本文整理汇总了C++中pixval32f函数的典型用法代码示例。如果您正苦于以下问题：C++ pixval32f函数的具体用法？C++ pixval32f怎么用？C++ pixval32f使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了pixval32f函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: extremum
/*
Determines whether a pixel is a scale-space extremum by comparing it to it's 3x3x3 pixel neighborhood.
@param dog_pyr DoG scale space pyramid
@param octv pixel's scale space octave
@param intvl pixel's within-octave interval
@param r pixel's image row
@param c pixel's image col
@return Returns 1 if the specified pixel is an extremum (max or min) among it's 3x3x3 pixel neighborhood.
*/
static int is_extremum( IplImage*** dog_pyr, int octv, int intvl, int r, int c )
{
//调用函数pixval32f获取图像dog_pyr[octv][intvl]的第r行第c列的点的坐标值
float val = pixval32f( dog_pyr[octv][intvl], r, c );
int i, j, k;
//检查是否最大值
if( val > 0 )
{
for( i = -1; i <= 1; i++ )//层
for( j = -1; j <= 1; j++ )//行
for( k = -1; k <= 1; k++ )//列
if( val < pixval32f( dog_pyr[octv][intvl+i], r + j, c + k ) )
return 0;
}
//检查是否最小值
else
{
for( i = -1; i <= 1; i++ )//层
for( j = -1; j <= 1; j++ )//行
for( k = -1; k <= 1; k++ )//列
if( val > pixval32f( dog_pyr[octv][intvl+i], r + j, c + k ) )
return 0;
}
return 1;
}示例2: extremum
/*
Determines whether a pixel is a scale-space extremum by comparing it to it's
3x3x3 pixel neighborhood.
@param dog_pyr DoG scale space pyramid
@param octv pixel's scale space octave
@param intvl pixel's within-octave interval
@param r pixel's image row
@param c pixel's image col
@return Returns 1 if the specified pixel is an extremum (max or min) among
it's 3x3x3 pixel neighborhood.
*/
int is_extremum( IplImage*** dog_pyr, int octv, int intvl, int r, int c )
{
float val = pixval32f( dog_pyr[octv][intvl], r, c );
int i, j, k;
/* check for maximum */
if( val > 0 )
{
for( i = -1; i <= 1; i++ )
for( j = -1; j <= 1; j++ )
for( k = -1; k <= 1; k++ )
if( val < pixval32f( dog_pyr[octv][intvl+i], r + j, c + k ) )
return 0;
}
/* check for minimum */
else
{
for( i = -1; i <= 1; i++ )
for( j = -1; j <= 1; j++ )
for( k = -1; k <= 1; k++ )
if( val > pixval32f( dog_pyr[octv][intvl+i], r + j, c + k ) )
return 0;
}
return 1;
}示例3: get_complax
float get_complax(IplImage *img, int r, int c, int w, int h) {
float sum = 0.0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
if (mask[y * h + x]) {
sum += pixval32f(img, r - w / 2 + x, c - h / 2 + y);
}
}
}
float avg = sum / num;
float complaxVal = 0.0;
// printf("%f %d %d\n", avg, r, c);
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
if (mask[y * h + x]) {
float tmp = pixval32f(img, r - w / 2 + x, c - h / 2 + y) - avg;
complaxVal += tmp * tmp;
}
}
}
return complaxVal;
}示例4: at
/*
Determines whether a feature is too edge like to be stable by computing the
ratio of principal curvatures at that feature. Based on Section 4.1 of Lowe's paper.
@param dog_img image from the DoG pyramid in which feature was detected
@param r feature row
@param c feature col
@param curv_thr high threshold on ratio of principal curvatures
@return Returns 0 if the feature at (r,c) in dog_img is sufficiently corner-like or 1 otherwise.
