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78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
# LICENSE HEADER MANAGED BY add-license-header
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#
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# Copyright 2018 Kornia Team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import pytest
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import torch
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from kornia.enhance.threshold import ThresholdType, threshold
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class TestThreshold:
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@pytest.mark.parametrize(
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"ttype",
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[
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ThresholdType.THRESH_BINARY,
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ThresholdType.THRESH_BINARY_INV,
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ThresholdType.THRESH_TRUNC,
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ThresholdType.THRESH_TOZERO,
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ThresholdType.THRESH_TOZERO_INV,
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],
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)
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@pytest.mark.parametrize("shape", [(1, 1, 5, 7), (2, 3, 11, 9)])
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def test_output_properties(self, ttype, shape, device, dtype):
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x = torch.rand(shape, device=device, dtype=dtype)
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out = threshold(x, thresh=0.5, maxval=1.0, type=ttype)
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assert out.shape == x.shape
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assert out.dtype == x.dtype
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assert out.device == x.device
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def test_binary_rule_strict_greater(self, device, dtype):
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x = torch.tensor([0.2, 0.5, 0.7], device=device, dtype=dtype)
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out = threshold(x, thresh=0.5, maxval=9.0, type=ThresholdType.THRESH_BINARY)
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expected = torch.tensor([0.0, 0.0, 9.0], device=device, dtype=dtype)
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assert torch.allclose(out, expected)
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def test_binary_inv_rule_strict_greater(self, device, dtype):
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x = torch.tensor([0.2, 0.5, 0.7], device=device, dtype=dtype)
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out = threshold(x, thresh=0.5, maxval=9.0, type=ThresholdType.THRESH_BINARY_INV)
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expected = torch.tensor([9.0, 9.0, 0.0], device=device, dtype=dtype)
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assert torch.allclose(out, expected)
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def test_trunc(self, device, dtype):
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x = torch.tensor([0.2, 0.5, 0.7], device=device, dtype=dtype)
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out = threshold(x, thresh=0.5, maxval=9.0, type=ThresholdType.THRESH_TRUNC)
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expected = torch.tensor([0.2, 0.5, 0.5], device=device, dtype=dtype)
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assert torch.allclose(out, expected)
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def test_tozero(self, device, dtype):
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x = torch.tensor([0.2, 0.5, 0.7], device=device, dtype=dtype)
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out = threshold(x, thresh=0.5, maxval=9.0, type=ThresholdType.THRESH_TOZERO)
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expected = torch.tensor([0.0, 0.0, 0.7], device=device, dtype=dtype)
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assert torch.allclose(out, expected)
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def test_tozero_inv(self, device, dtype):
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x = torch.tensor([0.2, 0.5, 0.7], device=device, dtype=dtype)
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out = threshold(x, thresh=0.5, maxval=9.0, type=ThresholdType.THRESH_TOZERO_INV)
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expected = torch.tensor([0.2, 0.5, 0.0], device=device, dtype=dtype)
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assert torch.allclose(out, expected)
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def test_otsu_raises(self, device, dtype):
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x = torch.rand(1, 1, 5, 5, device=device, dtype=dtype)
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with pytest.raises(NotImplementedError):
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threshold(x, thresh=0.0, maxval=1.0, type=ThresholdType.THRESH_OTSU)
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