120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
from __future__ import annotations
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import warnings
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from collections.abc import Callable
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import numpy as np
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import pytest
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from supervision.annotators.core import PercentageBarAnnotator
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from supervision.annotators.utils import validate_labels
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from supervision.detection.core import Detections, validate_fields_both_defined_or_none
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from supervision.detection.vlm import VLM, validate_vlm_parameters
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from supervision.metrics.detection import validate_input_tensors
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from supervision.validators import (
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_validate_detections_fields,
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_validate_resolution,
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validate_class_id,
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validate_confidence,
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validate_data,
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validate_detections_fields,
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validate_key_point_confidence,
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validate_key_points_fields,
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validate_keypoint_confidence,
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validate_keypoints_fields,
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validate_mask,
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validate_resolution,
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validate_tracker_id,
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validate_xy,
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validate_xyxy,
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)
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def _detections() -> Detections:
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return Detections(
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xyxy=np.array([[0, 0, 1, 1]]),
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confidence=np.array([0.5]),
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class_id=np.array([0]),
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)
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@pytest.mark.parametrize(
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("call", "version"),
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[
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(lambda: validate_xyxy(np.array([[0, 0, 1, 1]])), "0.29.0"),
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(lambda: validate_mask(np.array([[[True]]]), 1), "0.29.0"),
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(lambda: validate_class_id(np.array([0]), 1), "0.29.0"),
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(lambda: validate_confidence(np.array([0.5]), 1), "0.29.0"),
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(lambda: validate_key_point_confidence(np.array([[0.5]]), 1, 1), "0.29.0"),
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(lambda: validate_keypoint_confidence(np.array([[0.5]]), 1, 1), "0.27.0"),
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(lambda: validate_tracker_id(np.array([1]), 1), "0.29.0"),
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(lambda: validate_data({"id": [1]}, 1), "0.29.0"),
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(lambda: validate_xy(np.array([[[0, 0]]]), 1, 1), "0.29.0"),
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(
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lambda: validate_detections_fields(
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np.array([[0, 0, 1, 1]]),
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None,
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None,
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None,
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None,
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{},
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),
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"0.29.0",
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),
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(
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lambda: validate_key_points_fields(np.array([[[0, 0]]]), None, None, {}),
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"0.29.0",
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),
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(
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lambda: validate_keypoints_fields(np.array([[[0, 0]]]), None, None, {}),
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"0.27.0",
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),
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(lambda: validate_resolution((1, 1)), "0.29.0"),
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(
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lambda: validate_vlm_parameters(
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VLM.PALIGEMMA, "", {"resolution_wh": (1, 1)}
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),
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"0.29.0",
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),
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(
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lambda: validate_fields_both_defined_or_none(_detections(), _detections()),
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"0.29.0",
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),
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(lambda: validate_labels(None, _detections()), "0.29.0"),
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(
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lambda: PercentageBarAnnotator.validate_custom_values([0.5], _detections()),
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"0.29.0",
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),
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(
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lambda: validate_input_tensors(
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[np.empty((0, 6), dtype=np.float32)],
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[np.empty((0, 5), dtype=np.float32)],
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),
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"0.29.0",
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),
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],
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)
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def test_validate_public_shims_warn(call: Callable[[], object], version: str) -> None:
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with pytest.warns(FutureWarning, match=f"deprecated since v{version}"):
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call()
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def test_private_validation_paths_do_not_warn() -> None:
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with warnings.catch_warnings(record=True) as recorded_warnings:
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warnings.simplefilter("always")
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_validate_resolution((1, 1))
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_validate_detections_fields(
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np.array([[0, 0, 1, 1]]),
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None,
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None,
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None,
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None,
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{},
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)
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PercentageBarAnnotator._validate_custom_values([0.5], _detections())
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future_warnings = [
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warning for warning in recorded_warnings if warning.category is FutureWarning
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]
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assert future_warnings == []
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