Ground Truth usage cases

We provide for each image a single (image-like) ground truth file encoding semantic-, instance-, and parts- levels annotations. Our compact Label format together with panoptic_parts.utils.format.decode_uids() function enable easy decoding of the labels for various image understanding tasks including:

# labels: Python int, or np.ndarray, or tf.Tensor, or torch.tensor

# Semantic Segmentation
semantic_ids, _, _ = decode_uids(labels)

# Instance Segmentation
semantic_ids, instance_ids, _ = decode_uids(labels)

# Panoptic Segmentation
_, _, _, semantic_instance_ids = decode_uids(labels, return_sids_iids=True)

# Parts Segmentation / Parts Parsing
_, _, _, semantic_parts_ids = decode_uids(labels, return_sids_pids=True)

# Instance-level Parts Parsing
semantic_ids, instance_ids, parts_ids = decode_uids(labels)

# Parts-level Panoptic Segmentation
_, _, _, semantic_instance_ids, semantic_parts_ids = decode_uids(labels, return_sids_iids=True, return_sids_pids=True)