How to use the pyntcloud.core_class.PyntCloud function in pyntcloud

voxelgrid_highest


        **USE POINTS**

            points_random
                n: int
                    Number of points to be sampled.
        """
        if name in ALL_SAMPLERS:
            sampler = ALL_SAMPLERS[name](pyntcloud=self, **kwargs)
            sampler.extract_info()
            sample = sampler.compute()

            if as_PyntCloud:
                return PyntCloud(sample)

            return sample

        else:
            raise ValueError("Unsupported sampling method. Check docstring")

0 on voxels further than 2 * voxel side.

    target_size : [int, int, int], optional (Default [30, 30, 30])
        Dimensions of voxelgrid in case voxelize is True.

    Returns
    -------
    feature_vector : ndarray
        (target_size[0], target_size[1], target_size[2])

    Raises
    ------
    ValueError: if 3D format is not valid.

    """
    point_cloud = PyntCloud.from_file(path)

    if point_cloud.mesh is not None:
        point_cloud = PyntCloud(point_cloud.get_sample(
            "mesh_random", n=n_sampling))

    if voxelize:
        vgrid_id = point_cloud.add_structure("voxelgrid", x_y_z=target_size)
        voxelgrid = point_cloud.structures[vgrid_id]

        if voxel_mode == "binary":
            feature_vector = voxelgrid.get_feature_vector(mode="binary")
        elif voxel_mode == "density":
            feature_vector = voxelgrid.get_feature_vector(mode="density")
        elif voxel_mode == "TDF":
            feature_vector = voxelgrid.get_feature_vector(mode="TDF")
        else:

Dimensions of voxelgrid in case voxelize is True.

    Returns
    -------
    feature_vector : ndarray
        (target_size[0], target_size[1], target_size[2])

    Raises
    ------
    ValueError: if 3D format is not valid.

    """
    point_cloud = PyntCloud.from_file(path)

    if point_cloud.mesh is not None:
        point_cloud = PyntCloud(point_cloud.get_sample(
            "mesh_random", n=n_sampling))

    if voxelize:
        vgrid_id = point_cloud.add_structure("voxelgrid", x_y_z=target_size)
        voxelgrid = point_cloud.structures[vgrid_id]

        if voxel_mode == "binary":
            feature_vector = voxelgrid.get_feature_vector(mode="binary")
        elif voxel_mode == "density":
            feature_vector = voxelgrid.get_feature_vector(mode="density")
        elif voxel_mode == "TDF":
            feature_vector = voxelgrid.get_feature_vector(mode="TDF")
        else:
            raise ValueError("Invalid mode; available modes are: {}".format(
                {"binary", "density", "TDF"}))

and_return: boolean, optional
            Default: False
            If True, return a list with the splits.

        save_format: str, optional
            Default: "ply"
            Extension used to save the generated PyntClouds.
            Must be of one of the formats present in pyntcloud.io.TO

        save_path: str, optional
            Default: "."
            Path where the PyntClouds will be saved.
        """
        scalar_field = self.points[scalar_field]

        splits = {x: PyntCloud(self.points.loc[scalar_field == x]) for x in scalar_field.unique()}

        if not os.path.exists(save_path):
            os.makedirs(save_path)

        for key, val in splits.items():
            val.to_file("{}/{}.{}".format(save_path, key, save_format))

        if and_return:
            return splits