(K3);%% wiener2 函数实现Wiener（维纳）滤波a=imread('4.jpg');I=rgb2gray(a);subplot(2,2,1),imshow(I);K1=wiener2(I,[3,3]); % 3*3 wiener滤波K2=wiener2(I,[5,5]); % 5*5 wiener滤波K3=wiener2(I,[7,7]); % 7*7 wiener滤波subplot(2,2,2),imshow(K1);subplot(2,2,3),imshow(K2);subplot(2,2,4),imshow(K3);%% medfilt2 函数实现中值滤波a=imread('4.jpg');I=rgb2gray(a);subplot(2,2,1),imshow(I);K1=medfilt2(I,[3,3]); % 3*3 中值滤波K2=medfilt2(I,[5,5]); % 5*5 中值滤波K3=medfilt2(I,[7,7]); % 7*7 中值滤波subplot(2,2,2),imshow(K1);subplot(2,2,3),imshow(K2);subplot(2,2,4),imshow(K3);%}% ----------------图像模糊及复原------------------%{% deconvwnr 函数：使用维纳滤波器I=imread('qier.jpg');imshow(I);% 对图像进行模糊处理LEN=31;THETA=11;PSF1=fspecial('motion',LEN,THETA); % 运动模糊PSF2=fspecial('gaussian',10,5); % 高斯模糊Blurred1=imfilter(I,PSF1,'circular','conv'); % 得到运动模糊图像Blurred2=imfilter(I,PSF2,'conv'); % 得到高斯噪声模糊图像figure;subplot(1,2,1);imshow(Blurred1);title('Blurred1--"motion"'); subplot(1,2,2);imshow(Blurred2);title('Blurred2--"gaussian"');% 对模糊图像加噪声V=0.002;BlurredNoisy1=imnoise(Blurred1,'gaussian',0,V); % 加高斯噪声BlurredNoisy2=imnoise(Blurred2,'gaussian',0,V); % 加高斯噪声figure;subplot(1,2,1);imshow(BlurredNoisy1);title('BlurredNoisy1'); subplot(1,2,2);imshow(BlurredNoisy2);title('BlurredNoisy2');% 进行维纳滤波wnr1=deconvwnr(Blurred1,PSF1); % 维纳滤波wnr2=deconvwnr(Blurred2,PSF2); % 维纳滤波figure;subplot(1,2,1);imshow(wnr1);title('Restored1,True PSF'); subplot(1,2,2);imshow(wnr2);title('Restored2,True PSF');%% deconvreg函数：使用约束最小二乘滤波器I=imread('qier.jpg');imshow(I);% 对图像进行模糊处理LEN=31;THETA=11;PSF1=fspecial('motion',LEN,THETA); % 运动模糊PSF2=fspecial('gaussian',10,5); % 高斯模糊Blurred1=imfilter(I,PSF1,'circular','conv'); % 得到运动模糊图像Blurred2=imfilter(I,PSF2,'conv'); % 得到高斯噪声模糊图像figure;subplot(1,2,1);imshow(Blurred1);title('Blurred1--"motion"');subplot(1,2,2);imshow(Blurred2);title('Blurred2--"gaussian"');% 对模糊图像加噪声V=0.002;BlurredNoisy1=imnoise(Blurred1,'gaussian',0,V); % 加高斯噪声BlurredNoisy2=imnoise(Blurred2,'gaussian',0,V); % 加高斯噪声figure;subplot(1,2,1);imshow(BlurredNoisy1);title('BlurredNoisy1');subplot(1,2,2);imshow(BlurredNoisy2);title('BlurredNoisy2');NP=V*prod(size(I));reg1=deconvreg(BlurredNoisy1,PSF1,NP); % 约束最小二乘滤波reg2=deconvreg(BlurredNoisy2,PSF2,NP); % 约束最小二乘滤波figure;subplot(1,2,1);imshow(reg1);title('Restored1 with NP');subplot(1,2,2);imshow(reg2);title('Restored2 with NP');%% deconvlucy函数：使用Lucy-Richardson滤波器I=imread('qier.jpg');imshow(I);% 对图像进行模糊处理LEN=31;THETA=11;PSF1=fspecial('motion',LEN,THETA); % 运动模糊PSF2=fspecial('gaussian',10,5); % 高斯模糊Blurred1=imfilter(I,PSF1,'circular','conv'); % 得到运动模糊图像Blurred2=imfilter(I,PSF2,'conv'); % 得到高斯噪声模糊图像figure;subplot(1,2,1);imshow(Blurred1);title('Blurred1--"motion"');subplot(1,2,2);imshow(Blurred2);title('Blurred2--"gaussian"');% 对模糊图像加噪声V=0.002;BlurredNoisy1=imnoise(Blurred1,'gaussian',0,V); % 加高斯噪声BlurredNoisy2=imnoise(Blurred2,'gaussian',0,V); % 加高斯噪声figure;subplot(1,2,1);imshow(BlurredNoisy1);title('BlurredNoisy1');subplot(1,2,2);imshow(BlurredNoisy2);title('BlurredNoisy2');luc1=deconvlucy(BlurredNoisy1,PSF1,5); % 使用Lucy-Richardson滤波luc2=deconvlucy(BlurredNoisy1,PSF1,15); % 使用Lucy-Richardson滤波figure;subplot(1,2,1);imshow(luc1);title('Restored Image,NUMIT=5'); subplot(1,2,2);imshow(luc2);title('Restored Image,NUMIT=15');%}% deconvblind 函数：使用盲卷积算法a=imread('4.jpg');I=rgb2gray(a);figure;imshow(I);title('Original Image');PSF=fspecial('motion',13,45); % 运动模糊figure;imshow(PSF);Blurred=imfilter(I,PSF,'circ','conv'); % 得到运动模糊图像figure;imshow(Blurred);title('Blurred Image');INITPSF=ones(size(PSF));[J,P]=deconvblind(Blurred,INITPSF,30); % 使用盲卷积figure;imshow(J);figure;imshow(P,[],'notruesize');% *-*--*-*-*-*-*-*-*-*-*-*-*图像处理*-*-*-*-*-*-*-*-*-*-*-* %{% 对图像进行减采样a=imread('lena.jpg');%subplot(1,4,1);figure;imshow(a);title('原图');b=rgb2gray(a);%subplot(1,4,2);figure;imshow(b);title('原图的灰度图');[wid,hei]=size(b);%---4倍减采样----quartimg=zeros(wid/2+1,hei/2+1);i1=1;j1=1;for i=1:2:widfor j=1:2:heiquartimg(i1,j1)=b(i,j);j1=j1+1;endi1=i1+1;j1=1;end%subplot(1,4,3);figure;imshow(uint8(quartimg));title('4倍减采样')% ---16倍减采样---quanrtimg=zeros(wid/4+1,hei/4+1);i1=1;j1=1;for i=1:4:widfor j=1:4:heiquanrtimg(i1,j1)=b(i,j);j1=j1+1;endi1=i1+1;j1=1;end%subplot(1,4,4);.figure;imshow(uint8(quanrtimg));title('16倍减采样');%}% 图像类型% 将图像转换为256级灰度图像，64级灰度图像，32级灰度图像，8级灰度图像，2级灰度图像a=imread('4.jpg');%figure;subplot(2,3,1);imshow(a);title('原图');b=rgb2gray(a); % 这是256灰度级的图像%figure;subplot(2,3,2);imshow(b);title('原图的灰度图像');[wid,hei]=size(b);img64=zeros(wid,hei);img32=zeros(wid,hei);img8=zeros(wid,hei);img2=zeros(wid,hei);for i=1:widfor j=j:heiimg64(i,j)=floor(b(i,j)/4); % 转化为64灰度级endend%figure;subplot(2,3,3);imshow(uint8(img64),[0,63]);title('64级灰度图像');for i=1:widfor j=1:heiimg32(i,j)=floor(b(i,j)/8);% 转化为32灰度级endend%figure;subplot(2,3,4);imshow(uint8(img32),[0,31]);title('32级灰度图像');for i=1:widfor j=1:heiimg8(i,j)=floor(b(i,j)/32);% 转化为8灰度级endend%figure;subplot(2,3,5);imshow(uint8(img8),[0,7]);title('8级灰度图像');for i=1:widfor j=1:heiimg2(i,j)=floor(b(i,j)/128);% 转化为2灰度级endend%figure;subplot(2,3,6);imshow(uint8(img2),[0,1]);title('2级灰度图像');% *-*--*-*-*-*-*-*-*-*-*-*-*图像处理*-*-*-*-*-*-*-*-*-*-*-* %{% ------------------ 图像的点运算------------------I=imread('lena.jpg');figure;subplot(1,3,1);imshow(I);title('原图的灰度图');J=imadjust(I,[0.3;0.6],[0.1;0.9]); % 设置灰度变换的范围subplot(1,3,2);imshow(J);title('线性扩展');I1=double(I); % 将图像转换为double类型I2=I1/255; % 归一化此图像C=2; % 非线性扩展函数的参数K=C*log(1+I2); % 对图像的对数变换subplot(1,3,3);imshow(K);title('非线性扩展');M=255-I;figure;subplot(1,3,1);imshow(M);title('灰度倒置');N1=im2bw(I,0.4); % 将此图像二值化，阈值为0.4N2=im2bw(I,0.7); % 将此图像二值化，阈值为0.7 subplot(1,3,2);imshow(N1);title('二值化阈值0.4');subplot(1,3,3);imshow(N2);title('二值化阈值0.7');%}%{% ------------------ 图像的代数运算------------------% 将两幅图像进行加法运算I=imread('lena.jpg');I=rgb2gray(I);J=imread('rice.png');% 以下把两幅图转化为大小一样for i=1:size(I)for j=size(J):size(I)J(i,j)=0;endendI=im2double(I); % 将图像转化为double型J=im2double(J);% imshow(I);figure;imshow(J);K=I+0.3*J; % 将两幅图像相加subplot(1,3,1);imshow(I);title('人物图');subplot(1,3,2);imshow(J);title('背景图');subplot(1,3,3);imshow(K);title('相加后的图');imwrite(K,'i_lena1.jpg');%%% 将两幅图像做减运算，分离背景与原图A=imread('i_lena1.jpg');B=imread('rice.png');% 以下把两幅图转化为大小一样for i=1:size(A)for j=size(B):size(A)B(i,j)=0;endendC=A-0.3*B;a=imread('lena.jpg');subplot(2,2,1);imshow(a);title('原图图');subplot(2,2,2);imshow(A);title('混合图');subplot(2,2,3);imshow(B);title('背景图');subplot(2,2,4);imshow(C);title('分离后的图');%% 设置掩模，需要保留下来的区域，掩模图像的值为1，否则为0 A=imread('drum.jpg');A=rgb2gray(A);A=im2double(A);sizeA=size(A);subplot(1,2,1);imshow(A);title('原图');B=zeros(sizeA(1),sizeA(2)); % 设置模板B(100:400,100:500)=1;K=A.*B; % 两幅图像相乘subplot(1,2,2);imshow(K);title('局部图');%}%{% ------------------ 图像的缩放------------------A=imread('drum.jpg');B1=imresize(A,1.5); % 比例放大1.5杯，默认采用的是最近邻法进行线性插值B2=imresize(A,[420 384]); % 非比例放大到420:384C1=imresize(A,0.7); % 比例缩小0.7倍C2=imresize(A,[150 180]); % 非比例缩小到150:180figure;imshow(B1);title('比例放大图');figure;imshow(B2);title('非比例放大图');figure;imshow(C1);title('比例缩小图');figure;imshow(C2);title('非比例缩小图');% 检测非比例缩放得到的图片是否能还原到原图a=size(A)d=imresize(C2,[a(1),a(2)]);figure;imshow(d);%}% ------------------ 图像的旋转------------------I=imread('drum.jpg');J=imrotate(I,45); % 图像进行逆时针旋转，默认采用最近邻插值法进行插值处理K=imrotate(I,90); % 默认旋转出界的部分不被截出subplot(1,3,1);imshow(I);subplot(1,3,2);imshow(J);subplot(1,3,3);imshow(K);% 检测旋转后的图像是否失真P=imrotate(K,270);figure;imshow(P);。
ddim difusiondet 源代码解析
ddim difusiondet 源代码解析1. 引言1.1 概述本文旨在对"ddim diffusiondet"源代码进行解析，并深入分析该算法的实现原理和关键代码。
"ddim diffusiondet"是一种用于目标检测的图像处理算法，主要应用于计算机视觉领域。
通过对源代码的解析，可以更好地了解该算法的背景、实现原理以及相关代码细节。
1.2 文章结构本文将按照以下结构进行论述："ddim difusiondet 源代码解析"部分将详细介绍该算法的背景、实现原理和关键代码分析；"结果与讨论"部分将涵盖实验设置与数据集、实验结果分析以及相关讨论和启示；"总结与展望"部分将对主要观点进行总结，并展望未来的研究方向；最后，在"结论"部分对整个研究工作进行总结，并探讨了"ddim diffusiondet"在未来的应用前景。
1.3 目的本文旨在提供一个详细而清晰的关于“ddim difusiondet”源码解析的指南，为读者提供深入了解该算法的基础知识和实现细节。
通过对源码中关键部分的剖析，读者能够更好地理解该算法的工作原理，并为相关研究和应用提供有价值的参考。
同时，本文也希望能够激发读者对目标检测和计算机视觉领域更广泛的探索兴趣。
2. ddim difusiondet 源代码解析2.1 背景介绍在本节中，我们将介绍ddim difusiondet的背景信息。
ddim difusiondet是一个基于深度学习技术的目标检测算法，主要用于检测并定位图像或视频中的对象。
2.2 实现原理在这一节中，我们将详细解释ddim difusiondet的实现原理。
首先，该算法采用了深层残差网络（Deep Residual Network）作为其主干网络。
接下来，在主干网络上构建了多个特征金字塔模块（Feature Pyramid Network），以便对不同尺度的特征进行提取和处理。
opencv的warpaffine函数的源代码实现 -回复
opencv的warpaffine函数的源代码实现-回复OpenCV是一个强大的计算机视觉库，提供了许多图像处理和计算机视觉算法。
其中，warpAffine函数是OpenCV中的一个重要函数，用于对图像进行仿射变换。
在本文中，我们将一步一步地回答"[opencv的warpaffine函数的源代码实现]" 这个问题，探讨warpAffine函数的实现细节。
首先，我们需要了解仿射变换的概念。
仿射变换是一种平面几何变换，保持了一些线段的相对平行性和长度比例。
它可以描述平面上的旋转、平移、缩放和剪切等几何变换操作。
在OpenCV中，warpAffine函数的原型如下所示：cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]])其中，参数说明如下：- src: 输入图像，可以是任意维度的数组。
- M: 2x3的仿射变换矩阵。
- dsize: 输出图像的大小，即变换后的图像尺寸。
- dst: 可选参数，输出图像。
- flags: 可选参数，用于确定插值方法。
默认为线性插值。
- borderMode: 可选参数，用于确定边界像素的处理方式。
默认为边界像素的复制。
- borderValue: 可选参数，用于设置边界像素的值。
默认为0。
了解了warpAffine函数的参数，接下来我们将一步一步讨论函数的源代码实现。
首先，我们需要导入相应的库。
pythonimport numpy as np接下来，我们将创建一个函数来实现warpAffine功能。
pythondef warpAffine(src, M, dsize, dst=None, flags=None, borderMode=None, borderValue=None):# 确定输出图像的大小if dst is None:dst = np.zeros(dsize, dtype=src.dtype)else:assert dst.shape == dsize# 提取仿射变换矩阵的参数M = np.float32(M[:2])# 计算输出图像的尺寸rows, cols = dst.shape[:2]# 循环遍历输出图像的每个像素for row in range(rows):for col in range(cols):# 计算输入图像中的对应坐标input_coords = M.dot(np.array([col, row, 1]))x, y = input_coords[:2] / input_coords[2]# 判断坐标是否在输入图像范围内if x >= 0 and x < src.shape[1] and y >= 0 and y < src.shape[0]:# 使用插值方法计算输出图像中的像素值if flags is not None and flags !=cv2.INTER_NEAREST:# 使用双线性插值f_x = int(x)c_x = f_x + 1 if f_x < src.shape[1] - 1 else f_xf_y = int(y)c_y = f_y + 1 if f_y < src.shape[0] - 1 else f_yalpha = x - f_xbeta = y - f_ydst[row, col] = (1 - alpha) * (1 - beta) * src[f_y, f_x] + \(1 - alpha) * beta * src[c_y, f_x] + \alpha * (1 - beta) * src[f_y, c_x] + \alpha * beta * src[c_y, c_x]else:# 使用最近邻插值dst[row, col] = src[int(y), int(x)]# 处理边界像素elif borderMode is not None:if borderMode == cv2.BORDER_CONSTANT:# 使用常数填充边界像素dst[row, col] = borderValueelif borderMode == cv2.BORDER_REPLICATE:# 使用复制边界像素x = min(max(x, 0), src.shape[1] - 1)y = min(max(y, 0), src.shape[0] - 1)dst[row, col] = src[int(y), int(x)]return dst让我们逐行解释一下这段源代码的实现。
