C# Discussion Boards - CodeProject

ri19872-May-12 21:13

ri1987

2-May-12 21:13

using System;
using System.Data;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Emgu.CV;
using Emgu.CV.Structure;
using Emgu.Util;
using System.Drawing;
namespace SiftLib
{
public class Sift
{

const double SIFT_INIT_SIGMA = .5;
const int SIFT_IMG_BORDER = 5;
const int SIFT_MAX_INTERP_STEPS = 5;
const int SIFT_ORI_HIST_BINS = 36;
const double SIFT_ORI_RADIUS = 3 * SIFT_ORI_SIG_FCTR;
const double SIFT_ORI_SIG_FCTR = 1.5;
const int SIFT_ORI_SMOOTH_PASSES = 2;
const double SIFT_ORI_PEAK_RATIO = .8;
const double SIFT_DESCR_SCL_FCTR = 3.0;
const double SIFT_DESCR_MAG_THR = .2;
const double SIFT_INT_DESCR_FCTR = 512.0;

const int SIFT_INTVLS = 3;
const double SIFT_SIGMA = 1.6;
const double SIFT_CONTR_THR = 0.04;
const int SIFT_CURV_THR = 10;
const int SIFT_IMG_DBL = 1;
const int SIFT_DESCR_WIDTH = 4;
const int SIFT_DESCR_HIST_BINS = 8;

public List<feature> sift_features(Image<gray, float=""> img)
{
return _sift_features(img, SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR,
SIFT_CURV_THR, SIFT_IMG_DBL, SIFT_DESCR_WIDTH,
SIFT_DESCR_HIST_BINS);
}
public List<feature> _sift_features(Image<gray, float=""> img, int intvls,
double sigma, double contr_thr, int curv_thr,
int img_dbl, int descr_width, int descr_hist_bins)
{
Image<gray, single=""> init_img;
Image<gray, single="">[,] gauss_pyr, dog_pyr;
List<feature> features;
init_img = create_init_img(img, img_dbl, sigma);
List<feature> feat = new List<feature>();
int octvs = (int)(Math.Log(Math.Min(init_img.Width, init_img.Height)) / Math.Log(2) - 2);
gauss_pyr = build_gauss_pyr(init_img, octvs, intvls, sigma);
dog_pyr = build_dog_pyr(gauss_pyr, octvs, intvls);
features = scale_space_extrema(dog_pyr, octvs, intvls, contr_thr, curv_thr);
calc_feature_scales(ref features, sigma, intvls);
if (img_dbl != 0)
adjust_for_img_dbl(ref features);

calc_feature_oris(ref features, gauss_pyr);
compute_descriptors(ref features, gauss_pyr, descr_width, descr_hist_bins);
features.Sort(new Comparison<feature>(delegate(Feature a, Feature b) { if (a.scl < b.scl)return 1; if (a.scl > b.scl) return -1; return 0; }));
int n = features.Count;
feat = features;

return feat;

}

void compute_descriptors(ref List<feature> features, Image<gray, float="">[,] gauss_pyr, int d, int n)
{
Feature feat;
detection_data ddata;
float[, ,] hist;
int i, k = features.Count;

for (i = 0; i < k; i++)
{
feat = features[i];
ddata = feat.feature_data;
hist = descr_hist(gauss_pyr[ddata.octv, ddata.intvl], ddata.r,
ddata.c, feat.ori, ddata.scl_octv, d, n);
hist_to_descr(hist, d, n, ref feat);
//release_descr_hist(&hist, d);
}
}
void hist_to_descr(float[, ,] hist, int d, int n, ref Feature feat)
{
int int_val, i, r, c, o, k = 0;
feat.descr = new double[d * d * n];

for (r = 0; r < d; r++)
for (c = 0; c < d; c++)
for (o = 0; o < n; o++)
feat.descr[k++] = hist[r, c, o];

feat.d = k;
normalize_descr(feat);
for (i = 0; i < k; i++)
if (feat.descr[i] > SIFT_DESCR_MAG_THR)
feat.descr[i] = SIFT_DESCR_MAG_THR;
normalize_descr(feat);

