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));
}
private bool query(FreakFeaturePointStack query_keyframe, KeyframeMap i_keymap, FeaturePairStack i_result)
{
// mMatchedInliers.clear();
HomographyMat H = this._H;
InverseHomographyMat hinv = this._hinv;
hinv = new InverseHomographyMat_O1();
int num_of_query_frame = query_keyframe.getLength();
//ワークエリアの設定
if (num_of_query_frame > this._tmp_pair_stack[0].getArraySize())
{
this._tmp_pair_stack[0] = new FeaturePairStack(num_of_query_frame + 10);
this._tmp_pair_stack[1] = new FeaturePairStack(num_of_query_frame + 10);
}
int tmp_ch = 0;
int last_inliers = 0;
foreach (KeyValuePair <int, Keyframe> i in i_keymap)
{
Keyframe second = i.Value;
FreakMatchPointSetStack ref_points = second.getFeaturePointSet();
//新しいワークエリアを作る。
FeaturePairStack match_result = this._tmp_pair_stack[tmp_ch];
//ワークエリア初期化
match_result.clear();
//特徴量同士のマッチング
if (this._matcher.match(query_keyframe, second, match_result) < this.mMinNumInliers)
{
continue;
}
// Vote for a transformation based on the correspondences
if (!this.mHoughSimilarityVoting.extractMatches(match_result, second.width(), second.height()))
{
continue;
}
// Estimate the transformation between the two images
if (!this.mRobustHomography.PreemptiveRobustHomography(H, match_result, second.width(), second.height()))
{
continue;
}
//ここでHInv計算
if (!hinv.inverse(H))
{
continue;
}
// Apply some heuristics to the homography
if (!hinv.checkHomographyHeuristics(second.width(), second.height()))
{
continue;
}
// Find the inliers
this._find_inliner.extructMatches(H, match_result);
if (match_result.getLength() < mMinNumInliers)
{
continue;
}
//
// Use the estimated homography to find more inliers
match_result.clear();
if (_matcher.match(query_keyframe, ref_points, hinv, 10, match_result) < mMinNumInliers)
{
continue;
}
//
// Vote for a similarity with new matches
if (!this.mHoughSimilarityVoting.extractMatches(match_result, second.width(), second.height()))
{
continue;
}
//
// Re-estimate the homography
if (!this.mRobustHomography.PreemptiveRobustHomography(H, match_result, second.width(), second.height()))
{
continue;
}
// Apply some heuristics to the homography
if (!hinv.inverse(H))
{
continue;
}
if (!hinv.checkHomographyHeuristics(second.width(), second.height()))
{
continue;
}
//
// Check if this is the best match based on number of inliers
this._find_inliner.extructMatches(H, match_result);
//ポイント数が最小値より大きい&&最高成績ならテンポラリチャンネルを差し替える。
if (match_result.getLength() >= mMinNumInliers && match_result.getLength() > last_inliers)
{
//出力チャンネルを切り替え
tmp_ch = (tmp_ch + 1) % 2;
last_inliers = match_result.getLength();
}
}
//出力は last_inlines>0の場合に[(tmp_ch+1)%2]にある。
if (last_inliers <= 0)
{
return(false);
}
{
FeaturePairStack match_result = this._tmp_pair_stack[(tmp_ch + 1) % 2];
FeaturePairStack.Item[] dest = match_result.getArray();
for (int i = 0; i < match_result.getLength(); i++)
{
FeaturePairStack.Item t = i_result.prePush();
if (t == null)
{
System.Console.WriteLine("Push overflow!");
break;
}
t.query = dest[i].query;
t.ref_ = dest[i].ref_;
}
}
return(true);
}
