教你一步一步用c语言实现sift算法、下

作者：July、二零一一年三月十二日
出处：http://blog.csdn.net/v_JULY_v。
参考：Rob Hess维护的sift 库
环境：windows xp+vc6.0
条件：c语言实现。
说明：本BLOG内会陆续一一实现所有经典算法。
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本文接上，教你一步一步用c语言实现sift算法、上，而来：
函数编写
    ok，接上文，咱们一个一个的来编写main函数中所涉及到所有函数，这也是本文的关键部分：
//下采样原来的图像，返回缩小2倍尺寸的图像 CvMat * halfSizeImage(CvMat * im) { unsigned int i,j; int w = im->cols/2; int h = im->rows/2; CvMat *imnew = cvCreateMat(h, w, CV_32FC1); #define Im(ROW,COL) ((float *)(im->data.fl + im->step/sizeof(float) *(ROW)))[(COL)] #define Imnew(ROW,COL) ((float *)(imnew->data.fl + imnew->step/sizeof(float) *(ROW)))[(COL)] for ( j = 0; j < h; j++) for ( i = 0; i < w; i++) Imnew(j,i)=Im(j*2, i*2); return imnew; } //上采样原来的图像，返回放大2倍尺寸的图像 CvMat * doubleSizeImage(CvMat * im) { unsigned int i,j; int w = im->cols*2; int h = im->rows*2; CvMat *imnew = cvCreateMat(h, w, CV_32FC1); #define Im(ROW,COL) ((float *)(im->data.fl + im->step/sizeof(float) *(ROW)))[(COL)] #define Imnew(ROW,COL) ((float *)(imnew->data.fl + imnew->step/sizeof(float) *(ROW)))[(COL)] for ( j = 0; j < h; j++) for ( i = 0; i < w; i++) Imnew(j,i)=Im(j/2, i/2); return imnew; } //上采样原来的图像，返回放大2倍尺寸的线性插值图像 CvMat * doubleSizeImage2(CvMat * im) { unsigned int i,j; int w = im->cols*2; int h = im->rows*2; CvMat *imnew = cvCreateMat(h, w, CV_32FC1); #define Im(ROW,COL) ((float *)(im->data.fl + im->step/sizeof(float) *(ROW)))[(COL)] #define Imnew(ROW,COL) ((float *)(imnew->data.fl + imnew->step/sizeof(float) *(ROW)))[(COL)] // fill every pixel so we don't have to worry about skipping pixels later for ( j = 0; j < h; j++) { for ( i = 0; i < w; i++) { Imnew(j,i)=Im(j/2, i/2); } } /* A B C E F G H I J pixels A C H J are pixels from original image pixels B E G I F are interpolated pixels */ // interpolate pixels B and I for ( j = 0; j < h; j += 2) for ( i = 1; i < w - 1; i += 2) Imnew(j,i)=0.5*(Im(j/2, i/2)+Im(j/2, i/2+1)); // interpolate pixels E and G for ( j = 1; j < h - 1; j += 2) for ( i = 0; i < w; i += 2) Imnew(j,i)=0.5*(Im(j/2, i/2)+Im(j/2+1, i/2)); // interpolate pixel F for ( j = 1; j < h - 1; j += 2) for ( i = 1; i < w - 1; i += 2) Imnew(j,i)=0.25*(Im(j/2, i/2)+Im(j/2+1, i/2)+Im(j/2, i/2+1)+Im(j/2+1, i/2+1)); return imnew; } //双线性插值，返回像素间的灰度值 float getPixelBI(CvMat * im, float col, float row) { int irow, icol; float rfrac, cfrac; float row1 = 0, row2 = 0; int width=im->cols; int height=im->rows; #define ImMat(ROW,COL) ((float *)(im->data.fl + im->step/sizeof(float) *(ROW)))[(COL)] irow = (int) row; icol = (int) col; if (irow < 0 || irow >= height || icol < 0 || icol >= width) return 0; if (row > height - 1) row = height - 1; if (col > width - 1) col = width - 1; rfrac = 1.0 - (row - (float) irow); cfrac = 1.0 - (col - (float) icol); if (cfrac < 1) { row1 = cfrac * ImMat(irow,icol) + (1.0 - cfrac) * ImMat(irow,icol+1); } else { row1 = ImMat(irow,icol); } if (rfrac < 1) { if (cfrac < 1) { row2 = cfrac * ImMat(irow+1,icol) + (1.0 - cfrac) * ImMat(irow+1,icol+1); } else { row2 = ImMat(irow+1,icol); } } return rfrac * row1 + (1.0 - rfrac) * row2; } //矩阵归一化 void normalizeMat(CvMat* mat) { #define Mat(ROW,COL) ((float *)(mat->data.fl + mat->step/sizeof(float) *(ROW)))[(COL)] float sum = 0; for (unsigned int j = 0; j < mat->rows; j++) for (unsigned int i = 0; i < mat->cols; i++) sum += Mat(j,i); for ( j = 0; j < mat->rows; j++) for (unsigned int i = 0; i < mat->rows; i++) Mat(j,i) /= sum; } //向量归一化 void normalizeVec(float* vec, int dim) { unsigned int i; float sum = 0; for ( i = 0; i < dim; i++) sum += vec[i]; for ( i = 0; i < dim; i++) vec[i] /= sum; } //得到向量的欧式长度，2-范数 float GetVecNorm( float* vec, int dim ) { float sum=0.0; for (unsigned int i=0;i<dim;i++) sum+=vec[i]*vec[i]; return sqrt(sum); } //产生1D高斯核 float* GaussianKernel1D(float sigma, int dim) { unsigned int i; //printf("GaussianKernel1D(): Creating 1x%d vector for sigma=%.3f gaussian kernel/n", dim, sigma); float *kern=(float*)malloc( dim*sizeof(float) ); float s2 = sigma * sigma; int c = dim / 2; float m= 1.0/(sqrt(2.0 * CV_PI) * sigma); double v; for ( i = 0; i < (dim + 1) / 2; i++) { v = m * exp(-(1.0*i*i)/(2.0 * s2)) ; kern[c+i] = v; kern[c-i] = v; } // normalizeVec(kern, dim); // for ( i = 0; i < dim; i++) // printf("%f ", kern[i]); // printf("/n"); return kern; } //产生2D高斯核矩阵 CvMat* GaussianKernel2D(float sigma) { // int dim = (int) max(3.0f, GAUSSKERN * sigma); int dim = (int) max(3.0f, 2.0 * GAUSSKERN *sigma + 1.0f); // make dim odd if (dim % 2 == 0) dim++; //printf("GaussianKernel(): Creating %dx%d matrix for sigma=%.3f gaussian/n", dim, dim, sigma); CvMat* mat=cvCreateMat(dim, dim, CV_32FC1); #define Mat(ROW,COL) ((float *)(mat->data.fl + mat->step/sizeof(float) *(ROW)))[(COL)] float s2 = sigma * sigma; int c = dim / 2; //printf("%d %d/n", mat.size(), mat[0].size()); float m= 1.0/(sqrt(2.0 * CV_PI) * sigma); for (int i = 0; i < (dim + 1) / 2; i++) { for (int j = 0; j < (dim + 1) / 2; j++) { //printf("%d %d %d/n", c, i, j); float v = m * exp(-(1.0*i*i + 1.0*j*j) / (2.0 * s2)); Mat(c+i,c+j) =v; Mat(c-i,c+j) =v; Mat(c+i,c-j) =v; Mat(c-i,c-j) =v; } } // normalizeMat(mat); return