批量下载网页图片源代码
$new_image_ext = 'xbm';
break;
default:
$image_create_func = 'ImageCreateFromJPEG';
$image_save_func = 'ImageJPEG';
$new_image_ext = 'jpg';
$image_save_func = 'ImageBMP';
$new_image_ext = 'bmp';
break;
case 'gif':
$image_create_func = 'ImageCreateFromGIF';
$image_save_func = 'ImageGIF';
$new_image_ext = 'gif';
}
//根据‘指定扩展名标志’set_extension属性来合成本地图片文件名
if(isSet($this->set_extension)){
$ext = strrchr($this->source, ".");
$strlen = strlen($ext);
$new_name = basename(substr($this->source, 0, -$strlen)).'.'.$new_image_ext;
2012-05-02 14:22 php批量下载网页图片源代码 有GetAllPic.php、DownImage.class.php两个文件和data放置下载图片的文件夹。GetAllPic.php为主文件，DownImage.class.php为类文件
BMP图片转JPEG图片C程序源代码
// A BMP truecolor to JPEG encoder// Copyright 1999 Cristian Cuturicu #include <stdio.h>#include <stdlib.h>#include <string.h>#include "jtypes.h"#include "jglobals.h"#include "jtables.h"void write_APP0info()//Nothing to overwrite for APP0info{writeword(APP0info.marker);writeword(APP0info.length);writebyte('J');writebyte('F');writebyte('I');writebyte('F');writebyte(0);writebyte(APP0info.versionhi);writebyte(APP0info.versionlo);writebyte(APP0info.xyunits);writeword(APP0info.xdensity);writeword(APP0info.ydensity);writebyte(APP0info.thumbnwidth);writebyte(APP0info.thumbnheight);}void write_SOF0info()// We should overwrite width and height{writeword(SOF0info.marker);writeword(SOF0info.length);writebyte(SOF0info.precision);writeword(SOF0info.height);writeword(SOF0info.width);writebyte(SOF0info.nrofcomponents);writebyte(SOF0info.IdY);writebyte(SOF0info.HVY);writebyte(SOF0info.QTY);writebyte(SOF0info.IdCb);writebyte(SOF0info.HVCb);writebyte(SOF0info.QTCb);writebyte(SOF0info.IdCr);writebyte(SOF0info.HVCr);writebyte(SOF0info.QTCr);}void write_DQTinfo(){BYTE i;writeword(DQTinfo.marker);writeword(DQTinfo.length);writebyte(DQTinfo.QTYinfo);for (i=0; i<64; i++)writebyte(DQTinfo.Ytable[i]);writebyte(DQTinfo.QTCbinfo);for (i=0; i<64; i++)writebyte(DQTinfo.Cbtable[i]);}void set_quant_table(BYTE *basic_table, BYTE scale_factor, BYTE *newtable)// Set quantization table and zigzag reorder it{BYTE i;long temp;for (i=0; i<64; i++){temp = ((long) basic_table[i] * scale_factor + 50L) / 100L;// limit the values to the valid rangeif (temp <= 0L)temp = 1L;if (temp > 255L)temp = 255L;newtable[zigzag[i]] = (BYTE) temp;}}void set_DQTinfo(){BYTE scalefactor = 50;// scalefactor controls the visual quality of the image// the smaller is the better image we'll get, and the smaller// compression we'll achieveDQTinfo.marker = 0xFFDB;DQTinfo.length = 132;DQTinfo.QTYinfo = 0;DQTinfo.QTCbinfo = 1;set_quant_table(std_luminance_qt, scalefactor, DQTinfo.Ytable);set_quant_table(std_chrominance_qt, scalefactor, DQTinfo.Cbtable);}void write_DHTinfo(){BYTE i;writeword(DHTinfo.marker);writeword(DHTinfo.length);writebyte(DHTinfo.HTYDCinfo);for (i=0; i<16; i++)writebyte(DHTinfo.YDC_nrcodes[i]);for (i=0; i<12; i++)writebyte(DHTinfo.YDC_values[i]);writebyte(DHTinfo.HTYACinfo);for (i=0; i<16; i++)writebyte(DHTinfo.YAC_nrcodes[i]);for (i=0; i<162; i++)writebyte(DHTinfo.YAC_values[i]);writebyte(DHTinfo.HTCbDCinfo);for (i=0; i<16; i++)writebyte(DHTinfo.CbDC_nrcodes[i]);for (i=0; i<12; i++)writebyte(DHTinfo.CbDC_values[i]);writebyte(DHTinfo.HTCbACinfo);for (i=0; i<16; i++)writebyte(DHTinfo.CbAC_nrcodes[i]);for (i=0; i<162; i++)writebyte(DHTinfo.CbAC_values[i]);}void set_DHTinfo(){BYTE i;// fill the DHTinfo structure [get the values from the standard Huffman tables]DHTinfo.marker = 0xFFC4;DHTinfo.length = 0x01A2;DHTinfo.HTYDCinfo = 0;for (i=0; i<16; i++)DHTinfo.YDC_nrcodes[i] = std_dc_luminance_nrcodes[i+1];for (i=0; i<12; i++)DHTinfo.YDC_values[i] = std_dc_luminance_values[i];DHTinfo.HTYACinfo = 0x10;for (i=0; i<16; i++)DHTinfo.YAC_nrcodes[i] = std_ac_luminance_nrcodes[i+1];for (i=0; i<162; i++)DHTinfo.YAC_values[i] = std_ac_luminance_values[i];DHTinfo.HTCbDCinfo = 1;for (i=0; i<16; i++)DHTinfo.CbDC_nrcodes[i] = std_dc_chrominance_nrcodes[i+1];for (i=0; i<12; i++)DHTinfo.CbDC_values[i] = std_dc_chrominance_values[i];DHTinfo.HTCbACinfo = 0x11;for (i=0; i<16; i++)DHTinfo.CbAC_nrcodes[i] = std_ac_chrominance_nrcodes[i+1];for (i=0; i<162; i++)DHTinfo.CbAC_values[i] = std_ac_chrominance_values[i];}void write_SOSinfo()//Nothing to overwrite for SOSinfo{writeword(SOSinfo.marker);writeword(SOSinfo.length);writebyte(SOSinfo.nrofcomponents);writebyte(SOSinfo.IdY);writebyte(SOSinfo.HTY);writebyte(SOSinfo.IdCb);writebyte(SOSinfo.HTCb);writebyte(SOSinfo.IdCr);writebyte(SOSinfo.HTCr);writebyte(SOSinfo.Ss);writebyte(SOSinfo.Se);writebyte(SOSinfo.Bf);}void write_comment(BYTE *comment){WORD i, length;writeword(0xFFFE); // The COM markerlength = strlen((const char *)comment);writeword(length + 2);for (i=0; i<length; i++)writebyte(comment[i]);}void writebits(bitstring bs)// A portable version; it should be done in assembler{WORD value;SBYTE posval;// bit position in the bitstring we read, should be <=15 and >=0value = bs.value;posval = bs.length - 1;while (posval >= 0){if (value & mask[posval])bytenew |= mask[bytepos];posval--;bytepos--;if (bytepos < 0){// write itif (bytenew == 0xFF){// special casewritebyte(0xFF);writebyte(0);}elsewritebyte(bytenew);// reinitbytepos = 7;bytenew = 0;}}}void compute_Huffman_table(BYTE *nrcodes, BYTE *std_table, bitstring *HT){BYTE k,j;BYTE pos_in_table;WORD codevalue;codevalue = 0;pos_in_table = 0;for (k=1; k<=16; k++){for (j=1; j<=nrcodes[k]; j++){HT[std_table[pos_in_table]].value = codevalue;HT[std_table[pos_in_table]].length = k;pos_in_table++;codevalue++;}codevalue <<= 1;}}void init_Huffman_tables(){// Compute the Huffman tables used for encodingcompute_Huffman_table(std_dc_luminance_nrcodes, std_dc_luminance_values, YDC_HT);compute_Huffman_table(std_ac_luminance_nrcodes, std_ac_luminance_values, YAC_HT);compute_Huffman_table(std_dc_chrominance_nrcodes, std_dc_chrominance_values, CbDC_HT);compute_Huffman_table(std_ac_chrominance_nrcodes, std_ac_chrominance_values, CbAC_HT); }void exitmessage(char *error_message){printf("%s\n",error_message);exit(EXIT_FAILURE);}void set_numbers_category_and_bitcode(){SDWORD nr;SDWORD nrlower, nrupper;BYTE cat;category_alloc = (BYTE *)malloc(65535*sizeof(BYTE));if (category_alloc == NULL)exitmessage("Not enough memory.");//allow negative subscriptscategory = category_alloc + 32767;bitcode_alloc=(bitstring *)malloc(65535*sizeof(bitstring));if (bitcode_alloc==NULL)exitmessage("Not enough memory.");bitcode = bitcode_alloc + 32767;nrlower = 1;nrupper = 2;for (cat=1; cat<=15; cat++){//Positive numbersfor (nr=nrlower; nr<nrupper; nr++){category[nr] = cat;bitcode[nr].length = cat;bitcode[nr].value = (WORD)nr;}//Negative numbersfor (nr=-(nrupper-1); nr<=-nrlower; nr++){category[nr] = cat;bitcode[nr].length = cat;bitcode[nr].value = (WORD)(nrupper-1+nr);}nrlower <<= 1;nrupper <<= 1;}}void precalculate_YCbCr_tables(){WORD R,G,B;for (R=0; R<256; R++){YRtab[R] = (SDWORD)(65536*0.299+0.5)*R;CbRtab[R] = (SDWORD)(65536*-0.16874+0.5)*R;CrRtab[R] = (SDWORD)(32768)*R;}for (G=0; G<256; G++){YGtab[G] = (SDWORD)(65536*0.587+0.5)*G;CbGtab[G] = (SDWORD)(65536*-0.33126+0.5)*G;CrGtab[G] = (SDWORD)(65536*-0.41869+0.5)*G;}for (B=0; B<256; B++){YBtab[B] = (SDWORD)(65536*0.114+0.5)*B;CbBtab[B] = (SDWORD)(32768)*B;CrBtab[B] = (SDWORD)(65536*-0.08131+0.5)*B;}}// Using a bit modified form of the FDCT routine from IJG's C source:// Forward DCT routine idea taken from Independent JPEG Group's C source for// JPEG encoders/decoders/* For float AA&N IDCT method, divisors are equal to quantization coefficients scaled by scalefactor[row]*scalefactor[col], wherescalefactor[0] = 1scalefactor[k] = cos(k*PI/16) * sqrt(2) for k=1..7We apply a further scale factor of 8.What's actually stored is 1/divisor so that the inner loop canuse a multiplication rather than a division. */void prepare_quant_tables(){double aanscalefactor[8] = {1.0, 1.387039845, 1.306562965, 1.175875602,1.0, 0.785694958, 0.541196100, 0.275899379};BYTE row, col;BYTE i = 0;for (row = 0; row < 8; row++){for (col = 0; col < 8; col++){fdtbl_Y[i] = (float) (1.0 / ((double) DQTinfo.Ytable[zigzag[i]] *aanscalefactor[row] * aanscalefactor[col] * 8.0));fdtbl_Cb[i] = (float) (1.0 / ((double) DQTinfo.Cbtable[zigzag[i]] *aanscalefactor[row] * aanscalefactor[col] * 8.0));i++;}}}void fdct_and_quantization(SBYTE *data, float *fdtbl, SWORD *outdata){float tmp0, tmp1, tmp2, tmp3, tmp4, tmp5, tmp6, tmp7;float tmp10, tmp11, tmp12, tmp13;float z1, z2, z3, z4, z5, z11, z13;float *dataptr;float datafloat[64];float temp;SBYTE ctr;BYTE i;for (i=0; i<64; i++)datafloat[i] = data[i];/* Pass 1: process rows. */dataptr = datafloat;for (ctr = 7; ctr >= 0; ctr--){tmp0 = dataptr[0] + dataptr[7];tmp7 = dataptr[0] - dataptr[7];tmp1 = dataptr[1] + dataptr[6];tmp6 = dataptr[1] - dataptr[6];tmp2 = dataptr[2] + dataptr[5];tmp5 = dataptr[2] - dataptr[5];tmp3 = dataptr[3] + dataptr[4];tmp4 = dataptr[3] - dataptr[4];/* Even part */tmp10 = tmp0 + tmp3; /* phase 2 */tmp13 = tmp0 - tmp3;tmp11 = tmp1 + tmp2;tmp12 = tmp1 - tmp2;dataptr[0] = tmp10 + tmp11; /* phase 3 */dataptr[4] = tmp10 - tmp11;z1 = (tmp12 + tmp13) * ((float) 0.707106781); /* c4 */dataptr[2] = tmp13 + z1; /* phase 5 */dataptr[6] = tmp13 - z1;/* Odd part */tmp10 = tmp4 + tmp5; /* phase 2 */tmp11 = tmp5 + tmp6;tmp12 = tmp6 + tmp7;/* The rotator is modified from fig 4-8 to avoid extra negations. */z5 = (tmp10 - tmp12) * ((float) 0.382683433); /* c6 */z2 = ((float) 0.541196100) * tmp10 + z5; /* c2-c6 */z4 = ((float) 1.306562965) * tmp12 + z5; /* c2+c6 */z3 = tmp11 * ((float) 0.707106781); /* c4 */z11 = tmp7 + z3; /* phase 5 */z13 = tmp7 - z3;dataptr[5] = z13 + z2; /* phase 6 */dataptr[3] = z13 - z2;dataptr[1] = z11 + z4;dataptr[7] = z11 - z4;dataptr += 8; /* advance pointer to next row */ }/* Pass 2: process columns. */dataptr = datafloat;for (ctr = 7; ctr >= 0; ctr--){tmp0 = dataptr[0] + dataptr[56];tmp7 = dataptr[0] - dataptr[56];tmp1 = dataptr[8] + dataptr[48];tmp6 = dataptr[8] - dataptr[48];tmp2 = dataptr[16] + dataptr[40];tmp5 = dataptr[16] - dataptr[40];tmp3 = dataptr[24] + dataptr[32];tmp4 = dataptr[24] - dataptr[32];/* Even part */tmp10 = tmp0 + tmp3; /* phase 2 */tmp13 = tmp0 - tmp3;tmp11 = tmp1 + tmp2;tmp12 = tmp1 - tmp2;dataptr[0] = tmp10 + tmp11; /* phase 3 */dataptr[32] = tmp10 - tmp11;z1 = (tmp12 + tmp13) * ((float) 0.707106781); /* c4 */dataptr[16] = tmp13 + z1; /* phase 5 */dataptr[48] = tmp13 - z1;/* Odd part */tmp10 = tmp4 + tmp5; /* phase 2 */tmp11 = tmp5 + tmp6;tmp12 = tmp6 + tmp7;/* The rotator is modified from fig 4-8 to avoid extra negations. */z5 = (tmp10 - tmp12) * ((float) 0.382683433); /* c6 */z2 = ((float) 0.541196100) * tmp10 + z5; /* c2-c6 */z4 = ((float) 1.306562965) * tmp12 + z5; /* c2+c6 */z3 = tmp11 * ((float) 0.707106781); /* c4 */z11 = tmp7 + z3; /* phase 5 */z13 = tmp7 - z3;dataptr[40] = z13 + z2; /* phase 6 */dataptr[24] = z13 - z2;dataptr[8] = z11 + z4;dataptr[56] = z11 - z4;dataptr++; /* advance pointer to next column */ }/* Quantize/descale the coefficients, and store into output array */for (i = 0; i < 64; i++){/* Apply the quantization and scaling factor */temp = datafloat[i] * fdtbl[i];/* Round to nearest integer.Since C does not specify the direction of rounding for negativequotients, we have to force the dividend positive for portability.The maximum coefficient size is +-16K (for 12-bit data), so thiscode should work for either 16-bit or 32-bit ints.