hello.fiftyone.view module

hello.fiftyone.view.exclude_labels(dataset, labels=None, ids=None, tags=None, fields=None, omit_empty=True)

Excludes the specified labels from the collection.

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• labels (None) – a list of dicts specifying the labels to exclude in the format returned by fiftyone.core.session.Session.selected_labels()

• ids (None) – an ID or iterable of IDs of the labels to exclude

• tags (None) – a tag or iterable of tags of labels to exclude

• fields (None) – a field or iterable of fields from which to exclude

• omit_empty (True) – whether to omit samples that have no labels after filtering

Returns:

a fiftyone.core.view.DatasetView

hello.fiftyone.view.filter_duplicate_labels(dataset, label_field, iou_thresh=0.999, method='simple', iscrowd=None, classwise=False)

Delete duplicate labels in the given field of the dataset, as defined as labels with an IoU greater than a chosen threshold with another label in the field.

Parameters:

• dataset – a fiftyone.core.dataset.Dataset

• label_field – a label field of type fiftyone.core.labels.Detections or fiftyone.core.labels.Polylines

• iou_thresh (0.999) – the IoU threshold to use to determine whether labels are duplicates

• method ("simple") – supported values are ("simple", "greedy")

• iscrowd (None) – an optional name of a boolean attribute

• classwise (False) – different label values as always non-overlapping

hello.fiftyone.view.filter_field(dataset, field, a, op, b, only_matches=True)

Filters the values of a field or embedded field of each sample in the collection.

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• field – the label field to filter

• only_matches (True) – whether to only include samples with at least one label after filtering (True) or include all samples (False)

hello.fiftyone.view.filter_labels(dataset, field, expression, only_matches=True)

Filters the fiftyone.core.labels.Label field of each sample in the collection.

>>> from fiftyone import ViewField as F
>>> filter_labels(dataset, "ground_truth", F("label") == "house")
>>> filter_labels(dataset, "ground_truth", F("label").is_in(["cat", "dog"]))
>>> filter_labels(dataset, "ground_truth", ~F("label").is_in(["cat", "dog"]), only_matches=False)
>>> filter_labels(dataset, "predictions", F("confidence") > 0.8)

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• field – the label field to filter

• expression – a fiftyone.core.expressions.ViewExpression

• only_matches (True) – whether to only include samples with at least one label after filtering (True) or include all samples (False)

hello.fiftyone.view.filter_samples(dataset, expression)

Filters the samples in the collection by the given filter.

>>> from fiftyone import ViewField as F
>>> filter_samples(dataset, F("filepath").ends_with(".jpg"))
>>> filter_samples(dataset, F("predictions.detections").length() >= 2)
>>> # Only include samples whose `predictions` field contains at least
>>> # one object with area smaller than 0.2
>>> bbox = F("bounding_box")
>>> bbox_area = bbox[2] * bbox[3]
>>> small_boxes = F("predictions.detections").filter(bbox_area < 0.2)
>>> view = dataset.match(small_boxes.length() > 0)

Parameters:

• dataset – a fiftyone.core.dataset.Dataset

• expression – a fiftyone.core.expressions.ViewExpression

hello.fiftyone.view.labeled(dataset, field_name='ground_truth')

hello.fiftyone.view.match_labels(dataset, labels=None, ids=None, tags=None, filter=None, fields=None, bool=None)

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

Note that, unlike select_labels() and filter_labels(), this stage will not filter the labels themselves; it only selects the corresponding samples.

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• labels (None) – by fiftyone.core.session.Session.selected_labels()

• ids (None) – an ID or iterable of IDs of the labels to select

• tags (None) – a tag or iterable of tags of labels to select

• filter (None) – a fiftyone.core.expressions.ViewExpression

• fields (None) – a field or iterable of fields from which to select

• bool (None) – have (None or True) or do not have (False) at least one label

Returns:

a fiftyone.core.view.DatasetView

hello.fiftyone.view.match_tags(dataset, tags, bool=None)

Returns a view containing the samples in the collection that have (or do not have) any of the given tag(s).

Parameters:

• dataset – a fiftyone.core.dataset.Dataset

• tags – the tag or iterable of tags to match

• bool (None) – whether to match samples that have (None or True) or do not have (False) the given tags

Returns:

a DatasetView

hello.fiftyone.view.select_labels(dataset, labels=None, ids=None, tags=None, fields=None, omit_empty=True)

Selects only the specified labels from the collection.

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• labels (None) – a list of dicts specifying the labels to select in the format returned by fiftyone.core.session.Session.selected_labels()

• ids (None) – an ID or iterable of IDs of the labels to select

• tags (None) – a tag or iterable of tags of labels to select

• fields (None) – a field or iterable of fields from which to select

• omit_empty (True) – whether to omit samples that have no labels after filtering

Returns:

a fiftyone.core.view.DatasetView

hello.fiftyone.view.set_label_values(dataset, field_name, values, **kwargs)

Sets the fields of the specified labels in the collection to the given values.

Examples:

view = dataset.filter_labels("predictions", F("confidence") > 0.99)

label_ids = view.values("predictions.detections.id", unwind=True)
values = {_id: True for _id in label_ids}

dataset.set_label_values("predictions.detections.high_conf", values)

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• field_name – a field or embedded.field.name

• values – a dict mapping label IDs to values

hello.fiftyone.view.set_values(dataset, field_name, values, **kwargs)

Sets the field or embedded field on each sample or frame in the collection to the given values.

Parameters:

• dataset – a fiftyone.core.collections.SampleCollection

• field_name – a field or embedded.field.name

• values – an iterable of values, one for each sample in the collection

hello.fiftyone.view.sort_by_filename(dataset)

hello.fiftyone.view.uniqueness(dataset, count, brain_key='img_sim', model=None)

The uniqueness of a Dataset.

Parameters:

• dataset – a fiftyone.core.dataset.Dataset

• count (int, float) – specific int value or percentage

• model (None) – a fiftyone.core.models.Model or the name of a model

Examples:

>>> model: 'clip-vit-base32-torch'  # pip install ftfy regex
>>> model: 'mobilenet-v2-imagenet-torch'
>>> model: 'resnet50-imagenet-torch', 'resnet101-imagenet-torch', 'resnet152-imagenet-torch'
>>> model: 'resnext50-32x4d-imagenet-torch', 'resnext101-32x8d-imagenet-torch'
>>> model: 'wide-resnet50-2-imagenet-torch', 'wide-resnet101-2-imagenet-torch'

Returns:

a DatasetView

hello.fiftyone.view.unlabeled(dataset, field_name='ground_truth')