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272 lines
9.6 KiB
Python
272 lines
9.6 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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from typing import Tuple
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import pytest
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import torch
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from kornia import enhance
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from kornia.geometry import rotate
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from testing.base import BaseTester
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class TestEqualization(BaseTester):
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def test_smoke(self, device, dtype):
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C, H, W = 1, 10, 20
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img = torch.rand(C, H, W, device=device, dtype=dtype)
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res = enhance.equalize_clahe(img)
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assert isinstance(res, torch.Tensor)
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assert res.shape == img.shape
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assert res.device == img.device
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assert res.dtype == img.dtype
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@pytest.mark.parametrize("B, C", [(None, 1), (None, 3), (1, 1), (1, 3), (4, 1), (4, 3)])
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def test_cardinality(self, B, C, device, dtype):
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H, W = 10, 20
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if B is None:
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img = torch.rand(C, H, W, device=device, dtype=dtype)
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else:
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img = torch.rand(B, C, H, W, device=device, dtype=dtype)
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res = enhance.equalize_clahe(img)
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assert res.shape == img.shape
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@pytest.mark.parametrize("clip, grid", [(0.0, None), (None, (2, 2)), (2.0, (2, 2))])
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def test_optional_params(self, clip, grid, device, dtype):
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C, H, W = 1, 10, 20
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img = torch.rand(C, H, W, device=device, dtype=dtype)
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if clip is None:
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res = enhance.equalize_clahe(img, grid_size=grid)
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elif grid is None:
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res = enhance.equalize_clahe(img, clip_limit=clip)
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else:
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res = enhance.equalize_clahe(img, clip, grid)
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assert isinstance(res, torch.Tensor)
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assert res.shape == img.shape
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@pytest.mark.parametrize(
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"B, clip, grid, exception_type, expected_error_msg",
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[
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(0, 1.0, (2, 2), ValueError, "Invalid input tensor, it is empty."), # from perform_keep_shape_image
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(1, 1, (2, 2), TypeError, "Input clip_limit type is not float. Got"),
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(1, 2.0, 2, TypeError, "Input grid_size type is not Tuple. Got"),
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(1, 2.0, (2, 2, 2), TypeError, "Input grid_size is not a Tuple with 2 elements. Got 3"),
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(1, 2.0, (2, 2.0), TypeError, "Input grid_size type is not valid, must be a Tuple[int, int]"),
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(1, 2.0, (2, 0), ValueError, "Input grid_size elements must be positive. Got"),
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],
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)
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def test_exception(self, B, clip, grid, exception_type, expected_error_msg):
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C, H, W = 1, 10, 20
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img = torch.rand(B, C, H, W)
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with pytest.raises(exception_type) as errinfo:
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enhance.equalize_clahe(img, clip, grid)
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assert expected_error_msg in str(errinfo)
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@pytest.mark.parametrize("dims", [(1, 1, 1, 1, 1), (1, 1)])
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def test_exception_tensor_dims(self, dims):
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img = torch.rand(dims)
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with pytest.raises(ValueError):
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enhance.equalize_clahe(img)
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def test_exception_tensor_type(self):
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with pytest.raises(TypeError):
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enhance.equalize_clahe([1, 2, 3])
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def test_gradcheck(self, device):
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torch.random.manual_seed(4)
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bs, channels, height, width = 1, 1, 11, 11
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inputs = torch.rand(bs, channels, height, width, device=device, dtype=torch.float64)
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def grad_rot(data, a, b, c):
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rot = rotate(data, torch.tensor(30.0, dtype=data.dtype, device=device))
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return enhance.equalize_clahe(rot, a, b, c)
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self.gradcheck(grad_rot, (inputs, 40.0, (2, 2), True), nondet_tol=1e-4)
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@pytest.mark.skip(reason="args and kwargs in decorator")
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def test_jit(self, device, dtype):
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batch_size, channels, height, width = 1, 2, 10, 20
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inp = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype)
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op = enhance.equalize_clahe
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op_script = torch.jit.script(op)
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self.assert_close(op(inp), op_script(inp))
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def test_module(self):
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# equalize_clahe is only a function
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pass
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@pytest.fixture()
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def img(self, device, dtype):
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height, width = 20, 20
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# TODO: test with a more realistic pattern
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img = torch.arange(width, device=device).div(float(width - 1))[None].expand(height, width)[None][None]
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return img
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def test_he(self, img):
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# should be similar to enhance.equalize but slower. Similar because the lut is computed in a different way.
