Source code for torch_points3d.core.data_transform.filters

import numpy as np
import torch
import random
from torch_points3d.core.data_transform.features import PCACompute, compute_planarity

[docs]class FCompose(object): """ allow to compose different filters using the boolean operation Parameters ---------- list_filter: list list of different filter functions we want to apply boolean_operation: function, optional boolean function to compose the filter (take a pair and return a boolean) """ def __init__(self, list_filter, boolean_operation=np.logical_and): self.list_filter = list_filter self.boolean_operation = boolean_operation def __call__(self, data): assert len(self.list_filter) > 0 res = self.list_filter[0](data) for filter_fn in self.list_filter: res = self.boolean_operation(res, filter_fn(data)) return res def __repr__(self): rep = "{}([".format(self.__class__.__name__) for filt in self.list_filter: rep = rep + filt.__repr__() + ", " rep = rep + "])" return rep
[docs]class PlanarityFilter(object): """ compute planarity and return false if the planarity of a pointcloud is above or below a threshold Parameters ---------- thresh: float, optional threshold to filter low planar pointcloud is_leq: bool, optional choose whether planarity should be lesser or equal than the threshold or greater than the threshold. """ def __init__(self, thresh=0.3, is_leq=True): self.thresh = thresh self.is_leq = is_leq def __call__(self, data): if getattr(data, "eigenvalues", None) is None: data = PCACompute()(data) planarity = compute_planarity(data.eigenvalues) if self.is_leq: return planarity <= self.thresh else: return planarity > self.thresh def __repr__(self): return "{}(thresh={}, is_leq={})".format(self.__class__.__name__, self.thresh, self.is_leq)
[docs]class RandomFilter(object): """ Randomly select an elem of the dataset (to have smaller dataset) with a bernouilli distribution of parameter thresh. Parameters ---------- thresh: float, optional the parameter of the bernouilli function """ def __init__(self, thresh=0.3): self.thresh = thresh def __call__(self, data): return random.random() < self.thresh def __repr__(self): return "{}(thresh={})".format(self.__class__.__name__, self.thresh)