*/outdata[i] = (SWORD) ((SWORD)(temp + 16384.5) - 16384);}}void process_DU(SBYTE *ComponentDU,float *fdtbl,SWORD *DC,bitstring *HTDC,bitstring *HTAC){bitstring EOB = HTAC[0x00];bitstring M16zeroes = HTAC[0xF0];BYTE i;BYTE startpos;BYTE end0pos;BYTE nrzeroes;BYTE nrmarker;SWORD Diff;fdct_and_quantization(ComponentDU, fdtbl, DU_DCT);// zigzag reorderfor (i=0; i<64; i++)DU[zigzag[i]]=DU_DCT[i];// Encode DCDiff = DU[0] - *DC;*DC = DU[0];if (Diff == 0)writebits(HTDC[0]); //Diff might be 0else{writebits(HTDC[category[Diff]]);writebits(bitcode[Diff]);}// Encode ACsfor (end0pos=63; (end0pos>0)&&(DU[end0pos]==0); end0pos--) ;//end0pos = first element in reverse order != 0i = 1;while (i <= end0pos){startpos = i;for (; (DU[i]==0) && (i<=end0pos); i++) ;nrzeroes = i - startpos;if (nrzeroes >= 16){for (nrmarker=1; nrmarker<=nrzeroes/16; nrmarker++)writebits(M16zeroes);nrzeroes = nrzeroes%16;}writebits(HTAC[nrzeroes*16+category[DU[i]]]);writebits(bitcode[DU[i]]);i++;}if (end0pos != 63)writebits(EOB);}void load_data_units_from_RGB_buffer(WORD xpos, WORD ypos){BYTE x, y;BYTE pos = 0;DWORD location;BYTE R, G, B;location = ypos * width + xpos;for (y=0; y<8; y++){for (x=0; x<8; x++){R = RGB_buffer[location].R;G = RGB_buffer[location].G;B = RGB_buffer[location].B;// convert to YCbCrYDU[pos] = Y(R,G,B);CbDU[pos] = Cb(R,G,B);CrDU[pos] = Cr(R,G,B);location++;pos++;}location += width - 8;}}void main_encoder(){SWORD DCY = 0, DCCb = 0, DCCr = 0; //DC coefficients used for differential encoding WORD xpos, ypos;for (ypos=0; ypos<height; ypos+=8){for (xpos=0; xpos<width; xpos+=8){load_data_units_from_RGB_buffer(xpos, ypos);process_DU(YDU, fdtbl_Y, &DCY, YDC_HT, YAC_HT);process_DU(CbDU, fdtbl_Cb, &DCCb, CbDC_HT, CbAC_HT);process_DU(CrDU, fdtbl_Cb, &DCCr, CbDC_HT, CbAC_HT);}}}void load_bitmap(char *bitmap_name, WORD *width_original, WORD *height_original){WORD widthDiv8, heightDiv8; // closest multiple of 8 [ceil]BYTE nr_fillingbytes;//The number of the filling bytes in the BMP file// (the dimension in bytes of a BMP line on the disk is divisible by 4)colorRGB lastcolor;WORD column;BYTE TMPBUF[256];WORD nrline_up, nrline_dn, nrline;WORD dimline;colorRGB *tmpline;FILE *fp_bitmap = fopen(bitmap_name,"rb");if (fp_bitmap==NULL)exitmessage("Cannot open bitmap file.File not found ?");if (fread(TMPBUF, 1, 54, fp_bitmap) != 54)exitmessage("Need a truecolor BMP to encode.");if ((TMPBUF[0]!='B')||(TMPBUF[1]!='M')||(TMPBUF[28]!=24))exitmessage("Need a truecolor BMP to encode.");width = (WORD)TMPBUF[19]*256+TMPBUF[18];height = (WORD)TMPBUF[23]*256+TMPBUF[22];// Keep the old dimensions of the image*width_original = width;*height_original = height;if (width%8 != 0)widthDiv8 = (width/8)*8+8;elsewidthDiv8 = width;if (height%8 != 0)heightDiv8 = (height/8)*8+8;elseheightDiv8 = height;// The image we encode shall be filled with the last line and the last column// from the original bitmap, until width and height are divisible by 8// Load BMP image from disk and complete XRGB_buffer = (colorRGB *)(malloc(3*widthDiv8*heightDiv8));if (RGB_buffer == NULL)exitmessage("Not enough memory for the bitmap image.");if ( (width*3)%4 != 0)nr_fillingbytes = 4 - ( (width*3)%4);elsenr_fillingbytes = 0;for (nrline=0; nrline<height; nrline++){fread(RGB_buffer + nrline*widthDiv8, 1, width*3, fp_bitmap);fread(TMPBUF, 1, nr_fillingbytes, fp_bitmap);// complete Xmemcpy(&lastcolor, RGB_buffer + nrline*widthDiv8 + width-1, 3);for (column=width; column<widthDiv8; column++)memcpy(RGB_buffer+nrline*widthDiv8+column, &lastcolor, 3);}width = widthDiv8;dimline = width*3;tmpline = (colorRGB *)malloc(dimline);if (tmpline == NULL)exitmessage("Not enough memory.");// Reorder in memory the inversed bitmapfor (nrline_up=height-1,nrline_dn=0; nrline_up>nrline_dn; nrline_up--,nrline_dn++){memcpy(tmpline, RGB_buffer+nrline_up*width, dimline);memcpy(RGB_buffer+nrline_up*width, RGB_buffer+nrline_dn*width, dimline);memcpy(RGB_buffer+nrline_dn*width, tmpline, dimline);}// Y completion:memcpy(tmpline, RGB_buffer+(height-1)*width, dimline);for (nrline=height; nrline<heightDiv8; nrline++)memcpy(RGB_buffer+nrline*width, tmpline, dimline);height = heightDiv8;free(tmpline);fclose(fp_bitmap);}void init_all(){set_DQTinfo();set_DHTinfo();init_Huffman_tables();set_numbers_category_and_bitcode();precalculate_YCbCr_tables();prepare_quant_tables();}void main(int argc, char *argv[]){char BMP_filename[64];char JPG_filename[64];WORD width_original,height_original; //the original image dimensions, // before we made them divisible by 8BYTE len_filename;bitstring fillbits; //filling bitstring for the bit alignment of the EOI markerif (argc>1){strcpy(BMP_filename,argv[1]);if (argc>2)strcpy(JPG_filename,argv[2]);else{// replace ".bmp" with ".jpg"strcpy(JPG_filename, BMP_filename);len_filename=strlen(BMP_filename);strcpy(JPG_filename+(len_filename-3),"jpg");}}elseexitmessage("Syntax: enc fis.bmp [fis.jpg]");load_bitmap(BMP_filename, &width_original, &height_original);fp_jpeg_stream = fopen(JPG_filename,"wb");init_all();SOF0info.width = width_original;SOF0info.height = height_original;writeword(0xFFD8); // SOIwrite_APP0info();// write_comment("Cris made this JPEG with his own encoder");write_DQTinfo();write_SOF0info();write_DHTinfo();write_SOSinfo();// init global variablesbytenew = 0; // current bytebytepos = 7; // bit position in this bytemain_encoder();// Do the bit alignment of the EOI markerif (bytepos >= 0){fillbits.length = bytepos + 1;fillbits.value = (1<<(bytepos+1)) - 1;writebits(fillbits);}writeword(0xFFD9); // EOIfree(RGB_buffer);free(category_alloc);free(bitcode_alloc);fclose(fp_jpeg_stream);}//JGLOBALS.Hstatic BYTE bytenew=0; // The byte that will be written in the JPG filestatic SBYTE bytepos=7; // bit position in the byte we write (bytenew)//should be<=7 and >=0static WORD mask[16]={1,2,4,8,16,32,64,128,256,512,1024,2048,4096,8192,16384,32768}; // The Huffman tables we'll use:static bitstring YDC_HT[12];static bitstring CbDC_HT[12];static bitstring YAC_HT[256];static bitstring CbAC_HT[256];static BYTE *category_alloc;static BYTE *category; //Here we'll keep the category of the numbers in range: -32767..32767 static bitstring *bitcode_alloc;static bitstring *bitcode; // their bitcoded representation//Precalculated tables for a faster YCbCr->RGB transformation// We use a SDWORD table because we'll scale values by 2^16 and work with integersstatic SDWORD YRtab[256],YGtab[256],YBtab[256];static SDWORD CbRtab[256],CbGtab[256],CbBtab[256];static SDWORD CrRtab[256],CrGtab[256],CrBtab[256];static float fdtbl_Y[64];static float fdtbl_Cb[64]; //the same with the fdtbl_Cr[64]colorRGB *RGB_buffer; //image to be encodedWORD width, height;// image dimensions divisible by 8static SBYTE YDU[64]; // This is the Data Unit of Y after YCbCr->RGB transformation static SBYTE CbDU[64];static SBYTE CrDU[64];static SWORD DU_DCT[64]; // Current DU (after DCT and quantization) which we'll zigzag static SWORD DU[64]; //zigzag reordered DU which will be Huffman codedFILE *fp_jpeg_stream;//JTABLES.Hstatic BYTE zigzag[64]={ 0, 1, 5, 6,14,15,27,28,2, 4, 7,13,16,26,29,42,3, 8,12,17,25,30,41,43,9,11,18,24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 };/* These are the sample quantization tables given in JPEG spec section K.1.The spec says that the values given produce "good" quality, andwhen divided by 2, "very good" quality.