Class GPCForest5

Namespace

OpenCvSharp.OptFlow

Assembly

OpenCvSharp.dll

Class for computing the correspondences between two images using the Global Patch Collider (GPC) algorithm.

public class GPCForest5 : Algorithm, IDisposable

Inheritance

object

CvObject

CvPtrObject

Algorithm

GPCForest5

Implements

IDisposable

Inherited Members

Algorithm.Write(FileStorage)

Algorithm.Read(FileNode)

Algorithm.Empty

Algorithm.Save(string)

Algorithm.GetDefaultName()

CvPtrObject.RawPtr

CvObject.IsDisposed

CvObject.ptr

CvObject.SetSafeHandle(OpenCvSafeHandle)

CvObject.SetPostReleaseAction(Action)

CvObject.Dispose()

CvObject.Dispose(bool)

CvObject.DisposeManaged()

CvObject.DisposeUnmanaged()

CvObject.ThrowIfDisposed()

CvObject.CvPtr

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

This wraps cv::optflow::GPCForest<T>, a C++ template class, with a single fixed explicit instantiation at T = 5 (matching what upstream OpenCV's own samples/tests universally hardcode).

Methods

Create()

Creates an empty GPC forest of 5 trees.

FindCorrespondences(InputArray, InputArray, out Point[], out Point[], GPCMatchingParams?)

Finds correspondences between two images using the trained forest.

Train(GPCTrainingSamples, GPCTrainingParams?)

Trains the forest using one sample set for every tree. Consider using the overload taking image/ground-truth-flow triples instead for better quality.

Train(IEnumerable<Mat>, IEnumerable<Mat>, IEnumerable<Mat>, GPCTrainingParams?)

Trains the forest using individual samples extracted for each tree from the given image pairs and ground-truth flow. Generally produces better quality than the single-sample-set overload.