def test_reinhard_normalise(_source_image, _norm_reinhard):
"""Test for Reinhard colour normalisation."""
file_parent_dir = pathlib.Path(__file__).parent
source_img = imread(pathlib.Path(_source_image))
target_img = imread(file_parent_dir.joinpath("../data/target_image.png"))
reinhard_img = imread(pathlib.Path(_norm_reinhard))
norm = get_normaliser("reinhard")
norm.fit(target_img) # get stain information of target image
transform = norm.transform(source_img) # transform source image
assert np.shape(transform) == np.shape(source_img)
assert np.mean(
np.absolute(reinhard_img / 255.0 - transform / 255.0)) < 1e-2
def test_macenko_normalise(_source_image, _norm_macenko):
"""Test for stain normalisation with stain matrix from Macenko et al."""
file_parent_dir = pathlib.Path(__file__).parent
source_img = imread(pathlib.Path(_source_image))
target_img = imread(file_parent_dir.joinpath("../data/target_image.png"))
macenko_img = imread(pathlib.Path(_norm_macenko))
# init class with Macenko method
norm = get_normaliser("macenko")
norm.fit(target_img) # get stain information of target image
transform = norm.transform(source_img) # transform source image
assert np.shape(transform) == np.shape(source_img)
assert np.mean(np.absolute(macenko_img / 255.0 - transform / 255.0)) < 1e-2
def test_ruifrok_normalise(_source_image, _norm_ruifrok):
"""Test for stain normalisation with stain matrix from Ruifrok and Johnston."""
file_parent_dir = pathlib.Path(__file__).parent
source_img = imread(pathlib.Path(_source_image))
target_img = imread(file_parent_dir.joinpath("../data/target_image.png"))
ruifrok_img = imread(pathlib.Path(_norm_ruifrok))
# init class with Ruifrok & Johnston method
norm = get_normaliser("ruifrok")
norm.fit(target_img) # get stain information of target image
transform = norm.transform(source_img) # transform source image
assert np.shape(transform) == np.shape(source_img)
assert np.mean(np.absolute(ruifrok_img / 255.0 - transform / 255.0)) < 1e-2
def test_custom_normalise(_source_image, _norm_ruifrok):
"""Test for stain normalisation with user-defined stain matrix."""
file_parent_dir = pathlib.Path(__file__).parent
source_img = imread(pathlib.Path(_source_image))
target_img = imread(file_parent_dir.joinpath("../data/target_image.png"))
custom_img = imread(pathlib.Path(_norm_ruifrok))
# init class with custom method - test with ruifrok stain matrix
stain_matrix = np.array([[0.65, 0.70, 0.29], [0.07, 0.99, 0.11]])
norm = get_normaliser("custom", stain_matrix=stain_matrix)
norm.fit(target_img) # get stain information of target image
transform = norm.transform(source_img) # transform source image
assert np.shape(transform) == np.shape(source_img)
assert np.mean(np.absolute(custom_img / 255.0 - transform / 255.0)) < 1e-2
def test_get_patch_extractor(_source_image, _patch_extr_csv):
"""Test get_patch_extractor returns the right object."""
input_img = misc.imread(pathlib.Path(_source_image))
locations_list = pathlib.Path(_patch_extr_csv)
points = patchextraction.get_patch_extractor(
input_img=input_img,
locations_list=locations_list,
method_name="point",
patch_size=(200, 200),
)
assert isinstance(points, patchextraction.PointsPatchExtractor)
with pytest.raises(MethodNotSupported):
points.merge_patches()
fixed_window = patchextraction.get_patch_extractor(
input_img=input_img,
method_name="fixedwindow",
patch_size=(200, 200),
)
assert isinstance(fixed_window, patchextraction.FixedWindowPatchExtractor)
variable_window = patchextraction.get_patch_extractor(
input_img=input_img,
method_name="variablewindow",
patch_size=(200, 200),
)
assert isinstance(variable_window, patchextraction.VariableWindowPatchExtractor)
with pytest.raises(MethodNotSupported):
patchextraction.get_patch_extractor("unknown")
def test_patch_extractor(_source_image):
"""Test base class patch extractor."""
input_img = misc.imread(pathlib.Path(_source_image))
patches = patchextraction.PatchExtractor(input_img=input_img, patch_size=(20, 20))
next_patches = iter(patches)
assert next_patches.n == 0
def test_fixed_window_patch_extractor(_patch_extr_vf_image):
"""Test FixedWindowPatchExtractor for VF."""
input_img = pathlib.Path(_patch_extr_vf_image)
stride = (20, 20)
patch_size = (200, 200)
img = misc.imread(input_img)
img_h = img.shape[1]
img_w = img.shape[0]
num_patches_img_h = int(math.ceil((img_h - patch_size[1]) / stride[1] + 1))
num_patches_img_w = int(math.ceil(((img_w - patch_size[0]) / stride[0] + 1)))
num_patches_img = num_patches_img_h * num_patches_img_w
iter_tot = 0
img_patches = np.zeros(
(num_patches_img, patch_size[1], patch_size[0], 3), dtype=img.dtype
)
for h in range(num_patches_img_h):
for w in range(num_patches_img_w):
start_h = h * stride[1]
end_h = (h * stride[1]) + patch_size[1]
start_w = w * stride[0]
end_w = (w * stride[0]) + patch_size[0]
if end_h > img_h:
start_h = img_h - patch_size[1]
end_h = img_h
if end_w > img_w:
start_w = img_w - patch_size[0]
end_w = img_w
img_patches[iter_tot, :, :, :] = img[start_h:end_h, start_w:end_w, :]
iter_tot += 1
patches = patchextraction.get_patch_extractor(
input_img=input_img,
method_name="fixedwindow",
patch_size=patch_size,
resolution=0,
units="level",
stride=stride,
)
assert np.all(img_patches[0] == patches[0])
img_patches_test = []
for patch in patches:
img_patches_test.append(patch)
img_patches_test = np.array(img_patches_test)
assert np.all(img_patches == img_patches_test)
# Test for integer (single) patch_size and stride input
patches = patchextraction.get_patch_extractor(
input_img=input_img,
method_name="fixedwindow",
patch_size=patch_size[0],
resolution=0,
units="level",
stride=stride[0],
)
assert np.all(img_patches[0] == patches[0])
img_patches_test = []
for patch in patches:
img_patches_test.append(patch)
img_patches_test = np.array(img_patches_test)
assert np.all(img_patches == img_patches_test)