*/static BYTE std_luminance_qt[64] = {16, 11, 10, 16, 24, 40, 51, 61,12, 12, 14, 19, 26, 58, 60, 55,14, 13, 16, 24, 40, 57, 69, 56,14, 17, 22, 29, 51, 87, 80, 62,18, 22, 37, 56, 68, 109, 103, 77,24, 35, 55, 64, 81, 104, 113, 92,49, 64, 78, 87, 103, 121, 120, 101,72, 92, 95, 98, 112, 100, 103, 99};static BYTE std_chrominance_qt[64] = {17, 18, 24, 47, 99, 99, 99, 99,18, 21, 26, 66, 99, 99, 99, 99,24, 26, 56, 99, 99, 99, 99, 99,47, 66, 99, 99, 99, 99, 99, 99,99, 99, 99, 99, 99, 99, 99, 99,99, 99, 99, 99, 99, 99, 99, 99,99, 99, 99, 99, 99, 99, 99, 99,99, 99, 99, 99, 99, 99, 99, 99};// Standard Huffman tables (cf. JPEG standard section K.3) */static BYTE std_dc_luminance_nrcodes[17]={0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; static BYTE std_dc_luminance_values[12]={0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};static BYTE std_dc_chrominance_nrcodes[17]={0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; static BYTE std_dc_chrominance_values[12]={0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};static BYTE std_ac_luminance_nrcodes[17]={0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d }; static BYTE std_ac_luminance_values[162]= {0x01, 0x02, 0x03, 0x00, 0x04, 0x11, 0x05, 0x12,0x21, 0x31, 0x41, 0x06, 0x13, 0x51, 0x61, 0x07,0x22, 0x71, 0x14, 0x32, 0x81, 0x91, 0xa1, 0x08,0x23, 0x42, 0xb1, 0xc1, 0x15, 0x52, 0xd1, 0xf0,0x24, 0x33, 0x62, 0x72, 0x82, 0x09, 0x0a, 0x16,0x17, 0x18, 0x19, 0x1a, 0x25, 0x26, 0x27, 0x28,0x29, 0x2a, 0x34, 0x35, 0x36, 0x37, 0x38, 0x39,0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48, 0x49,0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58, 0x59,0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68, 0x69,0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78, 0x79,0x7a, 0x83, 0x84, 0x85, 0x86, 0x87, 0x88, 0x89,0x8a, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97, 0x98,0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6,0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3, 0xc4, 0xc5,0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2, 0xd3, 0xd4,0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda, 0xe1, 0xe2,0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9, 0xea,0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,0xf9, 0xfa };static BYTE std_ac_chrominance_nrcodes[17]={0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; static BYTE std_ac_chrominance_values[162]={0x00, 0x01, 0x02, 0x03, 0x11, 0x04, 0x05, 0x21,0x31, 0x06, 0x12, 0x41, 0x51, 0x07, 0x61, 0x71,0x13, 0x22, 0x32, 0x81, 0x08, 0x14, 0x42, 0x91,0xa1, 0xb1, 0xc1, 0x09, 0x23, 0x33, 0x52, 0xf0,0x15, 0x62, 0x72, 0xd1, 0x0a, 0x16, 0x24, 0x34,0xe1, 0x25, 0xf1, 0x17, 0x18, 0x19, 0x1a, 0x26,0x27, 0x28, 0x29, 0x2a, 0x35, 0x36, 0x37, 0x38,0x39, 0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48,0x49, 0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58,0x59, 0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68,0x69, 0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78,0x79, 0x7a, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,0x88, 0x89, 0x8a, 0x92, 0x93, 0x94, 0x95, 0x96,0x97, 0x98, 0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5,0xa6, 0xa7, 0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4,0xb5, 0xb6, 0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3,0xc4, 0xc5, 0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2,0xd3, 0xd4, 0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda,0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9,0xea, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,0xf9, 0xfa };//JTYPES.H#define BYTE unsigned char#define SBYTE signed char#define SWORD signed short int#define WORD unsigned short int#define DWORD unsigned long int#define SDWORD signed long intstatic struct APP0infotype {WORD marker;// = 0xFFE0WORD length; // = 16 for usual JPEG, no thumbnailBYTE JFIFsignature[5]; // = "JFIF",'\0'BYTE versionhi; // 1BYTE versionlo; // 1BYTE xyunits; // 0 = no units, normal densityWORD xdensity; // 1WORD ydensity; // 1BYTE thumbnwidth; // 0BYTE thumbnheight; // 0} APP0info={0xFFE0,16,'J','F','I','F',0,1,1,0,1,1,0,0};static struct SOF0infotype {WORD marker; // = 0xFFC0WORD length; // = 17 for a truecolor YCbCr JPGBYTE precision ;// Should be 8: 8 bits/sampleWORD height ;WORD width;BYTE nrofcomponents;//Should be 3: We encode a truecolor JPGBYTE IdY; // = 1BYTE HVY; // sampling factors for Y (bit 0-3 vert., 4-7 hor.)BYTE QTY; // Quantization Table number for Y = 0BYTE IdCb; // = 2BYTE HVCb;BYTE QTCb; // 1BYTE IdCr; // = 3BYTE HVCr;BYTE QTCr; // Normally equal to QTCb = 1} SOF0info = { 0xFFC0,17,8,0,0,3,1,0x11,0,2,0x11,1,3,0x11,1};// Default sampling factors are 1,1 for every image component: No downsampling static struct DQTinfotype {WORD marker; // = 0xFFDBWORD length; // = 132BYTE QTYinfo;// = 0: bit 0..3: number of QT = 0 (table for Y)// bit 4..7: precision of QT, 0 = 8 bit BYTE Ytable[64];BYTE QTCbinfo; // = 1 (quantization table for Cb,Cr}BYTE Cbtable[64];} DQTinfo;// Ytable from DQTinfo should be equal to a scaled and zizag reordered version// of the table which can be found in "tables.h": std_luminance_qt// Cbtable , similar = std_chrominance_qt// We'll init them in the program using set_DQTinfo functionstatic struct DHTinfotype {WORD marker; // = 0xFFC4WORD length; //0x01A2BYTE HTYDCinfo; // bit 0..3: number of HT (0..3), for Y =0//bit 4 :type of HT, 0 = DC table,1 = AC table//bit 5..7: not used, must be 0BYTE YDC_nrcodes[16]; //at index i = nr of codes with length iBYTE YDC_values[12];BYTE HTYACinfo; // = 0x10BYTE YAC_nrcodes[16];BYTE YAC_values[162];//we'll use the standard Huffman tablesBYTE HTCbDCinfo; // = 1BYTE CbDC_nrcodes[16];BYTE CbDC_values[12];BYTE HTCbACinfo; // = 0x11BYTE CbAC_nrcodes[16];BYTE CbAC_values[162];} DHTinfo;static struct SOSinfotype {WORD marker; // = 0xFFDAWORD length; // = 12BYTE nrofcomponents; // Should be 3: truecolor JPGBYTE IdY; //1BYTE HTY; //0 // bits 0..3: AC table (0..3)// bits 4..7: DC table (0..3)BYTE IdCb; //2BYTE HTCb; //0x11BYTE IdCr; //3BYTE HTCr; //0x11BYTE Ss,Se,Bf; // not interesting, they should be 0,63,0} SOSinfo={0xFFDA,12,3,1,0,2,0x11,3,0x11,0,0x3F,0};typedef struct { BYTE B,G,R; } colorRGB;typedef struct { BYTE length;WORD value;} bitstring;#define Y(R,G,B) ((BYTE)( (YRtab[(R)]+YGtab[(G)]+YBtab[(B)])>>16 ) - 128) #define Cb(R,G,B) ((BYTE)( (CbRtab[(R)]+CbGtab[(G)]+CbBtab[(B)])>>16 ) ) #define Cr(R,G,B) ((BYTE)( (CrRtab[(R)]+CrGtab[(G)]+CrBtab[(B)])>>16 ) )#define writebyte(b) fputc((b),fp_jpeg_stream)#define writeword(w) writebyte((w)/256);writebyte((w)%256);。
数字图像处理代码大全
1.图像反转MATLAB程序实现如下：I=imread('xian.bmp');J=double(I);J=-J+(256-1); %图像反转线性变换H=uint8(J);subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(H);2.灰度线性变换MATLAB程序实现如下：I=imread('xian.bmp');subplot(2,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);axis on; %显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axis on; %显示坐标系J=imadjust(I1,[0.1 0.5],[]); %局部拉伸，把[0.1 0.5]内的灰度拉伸为[0 1]subplot(2,2,3),imshow(J);title('线性变换图像[0.1 0.5]');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系K=imadjust(I1,[0.3 0.7],[]); %局部拉伸，把[0.3 0.7]内的灰度拉伸为[0 1]subplot(2,2,4),imshow(K);title('线性变换图像[0.3 0.7]');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系3.非线性变换MATLAB程序实现如下：I=imread('xian.bmp');I1=rgb2gray(I);subplot(1,2,1),imshow(I1);title('灰度图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系J=double(I1);J=40*(log(J+1));H=uint8(J);subplot(1,2,2),imshow(H);title('对数变换图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系4.直方图均衡化MATLAB程序实现如下：I=imread('xian.bmp');I=rgb2gray(I);figure;subplot(2,2,1);imshow(I);subplot(2,2,2);imhist(I);I1=histeq(I);figure;subplot(2,2,1);imshow(I1);subplot(2,2,2);imhist(I1);5.线性平滑滤波器用MATLAB实现领域平均法抑制噪声程序：I=imread('xian.bmp');subplot(231)imshow(I)title('原始图像')I=rgb2gray(I);I1=imnoise(I,'salt & pepper',0.02);subplot(232)imshow(I1)title('添加椒盐噪声的图像')k1=filter2(fspecial('average',3),I1)/255; %进行3*3模板平滑滤波k2=filter2(fspecial('average',5),I1)/255; %进行5*5模板平滑滤波k3=filter2(fspecial('average',7),I1)/255; %进行7*7模板平滑滤波k4=filter2(fspecial('average',9),I1)/255; %进行9*9模板平滑滤波subplot(233),imshow(k1);title('3*3模板平滑滤波');subplot(234),imshow(k2);title('5*5模板平滑滤波');subplot(235),imshow(k3);title('7*7模板平滑滤波');subplot(236),imshow(k4);title('9*9模板平滑滤波'); 6.中值滤波器用MATLAB实现中值滤波程序如下：I=imread('xian.bmp');I=rgb2gray(I);J=imnoise(I,'salt&pepper',0.02);subplot(231),imshow(I);title('原图像');subplot(232),imshow(J);title('添加椒盐噪声图像');k1=medfilt2(J); %进行3*3模板中值滤波k2=medfilt2(J,[5,5]); %进行5*5模板中值滤波k3=medfilt2(J,[7,7]); %进行7*7模板中值滤波k4=medfilt2(J,[9,9]); %进行9*9模板中值滤波subplot(233),imshow(k1);title('3*3模板中值滤波'); subplot(234),imshow(k2);title('5*5模板中值滤波'); subplot(235),imshow(k3);title('7*7模板中值滤波'); subplot(236),imshow(k4);title('9*9模板中值滤波'); 7.用Sobel算子和拉普拉斯对图像锐化：I=imread('xian.bmp');subplot(2,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(2,2,2),imshow(I1);title('二值图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系H=fspecial('sobel'); %选择sobel算子J=filter2(H,I1); %卷积运算subplot(2,2,3),imshow(J);title('sobel算子锐化图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系h=[0 1 0,1 -4 1,0 1 0]; %拉普拉斯算子J1=conv2(I1,h,'same'); %卷积运算subplot(2,2,4),imshow(J1);title('拉普拉斯算子锐化图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系8.梯度算子检测边缘用MATLAB实现如下：I=imread('xian.bmp');subplot(2,3,1);imshow(I);title('原始图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(2,3,2);imshow(I1);title('二值图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I2=edge(I1,'roberts');figure;subplot(2,3,3);imshow(I2);title('roberts算子分割结果');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I3=edge(I1,'sobel');subplot(2,3,4);imshow(I3);title('sobel算子分割结果');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I4=edge(I1,'Prewitt');subplot(2,3,5);imshow(I4);title('Prewitt算子分割结果');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系9.LOG算子检测边缘用MATLAB程序实现如下：I=imread('xian.bmp');subplot(2,2,1);imshow(I);title('原始图像');I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title('灰度图像');I2=edge(I1,'log');subplot(2,2,3);imshow(I2);title('log算子分割结果'); 10.Canny算子检测边缘用MATLAB程序实现如下：I=imread('xian.bmp'); subplot(2,2,1);imshow(I);title('原始图像')I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title('灰度图像');I2=edge(I1,'canny'); subplot(2,2,3);imshow(I2);title('canny算子分割结果');11.边界跟踪（bwtraceboundary函数）clcclear allI=imread('xian.bmp');figureimshow(I);title('原始图像');I1=rgb2gray(I); %将彩色图像转化灰度图像threshold=graythresh(I1); %计算将灰度图像转化为二值图像所需的门限BW=im2bw(I1, threshold); %将灰度图像转化为二值图像figureimshow(BW);title('二值图像');dim=size(BW);col=round(dim(2)/2)-90; %计算起始点列坐标row=find(BW(:,col),1); %计算起始点行坐标connectivity=8;num_points=180;contour=bwtraceboundary(BW,[row,col],'N',connectivity,num_p oints);%提取边界figureimshow(I1);hold on;plot(contour(:,2),contour(:,1), 'g','LineWidth' ,2); title('边界跟踪图像');12.Hough变换I= imread('xian.bmp');rotI=rgb2gray(I);subplot(2,2,1);imshow(rotI);title('灰度图像');axis([50,250,50,200]);grid on;axis on;BW=edge(rotI,'prewitt');subplot(2,2,2);imshow(BW);title('prewitt算子边缘检测后图像');axis([50,250,50,200]);grid on;axis on;[H,T,R]=hough(BW);subplot(2,2,3);imshow(H,[],'XData',T,'YData',R,'InitialMagnification','fit'); title('霍夫变换图');xlabel('\theta'),ylabel('\rho');axis on , axis normal, hold on;P=houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));x=T(P(:,2));y=R(P(:,1));plot(x,y,'s','color','white');lines=houghlines(BW,T,R,P,'FillGap',5,'MinLength',7); subplot(2,2,4);,imshow(rotI);title('霍夫变换图像检测');axis([50,250,50,200]);grid on;axis on;hold on;max_len=0;for k=1:length(lines)xy=[lines(k).point1;lines(k).point2];plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');len=norm(lines(k).point1-lines(k).point2);if(len>max_len)max_len=len;xy_long=xy;endendplot(xy_long(:,1),xy_long(:,2),'LineWidth',2,'Color','cyan'); 13.