PyImSegm

Provides a complete workflow from superpixel segmentation to evaluation, as shown in the sample notebooks for supervised and unsupervised approaches, enabling reproducible research.

Includes compiled Cython functions for descriptor computation, significantly accelerating processing compared to pure NumPy, with automatic fallback if compilation fails.

Implements region growing with learned statistical shape properties, such as ray features, to ensure plausible segmentations for objects like Drosophila egg chambers.

Supports both supervised and unsupervised segmentation, accommodating varying annotation levels, as detailed in the notebooks for different use cases.