private void add_good_ori_features(ref List<Feature> features, double[] hist, int n,
double magThr, Feature 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] >= magThr)
{
bin = i + interp_hist_peak(hist[l], hist[i], hist[r]);
bin = (bin < 0) ? n + bin : (bin >= n) ? bin - n : bin;
var newFeat = (Feature) feat.Clone();
newFeat.ori = ((pi2*bin)/n) - Math.PI;
features.Add(newFeat);
}
}
}
public object Clone()
{
Feature feat = new Feature();
feat.a = a;
feat.b = b;
feat.bck_match = bck_match;
feat.c = c;
feat.category = category;
feat.d = d;
feat.descr = (double[]) descr.Clone();
feat.feature_data = (detection_data)feature_data.Clone();
feat.fwd_match = fwd_match;
feat.img_pt = img_pt;
feat.mdl_match = mdl_match;
feat.mdl_pt = mdl_pt;
feat.ori = ori;
feat.scl = scl;
feat.type = type;
feat.x = x;
feat.y = y;
return feat;
}
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;
}
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 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);
}
}
public object Clone()
{
Feature feat = new Feature
{
a = a,
b = b,
bck_match = bck_match,
c = c,
category = category,
d = d,
descr = (double[]) descr.Clone(),
feature_data = (DetectionData) feature_data.Clone(),
fwd_match = fwd_match,
img_pt = img_pt,
mdl_match = mdl_match,
mdl_pt = mdl_pt,
ori = ori,
scl = scl,
type = type,
x = x,
y = y
};
return feat;
}
