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medial_lines

Compute medial lines for the input GeoDataFrame polygon geometries.

Parameters:

Name Type Description Default
gdf GeoDataFrame

GeoDataFrame containing polygons to compute medial lines for.

 required
num_points int

Number of resampled points in the input polygons.

 500
delta float

Distance between resampled polygon points. Ignored if num_points is not None.

 0.3
simplify_level float

Level of simplification to apply to the input geometries before computing medial lines. This helps to reduce noise from the voronoi triangulation.

 30.0
parallel bool

Whether to run the computation in parallel.

 False
num_processes int

Number of processes to use for parallel computation.

 1

Returns:

Type Description
GeoDataFrame

gpd.GeoDataFrame: GeoDataFrame containing the computed medial lines.
Note

Returns an empty GeoDataFrame if the input is empty.

Examples:

>>> from histolytics.spatial_geom.medial_lines import medial_lines
>>> from histolytics.data import cervix_tissue
>>> import geopandas as gpd
>>>
>>> # Create a simple polygon
>>> cervix_tis = cervix_tissue()
>>> lesion = cervix_tis[cervix_tis["class_name"] == "cin"]
>>>
>>> # Compute medial lines for the largest lesion segmentation
>>> medials = medial_lines(lesion, num_points=500, simplify_level=50)
>>> ax = cervix_tis.plot(column="class_name", figsize=(5, 5), aspect=1, alpha=0.5)
>>> medials.plot(ax=ax, color="red", lw=1, alpha=0.5)
>>> ax.set_axis_off()

out

Source code in src/histolytics/spatial_geom/medial_lines.py 
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def medial_lines(
    gdf: gpd.GeoDataFrame,
    num_points: int = 500,
    delta: float = 0.3,
    simplify_level: float = 30.0,
    parallel: bool = False,
    num_processes: int = 1,
) -> gpd.GeoDataFrame:
    """Compute medial lines for the input GeoDataFrame polygon geometries.

    Parameters:
        gdf (gpd.GeoDataFrame):
            GeoDataFrame containing polygons to compute medial lines for.
        num_points (int):
            Number of resampled points in the input polygons.
        delta (float):
            Distance between resampled polygon points. Ignored
            if `num_points` is not None.
        simplify_level (float):
            Level of simplification to apply to the input geometries before computing
            medial lines. This helps to reduce noise from the voronoi triangulation.
        parallel (bool):
            Whether to run the computation in parallel.
        num_processes (int):
            Number of processes to use for parallel computation.

    Returns:
        gpd.GeoDataFrame:
            GeoDataFrame containing the computed medial lines.

    Note:
        Returns an empty GeoDataFrame if the input is empty.

    Examples:
        >>> from histolytics.spatial_geom.medial_lines import medial_lines
        >>> from histolytics.data import cervix_tissue
        >>> import geopandas as gpd
        >>>
        >>> # Create a simple polygon
        >>> cervix_tis = cervix_tissue()
        >>> lesion = cervix_tis[cervix_tis["class_name"] == "cin"]
        >>>
        >>> # Compute medial lines for the largest lesion segmentation
        >>> medials = medial_lines(lesion, num_points=500, simplify_level=50)
        >>> ax = cervix_tis.plot(column="class_name", figsize=(5, 5), aspect=1, alpha=0.5)
        >>> medials.plot(ax=ax, color="red", lw=1, alpha=0.5)
        >>> ax.set_axis_off()
    ![out](../../img/medial_lines.png)
    """
    if gdf.empty:
        return gpd.GeoDataFrame(columns=["geometry", "class_name"])

    gdf = gdf.assign(geometry=gdf["geometry"].simplify(simplify_level))

    medials = gdf_apply(
        gdf,
        partial(_compute_medial_line, num_points=num_points, delta=delta),
        columns=["geometry"],
        parallel=parallel,
        num_processes=num_processes,
    )

    ret = gpd.GeoDataFrame(geometry=medials)
    ret.set_crs(gdf.crs, inplace=True)
    ret["class_name"] = "medial"

    return ret