def test_optional_params(self, clip, grid, device, dtype):
C, H, W = 1, 10, 20
img = torch.rand(C, H, W, device=device, dtype=dtype)
if clip is None:
res = enhance.equalize_clahe(img, grid_size=grid)
elif grid is None:
res = enhance.equalize_clahe(img, clip_limit=clip)
else:
res = enhance.equalize_clahe(img, clip, grid)
assert isinstance(res, torch.Tensor)
assert res.shape == img.shape
def test_cardinality(self, B, C, device, dtype):
H, W = 10, 20
if B is None:
img = torch.rand(C, H, W, device=device, dtype=dtype)
else:
img = torch.rand(B, C, H, W, device=device, dtype=dtype)
res = enhance.equalize_clahe(img)
assert res.shape == img.shape
def test_smoke(self, device, dtype):
C, H, W = 1, 10, 20
img = torch.rand(C, H, W, device=device, dtype=dtype)
res = enhance.equalize_clahe(img)
assert isinstance(res, torch.Tensor)
assert res.shape == img.shape
assert res.device == img.device
assert res.dtype == img.dtype
def test_clahe(self, img):
clip_limit: float = 2.0
grid_size: Tuple = (8, 8)
res = enhance.equalize_clahe(img,
clip_limit=clip_limit,
grid_size=grid_size)
res_diff = enhance.equalize_clahe(img,
clip_limit=clip_limit,
grid_size=grid_size,
slow_and_differentiable=True)
# NOTE: for next versions we need to improve the computation of the LUT
# and test with a better image
expected = torch.tensor([[[
0.1216, 0.8745, 0.9373, 0.9163, 0.8745, 0.8745, 0.9373, 0.8745,
0.8745, 0.8118, 0.9373, 0.8745, 0.8745, 0.8118, 0.8745, 0.8745,
0.8327, 0.8118, 0.8745, 1.0000
]]],
dtype=res.dtype,
device=res.device)
exp_diff = torch.tensor([[[
0.1250, 0.8752, 0.9042, 0.9167, 0.8401, 0.8852, 0.9302, 0.9120,
0.8750, 0.8370, 0.9620, 0.9077, 0.8750, 0.8754, 0.9204, 0.9167,
0.8370, 0.8806, 0.9096, 1.0000
]]],
dtype=res.dtype,
device=res.device)
assert torch.allclose(
res[..., 0, :],
expected,
atol=1e-04,
rtol=1e-04,
)
assert torch.allclose(
res_diff[..., 0, :],
exp_diff,
atol=1e-04,
rtol=1e-04,
)
def test_clahe(self, img):
clip_limit: float = 2.
grid_size: Tuple = (8, 8)
res = enhance.equalize_clahe(img,
clip_limit=clip_limit,
grid_size=grid_size)
# NOTE: for next versions we need to improve the computation of the LUT
# and test with a better image
assert torch.allclose(
res[..., 0, :],
torch.tensor([[[
0.1216, 0.8745, 0.9373, 0.9137, 0.8745, 0.8745, 0.9373, 0.8745,
0.8745, 0.8118, 0.9373, 0.8745, 0.8745, 0.8118, 0.8745, 0.8745,
0.8314, 0.8118, 0.8745, 1.0000
]]],
dtype=res.dtype,
device=res.device),
atol=1e-04,
rtol=1e-04)
def test_ahe(self, img):
clip_limit: float = 0.
grid_size: Tuple = (8, 8)
res = enhance.equalize_clahe(img,
clip_limit=clip_limit,
grid_size=grid_size)
# NOTE: for next versions we need to improve the computation of the LUT
# and test with a better image
assert torch.allclose(
res[..., 0, :],
torch.tensor([[[
0.2471, 0.4980, 0.7490, 0.6667, 0.4980, 0.4980, 0.7490, 0.4980,
0.4980, 0.2471, 0.7490, 0.4980, 0.4980, 0.2471, 0.4980, 0.4980,
0.3333, 0.2471, 0.4980, 1.0000
]]],
dtype=res.dtype,
device=res.device),
atol=1e-04,
rtol=1e-04)
def test_he(self, img):
# should be similar to enhance.equalize but slower. Similar because the lut is computed in a different way.
clip_limit: float = 0.
grid_size: Tuple = (1, 1)
res = enhance.equalize_clahe(img,
clip_limit=clip_limit,
grid_size=grid_size)
# NOTE: for next versions we need to improve the computation of the LUT
# and test with a better image
assert torch.allclose(
res[..., 0, :],
torch.tensor([[[
0.0471, 0.0980, 0.1490, 0.2000, 0.2471, 0.2980, 0.3490, 0.3490,
0.4471, 0.4471, 0.5490, 0.5490, 0.6471, 0.6471, 0.6980, 0.7490,
0.8000, 0.8471, 0.8980, 1.0000
]]],
dtype=res.dtype,
device=res.device),
atol=1e-04,
rtol=1e-04)
def grad_rot(input, a, b, c):
rot = rotate(input,
torch.tensor(30., dtype=input.dtype, device=device))
return enhance.equalize_clahe(rot, a, b, c)
def test_exception_tensor_type(self):
with pytest.raises(TypeError):
enhance.equalize_clahe([1, 2, 3])
def test_exception_tensor_dims(self, dims):
img = torch.rand(dims)
with pytest.raises(ValueError):
enhance.equalize_clahe(img)
def test_exception(self, B, clip, grid, exception_type):
C, H, W = 1, 10, 20
img = torch.rand(B, C, H, W)
with pytest.raises(exception_type):
enhance.equalize_clahe(img, clip, grid)