fiftyone.core.patches._PatchesView

Undocumented

Undocumented

Deletes all patches that are not in this view from the underlying dataset.

Deletes all patch field(s) that have been excluded from this view from the underlying dataset.

Reloads the view.

Note that PatchView instances are not singletons, so any in-memory patches extracted from this view will not be updated by calling this method.

Saves the patches in this view to the underlying dataset.

If this view contains any additional fields that were not extracted from the underlying dataset, these fields are not saved.

This method does not delete patches from the underlying dataset that this view excludes.

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

Examples:

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

dataset = foz.load_zoo_dataset("quickstart")

#
# Populate a new boolean attribute on all high confidence labels
#

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)

print(dataset.count("predictions.detections"))
print(len(label_ids))
print(dataset.count_values("predictions.detections.high_conf"))

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

When setting a sample field embedded.field.name, this function is an efficient implementation of the following loop:

for sample, value in zip(sample_collection, values):
    sample.embedded.field.name = value
    sample.save()

When setting an embedded field that contains an array, say embedded.array.field.name, this function is an efficient implementation of the following loop:

for sample, array_values in zip(sample_collection, values):
    for doc, value in zip(sample.embedded.array, array_values):
        doc.field.name = value

    sample.save()

When setting a frame field frames.embedded.field.name, this function is an efficient implementation of the following loop:

for sample, frame_values in zip(sample_collection, values):
    for frame, value in zip(sample.frames.values(), frame_values):
        frame.embedded.field.name = value

    sample.save()

When setting an embedded frame field that contains an array, say frames.embedded.array.field.name, this function is an efficient implementation of the following loop:

for sample, frame_values in zip(sample_collection, values):
    for frame, array_values in zip(sample.frames.values(), frame_values):
        for doc, value in zip(frame.embedded.array, array_values):
            doc.field.name = value

    sample.save()

When values is a dict mapping keys in key_field to values, then this function is an efficient implementation of the following loop:

for key, value in values.items():
    sample = sample_collection.one(F(key_field) == key)
    sample.embedded.field.name = value
    sample.save()

When setting frame fields using the dict values syntax, each value in values may either be a list corresponding to the frames of the sample matching the given key, or each value may itself be a dict mapping frame numbers to values. In the latter case, this function is an efficient implementation of the following loop:

for key, frame_values in values.items():
    sample = sample_collection.one(F(key_field) == key)
    for frame_number, value in frame_values.items():
        frame = sample[frame_number]
        frame.embedded.field.name = value

    sample.save()

You can also update list fields using the dict values syntax, in which case this method is an efficient implementation of the natural nested list modifications of the above sample/frame loops.

The dual function of set_values is values, which can be used to efficiently extract the values of a field or embedded field of all samples in a collection as lists of values in the same structure expected by this method.

Examples:

import random

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

dataset = foz.load_zoo_dataset("quickstart")

#
# Create a new sample field
#

values = [random.random() for _ in range(len(dataset))]
dataset.set_values("random", values)

print(dataset.bounds("random"))

#
# Add a tag to all low confidence labels
#

view = dataset.filter_labels("predictions", F("confidence") < 0.06)

detections = view.values("predictions.detections")
for sample_detections in detections:
    for detection in sample_detections:
        detection.tags.append("low_confidence")

view.set_values("predictions.detections", detections)

print(dataset.count_label_tags())