private void addFeatureOrientations(GaussianScaleSpacePyramid i_pyramid, DogFeaturePoint dfp, DogFeaturePointStack i_ot_fps)
{
double[] tmp = this._addFeatureOrientations_tmp;
double x, y, s;
// Down sample the point to the detected octave
bilinear_downsample_point(tmp, dfp.x, dfp.y, dfp.sigma, dfp.octave);
x = tmp[0];
y = tmp[1];
s = tmp[2];
// Downsampling the point can cause (x,y) to leave the image bounds
// by
// a tiny amount. Here we just clip it to be within the image
// bounds.
x = ClipScalar(x, 0, i_pyramid.get(dfp.octave, 0).getWidth() - 1);
y = ClipScalar(y, 0, i_pyramid.get(dfp.octave, 0).getHeight() - 1);
// Compute dominant orientations
int num_angles = mOrientationAssignment.compute(dfp.octave, dfp.scale, x, y, s, this.mOrientations);
// Create a feature point for each angle
for (int j = 0; j < num_angles; j++)
{
// Copy the feature point
DogFeaturePoint fp = i_ot_fps.prePush();
if (fp == null)
{
//中断
// prepush_warning();
break;
}
fp.x = dfp.x;
fp.y = dfp.y;
fp.octave = dfp.octave;
fp.scale = dfp.scale;
fp.sp_scale = dfp.sp_scale;
fp.score = dfp.score;
fp.sigma = dfp.sigma;
fp.edge_score = dfp.edge_score;
fp.angle = mOrientations[j];
}
return;
}
/**
* Sample a receptor given (x,y,octave,scale) and a pyramid.
*/
private double SampleReceptor(GaussianScaleSpacePyramid pyramid, double x, double y, int octave, int scale)
{
// Get the correct image from the pyramid
KpmImage image = pyramid.get(octave, scale);
double a = 1.0f / (1 << octave);
double b = 0.5f * a - 0.5f;
return(image.bilinearInterpolation(ClipScalar(x * a + b, 0, image.getWidth() - 2), ClipScalar(y * a + b, 0, image.getHeight() - 2)));
}
/**
* Compute the Difference-of-Gaussian from a Gaussian Pyramid.
*/
public void compute(GaussianScaleSpacePyramid pyramid)
{
for (int i = 0; i < this.mNumOctaves; i++)
{
for (int j = 0; j < this.mNumScalesPerOctave; j++)
{
this.mImages[i * mNumScalesPerOctave + j].difference_image_binomial(pyramid.get(i, j), pyramid.get(i, j + 1));
}
}
}