*/
static int is_too_edge_like( IplImage* dog_img, int r, int c, int curv_thr )
{
double d, dxx, dyy, dxy, tr, det;
/*某点的主曲率与其海森矩阵的特征值成正比，为了避免直接计算特征值，这里只考虑特征值的比值
可通过计算海森矩阵的迹tr(H)和行列式det(H)来计算特征值的比值
设a是海森矩阵的较大特征值，b是较小的特征值，有a = r*b，r是大小特征值的比值
tr(H) = a + b; det(H) = a*b;
tr(H)^2 / det(H) = (a+b)^2 / ab = (r+1)^2/r
r越大，越可能是边缘点；伴随r的增大，(r+1)^2/r 的值也增大，所以可通过(r+1)^2/r 判断主曲率比值是否满足条件*/
/* principal curvatures are computed using the trace and det of Hessian */
d = pixval32f(dog_img, r, c);//调用函数pixval32f获取图像dog_img的第r行第c列的点的坐标值
//用差分近似代替偏导，求出海森矩阵的几个元素值
/* / dxx dxy \
\ dxy dyy / */
dxx = pixval32f( dog_img, r, c+1 ) + pixval32f( dog_img, r, c-1 ) - 2 * d;
dyy = pixval32f( dog_img, r+1, c ) + pixval32f( dog_img, r-1, c ) - 2 * d;
dxy = ( pixval32f(dog_img, r+1, c+1) - pixval32f(dog_img, r+1, c-1) -
pixval32f(dog_img, r-1, c+1) + pixval32f(dog_img, r-1, c-1) ) / 4.0;
tr = dxx + dyy;//海森矩阵的迹
det = dxx * dyy - dxy * dxy;//海森矩阵的行列式
//若行列式为负，表明曲率有不同的符号，去除此点
/* negative determinant -> curvatures have different signs; reject feature */
if( det <= 0 )
return 1;//返回1表明此点是边缘点
//通过式子：(r+1)^2/r 判断主曲率的比值是否满足条件，若小于阈值，表明不是边缘点
if( tr * tr / det < ( curv_thr + 1.0 )*( curv_thr + 1.0 ) / curv_thr )
return 0;//不是边缘点
return 1;//是边缘点
}示例5: scale_space_extrema
/*
Detects features at extrema in DoG scale space. Bad features are discarded
based on contrast and ratio of principal curvatures.
@param dog_pyr DoG scale space pyramid
@param octvs octaves of scale space represented by dog_pyr
@param intvls intervals per octave
@param contr_thr low threshold on feature contrast
@param curv_thr high threshold on feature ratio of principal curvatures
@param storage memory storage in which to store detected features
@return Returns an array of detected features whose scales, orientations,
and descriptors are yet to be determined.
*/
CvSeq* scale_space_extrema( IplImage*** dog_pyr, int octvs, int intvls,
double contr_thr, int curv_thr, CvMemStorage* storage ) {
CvSeq* features;
double prelim_contr_thr = 0.5 * contr_thr / intvls;
struct feature* feat;
struct detection_data* ddata;
int o, i, r, c;
features = cvCreateSeq( 0, sizeof(CvSeq), sizeof(struct feature), storage );
for( o = 0; o < octvs; o++ )
for( i = 1; i <= intvls; i++ )
for(r = SIFT_IMG_BORDER; r < dog_pyr[o][0]->height-SIFT_IMG_BORDER; r++)
for(c = SIFT_IMG_BORDER; c < dog_pyr[o][0]->width-SIFT_IMG_BORDER; c++)
/* perform preliminary check on contrast */
if( ABS( pixval32f( dog_pyr[o][i], r, c ) ) > prelim_contr_thr )
if( is_extremum( dog_pyr, o, i, r, c ) ) {
feat = interp_extremum(dog_pyr, o, i, r, c, intvls, contr_thr);
if( feat ) {
ddata = feat_detection_data( feat );
if( ! is_too_edge_like( dog_pyr[ddata->octv][ddata->intvl],
ddata->r, ddata->c, curv_thr ) ) {
cvSeqPush( features, feat );
}
else
free( ddata );
free( feat );
}
}
return features;
}示例6: pixel
/*
Calculates the gradient magnitude and orientation at a given pixel.
@param img image
@param r pixel row
@param c pixel col
@param mag output as gradient magnitude at pixel (r,c)
@param ori output as gradient orientation at pixel (r,c)
@return Returns 1 if the specified pixel is a valid one and sets mag and
ori accordingly; otherwise returns 0
*/
int calc_grad_mag_ori( IplImage* img, int r, int c, double* mag, double* ori )
{
double dx, dy;
if( r > 0 && r < img->height - 1 && c > 0 && c < img->width - 1 )
{
dx = pixval32f( img, r, c+1 ) - pixval32f( img, r, c-1 );
dy = pixval32f( img, r-1, c ) - pixval32f( img, r+1, c );
*mag = sqrt( dx*dx + dy*dy );
*ori = atan2( dy, dx );
return 1;
}
else
return 0;
}示例7: pixel
/*
Calculates the gradient magnitude and orientation at a given pixel.
