I built haralickt: > 20x faster GPU-accelerated Haralick texture features (GLCM) in PyTorch with identical precision to Mahotas

A developer released haralickt, an open-source PyTorch library that computes Gray-Level Co-occurrence Matrices (GLCM) and Haralick texture features on GPUs using vectorized matrix operations and CUDA. The library claims to run more than 20x faster than CPU implementations such as mahotas and scikit-image on high-resolution images and batches, while matching mahotas' numerical precision. It is available via pip under the MPL 2.0 license and works directly with torch.Tensor on GPU without CPU-GPU transfers.

Hi everyone If you work with texture analysis medical imaging, satellite data, or industrial defect detection , you've likely noticed that classic libraries like mahotas and scikit-image compute Gray-Level Co-occurrence Matrices GLCM and Haralick features strictly on the CPU. On large datasets, this quickly becomes a major pipeline bottleneck. To solve this, I wrote and open-sourced haralickt — a library that offloads these computations to the GPU using vectorized matrix operations in PyTorch and CUDA. Key highlights: • 20x faster than traditional CPU implementations on high-resolution images and batches. • Identical numerical precision to classic implementations verified against mahotas . • PyTorch-native: works seamlessly with torch.Tensor on GPU without costly CPU-GPU data transfers. • Open Source MPL 2.0 : free to use in research and commercial projects. Quick installation: bash pip install haralickt Используйте код с осторожностью. GitHub repository & benchmarks: https://github.com/SergeiyVaryukhin/haralickt-1.0.0 https://github.com/SergeiyVaryukhin/haralickt-1.0.0 This is my first public release v1.0.0 , so I would really appreciate any feedback, bug reports, or ideas for new features. If you find it useful for your projects, a star on GitHub would mean a lot