mat; } //x方向像素处作卷积 float ConvolveLocWidth(float* kernel, int dim, CvMat * src, int x, int y) { #define Src(ROW,COL) ((float *)(src->data.fl + src->step/sizeof(float) *(ROW)))[(COL)] unsigned int i; float pixel = 0; int col; int cen = dim / 2; //printf("ConvolveLoc(): Applying convoluation at location (%d, %d)/n", x, y); for ( i = 0; i < dim; i++) { col = x + (i - cen); if (col < 0) col = 0; if (col >= src->cols) col = src->cols - 1; pixel += kernel[i] * Src(y,col); } if (pixel > 1) pixel = 1; return pixel; } //x方向作卷积 void Convolve1DWidth(float* kern, int dim, CvMat * src, CvMat * dst) { #define DST(ROW,COL) ((float *)(dst->data.fl + dst->step/sizeof(float) *(ROW)))[(COL)] unsigned int i,j; for ( j = 0; j < src->rows; j++) { for ( i = 0; i < src->cols; i++) { //printf("%d, %d/n", i, j); DST(j,i) = ConvolveLocWidth(kern, dim, src, i, j); } } } //y方向像素处作卷积 float ConvolveLocHeight(float* kernel, int dim, CvMat * src, int x, int y) { #define Src(ROW,COL) ((float *)(src->data.fl + src->step/sizeof(float) *(ROW)))[(COL)] unsigned int j; float pixel = 0; int cen = dim / 2; //printf("ConvolveLoc(): Applying convoluation at location (%d, %d)/n", x, y); for ( j = 0; j < dim; j++) { int row = y + (j - cen); if (row < 0) row = 0; if (row >= src->rows) row = src->rows - 1; pixel += kernel[j] * Src(row,x); } if (pixel > 1) pixel = 1; return pixel; } //y方向作卷积 void Convolve1DHeight(float* kern, int dim, CvMat * src, CvMat * dst) { #define Dst(ROW,COL) ((float *)(dst->data.fl + dst->step/sizeof(float) *(ROW)))[(COL)] unsigned int i,j; for ( j = 0; j < src->rows; j++) { for ( i = 0; i < src->cols; i++) { //printf("%d, %d/n", i, j); Dst(j,i) = ConvolveLocHeight(kern, dim, src, i, j); } } } //卷积模糊图像 int BlurImage(CvMat * src, CvMat * dst, float sigma) { float* convkernel; int dim = (int) max(3.0f, 2.0 * GAUSSKERN * sigma + 1.0f); CvMat *tempMat; // make dim odd if (dim % 2 == 0) dim++; tempMat = cvCreateMat(src->rows, src->cols, CV_32FC1); convkernel = GaussianKernel1D(sigma, dim); Convolve1DWidth(convkernel, dim, src, tempMat); Convolve1DHeight(convkernel, dim, tempMat, dst); cvReleaseMat(&tempMat); return dim; }

五个步骤

    ok，接下来，进入重点部分，咱们依据上文介绍的sift算法的几个步骤，来一一实现这些函数。
    为了版述清晰，再贴一下，主函数，顺便再加强下对sift 算法的五个步骤的认识：

1、SIFT算法第一步：图像预处理
CvMat *ScaleInitImage(CvMat * im) ;                  //金字塔初始化
2、SIFT算法第二步：建立高斯金字塔函数
ImageOctaves* BuildGaussianOctaves(CvMat * image) ;  //建立高斯金字塔
3、SIFT算法第三步：特征点位置检测，最后确定特征点的位置
int DetectKeypoint(int numoctaves, ImageOctaves *GaussianPyr);
4、SIFT算法第四步：计算高斯图像的梯度方向和幅值，计算各个特征点的主方向
void ComputeGrad_DirecandMag(int numoctaves, ImageOctaves *GaussianPyr);
5、SIFT算法第五步：抽取各个特征点处的特征描述字
void ExtractFeatureDescriptors(int numoctaves, ImageOctaves *GaussianPyr);

ok，接下来一一具体实现这几个函数：
SIFT算法第一步
    SIFT算法第一步：扩大图像，预滤波剔除噪声，得到金字塔的最底层-第一阶的第一层：
CvMat *ScaleInitImage(CvMat * im) { double sigma,preblur_sigma; CvMat *imMat; CvMat * dst; CvMat *tempMat; //首先对图像进行平滑滤波，抑制噪声 imMat = cvCreateMat(im->rows, im->cols, CV_32FC1); BlurImage(im, imMat, INITSIGMA); //针对两种情况分别进行处理：初始化放大原始图像或者在原图像基础上进行后续操作 //建立金字塔的最底层 if (DOUBLE_BASE_IMAGE_SIZE) { tempMat = doubleSizeImage2(imMat);//对扩大两倍的图像进行二次采样，采样率为0.5，采用线性插值 #define TEMPMAT(ROW,COL) ((float *)(tempMat->data.fl + tempMat->step/sizeof(float) * (ROW)))[(COL)] dst = cvCreateMat(tempMat->rows, tempMat->cols, CV_32FC1); preblur_sigma = 1.0;//sqrt(2 - 4*INITSIGMA*INITSIGMA); BlurImage(tempMat, dst, preblur_sigma); // The initial blurring for the first image of the first octave of the pyramid. sigma = sqrt( (4*INITSIGMA*INITSIGMA) + preblur_sigma * preblur_sigma ); // sigma = sqrt(SIGMA * SIGMA - INITSIGMA * INITSIGMA * 4); //printf("Init Sigma: %f/n", sigma); BlurImage(dst, tempMat, sigma); //得到金字塔的最底层-放大2倍的图像 cvReleaseMat( &dst ); return tempMat; } else { dst = cvCreateMat(im->rows, im->cols, CV_32FC1); //sigma = sqrt(SIGMA * SIGMA - INITSIGMA * INITSIGMA); preblur_sigma = 1.0;//sqrt(2 - 4*INITSIGMA*INITSIGMA); sigma = sqrt( (4*INITSIGMA*INITSIGMA) + preblur_sigma * preblur_sigma ); //printf("Init Sigma: %f/n", sigma); BlurImage(imMat, dst, sigma); //得到金字塔的最底层：原始图像大小 return dst; } }  

SIFT算法第二步
    SIFT第二步，建立Gaussian金字塔，给定金字塔第一阶第一层图像后，计算高斯金字塔其他尺度图像，
每一阶的数目由变量SCALESPEROCTAVE决定，给定一个基本图像，计算它的高斯金字塔图像，返回外部向量是阶梯指针，内部向量是每一个阶梯内部的不同尺度图像。
//SIFT算法第二步 ImageOctaves* BuildGaussianOctaves(CvMat * image) { ImageOctaves *octaves; CvMat *tempMat; CvMat *dst; CvMat *temp; int i,j; double k = pow(2, 1.0/((float)SCALESPEROCTAVE)); //方差倍数 float preblur_sigma, initial_sigma , sigma1,sigma2,sigma,absolute_sigma,sigma_f; //计算金字塔的阶梯数目 int dim = min(image->rows, image->cols); int numoctaves = (int) (log((double) dim) / log(2.0)) - 2; //金字塔阶数 //限定金字塔的阶梯数 numoctaves = min(numoctaves, MAXOCTAVES); //为高斯金塔和DOG金字塔分配内存 octaves=(ImageOctaves*) malloc( numoctaves * sizeof(ImageOctaves) ); DOGoctaves=(ImageOctaves*) malloc( numoctaves * sizeof(ImageOctaves) ); printf("BuildGaussianOctaves(): Base image dimension is %dx%d/n", (int)(0.5*(image->cols)), (int)(0.5*(image->rows)) ); printf("BuildGaussianOctaves(): Building %d octaves/n", numoctaves); // start with initial source image tempMat=cvCloneMat( image ); // preblur_sigma = 1.0;//sqrt(2 - 4*INITSIGMA*INITSIGMA); initial_sigma = sqrt(2);//sqrt( (4*INITSIGMA*INITSIGMA) + preblur_sigma * preblur_sigma ); // initial_sigma = sqrt(SIGMA * SIGMA - INITSIGMA * INITSIGMA * 4); //在每一阶金字塔图像中建立不同的尺度图像 for ( i = 0; i < numoctaves; i++) { //首先建立金字塔每一阶梯的最底层，其中0阶梯的最底层已经建立好 printf("Building octave %d of dimesion (%d, %d)/n", i, tempMat->cols,tempMat->rows); //为各个阶梯分配内存 octaves[i].Octave= (ImageLevels*) malloc( (SCALESPEROCTAVE + 3) * sizeof(ImageLevels) ); DOGoctaves[i].Octave= (ImageLevels*) malloc( (SCALESPEROCTAVE + 2) * sizeof(ImageLevels) ); //存储各个阶梯的最底层 (octaves[i].Octave)[0].Level=tempMat; octaves[i].col=tempMat->cols; octaves[i].row=tempMat->rows; DOGoctaves[i].col=tempMat->cols; DOGoctaves[i].row=tempMat->rows; if (DOUBLE_BASE_IMAGE_SIZE) octaves[i].subsample=pow(2,i)*0.5; else octaves[i].subsample=pow(2,i); if(i==0) { (octaves[0].Octave)[0].levelsigma = initial_sigma; (octaves[0].Octave)[0].absolute_sigma = initial_sigma; printf("0 scale and blur sigma : %f /n", (octaves[0].subsample) * ((octaves[0].Octave)[0].absolute_sigma)); } else { (octaves[i].Octave)[0].levelsigma = (octaves[i-1].Octave)[SCALESPEROCTAVE].levelsigma; (octaves[i].Octave)[0].absolute_sigma = (octaves[i-1].Octave)[SCALESPEROCTAVE].absolute_sigma; printf( "0 scale and blur sigma : %f /n", ((octaves[i].Octave)[0].absolute_sigma) ); } sigma = initial_sigma; //建立本阶梯其他层的图像 for ( j = 1; j < SCALESPEROCTAVE + 3; j++) { dst = cvCreateMat(tempMat->rows, tempMat->cols, CV_32FC1);//用于存储高斯层 temp = cvCreateMat(tempMat->rows, tempMat->cols, CV_32FC1);//用于存储DOG层 // 2 passes of 1D on original // if(i!=0) // { // sigma1 = pow(k, j - 1) * ((octaves[i-1].Octave)[j-1].levelsigma); // sigma2 = pow(k, j) * ((octaves[i].Octave)[j-1].levelsigma); // sigma = sqrt(sigma2*sigma2 - sigma1*sigma1); sigma_f= sqrt(k*k-1)*sigma; // } // else // { // sigma = sqrt(SIGMA * SIGMA - INITSIGMA * INITSIGMA * 4)*pow(k,j); // } sigma = k*sigma; absolute_sigma = sigma * (octaves[i].subsample); printf("%d scale and Blur sigma: %f /n", j, absolute_sigma); (octaves[i].Octave)[j].levelsigma = sigma; (octaves[i].Octave)[j].absolute_sigma = absolute_sigma; //产生高斯层 int length=BlurImage((octaves[i].Octave)[j-1].Level, dst, sigma_f);//相应尺度 (octaves[i].Octave)[j].levelsigmalength = length; (octaves[i].Octave)[j].Level=dst; //产生DOG层 cvSub( ((octaves[i].Octave)[j]).Level, ((octaves[i].Octave)[j-1]).Level, temp, 0 ); // cvAbsDiff( ((octaves[i].Octave)[j]).Level, ((octaves[i].Octave)[j-1]).Level, temp ); ((DOGoctaves[i].Octave)[j-1]).Level=temp; } // halve the image size for next iteration tempMat = halfSizeImage( ( (octaves[i].Octave)[SCALESPEROCTAVE].Level ) ); } return octaves; }  

SIFT算法第三步
    SIFT算法第三步，特征点位置检测，最后确定特征点的位置检测DOG金字塔中的局部最大值，找到之后，还要经过两个检验才能确认为特征点：一是它必须有明显的差异，二是他不应该是边缘点，（也就是说，在极值点处的主曲率比应该小于某一个阈值）。
//SIFT算法第三步，特征点位置检测， int DetectKeypoint(int numoctaves, ImageOctaves *GaussianPyr) { //计算用于DOG极值点检测的主曲率比的阈值 double curvature_threshold; curvature_threshold= ((CURVATURE_THRESHOLD + 1)*(CURVATURE_THRESHOLD + 1))/CURVATURE_THRESHOLD; #define ImLevels(OCTAVE,LEVEL,ROW,COL) ((float *)(DOGoctaves[(OCTAVE)].Octave[(LEVEL)].Level->data.fl + DOGoctaves[(OCTAVE)].Octave[(LEVEL)].Level->step/sizeof(float) *(ROW)))[(COL)] int keypoint_count = 0; for (int i=0; i<numoctaves; i++) { for(int j=1;j<SCALESPEROCTAVE+1;j++)//取中间的scaleperoctave个层 { //在图像的有效区域内寻找具有显著性特征的局部最大值 //float sigma=(GaussianPyr[i].Octave)[j].levelsigma; //int dim = (int) (max(3.0f, 2.0*GAUSSKERN *sigma + 1.0f)*0.5); int dim = (int)(0.5*((GaussianPyr[i].Octave)[j].levelsigmalength)+0.5); for (int m=dim;m<((DOGoctaves[i].row)-dim);m++) for(int n=dim;n<((DOGoctaves[i].col)-dim);n++) { if ( fabs(ImLevels(i,j,m,n))>= CONTRAST_THRESHOLD ) { if ( ImLevels(i,j,m,n)!=0.0 ) //1、首先是非零 { float inf_val=ImLevels(i,j,m,n); if(( (inf_val <= ImLevels(i,j-1,m-1,n-1))&& (inf_val <= ImLevels(i,j-1,m ,n-1))&& (inf_val <= ImLevels(i,j-1,m+1,n-1))&& (inf_val <= ImLevels(i,j-1,m-1,n ))&& (inf_val <= ImLevels(i,j-1,m ,n ))&& (inf_val <= ImLevels(i,j-1,m+1,n ))&& (inf_val <= ImLevels(i,j-1,m-1,n+1))&& (inf_val <= ImLevels(i,j-1,m ,n+1))&& (inf_val <= ImLevels(i,j-1,m+1,n+1))&& //底层的小尺度9 (inf_val <= ImLevels(i,j,m-1,n-1))&& (inf_val <= ImLevels(i,j,m ,n-1))&& (inf_val <= ImLevels(i,j,m+1,n-1))&& (inf_val <= ImLevels(i,j,m-1,n ))&& (inf_val <= ImLevels(i,j,m+1,n ))&& (inf_val <= ImLevels(i,j,m-1,n+1))&& (inf_val <= ImLevels(i,j,m ,n+1))&& (inf_val <= ImLevels(i,j,m+1,n+1))&& //当前层8 (inf_val <= ImLevels(i,j+1,m-1,n-1))&& (inf_val <= ImLevels(i,j+1,m ,n-1))&& (inf_val <= ImLevels(i,j+1,m+1,n-1))&& (inf_val <= ImLevels(i,j+1,m-1,n ))&& (inf_val <= ImLevels(i,j+1,m ,n ))&& (inf_val <= ImLevels(i,j+1,m+1,n ))&& (inf_val <= ImLevels(i,j+1,m-1,n+1))&& (inf_val <= ImLevels(i,j+1,m ,n+1))&& (inf_val <= ImLevels(i,j+1,m+1,n+1)) //下一层大尺度9 ) || ( (inf_val >= ImLevels(i,j-1,m-1,n-1))&& (inf_val >= ImLevels(i,j-1,m ,n-1))&& (inf_val >= ImLevels(i,j-1,m+1,n-1))&& (inf_val >= ImLevels(i,j-1,m-1,n ))&& (inf_val >= ImLevels(i,j-1,m ,n ))&& (inf_val >= ImLevels(i,j-1,m+1,n ))&& (inf_val >= ImLevels(i,j-1,m-1,n+1))&& (inf_val >= ImLevels(i,j-1,m ,n+1))&& (inf_val >= ImLevels(i,j-1,m+1,n+1))&& (inf_val >= ImLevels(i,j,m-1,n-1))&& (inf_val >= ImLevels(i,j,m ,n-1))&& (inf_val >= ImLevels(i,j,m+1,n-1))&& (inf_val >= ImLevels(i,j,m-1,n ))&& (inf_val >= ImLevels(i,j,m+1,n ))&& (inf_val >= ImLevels(i,j,m-1,n+1))&& (inf_val >= ImLevels(i,j,m ,n+1))&& (inf_val >= ImLevels(i,j,m+1,n+1))&& (inf_val >= ImLevels(i,j+1,m-1,n-1))&& (inf_val >= ImLevels(i,j+1,m ,n-1))&& (inf_val >= ImLevels(i,j+1,m+1,n-1))&& (inf_val >= ImLevels(i,j+1,m-1,n ))&& (inf_val >= ImLevels(i,j+1,m ,n ))&& (inf_val >= ImLevels(i,j+1,m+1,n ))&& (inf_val >= ImLevels(i,j+1,m-1,n+1))&& (inf_val >= ImLevels(i,j+1,m ,n+1))&& (inf_val >= ImLevels(i,j+1,m+1,n+1)) ) ) //2、满足26个中极值点 { //此处可存储 //然后必须具有明显的显著性，即必须大于CONTRAST_THRESHOLD=0.02 if ( fabs(ImLevels(i,j,m,n))>= CONTRAST_THRESHOLD ) { //最后显著处的特征点必须具有足够的曲率比，CURVATURE_THRESHOLD=10.0，首先计算Hessian矩阵 // Compute the entries of the Hessian matrix at the extrema location. /* 1 0 -1 0 0 0 -1 0 1 *0.25 */ // Compute the trace and the determinant of the Hessian. //Tr_H = Dxx + Dyy; //Det_H = Dxx*Dyy - Dxy^2; float Dxx,Dyy,Dxy,Tr_H,Det_H,curvature_ratio; Dxx = ImLevels(i,j,m,n-1) + ImLevels(i,j,m,n+1)-2.0*ImLevels(i,j,m,n); Dyy = ImLevels(i,j,m-1,n) + ImLevels(i,j,m+1,n)-2.0*ImLevels(i,j,m,n); Dxy = ImLevels(i,j,m-1,n-1) + ImLevels(i,j,m+1,n+1) - ImLevels(i,j,m+1,n-1) - ImLevels(i,j,m-1,n+1); Tr_H = Dxx + Dyy; Det_H = Dxx*Dyy - Dxy*Dxy; // Compute the ratio of the principal curvatures. curvature_ratio = (1.0*Tr_H*Tr_H)/Det_H; if ( (Det_H>=0.0) && (curvature_ratio <= curvature_threshold) ) //最后得到最具有显著性特征的特征点 { //将其存储起来，以计算后面的特征描述字 keypoint_count++; Keypoint k; /* Allocate memory for the keypoint. */ k = (Keypoint) malloc(sizeof(struct KeypointSt)); k->next = keypoints; keypoints = k; k->row = m*(GaussianPyr[i].subsample); k->col =n*(GaussianPyr[i].subsample); k->sy = m; //行 k->sx = n; //列 k->octave=i; k->level=j; k->scale = (GaussianPyr[i].Octave)[j].absolute_sigma; }//if >curvature_thresh }//if >contrast }//if inf value }//if non zero }//if >contrast } //for concrete image level col }//for levels }//for octaves return keypoint_count; } //在图像中，显示SIFT特征点的位置 void DisplayKeypointLocation(IplImage* image, ImageOctaves *GaussianPyr) { Keypoint p = keypoints; // p指向第一个结点 while(p) // 没到表尾 { cvLine( image, cvPoint((int)((p->col)-3),(int)(p->row)), cvPoint((int)((p->col)+3),(int)(p->row)), CV_RGB(255,255,0), 1, 8, 0 ); cvLine( image, cvPoint((int)(p->col),(int)((p->row)-3)), cvPoint((int)(p->col),(int)((p->row)+3)), CV_RGB(255,255,0), 1, 8, 0 ); // cvCircle(image,cvPoint((uchar)(p->col),(uchar)(p->row)), // (int)((GaussianPyr[p->octave].Octave)[p->level].absolute_sigma), // CV_RGB(255,0,0),1,8,0); p=p->next; } } // Compute the gradient direction and magnitude of the gaussian pyramid images void ComputeGrad_DirecandMag(int numoctaves, ImageOctaves *GaussianPyr) { // ImageOctaves *mag_thresh ; mag_pyr=(ImageOctaves*) malloc( numoctaves * sizeof(ImageOctaves) ); grad_pyr=(ImageOctaves*) malloc( numoctaves * sizeof(ImageOctaves) ); // float sigma=( (GaussianPyr[0].Octave)[SCALESPEROCTAVE+2].absolute_sigma ) / GaussianPyr[0].subsample; // int dim = (int) (max(3.0f, 2 * GAUSSKERN *sigma + 1.0f)*0.5+0.5); #define ImLevels(OCTAVE,LEVEL,ROW,COL) ((float *)(GaussianPyr[(OCTAVE)].Octave[(LEVEL)].Level->data.fl + GaussianPyr[(OCTAVE)].Octave[(LEVEL)].Level->step/sizeof(float) *(ROW)))[(COL)] for (int i=0; i<numoctaves; i++) { mag_pyr[i].Octave= (ImageLevels*) malloc( (SCALESPEROCTAVE) * sizeof(ImageLevels) ); grad_pyr[i].Octave= (ImageLevels*) malloc( (SCALESPEROCTAVE) * sizeof(ImageLevels) ); for(int j=1;j<SCALESPEROCTAVE+1;j++)//取中间的scaleperoctave个层 { CvMat *Mag = cvCreateMat(GaussianPyr[i].row, GaussianPyr[i].col, CV_32FC1); CvMat *Ori = cvCreateMat(GaussianPyr[i].row, GaussianPyr[i].col, CV_32FC1); CvMat *tempMat1 = cvCreateMat(GaussianPyr[i].row, GaussianPyr[i].col, CV_32FC1); CvMat *tempMat2 = cvCreateMat(GaussianPyr[i].row, GaussianPyr[i].col, CV_32FC1); cvZero(Mag); cvZero(Ori); cvZero(tempMat1); cvZero(tempMat2); #define MAG(ROW,COL) ((float *)(Mag->data.fl + Mag->step/sizeof(float) *(ROW)))[(COL)] #define ORI(ROW,COL) ((float *)(Ori->data.fl + Ori->step/sizeof(float) *(ROW)))[(COL)] #define TEMPMAT1(ROW,COL) ((float *)(tempMat1->data.fl + tempMat1->step/sizeof(float) *(ROW)))[(COL)] #define TEMPMAT2(ROW,COL) ((float *)(tempMat2->data.fl + tempMat2->step/sizeof(float) *(ROW)))[(COL)] for (int m=1;m<(GaussianPyr[i].row-1);m++) for(int n=1;n<(GaussianPyr[i].col-1);n++) { //计算幅值 TEMPMAT1(m,n) = 0.5*( ImLevels(i,j,m,n+1)-ImLevels(i,j,m,n-1) ); //dx TEMPMAT2(m,n) = 0.5*( ImLevels(i,j,m+1,n)-ImLevels(i,j,m-1,n) ); //dy MAG(m,n) = sqrt(TEMPMAT1(m,n)*TEMPMAT1(m,n)+TEMPMAT2(m,n)*TEMPMAT2(m,n)); //mag //计算方向 ORI(m,n) =atan( TEMPMAT2(m,n)/TEMPMAT1(m,n) ); if (ORI(m,n)==CV_PI) ORI(m,n)=-CV_PI; } ((mag_pyr[i].Octave)[j-1]).Level=Mag; ((grad_pyr[i].Octave)[j-1]).Level=Ori; cvReleaseMat(&tempMat1); cvReleaseMat(&tempMat2); }//for levels }//for octaves }
SIFT算法第四步