/* convert floating-point descriptor to integer valued descriptor */
for (i = 0; i < k; i++)
{
int_val = (int)(SIFT_INT_DESCR_FCTR * feat.descr[i]);
feat.descr[i] = Math.Min(255, int_val);
}
}
void normalize_descr(Feature feat)
{
double cur, len_inv, len_sq = 0.0;
int i, d = feat.d;

for (i = 0; i < d; i++)
{
cur = feat.descr[i];
len_sq += cur * cur;
}
len_inv = 1.0 / Math.Sqrt(len_sq);
for (i = 0; i < d; i++)
feat.descr[i] *= len_inv;
}
float[, ,] descr_hist(Image<gray, float=""> img, int r, int c, double ori,
double scl, int d, int n)
{
float[, ,] hist;
double cos_t, sin_t, hist_width, exp_denom, r_rot, c_rot, grad_mag,
grad_ori, w, rbin, cbin, obin, bins_per_rad, PI2 = 2.0 * Math.PI;
int radius, i, j;

hist = new float[d, d, n];

cos_t = Math.Cos(ori);
sin_t = Math.Sin(ori);
bins_per_rad = n / PI2;
exp_denom = d * d * 0.5;
hist_width = SIFT_DESCR_SCL_FCTR * scl;
radius = (int)(hist_width * Math.Sqrt(2) * (d + 1.0) * 0.5 + 0.5);
for (i = -radius; i <= radius; i++)
for (j = -radius; j <= radius; j++)
{
/*
Calculate sample's histogram array coords rotated relative to ori.
Subtract 0.5 so samples that fall e.g. in the center of row 1 (i.e.
r_rot = 1.5) have full weight placed in row 1 after interpolation.
*/
c_rot = (j * cos_t - i * sin_t) / hist_width;
r_rot = (j * sin_t + i * cos_t) / hist_width;
rbin = r_rot + d / 2 - 0.5;
cbin = c_rot + d / 2 - 0.5;

if (rbin > -1.0 && rbin < d && cbin > -1.0 && cbin < d)
if (calc_grad_mag_ori(img, r + i, c + j, out grad_mag, out grad_ori) != 0)
{
grad_ori -= ori;
while (grad_ori < 0.0)
grad_ori += PI2;
while (grad_ori >= PI2)
grad_ori -= PI2;

obin = grad_ori * bins_per_rad;
w = Math.Exp(-(c_rot * c_rot + r_rot * r_rot) / exp_denom);
interp_hist_entry(ref hist, rbin, cbin, obin, grad_mag * w, d, n);
}
}

return hist;
}
void interp_hist_entry(ref float[, ,] hist, double rbin, double cbin,
double obin, double mag, int d, int n)
{
float d_r, d_c, d_o, v_r, v_c, v_o;

int r0, c0, o0, rb, cb, ob, r, c, o;

r0 = (int)Math.Floor(rbin);
c0 = (int)Math.Floor(cbin);
o0 = (int)Math.Floor(obin);
d_r = (float)rbin - r0;
d_c = (float)cbin - c0;
d_o = (float)obin - o0;

/*
The entry is distributed into up to 8 bins. Each entry into a bin
is multiplied by a weight of 1 - d for each dimension, where d is the
distance from the center value of the bin measured in bin units.
*/
for (r = 0; r <= 1; r++)
{
rb = r0 + r;
if (rb >= 0 && rb < d)
{
v_r = (float)mag * ((r == 0) ? 1.0F - d_r : d_r);

for (c = 0; c <= 1; c++)
{
cb = c0 + c;
if (cb >= 0 && cb < d)
{
v_c = v_r * ((c == 0) ? 1.0F - d_c : d_c);

for (o = 0; o <= 1; o++)
{
ob = (o0 + o) % n;
v_o = v_c * ((o == 0) ? 1.0F - d_o : d_o);
hist[rb, cb, ob] += v_o;
}
}
}
}
}
}

void calc_feature_oris(ref List<feature> features, Image<gray, float="">[,] gauss_pyr)
{
Feature feat;
detection_data ddata;
double[] hist;
double omax;
int i, j, n = features.Count;

