/**
* 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;
}
public static NyARNftFreakFsetFile genFeatureSet3(NyARNftIsetFile i_iset_file)
{
int max_features = 500;
DogFeaturePointStack _dog_feature_points = new DogFeaturePointStack(max_features);
FreakFeaturePointStack query_keypoint = new FreakFeaturePointStack(max_features);
//
List <NyARNftFreakFsetFile.RefDataSet> refdataset = new List <NyARNftFreakFsetFile.RefDataSet>();
List <NyARNftFreakFsetFile.ImageInfo> imageinfo = new List <NyARNftFreakFsetFile.ImageInfo>();
for (int ii = 0; ii < i_iset_file.items.Length; ii++)
{
NyARNftIsetFile.ReferenceImage rimg = i_iset_file.items[ii];
FREAKExtractor mFeatureExtractor = new FREAKExtractor();
int octerves = BinomialPyramid32f.octavesFromMinimumCoarsestSize(rimg.width, rimg.height, 8);
BinomialPyramid32f _pyramid = new BinomialPyramid32f(rimg.width, rimg.height, octerves, 3);
DoGScaleInvariantDetector _dog_detector = new DoGScaleInvariantDetector(rimg.width, rimg.height, octerves, 3, 3, 4, max_features);
//RefDatasetの作成
_pyramid.build(NyARGrayscaleRaster.createInstance(rimg.width, rimg.height, NyARBufferType.INT1D_GRAY_8, rimg.img));
// Detect feature points
_dog_feature_points.clear();
_dog_detector.detect(_pyramid, _dog_feature_points);
// Extract features
query_keypoint.clear();
mFeatureExtractor.extract(_pyramid, _dog_feature_points, query_keypoint);
for (int i = 0; i < query_keypoint.getLength(); i++)
{
FreakFeaturePoint ffp = query_keypoint.getItem(i);
NyARNftFreakFsetFile.RefDataSet rds = new NyARNftFreakFsetFile.RefDataSet();
rds.pageNo = 1;
rds.refImageNo = ii;
rds.coord2D.setValue(ffp.x, ffp.y);
rds.coord3D.setValue((ffp.x + 0.5f) / rimg.dpi * 25.4f, ((rimg.height - 0.5f) - ffp.y) / rimg.dpi * 25.4f);
rds.featureVec.angle = ffp.angle;
rds.featureVec.maxima = ffp.maxima ? 1 : 0;
rds.featureVec.scale = ffp.scale;
ffp.descripter.getValueLe(rds.featureVec.v);
refdataset.Add(rds);
}
imageinfo.Add(new NyARNftFreakFsetFile.ImageInfo(rimg.width, rimg.height, ii));
}
NyARNftFreakFsetFile.PageInfo[] pi = new NyARNftFreakFsetFile.PageInfo[1];
pi[0] = new NyARNftFreakFsetFile.PageInfo(1, imageinfo.ToArray());
return(new NyARNftFreakFsetFile(refdataset.ToArray(), pi));
}