@param img image
@param r pixel row
@param c pixel col
@param mag output as gradient magnitude at pixel (r,c)
@param ori output as gradient orientation at pixel (r,c)
@return Returns 1 if the specified pixel is a valid one and sets mag and
ori accordingly; otherwise returns 0
*/
static int calc_grad_mag_ori( IplImage* img, int r, int c, double* mag, double* ori )
{
double dx, dy;
//对输入的坐标值进行检查
if( r > 0 && r < img->height - 1 && c > 0 && c < img->width - 1 )
{
//用差分近似代替偏导，来求梯度的幅值和方向
dx = pixval32f( img, r, c+1 ) - pixval32f( img, r, c-1 );//x方向偏导
dy = pixval32f( img, r-1, c ) - pixval32f( img, r+1, c );//y方向偏导
*mag = sqrt( dx*dx + dy*dy );//梯度的幅值，即梯度的模
*ori = atan2( dy, dx );//梯度的方向
return 1;
}
//行列坐标值不合法，返回0
else
return 0;
}示例8: print_32f_image
void print_32f_image( const IplImage *src ) {
for ( int y = 0; y < src->height; y++ ) {
for ( int x = 0; x < src->width; x++ ) {
std::cout << pixval32f(src, x, y) << " ";
}
std::cout << endl;
}
std::cout << "*********************\n";
}示例9: calc_grad_mag_ori
int calc_grad_mag_ori(const IplImage *src, int x, int y, float &mag, float &ori) {
float dx, dy;
if (x > 0 && x < src->width - 1 &&
y > 0 && y < src->height - 1) {
dy = pixval32f(src, x, y - 1) - pixval32f(src, x, y + 1);
dx = pixval32f(src, x + 1, y) - pixval32f(src, x - 1, y);
mag = sqrt(dx * dx + dy * dy);
ori = atan2(dy, dx);
// print_point(x, y - 1, pixval32f(src, x, y - 1));
// print_point(x, y + 1, pixval32f(src, x, y + 1));
// cout << dy << " " << dx << " " << ori << endl;
// cout << cvRound(8 * (ori + CV_PI) / (2 * CV_PI)) << endl;
// cout << "-------------\n";
// cout << mag << endl;
return 1;
}
return 0;
}示例10: interp_contr
/*
Calculates interpolated pixel contrast. Based on Eqn. (3) in Lowe's paper.
@param dog_pyr difference of Gaussians scale space pyramid
@param octv octave of scale space
@param intvl within-octave interval
@param r pixel row
@param c pixel column
@param xi interpolated subpixel increment to interval
@param xr interpolated subpixel increment to row
@param xc interpolated subpixel increment to col
@param Returns interpolated contrast.
*/
double interp_contr( IplImage*** dog_pyr, int octv, int intvl, int r,
int c, double xi, double xr, double xc )
{
CvMat* dD, X, T;
double t[1], x[3] = { xc, xr, xi };
cvInitMatHeader( &X, 3, 1, CV_64FC1, x, CV_AUTOSTEP );
cvInitMatHeader( &T, 1, 1, CV_64FC1, t, CV_AUTOSTEP );
dD = deriv_3D( dog_pyr, octv, intvl, r, c );
cvGEMM( dD, &X, 1, NULL, 0, &T, CV_GEMM_A_T );
cvReleaseMat( &dD );
return pixval32f( dog_pyr[octv][intvl], r, c ) + t[0] * 0.5;
}示例11: at
/*
Determines whether a feature is too edge like to be stable by computing the
ratio of principal curvatures at that feature. Based on Section 4.1 of
Lowe's paper.
@param dog_img image from the DoG pyramid in which feature was detected
@param r feature row
@param c feature col
@param curv_thr high threshold on ratio of principal curvatures
@return Returns 0 if the feature at (r,c) in dog_img is sufficiently
corner-like or 1 otherwise.
