pymeshoptimizer 0.1.1

High-level abstractions and utilities for working with meshoptimizer

PyMeshoptimizer

This package provides high-level abstractions and utilities for working with meshoptimizer, making it easier to use the core functionality in common workflows.

Installation

pip install pymeshoptimizer

Features

Mesh Representation

• Mesh class: A high-level representation of a 3D mesh with methods for optimization and simplification
• EncodedMesh class: A container for encoded mesh data
• Encoding and decoding functions for meshes

File I/O

• Save and load arrays to/from files
• Save and load meshes to/from ZIP files
• Save and load multiple arrays to/from ZIP files
• Combined storage of meshes and arrays in a single ZIP file

Usage Examples

Working with Meshes

from meshoptimizer.export import Mesh, encode_mesh, decode_mesh

# Create a mesh
mesh = Mesh(vertices, indices)

# Optimize the mesh
mesh.optimize_vertex_cache()
mesh.optimize_overdraw()
mesh.optimize_vertex_fetch()

# Simplify the mesh
mesh.simplify(target_ratio=0.5)  # Keep 50% of triangles

# Encode the mesh for storage or transmission
encoded_mesh = mesh.encode()
# or
encoded_mesh = encode_mesh(vertices, indices)

# Decode the mesh
decoded_mesh = Mesh.decode(encoded_mesh)
# or
decoded_vertices, decoded_indices = decode_mesh(encoded_mesh)

File I/O

from meshoptimizer.export import (
    save_array_to_file, load_array_from_file,
    save_mesh_to_zip, load_mesh_from_zip,
    save_arrays_to_zip, load_arrays_from_zip
)

# Save and load arrays to/from files
save_array_to_file(array, "array.bin")
loaded_array = load_array_from_file("array.bin")

# Save and load meshes to/from ZIP files
save_mesh_to_zip(mesh, "mesh.zip")
loaded_mesh = load_mesh_from_zip(Mesh, "mesh.zip")

# Save and load multiple arrays to/from ZIP files
arrays = {"positions": positions, "normals": normals, "colors": colors}
save_arrays_to_zip(arrays, "arrays.zip")
loaded_arrays = load_arrays_from_zip("arrays.zip")

Combined Storage

from meshoptimizer.export import save_combined_data_to_zip, load_combined_data_from_zip

# Save mesh and arrays together
save_combined_data_to_zip(
    encoded_mesh=encoded_mesh,
    encoded_arrays={"normals": encoded_normals, "colors": encoded_colors},
    metadata={"name": "MyMesh", "version": "1.0"},
    zip_path="combined_data.zip"
)

# Load combined data
loaded_mesh, loaded_arrays, loaded_metadata = load_combined_data_from_zip("combined_data.zip")

Integration with Other Tools

This package is designed to work well with other tools and libraries:

• Use with NumPy for efficient array operations
• Export optimized meshes to game engines
• Store compressed mesh data efficiently
• Process large datasets with minimal memory usage

Performance Considerations

• Mesh encoding significantly reduces data size (typically 3-5x compression)
• ZIP compression provides additional size reduction
• Optimized meshes render faster on GPUs
• Simplified meshes maintain visual quality with fewer triangles

Development and Contributing

Testing

Run the test suite with unittest:

python -m unittest discover

Continuous Integration

This project uses GitHub Actions for continuous integration:

• Automated tests run on push to main and on pull requests
• Tests run on multiple Python versions (3.8, 3.9, 3.10, 3.11)

Releasing to PyPI

To release a new version:

1. Update the version number manually in setup.py
2. Create a new release on GitHub with a tag matching the version (e.g., v0.1.0)
3. The GitHub Actions workflow will automatically build and publish the package to PyPI

Note: Publishing to PyPI requires a PyPI API token stored as a GitHub secret named PYPI_API_TOKEN.