class WorldTest(TestCase):
def setUp(self):
ev.events
self.world = World()
self.world.register_listener(ev)
def tearDown(self):
self.world.unregister_listener(ev)
ev.events
del self.world
def test_world_objects(self):
wo = WorldObject(name='o', data=1)
assert wo.name == 'o'
assert wo.data == 1
def test_world(self):
obj = self.world.add(1, 'a')
events = ev.events
self.check_events(events,
names=['world_object_changed'],
values={0: {0: self.world, 1: None, 2: obj}}
)
attribute_dict = dict(name='attr1', value=1, interface='IInt', label="Attribute 1")
final_dict = dict(name='attr1', value=1, interface='IInt', label="Attribute 1", constraints=None)
obj.set_attribute(**attribute_dict)
events = ev.events
self.check_events(events,
names=['world_object_item_changed'],
values={0: {0: self.world, 1: obj, 2: "attribute", 3: None, 4: final_dict}}
)
assert obj['attr1'] == attribute_dict['value']
class WorldTest(TestCase):
def setUp(self):
ev.events
self.world = World()
self.world.register_listener(ev)
def tearDown(self):
self.world.unregister_listener(ev)
ev.events
del self.world
def test_world_objects(self):
wo = WorldObject(name='o', data=1)
assert wo.name == 'o'
assert wo.data == 1
def test_world(self):
obj = self.world.add(1, 'a')
events = ev.events
self.check_events(events,
names=['world_object_changed'],
values={0: {
0: self.world,
1: None,
2: obj
}})
attribute_dict = dict(name='attr1',
value=1,
interface='IInt',
label="Attribute 1")
final_dict = dict(name='attr1',
value=1,
interface='IInt',
label="Attribute 1",
constraints=None)
obj.set_attribute(**attribute_dict)
events = ev.events
self.check_events(events,
names=['world_object_item_changed'],
values={
0: {
0: self.world,
1: obj,
2: "attribute",
3: None,
4: final_dict
}
})
assert obj['attr1'] == attribute_dict['value']
def nuclei_active_region_segmentation(input_img,
positions,
omega_energies=dict(intensity=1.0,
gradient=1.5,
smoothness=10000.0),
intensity_min=20000.,
iterations=10,
display=False):
"""
3D extension of the multiple region extension of the binary level set implementation
"""
segmentation_start_time = time()
print "--> Active region segmentation (", len(positions), " regions )"
from copy import deepcopy
reference_img = deepcopy(input_img)
size = np.array(reference_img.shape)
voxelsize = np.array(reference_img.voxelsize)
if display:
from openalea.core.world import World
world = World()
if omega_energies.has_key('gradient'):
from scipy.ndimage.filters import gaussian_gradient_magnitude
start_time = time()
print " --> Computing image gradient"
gradient = gaussian_gradient_magnitude(
np.array(reference_img, np.float64),
sigma=0.5 / np.array(reference_img.voxelsize))
gradient_img = SpatialImage(np.array(gradient, np.uint16),
voxelsize=reference_img.voxelsize)
end_time = time()
print " <-- Computing image gradient [", end_time - start_time, " s]"
start_time = time()
print " --> Creating seed image"
print positions.keys()
seed_img = seed_image_from_points(size,
voxelsize,
positions,
point_radius=1.0)
regions_img = np.copy(seed_img)
end_time = time()
print " <-- Creating seed image (", len(np.unique(
regions_img)) - 1, " regions ) [", end_time - start_time, " s]"
if display:
world.add(seed_img,
'active_regions_seeds',
colormap='glasbey',
voxelsize=voxelsize,
alphamap='constant',
volume=False,
cut_planes=True)
raw_input()
for iteration in xrange(iterations):
start_time = time()
print " --> Active region energy gradient descent : iteration", iteration, " (", len(
np.unique(regions_img)) - 1, " regions )"
previous_regions_img = np.copy(regions_img)
regions_img = active_regions_energy_gradient_descent(
regions_img,
reference_img,
omega_energies=omega_energies,
intensity_min=intensity_min,
gradient_img=gradient)
change = ((regions_img - previous_regions_img) != 0.).sum() / float(
(regions_img > 1.).sum())
end_time = time()
print " --> Active region energy gradient descent : iteration", iteration, " (Evolution : ", int(
100 * change), " %) ", "[", end_time - start_time, " s]"
if display:
world.add(regions_img,
'active_regions',
colormap='invert_grey',
voxelsize=voxelsize,
intensity_range=(1, 2))
segmented_img = SpatialImage(regions_img,
voxelsize=reference_img.voxelsize)
if display:
world.add(segmented_img,
'active_regions',
colormap='glasbey',
voxelsize=voxelsize,
alphamap='constant')
raw_input()
segmentation_end_time = time()
print "<-- Active region segmentation (", len(
np.unique(segmented_img)
) - 1, " regions ) [", segmentation_end_time - segmentation_start_time, " s]"
return segmented_img