直方图阈值法用MATLAB实现直方图阈值法：I=imread('xian.bmp');I1=rgb2gray(I);figure;subplot(2,2,1);imshow(I1);title('灰度图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系[m,n]=size(I1); %测量图像尺寸参数GP=zeros(1,256); %预创建存放灰度出现概率的向量for k=0:255GP(k+1)=length(find(I1==k))/(m*n); %计算每级灰度出现的概率，将其存入GP中相应位置endsubplot(2,2,2),bar(0:255,GP,'g') %绘制直方图title('灰度直方图')xlabel('灰度值')ylabel('出现概率')I2=im2bw(I,150/255);subplot(2,2,3),imshow(I2);title('阈值150的分割图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I3=im2bw(I,200/255); %subplot(2,2,4),imshow(I3);title('阈值200的分割图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系14. 自动阈值法：Otsu法用MATLAB实现Otsu算法：clcclear allI=imread('xian.bmp');subplot(1,2,1),imshow(I);title('原始图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系level=graythresh(I); %确定灰度阈值BW=im2bw(I,level);subplot(1,2,2),imshow(BW);title('Otsu法阈值分割图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系15.膨胀操作I=imread('xian.bmp'); %载入图像I1=rgb2gray(I);subplot(1,2,1);imshow(I1);title('灰度图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系se=strel('disk',1); %生成圆形结构元素I2=imdilate(I1,se); %用生成的结构元素对图像进行膨胀subplot(1,2,2);imshow(I2);title('膨胀后图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系16.腐蚀操作MATLAB实现腐蚀操作I=imread('xian.bmp'); %载入图像I1=rgb2gray(I);subplot(1,2,1);imshow(I1);title('灰度图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系se=strel('disk',1); %生成圆形结构元素I2=imerode(I1,se); %用生成的结构元素对图像进行腐蚀subplot(1,2,2);imshow(I2);title('腐蚀后图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系17.开启和闭合操作用MATLAB实现开启和闭合操作I=imread('xian.bmp'); %载入图像subplot(2,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);axis on; %显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axis on; %显示坐标系se=strel('disk',1); %采用半径为1的圆作为结构元素I3=imclose(I1,se); %闭合操作subplot(2,2,3),imshow(I2);title('开启运算后图像');axis([50,250,50,200]);axis on; %显示坐标系subplot(2,2,4),imshow(I3);title('闭合运算后图像');axis([50,250,50,200]);axis on; %显示坐标系18.开启和闭合组合操作I=imread('xian.bmp'); %载入图像subplot(3,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);axis on; %显示坐标系I1=rgb2gray(I);subplot(3,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axis on; %显示坐标系se=strel('disk',1);I3=imclose(I1,se); %闭合操作subplot(3,2,3),imshow(I2);title('开启运算后图像');axis([50,250,50,200]);axis on; %显示坐标系subplot(3,2,4),imshow(I3);title('闭合运算后图像');axis([50,250,50,200]);axis on; %显示坐标系se=strel('disk',1);I4=imopen(I1,se);I5=imclose(I4,se);subplot(3,2,5),imshow(I5); %开—闭运算图像title('开—闭运算图像');axis([50,250,50,200]);axis on; %显示坐标系I6=imclose(I1,se);I7=imopen(I6,se);subplot(3,2,6),imshow(I7); %闭—开运算图像title('闭—开运算图像');axis([50,250,50,200]);axis on; %显示坐标系19.形态学边界提取利用MATLAB实现如下：I=imread('xian.bmp'); %载入图像subplot(1,3,1),imshow(I);title('原始图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(1,3,2),imshow(I1);title('二值化图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I2=bwperim(I1); %获取区域的周长subplot(1,3,3),imshow(I2);title('边界周长的二值图像');axis([50,250,50,200]);grid on;axis on;20.形态学骨架提取利用MATLAB实现如下：I=imread('xian.bmp'); subplot(2,2,1),imshow(I); title('原始图像');axis([50,250,50,200]); axis on;I1=im2bw(I);subplot(2,2,2),imshow(I1); title('二值图像');axis([50,250,50,200]); axis on;I2=bwmorph(I1,'skel',1); subplot(2,2,3),imshow(I2); title('1次骨架提取');axis([50,250,50,200]); axis on;I3=bwmorph(I1,'skel',2); subplot(2,2,4),imshow(I3); title('2次骨架提取');axis([50,250,50,200]); axis on;21.直接提取四个顶点坐标I = imread('xian.bmp');I = I(:,:,1);BW=im2bw(I);figureimshow(~BW)[x,y]=getpts。
internimage源码解析
internimage源码解析InternImage是一个开源项目，旨在提供一种用于图像处理和分析的Python库。
它基于Pillow库，并提供了一些额外的功能和工具，以简化图像处理的流程。
下面我将对InternImage的源代码进行解析，并分析其主要功能和特点。
InternImage的源代码主要包含以下几个方面的内容：图像加载和保存、图像缩放和旋转、图像滤波和变换、图像特征提取和匹配、图像分割和识别等。
下面我将逐个进行解析。
图像加载和保存部分，InternImage通过使用Pillow库提供的Image.open函数来加载图像。
加载后的图像数据存储在一个PIL.Image对象中。
加载完图像后，可以使用PIL.Image对象的save方法来保存图像。
图像缩放和旋转部分，InternImage提供了resize和rotate两个函数。
resize函数可以将图像按照指定的大小进行缩放，参数包括目标尺寸和插值方法。
rotate函数可以将图像按照指定的角度进行旋转，参数为旋转角度和插值方法。
这些函数利用了Pillow库提供的resize 和rotate方法，简化了图像缩放和旋转的操作。
图像滤波和变换部分，InternImage提供了一些常用的图像滤波函数，如均值滤波、高斯滤波、中值滤波等。
这些函数利用了Pillow库提供的filter方法，将滤波操作应用于图像数据。
此外，InternImage还提供了一些图像变换函数，如直方图均衡化、图像加噪等。
图像特征提取和匹配部分，InternImage利用了一些经典的图像处理算法来提取图像的特征，并将其表示为特征向量。
其中包括SIFT算法、SURF算法、ORB算法等。
提取到的特征向量可以用于图像匹配和识别。
InternImage还提供了一些常用的图像匹配算法，如最邻近匹配、RANSAC匹配等。
图像分割和识别部分，InternImage利用了一些机器学习算法和深度学习算法来实现图像分割和识别任务。
8种常用图像处理算法(函数)
8种常用图像处理算法（函数）这是我毕业设计的一部分代码/*************************************************************************** 函数名称：** VertMirror()** 参数:** LPSTR lpDIB //指向源DIB图像指针** 返回值:** BOOL //镜像成功返回TRUE，否则返回FALSE。
** 说明:** 该函数用来实现DIB图像的垂直镜像。
*************************************************************************/BOOL WINAPI VertMirror(LPSTR lpDIB){//原图象宽度LONG lWidth;//原图象高度LONG lHeight;//原图象的颜色数WORD wNumColors;//原图象的信息头结构指针LPBITMAPINFOHEADER lpbmi;//指向原图象和目的图象的像素的指针LPBYTE lpSrc,lpDst;//平移后剩余图像在源图像中的位置（矩形区域）CRect rectSrc;//指向原图像像素的指针LPBYTE lpDIBBits;//指向复制图像像素的指针LPBYTE lpNewDIBBits;//内存句柄HLOCAL h;//循环变量LONG i;//图像每行的字节数LONG lLineBytes;//获取图象的信息头结构的指针lpbmi=(LPBITMAPINFOHEADER)lpDIB;//找到图象的像素位置lpDIBBits=(LPBYTE)::FindDIBBits(lpDIB);//获取图象的宽度lWidth=::DIBWidth(lpDIB);//获取图象的高度lHeight=::DIBHeight(lpDIB);//获取图象的颜色数wNumColors=::DIBNumColors(lpDIB);//计算图像每行的字节数lLineBytes = WIDTHBYTES(lWidth *(lpbmi->biBitCount));// 暂时分配内存，以保存新图像h= LocalAlloc(LHND, lLineBytes);// 分配内存失败，直接返回if (!h)return FALSE;// 锁定内存lpNewDIBBits = (LPBYTE)LocalLock(h);//如果是256色位图或真彩色位图if(wNumColors==256||wNumColors==0){//平移图像，每次移动一行for(i = 0; i<lHeight/2; i++){//指向原图象倒数第i行像素起点的指针lpSrc =(LPBYTE)lpDIBBits + lLineBytes * i;//目标区域同样要注意上下倒置的问题lpDst =(LPBYTE)lpDIBBits+lLineBytes * (lHeight-i-1) ;//备份一行memcpy(lpNewDIBBits, lpDst, lLineBytes);//将倒数第i行像素复制到第i行memcpy(lpDst, lpSrc, lLineBytes);//将第i行像素复制到第i行memcpy(lpSrc, lpNewDIBBits, lLineBytes);}}else{AfxMessageBox("只支持256色和真彩色位图");// 释放内存LocalUnlock(h);LocalFree(h);return false;}// 释放内存LocalUnlock(h);LocalFree(h);// 返回return TRUE;}/************************************************************************* ** 函数名称：** RotateDIB ()** 参数:** LPSTR lpDIB //指向源DIB图像指针** int iAngle* 说明:** 该函数用来实现DIB图像的旋转。
(完整版)数字图像处理代码大全
1.图像反转MATLAB程序实现如下：I=imread('xian.bmp');J=double(I);J=-J+(256-1); %图像反转线性变换H=uint8(J);subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(H);2.灰度线性变换MATLAB程序实现如下：I=imread('xian.bmp');subplot(2,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);axis on; %显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axis on; %显示坐标系J=imadjust(I1,[0.1 0.5],[]); %局部拉伸，把[0.1 0.5]内的灰度拉伸为[0 1]subplot(2,2,3),imshow(J);title('线性变换图像[0.1 0.5]');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系K=imadjust(I1,[0.3 0.7],[]); %局部拉伸，把[0.3 0.7]内的灰度拉伸为[0 1]subplot(2,2,4),imshow(K);title('线性变换图像[0.3 0.7]');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系3.非线性变换MATLAB程序实现如下：I=imread('xian.bmp');I1=rgb2gray(I);subplot(1,2,1),imshow(I1);title('灰度图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系J=double(I1);J=40*(log(J+1));H=uint8(J);subplot(1,2,2),imshow(H);title('对数变换图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系4.直方图均衡化MATLAB程序实现如下：I=imread('xian.bmp');I=rgb2gray(I);figure;subplot(2,2,1);imshow(I);subplot(2,2,2);imhist(I);I1=histeq(I);figure;subplot(2,2,1);imshow(I1);subplot(2,2,2);imhist(I1);5.线性平滑滤波器用MATLAB实现领域平均法抑制噪声程序：I=imread('xian.bmp');subplot(231)imshow(I)title('原始图像')I=rgb2gray(I);I1=imnoise(I,'salt & pepper',0.02);subplot(232)imshow(I1)title('添加椒盐噪声的图像')k1=filter2(fspecial('average',3),I1)/255; %进行3*3模板平滑滤波k2=filter2(fspecial('average',5),I1)/255; %进行5*5模板平滑滤波k3=filter2(fspecial('average',7),I1)/255; %进行7*7模板平滑滤波k4=filter2(fspecial('average',9),I1)/255; %进行9*9模板平滑滤波subplot(233),imshow(k1);title('3*3模板平滑滤波');subplot(234),imshow(k2);title('5*5模板平滑滤波');subplot(235),imshow(k3);title('7*7模板平滑滤波');subplot(236),imshow(k4);title('9*9模板平滑滤波'); 6.中值滤波器用MATLAB实现中值滤波程序如下：I=imread('xian.bmp');I=rgb2gray(I);J=imnoise(I,'salt&pepper',0.02);subplot(231),imshow(I);title('原图像');subplot(232),imshow(J);title('添加椒盐噪声图像');k1=medfilt2(J); %进行3*3模板中值滤波k2=medfilt2(J,[5,5]); %进行5*5模板中值滤波k3=medfilt2(J,[7,7]); %进行7*7模板中值滤波k4=medfilt2(J,[9,9]); %进行9*9模板中值滤波subplot(233),imshow(k1);title('3*3模板中值滤波'); subplot(234),imshow(k2);title('5*5模板中值滤波'); subplot(235),imshow(k3);title('7*7模板中值滤波'); subplot(236),imshow(k4);title('9*9模板中值滤波'); 7.用Sobel算子和拉普拉斯对图像锐化：I=imread('xian.bmp');subplot(2,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(2,2,2),imshow(I1);title('二值图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系H=fspecial('sobel'); %选择sobel算子J=filter2(H,I1); %卷积运算subplot(2,2,3),imshow(J);title('sobel算子锐化图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系h=[0 1 0,1 -4 1,0 1 0]; %拉普拉斯算子J1=conv2(I1,h,'same'); %卷积运算subplot(2,2,4),imshow(J1);title('拉普拉斯算子锐化图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系8.梯度算子检测边缘用MATLAB实现如下：I=imread('xian.bmp');subplot(2,3,1);imshow(I);title('原始图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(2,3,2);imshow(I1);title('二值图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I2=edge(I1,'roberts');figure;subplot(2,3,3);imshow(I2);title('roberts算子分割结果');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I3=edge(I1,'sobel');subplot(2,3,4);imshow(I3);title('sobel算子分割结果');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I4=edge(I1,'Prewitt');subplot(2,3,5);imshow(I4);title('Prewitt算子分割结果');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系9.LOG算子检测边缘用MATLAB程序实现如下：I=imread('xian.bmp');subplot(2,2,1);imshow(I);title('原始图像');I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title('灰度图像');I2=edge(I1,'log');subplot(2,2,3);imshow(I2);title('log算子分割结果'); 10.Canny算子检测边缘用MATLAB程序实现如下：I=imread('xian.bmp'); subplot(2,2,1);imshow(I);title('原始图像')I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title('灰度图像');I2=edge(I1,'canny'); subplot(2,2,3);imshow(I2);title('canny算子分割结果');11.