for (i = 0; i < n; i++)
{
feat = features[0];
features.RemoveAt(0);
ddata = feat.feature_data;
hist = ori_hist(gauss_pyr[ddata.octv, ddata.intvl],
ddata.r, ddata.c, SIFT_ORI_HIST_BINS,
(int)Math.Round(SIFT_ORI_RADIUS * ddata.scl_octv),
SIFT_ORI_SIG_FCTR * ddata.scl_octv);
for (j = 0; j < SIFT_ORI_SMOOTH_PASSES; j++)
smooth_ori_hist(ref hist, SIFT_ORI_HIST_BINS);
omax = dominant_ori(ref hist, SIFT_ORI_HIST_BINS);
add_good_ori_features(ref features, hist, SIFT_ORI_HIST_BINS,
omax * SIFT_ORI_PEAK_RATIO, feat);
//free( ddata );
//free( feat );
//free( hist );
}
}
void add_good_ori_features(ref List<feature> features, double[] hist, int n,
double mag_thr, Feature feat)
{
Feature new_feat;
double bin, PI2 = Math.PI * 2.0;
int l, r, i;

for (i = 0; i < n; i++)
{
l = (i == 0) ? n - 1 : i - 1;
r = (i + 1) % n;

if (hist[i] > hist[l] && hist[i] > hist[r] && hist[i] >= mag_thr)
{
bin = i + interp_hist_peak(hist[l], hist[i], hist[r]);
bin = (bin < 0) ? n + bin : (bin >= n) ? bin - n : bin;
new_feat = (Feature)feat.Clone();
new_feat.ori = ((PI2 * bin) / n) - Math.PI;
features.Add(new_feat);
//free( new_feat );
}
}
}
double interp_hist_peak(double l, double c, double r) { return 0.5 * ((l) - (r)) / ((l) - 2.0 * (c) + (r)); }
double dominant_ori(ref double[] hist, int n)
{
double omax;
int maxbin, i;

omax = hist[0];
maxbin = 0;
for (i = 1; i < n; i++)
if (hist[i] > omax)
{
omax = hist[i];
maxbin = i;
}
return omax;
}
void smooth_ori_hist(ref double[] hist, int n)
{

double prev, tmp, h0 = hist[0];
int i;

prev = hist[n - 1];
for (i = 0; i < n; i++)
{
tmp = hist[i];
hist[i] = 0.25 * prev + 0.5 * hist[i] +
0.25 * ((i + 1 == n) ? h0 : hist[i + 1]);
prev = tmp;
}
}
double[] ori_hist(Image<gray, single=""> img, int r, int c, int n, int rad, double sigma)
{
double[] hist;
double mag, ori, w, exp_denom, PI2 = Math.PI * 2.0;
int bin, i, j;

hist = new double[n];
exp_denom = 2.0 * sigma * sigma;
for (i = -rad; i <= rad; i++)
for (j = -rad; j <= rad; j++)
if (calc_grad_mag_ori(img, r + i, c + j, out mag, out ori) == 1)
{
w = Math.Exp(-(i * i + j * j) / exp_denom);
bin = (int)Math.Round(n * (ori + Math.PI) / PI2);
bin = (bin < n) ? bin : 0;
hist[bin] += w * mag;
}

return hist;
}
int calc_grad_mag_ori(Image<gray, single=""> img, int r, int c, out double mag, out double ori)
{
double dx, dy;

if (r > 0 && r < img.Height - 1 && c > 0 && c < img.Width - 1)
{
dx = img[r, c + 1].Intensity - img[r, c - 1].Intensity;
dy = img[r - 1, c].Intensity - img[r + 1, c].Intensity;
mag = Math.Sqrt(dx * dx + dy * dy);
ori = Math.Atan2(dy, dx);
return 1;
}

else
{
mag = 0;
ori = 0;
return 0;
}
}
void adjust_for_img_dbl(ref List<feature> features)
{
Feature feat;
int i, n;

