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import fiftyone as fo
import fiftyone.utils.cvat as fouc

dataset = fo.Dataset("my-dataset")
fouc.import_annotations(
    dataset, 
    task_ids=[...], 
    download_media=True,
)

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view = dataset.match(
    F("ground_truth.detections")
    .filter(F("condition") == True)
    .length() > 0
)

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for batch in range(0, len(dataset), batch_size):
    view = dataset[batch*batch_size : (batch+1) * batch_size]
    results = view.annotate(..., backend="labelstudio", project_name="my_project")

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mean_agg = fo.Mean("predictions.detections.confidence")
mean_value1 = dataset1.aggregate(mean)
mean_value2 = dataset2.aggregate(mean)

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# will count the number of samples in a dataset
sample_count = fo.Count()

# will retrieve the distinct labels in the `ground_truth` field
distinct_labels = fo.Distinct("ground_truth.detections.label")

# will compute a histogram of the `uniqueness` field
histogram_values = fo.HistogramValues("uniqueness")

# efficiently compute all three results

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## Option 1
aggregation = fo.Mean(F("predictions.detections.confidence") ** 2)
squared_conf_mean = dataset.aggregate(aggregation)

## Option 2
squared_conf_mean = dataset.mean(F("predictions.detections.confidence") ** 2))

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results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
    compute_mAP=True,
)

# Get the 10 most common classes in the dataset
counts = dataset.count_values("ground_truth.detections.label")
classes_top10 = sorted(counts, key=counts.get, reverse=True)[:10]

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results = dataset.evaluate_classifications(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
    method="binary",
    classes=["classA", "classB"],
)

results.print_report()

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pred_conf_field = "predictions.detections.confidence"
pred_confs_jagged = dataset.values(pred_conf_field)

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pred_conf_field = "predictions.detections.confidence"
pred_confs = dataset.values(pred_conf_field, unwind=True)