边界跟踪（bwtraceboundary函数）clcclear allI=imread('xian.bmp');figureimshow(I);title('原始图像');I1=rgb2gray(I); %将彩色图像转化灰度图像threshold=graythresh(I1); %计算将灰度图像转化为二值图像所需的门限BW=im2bw(I1, threshold); %将灰度图像转化为二值图像figureimshow(BW);title('二值图像');dim=size(BW);col=round(dim(2)/2)-90; %计算起始点列坐标row=find(BW(:,col),1); %计算起始点行坐标connectivity=8;num_points=180;contour=bwtraceboundary(BW,[row,col],'N',connectivity,num_p oints);%提取边界figureimshow(I1);hold on;plot(contour(:,2),contour(:,1), 'g','LineWidth' ,2); title('边界跟踪图像');12.Hough变换I= imread('xian.bmp');rotI=rgb2gray(I);subplot(2,2,1);imshow(rotI);title('灰度图像');axis([50,250,50,200]);grid on;axis on;BW=edge(rotI,'prewitt');subplot(2,2,2);imshow(BW);title('prewitt算子边缘检测后图像');axis([50,250,50,200]);grid on;axis on;[H,T,R]=hough(BW);subplot(2,2,3);imshow(H,[],'XData',T,'YData',R,'InitialMagnification','fit'); title('霍夫变换图');xlabel('\theta'),ylabel('\rho');axis on , axis normal, hold on;P=houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));x=T(P(:,2));y=R(P(:,1));plot(x,y,'s','color','white');lines=houghlines(BW,T,R,P,'FillGap',5,'MinLength',7); subplot(2,2,4);,imshow(rotI);title('霍夫变换图像检测');axis([50,250,50,200]);grid on;axis on;hold on;max_len=0;for k=1:length(lines)xy=[lines(k).point1;lines(k).point2];plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');len=norm(lines(k).point1-lines(k).point2);if(len>max_len)max_len=len;xy_long=xy;endendplot(xy_long(:,1),xy_long(:,2),'LineWidth',2,'Color','cyan'); 13.直方图阈值法用MATLAB实现直方图阈值法：I=imread('xian.bmp');I1=rgb2gray(I);figure;subplot(2,2,1);imshow(I1);title('灰度图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系[m,n]=size(I1); %测量图像尺寸参数GP=zeros(1,256); %预创建存放灰度出现概率的向量for k=0:255GP(k+1)=length(find(I1==k))/(m*n); %计算每级灰度出现的概率，将其存入GP中相应位置endsubplot(2,2,2),bar(0:255,GP,'g') %绘制直方图title('灰度直方图')xlabel('灰度值')ylabel('出现概率')I2=im2bw(I,150/255);subplot(2,2,3),imshow(I2);title('阈值150的分割图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I3=im2bw(I,200/255); %subplot(2,2,4),imshow(I3);title('阈值200的分割图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系14. 自动阈值法：Otsu法用MATLAB实现Otsu算法：clcclear allI=imread('xian.bmp');subplot(1,2,1),imshow(I);title('原始图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系level=graythresh(I); %确定灰度阈值BW=im2bw(I,level);subplot(1,2,2),imshow(BW);title('Otsu法阈值分割图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系15.膨胀操作I=imread('xian.bmp'); %载入图像I1=rgb2gray(I);subplot(1,2,1);imshow(I1);title('灰度图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系se=strel('disk',1); %生成圆形结构元素I2=imdilate(I1,se); %用生成的结构元素对图像进行膨胀subplot(1,2,2);imshow(I2);title('膨胀后图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系16.腐蚀操作MATLAB实现腐蚀操作I=imread('xian.bmp'); %载入图像I1=rgb2gray(I);subplot(1,2,1);imshow(I1);title('灰度图像')axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系se=strel('disk',1); %生成圆形结构元素I2=imerode(I1,se); %用生成的结构元素对图像进行腐蚀subplot(1,2,2);imshow(I2);title('腐蚀后图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系17.开启和闭合操作用MATLAB实现开启和闭合操作I=imread('xian.bmp'); %载入图像subplot(2,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);axis on; %显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axis on; %显示坐标系se=strel('disk',1); %采用半径为1的圆作为结构元素I3=imclose(I1,se); %闭合操作subplot(2,2,3),imshow(I2);title('开启运算后图像');axis([50,250,50,200]);axis on; %显示坐标系subplot(2,2,4),imshow(I3);title('闭合运算后图像');axis([50,250,50,200]);axis on; %显示坐标系18.开启和闭合组合操作I=imread('xian.bmp'); %载入图像subplot(3,2,1),imshow(I);title('原始图像');axis([50,250,50,200]);axis on; %显示坐标系I1=rgb2gray(I);subplot(3,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axis on; %显示坐标系se=strel('disk',1);I3=imclose(I1,se); %闭合操作subplot(3,2,3),imshow(I2);title('开启运算后图像');axis([50,250,50,200]);axis on; %显示坐标系subplot(3,2,4),imshow(I3);title('闭合运算后图像');axis([50,250,50,200]);axis on; %显示坐标系se=strel('disk',1);I4=imopen(I1,se);I5=imclose(I4,se);subplot(3,2,5),imshow(I5); %开—闭运算图像title('开—闭运算图像');axis([50,250,50,200]);axis on; %显示坐标系I6=imclose(I1,se);I7=imopen(I6,se);subplot(3,2,6),imshow(I7); %闭—开运算图像title('闭—开运算图像');axis([50,250,50,200]);axis on; %显示坐标系19.形态学边界提取利用MATLAB实现如下：I=imread('xian.bmp'); %载入图像subplot(1,3,1),imshow(I);title('原始图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(1,3,2),imshow(I1);title('二值化图像');axis([50,250,50,200]);grid on; %显示网格线axis on; %显示坐标系I2=bwperim(I1); %获取区域的周长subplot(1,3,3),imshow(I2);title('边界周长的二值图像');axis([50,250,50,200]);grid on;axis on;20.形态学骨架提取利用MATLAB实现如下：I=imread('xian.bmp'); subplot(2,2,1),imshow(I); title('原始图像');axis([50,250,50,200]); axis on;I1=im2bw(I);subplot(2,2,2),imshow(I1); title('二值图像');axis([50,250,50,200]); axis on;I2=bwmorph(I1,'skel',1); subplot(2,2,3),imshow(I2); title('1次骨架提取');axis([50,250,50,200]); axis on;I3=bwmorph(I1,'skel',2); subplot(2,2,4),imshow(I3); title('2次骨架提取');axis([50,250,50,200]); axis on;21.直接提取四个顶点坐标I = imread('xian.bmp');I = I(:,:,1);BW=im2bw(I);figureimshow(~BW)[x,y]=getpts。
用Python编写简单的图像处理和图形生成工具
用Python编写简单的图像处理和图形生成工具Python是一种强大的编程语言，同时也具有广泛的图像处理和图形生成工具，可以帮助我们进行各种图片编辑和图形生成的任务。
接下来，我将介绍几个常用的Python图像处理和图形生成工具，并且提供一些使用示例。
1. PIL（Python Imaging Library）: PIL是Python中最常用的图像处理库之一，可以用来打开、编辑和保存各种图像格式的图片。
它提供了丰富的图像处理函数，如缩放、旋转、剪切、滤镜等，可以满足大部分的图像处理需求。
下面是一个使用PIL库进行图像缩放的示例：```pythonfrom PIL import Image#打开图片img = Image.open('input.jpg')#缩放图片new_img = img.resize((500, 500))#保存缩放后的图片new_img.save('output.jpg')```2. OpenCV：OpenCV是一种开源的计算机视觉库，可以用于处理图像和视频。
它支持各种图像处理和计算机视觉任务，如颜色转换、边缘检测、人脸识别等。
OpenCV具有强大的图像处理功能，而且还可以与其他机器学习库结合使用。
下面是一个使用OpenCV库进行图像边缘检测的示例：```pythonimport cv2#读取图片img = cv2.imread('input.jpg')#转换为灰度图gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#边缘检测edges = cv2.Canny(gray, 100, 200)#显示结果cv2.imshow('Edges', edges)cv2.waitKey(0)```3. Matplotlib：Matplotlib是一个绘图库，用于生成各种类型的图形，如折线图、散点图、柱状图等。
CC++BMP（24位真彩色）图像处理（2）------图像截取
CC++BMP（24位真彩⾊）图像处理（2）------图像截取对上⼀篇博客《C/C++ BMP（24位真彩⾊）图像处理（1）------图像打开与数据区处理》的代码做⼩部分的修改，就可以进⾏BMP图像的截取操作，代码如下：#include <string.h>#include <math.h>#include <stdio.h>#include <stdlib.h>#include <malloc.h>#include<time.h>//时间相关头⽂件，可⽤其中函数计算图像处理速度#define WIDTHBYTES(bits) (((bits)+31)/32*4)//⽤于使图像宽度所占字节数为4byte的倍数#define MYCUT_HEIGHT 100 //截取⾼度#define MYCUT_WIDTH 100 //截取宽度#define BEGIN_X 0 //截取位图开始位置X坐标#define BEGIN_Y 0 //截取位图开始位置Y坐标typedef unsigned char BYTE;typedef unsigned short WORD;typedef unsigned long DWORD;typedef long LONG;//位图⽂件头信息结构定义//其中不包含⽂件类型信息（由于结构体的内存结构决定，要是加了的话将不能正确读取⽂件信息）typedef struct tagBITMAPFILEHEADER {DWORD bfSize; //⽂件⼤⼩WORD bfReserved1; //保留字，不考虑WORD bfReserved2; //保留字，同上DWORD bfOffBits; //实际位图数据的偏移字节数，即前三个部分长度之和} BITMAPFILEHEADER;//信息头BITMAPINFOHEADER，也是⼀个结构，其定义如下：typedef struct tagBITMAPINFOHEADER{//public:DWORD biSize; //指定此结构体的长度，为40LONG biWidth; //位图宽LONG biHeight; //位图⾼WORD biPlanes; //平⾯数，为1WORD biBitCount; //采⽤颜⾊位数，可以是1，2，4，8，16，24，新的可以是32DWORD biCompression; //压缩⽅式，可以是0，1，2，其中0表⽰不压缩DWORD biSizeImage; //实际位图数据占⽤的字节数LONG biXPelsPerMeter; //X⽅向分辨率LONG biYPelsPerMeter; //Y⽅向分辨率DWORD biClrUsed; //使⽤的颜⾊数，如果为0，则表⽰默认值(2^颜⾊位数)DWORD biClrImportant; //重要颜⾊数，如果为0，则表⽰所有颜⾊都是重要的} BITMAPINFOHEADER;void main(){long now=0;now=clock();//存储图像处理开始时间BITMAPFILEHEADER bitHead,writebitHead;BITMAPINFOHEADER bitInfoHead,writebitInfoHead;FILE* pfile;//输⼊⽂件FILE* wfile;//输出⽂件char strFile[50]="E:\\testpicture\\1.bmp";//打开图像路径，BMP图像必须为24位真彩⾊格式char strFilesave[50]="E:\\testpicture\\2.bmp";//处理后图像存储路径pfile = fopen(strFile,"rb");//⽂件打开图像wfile = fopen(strFilesave,"wb");//打开⽂件为存储修改后图像做准备//读取位图⽂件头信息WORD fileType;fread(&fileType,1,sizeof(WORD),pfile);fwrite(&fileType,1,sizeof(WORD),wfile);if(fileType != 0x4d42){printf("file is not .bmp file!");return;}//读取位图⽂件头信息fread(&bitHead,1,sizeof(tagBITMAPFILEHEADER),pfile);writebitHead=bitHead;//由于截取图像头和源⽂件头相似，所以先将源⽂件头数据赋予截取⽂件头//读取位图信息头信息fread(&bitInfoHead,1,sizeof(BITMAPINFOHEADER),pfile);writebitInfoHead=bitInfoHead;//同位图⽂件头相似writebitInfoHead.biHeight=MYCUT_HEIGHT;//为截取⽂件重写位图⾼度writebitInfoHead.biWidth=MYCUT_WIDTH;//为截取⽂件重写位图宽度int mywritewidth=WIDTHBYTES(writebitInfoHead.biWidth*writebitInfoHead.biBitCount);//BMP图像实际位图数据区的宽度为4byte的倍数，在此计算实际数据区宽度 writebitInfoHead.biSizeImage=mywritewidth*writebitInfoHead.biHeight;//计算位图实际数据区⼤⼩writebitHead.bfSize=54+writebitInfoHead.biSizeImage;//位图⽂件头⼤⼩为位图数据区⼤⼩加上54bytefwrite(&writebitHead,1,sizeof(tagBITMAPFILEHEADER),wfile);//写回位图⽂件头信息到输出⽂件fwrite(&writebitInfoHead,1,sizeof(BITMAPINFOHEADER),wfile);//写回位图信息头信息到输出⽂件int width = bitInfoHead.biWidth;int height = bitInfoHead.biHeight;//分配内存空间把源图存⼊内存int l_width = WIDTHBYTES(width*bitInfoHead.biBitCount);//计算位图的实际宽度并确保它为4byte的倍数int write_width = WIDTHBYTES(writebitInfoHead.biWidth*writebitInfoHead.biBitCount);//计算写位图的实际宽度并确保它为4byte的倍数BYTE *pColorData=(BYTE *)malloc(height*l_width);//开辟内存空间存储图像数据memset(pColorData,0,height*l_width);BYTE *pColorDataMid=(BYTE *)malloc(mywritewidth*MYCUT_HEIGHT);//开辟内存空间存储图像处理之后数据memset(pColorDataMid,0,mywritewidth*MYCUT_HEIGHT);long nData = height*l_width;long write_nData = mywritewidth*MYCUT_HEIGHT;//截取的位图数据区长度定义//把位图数据信息读到数组⾥fread(pColorData,1,nData,pfile);//图像处理可通过操作这部分数据加以实现//截取图像数据区操作，在操作过程中注意截取图像是否越界，可在此处加⼊代码进⾏越界处理for(int hnum=height-BEGIN_Y-MYCUT_HEIGHT;hnum<height-BEGIN_Y;hnum++)//由于BMP图像的数据存储格式起点是图像的左下⾓，所以需要进⾏坐标换算操作 for(int wnum=BEGIN_X;wnum<BEGIN_X+MYCUT_WIDTH;wnum++){int pixel_point=hnum*l_width+wnum*3;//数组位置偏移量，对应于图像的各像素点RGB的起点int write_pixel_point=(hnum-height+BEGIN_Y+MYCUT_HEIGHT)*mywritewidth+(wnum-BEGIN_X)*3;pColorDataMid[write_pixel_point]=pColorData[pixel_point];pColorDataMid[write_pixel_point+1]=pColorData[pixel_point+1];pColorDataMid[write_pixel_point+2]=pColorData[pixel_point+2];}fwrite(pColorDataMid,1,write_nData,wfile); //将处理完图像数据区写回⽂件fclose(pfile);fclose(wfile);printf("图像处理完成\n");printf("运⾏时间为：%dms\n",int(((double)(clock()-now))/CLOCKS_PER_SEC*1000));//输出图像处理花费时间信息}。
matlab数字图像处理源代码
数字图像去噪典型算法及matlab实现希望得到大家的指点和帮助图像去噪是数字图像处理中的重要环节和步骤。
去噪效果的好坏直接影响到后续的图像处理工作如图像分割、边缘检测等。
图像信号在产生、传输过程中都可能会受到噪声的污染，一般数字图像系统中的常见噪声主要有：高斯噪声（主要由阻性元器件内部产生）、椒盐噪声（主要是图像切割引起的黑图像上的白点噪声或光电转换过程中产生的泊松噪声）等；目前比较经典的图像去噪算法主要有以下三种：均值滤波算法：也称线性滤波，主要思想为邻域平均法，即用几个像素灰度的平均值来代替每个像素的灰度。
