def visualize(labels: Union[List[Label], List[Dict[str, Label]]],
filepaths: Optional[List[str]] = None,
wait: Optional[bool] = True,
label_field: Optional[str] = "predictions",
**kwargs) -> Optional[Session]:
"""Use the result of a FiftyOne serializer to visualize predictions in the
FiftyOne App.
Args:
labels: Either a list of FiftyOne labels that will be applied to the
corresponding filepaths provided with through `filepath` or
`datamodule`. Or a list of dictionaries containing image/video
filepaths and corresponding FiftyOne labels.
filepaths: A list of filepaths to images or videos corresponding to the
provided `labels`.
wait: A boolean determining whether to launch the FiftyOne session and
wait until the session is closed or whether to return immediately.
label_field: The string of the label field in the FiftyOne dataset
containing predictions
"""
if not _FIFTYONE_AVAILABLE:
raise ModuleNotFoundError("Please, `pip install fiftyone`.")
if flash._IS_TESTING:
return None
# Flatten list if batches were used
if all(isinstance(fl, list) for fl in labels):
labels = list(chain.from_iterable(labels))
if all(isinstance(fl, dict) for fl in labels):
filepaths = [lab["filepath"] for lab in labels]
labels = [lab["predictions"] for lab in labels]
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_labeled_images(
list(zip(filepaths, labels)),
LabeledImageTupleSampleParser(),
label_field=label_field,
)
session = fo.launch_app(dataset, **kwargs)
if wait:
session.wait()
return session
def app_test():
dataset = foz.load_zoo_dataset("quickstart")
#dataset = foz.load_zoo_dataset("quickstart-video")
#dataset = foz.load_zoo_dataset("cifar10")
session = fo.launch_app(dataset, port=5151)
#session.show()
if False:
# View the dataset in the App.
session.dataset = dataset
elif False:
# Object patches.
# Convert to ground truth patches.
gt_patches = dataset.to_patches("ground_truth")
print(gt_patches)
# View patches in the App.
session.view = gt_patches
elif False:
# Evaluation patches.
# Evaluate 'predictions' w.r.t. labels in 'ground_truth' field.
dataset.evaluate_detections("predictions", gt_field="ground_truth", eval_key="eval")
# Convert to evaluation patches.
eval_patches = dataset.to_evaluation_patches("eval")
print(eval_patches)
print(eval_patches.count_values("type"))
# View patches in the App.
session.view = eval_patches
# Blocks execution until the App is closed.
session.wait()
)
trainer.save_checkpoint("image_classification_model.pt")
# 5 Predict from checkpoint on data with ground truth
model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
model.serializer = FiftyOneLabels(return_filepath=False)
datamodule = ImageClassificationData.from_fiftyone(predict_dataset=test_dataset)
predictions = trainer.predict(model, datamodule=datamodule)
predictions = list(chain.from_iterable(predictions)) # flatten batches
# 6 Add predictions to dataset
test_dataset.set_values("predictions", predictions)
# 7 Visualize labels in the App
session = fo.launch_app(test_dataset)
# 8 Evaluate your model
results = test_dataset.evaluate_classifications(
"predictions",
gt_field="ground_truth",
eval_key="eval",
)
results.print_report()
plot = results.plot_confusion_matrix()
plot.show()
# Only when running this in a script
# Block until the FiftyOne App is closed
session.wait()
def launch_fiftyone_app(coco_image_dir: str, coco_json_path: str):
dataset = create_fiftyone_dataset_from_coco_file(coco_image_dir,
coco_json_path)
session = fo.launch_app()
session.dataset = dataset
return session
import fiftyone as fo
import fiftyone.zoo as foz
# Blog: https://blog.csdn.net/fengbingchun/article/details/121284157
# reference: https://voxel51.com/docs/fiftyone/tutorials/evaluate_detections.html
datasets = foz.list_zoo_datasets()
print("available datasets:", datasets)
dataset = foz.load_zoo_dataset("coco-2017", split="validation", dataset_name="evaluate-detections-tutorial")
dataset.persistent = True
session = fo.launch_app(dataset)
# print some information about the dataset
print("dataset info:", dataset)
# print a ground truth detection
sample = dataset.first()
print("ground truth:", sample.ground_truth.detections[0])
session.wait()
import fiftyone as fo # Load your FiftyOne dataset dataset = ... # Launch the app locally # (if you're reading this from the app, you've already done this!) session = fo.launch_app() # Load a dataset session.dataset = dataset # Load a specific view into your dataset session.view = view
import fiftyone as fo # Load your FiftyOne dataset dataset = ... # Launch the app that you'll connect to from your local machine session = fo.launch_app(remote=True) # Load a dataset session.dataset = dataset # Load a specific view into your dataset session.view = view