def test_read_3D(self):
sp_img_inr = imread(data_path('time_0_cut.inr'))
img_path_tiff = data_path('time_0_cut.tiff')
imsave(img_path_tiff, sp_img_inr)
sp_img_tiff = imread(img_path_tiff)
self.assertDictEqual(sp_img_inr.get_metadata(), sp_img_tiff.get_metadata())
np.testing.assert_array_equal(sp_img_inr, sp_img_tiff)
def test_read_3D(self):
sp_img_inr = imread(data_path('time_0_cut.inr'))
img_path_tiff = data_path('time_0_cut.tiff')
imsave(img_path_tiff, sp_img_inr)
sp_img_tiff = imread(img_path_tiff)
self.assertDictEqual(sp_img_inr.get_metadata(),
sp_img_tiff.get_metadata())
np.testing.assert_array_equal(sp_img_inr, sp_img_tiff)
# print the SpatialImage metadata
print('Metadata :'), metadata
# metadata modification
metadata['filename'] = img_path
# set the SpatialImage metadata, mutator
sp_img.set_metadata(metadata)
# print the SpatialImage metadata
print('New metadata :'), sp_img.get_metadata()
# get the SpatialImage voxelsize (list of floating numbers)
vox = sp_img.get_voxelsize()
# print the SpatialImage voxelsize
print('Voxelsize :'), vox
# voxelsize modification
new_vox = [] # empty list
for ind, val in enumerate(vox):
new_vox.append(2.0*val)
# set the SpatialImage voxelsize, mutator
sp_img.set_voxelsize(new_vox)
print('New voxelsize :'), sp_img.get_voxelsize()
# output
# filename
res_name = 'example_input_output.tif'
# SpatialImage to .tif format
imsave(out_path+res_name, sp_img)
# filename
res_name = 'example_input_output.inr'
# SpatialImage to .inr format
imsave(out_path+res_name, sp_img)
# print the SpatialImage metadata
print('Metadata :'), metadata
# metadata modification
metadata['filename'] = img_path
# set the SpatialImage metadata, mutator
sp_img.set_metadata(metadata)
# print the SpatialImage metadata
print('New metadata :'), sp_img.get_metadata()
# get the SpatialImage voxelsize (list of floating numbers)
vox = sp_img.get_voxelsize()
# print the SpatialImage voxelsize
print('Voxelsize :'), vox
# voxelsize modification
new_vox = [] # empty list
for ind, val in enumerate(vox):
new_vox.append(2.0 * val)
# set the SpatialImage voxelsize, mutator
sp_img.set_voxelsize(new_vox)
print('New voxelsize :'), sp_img.get_voxelsize()
# output
# filename
res_name = 'example_input_output.tif'
# SpatialImage to .tif format
imsave(out_path + res_name, sp_img)
# filename
res_name = 'example_input_output.inr'
# SpatialImage to .inr format
imsave(out_path + res_name, sp_img)
# time_1 is first the local reference image,
# and becames the floating image
# time_2 is the global reference image
times = [0, 1, 2]
# list of SpatialImage instances
list_images = [imread(data_path('time_' + str(time) + '.inr'))
for time in times]
# Rigid registration along the whole sequence:
# list_compo_trsf : list of transformations
# list_res_img : list of resulting images
# the primitive embeds an iterative registration
list_compo_trsf, list_res_img = sequence_registration(list_images,
method='sequence_rigid_registration')
for ind, img in enumerate(list_res_img):
# filenames
res_name = ''.join(['example_seq_reg_rigid_', str(ind), '_',
str(times[-1]), '.inr'])
# SpatialImage to .inr format
imsave(out_path+res_name, img)
# Affine registration alog the whole sequence:
list_compo_trsf, list_res_img = sequence_registration(list_images,
method='sequence_affine_registration')
for ind, img in enumerate(list_res_img):
# filenames
res_name = ''.join(['example_seq_reg_affine_', str(ind), '_',
str(times[-1]), '.inr'])
# SpatialImage to .inr format
imsave(out_path+res_name, img)