n = features.Count;
for (i = 0; i < n; i++)
{
feat = features[i];
feat.x /= 2.0;
feat.y /= 2.0;
feat.scl /= 2.0;
feat.img_pt.X /= 2.0F;
feat.img_pt.Y /= 2.0F;
}
}
void calc_feature_scales(ref List<feature> features, double sigma, int intvls)
{
Feature feat;
detection_data ddata;
double intvl;
int i, n;

n = features.Count;
for (i = 0; i < n; i++)
{
feat = features[i];
intvl = feat.feature_data.intvl + feat.feature_data.subintvl;
feat.scl = sigma * Math.Pow(2.0, feat.feature_data.octv + intvl / intvls);
feat.feature_data.scl_octv = sigma * Math.Pow(2.0, intvl / intvls);
}
}

Image<gray, single=""> create_init_img(Image<gray, float=""> img, int img_dbl, double sigma)
{
Image<gray, single=""> gray;
Image<gray, single=""> dbl;
float sig_diff;

gray = convert_to_gray32(img);
if (img_dbl != 0)
{
sig_diff = (float)Math.Sqrt(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4);
dbl = new Image<gray, float="">(new Size(img.Width * 2, img.Height * 2));
dbl = gray.Resize(dbl.Width, dbl.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
dbl = dbl.SmoothGaussian(0, 0, sig_diff, sig_diff);
return dbl;
}
else
{
sig_diff = (float)Math.Sqrt(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA);
gray.SmoothGaussian(0, 0, sig_diff, sig_diff);
return gray;
}
}

Image<gray, single=""> convert_to_gray32(Image<gray, float=""> img)
{
Image<gray, byte=""> gray8;
Image<gray, single=""> gray32;

gray32 = new Image<gray, single="">(img.Width, img.Height);

using (gray8 = img.Convert<gray, byte="">())
{

gray32 = gray8.ConvertScale<single>(1.0 / 255.0, 0);
}

return gray32;
}

Image<gray, single="">[,] build_gauss_pyr(Image<gray, single=""> basepic, int octvs,
int intvls, double sigma)
{
Image<gray, single="">[,] gauss_pyr = new Image<gray, float="">[octvs, intvls + 3];
double[] sig = new double[intvls + 3];
double sig_total, sig_prev, k;
int i, o;

/*
precompute Gaussian sigmas using the following formula:

\sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
*/
sig[0] = sigma;
k = Math.Pow(2.0, 1.0 / intvls);
for (i = 1; i < intvls + 3; i++)
{
sig_prev = Math.Pow(k, i - 1) * sigma;
sig_total = sig_prev * k;
sig[i] = Math.Sqrt(sig_total * sig_total - sig_prev * sig_prev);
}

for (o = 0; o < octvs; o++)
for (i = 0; i < intvls + 3; i++)
{
if (o == 0 && i == 0)
gauss_pyr[o, i] = basepic.Clone();

/* base of new octvave is halved image from end of previous octave */
else if (i == 0)
gauss_pyr[o, i] = downsample(gauss_pyr[o - 1, intvls]);

/* blur the current octave's last image to create the next one */
else
{

gauss_pyr[o, i] = gauss_pyr[o, i - 1].SmoothGaussian(0, 0, sig[i], sig[i]);
}
}

return gauss_pyr;
}
Image<gray, single=""> downsample(Image<gray, single=""> img)
{
Image<gray, single=""> smaller;
smaller = img.Resize(img.Width / 2, img.Height / 2, Emgu.CV.CvEnum.INTER.CV_INTER_NN);

return smaller;
}

Image<gray, single="">[,] build_dog_pyr(Image<gray, single="">[,] gauss_pyr, int octvs, int intvls)
{
Image<gray, single="">[,] dog_pyr;
int i, o;

dog_pyr = new Image<gray, float="">[octvs, intvls + 2];

for (o = 0; o < octvs; o++)
for (i = 0; i < intvls + 2; i++)
{
dog_pyr[o, i] = gauss_pyr[o, i + 1].Sub(gauss_pyr[o, i]);
}

return dog_pyr;
}
List<feature> scale_space_extrema(Image<gray, single="">[,] dog_pyr, int octvs, int intvls,
double contr_thr, int curv_thr)
{
List<feature> features = new List<feature>();
double prelim_contr_thr = 0.5 * contr_thr / intvls;
Feature feat;
detection_data ddata;
int o, i, r, c;

