smoothify 0.3.3

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A Python package for smoothing and refining geometries derived from raster data classifications. Smoothify transforms jagged polygons and lines resulting from raster-to-vector conversion into smooth, visually appealing features using an optimized implementation of Chaikin's corner-cutting algorithm.

Problem

Polygons and lines derived from classified raster data (e.g., ML model predictions, spectral indices, or remote sensing classifications) often have unnatural "stair-stepped" or "pixelated" edges that:

• Are visually unappealing in maps and GIS applications
• Can be difficult to work with in downstream vector processing
• Don't represent the real-world features they're meant to depict

Solution

Smoothify applies an optimized implementation of Chaikin's corner-cutting algorithm along with other geometric processing to create smooth, natural-looking features while:

• Preserving the general shape and area of polygons
• Supporting all shapely geometry types
• Handling shapes with interior holes
• Efficiently processing large datasets with multiprocessing

Installation

uv add smoothify

or

pip install smoothify

or

conda install conda-forge::smoothify

Quick Start

import geopandas as gpd
from smoothify import smoothify

# Load your polygonized raster data
polygon_gdf = gpd.read_file("path/to/your/polygons.gpkg")

# Apply smoothing (segment_length auto-detected from geometry)
smoothed_gdf = smoothify(
    geom=polygon_gdf,
    smooth_iterations=3,  # More iterations = smoother result
    num_cores=4  # Use parallel processing for large datasets
)

# Or specify segment_length explicitly (generally recommended)
smoothed_gdf = smoothify(
    geom=polygon_gdf,
    segment_length=10.0,  # Use the original raster resolution
    smooth_iterations=3,
    num_cores=4
)

# Save the result
smoothed_gdf.to_file("smoothed_polygons.gpkg")

Examples

Example notebooks:

• Usage examples
• Smoothify vs. Shapely comparison
• Real-world water example
• Merging holes

Basic Polygon Smoothing

Transform pixelated polygons from raster data into smooth, natural-looking features:

LineString Smoothing

Works perfectly for roads, streams, and other linear features:

Controlling Smoothness with Iterations

The smooth_iterations parameter controls how smooth the result will be:

Merging Adjacent Geometries

When processing multiple adjacent polygons, allowing merge_collection = True produces a combined result:

General Usage

The smoothify() function accepts three types of input:

1. GeoDataFrame

import geopandas as gpd
from smoothify import smoothify
# By default this will dissolve adjacent polygons before smoothing
gdf = gpd.read_file("polygons.gpkg")
smoothed_gdf = smoothify(
    geom=gdf,
    segment_length=10.0,
    smooth_iterations=3,
    num_cores=4
)

# Dissolve geometries by a specific field before smoothing
# Useful for merging adjacent polygons with the same classification
gdf_with_classes = gpd.read_file("classified_polygons.gpkg")
smoothed_by_class = smoothify(
    geom=gdf_with_classes,
    segment_length=10.0,
    smooth_iterations=3,
    merge_collection=True,
    merge_field="land_type",  # Merge adjacent geometries with same land_type
    num_cores=4
)

2. Single Geometry

from shapely.geometry import Polygon
from smoothify import smoothify

polygon = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)])
smoothed_polygon = smoothify(
    geom=polygon,
    smooth_iterations=3
)

3. List of Geometries or GeometryCollection

from shapely.geometry import Polygon, LineString
from smoothify import smoothify

geometries = [
    Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]),
    LineString([(0, 0), (5, 5), (10, 0)])
]
smoothed = smoothify(
    geom=geometries,
    segment_length=1.0,
    smooth_iterations=3
)

Parameters

How It Works

Smoothify uses an advanced multi-step smoothing pipeline. The numbered steps below correspond to the panels in the figure:

1. Pixelated input — a polygon straight from raster-to-vector conversion, with a stair-stepped boundary
2. Multiple variants (for Polygons) that start at evenly spaced arc-length positions, each simplified to strip staircase noise, so no artifact is tied to a fixed start vertex
3. Chaikin corner cutting applied to each variant
4. Per-point median merge — a start-invariant consensus that resolves the variants' disagreements
5. Final smoothing pass on the merged result
6. Restore original area via buffering (for Polygons, when preserve_area=True)

Two steps are not shown in the figure: before step 2, touching holes are joined (for Polygons, when merge_holes=True) so they smooth as one opening; and after step 6, the result is checked for sharp concave folds left by features near the smoothing scale (e.g. one-pixel-wide arms) and repaired with a small morphological opening/closing bounded at segment_length / 4.

Invalid Geometries

Smoothify does not repair invalid input. If it encounters an invalid geometry (e.g. a self-intersecting polygon), it returns that geometry unchanged and emits a warning, instead of crashing or silently producing an empty geometry. This is consistent whether you pass a single geometry, a list/collection, or a GeoDataFrame.

If you want invalid geometries smoothed, repair them first with shapely's make_valid():

# GeoDataFrame
gdf.geometry = gdf.geometry.make_valid()
smoothed_gdf = smoothify(gdf, segment_length=10.0)

# Single geometry
from shapely import make_valid
smoothed = smoothify(make_valid(polygon), segment_length=1.0)

Performance Considerations

• Parallel Processing: For large GeoDataFrames or collections, use num_cores = 0 to enable parallel processing
• Duplicate Shapes: Geometries that are translated copies of the same shape (common in raster-derived data, e.g. single-pixel polygons) are automatically smoothed once and the result reused
• Smoothing Iterations: Values of 3-5 typically provide good results. Higher values create smoother output but increase processing time and vertex count
• Memory Usage: Scales with geometry complexity. The algorithm creates multiple variants during smoothing
• Optimal segment_length: Anything from about half the original raster pixel size and up should produce reasonable output — larger values produce more rounded output, smaller values stay more faithful to the original geometry

Running the Tests

Smoothify uses pytest. After cloning the repository, install the development dependencies and run the suite with uv:

# Install dependencies (including the dev group)
uv sync

# Run all tests (parallelised across CPU cores by default via pytest-xdist)
uv run pytest tests/

# Run with coverage
uv run pytest tests/ --cov=smoothify --cov-report=html

# Run a single test (add `-n 0` to disable parallelism for clearer output)
uv run pytest tests/test_chaikin.py::TestChaikinCornerCutting::test_simple_square_polygon -n 0

The suite runs in parallel by default (-n auto in pytest.ini); pass -n 0 to run serially when debugging. If you prefer not to use uv, install the dev dependencies into your environment and run pytest tests/ directly.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.