public FreakKeypointMatching(NyARParam i_ref_cparam)
{
NyARIntSize size = i_ref_cparam.getScreenSize();
this._ref_cparam = i_ref_cparam;
this.mFeatureExtractor = new FREAKExtractor();
int octerves = BinomialPyramid32f.octavesFromMinimumCoarsestSize(size.w, size.h, kMinCoarseSize);
this._pyramid = new BinomialPyramid32f(size.w, size.h, octerves, 3);
this._dog_detector = new DoGScaleInvariantDetector(size.w, size.h, octerves, 3, kLaplacianThreshold, kEdgeThreshold, kMaxNumFeatures);
this.mMinNumInliers = kMinNumInliers;
//
this._tmp_pair_stack[0] = new FeaturePairStack(kMaxNumFeatures);
this._tmp_pair_stack[1] = new FeaturePairStack(kMaxNumFeatures);
this._find_inliner = new FindInliers_O1(kHomographyInlierThreshold);
double dx = size.w + (size.w * 0.2f);
double dy = size.h + (size.h * 0.2f);
this.mHoughSimilarityVoting = new HoughSimilarityVoting_O3(-dx, dx, -dy, dy, 12, 10);
this._matcher = new BinaryHirerarchialClusteringMatcher();
}
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));
}