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 (Math.Abs(dog_pyr[o, i][r, c].Intensity) > prelim_contr_thr)
if (is_extremum(dog_pyr, o, i, r, c) == 1)
{
feat = interp_extremum(dog_pyr, o, i, r, c, intvls, contr_thr);
if (feat != null)
{
ddata = feat.feature_data;
if ((is_too_edge_like(dog_pyr[ddata.octv, ddata.intvl],
ddata.r, ddata.c, curv_thr) == 0))
{
features.Insert(0, feat);//cvSeqPush( features, feat );
}

}
}

return features;
}

int is_extremum(Image<gray, single="">[,] dog_pyr, int octv, int intvl, int r, int c)
{
float val = (float)dog_pyr[octv, intvl][r, c].Intensity;
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 < dog_pyr[octv, intvl + i][r + j, c + k].Intensity)
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 > dog_pyr[octv, intvl + i][r + j, c + k].Intensity)
return 0;
}

return 1;
}
Feature interp_extremum(Image<gray, single="">[,] dog_pyr, int octv, int intvl,
int r, int c, int intvls, double contr_thr)
{
Feature feat;
detection_data ddata;
double xi = 0, xr = 0, xc = 0, contr;
int i = 0;

while (i < SIFT_MAX_INTERP_STEPS)
{
interp_step(dog_pyr, octv, intvl, r, c, out xi, out xr, out xc);
if (Math.Abs(xi) < 0.5 && Math.Abs(xr) < 0.5 && Math.Abs(xc) < 0.5)
break;

c += (int)Math.Round(xc);
r += (int)Math.Round(xr);
intvl += (int)Math.Round(xi);

if (intvl < 1 ||
intvl > intvls ||
c < SIFT_IMG_BORDER ||
r < SIFT_IMG_BORDER ||
c >= dog_pyr[octv, 0].Width - SIFT_IMG_BORDER ||
r >= dog_pyr[octv, 0].Height - SIFT_IMG_BORDER)
{
return null;
}

i++;
}

/* ensure convergence of interpolation */
if (i >= SIFT_MAX_INTERP_STEPS)
return null;

contr = interp_contr(dog_pyr, octv, intvl, r, c, xi, xr, xc);
if (Math.Abs(contr) < contr_thr / intvls)
return null;

feat = new_feature();
ddata = feat.feature_data;
feat.img_pt.X = (float)(feat.x = (c + xc) * Math.Pow(2.0, octv));
feat.img_pt.Y = (float)(feat.y = (double)((r + xr) * Math.Pow(2.0, octv)));
ddata.r = r;
ddata.c = c;
ddata.octv = octv;
ddata.intvl = intvl;
ddata.subintvl = xi;
feat.feature_data = ddata;
return feat;
}
Feature new_feature()
{
Feature feat = new Feature();
detection_data ddata = new detection_data();

feat.feature_data = ddata;
feat.type = feature_type.FEATURE_LOWE;

return feat;
}
void interp_step(Image<gray, single="">[,] dog_pyr, int octv, int intvl, int r, int c,
out double xi, out double xr, out double xc)
{
Matrix<double> dD, H, H_inv, X = new Matrix<double>(3, 1);
double[] x = new double[] { 0, 0, 0 };

dD = deriv_3D(dog_pyr, octv, intvl, r, c);
H = hessian_3D(dog_pyr, octv, intvl, r, c);
H_inv = H.Clone();

CvInvoke.cvInvert(H, H_inv.Ptr, Emgu.CV.CvEnum.INVERT_METHOD.CV_SVD);
unsafe
{
fixed (double* a = &x[0])
{
CvInvoke.cvInitMatHeader(X.Ptr, 3, 1, Emgu.CV.CvEnum.MAT_DEPTH.CV_64F, new IntPtr(a), 0x7fffffff);
}
}
CvInvoke.cvGEMM(H_inv.Ptr, dD.Ptr, -1, IntPtr.Zero, 0, X.Ptr, 0);

