/**
* Detect scale-invariant feature points given a pyramid.
* @param _i_dog_feature_points
* 検出したDOG特徴点
*/
public void detect(GaussianScaleSpacePyramid i_pyramid, DogFeaturePointStack i_dog_feature_points)
{
//clean up 1st feature stack
DogFeaturePointStack tmp_fp = this._tmp_fps;
tmp_fp.clear();
// Compute Laplacian images (DoG)
this.mLaplacianPyramid.compute(i_pyramid);
// Detect minima and maximum in Laplacian images
this.extractFeatures(i_pyramid, this.mLaplacianPyramid, tmp_fp);
// Sub-pixel refinement
this.findSubpixelLocations(i_pyramid, tmp_fp);
// Compute the gradient pyramid
this.mOrientationAssignment.computeGradients(i_pyramid);
AreaBuckit abuckit = this.mBuckets;
if (tmp_fp.getLength() <= abuckit._buckit.Length)
{
//特徴点の数が要求数以下なら全てのポイントを使う。
for (int i = 0; i < tmp_fp.getLength(); i++)
{
this.addFeatureOrientations(i_pyramid, tmp_fp.getItem(i), i_dog_feature_points);
}
}
else
{
//特徴点を選別(Prune features)
// Clear the previous state
abuckit.clear();
// Insert each features into a bucket
for (int i = 0; i < tmp_fp.getLength(); i++)
{
DogFeaturePoint p = tmp_fp.getItem(i);
abuckit.put(p.x, p.y, i, Math.Abs(p.score));
}
// Compute an orientation for each feature point
for (int i = 0; i < abuckit._buckit.Length; i++)
{
if (abuckit._buckit[i].first == 0)
{
continue;
}
this.addFeatureOrientations(i_pyramid, tmp_fp.getItem(abuckit._buckit[i].second), i_dog_feature_points);
}
}
return;
}
/**
* Sub-pixel refinement.
*/
private void findSubpixelLocations(GaussianScaleSpacePyramid pyramid, DogFeaturePointStack i_dog_fp)
{
int num_points;
double laplacianSqrThreshold;
double hessianThreshold;
num_points = 0;
laplacianSqrThreshold = (this.mLaplacianThreshold * this.mLaplacianThreshold);
double te = (mEdgeThreshold + 1);
hessianThreshold = ((te * te) / mEdgeThreshold);
for (int i = 0; i < i_dog_fp.getLength(); i++)
{
DogFeaturePoint kp = i_dog_fp.getItem(i);
//assert kp.scale < mLaplacianPyramid.numScalePerOctave();
// ASSERT(kp.scale < mLaplacianPyramid.numScalePerOctave(),
// "Feature point scale is out of bounds");
int lap_index = kp.octave * mLaplacianPyramid.numScalePerOctave() + kp.scale;
// Get Laplacian images
LaplacianImage lap0 = mLaplacianPyramid.get(lap_index - 1);
LaplacianImage lap1 = mLaplacianPyramid.get(lap_index);
LaplacianImage lap2 = mLaplacianPyramid.get(lap_index + 1);
// Compute the Hessian
if (!this.updateLocation(kp, lap0, lap1, lap2))
{
continue;
}
if (Math.Abs(kp.edge_score) < hessianThreshold && (kp.score * kp.score) >= laplacianSqrThreshold &&
kp.x >= 0 && kp.x < mLaplacianPyramid.get(0).getWidth() && kp.y >= 0 &&
kp.y < mLaplacianPyramid.get(0).getHeight())
{
// Update the sigma
kp.sigma = pyramid.effectiveSigma(kp.octave, kp.sp_scale);
i_dog_fp.swap(i, num_points++);
}
}
i_dog_fp.setLength(num_points);
}
/**
* Extract the descriptors for all the feature points.
*/
private void ExtractFREAK84(FreakFeaturePointStack store,
GaussianScaleSpacePyramid pyramid, DogFeaturePointStack points,
double[] points_ring0, double[] points_ring1, double[] points_ring2,
double[] points_ring3, double[] points_ring4, double[] points_ring5,
double sigma_center, double sigma_ring0, double sigma_ring1,
double sigma_ring2, double sigma_ring3, double sigma_ring4,
double sigma_ring5, double expansion_factor)
{
// ASSERT(pyramid, "Pyramid is NULL");
// ASSERT(store.size() == points.size(),
// "Feature store has not been allocated");
for (int i = 0; i < points.getLength(); i++)
{
FreakFeaturePoint sp = store.prePush();
if (sp == null)
{
prepush_wawning();
break;
}
DogFeaturePoint pt = points.getItem(i);
if (!ExtractFREAK84(sp.descripter,
pyramid, pt, points_ring0, points_ring1,
points_ring2, points_ring3, points_ring4, points_ring5,
sigma_center, sigma_ring0, sigma_ring1, sigma_ring2,
sigma_ring3, sigma_ring4, sigma_ring5, expansion_factor
))
{
store.pop();
continue;
}
sp.angle = pt.angle;
sp.x = pt.x;
sp.y = pt.y;
sp.scale = pt.sigma;
sp.maxima = pt.score > 0;
// store.point(num_points).set(points[i]);
}
}
