/// <summary>
/// Find best matches for each query descriptor which have distance less than
/// maxDistance (in increasing order of distances).
/// </summary>
/// <param name="queryDescriptors"></param>
/// <param name="maxDistance"></param>
/// <param name="masks"></param>
/// <param name="compactResult"></param>
/// <returns></returns>
public DMatch[][] RadiusMatch(Mat queryDescriptors, float maxDistance, Mat[] masks = null, bool compactResult = false)
{
ThrowIfDisposed();
if (queryDescriptors == null)
{
throw new ArgumentNullException(nameof(queryDescriptors));
}
var masksPtrs = new IntPtr[0];
if (masks != null)
{
masksPtrs = EnumerableEx.SelectPtrs(masks);
}
using (var matchesVec = new VectorOfVectorDMatch())
{
NativeMethods.features2d_DescriptorMatcher_radiusMatch2(
ptr, queryDescriptors.CvPtr, matchesVec.CvPtr, maxDistance,
masksPtrs, masksPtrs.Length, compactResult ? 1 : 0);
GC.KeepAlive(this);
GC.KeepAlive(queryDescriptors);
GC.KeepAlive(masks);
return(matchesVec.ToArray());
}
}
/// <summary>
/// Find k best matches for each query descriptor (in increasing order of distances).
/// compactResult is used when mask is not empty. If compactResult is false matches
/// vector will have the same size as queryDescriptors rows. If compactResult is true
/// matches vector will not contain matches for fully masked out query descriptors.
/// </summary>
/// <param name="queryDescriptors"></param>
/// <param name="k"></param>
/// <param name="masks"></param>
/// <param name="compactResult"></param>
/// <returns></returns>
public DMatch[][] KnnMatch(Mat queryDescriptors, int k, Mat[]?masks = null, bool compactResult = false)
{
ThrowIfDisposed();
if (queryDescriptors == null)
{
throw new ArgumentNullException(nameof(queryDescriptors));
}
var masksPtrs = Array.Empty <IntPtr>();
if (masks != null)
{
masksPtrs = masks.Select(x => x.CvPtr).ToArray();
}
using var matchesVec = new VectorOfVectorDMatch();
NativeMethods.HandleException(
NativeMethods.features2d_DescriptorMatcher_knnMatch2(
ptr, queryDescriptors.CvPtr, matchesVec.CvPtr, k,
masksPtrs, masksPtrs.Length, compactResult ? 1 : 0));
GC.KeepAlive(this);
GC.KeepAlive(queryDescriptors);
GC.KeepAlive(masks);
return(matchesVec.ToArray());
}
/// <summary>
/// Find k best matches for each query descriptor (in increasing order of distances).
/// compactResult is used when mask is not empty. If compactResult is false matches
/// vector will have the same size as queryDescriptors rows. If compactResult is true
/// matches vector will not contain matches for fully masked out query descriptors.
/// </summary>
/// <param name="queryDescriptors"></param>
/// <param name="trainDescriptors"></param>
/// <param name="k"></param>
/// <param name="mask"></param>
/// <param name="compactResult"></param>
/// <returns></returns>
public DMatch[][] KnnMatch(Mat queryDescriptors, Mat trainDescriptors,
int k, Mat? mask = null, bool compactResult = false)
{
ThrowIfDisposed();
if (queryDescriptors == null)
throw new ArgumentNullException(nameof(queryDescriptors));
if (trainDescriptors == null)
throw new ArgumentNullException(nameof(trainDescriptors));
using var matchesVec = new VectorOfVectorDMatch();
NativeMethods.HandleException(
NativeMethods.features2d_DescriptorMatcher_knnMatch1(
ptr, queryDescriptors.CvPtr, trainDescriptors.CvPtr,
matchesVec.CvPtr, k, Cv2.ToPtr(mask), compactResult ? 1 : 0));
GC.KeepAlive(this);
GC.KeepAlive(queryDescriptors);
GC.KeepAlive(trainDescriptors);
GC.KeepAlive(mask);
return matchesVec.ToArray();
}
/// <summary>
/// Find best matches for each query descriptor which have distance less than
/// maxDistance (in increasing order of distances).
/// </summary>
/// <param name="queryDescriptors"></param>
/// <param name="trainDescriptors"></param>
/// <param name="maxDistance"></param>
/// <param name="mask"></param>
/// <param name="compactResult"></param>
/// <returns></returns>
public DMatch[][] RadiusMatch(Mat queryDescriptors, Mat trainDescriptors,
float maxDistance, Mat mask = null, bool compactResult = false)
{
ThrowIfDisposed();
if (queryDescriptors == null)
{
throw new ArgumentNullException("queryDescriptors");
}
if (trainDescriptors == null)
{
throw new ArgumentNullException("trainDescriptors");
}
using (var matchesVec = new VectorOfVectorDMatch())
{
NativeMethods.features2d_DescriptorMatcher_radiusMatch1(
ptr, queryDescriptors.CvPtr, trainDescriptors.CvPtr,
matchesVec.CvPtr, maxDistance, Cv2.ToPtr(mask), compactResult ? 1 : 0);
return(matchesVec.ToArray());
}
}
/// <summary>
/// Find k best matches for each query descriptor (in increasing order of distances).
/// compactResult is used when mask is not empty. If compactResult is false matches
/// vector will have the same size as queryDescriptors rows. If compactResult is true
/// matches vector will not contain matches for fully masked out query descriptors.
/// </summary>
/// <param name="queryDescriptors"></param>
/// <param name="k"></param>
/// <param name="masks"></param>
/// <param name="compactResult"></param>
/// <returns></returns>
public DMatch[][] KnnMatch(Mat queryDescriptors, int k, Mat[] masks = null, bool compactResult = false)
{
ThrowIfDisposed();
if (queryDescriptors == null)
{
throw new ArgumentNullException("queryDescriptors");
}
var masksPtrs = new IntPtr[0];
if (masks != null)
{
masksPtrs = EnumerableEx.SelectPtrs(masks);
}
using (var matchesVec = new VectorOfVectorDMatch())
{
NativeMethods.features2d_DescriptorMatcher_knnMatch2(
ptr, queryDescriptors.CvPtr, matchesVec.CvPtr, k,
masksPtrs, masksPtrs.Length, compactResult ? 1 : 0);
return(matchesVec.ToArray());
}
}
