|
public FindFeatures ( float descriptors, Matrix |
||
| descriptors | float | The m feature descriptors to be searched from the feature tree |
| results | Matrix |
/// The results of the best |
| dist | Matrix |
/// A m x |
| k | int | The number of neighbors to find |
| emax | int | For k-d tree only: the maximum number of leaves to visit. Use 20 if not sure |
| Résultat | void | |
public void TestKDTree()
{
float[][] features = new float[10][];
for (int i = 0; i < features.Length; i++)
features[i] = new float[] { (float)i };
FeatureTree tree = new FeatureTree(features);
Matrix<Int32> result;
Matrix<double> distance;
float[][] features2 = new float[1][];
features2[0] = new float[] { 5.0f };
tree.FindFeatures(features2, out result, out distance, 1, 20);
Assert.AreEqual(result[0, 0], 5);
Assert.AreEqual(distance[0, 0], 0.0);
}
static void Run()
{
Image<Gray, Byte> modelImage = new Image<Gray, byte>("box.png");
#region extract features from the object image
MCvSURFParams param1 = new MCvSURFParams(500, false);
SURFFeature[] modelFeatures = modelImage.ExtractSURF(ref param1);
SURFFeature[] modelFeaturesPositiveLaplacian = Array.FindAll<SURFFeature>(modelFeatures, delegate(SURFFeature f) { return f.Point.laplacian >= 0; });
SURFFeature[] modelFeaturesNegativeLaplacian = Array.FindAll<SURFFeature>(modelFeatures, delegate(SURFFeature f) { return f.Point.laplacian < 0; });
//Create feature trees for the given features
FeatureTree featureTreePositiveLaplacian = new FeatureTree(
Array.ConvertAll<SURFFeature, Matrix<float>>(
modelFeaturesPositiveLaplacian,
delegate(SURFFeature f) { return f.Descriptor; }));
FeatureTree featureTreeNegativeLaplacian = new FeatureTree(
Array.ConvertAll<SURFFeature, Matrix<float>>(
modelFeaturesNegativeLaplacian,
delegate(SURFFeature f) { return f.Descriptor; }));
#endregion
Image<Gray, Byte> observedImage = new Image<Gray, byte>("box_in_scene.png");
#region extract features from the observed image
MCvSURFParams param2 = new MCvSURFParams(500, false);
SURFFeature[] imageFeatures = observedImage.ExtractSURF(ref param2);
SURFFeature[] imageFeaturesPositiveLaplacian = Array.FindAll<SURFFeature>(imageFeatures, delegate(SURFFeature f) { return f.Point.laplacian >= 0; });
SURFFeature[] imageFeaturesNegativeLaplacian = Array.FindAll<SURFFeature>(imageFeatures, delegate(SURFFeature f) { return f.Point.laplacian < 0; });
#endregion
#region Merge the object image and the observed image into one image for display
Image<Gray, Byte> res = new Image<Gray, byte>(Math.Max(modelImage.Width, observedImage.Width), modelImage.Height + observedImage.Height);
res.ROI = new System.Drawing.Rectangle(0, 0, modelImage.Width, modelImage.Height);
modelImage.Copy(res, null);
res.ROI = new System.Drawing.Rectangle(0, modelImage.Height, observedImage.Width, observedImage.Height);
observedImage.Copy(res, null);
res.ROI = Rectangle.Empty;
#endregion
double matchDistanceRatio = 0.8;
List<PointF> modelPoints = new List<PointF>();
List<PointF> observePoints = new List<PointF>();
#region using Feature Tree to match feature
Matrix<float>[] imageFeatureDescriptorsPositiveLaplacian = Array.ConvertAll<SURFFeature, Matrix<float>>(
imageFeaturesPositiveLaplacian,
delegate(SURFFeature f) { return f.Descriptor; });
Matrix<float>[] imageFeatureDescriptorsNegativeLaplacian = Array.ConvertAll<SURFFeature, Matrix<float>>(
imageFeaturesNegativeLaplacian,
delegate(SURFFeature f) { return f.Descriptor; });
Matrix<Int32> result1;
Matrix<double> dist1;
featureTreePositiveLaplacian.FindFeatures(imageFeatureDescriptorsPositiveLaplacian, out result1, out dist1, 2, 20);
MatchSURFFeatureWithFeatureTree(
modelFeaturesPositiveLaplacian,
imageFeaturesPositiveLaplacian,
matchDistanceRatio, result1.Data, dist1.Data, modelPoints, observePoints);
featureTreeNegativeLaplacian.FindFeatures(imageFeatureDescriptorsNegativeLaplacian, out result1, out dist1, 2, 20);
MatchSURFFeatureWithFeatureTree(
modelFeaturesNegativeLaplacian,
imageFeaturesNegativeLaplacian,
matchDistanceRatio, result1.Data, dist1.Data, modelPoints, observePoints);
#endregion
Matrix<float> homographyMatrix = CameraCalibration.FindHomography(
modelPoints.ToArray(), //points on the object image
observePoints.ToArray(), //points on the observed image
HOMOGRAPHY_METHOD.RANSAC,
3).Convert<float>();
#region draw the projected object in observed image
for (int i = 0; i < modelPoints.Count; i++)
{
PointF p = observePoints[i];
p.Y += modelImage.Height;
res.Draw(new LineSegment2DF(modelPoints[i], p), new Gray(0), 1);
}
System.Drawing.Rectangle rect = modelImage.ROI;
Matrix<float> orginalCornerCoordinate = new Matrix<float>(new float[,]
{{ rect.Left, rect.Bottom, 1.0f},
{ rect.Right, rect.Bottom, 1.0f},
{ rect.Right, rect.Top, 1.0f},
{ rect.Left, rect.Top, 1.0f}});
Matrix<float> destCornerCoordinate = homographyMatrix * orginalCornerCoordinate.Transpose();
float[,] destCornerCoordinateArray = destCornerCoordinate.Data;
Point[] destCornerPoints = new Point[4];
for (int i = 0; i < destCornerPoints.Length; i++)
{
float denominator = destCornerCoordinateArray[2, i];
destCornerPoints[i] = new Point(
(int)(destCornerCoordinateArray[0, i] / denominator),
(int)(destCornerCoordinateArray[1, i] / denominator) + modelImage.Height);
}
res.DrawPolyline(destCornerPoints, true, new Gray(255.0), 5);
#endregion
ImageViewer.Show(res);
}