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51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
# ------------------------------------------------------------------------
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# RF-DETR
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# Copyright (c) 2025 Roboflow. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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"""Focused predict() contract tests for keypoint and non-keypoint outputs."""
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import numpy as np
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import PIL.Image
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import supervision as sv
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from .helpers import _DummyModel, _DummyRFDETR
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def test_predict_returns_supervision_keypoints() -> None:
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"""Keypoint model predictions return ``sv.KeyPoints`` with detection details."""
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image = PIL.Image.new("RGB", (64, 48), color=(128, 128, 128))
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model = _DummyRFDETR()
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model.model = _DummyModel(labels=[0, 1], include_keypoints=True)
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key_points = model.predict(image)
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assert isinstance(key_points, sv.KeyPoints)
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assert key_points.xy.shape == (2, 17, 2)
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assert key_points.keypoint_confidence.shape == (2, 17)
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assert key_points.data["xyxy"].shape == (2, 4)
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assert key_points.detection_confidence.shape == (2,)
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assert np.isfinite(key_points.xy).all()
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assert np.isfinite(key_points.keypoint_confidence).all()
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assert "keypoint_precision_cholesky" in key_points.data
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keypoint_precision = key_points.data["keypoint_precision_cholesky"]
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assert isinstance(keypoint_precision, np.ndarray)
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assert keypoint_precision.shape == (2, 17, 3)
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assert np.isfinite(keypoint_precision).all()
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assert "source_image" in key_points.data
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assert len(key_points.data["source_image"]) == 2
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def test_predict_default_detection_without_keypoints_unchanged() -> None:
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"""Default detection prediction keeps legacy output structure."""
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image = PIL.Image.new("RGB", (64, 48), color=(128, 128, 128))
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model = _DummyRFDETR()
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detections = model.predict(image)
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assert "keypoints" not in detections.data
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assert not hasattr(detections, "keypoints")
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assert "class_name" in detections.data
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assert "source_shape" in detections.data
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assert detections.data["source_shape"].shape[1] == 2
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