//SIFT算法第四步：计算各个特征点的主方向，确定主方向 void AssignTheMainOrientation(int numoctaves, ImageOctaves *GaussianPyr,ImageOctaves *mag_pyr,ImageOctaves *grad_pyr) { // Set up the histogram bin centers for a 36 bin histogram. int num_bins = 36; float hist_step = 2.0*PI/num_bins; float hist_orient[36]; for (int i=0;i<36;i++) hist_orient[i]=-PI+i*hist_step; float sigma1=( ((GaussianPyr[0].Octave)[SCALESPEROCTAVE].absolute_sigma) ) / (GaussianPyr[0].subsample);//SCALESPEROCTAVE+2 int zero_pad = (int) (max(3.0f, 2 * GAUSSKERN *sigma1 + 1.0f)*0.5+0.5); //Assign orientations to the keypoints. #define ImLevels(OCTAVES,LEVELS,ROW,COL) ((float *)((GaussianPyr[(OCTAVES)].Octave[(LEVELS)].Level)->data.fl + (GaussianPyr[(OCTAVES)].Octave[(LEVELS)].Level)->step/sizeof(float) *(ROW)))[(COL)] int keypoint_count = 0; Keypoint p = keypoints; // p指向第一个结点 while(p) // 没到表尾 { int i=p->octave; int j=p->level; int m=p->sy; //行 int n=p->sx; //列 if ((m>=zero_pad)&&(m<GaussianPyr[i].row-zero_pad)&& (n>=zero_pad)&&(n<GaussianPyr[i].col-zero_pad) ) { float sigma=( ((GaussianPyr[i].Octave)[j].absolute_sigma) ) / (GaussianPyr[i].subsample); //产生二维高斯模板 CvMat* mat = GaussianKernel2D( sigma ); int dim=(int)(0.5 * (mat->rows)); //分配用于存储Patch幅值和方向的空间 #define MAT(ROW,COL) ((float *)(mat->data.fl + mat->step/sizeof(float) *(ROW)))[(COL)] //声明方向直方图变量 double* orienthist = (double *) malloc(36 * sizeof(double)); for ( int sw = 0 ; sw < 36 ; ++sw) { orienthist[sw]=0.0; } //在特征点的周围统计梯度方向 for (int x=m-dim,mm=0;x<=(m+dim);x++,mm++) for(int y=n-dim,nn=0;y<=(n+dim);y++,nn++) { //计算特征点处的幅值 double dx = 0.5*(ImLevels(i,j,x,y+1)-ImLevels(i,j,x,y-1)); //dx double dy = 0.5*(ImLevels(i,j,x+1,y)-ImLevels(i,j,x-1,y)); //dy double mag = sqrt(dx*dx+dy*dy); //mag //计算方向 double Ori =atan( 1.0*dy/dx ); int binIdx = FindClosestRotationBin(36, Ori); //得到离现有方向最近的直方块 orienthist[binIdx] = orienthist[binIdx] + 1.0* mag * MAT(mm,nn);//利用高斯加权累加进直方图相应的块 } // Find peaks in the orientation histogram using nonmax suppression. AverageWeakBins (orienthist, 36); // find the maximum peak in gradient orientation double maxGrad = 0.0; int maxBin = 0; for (int b = 0 ; b < 36 ; ++b) { if (orienthist[b] > maxGrad) { maxGrad = orienthist[b]; maxBin = b; } } // First determine the real interpolated peak high at the maximum bin // position, which is guaranteed to be an absolute peak. double maxPeakValue=0.0; double maxDegreeCorrection=0.0; if ( (InterpolateOrientation ( orienthist[maxBin == 0 ? (36 - 1) : (maxBin - 1)], orienthist[maxBin], orienthist[(maxBin + 1) % 36], &maxDegreeCorrection, &maxPeakValue)) == false) printf("BUG: Parabola fitting broken"); // Now that we know the maximum peak value, we can find other keypoint // orientations, which have to fulfill two criterias: // // 1. They must be a local peak themselves. Else we might add a very // similar keypoint orientation twice (imagine for example the // values: 0.4 1.0 0.8, if 1.0 is maximum peak, 0.8 is still added // with the default threshhold, but the maximum peak orientation // was already added). // 2. They must have at least peakRelThresh times the maximum peak // value. bool binIsKeypoint[36]; for ( b = 0 ; b < 36 ; ++b) { binIsKeypoint[b] = false; // The maximum peak of course is if (b == maxBin) { binIsKeypoint[b] = true; continue; } // Local peaks are, too, in case they fulfill the threshhold if (orienthist[b] < (peakRelThresh * maxPeakValue)) continue; int leftI = (b == 0) ? (36 - 1) : (b - 1); int rightI = (b + 1) % 36; if (orienthist[b] <= orienthist[leftI] || orienthist[b] <= orienthist[rightI]) continue; // no local peak binIsKeypoint[b] = true; } // find other possible locations double oneBinRad = (2.0 * PI) / 36; for ( b = 0 ; b < 36 ; ++b) { if (binIsKeypoint[b] == false) continue; int bLeft = (b == 0) ? (36 - 1) : (b - 1); int bRight = (b + 1) % 36; // Get an interpolated peak direction and value guess. double peakValue; double degreeCorrection; double maxPeakValue, maxDegreeCorrection; if (InterpolateOrientation ( orienthist[maxBin == 0 ? (36 - 1) : (maxBin - 1)], orienthist[maxBin], orienthist[(maxBin + 1) % 36], °reeCorrection, &peakValue) == false) { printf("BUG: Parabola fitting broken"); } double degree = (b + degreeCorrection) * oneBinRad - PI; if (degree < -PI) degree += 2.0 * PI; else if (degree > PI) degree -= 2.0 * PI; //存储方向，可以直接利用检测到的链表进行该步主方向的指定; //分配内存重新存储特征点 Keypoint k; /* Allocate memory for the keypoint Descriptor. */ k = (Keypoint) malloc(sizeof(struct KeypointSt)); k->next = keyDescriptors; keyDescriptors = k; k->descrip = (float*)malloc(LEN * sizeof(float)); k->row = p->row; k->col = p->col; k->sy = p->sy; //行 k->sx = p->sx; //列 k->octave = p->octave; k->level = p->level; k->scale = p->scale; k->ori = degree; k->mag = peakValue; }//for free(orienthist); } p=p->next; } } //寻找与方向直方图最近的柱，确定其index int FindClosestRotationBin (int binCount, float angle) { angle += CV_PI; angle /= 2.0 * CV_PI; // calculate the aligned bin angle *= binCount; int idx = (int) angle; if (idx == binCount) idx = 0; return (idx); } // Average the content of the direction bins. void AverageWeakBins (double* hist, int binCount) { // TODO: make some tests what number of