Tagging

Scopes

Note that there are two different scopes to which you can apply tags. One is the TagScope.DATA scope which applies tags to the images. The second one is the TagScope.LABEL, which applies to individual labels. The TagScope.LABEL can be present multiple times in the same image if there are multiple labels with that tag.

Tagging Data

Tagging data is based on unique identifiers of the data you are tagging. For data tagging, the identifier is a composition of label_hash, data_hash, and frame.

To iterate over the data in a project and add tags is to follow this structure.

from pathlib import Path

from encord_active.lib.common.iterator import DatasetIterator
from encord_active.lib.db.connection import DBConnection
from encord_active.lib.db.merged_metrics import MergedMetrics
from encord_active.lib.project.project_file_structure import ProjectFileStructure

project_path = Path("/path/to/your/project/root")
with DBConnection(ProjectFileStructure(project_path)) as conn:
    metrics = MergedMetrics(conn).all()

iterator = DatasetIterator(project_path)
for data_unit, image in iterator.iterate():
    identifier = f"{iterator.label_hash}_{iterator.du_hash}_{iterator.frame:05d}"
    tags = metrics.get_row(identifier).tags
    tags.append(data_tag)  # Only use TagScope.DATA tags here.
    metrics.update_tags(identifier, tags)

Tagging Labels

Tagging labels is very similar to tagging data. You will need to append the classificationHash or the objectHash for the label you are tagging to the identifier - as done below.

from pathlib import Path

from encord_active.lib.common.iterator import DatasetIterator
from encord_active.lib.db.connection import DBConnection
from encord_active.lib.db.merged_metrics import MergedMetrics
from encord_active.lib.project.project_file_structure import ProjectFileStructure

project_path = Path("/path/to/your/project/root")
with DBConnection(ProjectFileStructure(project_path)) as conn:
    metrics = MergedMetrics(conn).all()

iterator = DatasetIterator(project_path)
for data_unit, image in iterator.iterate():
    # For bounding boxes and polygons
    for obj in data_unit.get("labels", {}).get("objects", []):
        obj_hash = obj["objectHash"]
        identifier = f"{iterator.label_hash}_{iterator.du_hash}_{iterator.frame:05d}_{obj_hash}"

        tags = metrics.get_row(identifier).tags
        tags.append(label_tag)  # Only use TagScope.LABEL tags here.
        metrics.update_tags(identifier, tags)

    # For frame-level classifications
    for obj in data_unit.get("labels", {}).get("classifications", []):
        clf_hash = obj["classificationHash"]
        identifier = f"{iterator.label_hash}_{iterator.du_hash}_{iterator.frame:05d}_{clf_hash}"

        tags = metrics.get_row(identifier).tags
        tags.append(label_tag)  # Only use TagScope.LABEL tags here.
        metrics.update_tags(identifier, tags)