有效抑制加性噪声，但容易引起图像模糊，可以对其进行改进，主要避开对景物边缘的平滑处理。
中值滤波：基于排序统计理论的一种能有效抑制噪声的非线性平滑滤波信号处理技术。
中值滤波的特点即是首先确定一个以某个像素为中心点的邻域，一般为方形邻域，也可以为圆形、十字形等等，然后将邻域中各像素的灰度值排序，取其中间值作为中心像素灰度的新值，这里领域被称为窗口，当窗口移动时，利用中值滤波可以对图像进行平滑处理。
其算法简单，时间复杂度低，但其对点、线和尖顶多的图像不宜采用中值滤波。
很容易自适应化。
Wiener维纳滤波：使原始图像和其恢复图像之间的均方误差最小的复原方法，是一种自适应滤波器，根据局部方差来调整滤波器效果。
对于去除高斯噪声效果明显。
实验一：均值滤波对高斯噪声的效果I=imread('C:\Documents and Settings\Administrator\桌面\1.gif');%读取图像J=imnoise(I,'gaussian',0,0.005);%加入均值为0，方差为0.005的高斯噪声subplot(2,3,1);imshow(I);title('原始图像');subplot(2,3,2); imshow(J);title('加入高斯噪声之后的图像');%采用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波K1=filter2(fspecial('average',3),J)/255; %模板尺寸为3K2=filter2(fspecial('average',5),J)/255;% 模板尺寸为5K3=filter2(fspecial('average',7),J)/255; %模板尺寸为7K4= filter2(fspecial('average',9),J)/255; %模板尺寸为9subplot(2,3,3);imshow(K1);title('改进后的图像1');subplot(2,3,4); imshow(K2);title('改进后的图像2');subplot(2,3,5);imshow(K3);title('改进后的图像3');subplot(2,3,6);imshow(K4);title('改进后的图像4');PS：filter2用法：filter2用法fspecial函数用于创建预定义的滤波算子，其语法格式为：h = fspecial(type)h = fspecial(type,parameters)参数type制定算子类型，parameters指定相应的参数，具体格式为：type='average'，为均值滤波，参数为n，代表模版尺寸，用向量表示，默认值为[3,3]。
图像采集源代码
图像采集源代码#include "stdafx.h"#include "CaptureVideo.h"#include "CaptureVideoDlg.h"#include <string>#include "FileDialog1.h"using namespace std;#include <vfw.h>#ifdef _DEBUG#define new DEBUG_NEW#undef THIS_FILEstatic char THIS_FILE[] = __FILE__;#endif/////////////////////////////////////////////////////////////////////////////// CAboutDlg dialog used for App Aboutclass CAboutDlg : public CDialog{public:CAboutDlg();// Dialog Data//{{AFX_DATA(CAboutDlg)enum { IDD = IDD_ABOUTBOX };//}}AFX_DATA// ClassWizard generated virtual function overrides//{{AFX_VIRTUAL(CAboutDlg)protected:virtual void DoDataExchange(CDataExchange* pDX); // DDX/DDV support //}}AFX_VIRTUAL// Implementationprotected://{{AFX_MSG(CAboutDlg)//}}AFX_MSGDECLARE_MESSAGE_MAP()};CAboutDlg::CAboutDlg() : CDialog(CAboutDlg::IDD){//{{AFX_DATA_INIT(CAboutDlg)//}}AFX_DATA_INIT}void CAboutDlg::DoDataExchange(CDataExchange* pDX){CDialog::DoDataExchange(pDX);//{{AFX_DATA_MAP(CAboutDlg)//}}AFX_DATA_MAP}BEGIN_MESSAGE_MAP(CAboutDlg, CDialog)//{{AFX_MSG_MAP(CAboutDlg)// No message handlers//}}AFX_MSG_MAPEND_MESSAGE_MAP()/////////////////////////////////////////////////////////////////////////////// CCaptureVideoDlg dialogCCaptureVideoDlg::CCaptureVideoDlg(CWnd* pParent /*=NULL*/) : CDialog(CCaptureVideoDlg::IDD, pParent){//{{AFX_DATA_INIT(CCaptureVideoDlg)// NOTE: the ClassWizard will add member initialization here //}}AFX_DATA_INIT// Note that LoadIcon does not require a subsequent DestroyIcon in Win32 m_hIcon = AfxGetApp()->LoadIcon(IDR_MAINFRAME);}void CCaptureVideoDlg::DoDataExchange(CDataExchange* pDX){CDialog::DoDataExchange(pDX);//{{AFX_DATA_MAP(CCaptureVideoDlg)// NOTE: the ClassWizard will add DDX and DDV calls here //}}AFX_DATA_MAP}BEGIN_MESSAGE_MAP(CCaptureVideoDlg, CDialog)//{{AFX_MSG_MAP(CCaptureVideoDlg)ON_WM_SYSCOMMAND()ON_WM_PAINT()ON_WM_QUERYDRAGICON()ON_BN_CLICKED(IDC_STARTCAP, OnStartcap)ON_BN_CLICKED(IDC_GrabFrame, OnGrabFrame)ON_BN_CLICKED(IDC_SaveImage, OnSaveImage)ON_BN_CLICKED(IDC_SaveVideoFile, OnSaveVideoFile)ON_BN_CLICKED(IDC_SetupVideoFormat, OnSetupVideoFormat)ON_BN_CLICKED(IDC_Exit, OnExit)ON_BN_CLICKED(IDC_CapSetup, OnCapSetup)ON_BN_CLICKED(IDC_StopCap, OnStopCap)ON_BN_CLICKED(IDC_BUTTON1, OnSaveAs)//}}AFX_MSG_MAPEND_MESSAGE_MAP()/////////////////////////////////////////////////////////////////////////////// CCaptureVideoDlg message handlersBOOL CCaptureVideoDlg::OnInitDialog(){CDialog::OnInitDialog();// Add "About..." menu item to system menu.// IDM_ABOUTBOX must be in the system command range.ASSERT((IDM_ABOUTBOX & 0xFFF0) == IDM_ABOUTBOX);ASSERT(IDM_ABOUTBOX < 0xF000);CMenu* pSysMenu = GetSystemMenu(FALSE);if (pSysMenu != NULL){CString strAboutMenu;strAboutMenu.LoadString(IDS_ABOUTBOX);if (!strAboutMenu.IsEmpty()){pSysMenu->AppendMenu(MF_SEPARATOR);pSysMenu->AppendMenu(MF_STRING, IDM_ABOUTBOX, strAboutMenu);}}// Set the icon for this dialog. The framework does this automatically// when the application's main window is not a dialogSetIcon(m_hIcon, TRUE); // Set big iconSetIcon(m_hIcon, FALSE); // Set small icon// TODO: Add extra initialization herereturn TRUE; // return TRUE unless you set the focus to a control}void CCaptureVideoDlg::OnSysCommand(UINT nID, LPARAM lParam){if ((nID & 0xFFF0) == IDM_ABOUTBOX){CAboutDlg dlgAbout;dlgAbout.DoModal();}else{CDialog::OnSysCommand(nID, lParam);}}// If you add a minimize button to your dialog, you will need the code below// to draw the icon. For MFC applications using the document/view model,// this is automatically done for you by the framework.void CCaptureVideoDlg::OnPaint(){if (IsIconic()){CPaintDC dc(this); // device context for paintingSendMessage(WM_ICONERASEBKGND, (WPARAM) dc.GetSafeHdc(), 0);// Center icon in client rectangleint cxIcon = GetSystemMetrics(SM_CXICON);int cyIcon = GetSystemMetrics(SM_CYICON);CRect rect;GetClientRect(&rect);int x = (rect.Width() - cxIcon -1) / 2;int y = (rect.Height() - cyIcon -1) / 2;// Draw the icondc.DrawIcon(x, y, m_hIcon);}else{CDialog::OnPaint();}}// The system calls this to obtain the cursor to display while the user drags// the minimized window.HCURSOR CCaptureVideoDlg::OnQueryDragIcon(){return (HCURSOR) m_hIcon;}void CCaptureVideoDlg::OnStartcap(){// TODO: Add your control notification handler code hereif(GetDlgItem(IDC_GrabFrame)->IsWindowEnabled()){//如果当前状态为画面冻结capOverlay (m_hCapture, TRUE);//使得当前窗口为Overlay窗口GetDlgItem(IDC_SaveVideoFile)->EnableWindow(true);//使开始录象按钮有效，能启动录象功能}else{//当前状态不是画面冻结GetDlgItem(IDC_GrabFrame)->EnableWindow(true);//使冻结画面按钮有效GetDlgItem(IDC_SaveImage)->EnableWindow(true);//使保存画面按钮有效IDC_BUTTON1GetDlgItem(IDC_BUTTON1)->EnableWindow(true);GetDlgItem(IDC_SaveVideoFile)->EnableWindow(true);//使开始录象按钮有效CAPTUREPARMS m_CaptureParms;//声明一个结构体m_hCapture = capCreateCaptureWindow ("Cap",WS_EX_CONTROLPARENT| WS_CHILD | WS_VISIBLE, 5, 6, 640, 480, m_hWnd, 0);//创建捕捉窗口m_hCapturecapDriverConnect (m_hCapture, 0);//将m_hCapture窗口与驱动程序连接capCaptureGetSetup (m_hCapture, &m_CaptureParms, sizeof (m_CaptureParms));//得到有关设置capOverlay (m_hCapture, TRUE);//使得当前窗口为Overlay窗口}}void CCaptureVideoDlg::OnGrabFrame(){// TODO: Add your control notification handler code herecapGrabFrame(m_hCapture);//冻结当前画面GetDlgItem(IDC_SaveVideoFile)->EnableWindow(false);//使开始录象按钮无效}void CCaptureVideoDlg::OnSaveImage(){// TODO: Add your control notification handler code herecapFileSaveDIB(m_hCapture,"C:\\image.bmp");//图像保存默认为C:\\image.bmpAfxMessageBox("文件成功保存在C:\\image.bmp");}void CCaptureVideoDlg::OnSaveVideoFile(){// TODO: Add your control notification handler code hereGetDlgItem(IDC_StopCap)->EnableWindow(true);//停止录像有效capCaptureSequence(m_hCapture);}void CCaptureVideoDlg::OnSetupVideoFormat(){// TODO: Add your control notification handler code herecapDlgVideoFormat(m_hCapture);}void CCaptureVideoDlg::OnExit(){// TODO: Add your control notification handler code herecapDriverDisconnect(m_hCapture);CDialog::OnCancel();}void CCaptureVideoDlg::OnCapSetup(){// TODO: Add your control notification handler code here capDlgVideoSource(m_hCapture);}void CCaptureVideoDlg::OnStopCap(){// TODO: Add your control notification handler code here capCaptureStop(m_hCapture);capDriverDisconnect(m_hCapture);}/*void CCaptureVideoDlg::OnSaveAS(){// TODO: Add your control notification handler code here CString FilePathName;CFileDialog dlg(false);if(dlg.DoModal()==IDOK)FilePathName=dlg.GetPathName();int strLength = FilePathName.GetLength() + 1;char *pValue = new char[strLength];strncpy(pValue, FilePathName, strLength);capFileSaveDIB(m_hCapture,pValue);}*/void CCaptureVideoDlg::OnSaveAs(){// TODO: Add your control notification handler code here CString FilePathName;CFileDialog dlg(false);if(dlg.DoModal()==IDOK)FilePathName=dlg.GetPathName();int strLength = FilePathName.GetLength() + 1;char *pValue = new char[strLength];strncpy(pValue, FilePathName, strLength);capFileSaveDIB(m_hCapture,pValue);AfxMessageBox("文件成功保存");}void CCaptureVideoDlg::OnSaveAsA VI(){// TODO: Add your control notification handler code here CString FilePathName;CFileDialog dlg(false);if(dlg.DoModal()==IDOK)FilePathName=dlg.GetPathName();int strLength = FilePathName.GetLength() + 1;char *pValue = new char[strLength];strncpy(pValue, FilePathName, strLength);capCaptureStop(m_hCapture);capDriverDisconnect(m_hCapture,pValue);}。
opencv 源码编译
opencv 源码编译OpenCV是一个流行的计算机视觉库，它提供了许多基本的图像处理功能和算法。
虽然OpenCV提供了预编译的二进制文件，但有时需要从源代码编译OpenCV以获取更多的灵活性和控制权。
本文将介绍如何从源代码编译OpenCV。
1. 准备工作在开始编译OpenCV之前，您需要准备一些工具和依赖项。
首先，您需要安装一个C++编译器。
对于Windows用户，建议使用Visual Studio。
对于Linux用户，建议使用GCC编译器。
您还需要安装CMake，这是一个跨平台的构建工具，它可以生成Makefile或Visual Studio项目文件。
最后，您需要安装OpenCV的依赖项，例如FFmpeg、GTK+、JPEG、PNG、TIFF等。
可以使用包管理器来安装它们，例如apt-get、yum、brew等。
2. 下载OpenCV源代码在开始编译OpenCV之前，您需要从官方网站下载OpenCV源代码。
打开OpenCV的官方网站，选择“下载”选项卡。
在“源代码”部分中，选择最新版本的OpenCV源代码，并下载到本地计算机上。
3. 使用CMake生成Makefile或Visual Studio项目文件一旦您已经下载了OpenCV源代码，您可以使用CMake来生成Makefile或Visual Studio项目文件。
打开CMake GUI，并选择OpenCV源代码所在的文件夹。
然后，您需要选择生成器，例如“V isual Studio 2019”或“Unix Makefiles”。
接下来，您需要设置一些参数，例如安装路径、编译选项、依赖项路径等。
这些参数将控制OpenCV的编译过程和生成的库文件。
最后，单击“Generate”按钮，CMake将生成Makefile或Visual Studio项目文件。
4. 编译OpenCV一旦您已经生成了Makefile或Visual Studio项目文件，您可以开始编译OpenCV。
图像 锐化处理代码
Visual C++实现数字图像增强处理分类：图像处理/OpenCV2012-12-10 17:08 266人阅读评论(1) 收藏举报前言对于一个图像处理系统来说，可以将流程分为三个阶段，在获取原始图像后，首先是图像预处理阶段、第二是特征抽取阶段、第三是识别分析阶段。
图像预处理阶段尤为重要，如果这阶段处理不好，后面的工作根本无法展开。
在实际应用中，我们的系统获取的原始图像不是完美的，例如对于系统获取的原始图像，由于噪声、光照等原因，图像的质量不高，所以需要进行预处理，以有利于提取我们感兴趣的信息。
图像的预处理包括图像增强、平滑滤波、锐化等内容。
图像的预处理既可以在空间域实现，也可以在频域内实现，我们主要介绍在空间域内对图像进行点运算，它是一种既简单又重要的图像处理技术，它能让用户改变图像上像素点的灰度值，这样通过点运算处理将产生一幅新图像。
下面我们开始介绍与图像点运算的相关知识。
一、图像的直方图图像直方图是图像处理中一种十分重要的图像分析工具，它描述了一幅图像的灰度级内容，任何一幅图像的直方图都包含了丰富的信息，它主要用在图象分割，图像灰度变换等处理过程中。
从数学上来说图像直方图是图像各灰度值统计特性与图像灰度值的函数，它统计一幅图像中各个灰度级出现的次数或概率；从图形上来说，它是一个二维图，横坐标表示图像中各个像素点的灰度级，纵坐标为各个灰度级上图像各个像素点出现的次数或概率。
如果不特别说明，本讲座中的直方图的纵坐标都对应着该灰度级在图像中出现的概率。