passes is the best. (its clear // one is not enough, as we may have something like // ( 0.4, 0.4, 0.3, 0.4, 0.4 )) for (int sn = 0 ; sn < 2 ; ++sn) { double firstE = hist[0]; double last = hist[binCount-1]; for (int sw = 0 ; sw < binCount ; ++sw) { double cur = hist[sw]; double next = (sw == (binCount - 1)) ? firstE : hist[(sw + 1) % binCount]; hist[sw] = (last + cur + next) / 3.0; last = cur; } } } // Fit a parabol to the three points (-1.0 ; left), (0.0 ; middle) and // (1.0 ; right). // Formulas: // f(x) = a (x - c)^2 + b // c is the peak offset (where f'(x) is zero), b is the peak value. // In case there is an error false is returned, otherwise a correction // value between [-1 ; 1] is returned in 'degreeCorrection', where -1 // means the peak is located completely at the left vector, and -0.5 just // in the middle between left and middle and > 0 to the right side. In // 'peakValue' the maximum estimated peak value is stored. bool InterpolateOrientation (double left, double middle,double right, double *degreeCorrection, double *peakValue) { double a = ((left + right) - 2.0 * middle) / 2.0; //抛物线捏合系数a // degreeCorrection = peakValue = Double.NaN; // Not a parabol if (a == 0.0) return false; double c = (((left - middle) / a) - 1.0) / 2.0; double b = middle - c * c * a; if (c < -0.5 || c > 0.5) return false; *degreeCorrection = c; *peakValue = b; return true; } //显示特征点处的主方向 void DisplayOrientation (IplImage* image, ImageOctaves *GaussianPyr) { Keypoint p = keyDescriptors; // p指向第一个结点 while(p) // 没到表尾 { float scale=(GaussianPyr[p->octave].Octave)[p->level].absolute_sigma; float autoscale = 3.0; float uu=autoscale*scale*cos(p->ori); float vv=autoscale*scale*sin(p->ori); float x=(p->col)+uu; float y=(p->row)+vv; cvLine( image, cvPoint((int)(p->col),(int)(p->row)), cvPoint((int)x,(int)y), CV_RGB(255,255,0), 1, 8, 0 ); // Arrow head parameters float alpha = 0.33; // Size of arrow head relative to the length of the vector float beta = 0.33; // Width of the base of the arrow head relative to the length float xx0= (p->col)+uu-alpha*(uu+beta*vv); float yy0= (p->row)+vv-alpha*(vv-beta*uu); float xx1= (p->col)+uu-alpha*(uu-beta*vv); float yy1= (p->row)+vv-alpha*(vv+beta*uu); cvLine( image, cvPoint((int)xx0,(int)yy0), cvPoint((int)x,(int)y), CV_RGB(255,255,0), 1, 8, 0 ); cvLine( image, cvPoint((int)xx1,(int)yy1), cvPoint((int)x,(int)y), CV_RGB(255,255,0), 1, 8, 0 ); p=p->next; } }

SIFT算法第五步
    SIFT算法第五步：抽取各个特征点处的特征描述字，确定特征点的描述字。描述字是Patch网格内梯度方向的描述，旋转网格到主方向，插值得到网格处梯度值。
一个特征点可以用2*2*8=32维的向量，也可以用4*4*8=128维的向量更精确的进行描述。
void ExtractFeatureDescriptors(int numoctaves, ImageOctaves *GaussianPyr) { // The orientation histograms have 8 bins float orient_bin_spacing = PI/4; float orient_angles[8]={-PI,-PI+orient_bin_spacing,-PI*0.5, -orient_bin_spacing, 0.0, orient_bin_spacing, PI*0.5, PI+orient_bin_spacing}; //产生描述字中心各点坐标 float *feat_grid=(float *) malloc( 2*16 * sizeof(float)); for (int i=0;i<GridSpacing;i++) { for (int j=0;j<2*GridSpacing;++j,++j) { feat_grid[i*2*GridSpacing+j]=-6.0+i*GridSpacing; feat_grid[i*2*GridSpacing+j+1]=-6.0+0.5*j*GridSpacing; } } //产生网格 float *feat_samples=(float *) malloc( 2*256 * sizeof(float)); for ( i=0;i<4*GridSpacing;i++) { for (int j=0;j<8*GridSpacing;j+=2) { feat_samples[i*8*GridSpacing+j]=-(2*GridSpacing-0.5)+i; feat_samples[i*8*GridSpacing+j+1]=-(2*GridSpacing-0.5)+0.5*j; } } float feat_window = 2*GridSpacing; Keypoint p = keyDescriptors; // p指向第一个结点 while(p) // 没到表尾 { float scale=(GaussianPyr[p->octave].Octave)[p->level].absolute_sigma; float sine = sin(p->ori); float cosine = cos(p->ori); //计算中心点坐标旋转之后的位置 float *featcenter=(float *) malloc( 2*16 * sizeof(float)); for (int i=0;i<GridSpacing;i++) { for (int j=0;j<2*GridSpacing;j+=2) { float x=feat_grid[i*2*GridSpacing+j]; float y=feat_grid[i*2*GridSpacing+j+1]; featcenter[i*2*GridSpacing+j]=((cosine * x + sine * y) + p->sx); featcenter[i*2*GridSpacing+j+1]=((-sine * x + cosine * y) + p->sy); } } // calculate sample window coordinates (rotated along keypoint) float *feat=(float *) malloc( 2*256 * sizeof(float)); for ( i=0;i<64*GridSpacing;i++,i++) { float x=feat_samples[i]; float y=feat_samples[i+1]; feat[i]=((cosine * x + sine * y) + p->sx); feat[i+1]=((-sine * x + cosine * y) + p->sy); } //Initialize the feature descriptor. float *feat_desc = (float *) malloc( 128 * sizeof(float)); for (i=0;i<128;i++) { feat_desc[i]=0.0; // printf("%f ",feat_desc[i]); } //printf("/n"); for ( i=0;i<512;++i,++i) { float x_sample = feat[i]; float y_sample = feat[i+1]; // Interpolate the gradient at the sample position /* 0 1 0 1 * 1 0 1 0 具体插值策略如图示 */ float sample12=getPixelBI(((GaussianPyr[p->octave].Octave)[p->level]).Level, x_sample, y_sample-1); float sample21=getPixelBI(((GaussianPyr[p->octave].Octave)[p->level]).Level, x_sample-1, y_sample); float sample22=getPixelBI(((GaussianPyr[p->octave].Octave)[p->level]).Level, x_sample, y_sample); float sample23=getPixelBI(((GaussianPyr[p->octave].Octave)[p->level]).Level, x_sample+1, y_sample); float sample32=getPixelBI(((GaussianPyr[p->octave].Octave)[p->level]).Level, x_sample, y_sample+1); //float