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Add Cloud2CloudDistance
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‎wrapper/cccorelib/src/DistanceComputationTools.cpp‎

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@@ -18,6 +18,10 @@
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#include <pybind11/pybind11.h>
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#include <DistanceComputationTools.h>
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#include <ReferenceCloud.h>
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#include <ScalarField.h>
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#include <GenericIndexedCloudPersist.h>
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#include <PointCloud.h>
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namespace py = pybind11;
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using namespace pybind11::literals;
@@ -27,6 +31,227 @@ void define_DistanceComputationTools(py::module &cccorelib)
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py::class_<CCCoreLib::DistanceComputationTools> DistanceComputationTools(cccorelib,
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"DistanceComputationTools");
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py::class_<CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams>(
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DistanceComputationTools, "Cloud2CloudDistancesComputationParams")
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.def(py::init<>())
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.def_readwrite(
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"octreeLevel",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::octreeLevel,
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R"doc(
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Level of subdivision of the octree at witch to apply the distance computation algorithm
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If set to 0 (default) the algorithm will try to guess the best level automatically.
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)doc")
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.def_readwrite(
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"maxSearchDist",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::maxSearchDist,
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R"doc(
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Maximum search distance (true distance won't be computed if greater)
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Set to -1 to deactivate (default).
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Not compatible with closest point set determination (see CPSet)
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)doc")
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.def_readwrite(
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"multiThread",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::multiThread,
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R"doc(
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Whether to use multi-thread or single thread mode
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If maxSearchDist > 0, single thread mode will be forced.
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)doc")
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.def_readwrite(
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"maxThreadCount",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::maxThreadCount,
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R"doc(
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Maximum number of threads to use (0 = max)
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)doc")
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.def_readwrite(
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"localModel",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::localModel,
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R"doc(
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Type of local 3D modeling to use
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Default: NO_MODEL. Otherwise see CC_LOCAL_MODEL_TYPES.
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)doc")
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.def_readwrite("useSphericalSearchForLocalModel",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::
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useSphericalSearchForLocalModel,
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R"doc(
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Whether to use a fixed number of neighbors or a (sphere) radius for nearest neighbors search
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For local models only (i.e. ignored if localModel = NO_MODEL).
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)doc")
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.def_readwrite(
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"kNNForLocalModel",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::kNNForLocalModel,
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R"doc(
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Number of neighbors for nearest neighbors search (local model)
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For local models only (i.e. ignored if localModel = NO_MODEL).
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Ignored if useSphericalSearchForLocalModel is true.
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)doc")
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.def_readwrite(
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"radiusForLocalModel",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::radiusForLocalModel,
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R"doc(
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Radius for nearest neighbors search (local model)
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For local models only (i.e. ignored if localModel = NO_MODEL).
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Ignored if useSphericalSearchForLocalModel is true.
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)doc")
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.def_readwrite("reuseExistingLocalModels",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::
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reuseExistingLocalModels,
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R"doc(
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Whether to use an approximation for local model computation
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For local models only (i.e. ignored if localModel = NO_MODEL).
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Ignored if useSphericalSearchForLocalModel is true.
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)doc")
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.def_readwrite("CPSet",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::CPSet,
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R"doc(
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Container of (references to) points to store the "Closest Point Set"
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The Closest Point Set corresponds to (the reference to) each compared point's closest neighbor.
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Not compatible with max search distance (see maxSearchDist)
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)doc")
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.def_property(
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"splitDistances",
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[](CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams &self) {
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return py::make_tuple(self.splitDistances[0], self.splitDistances[1], self.splitDistances[2]);
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},
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[](CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams &self,
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const py::sequence &list)
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{
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self.splitDistances[0] = list[0].cast<CCCoreLib::ScalarField *>();
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self.splitDistances[1] = list[1].cast<CCCoreLib::ScalarField *>();
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self.splitDistances[2] = list[2].cast<CCCoreLib::ScalarField *>();
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},
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R"doc(Split distances (one scalar field per dimension: X, Y and Z))doc")
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.def_readwrite(
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"resetFormerDistances",
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&CCCoreLib::DistanceComputationTools::Cloud2CloudDistancesComputationParams::resetFormerDistances,
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R"doc(
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Whether to keep the existing distances as is (if any) or not
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By default, any previous distances/scalar values stored in the 'enabled' scalar field will be
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reset before computing them again.
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)doc");
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DistanceComputationTools.def_static("computeCloud2CloudDistances",
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&CCCoreLib::DistanceComputationTools::computeCloud2CloudDistances,
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"compareCloud"_a,
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"referenceCloud"_a,
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"params"_a,
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"progressCb"_a = nullptr,
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"compOctree"_a = nullptr,
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"refOctree"_a = nullptr,
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R"doc(
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Computes the 'nearest neighbor' distances between two point clouds (formerly named "Hausdorff distance")
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The main algorithm and its different versions (with or without local modeling) are described in
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Daniel Girardeau-Montaut's PhD manuscript (Chapter 2, section 2.3). It is the standard way to compare
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directly two dense point clouds.