]
floating_img, reference_img = list_images[0], list_images[1]
# Rigid registration:
trsf_rig, res_rig = registration(floating_img,
reference_img,
method='rigid_registration')
# display the spatial transformation (4x4 matrix):
trsf_rig.c_display()
# save the spatial transformation:
res_name = 'example_trsf_rigid.trsf' # filename
trsf_rig.write(out_path + res_name)
# save the result image:
res_name = 'example_reg_rigid_1_2.tif' # filename
# SpatialImage to .tif format
imsave(out_path + res_name, res_rig)
# Affine registration:
trsf_aff, res_aff = registration(floating_img,
reference_img,
method='affine_registration')
res_name = 'example_reg_affine_1_2.tif' # filename
# SpatialImage to .tif format
imsave(out_path + res_name, res_aff)
# Deformable registration:
trsf_def, res_def = registration(floating_img,
reference_img,
method='deformable_registration')
res_name = 'example_reg_deformable_1_2.tif' # filename
# SpatialImage to .tif format
img = imread(data_path('time_' + str(val) + '_seg.inr'))
# subimage extraction
shape = img.get_shape()
indices = [0,shape[0]-1,0,shape[1]-1,0,5]
img = img.get_region(indices=indices)
# remove small cells
labels = np.unique(img).tolist()
img = labels_post_processing(img, method='labels_erosion', radius=2)
img[img==0] = np.min(labels)
img = SpatialImage(img, voxelsize=img.get_voxelsize())
# save input labeled images
res_name = 'example_track_time_' + str(val) + '_seg.inr'
imsave(out_path+res_name, img)
labels = np.unique(img).tolist() # list of labels
back_id = np.min(labels) # background identifier
back_id_list.append(back_id)
labels.remove(back_id)
# feature space computation
obj_gf = GeometricalFeatures(img, label=labels)
feature_space = obj_gf.compute_feature_space()
segmentation_list.append(img), feature_space_list.append(feature_space)
# temporal matching
obj_tm = TemporalMatching(segmentation_list=segmentation_list,
feature_space_list=feature_space_list,
background_id_list=back_id_list)
for time in times]
floating_img, reference_img = list_images[0], list_images[1]
# Rigid registration:
trsf_rig, res_rig = registration(floating_img,
reference_img,
method='rigid_registration')
# display the spatial transformation (4x4 matrix):
trsf_rig.c_display()
# save the spatial transformation:
res_name = 'example_trsf_rigid.trsf' # filename
trsf_rig.write(out_path+res_name)
# save the result image:
res_name = 'example_reg_rigid_1_2.tif' # filename
# SpatialImage to .tif format
imsave(out_path+res_name, res_rig)
# Affine registration:
trsf_aff, res_aff = registration(floating_img,
reference_img,
method='affine_registration')
res_name = 'example_reg_affine_1_2.tif' # filename
# SpatialImage to .tif format
imsave(out_path+res_name, res_aff)
# Deformable registration:
trsf_def, res_def = registration(floating_img,
reference_img,
method=
'deformable_registration')
res_name = 'example_reg_deformable_1_2.tif' # filename
opening2 = grey_erosion(input_img>30, 10)
"""
threshold = int(sys.argv[3])
ext_img = h_transform(asf_img, h=threshold, method='h_transform_min')
print 'done transform'
con_img = region_labeling(ext_img,
low_threshold=1,
high_threshold=threshold,
method='connected_components')
print 'done labelling'
seg_img = segmentation(smooth_img,
con_img,
control='first',
method='seeded_watershed')
print 'done seg'
# optional post processig block
#pp_img = labels_post_processing(seg_img, radius=1,
# iterations=1,
# method='labels_erosion')
res_name = 'example_segmentation.tif'
imsave(sys.argv[2], seg_img)