def mean_averaging(list_images, **kwds):
"""
Mean image from a list of *SpatialImages*.
Parameters
----------
:param list list_images: list of *SpatialImage*
Returns
----------
:return: *SpatialImage* instance -- image and associated informations
"""
sp_img = mean_images(list_images, param_str_2='-mean')
return sp_img
def mean_averaging(list_images, **kwds):
"""
Mean image from a list of *SpatialImages*.
Parameters
----------
:param list list_images: list of *SpatialImage*
Returns
----------
:return: *SpatialImage* instance -- image and associated informations
"""
sp_img = mean_images(list_images, param_str_2='-mean')
return sp_img
def fusion(list_images, iterations=None):
"""
Multiview reconstruction (registration)
Parameters
----------
:param list list_images: list of input ``SpatialImage``
:param int iterations: number of iterations, optional. Default: 5
Returns
----------
:return: ``SpatialImage`` instance -- image and metadata
Example
-------
>>> from timagetk.util import data_path
>>> from timagetk.components import imread
>>> from timagetk.algorithms import fusion
>>> vues = [0, 1, 2]
>>> list_images = [imread(data_path('fusion_img_' + str(vue) + '.inr'))
for vue in vues]
>>> fus_img = fusion(list_images)
"""
if iterations is None:
iterations = 5
else:
iterations = int(abs(iterations))
#--- check: list of SpatialImage images
conds_init = isinstance(list_images, list) and len(list_images)>=2
conds_list_img = [0 if isinstance(sp_img, SpatialImage)
else 1 for sp_img in list_images]
#--- end check
if conds_init and 1 not in conds_list_img:
succ_ref_img = []
vox_list = [sp_img.get_voxelsize() for sp_img in list_images]
vox_list = [i for i in itertools.chain.from_iterable(vox_list)] # voxel list
ext_list = [sp_img.get_extent() for sp_img in list_images]
ext_list = [i for i in itertools.chain.from_iterable(ext_list)] # extent list
if list_images[0].get_dim() == 3:
min_vox, val = np.min(vox_list), int(np.max(ext_list)/np.min(vox_list))
tmp_arr = np.zeros((val, val, val), dtype=list_images[0].dtype)
template_img = SpatialImage(tmp_arr, voxelsize=[min_vox, min_vox, min_vox])
init_ref = apply_trsf(list_images[0], bal_transformation=None, template_img=template_img)
succ_ref_img.append(init_ref)
init_trsf_list, init_img_list = [], []
for ind, sp_img in enumerate(list_images):
if ind>0:
trsf_rig, res_rig = blockmatching(sp_img, init_ref,
param_str_2='-trsf-type rigid -py-ll 1')
trsf_aff, res_aff = blockmatching(sp_img, init_ref,
left_transformation=trsf_rig,
param_str_2='-trsf-type affine')
tmp_trsf = compose_trsf([trsf_rig, trsf_aff])
trsf_def, res_def = blockmatching(sp_img, init_ref,
init_result_transformation=tmp_trsf,
param_str_2='-trsf-type vectorfield')
out_trsf = BalTransformation(c_bal_trsf=trsf_def)
init_trsf_list.append(out_trsf)
init_img_list.append(res_def)
init_img_list.append(init_ref)
mean_ref = mean_images(init_img_list)
mean_trsf = mean_trsfs(init_trsf_list)
mean_trsf_inv = inv_trsf(mean_trsf)
mean_ref_update = apply_trsf(mean_ref, mean_trsf_inv, template_img=template_img)
succ_ref_img.append(mean_ref_update)
for index in range(0, iterations):
init_trsf_list, init_img_list = [], []
for ind, sp_img in enumerate(list_images):
trsf_rig, res_rig = blockmatching(sp_img, mean_ref_update,
param_str_2='-trsf-type rigid -py-ll 1')
trsf_aff, res_aff = blockmatching(sp_img, mean_ref_update,
left_transformation=trsf_rig,
param_str_2='-trsf-type affine')
tmp_trsf = compose_trsf([trsf_rig, trsf_aff])
trsf_def, res_def = blockmatching(sp_img, mean_ref_update,
init_result_transformation=tmp_trsf,
param_str_2='-trsf-type vectorfield')
out_trsf = BalTransformation(c_bal_trsf=trsf_def)
init_trsf_list.append(out_trsf)
init_img_list.append(res_def)
init_img_list.append(mean_ref_update)
mean_ref = mean_images(init_img_list)
mean_trsf = mean_trsfs(init_trsf_list)
mean_trsf_inv = inv_trsf(mean_trsf)
mean_ref_update = apply_trsf(mean_ref, mean_trsf_inv, template_img=template_img)
succ_ref_img.append(mean_ref_update)
return succ_ref_img[-1]
else:
print('Incorrect specification')
return
def test_plugin():
out_1 = averaging([image_1, image_2], method='mean_averaging')
out_2 = mean_images([image_1, image_2])
np.testing.assert_array_equal(out_1, out_2)
def test_mean_image():
out = mean_images([image_1, image_2])
np.testing.assert_array_equal(out, mean_image)
def fusion(list_images, iterations=None):
"""
Multiview reconstruction (registration)
Parameters
----------
:param list list_images: list of input ``SpatialImage``
:param int iterations: number of iterations, optional. Default: 5
Returns
----------
:return: ``SpatialImage`` instance -- image and metadata
Example
-------
>>> from timagetk.util import data_path
>>> from timagetk.components import imread
>>> from timagetk.algorithms import fusion
>>> vues = [0, 1, 2]
>>> list_images = [imread(data_path('fusion_img_' + str(vue) + '.inr'))
for vue in vues]
>>> fus_img = fusion(list_images)
"""
if iterations is None:
iterations = 5
else:
iterations = int(abs(iterations))
#--- check: list of SpatialImage images
conds_init = isinstance(list_images, list) and len(list_images) >= 2
conds_list_img = [
0 if isinstance(sp_img, SpatialImage) else 1 for sp_img in list_images
]
#--- end check
if conds_init and 1 not in conds_list_img:
succ_ref_img = []
vox_list = [sp_img.get_voxelsize() for sp_img in list_images]
vox_list = [i for i in itertools.chain.from_iterable(vox_list)
] # voxel list
ext_list = [sp_img.get_extent() for sp_img in list_images]
ext_list = [i for i in itertools.chain.from_iterable(ext_list)
] # extent list
if list_images[0].get_dim() == 3:
min_vox, val = np.min(vox_list), int(
np.max(ext_list) / np.min(vox_list))
tmp_arr = np.zeros((val, val, val), dtype=list_images[0].dtype)
template_img = SpatialImage(tmp_arr,
voxelsize=[min_vox, min_vox, min_vox])
init_ref = apply_trsf(list_images[0],
bal_transformation=None,
template_img=template_img)
succ_ref_img.append(init_ref)
init_trsf_list, init_img_list = [], []
for ind, sp_img in enumerate(list_images):
if ind > 0:
trsf_rig, res_rig = blockmatching(
sp_img, init_ref, param_str_2='-trsf-type rigid -py-ll 1')
trsf_aff, res_aff = blockmatching(
sp_img,
init_ref,
left_transformation=trsf_rig,
param_str_2='-trsf-type affine')
tmp_trsf = compose_trsf([trsf_rig, trsf_aff])
trsf_def, res_def = blockmatching(
sp_img,
init_ref,
init_result_transformation=tmp_trsf,
param_str_2='-trsf-type vectorfield')
out_trsf = BalTransformation(c_bal_trsf=trsf_def)
init_trsf_list.append(out_trsf)
init_img_list.append(res_def)
init_img_list.append(init_ref)
mean_ref = mean_images(init_img_list)
mean_trsf = mean_trsfs(init_trsf_list)
mean_trsf_inv = inv_trsf(mean_trsf)
mean_ref_update = apply_trsf(mean_ref,
mean_trsf_inv,
template_img=template_img)
succ_ref_img.append(mean_ref_update)
for index in range(0, iterations):
init_trsf_list, init_img_list = [], []
for ind, sp_img in enumerate(list_images):
trsf_rig, res_rig = blockmatching(
sp_img,
mean_ref_update,
param_str_2='-trsf-type rigid -py-ll 1')
trsf_aff, res_aff = blockmatching(
sp_img,
mean_ref_update,
left_transformation=trsf_rig,
param_str_2='-trsf-type affine')
tmp_trsf = compose_trsf([trsf_rig, trsf_aff])
trsf_def, res_def = blockmatching(
sp_img,
mean_ref_update,
init_result_transformation=tmp_trsf,
param_str_2='-trsf-type vectorfield')
out_trsf = BalTransformation(c_bal_trsf=trsf_def)
init_trsf_list.append(out_trsf)
init_img_list.append(res_def)
init_img_list.append(mean_ref_update)
mean_ref = mean_images(init_img_list)
mean_trsf = mean_trsfs(init_trsf_list)
mean_trsf_inv = inv_trsf(mean_trsf)
mean_ref_update = apply_trsf(mean_ref,
mean_trsf_inv,
template_img=template_img)
succ_ref_img.append(mean_ref_update)
return succ_ref_img[-1]
else:
print('Incorrect specification')
return
def test_plugin():
out_1 = averaging([image_1, image_2], method='mean_averaging')
out_2 = mean_images([image_1, image_2])
np.testing.assert_array_equal(out_1, out_2)
def test_mean_image():
out = mean_images([image_1, image_2])
np.testing.assert_array_equal(out, mean_image)