我们的例子是在一个对话框中显示一个图像的直方图，为实现该目的，我们定义了一个名为"ZFT"的对话框类用来显示图像的直方图，具体实现代码和效果图如下（关于代码实现部分可以参考笔者2001年在天极网上发表的一篇VC实现数字图像处理的文章）：[cpp]view plaincopyprint?1.//////////////////////////////////直方图对话框构造函数；2.ZFT::ZFT(CWnd* pParent /*=NULL*/)3.: CDialog(ZFT::IDD, pParent)//ZFT为定义的用来显示直方图的对话框类；4.{5.Width=Height=0;//对话框初始化阶段设置图像的宽和高为"0"；6.}7.////////////////////////对话框重画函数；8.void ZFT::OnPaint()9.{10.CRect rect;//矩形区域对象；11.CWnd *pWnd;//得到图片框的窗口指针；12.pWnd=GetDlgItem(IDC_Graphic);//得到ZFT对话框内的"Frame"控件的指针；13.file://（IDC_Graphic为放置在对话框上的一个"Picture"控件，并讲类型设置为"Frame"）。
FCM图像分割算法MATLAB源代码
FCM图像分割算法function fcmapp(file, cluster_n)% FCMAPP% fcmapp(file, cluter_n) segments a image named file using the algorithm% FCM.% [in]% file: the path of the image to be clustered.% cluster_n: the number of cluster for FCM.eval(['info=imfinfo(''',file, ''');']);switch info.ColorTypecase 'truecolor'eval(['RGB=imread(''',file, ''');']);% [X, map] = rgb2ind(RGB, 256);I = rgb2gray(RGB);clear RGB;case 'indexed'eval(['[X, map]=imread(''',file, ''');']);I = ind2gray(X, map);clear X;case 'grayscale'eval(['I=imread(''',file, ''');']);end;I = im2double(I);filename = file(1 : find(file=='.')-1);data = reshape(I, numel(I), 1);tic[center, U, obj_fcn]=fcm(data, cluster_n);elapsedtime = toc;%eval(['save(', filename, int2str(cluster_n),'.mat'', ''center'', ''U'', ''obj_fcn'', ''elapsedtime'');']); fprintf('elapsedtime = %d', elapsedtime);maxU=max(U);temp = sort(center, 'ascend');for n = 1:cluster_n;eval(['cluster',int2str(n), '_index = find(U(', int2str(n), ',:) == maxU);']);index = find(temp == center(n));switch indexcase 1color_class = 0;case cluster_ncolor_class = 255;otherwisecolor_class = fix(255*(index-1)/(cluster_n-1));endeval(['I(cluster',int2str(n), '_index(:))=', int2str(color_class),';']);end;filename = file(1:find(file=='.')-1);I = mat2gray(I);%eval(['imwrite(I,', filename,'_seg', int2str(cluster_n), '.bmp'');']);imwrite(I, 'temp\tu2_4.bmp','bmp');imview(I);function fcmapp(file, cluster_n)% FCMAPP% fcmapp(file, cluter_n) segments a image named file using the algorithm% FCM.% [in]% file: the path of the image to be clustered.% cluster_n: the number of cluster for FCM.eval(['info=imfinfo(''',file, ''');']);switch info.ColorTypecase 'truecolor'eval(['RGB=imread(''',file, ''');']);% [X, map] = rgb2ind(RGB, 256);I = rgb2gray(RGB);clear RGB;case 'indexed'eval(['[X, map]=imread(''',file, ''');']);I = ind2gray(X, map);clear X;case 'grayscale'eval(['I=imread(''',file, ''');']);end;I = im2double(I);filename = file(1 : find(file=='.')-1);data = reshape(I, numel(I), 1);tic[center, U, obj_fcn]=fcm(data, cluster_n);elapsedtime = toc;%eval(['save(', filename, int2str(cluster_n),'.mat'', ''center'', ''U'', ''obj_fcn'', ''elapsedtime'');']); fprintf('elapsedtime = %d', elapsedtime);maxU=max(U);temp = sort(center, 'ascend');for n = 1:cluster_n;eval(['cluster',int2str(n), '_index = find(U(', int2str(n), ',:) == maxU);']);index = find(temp == center(n));switch indexcase 1color_class = 0;case cluster_ncolor_class = 255;otherwisecolor_class = fix(255*(index-1)/(cluster_n-1));endeval(['I(cluster',int2str(n), '_index(:))=', int2str(color_class),';']); end;filename = file(1:find(file=='.')-1);I = mat2gray(I);%eval(['imwrite(I,', filename,'_seg', int2str(cluster_n), '.bmp'');']); imwrite(I, 'r.bmp');imview(I);主程序1ImageDir='.\';%directory containing the images%path('..') ;%cmpviapath('..') ;img=im2double(imresize(imread([ImageDir '12.png']),2)) ;figure(1) ; imagesc(img) ; axis image[ny,nx,nc]=size(img) ;imgc=applycform(img,makecform('srgb2lab')) ;d=reshape(imgc(:,:,2:3),ny*nx,2) ;d(:,1)=d(:,1)/max(d(:,1)) ; d(:,2)=d(:,2)/max(d(:,2)) ;%d=d ./ (repmat(sqrt(sum(d.^2,2)),1,3)+eps()) ;k=4 ; % number of clusters%[l0 c] = kmeans(d, k,'Display','iter','Maxiter',100);[l0 c] = kmeans(d, k,'Maxiter',100);l0=reshape(l0,ny,nx) ;figure(2) ; imagesc(l0) ; axis image ;%c=[ 0.37 0.37 0.37 ; 0.77 0.73 0.66 ; 0.64 0.77 0.41 ; 0.81 0.76 0.58 ; ...%0.85 0.81 0.73 ] ;%c=[0.99 0.76 0.15 ; 0.55 0.56 0.15 ] ;%c=[ 0.64 0.64 0.67 ; 0.27 0.45 0.14 ] ;%c=c ./ (repmat(sqrt(sum(c.^2,2)),1,3)+eps()) ;% Data termDc=zeros(ny,nx,k) ;for i=1:k,dif=d-repmat(c(i,:),ny*nx,1) ;Dc(:,:,i)= reshape(sum(dif.^2,2),ny,nx) ;end ;% Smoothness termSc=(ones(k)-eye(k)) ;% Edge termsg = fspecial('gauss', [13 13], 2);dy = fspecial('sobel');vf = conv2(g, dy, 'valid');Vc = zeros(ny,nx);Hc = Vc;for b=1:nc,Vc = max(Vc, abs(imfilter(img(:,:,b), vf, 'symmetric')));Hc = max(Hc, abs(imfilter(img(:,:,b), vf', 'symmetric'))); endgch=char;gch = GraphCut('open', 1*Dc, Sc,exp(-5*Vc),exp(-5*Hc)); [gch l] = GraphCut('expand',gch);gch = GraphCut('close', gch);label=l(100,200) ;lb=(l==label) ;lb=imdilate(lb,strel('disk',1))-lb ;figure(3) ; image(img) ; axis image ; hold on ;contour(lb,[1 1],'r') ; hold off ; title('no edges') ;figure(4) ; imagesc(l) ; axis image ; title('no edges') ;gch = GraphCut('open', Dc, 5*Sc,exp(-10*Vc),exp(-10*Hc)); [gch l] = GraphCut('expand',gch);gch = GraphCut('close', gch);lb=(l==label) ;lb=imdilate(lb,strel('disk',1))-lb ;figure(5) ; image(img) ; axis image ; hold on ;contour(lb,[1 1],'r') ; hold off ; title('edges') ;figure(6) ; imagesc(l) ; axis image ; title('edges') ;主程序2I = imread( '12.png' );I = rgb2gray(I);subplot(5,3,1),imshow(I);k=medfilt2(I,[5,5]);subplot(5,3,2),imshow(k);title('5*5中值滤波图像');%f=imread('tuxiang1.tif');%subplot(1,2,1),imshow(f);%title('原图像');g1=histeq(k,256);subplot(5,3,3),imshow(g1);title('直方图匹配');%g2=histeq(k2,256);%subplot(2,2,2),imshow(g2);%title('5*5直方图匹配');%k=medfilt2(f,[5,5]);%k2=medfilt2(f,[5,5]);%j=imnoise(f,'gaussian',0,0.005);%subplot(1,3,3),imshow(k2);%title('5*5中值滤波图像');hy = fspecial( 'sobel' );hx = hy;Iy = imfilter(double(g1), hy, 'replicate' );Ix = imfilter(double(g1), hx, 'replicate' );gradmag = sqrt(Ix.^2 + Iy.^2);subplot(5,3,4), imshow(gradmag,[ ]), title( 'gradmag' );L = watershed(gradmag);Lrgb = label2rgb(L);subplot(5,3,5), imshow(Lrgb), title( 'Lrgb' );se = strel( 'disk' , 9);Io = imopen(g1, se);subplot(5,3,6), imshow(Io), title( 'Io' )Ie = imerode(g1, se);Iobr = imreconstruct(Ie, g1);subplot(5,3,7), imshow(Iobr), title( 'Iobr' );Ioc = imclose(Io, se);subplot(5,3,8), imshow(Ioc), title( 'Ioc' );Iobrd = imdilate(Iobr, se);Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr)); Iobrcbr = imcomplement(Iobrcbr);subplot(5,3,9), imshow(Iobrcbr), title( 'Iobrcbr' );fgm = imregionalmax(Iobrcbr);subplot(5,3,10), imshow(fgm), title( 'fgm' );I2 = g1; I2(fgm) = 255;subplot(5,3,11),imshow(I2), title( 'fgm superimposed on original image' );se2 = strel(ones(5,5)); I3 = g1; I3(fgm) = 255;subplot(5,3,12) ,imshow(I3);title( 'fgm4 superimposed on original image' );bw = im2bw(Iobrcbr, graythresh(Iobrcbr));subplot(5,3,13) , imshow(bw), title( 'bw' );D = bwdist(bw); DL = watershed(D);bgm = DL == 0;subplot(5,3,14) , imshow(bgm), title( 'bgm' );gradmag2 = imimposemin(gradmag, bgm | fgm);L = watershed(gradmag2);I4 = g1;I4(imdilate(L == 0, ones(3, 3)) | bgm | fgm) = 255;figure, imshow(I4);title( 'Markers and object boundaries superimposed on original image' ); Lrgb = label2rgb(L, 'jet' , 'w' , 'shuffle' );figure, imshow(Lrgb);title( 'Lrgb' );figure, imshow(I), hold onhimage = imshow(Lrgb);set(himage, 'AlphaData' , 0.3);title( 'Lrgb superimposed transparently on original image' );。
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if( !CMDIChildWnd::PreCreateWindow(cs) )
return FALSE;ห้องสมุดไป่ตู้
return TRUE;
}
/////////////////////////////////////////////////////////////////////////////
// CChildFrame message handlers
// ChildFrm.cpp : implementation of the CChildFrame class
//
#include "stdafx.h"
#include "ImageProcessing.h"
// CChildFrame diagnostics
#ifdef _DEBUG
void CChildFrame::AssertValid() const
{
CMDIChildWnd::AssertValid();
}
void CChildFrame::Dump(CDumpContext& dc) const
CChildFrame::CChildFrame()
{
// TODO: add member initialization code here
}
CChildFrame::~CChildFrame()
{
}
#include "ChildFrm.h"
#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[] = __FILE__;
#endif
/////////////////////////////////////////////////////////////////////////////
// ChildFrm.cpp : implementation of the CChildFrame class
//
#include "stdafx.h"
#include "ImageProcessing.h"
#include "ChildFrm.h"
// CChildFrame construction/destruction
CChildFrame::CChildFrame()
{
// TODO: add member initialization code here
}
CChildFrame::~CChildFrame()
// CChildFrame
IMPLEMENT_DYNCREATE(CChildFrame, CMDIChildWnd)
BEGIN_MESSAGE_MAP(CChildFrame, CMDIChildWnd)
//{{AFX_MSG_MAP(CChildFrame)
{
CMDIChildWnd::Dump(dc);
}
#endif //_DEBUG
/////////////////////////////////////////////////////////////////////////////
// CChildFrame message handlers
{
}
BOOL CChildFrame::PreCreateWindow(CREATESTRUCT& cs)
{
// TODO: Modify the Window class or styles here by modifying
// the CREATESTRUCT cs
// NOTE - the ClassWizard will add and remove mapping macros here.
// DO NOT EDIT what you see in these blocks of generated code !
#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[] = __FILE__;
#endif
/////////////////////////////////////////////////////////////////////////////
// CChildFrame diagnostics
#ifdef _DEBUG
void CChildFrame::AssertValid() const
{
CMDIChildWnd::AssertValid();
}
void CChildFrame::Dump(CDumpContext& dc) const
if( !CMDIChildWnd::PreCreateWindow(cs) )
return FALSE;
return TRUE;
}
/////////////////////////////////////////////////////////////////////////////
//}}AFX_MSG_MAP
END_MESSAGE_MAP()
/////////////////////////////////////////////////////////////////////////////
// CChildFrame construction/destruction
// CChildFrame
IMPLEMENT_DYNCREATE(CChildFrame, CMDIChildWnd)
BEGIN_MESSAGE_MAP(CChildFrame, CMDIChildWnd)
//{{AFX_MSG_MAP(CChildFrame)
// NOTE - the ClassWizard will add and remove mapping macros here.
// DO NOT EDIT what you see in these blocks of generated code !
//}}AFX_MSG_MAP
END_MESSAGE_MAP()
/////////////////////////////////////////////////////////////////////////////
BOOL CChildFrame::PreCreateWindow(CREATESTRUCT& cs)
{
// TODO: Modify the Window class or styles here by modifying
// the CREATESTRUCT cs
{
CMDIChildWnd::Dump(dc);
}
#endif //_DEBUG
/////////////////////////////////////////////////////////////////////////////