//cvReleaseMat( &dD );
//cvReleaseMat( &H );
//cvReleaseMat( &H_inv );

xi = x[2];
xr = x[1];
xc = x[0];
}
Matrix<double> deriv_3D(Image<gray, single="">[,] dog_pyr, int octv, int intvl, int r, int c)
{
Matrix<double> dI;
double dx, dy, ds;

dx = (dog_pyr[octv, intvl][r, c + 1].Intensity -
dog_pyr[octv, intvl][r, c - 1].Intensity) / 2.0;
dy = (dog_pyr[octv, intvl][r + 1, c].Intensity -
dog_pyr[octv, intvl][r - 1, c].Intensity) / 2.0;
ds = (dog_pyr[octv, intvl + 1][r, c].Intensity -
dog_pyr[octv, intvl - 1][r, c].Intensity) / 2.0;

dI = new Matrix<double>(3, 1);
dI[0, 0] = dx;
dI[1, 0] = dy;
dI[2, 0] = ds;

return dI;
}
Matrix<double> hessian_3D(Image<gray, single="">[,] dog_pyr, int octv, int intvl, int r, int c)
{
Matrix<double> H;
double v, dxx, dyy, dss, dxy, dxs, dys;

v = dog_pyr[octv, intvl][r, c].Intensity;
dxx = dog_pyr[octv, intvl][r, c + 1].Intensity + dog_pyr[octv, intvl][r, c - 1].Intensity - 2 * v;
dyy = dog_pyr[octv, intvl][r + 1, c].Intensity +
dog_pyr[octv, intvl][r - 1, c].Intensity - 2 * v;
dss = dog_pyr[octv, intvl + 1][r, c].Intensity +
dog_pyr[octv, intvl - 1][r, c].Intensity - 2 * v;
dxy = (dog_pyr[octv, intvl][r + 1, c + 1].Intensity -
dog_pyr[octv, intvl][r + 1, c - 1].Intensity -
dog_pyr[octv, intvl][r - 1, c + 1].Intensity +
dog_pyr[octv, intvl][r - 1, c - 1].Intensity) / 4.0;
dxs = (dog_pyr[octv, intvl + 1][r, c + 1].Intensity -
dog_pyr[octv, intvl + 1][r, c - 1].Intensity -
dog_pyr[octv, intvl - 1][r, c + 1].Intensity +
dog_pyr[octv, intvl - 1][r, c - 1].Intensity) / 4.0;
dys = (dog_pyr[octv, intvl + 1][r + 1, c].Intensity -
dog_pyr[octv, intvl + 1][r - 1, c].Intensity -
dog_pyr[octv, intvl - 1][r + 1, c].Intensity +
dog_pyr[octv, intvl - 1][r - 1, c].Intensity) / 4.0;

H = new Matrix<double>(3, 3);
H[0, 0] = dxx;
H[0, 1] = dxy;
H[0, 2] = dxs;
H[1, 0] = dxy;
H[1, 1] = dyy;
H[1, 2] = dys;
H[2, 0] = dxs;
H[2, 1] = dys;
H[2, 2] = dss;

return H;
}
double interp_contr(Image<gray, single="">[,] dog_pyr, int octv, int intvl, int r,
int c, double xi, double xr, double xc)
{
Matrix<double> dD, X = new Matrix<double>(3, 1), T = new Matrix<double>(1, 1);
double[] t = new double[1];
double[] x = new double[3] { xc, xr, xi };

unsafe
{
fixed (double* a = &x[0])
{
CvInvoke.cvInitMatHeader(X.Ptr, 3, 1, Emgu.CV.CvEnum.MAT_DEPTH.CV_64F, new IntPtr(a), 0x7fffffff);
}
}
unsafe
{
fixed (double* a = &t[0])
{
CvInvoke.cvInitMatHeader(T.Ptr, 1, 1, Emgu.CV.CvEnum.MAT_DEPTH.CV_64F, new IntPtr(a), 0x7fffffff);
}
}
dD = deriv_3D(dog_pyr, octv, intvl, r, c);
CvInvoke.cvGEMM(dD.Ptr, X.Ptr, 1, IntPtr.Zero, 0, T.Ptr, Emgu.CV.CvEnum.GEMM_TYPE.CV_GEMM_A_T);
//cvReleaseMat( &dD );