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clip_limit: float = 0.0
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grid_size: Tuple = (1, 1)
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res = enhance.equalize_clahe(img, clip_limit=clip_limit, grid_size=grid_size)
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# NOTE: for next versions we need to improve the computation of the LUT
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# and test with a better image
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self.assert_close(
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res[..., 0, :],
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torch.tensor(
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[
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[
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[
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0.0471,
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0.0980,
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0.1490,
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0.2000,
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0.2471,
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0.2980,
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0.3490,
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0.3490,
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0.4471,
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0.4471,
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0.5490,
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0.5490,
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0.6471,
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0.6471,
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0.6980,
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0.7490,
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0.8000,
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0.8471,
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0.8980,
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1.0000,
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]
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]
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],
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dtype=res.dtype,
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device=res.device,
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),
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low_tolerance=True,
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)
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def test_ahe(self, img):
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clip_limit: float = 0.0
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grid_size: Tuple = (8, 8)
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res = enhance.equalize_clahe(img, clip_limit=clip_limit, grid_size=grid_size)
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# NOTE: for next versions we need to improve the computation of the LUT
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# and test with a better image
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self.assert_close(
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res[..., 0, :],
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torch.tensor(
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[
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[
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[
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0.2471,
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0.4980,
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0.7490,
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0.6667,
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0.4980,
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0.4980,
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0.7490,
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0.4993,
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0.4980,
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0.2471,
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0.7490,
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0.4993,
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0.4980,
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0.2471,
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0.4980,
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0.4993,
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0.3333,
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0.2471,
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0.4980,
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1.0000,
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]
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]
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],
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dtype=res.dtype,
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device=res.device,
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),
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low_tolerance=True,
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)
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def test_clahe(self, img):
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clip_limit: float = 2.0
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grid_size: Tuple = (8, 8)
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res = enhance.equalize_clahe(img, clip_limit=clip_limit, grid_size=grid_size)
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res_diff = enhance.equalize_clahe(img, clip_limit=clip_limit, grid_size=grid_size, slow_and_differentiable=True)
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# NOTE: for next versions we need to improve the computation of the LUT
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# and test with a better image
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expected = torch.tensor(
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[
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[
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[
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0.1216,
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0.8745,
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0.9373,
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0.9163,
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0.8745,
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0.8745,
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0.9373,
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0.8745,
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0.8745,
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0.8118,
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0.9373,
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0.8745,
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0.8745,
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0.8118,
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0.8745,
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0.8745,
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0.8327,
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0.8118,
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0.8745,
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1.0000,
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]
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]
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],
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dtype=res.dtype,
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device=res.device,
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)
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exp_diff = torch.tensor(
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[
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[
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[
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0.1250,
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0.8752,
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0.9042,
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0.9167,
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0.8401,
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0.8852,
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0.9302,
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0.9120,
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0.8750,
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0.8370,
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0.9620,
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0.9077,
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0.8750,
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0.8754,
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0.9204,
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0.9167,
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0.8370,
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0.8806,
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0.9096,
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1.0000,
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]
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]
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],
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dtype=res.dtype,
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device=res.device,
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)
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self.assert_close(res[..., 0, :], expected, low_tolerance=True)
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self.assert_close(res_diff[..., 0, :], exp_diff, low_tolerance=True)
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