fiftyone.core.stages.MatchLabels

Selects the samples from a collection that contain (or do not contain) at least one label that matches the specified criteria.

Note that, unlike SelectLabels and FilterLabels, this stage will not filter the labels themselves; it only selects the corresponding samples.

You can perform a selection via one or more of the following methods:

• Provide the labels argument, which should contain a list of dicts in the format returned by fiftyone.core.session.Session.selected_labels, to match specific labels
• Provide the ids argument to match labels with specific IDs
• Provide the tags argument to match labels with specific tags
• Provide the filter argument to match labels based on a boolean fiftyone.core.expressions.ViewExpression that is applied to each individual fiftyone.core.labels.Label element
• Pass bool=False to negate the operation and instead match samples that do not contain at least one label matching the specified criteria

If multiple criteria are specified, labels must match all of them in order to trigger a sample match.

By default, the selection is applied to all fiftyone.core.labels.Label fields, but you can provide the fields argument to explicitly define the field(s) in which to search.

Examples:

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart")

#
# Only show samples whose labels are currently selected in the App
#

session = fo.launch_app(dataset)

# Select some labels in the App...

stage = fo.MatchLabels(labels=session.selected_labels)
view = dataset.add_stage(stage)

#
# Only include samples that contain labels with the specified IDs
#

# Grab some label IDs
ids = [
    dataset.first().ground_truth.detections[0].id,
    dataset.last().predictions.detections[0].id,
]

stage = fo.MatchLabels(ids=ids)
view = dataset.add_stage(stage)

print(len(view))
print(view.count("ground_truth.detections"))
print(view.count("predictions.detections"))

#
# Only include samples that contain labels with the specified tags
#

# Grab some label IDs
ids = [
    dataset.first().ground_truth.detections[0].id,
    dataset.last().predictions.detections[0].id,
]

# Give the labels a "test" tag
dataset = dataset.clone()  # create copy since we're modifying data
dataset.select_labels(ids=ids).tag_labels("test")

print(dataset.count_values("ground_truth.detections.tags"))
print(dataset.count_values("predictions.detections.tags"))

# Retrieve the labels via their tag
stage = fo.MatchLabels(tags="test")
view = dataset.add_stage(stage)

print(len(view))
print(view.count("ground_truth.detections"))
print(view.count("predictions.detections"))

#
# Only include samples that contain labels matching a filter
#

filter = F("confidence") > 0.99
stage = fo.MatchLabels(filter=filter, fields="predictions")
view = dataset.add_stage(stage)

print(len(view))
print(view.count("ground_truth.detections"))
print(view.count("predictions.detections"))