diff_x = 0.5*(sample23 - sample21); //float diff_y = 0.5*(sample32 - sample12); float diff_x = sample23 - sample21; float diff_y = sample32 - sample12; float mag_sample = sqrt( diff_x*diff_x + diff_y*diff_y ); float grad_sample = atan( diff_y / diff_x ); if(grad_sample == CV_PI) grad_sample = -CV_PI; // Compute the weighting for the x and y dimensions. float *x_wght=(float *) malloc( GridSpacing * GridSpacing * sizeof(float)); float *y_wght=(float *) malloc( GridSpacing * GridSpacing * sizeof(float)); float *pos_wght=(float *) malloc( 8*GridSpacing * GridSpacing * sizeof(float));; for (int m=0;m<32;++m,++m) { float x=featcenter[m]; float y=featcenter[m+1]; x_wght[m/2] = max(1 - (fabs(x - x_sample)*1.0/GridSpacing), 0); y_wght[m/2] = max(1 - (fabs(y - y_sample)*1.0/GridSpacing), 0); } for ( m=0;m<16;++m) for (int n=0;n<8;++n) pos_wght[m*8+n]=x_wght[m]*y_wght[m]; free(x_wght); free(y_wght); //计算方向的加权，首先旋转梯度场到主方向，然后计算差异 float diff[8],orient_wght[128]; for ( m=0;m<8;++m) { float angle = grad_sample-(p->ori)-orient_angles[m]+CV_PI; float temp = angle / (2.0 * CV_PI); angle -= (int)(temp) * (2.0 * CV_PI); diff[m]= angle - CV_PI; } // Compute the gaussian weighting. float x=p->sx; float y=p->sy; float g = exp(-((x_sample-x)*(x_sample-x)+(y_sample-y)*(y_sample-y))/(2*feat_window*feat_window))/(2*CV_PI*feat_window*feat_window); for ( m=0;m<128;++m) { orient_wght[m] = max((1.0 - 1.0*fabs(diff[m%8])/orient_bin_spacing),0); feat_desc[m] = feat_desc[m] + orient_wght[m]*pos_wght[m]*g*mag_sample; } free(pos_wght); } free(feat); free(featcenter); float norm=GetVecNorm( feat_desc, 128); for (int m=0;m<128;m++) { feat_desc[m]/=norm; if (feat_desc[m]>0.2) feat_desc[m]=0.2; } norm=GetVecNorm( feat_desc, 128); for ( m=0;m<128;m++) { feat_desc[m]/=norm; printf("%f ",feat_desc[m]); } printf("/n"); p->descrip = feat_desc; p=p->next; } free(feat_grid); free(feat_samples); } //为了显示图象金字塔，而作的图像水平拼接 CvMat* MosaicHorizen( CvMat* im1, CvMat* im2 ) { int row,col; CvMat *mosaic = cvCreateMat( max(im1->rows,im2->rows),(im1->cols+im2->cols),CV_32FC1); #define Mosaic(ROW,COL) ((float*)(mosaic->data.fl + mosaic->step/sizeof(float)*(ROW)))[(COL)] #define Im11Mat(ROW,COL) ((float *)(im1->data.fl + im1->step/sizeof(float) *(ROW)))[(COL)] #define Im22Mat(ROW,COL) ((float *)(im2->data.fl + im2->step/sizeof(float) *(ROW)))[(COL)] cvZero(mosaic); /* Copy images into mosaic1. */ for ( row = 0; row < im1->rows; row++) for ( col = 0; col < im1->cols; col++) Mosaic(row,col)=Im11Mat(row,col) ; for ( row = 0; row < im2->rows; row++) for ( col = 0; col < im2->cols; col++) Mosaic(row, (col+im1->cols) )= Im22Mat(row,col) ; return mosaic; } //为了显示图象金字塔，而作的图像垂直拼接 CvMat* MosaicVertical( CvMat* im1, CvMat* im2 ) { int row,col; CvMat *mosaic = cvCreateMat(im1->rows+im2->rows,max(im1->cols,im2->cols), CV_32FC1); #define Mosaic(ROW,COL) ((float*)(mosaic->data.fl + mosaic->step/sizeof(float)*(ROW)))[(COL)] #define Im11Mat(ROW,COL) ((float *)(im1->data.fl + im1->step/sizeof(float) *(ROW)))[(COL)] #define Im22Mat(ROW,COL) ((float *)(im2->data.fl + im2->step/sizeof(float) *(ROW)))[(COL)] cvZero(mosaic); /* Copy images into mosaic1. */ for ( row = 0; row < im1->rows; row++) for ( col = 0; col < im1->cols; col++) Mosaic(row,col)= Im11Mat(row,col) ; for ( row = 0; row < im2->rows; row++) for ( col = 0; col < im2->cols; col++) Mosaic((row+im1->rows),col)=Im22Mat(row,col) ; return mosaic; }

ok，为了版述清晰，再贴一下上文所述的主函数（注，上文已贴出，此是为了版述清晰，重复造轮）：

int main( void ) { //声明当前帧IplImage指针 IplImage* src = NULL; IplImage* image1 = NULL; IplImage* grey_im1 = NULL; IplImage* DoubleSizeImage = NULL; IplImage* mosaic1 = NULL; IplImage* mosaic2 = NULL; CvMat* mosaicHorizen1 = NULL; CvMat* mosaicHorizen2 = NULL; CvMat* mosaicVertical1 = NULL; CvMat* image1Mat = NULL; CvMat* tempMat=NULL; ImageOctaves *Gaussianpyr; int rows,cols; #define Im1Mat(ROW,COL) ((float *)(image1Mat->data.fl + image1Mat->step/sizeof(float) *(ROW)))[(COL)] //灰度图象像素的数据结构 #define Im1B(ROW,COL) ((uchar*)(image1->imageData + image1->widthStep*(ROW)))[(COL)*3] #define Im1G(ROW,COL) ((uchar*)(image1->imageData + image1->widthStep*(ROW)))[(COL)*3+1] #define Im1R(ROW,COL) ((uchar*)(image1->imageData + image1->widthStep*(ROW)))[(COL)*3+2] storage = cvCreateMemStorage(0); //读取图片 if( (src = cvLoadImage( "street1.jpg", 1)) == 0 ) // test1.jpg einstein.pgm back1.bmp return -1; //为图像分配内存 image1 = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U,3); grey_im1 = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U,1); DoubleSizeImage = cvCreateImage(cvSize(2*(src->width), 2*(src->height)), IPL_DEPTH_8U,3); //为图像阵列分配内存，假设两幅图像的大小相同，tempMat跟随image1的大小 image1Mat = cvCreateMat(src->height, src->width, CV_32FC1); //转化成单通道图像再处理 cvCvtColor(src, grey_im1, CV_BGR2GRAY); //转换进入Mat数据结构,图像操作使用的是浮点型操作 cvConvert(grey_im1, image1Mat); double t = (double)cvGetTickCount(); //图像归一化 cvConvertScale( image1Mat, image1Mat, 1.0/255, 0 ); int dim = min(image1Mat->rows, image1Mat->cols); numoctaves = (int) (log((double) dim) / log(2.0)) - 2; //金字塔阶数 numoctaves = min(numoctaves, MAXOCTAVES); //SIFT算法第一步，预滤波除噪声，建立金字塔底层 tempMat = ScaleInitImage(image1Mat) ; //SIFT算法第二步，建立Guassian金字塔和DOG金字塔 Gaussianpyr = BuildGaussianOctaves(tempMat) ; t = (double)cvGetTickCount() - t; printf( "the time of build Gaussian pyramid and DOG pyramid is %.1f/n", t/(cvGetTickFrequency()*1000.) ); #define ImLevels(OCTAVE,LEVEL,ROW,COL) ((float *)(Gaussianpyr[(OCTAVE)].Octave[(LEVEL)].Level->data.fl + Gaussianpyr[(OCTAVE)].Octave[(LEVEL)].Level->step/sizeof(float) *(ROW)))[(COL)] //显示高斯金字塔 for (int i=0; i<numoctaves;i++) { if (i==0) { mosaicHorizen1=MosaicHorizen( (Gaussianpyr[0].Octave)[0].Level, (Gaussianpyr[0].Octave)[1].Level ); for (int j=2;j<SCALESPEROCTAVE+3;j++) mosaicHorizen1=MosaicHorizen( mosaicHorizen1, (Gaussianpyr[0].Octave)[j].Level ); for ( j=0;j<NUMSIZE;j++) mosaicHorizen1=halfSizeImage(mosaicHorizen1); } else if (i==1) { mosaicHorizen2=MosaicHorizen( (Gaussianpyr[1].Octave)[0].Level, (Gaussianpyr[1].Octave)[1].Level ); for (int j=2;j<SCALESPEROCTAVE+3;j++) mosaicHorizen2=MosaicHorizen( mosaicHorizen2, (Gaussianpyr[1].Octave)[j].Level ); for ( j=0;j<NUMSIZE;j++) mosaicHorizen2=halfSizeImage(mosaicHorizen2); mosaicVertical1=MosaicVertical( mosaicHorizen1, mosaicHorizen2 ); } else { mosaicHorizen1=MosaicHorizen( (Gaussianpyr[i].Octave)[0].Level, (Gaussianpyr[i].Octave)[1].Level ); for (int j=2;j<SCALESPEROCTAVE+3;j++) mosaicHorizen1=MosaicHorizen( mosaicHorizen1, (Gaussianpyr[i].Octave)[j].Level ); for ( j=0;j<NUMSIZE;j++) mosaicHorizen1=halfSizeImage(mosaicHorizen1); mosaicVertical1=MosaicVertical( mosaicVertical1, mosaicHorizen1 ); } } mosaic1 = cvCreateImage(cvSize(mosaicVertical1->width, mosaicVertical1->height), IPL_DEPTH_8U,1); cvConvertScale( mosaicVertical1, mosaicVertical1, 255.0, 0 ); cvConvertScaleAbs( mosaicVertical1, mosaic1, 1, 0 ); // cvSaveImage("GaussianPyramid of me.jpg",mosaic1); cvNamedWindow("mosaic1",1); cvShowImage("mosaic1", mosaic1); cvWaitKey(0); cvDestroyWindow("mosaic1"); //显示DOG金字塔 for ( i=0; i<numoctaves;i++) { if (i==0) { mosaicHorizen1=MosaicHorizen( (DOGoctaves[0].Octave)[0].Level, (DOGoctaves[0].Octave)[1].Level ); for (int j=2;j<SCALESPEROCTAVE+2;j++) mosaicHorizen1=MosaicHorizen( mosaicHorizen1, (DOGoctaves[0].Octave)[j].Level ); for ( j=0;j<NUMSIZE;j++) mosaicHorizen1=halfSizeImage(mosaicHorizen1); } else if (i==1) { mosaicHorizen2=MosaicHorizen( (DOGoctaves[1].Octave)[0].Level, (DOGoctaves[1].Octave)[1].Level ); for (int j=2;j<SCALESPEROCTAVE+2;j++) mosaicHorizen2=MosaicHorizen( mosaicHorizen2, (DOGoctaves[1].Octave)[j].Level ); for ( j=0;j<NUMSIZE;j++) mosaicHorizen2=halfSizeImage(mosaicHorizen2); mosaicVertical1=MosaicVertical( mosaicHorizen1, mosaicHorizen2 ); } else { mosaicHorizen1=MosaicHorizen( (DOGoctaves[i].Octave)[0].Level, (DOGoctaves[i].Octave)[1].Level ); for (int j=2;j<SCALESPEROCTAVE+2;j++) mosaicHorizen1=MosaicHorizen( mosaicHorizen1, (DOGoctaves[i].Octave)[j].Level ); for ( j=0;j<NUMSIZE;j++) mosaicHorizen1=halfSizeImage(mosaicHorizen1); mosaicVertical1=MosaicVertical( mosaicVertical1, mosaicHorizen1 ); } } //考虑到DOG金字塔各层图像都会有正负，所以，必须寻找最负的，以将所有图像抬高一个台阶去显示 double min_val=0; double max_val=0; cvMinMaxLoc( mosaicVertical1, &min_val, &max_val,NULL, NULL, NULL ); if ( min_val<0.0 ) cvAddS( mosaicVertical1, cvScalarAll( (-1.0)*min_val ), mosaicVertical1, NULL ); mosaic2 = cvCreateImage(cvSize(mosaicVertical1->width, mosaicVertical1->height), IPL_DEPTH_8U,1); cvConvertScale( mosaicVertical1, mosaicVertical1, 255.0/(max_val-min_val), 0 ); cvConvertScaleAbs( mosaicVertical1, mosaic2, 1, 0 ); // cvSaveImage("DOGPyramid of me.jpg",mosaic2); cvNamedWindow("mosaic1",1); cvShowImage("mosaic1", mosaic2); cvWaitKey(0); //SIFT算法第三步：特征点位置检测，最后确定特征点的位置 int keycount=DetectKeypoint(numoctaves, Gaussianpyr); printf("the keypoints number are %d ;/n", keycount); cvCopy(src,image1,NULL); DisplayKeypointLocation( image1 ,Gaussianpyr); cvPyrUp( image1, DoubleSizeImage, CV_GAUSSIAN_5x5 ); cvNamedWindow("image1",1); cvShowImage("image1", DoubleSizeImage); cvWaitKey(0); cvDestroyWindow("image1"); //SIFT算法第四步：计算高斯图像的梯度方向和幅值，计算各个特征点的主方向 ComputeGrad_DirecandMag(numoctaves, Gaussianpyr); AssignTheMainOrientation( numoctaves, Gaussianpyr,mag_pyr,grad_pyr); cvCopy(src,image1,NULL); DisplayOrientation ( image1, Gaussianpyr); // cvPyrUp( image1, DoubleSizeImage, CV_GAUSSIAN_5x5 ); cvNamedWindow("image1",1); // cvResizeWindow("image1", 2*(image1->width), 2*(image1->height) ); cvShowImage("image1", image1); cvWaitKey(0); //SIFT算法第五步：抽取各个特征点处的特征描述字 ExtractFeatureDescriptors( numoctaves, Gaussianpyr); cvWaitKey(0); //销毁窗口 cvDestroyWindow("image1"); cvDestroyWindow("mosaic1"); //释放图像 cvReleaseImage(&image1); cvReleaseImage(&grey_im1); cvReleaseImage(&mosaic1); cvReleaseImage(&mosaic2); return 0; }

最后，再看一下，运行效果（图中美女为老乡+朋友，何姐08年照）：

完。 

updated

    有很多朋友都在本文评论下要求要本程序的完整源码包（注：本文代码未贴全，复制粘贴编译肯定诸多错误），但由于时隔太久，这份代码我自己也找不到了，不过，我可以提供一份sift + KD + BBF，且可以编译正确的代码供大家参考学习，有pudn帐号的朋友可以前去下载：http://www.pudn.com/downloads340/sourcecode/graph/texture_mapping/detail1486667.html（没有pudn账号的同学请加群：169056165，验证信息：sift，至群共享下载），然后用两幅不同的图片做了下匹配（当然，运行结果显示是不匹配的），效果还不错：http://weibo.com/1580904460/yDmzAEwcV#1348475194313! July、二零一二年十月十一日。