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HBB2OBB is a Python tool designed to convert horizontal bounding boxes (HBBs), also known as axis-aligned bounding boxes, into oriented bounding boxes (OBBs), also referred to as rotated bounding boxes, using segmentation models from the SAM (Segment Anything Model) family. This tool addresses a critical need in object detection tasks where objects appear in arbitrary orientations, such as in aerial imagery, satellite data, or traffic monitoring scenarios. The conversion utilizes user-provided HBB annotations as prompts for SAM models, leveraging their state-of-the-art segmentation capabilities to accurately delineate object boundaries and generate precise OBBs that better encapsulate non-upright objects.
The conversion process employs a model ensemble approach that combines masks from multiple segmentation models through majority voting, resulting in enhanced accuracy and robustness. The system implements spatial constraint techniques, including region-specific masking and contour refinement to ensure the segmentation remains within relevant object boundaries. The library supports flexible scaling of input HBBs (both positive and negative factors) to accommodate for potentially cropped object parts or overly conservative annotations. If no valid mask is detected, a fallback strategy maintains the original HBB as the OBB, ensuring consistent outputs.
Beyond conversion, HBB2OBB offers comprehensive evaluation tools to assess OBB accuracy against ground truth annotations, hyperparameter optimization capabilities to fine-tune the conversion process for specific datasets, and utilities for format conversion between COCO JSON and YOLO TXT. The package includes intuitive visualization features that render the conversion process transparently, displaying the progression from original HBBs through segmentation masks to final OBBs. Designed with ease of use in mind, HBB2OBB provides both an intuitive command-line interface and a flexible Python API for seamless integration into existing workflows.