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The current scalar field of the compared cloud should be enabled. By default it will be reset to
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NAN_VALUE but one can avoid this by defining the Cloud2CloudDistancesComputationParams::resetFormerDistances
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parameters to false. But even in this case, only values above Cloud2CloudDistancesComputationParams::maxSearchDist
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will remain untouched.
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Max search distance (Cloud2CloudDistancesComputationParams::maxSearchDist > 0) is not compatible with the
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determination of the Closest Point Set (Cloud2CloudDistancesComputationParams::CPSet)
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Parameters
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----------
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comparedCloud:the compared cloud (the distances will be computed for each point of this cloud)
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referenceCloud: the reference cloud (the nearest neigbhor will be determined among these points)
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params: distance computation parameters
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progressCb:the client application can get some notification of the process progress through this callback mechanism (see GenericProgressCallback)
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compOctree:the pre-computed octree of the compared cloud (warning: both octrees must have the same cubical bounding-box - it is automatically computed if 0)
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refOctree: the pre-computed octree of the reference cloud (warning: both octrees must have the same cubical bounding-box - it is automatically computed if 0)
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Returns
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-------
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0 if ok, a negative value otherwise
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)doc");
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py::class_<CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams>(cccorelib, "Cloud2MeshDistancesComputationParams", R"doc(
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Cloud-to-mesh distances computation parameters
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)doc")
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.def_readwrite("octreeLevel", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::octreeLevel, R"doc(
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The level of subdivision of the octree at witch to apply the algorithm
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)doc")
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.def_readwrite("maxSearchDist", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::maxSearchDist, R"doc(
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Max search distance (acceleration)
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Default value: 0. If greater than 0, then the algorithm won't compute distances over this value
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)doc")
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.def_readwrite("useDistanceMap", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::useDistanceMap, R"doc(
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Use distance map (acceleration)
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If true the distances will be approximated by a Distance Transform.
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Incompatible with signed distances or Closest Point Set.
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)doc")
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.def_readwrite("signedDistances", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::signedDistances, R"doc(
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Whether to compute signed distances or not
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If true, the computed distances will be signed (in this case, the Distance Transform can't be used
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and therefore useDistanceMap will be ignored)
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)doc")
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.def_readwrite("flipNormals", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::flipNormals, R"doc(
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Whether triangle normals should be computed in the 'direct' order (true) or 'indirect' (false)
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)doc")
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.def_readwrite("multiThread", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::multiThread, R"doc(
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Whether to use multi-thread or single thread mode (if maxSearchDist > 0, single thread mode is forced)
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)doc")
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.def_readwrite("maxThreadCount", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::maxThreadCount, R"doc(
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Maximum number of threads to use (0 = max)
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)doc")
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.def_readwrite("CPSet", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::CPSet, R"doc(
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Cloud to store the Closest Point Set
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The cloud should be initialized but empty on input. It will have the same size as the compared cloud on output.
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Not compatible with maxSearchDist > 0.
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)doc")
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.def_readwrite("robust", &CCCoreLib::DistanceComputationTools::Cloud2MeshDistancesComputationParams::robust, R"doc(
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Whether to compute distances in a robust way (trying to detect edge cases)
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Computation will be slightly slower, and a little bit more memory will be required
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)doc")
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.def(py::init<>());
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DistanceComputationTools.def_static("computeCloud2MeshDistances",
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&CCCoreLib::DistanceComputationTools::computeCloud2MeshDistances,
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"pointCloud"_a,
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"mesh"_a,
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"params"_a,
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"progressCb"_a = nullptr,
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"cloudOctree"_a = nullptr,
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R"doc(
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Computes the distances between a point cloud and a mesh
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The algorithm, inspired from METRO by Cignoni et al., is described
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in Daniel Girardeau-Montaut's PhD manuscript (Chapter 2, section 2.2).
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It is the general way to compare a point cloud with a triangular mesh.
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Parameters
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----------
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pointCloud: the compared cloud (the distances will be computed on these points)
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mesh: the reference mesh (the distances will be computed relatively to its triangles)
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params: distance computation parameters
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progressCb:the client application can get some notification of the process progress through this callback mechanism (see GenericProgressCallback)
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cloudOctree: the pre-computed octree of the compared cloud
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(warning: its bounding box should be equal to the union of both point cloud and mesh bbs
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and it should be cubical - it is automatically computed if 0)
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Returns
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-------
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0 if ok, a negative value otherwise
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)doc");
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py::enum_<CCCoreLib::DistanceComputationTools::ERROR_MEASURES>(DistanceComputationTools,
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"ERRPOR_MEASURES")
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.value("RMS", CCCoreLib::DistanceComputationTools::ERROR_MEASURES::RMS)

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