Majority rules raster generation over a vector area

majority_raster.py

import geopandas as gpd
import rasterio
import numpy as np
from rasterio.transform import from_bounds

# Function to apply majority rule cellular automaton
def majority_rule(array):
    # Create a copy of the array to store the updated values
    new_array = np.zeros_like(array)
    rows, cols = array.shape
    
    # Apply majority rule to each cell

    for i in range(1, rows-1):
        for j in range(1, cols-1):
            # Get the values of the cell and its neighbors
            neighbors = array[i-1:i+2, j-1:j+2].flatten()
            # Apply majority rule
            new_array[i, j] = np.median(neighbors) > 0.5
            
    return new_array

# Open the GeoPackage file using GeoPandas
gpkg_path = 'input.gpkg'
gdf = gpd.read_file(gpkg_path)

# Get the bounding box
minx, miny, maxx, maxy = gdf.total_bounds

# Calculate the number of rows and columns for the raster
cols, rows = 125, 125  # Adjust these values as needed

# Generate a cellular automaton raster
random_array = np.random.randint(2, size=(rows, cols), dtype=np.uint8)

# Apply majority rule cellular automaton
for _ in range(4):  # Repeat the majority rule 10 times as an example
    random_array = majority_rule(random_array)

# Define the metadata for the output raster
transform = from_bounds(minx, miny, maxx, maxy, cols, rows)

meta = {
    'driver': 'GTiff',
    'height': rows,
    'width': cols,
    'count': 1,
    'dtype': random_array.dtype,
    'crs': gdf.crs,
    'transform': transform
}

# Write the output raster
output_raster_path = 'output.tif'
with rasterio.open(output_raster_path, 'w', **meta) as dst:
    dst.write(random_array, 1)