Generating Voronoi in a Polygon

generate_polys.py

import geopandas as gpd
from shapely.ops import voronoi_diagram
from shapely.ops import unary_union
from shapely.geometry import shape
import random

def generate_voronoi_plot(df, n_points):
    # Read GeoDataFrame from file
    
    # Extract geometry for clipping
    clip_df = df.loc[0, 'geometry']
    
    # Sample points and create Voronoi diagram
    points = df['geometry'].sample_points(n_points)
    points = [shape(geometry) for geometry in points]
    mp = unary_union(points)
    vor = gpd.GeoDataFrame(gpd.GeoSeries(voronoi_diagram(mp, envelope=clip_df))).set_geometry(0).explode()
    
    # Clip Voronoi diagram
    vor = gpd.clip(vor, clip_df, keep_geom_type=True).set_crs(df.crs.to_epsg())
    
    # Generate random values
    def generate_random():
        return random.randint(0, 1)

    # Use the assign method to create a new column filled with random values
    vor = vor.assign(val=[generate_random() for _ in range(len(vor))])
    vor=vor.dissolve('val')
    return vor
    # Plot the Voronoi diagram
    
# Call the function with the file path
df=gpd.read_file('input.gpkg') #Only tested with a geopackage that contains only 1 polygon
n_points=75
shapes=generate_voronoi_plot(df, n_points)
shapes.to_file('output.gpkg')

Probably pass a .dissolve() at the end to make it easier for benchmarking later