return dog_pyr[octv, intvl][r, c].Intensity + t[0] * 0.5;
}
int is_too_edge_like(Image<gray, float=""> dog_img, int r, int c, int curv_thr)
{
double d, dxx, dyy, dxy, tr, det;
/*
* BT ADDED
*
* */
if ((c == 0) || (r == 0))
return 1;
/*
* BT ENDED
* /
/* principal curvatures are computed using the trace and det of Hessian */
d = dog_img[r, c].Intensity;
dxx = dog_img[r, c + 1].Intensity + dog_img[r, c - 1].Intensity - 2 * d;
dyy = dog_img[r + 1, c].Intensity + dog_img[r - 1, c].Intensity - 2 * d;
dxy = (dog_img[r + 1, c + 1].Intensity - dog_img[r + 1, c - 1].Intensity -
dog_img[r - 1, c + 1].Intensity + dog_img[r - 1, c - 1].Intensity) / 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;
}

}
}

ri19872-May-12 21:16

ri1987

2-May-12 21:16

i put it my program but it contain error plz help me to run the program .

PIEBALDconsult2-May-12 11:05

2-May-12 11:05

Maybe you need to contact the lord of the SIFT.

Pete O'Hanlon2-May-12 11:32

Pete O'Hanlon

2-May-12 11:32

He's just discovered that SIFT happens.

*pre-emptive celebratory nipple tassle jiggle* - Sean Ewington

"Mind bleach! Send me mind bleach!" - Nagy Vilmos

CodeStash - Online Snippet Management | My blog | MoXAML PowerToys | Mole 2010 - debugging made easier

How to Override FileSystemInfo Class Readonly properties (Exists, Extension, Name, and etc)?

Member 1239536924-Apr-16 23:11

Member 12395369

24-Apr-16 23:11

Pleaaaase , how can i see the result of my features in a listBox ?

ShadowUz1-May-12 19:34

ShadowUz

1-May-12 19:34

I am using a C++ DLL, where one of the parameters is a FileSystemInfo Class.
The problem is, I am retrieving all file properties from a server.
And have to assign all the properties to the class and return it to C++ DLL.

I have tried to create my own FileSystemInfo class with {get; set} properties, but getting this error:

Cannot convert MyClass.MyFileSystemInfo to System.IO.FileSystemInfo

Re: How to Override FileSystemInfo Class Readonly properties (Exists, Extension, Name, and etc)?

egenis1-May-12 20:05

egenis

1-May-12 20:05

Is this what you are looking for?

using System.IO;
...

public class MyFileSystemInfo : FileSystemInfo
{
    public string SomeProperty { get; set; }
    public string SomeOtherProperty { get; set; }

    public override void Delete()
    {
        throw new NotImplementedException();
    }

    public override bool Exists
    {
        get { throw new NotImplementedException(); }
    }

    public override string Name
    {
        get { throw new NotImplementedException(); }
    }
}

Implement the abstract class FileSystemInfo and add the properties you require. You can then wrap the readonly properties of the base class to suit your needs?

ShowingTabs After Authentication

AmbiguousName1-May-12 19:03

AmbiguousName

1-May-12 19:03

hello guys... In my program, user will first need to enter the credentials on Tab1, in order to be able to see the rest of the tabs (and controls on it). If wrong credentials are given then upon clicking any other tab, for a fraction of second it shows the other tab (onwhich I click: say Tab3) then switches almost immediately back to first tab, although the code works fine . Here is what I have tried so far

private void OnMouseDown(object sender, MouseEventArgs e) 
{
  if (e.Button == MouseButtons.Left)
  {
      if (tabMainTabCtrl.SelectedIndex != 0)
      {
        if (IsUserAuthenticated) // this is set when user is authenticated or not
          tabMainTabCtrl.SelectedIndex = m_iSelectedTab;
        else
          tabMainTabCtrl.SelectedIndex = 0;
      }
  }
}

private void tabMainTabCtrl_SelectedIndexChanged(object sender, EventArgs e)
{
     m_iSelectedTab = tabMainTabCtrl.SelectedIndex; //m_iSelectedTab: class level global variable
}

As I said, it works fine but it seems strange. So what can be a better solution? thnx for any input.

