protected override void DisposeObject()
{
if (_modelKeypoints != null)
{
_modelKeypoints.Dispose();
_modelKeypoints = null;
}
if (_modelDescriptors != null)
{
_modelDescriptors.Dispose();
_modelDescriptors = null;
}
if (_modelDescriptorMatcher != null)
{
_modelDescriptorMatcher.Dispose();
_modelDescriptorMatcher = null;
}
if (_octagon != null)
{
_octagon.Dispose();
_octagon = null;
}
}
/// <summary>
/// Match feature points using symmetry test and RANSAC
/// </summary>
/// <param name="image1">input image1</param>
/// <param name="image2">input image2</param>
/// <param name="keypoints1">output keypoint1</param>
/// <param name="keypoints2">output keypoint2</param>
/// <returns>return fundemental matrix</returns>
public Image <Bgr, Byte> Match(Image <Gray, Byte> image1, Image <Gray, Byte> image2,
ref VectorOfKeyPoint keypoints1, ref VectorOfKeyPoint keypoints2, bool computeModelFeatures)
{
//1a. Detection of the SURF features
keypoints2 = null;
if (computeModelFeatures == true)
{
keypoints1 = this._Detector.DetectKeyPointsRaw(image1, null);
}
keypoints2 = this._Detector.DetectKeyPointsRaw(image2, null);
//1b. Extraction of the SURF descriptors
Matrix <float> descriptors1 = this._Detector.ComputeDescriptorsRaw(image1, null, keypoints1);
Matrix <float> descriptors2 = this._Detector.ComputeDescriptorsRaw(image2, null, keypoints2);
//2. Match the two image descriptors
//Construction of the match
BruteForceMatcher <float> matcher = new BruteForceMatcher <float>(DistanceType.L2);
//from image 1 to image 2
//based on k nearest neighbours (with k=2)
matcher.Add(descriptors1);
//Number of nearest neighbors to search for
int k = 2;
int n = descriptors2.Rows;
//The resulting n*k matrix of descriptor index from the training descriptors
Matrix <int> trainIdx1 = new Matrix <int>(n, k);
//The resulting n*k matrix of distance value from the training descriptors
Matrix <float> distance1 = new Matrix <float>(n, k);
matcher.KnnMatch(descriptors2, trainIdx1, distance1, k, null);
matcher.Dispose();
//from image 1 to image 2
matcher = new BruteForceMatcher <float>(DistanceType.L2);
matcher.Add(descriptors2);
n = descriptors1.Rows;
//The resulting n*k matrix of descriptor index from the training descriptors
Matrix <int> trainIdx2 = new Matrix <int>(n, k);
//The resulting n*k matrix of distance value from the training descriptors
Matrix <float> distance2 = new Matrix <float>(n, k);
matcher.KnnMatch(descriptors1, trainIdx2, distance2, k, null);
//3. Remove matches for which NN ratio is > than threshold
int removed = RatioTest(ref trainIdx1, ref distance1);
removed = RatioTest(ref trainIdx2, ref distance2);
//4. Create symmetrical matches
Matrix <float> symMatches;
int symNumber = SymmetryTest(trainIdx1, distance1, trainIdx2, distance2, out symMatches);
//--------------modified code for zero matches------------
if (symNumber == 0) // no proper symmetrical matches, should retry in this case
{
return(null);
}
//-----------------end modified code----------------------
Matrix <double> fundementalMatrix = ApplyRANSAC(symMatches, keypoints1, keypoints2, symNumber);//, image2);
// Image<Bgr, Byte> resultImage = Features2DToolbox.DrawMatches(image1, modelKeyPoints, image2, observedKeyPoints,
//indices, new Bgr(255, 0, 0), new Bgr(0, 255, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
// return resultImage;
return(null); // we do our own drawing of correspondences
}
