import numpy
import pytest
from ..intensities import registration
from ..io import data_for_registration
from ..io.input_stack import InputStacksNumpy
from .assert_transformations import assert_allclose_transformations
_MAPPERS = {
f"mapper{'_'.join(k)}": v
for k, v in registration.IntensityMapping.get_subclass_items()
}
[docs]
@pytest.mark.parametrize("mapper", _MAPPERS)
@pytest.mark.parametrize(
"transfo_type",
["translation", "rigid", "affine", "similarity"],
)
def test_intensity_registration(transfo_type, mapper):
if transfo_type not in _MAPPERS[mapper].SUPPORTED_TRANSFORMATIONS:
pytest.skip(f"transformation type {transfo_type} not supported by {mapper}")
image = data_for_registration.generate_image()
image_stack, active_expected, passive_expected = (
data_for_registration.generate_image_stack(image, transfo_type, plot=0)
)
image_stacks = data_for_registration.generate_image_stacks(image_stack, nstacks=1)
mapper = _MAPPERS[mapper](transfo_type=transfo_type)
with InputStacksNumpy(image_stacks) as stacks:
transformations = registration.calculate_transformations(stacks, mapper)
assert_allclose_transformations(
transformations,
active_expected,
passive_expected,
rtol=0.1,
atol=0.1,
rtol_pixels=0.1,
atol_pixels=0.4,
)
[docs]
@pytest.mark.parametrize("mapper", _MAPPERS)
@pytest.mark.parametrize("transfo_type", ["translation", "affine"])
def test_intensity_registration_block(transfo_type, mapper):
if transfo_type not in _MAPPERS[mapper].SUPPORTED_TRANSFORMATIONS:
pytest.skip(f"transformation type {transfo_type} not supported by {mapper}")
image = data_for_registration.generate_image()
image_stack, active_expected, passive_expected = (
data_for_registration.generate_image_stack(
image, transfo_type, nimages=7, plot=0
)
)
image_stacks = data_for_registration.generate_image_stacks(image_stack, nstacks=1)
mapper = _MAPPERS[mapper](transfo_type=transfo_type)
with InputStacksNumpy(image_stacks) as stacks:
transformations = registration.calculate_transformations(
stacks, mapper, include=[0, 1, 2, 4, 5, 6], reference_image=0, block_size=3
)
for transformations_ in transformations.values():
assert len(transformations_) == 6
active_expected.pop(3)
passive_expected.pop(3)
assert_allclose_transformations(
transformations,
active_expected,
passive_expected,
rtol=0.01,
atol=0.1,
rtol_pixels=0.1,
atol_pixels=0.4,
)
[docs]
@pytest.mark.parametrize("mapper", _MAPPERS)
def test_intensity_registration_mask(mapper):
if mapper in ("mapperCrossCorrelation_Numpy", "mapperOptimization_Kornia"):
pytest.skip("mask not supported")
transfo_type = "translation"
image = data_for_registration.generate_image()
image_stack, _, _ = data_for_registration.generate_image_stack(
image, transfo_type, nimages=2
)
select_identical = numpy.zeros(image_stack[0].shape)
idx_identical = (slice(100, None), slice(None))
select_identical[idx_identical] = 1
select_not_identical = 1 - select_identical
image_stack[1][idx_identical] = image_stack[0][idx_identical]
image_stacks = data_for_registration.generate_image_stacks(image_stack, nstacks=1)
mapper_identical = _MAPPERS[mapper](
transfo_type=transfo_type, mask=select_identical
)
mapper_not_identical = _MAPPERS[mapper](
transfo_type=transfo_type, mask=select_not_identical
)
with InputStacksNumpy(image_stacks) as stacks:
transformations_identical = registration.calculate_transformations(
stacks, mapper_identical
)
transformations_not_identical = registration.calculate_transformations(
stacks, mapper_not_identical
)
active_matrix = transformations_identical["stack_0"][1].active_matrix
expected = numpy.identity(3)
numpy.testing.assert_allclose(active_matrix, expected)
active_matrix = transformations_not_identical["stack_0"][1].active_matrix
expected = numpy.identity(3)
expected[:2, 2] = [3, 2]
numpy.testing.assert_allclose(active_matrix, expected, rtol=0.1)