This world is going to explode due to international politics, SOON.

modified 2-May-12 1:12am.

Re: ShowingTabs After Authentication

Eddy Vluggen2-May-12 0:02

Eddy Vluggen

2-May-12 0:02

Create the controls in the tab when the user is authenticated; that way no-one will be able to circumvent the security by toggeling a "visible" bit.

Bastard Programmer from Hell Suspicious | :suss:

embed javascript code in c#

mrx1001-May-12 12:02

mrx100

1-May-12 12:02

please i want to embed javascript code in c# windows
form application

what should i do & thanks

Re: embed javascript code in c#

Dave Kreskowiak1-May-12 13:27

Dave Kreskowiak

1-May-12 13:27

You don't, directly anyway. You cannot embed JavaScript code directly into your C# app.

You can however use any one of about a dozen technologies to interop between the two.

Have a look at this[^].

A guide to posting questions on CodeProject[^]

Dave Kreskowiak

Re: embed javascript code in c#

Abhinav S1-May-12 16:36

Abhinav S

1-May-12 16:36

No you can't do that.

how can i add a midi file in my csharp project?

SeyedRezaFatemi1-May-12 10:20

SeyedRezaFatemi

1-May-12 10:20

how can i add a midi file in my csharp project?
*.mid

Re: how can i add a midi file in my csharp project?

Richard MacCutchan1-May-12 10:41

Richard MacCutchan

1-May-12 10:41

Add it where? In the resources at build time, or in the memory at execution time?

Binding 100,000 items to a list box can be just silly regardless of what pattern you are following. Jeremy Likness

Re: how can i add a midi file in my csharp project?

egenis1-May-12 20:28

egenis

1-May-12 20:28

I think Richard gave you a good hint - resource file...

How can i specify drive specification

Fred 341-May-12 9:11

Fred 34

1-May-12 9:11

In fact i want to know within my computer which drive is a cool disk?

Wes Aday1-May-12 9:42

Wes Aday

1-May-12 9:42

faraz34 wrote:
which drive is a cool disk

The one with the sunglasses?

Why is common sense not common?
Never argue with an idiot. They will drag you down to their level where they are an expert.
Sometimes it takes a lot of work to be lazy
Please stand in front of my pistol, smile and wait for the flash - JSOP 2012

Fred 341-May-12 9:51

Fred 34

1-May-12 9:51

ok i implement your advice, from now on i will never argue with an idiot, and i ask my question fron a polite expert.

Richard MacCutchan1-May-12 10:40

Richard MacCutchan

1-May-12 10:40

Instead of being abusive try asking a sensible question.

faraz34 wrote:
i want to know within my computer which drive is a cool disk?

What does this mean cool disk? How are we expected to know what is configured in your computer?

Binding 100,000 items to a list box can be just silly regardless of what pattern you are following. Jeremy Likness

PIEBALDconsult1-May-12 10:37

1-May-12 10:37

Yet it is well understood that nothing to do with computers is cool.

Wes Aday1-May-12 10:49

Wes Aday

1-May-12 10:49

PIEBALDconsult wrote:
nothing to do with computers is cool

Well apparently there are "cool drives"... so maybe computers are coming back into being cool. Next thing you know we will have cool motherboards. I live in a hot climate so maybe a cool mouse would help.

PIEBALDconsult1-May-12 11:10

1-May-12 11:10

Wes Aday wrote:
I live in a hot climate

Oh, do you? Anywhere near Tosche Station[^]?

Wes Aday1-May-12 11:26

Wes Aday

1-May-12 11:26

PIEBALDconsult wrote:
Tosche Station

Define "near"... A bit east and south. Passed through there on my way here.

PIEBALDconsult1-May-12 11:40

1-May-12 11:40

Wes Aday wrote:
A bit east and south

Then you're probably close enough.