*/
int is_too_edge_like( IplImage* dog_img, int r, int c, int curv_thr )
{
double d, dxx, dyy, dxy, tr, det;
/* principal curvatures are computed using the trace and det of Hessian */
d = pixval32f(dog_img, r, c);
dxx = pixval32f( dog_img, r, c+1 ) + pixval32f( dog_img, r, c-1 ) - 2 * d;
dyy = pixval32f( dog_img, r+1, c ) + pixval32f( dog_img, r-1, c ) - 2 * d;
dxy = ( pixval32f(dog_img, r+1, c+1) - pixval32f(dog_img, r+1, c-1) -
pixval32f(dog_img, r-1, c+1) + pixval32f(dog_img, r-1, c-1) ) / 4.0;
tr = dxx + dyy;
det = dxx * dyy - dxy * dxy;
/* negative determinant -> curvatures have different signs; reject feature */
if( det <= 0 )
return 1;
if( tr * tr / det < ( curv_thr + 1.0 )*( curv_thr + 1.0 ) / curv_thr )
return 0;
return 1;
}示例12: cvInitMatHeader
/*
Calculates interpolated pixel contrast. Based on Eqn. (3) in Lowe's paper.
@param dog_pyr difference of Gaussians scale space pyramid
@param octv octave of scale space
@param intvl within-octave interval
@param r pixel row
@param c pixel column
@param xi interpolated subpixel increment to interval
@param xr interpolated subpixel increment to row
@param xc interpolated subpixel increment to col
@param Returns interpolated contrast.
*/
float SiftGPU::InterpContr( IplImage*** dog_pyr, int octv, int intvl, int r,
int c, float xi, float xr, float xc )
{
CvMat* dD, X, T;
float t[1], x[3] = { xc, xr, xi };
cvInitMatHeader( &X, 3, 1, CV_64FC1, x, CV_AUTOSTEP );
cvInitMatHeader( &T, 1, 1, CV_64FC1, t, CV_AUTOSTEP );
dD = Deriv3D( dog_pyr, octv, intvl, r, c );
//cvGEMM( dD, &X, 1, NULL, 0, &T, CV_GEMM_A_T );
t[0] = cvGetReal2D(dD, 0, 0) * x[0] + cvGetReal2D(dD, 1, 0) * x[1] + cvGetReal2D(dD, 2, 0) * x[2];
cvReleaseMat( &dD );
return pixval32f( dog_pyr[octv][intvl], r, c ) + t[0] * 0.5;
}示例13: scale_space_extrema
/*
Detects features at extrema in DoG scale space. Bad features are discarded
based on contrast and ratio of principal curvatures.
@param dog_pyr DoG scale space pyramid
@param octvs octaves of scale space represented by dog_pyr
@param intvls intervals per octave
@param contr_thr low threshold on feature contrast
@param curv_thr high threshold on feature ratio of principal curvatures
@param storage memory storage in which to store detected features
@return Returns an array of detected features whose scales, orientations,
and descriptors are yet to be determined.
*/
static CvSeq* scale_space_extrema( IplImage*** dog_pyr, int octvs, int intvls,
double contr_thr, int curv_thr, CvMemStorage* storage )
{
CvSeq* features;//特征点序列
double prelim_contr_thr = 0.5 * contr_thr / intvls;//像素的对比度阈值
struct feature* feat;
struct detection_data* ddata;
int o, i, r, c;
//在存储器storage上创建存储极值点的序列，其中存储feature结构类型的数据
features = cvCreateSeq( 0, sizeof(CvSeq), sizeof(struct feature), storage );
/*遍历高斯差分金字塔，检测极值点*/
//SIFT_IMG_BORDER指明边界宽度，只检测边界线以内的极值点
for( o = 0; o < octvs; o++ )//第o组
for( i = 1; i <= intvls; i++ )//遍i层
for(r = SIFT_IMG_BORDER; r < dog_pyr[o][0]->height-SIFT_IMG_BORDER; r++)//第r行
for(c = SIFT_IMG_BORDER; c < dog_pyr[o][0]->width-SIFT_IMG_BORDER; c++)//第c列
//进行初步的对比度检查，只有当归一化后的像素值大于对比度阈值prelim_contr_thr时才继续检测此像素点是否可能是极值
