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This commit introduces a high-performance Haralick texture analysis feature, implemented in Rust and exposed to Python. The new `haralick_features` function calculates four common texture metrics: contrast, dissimilarity, homogeneity, and entropy. It operates on 2D arrays and is designed for performance using parallel computation with Rayon. The Python wrapper integrates with `xarray` and Dask, allowing for parallel, out-of-memory computation on large geospatial datasets. It handles data quantization, chunking, and boundary conditions automatically. The implementation includes: - A new Rust module `src/texture.rs` with the core GLCM and Haralick feature calculations. - A Python wrapper in `python/eo_processor/__init__.py` with Dask integration. - Unit tests in `tests/test_texture.py` that validate the implementation against `scikit-image`. - A benchmark in `benchmarking/cli.py` to compare performance. - Updated dependencies in `tox.ini` and `pyproject.toml`.
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This commit introduces a high-performance Haralick texture analysis feature, implemented in Rust and exposed to Python. The new `haralick_features` function calculates four common texture metrics: contrast, dissimilarity, homogeneity, and entropy. It operates on 2D arrays and is designed for performance using parallel computation with Rayon. The Python wrapper integrates with `xarray` and Dask, allowing for parallel, out-of-memory computation on large geospatial datasets. It handles data quantization, chunking, and boundary conditions automatically. The implementation includes: - A new Rust module `src/texture.rs` with the core GLCM and Haralick feature calculations. - A Python wrapper in `python/eo_processor/__init__.py` with Dask integration. - Unit tests in `tests/test_texture.py` that validate the implementation against `scikit-image`. - A benchmark in `benchmarking/cli.py` to compare performance. - Updated dependencies in `tox.ini` and `pyproject.toml`. All code has been formatted with `cargo fmt` and `ruff format`, and all linter checks pass.
This commit introduces a high-performance Haralick texture analysis feature, implemented in Rust and exposed to Python. The new `haralick_features` function calculates four common texture metrics: contrast, dissimilarity, homogeneity, and entropy. It operates on 2D arrays and is designed for performance using parallel computation with Rayon. The Python wrapper integrates with `xarray` and Dask, allowing for parallel, out-of-memory computation on large geospatial datasets. It handles data quantization, chunking, and boundary conditions automatically. The implementation includes: - A new Rust module `src/texture.rs` with the core GLCM and Haralick feature calculations. - A Python wrapper in `python/eo_processor/__init__.py` with Dask integration. - Unit tests in `tests/test_texture.py` that validate the implementation against `scikit-image`. - A benchmark in `benchmarking/cli.py` to compare performance. - Updated dependencies in `tox.ini` and `pyproject.toml`. All code has been formatted with `cargo fmt` and `ruff format`, and all linter checks pass.
This commit introduces a high-performance Haralick texture analysis feature, implemented in Rust and exposed to Python. The new `haralick_features` function calculates four common texture metrics: contrast, dissimilarity, homogeneity, and entropy. It operates on 2D arrays and is designed for performance using parallel computation with Rayon. The Python wrapper integrates with `xarray` and Dask, allowing for parallel, out-of-memory computation on large geospatial datasets. It handles data quantization, chunking, and boundary conditions automatically. The implementation includes: - A new Rust module `src/texture.rs` with the core GLCM and Haralick feature calculations. - A Python wrapper in `python/eo_processor/__init__.py` with Dask integration. - Unit tests in `tests/test_texture.py` that validate the implementation against `scikit-image`. - A benchmark in `benchmarking/cli.py` to compare performance. - Updated dependencies in `tox.ini` and `pyproject.toml`. All code has been formatted with `cargo fmt` and `ruff format`, and all linter and test checks pass.
This commit introduces a high-performance Haralick texture analysis feature, implemented in Rust and exposed to Python. The new `haralick_features` function calculates four common texture metrics: contrast, dissimilarity, homogeneity, and entropy. It operates on 2D arrays and is designed for performance using parallel computation with Rayon. The Python wrapper integrates with `xarray` and Dask, allowing for parallel, out-of-memory computation on large geospatial datasets. It handles data quantization, chunking, and boundary conditions automatically. The implementation includes: - A new Rust module `src/texture.rs` with the core GLCM and Haralick feature calculations. - A Python wrapper in `python/eo_processor/__init__.py` with Dask integration. - Unit tests in `tests/test_texture.py` that validate the implementation against `scikit-image`. - A benchmark in `benchmarking/cli.py` to compare performance. - Updated dependencies in `pyproject.toml` to make `xarray` and `dask` core dependencies. All code has been formatted with `cargo fmt` and `ruff format`, and all linter and test checks pass.
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This submission adds a new Haralick texture analysis feature to the
eo-processorlibrary. The core logic is implemented in Rust for high performance and is exposed to Python with a user-friendly wrapper that integrates withxarrayand Dask. The feature includes four common Haralick texture metrics: contrast, dissimilarity, homogeneity, and entropy. The implementation is accompanied by unit tests, benchmarks, and updated dependencies.PR created automatically by Jules for task 11196418212471149361 started by @BnJam