3 core components of the COCO data format—images, annotations, and categories—are programmatically generated by this suite of Python helper functions. The utility transforms binary masks and image metadata into the standardized JSON structure required for training object detection and instance segmentation models.
Use Cases
- Convert binary segmentation masks into COCO-compliant 'segmentation' polygons for training instance segmentation models.
- Automate the population of the 'images' list by extracting 'file_name', 'height', and 'width' from local image files.
- Map unique class labels to 'category_id' integers to build the 'categories' section of the annotation file.
Strengths
- Generates JSON files containing the standard 'images', 'annotations', and 'categories' root keys.
- Includes functions to convert binary masks into polygon coordinates for the 'segmentation' field.
- Supports the automated calculation of 'bbox' coordinates and 'area' attributes for each object instance.