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Source code for flash.core.integrations.fiftyone.utils

from itertools import chain
from typing import Dict, List, Optional, Type, Union

import flash
from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import, requires

if _FIFTYONE_AVAILABLE:
    fo = lazy_import("fiftyone")
    fol = lazy_import("fiftyone.core.labels")
    Label = "fiftyone.Label"
    Session = "fiftyone.Session"
    SampleCollection = "fiftyone.core.collections.SampleCollection"
else:
    fo = None
    fol = None
    Label = object
    Session = object
    SampleCollection = object


[docs]@requires("fiftyone") def visualize( predictions: Union[List[Label], List[Dict[str, Label]]], filepaths: Optional[List[str]] = None, label_field: Optional[str] = "predictions", wait: Optional[bool] = False, **kwargs, ) -> Optional[Session]: """Visualizes predictions from a model with a FiftyOne Output in the :ref:`FiftyOne App <fiftyone:fiftyone-app>`. This method can be used in all of the following environments: - **Local Python shell**: The App will launch in a new tab in your default web browser. - **Remote Python shell**: Pass the ``remote=True`` option to this method and then follow the instructions printed to your remote shell to open the App in your browser on your local machine. - **Jupyter notebook**: The App will launch in the output of your current cell. - **Google Colab**: The App will launch in the output of your current cell. - **Python script**: Pass the ``wait=True`` option to block execution of your script until the App is closed. See :ref:`this page <fiftyone:environments>` for more information about using the FiftyOne App in different environments. Args: predictions: Can be either a list of FiftyOne labels that will be matched with the corresponding ``filepaths``, or a list of dictionaries with "filepath" and "predictions" keys that contains the filepaths and predictions. filepaths: A list of filepaths to images or videos corresponding to the provided ``predictions``. label_field: The name of the label field in which to store the predictions in the FiftyOne dataset. wait: Whether to block execution until the FiftyOne App is closed. **kwargs: Optional keyword arguments for :meth:`fiftyone:fiftyone.core.session.launch_app`. Returns: a :class:`fiftyone:fiftyone.core.session.Session` """ if flash._IS_TESTING: return None # Flatten list if batches were used if all(isinstance(fl, list) for fl in predictions): predictions = list(chain.from_iterable(predictions)) if all(isinstance(fl, dict) for fl in predictions): filepaths = [lab["filepath"] for lab in predictions] labels = [lab["predictions"] for lab in predictions] else: labels = predictions if filepaths is None: raise ValueError("The `filepaths` argument is required if filepaths are not provided in `labels`.") dataset = fo.Dataset() if filepaths: dataset.add_samples([fo.Sample(filepath=f, **{label_field: l}) for f, l in zip(filepaths, labels)]) session = fo.launch_app(dataset, **kwargs) if wait: session.wait() return session
class FiftyOneLabelUtilities: """Helper class providing useful methods for working with ``SampleCollection`` datasets from FiftyOne. Args: label_field: The field in the ``SampleCollection`` containing the ground truth labels. label_cls: The ``FiftyOne.Label`` subclass to expect ground truth labels to be instances of. If ``None``, defaults to ``FiftyOne.Label``. """ def __init__(self, label_field: str = "ground_truth", label_cls: Optional[Type[Label]] = None): super().__init__() self.label_field = label_field self.label_cls = label_cls or fol.Label def validate(self, sample_collection: SampleCollection): label_type = sample_collection._get_label_field_type(self.label_field) if not issubclass(label_type, self.label_cls): raise ValueError(f"Expected field '{self.label_field}' to have type {self.label_cls}; found {label_type}") def get_classes(self, sample_collection: SampleCollection): classes = sample_collection.classes.get(self.label_field, None) if not classes: classes = sample_collection.default_classes if not classes: label_path = sample_collection._get_label_field_path(self.label_field, "label")[1] classes = sample_collection.distinct(label_path) return classes

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