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Filters Module

classify_ground_points(arrays, ignore_class='Classification[7:7]', cell=1.0, cut=0.0, returns='last,only', scalar=1.25, slope=0.15, threshold=0.5, window=18.0)

Applies the SMRF filter to classify ground points in the point cloud.

:param arrays: list Cleaned point cloud arrays after outlier removal. :type arrays: list :param ignore_class: str, classification codes to ignore during filtering. :param cell: float, cell size in meters. :param cut: float, cut net size (0 skips net cutting). :param returns: str, return types to include in output ("first", "last", "intermediate", "only"). :param scalar: float, elevation scalar. :param slope: float, slope threshold for ground classification. :param threshold: float, elevation threshold. :param window: float, max window size in meters. :return: list Point cloud arrays with classified ground points. :rtype: list :raises RuntimeError: If there's an error during the pipeline execution. :raises ValueError: If no data is returned after SMRF classification.

Source code in pyforestscan/filters.py 
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def classify_ground_points(
        arrays,
        ignore_class="Classification[7:7]",
        cell=1.0,
        cut=0.0,
        returns="last,only",
        scalar=1.25,
        slope=0.15,
        threshold=0.5,
        window=18.0
):
    """
    Applies the SMRF filter to classify ground points in the point cloud.

    :param arrays: list
        Cleaned point cloud arrays after outlier removal.
    :type arrays: list
    :param ignore_class: str, classification codes to ignore during filtering.
    :param cell: float, cell size in meters.
    :param cut: float, cut net size (0 skips net cutting).
    :param returns: str, return types to include in output ("first", "last", "intermediate", "only").
    :param scalar: float, elevation scalar.
    :param slope: float, slope threshold for ground classification.
    :param threshold: float, elevation threshold.
    :param window: float, max window size in meters.
    :return: list
        Point cloud arrays with classified ground points.
    :rtype: list
    :raises RuntimeError:
        If there's an error during the pipeline execution.
    :raises ValueError:
        If no data is returned after SMRF classification.
    """
    pipeline_stages = [
        _filter_smrf(
            cell=cell,
            cut=cut,
            ignore=ignore_class,
            returns=returns,
            scalar=scalar,
            slope=slope,
            threshold=threshold,
            window=window
        )
    ]

    try:
        pipeline = _build_pdal_pipeline(arrays, pipeline_stages)
    except RuntimeError as e:
        raise RuntimeError(f"SMRF Classification Pipeline Failed: {e}")

    processed_arrays = pipeline.arrays

    if not processed_arrays:
        raise ValueError("No data returned after SMRF classification.")

    return processed_arrays

filter_ground(arrays)

Applies a filter to remove ground points (classification 2) from the input arrays.

:param arrays: List of point cloud arrays to be processed. :type arrays: list :return: Processed point cloud arrays after removing ground points. :rtype: list

Source code in pyforestscan/filters.py 
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def filter_ground(arrays):
    """
    Applies a filter to remove ground points (classification 2) from the input arrays.

    :param arrays: List of point cloud arrays to be processed.
    :type arrays: list
    :return: Processed point cloud arrays after removing ground points.
    :rtype: list
    """
    pipeline = _build_pdal_pipeline(arrays, [_filter_ground()])
    return pipeline.arrays

filter_hag(arrays, lower_limit=0, upper_limit=None)

Applies a Height Above Ground (HAG) filter to the input arrays.

:param arrays: List of point cloud arrays to be processed. :type arrays: list :param lower_limit: The minimum value for the height filter, defaults to 0. :type lower_limit: int, optional :param upper_limit: The maximum value for the height filter, defaults to None. :type upper_limit: int, optional :return: Processed point cloud arrays after applying the HAG filter. :rtype: list :raises ValueError: If upper_limit is less than lower_limit.

Source code in pyforestscan/filters.py 
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def filter_hag(arrays, lower_limit=0, upper_limit=None):
    """
    Applies a Height Above Ground (HAG) filter to the input arrays.

    :param arrays: List of point cloud arrays to be processed.
    :type arrays: list
    :param lower_limit: The minimum value for the height filter, defaults to 0.
    :type lower_limit: int, optional
    :param upper_limit: The maximum value for the height filter, defaults to None.
    :type upper_limit: int, optional
    :return: Processed point cloud arrays after applying the HAG filter.
    :rtype: list
    :raises ValueError: If upper_limit is less than lower_limit.
    """
    pipeline = _build_pdal_pipeline(arrays, [_filter_hag(lower_limit, upper_limit)])
    return pipeline.arrays

filter_select_ground(arrays)

Applies a filter to select ground points (classification 2) from the input arrays.

:param arrays: List of point cloud arrays to be processed. :type arrays: list :return: Processed point cloud arrays after removing ground points. :rtype: list

Source code in pyforestscan/filters.py 
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def filter_select_ground(arrays):
    """
    Applies a filter to select ground points (classification 2) from the input arrays.

    :param arrays: List of point cloud arrays to be processed.
    :type arrays: list
    :return: Processed point cloud arrays after removing ground points.
    :rtype: list
    """
    pipeline = _build_pdal_pipeline(arrays, [_select_ground()])
    return pipeline.arrays

remove_outliers_and_clean(arrays, mean_k=8, multiplier=3.0)

Removes statistical outliers from the point cloud to enhance data quality.

:param arrays: list List of point cloud arrays obtained from read_lidar. :type point_cloud_arrays: list :param mean_k: int, number of nearest neighbors for outlier removal. :param multiplier: float, standard deviation multiplier for outlier removal. :return: list Cleaned array of point cloud data without outliers. :rtype: list :raises RuntimeError: If there's an error during the pipeline execution. :raises ValueError: If no data is returned after outlier removal.

Source code in pyforestscan/filters.py 
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def remove_outliers_and_clean(arrays, mean_k=8, multiplier=3.0):
    """
    Removes statistical outliers from the point cloud to enhance data quality.

    :param arrays: list
        List of point cloud arrays obtained from read_lidar.
    :type point_cloud_arrays: list
    :param mean_k: int, number of nearest neighbors for outlier removal.
    :param multiplier: float, standard deviation multiplier for outlier removal.
    :return: list
        Cleaned array of point cloud data without outliers.
    :rtype: list
    :raises RuntimeError:
        If there's an error during the pipeline execution.
    :raises ValueError:
        If no data is returned after outlier removal.
    """
    pipeline_stages = [
        _filter_statistical_outlier(mean_k=mean_k, multiplier=multiplier)
    ]

    try:
        pipeline = _build_pdal_pipeline(arrays, pipeline_stages)
    except RuntimeError as e:
        raise RuntimeError(f"Outlier Removal Pipeline Failed: {e}")

    processed_arrays = pipeline.arrays

    if not processed_arrays:
        raise ValueError("No data returned after outlier removal.")

    return processed_arrays