2022-02-24 09:24:25 +08:00
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# Copyright (c) OpenMMLab. All rights reserved.
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2021-12-24 10:56:48 +08:00
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import numpy as np
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import pytest
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import torch
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from mmcv.ops import points_in_polygons
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@pytest.mark.skipif(
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not torch.cuda.is_available(), reason='requires CUDA support')
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def test_points_in_polygons():
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points = np.array([[300., 300.], [400., 400.], [100., 100], [300, 250],
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[100, 0]])
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polygons = np.array([[200., 200., 400., 400., 500., 200., 400., 100.],
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[400., 400., 500., 500., 600., 300., 500., 200.],
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[300., 300., 600., 700., 700., 700., 700., 100.]])
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expected_output = np.array([[0., 0., 0.], [0., 0., 1.], [0., 0., 0.],
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[1., 0., 0.], [0., 0., 0.]])
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points = torch.from_numpy(points).cuda().float()
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polygons = torch.from_numpy(polygons).cuda().float()
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expected_output = torch.from_numpy(expected_output).cuda().float()
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assert torch.allclose(
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points_in_polygons(points, polygons), expected_output, 1e-3)
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