//调用函数pixval32f获取图像dog_pyr[o][i]的第r行第c列的点的坐标值，然后调用ABS宏求其绝对值
if( ABS( pixval32f( dog_pyr[o][i], r, c ) ) > prelim_contr_thr )
//通过在尺度空间中将一个像素点的值与其周围3*3*3邻域内的点比较来决定此点是否极值点(极大值或极小都行)
if( is_extremum( dog_pyr, o, i, r, c ) )//若是极值点
{
//由于极值点的检测是在离散空间中进行的，所以检测到的极值点并不一定是真正意义上的极值点
//因为真正的极值点可能位于两个像素之间，而在离散空间中只能精确到坐标点精度上
//通过亚像素级插值进行极值点精确定位(修正极值点坐标)，并去除低对比度的极值点，将修正后的特征点组成feature结构返回
feat = interp_extremum(dog_pyr, o, i, r, c, intvls, contr_thr);
//返回值非空，表明此点已被成功修正
if( feat )
{
//调用宏feat_detection_data来提取参数feat中的feature_data成员并转换为detection_data类型的指针
ddata = feat_detection_data( feat );
//去除边缘响应，即通过计算主曲率比值判断某点是否边缘点,返回值为0表示不是边缘点，可做特征点
if( ! is_too_edge_like( dog_pyr[ddata->octv][ddata->intvl], ddata->r, ddata->c, curv_thr ) )
{
cvSeqPush( features, feat );//向特征点序列features末尾插入新检测到的特征点feat
}
else
free( ddata );
free( feat );
}
}
return features;//返回特征点序列
}示例14: get_gray
float *get_image_dct( const IplImage *src, const int nSub, int &featLen ) {
IplImage *img = get_gray( src );
IplImage *subImage = resize_image( img, nSub );
IplImage *dctImage = cvCloneImage( subImage );
cvDCT( subImage, dctImage, CV_DXT_FORWARD );
// print_32f_image( dctImage );
// float *data = ( float * )( dctImage->imageData );
// qsort( data + 1, nSub * nSub - 1, sizeof( float ), cmp );
// print_32f_image( dctImage );
int subDct = 6;
featLen = subDct * subDct - 1;
int index = 0;
float *feature = new float[ featLen ];
/*copy only 35 points of dcts*/
for ( int y = 0; y < subDct; y++ ) {
for ( int x = 0; x < subDct; x++ ) {
if ( x == 0 && y == 0 )
continue;
feature[ index++ ] = pixval32f( dctImage, x, y );
}
}
/*print the feature without sort*/
// print_dct_feature( feature, subDct );
qsort( feature, featLen, sizeof( float ), cmp );
//sort( feature, feature + featLen );
/*print the feature after sort*/
//print_dct_feature( feature, subDct );
for ( int i = 0; i < featLen; i++ ) {
printf( "%.3f\t", feature[ i ] );
}
std::cout << endl;
show_image( img, "gray" );
cvReleaseImage( &img );
cvReleaseImage( &subImage );
cvReleaseImage( &dctImage );
return feature;
}示例15: interp_contr
/*
Calculates interpolated pixel contrast. Based on Eqn. (3) in Lowe's paper.
@param dog_pyr difference of Gaussians scale space pyramid
@param octv octave of scale space
@param intvl within-octave interval
@param r pixel row
@param c pixel column
@param xi interpolated subpixel increment to interval
@param xr interpolated subpixel increment to row
@param xc interpolated subpixel increment to col
@param Returns interpolated contrast.
*/
static double interp_contr( IplImage*** dog_pyr, int octv, int intvl, int r,
int c, double xi, double xr, double xc )
{
CvMat* dD, X, T;
double t[1], x[3] = { xc, xr, xi };
//偏移量组成的列向量X，其中是x，y，σ三方向上的偏移量
cvInitMatHeader( &X, 3, 1, CV_64FC1, x, CV_AUTOSTEP );
//矩阵乘法的结果T，是一个数值
cvInitMatHeader( &T, 1, 1, CV_64FC1, t, CV_AUTOSTEP );
//在DoG金字塔中计算某点的x方向、y方向以及尺度方向上的偏导数，结果存放在列向量dD中
dD = deriv_3D( dog_pyr, octv, intvl, r, c );
//矩阵乘法：T = dD^T * X
cvGEMM( dD, &X, 1, NULL, 0, &T, CV_GEMM_A_T );
cvReleaseMat( &dD );
//返回计算出的对比度值：D + 0.5 * dD^T * X (具体公式推导见SIFT算法说明)
return pixval32f( dog_pyr[octv][intvl], r, c ) + t[0] * 0.5;
}