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In my example, wb_map is the WebBrowser control on the Form. You need to replace it with the WebBrowser control in your application.
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loyal ginger wrote: In my example, wb_map is the WebBrowser control on the Form. You need to replace it with the WebBrowser control in your application
of course i replace wb_map by webBrowser1 control but i have error i don't know what causes this error
object reference not set to an instance of an object ????!!!!!!!!!!!
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Sorry I might have misunderstood you. In that case I really have no clue what went wrong. My original reply to your question was based on my error-free application, which happens to be very similar to your program. I don't have a complete picture of your program so it's hard for me to trouble shoot your program. Maybe somebody else here can give you a better answer? Thanks for your patience!
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when i put this line of code in webbrowser control it works thanks for all for answers 
if (e.Url.Equals(webBrowser1.Url))
{
webBrowser1.Document.InvokeScript("initialize", new object[] { latitude, longitude });
}
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Hi,
I need help in writing code on establish a PPTP (VPN Tunnel)connection in c#.
Thanks
Kalyan
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What sort of help? You need to be much more specific with your questions. Nobody here is going to write your application for you but they will help you if you have problems with particular parts of your own code. If you have no idea how to approach this project then Google is your friend.
Binding 100,000 items to a list box can be just silly regardless of what pattern you are following. Jeremy Likness
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Hi,
In my code, I am counting the number of records (sent emails) that are older than one month. In order to do that, I am using following snippet.
int totalSentEmailsOlderThanMonth = emailSendingService.ListSentEmails().ToArray().Count(x => (DateTime.Now - x.SentDateTime).TotalDays > 30);
The reason I am using .ToArray() in my code as (DateTime.Now - x.SentDateTime).TotalDays cannot be translated in LINQ to SQL query so, by calling ToArray(), I am loading all records into the memory and then counting. But there are more than hundreds of records in my database and loading all the records into the memory just for counting is not efficient at all. So, would anyone please give me an alternative more efficient snippet that can be used in this scenario.
[[ By the way, emailSendingService.ListSentEmails() returns IQueryable<MyRecordType> ]]
Regards.
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You need to re-write the predicate a little bit (I don't have time to check this, so consider it not-very psuedo psuedo code) and it might error:
DateTime latestSendDate = DateTime.Now.AddDays(-30);
int totalSentEmailsOlderThanMonth = emailSendingService.ListSentEmails().Count(x => x.SentDateTime <= latestSendDate);
This removes the "calculation" from the LINQ query count predicate and puts it into a format it should be able to generate SQL from. There may be an out by one error (you get 29/31 days).
I think this will work, as I say, I don't have time to check it. Let me know if there are any problems.
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Owe, thats the cute idea. Thanks a loooot. Sure, it is a working code and it is working perfectly.
Thank you very much for your help.
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Hi Every one .. please if u could help me in write a program of SIFT algorithem in image processing i need it plz
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Try something and then post your questions here. No one is going to help you write any program from scratch.
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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;
}
}
}
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i put it my program but it contain error plz help me to run the program .
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Maybe you need to contact the lord of the SIFT.
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He's just discovered that SIFT happens.
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Pleaaaase , how can i see the result of my features in a listBox ?
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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
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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?
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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)
tabMainTabCtrl.SelectedIndex = m_iSelectedTab;
else
tabMainTabCtrl.SelectedIndex = 0;
}
}
}
private void tabMainTabCtrl_SelectedIndexChanged(object sender, EventArgs e)
{
m_iSelectedTab = tabMainTabCtrl.SelectedIndex;
}
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.
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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 
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please i want to embed javascript code in c# windows
form application
what should i do & thanks
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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[^].
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