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radius_clustering.go
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/
radius_clustering.go
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package segmentation
import (
"context"
"github.com/golang/geo/r3"
"github.com/mitchellh/mapstructure"
"github.com/pkg/errors"
"go.viam.com/rdk/components/camera"
"go.viam.com/rdk/config"
pc "go.viam.com/rdk/pointcloud"
"go.viam.com/rdk/vision"
)
// RadiusClusteringConfig specifies the necessary parameters for 3D object finding.
type RadiusClusteringConfig struct {
MinPtsInPlane int `json:"min_points_in_plane"`
MinPtsInSegment int `json:"min_points_in_segment"`
ClusteringRadiusMm float64 `json:"clustering_radius_mm"`
MeanKFiltering int `json:"mean_k_filtering"`
Label string `json:"label,omitempty"`
}
// CheckValid checks to see in the input values are valid.
func (rcc *RadiusClusteringConfig) CheckValid() error {
if rcc.MinPtsInPlane <= 0 {
return errors.Errorf("min_points_in_plane must be greater than 0, got %v", rcc.MinPtsInPlane)
}
if rcc.MinPtsInSegment <= 0 {
return errors.Errorf("min_points_in_segment must be greater than 0, got %v", rcc.MinPtsInSegment)
}
if rcc.ClusteringRadiusMm <= 0 {
return errors.Errorf("clustering_radius_mm must be greater than 0, got %v", rcc.ClusteringRadiusMm)
}
return nil
}
// ConvertAttributes changes the AttributeMap input into a RadiusClusteringConfig.
func (rcc *RadiusClusteringConfig) ConvertAttributes(am config.AttributeMap) error {
decoder, err := mapstructure.NewDecoder(&mapstructure.DecoderConfig{TagName: "json", Result: rcc})
if err != nil {
return err
}
err = decoder.Decode(am)
if err == nil {
err = rcc.CheckValid()
}
return err
}
// NewRadiusClustering returns a Segmenter that removes the planes (if any) and returns
// a segmentation of the objects in a point cloud using a radius based clustering algo
// described in the paper "A Clustering Method for Efficient Segmentation of 3D Laser Data" by Klasing et al. 2008.
func NewRadiusClustering(params config.AttributeMap) (Segmenter, error) {
// convert attributes to appropriate struct
if params == nil {
return nil, errors.New("config for radius clustering segmentation cannot be nil")
}
cfg := &RadiusClusteringConfig{}
err := cfg.ConvertAttributes(params)
if err != nil {
return nil, err
}
return cfg.RadiusClustering, nil
}
// RadiusClustering applies the radius clustering algorithm directly on a given point cloud.
func (rcc *RadiusClusteringConfig) RadiusClustering(ctx context.Context, c camera.Camera) ([]*vision.Object, error) {
// get next point cloud
cloud, err := c.NextPointCloud(ctx)
if err != nil {
return nil, err
}
ps := NewPointCloudPlaneSegmentation(cloud, 10, rcc.MinPtsInPlane)
// if there are found planes, remove them, and keep all the non-plane points
_, nonPlane, err := ps.FindPlanes(ctx)
if err != nil {
return nil, err
}
// filter out the noise on the point cloud if mean K is greater than 0
if rcc.MeanKFiltering > 0.0 {
filter, err := pc.StatisticalOutlierFilter(rcc.MeanKFiltering, 1.25)
if err != nil {
return nil, err
}
nonPlane, err = filter(nonPlane)
if err != nil {
return nil, err
}
}
// do the segmentation
segments, err := segmentPointCloudObjects(nonPlane, rcc.ClusteringRadiusMm, rcc.MinPtsInSegment)
if err != nil {
return nil, err
}
objects, err := NewSegmentsFromSlice(segments, rcc.Label)
if err != nil {
return nil, err
}
return objects.Objects, nil
}
// segmentPointCloudObjects uses radius based nearest neighbors to segment the images, and then prunes away
// segments that do not pass a certain threshold of points.
func segmentPointCloudObjects(cloud pc.PointCloud, radius float64, nMin int) ([]pc.PointCloud, error) {
segments, err := radiusBasedNearestNeighbors(cloud, radius)
if err != nil {
return nil, err
}
segments = pc.PrunePointClouds(segments, nMin)
return segments, nil
}
// radiusBasedNearestNeighbors partitions the pointcloud, grouping points within a given radius of each other.
func radiusBasedNearestNeighbors(cloud pc.PointCloud, radius float64) ([]pc.PointCloud, error) {
kdt, ok := cloud.(*pc.KDTree)
if !ok {
kdt = pc.ToKDTree(cloud)
}
var err error
clusters := NewSegments()
c := 0
kdt.Iterate(0, 0, func(v r3.Vector, d pc.Data) bool {
// skip if point already is assigned cluster
if _, ok := clusters.Indices[v]; ok {
return true
}
// if not assigned, see if any of its neighbors are assigned a cluster
nn := kdt.RadiusNearestNeighbors(v, radius, false)
for _, neighbor := range nn {
nv := neighbor.P
ptIndex, ptOk := clusters.Indices[v]
neighborIndex, neighborOk := clusters.Indices[nv]
switch {
case ptOk && neighborOk:
if ptIndex != neighborIndex {
err = clusters.MergeClusters(ptIndex, neighborIndex)
}
case !ptOk && neighborOk:
err = clusters.AssignCluster(v, d, neighborIndex)
case ptOk && !neighborOk:
err = clusters.AssignCluster(neighbor.P, neighbor.D, ptIndex)
}
if err != nil {
return false
}
}
// if none of the neighbors were assigned a cluster, create a new cluster and assign all neighbors to it
if _, ok := clusters.Indices[v]; !ok {
err = clusters.AssignCluster(v, d, c)
if err != nil {
return false
}
for _, neighbor := range nn {
err = clusters.AssignCluster(neighbor.P, neighbor.D, c)
if err != nil {
return false
}
}
c++
}
return true
})
if err != nil {
return nil, err
}
return clusters.PointClouds(), nil
}