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Description:

itkransac 0.2.1

ITKRANSAC

Overview
This is the source code for a C++ templated implementation of the RANSAC
algorithm and associated Python wrapping. The implementation is
multi-threaded. This repository is only for pointset registratation and
differs slightly from the original generic implementation due to optimization.
For implementation related to plane and sphere estimation
please refer https://github.com/midas-journal/midas-journal-769 and the
associated Insight Journal article.
The code is "in the style of ITK". That is, it is very similar to the official
ITK style but does not follow all of the required conventions.
Manifest:

RANSAC {h,txx} - Multi-threaded implementation of the generic RANSAC algorithm.
ParametersEstimator.{h,hxx} - Super class of all parameter estimation objects
that can be used with the RANSAC algorithm. This is an abstract class that
defines an interface.
itkLandmarkRegistrationEstimator.{h,hxx} - Estimation code for landmark based pointset registration.
Testing/*.cxx - Test for the PointSet registration using landmark points.

Python wrapping installation:
pip install itk-ransac

Sample Usage in Python for 3D PointSet is shown here:
data = itk.vector[itk.Point[itk.D, 6]]()
agreeData = itk.vector[itk.Point[itk.D, 6]]()
GenerateData(data, agreeData)

transformParameters = itk.vector.D()

TransformType = itk.Similarity3DTransform.D

maximumDistance = inlier_value
RegistrationEstimatorType = itk.Ransac.LandmarkRegistrationEstimator[6, TransformType]
registrationEstimator = RegistrationEstimatorType.New()
registrationEstimator.SetMinimalForEstimate(number_of_ransac_points)
registrationEstimator.SetAgreeData(agreeData)
registrationEstimator.SetDelta(maximumDistance)
registrationEstimator.LeastSquaresEstimate(data, transformParameters)

desiredProbabilityForNoOutliers = 0.99
RANSACType = itk.RANSAC[itk.Point[itk.D, 6], itk.D, TransformType]
ransacEstimator = RANSACType.New()
ransacEstimator.SetData(data)
ransacEstimator.SetAgreeData(agreeData)
ransacEstimator.SetMaxIteration(number_of_iterations)
ransacEstimator.SetNumberOfThreads(8)
ransacEstimator.SetParametersEstimator(registrationEstimator)

percentageOfDataUsed = ransacEstimator.Compute( transformParameters, desiredProbabilityForNoOutliers )
for i in transformParameters:
print(i)

Landmarks can be obtained by performing feature matching.
For this one can use the ITKFPFH library.