Add an OrganSegmentation task

[jlegrand62]

It uses a DBSCAN clustering method on the segmented point cloud to detect organs.

[jlegrand62]

Can be tested with:

romi_run_task OrganSegmentation database_jcharlaix/scans_reconstructed/arabido_test2_ml/ --config configs/ml_pipe_vplants_4.toml

[jlegrand62]

Example of clustering on PEDICEL (left) & FRUIT (right):

[fabfabBesnard]

For clarity, I prefer adding some information (correct me if I am wrong):

• the input of new task OrganSegmentation is a labelled point cloud, where labels were given by deep learning from 2D images (ml pipeline only)

• the output is still a point cloud, but this time neighboring points with the same label are clustered in a unique object with a class, providing an instance segmentation of the point cloud. The class is derived from the classes of the point labels, on which the machine learning phase was trained initially (namely: stem, leaf, flower, fruit, pedicle)

• the output is stored in a dedicated folder, whose name starts looks like "OrganSegmentation_2_0_5_out_8a261572f8"

• In the ml pipe, this task replaces ClusteredMesh and connects directly SegmentedPointCloud to AnglesAndInternodes

[fabfabBesnard]

Note before merging: edit the romiscan/config/ml_pipe_full.toml with new task

[jlegrand62]

Note before merging: edit the romiscan/config/ml_pipe_full.toml with new task

Maybe I should wait to test OrganSegmentation for repeatability against ClusteredMesh and prove its more robust?!

[jlegrand62]

I have this robustness_comparison script ready in a branch that should be quickly mergeable with this one!