def __init__(self, sim,
iktype=openravepy.IkParameterization.Type.Transform6D):
Manipulator.__init__(self)
self.simulated = sim
self.iktype = iktype
robot = self.GetRobot()
env = robot.GetEnv()
with env:
dof_indices = self.GetIndices()
accel_limits = robot.GetDOFAccelerationLimits()
accel_limits[dof_indices[0:3]] = 1.2
accel_limits[dof_indices[3:6]] = 1.0
robot.SetDOFAccelerationLimits(accel_limits)
# Load or_nlopt_ik as the IK solver. Unfortunately, IKFast doesn't work
# on the Mico.
if iktype is not None:
self.iksolver = openravepy.RaveCreateIkSolver(env, 'NloptIK')
self.SetIKSolver(self.iksolver)
# Load simulation controllers.
if sim:
from prpy.simulation import ServoSimulator
self.controller = robot.AttachController(
self.GetName(), '', self.GetArmIndices(), 0, True)
self.servo_simulator = ServoSimulator(self, rate=20,
watchdog_timeout=0.1)
def __init__(self, sim, owd_namespace,
iktype=openravepy.IkParameterization.Type.Transform6D):
Manipulator.__init__(self)
self.simulated = sim
self._iktype = iktype
if iktype is not None:
self._SetupIK(iktype)
# Enable servo motions in simulation mode.
self.controller = self.GetRobot().AttachController(name=self.GetName(),
args='OWDController {0:s} {1:s}'.format('prpy', owd_namespace),
dof_indices=self.GetArmIndices(), affine_dofs=0, simulated=sim
)
if sim:
from prpy.simulation import ServoSimulator
self.servo_simulator = ServoSimulator(
self, rate=20, watchdog_timeout=0.1)
def __init__(self, name, buffers, clusters, representative_buffers=None, colors=None, vol_shape=None, representatives_line_width=5.0, streamlines_line_width=2.0, representatives_alpha=1.0, streamlines_alpha=1.0, affine=None, verbose=False, clustering_parameter=None, clustering_parameter_max=None, full_dissimilarity_matrix=None):
"""StreamlineLabeler is meant to explore and select subsets of
the streamlines. The exploration occurs through clustering in
order to simplify the scene.
"""
# super(StreamlineLabeler, self).__init__(name)
Actor.__init__(self, name) # direct call of the __init__ seems better in case of multiple inheritance
if affine is None: self.affine = np.eye(4, dtype = np.float32)
else: self.affine = affine
if vol_shape is not None:
I, J, K = vol_shape
centershift = img_to_ras_coords(np.array([[I/2., J/2., K/2.]]), affine)
centeraffine = from_matvec(np.eye(3), centershift.squeeze())
affine[:3,3] = affine[:3, 3] - centeraffine[:3, 3]
self.glaffine = (GLfloat * 16)(*tuple(affine.T.ravel()))
self.glaff = affine
self.mouse_x=None
self.mouse_y=None
self.buffers = buffers
self.clusters = clusters
self.save_init_set = True
# MBKM:
Manipulator.__init__(self, initial_clusters=clusters, clustering_function=mbkm_wrapper)
# We keep the representative_ids as list to preserve order,
# which is necessary for presentation purposes:
self.representative_ids_ordered = sorted(self.clusters.keys())
self.representatives_alpha = representatives_alpha
# representative buffers:
if representative_buffers is None:
representative_buffers = compute_buffers_representatives(buffers, self.representative_ids_ordered)
self.representatives_buffer = representative_buffers['buffer']
self.representatives_colors = representative_buffers['colors']
self.representatives_first = representative_buffers['first']
self.representatives_count = representative_buffers['count']
self.representatives = buffer2coordinates(self.representatives_buffer,
self.representatives_first,
self.representatives_count)
# full tractography buffers:
self.streamlines_buffer = buffers['buffer']
self.streamlines_colors = buffers['colors']
self.streamlines_first = buffers['first']
self.streamlines_count = buffers['count']
print('MBytes %f' % (self.streamlines_buffer.nbytes/2.**20,))
self.hide_representatives = False
self.expand = False
self.knnreset = False
self.representatives_line_width = representatives_line_width
self.streamlines_line_width = streamlines_line_width
self.vertices = self.streamlines_buffer # this is apparently requested by Actor
self.color_storage = {}
# This is the color of a selected representative.
self.color_selected = np.array([1.0, 1.0, 1.0, 1.0], dtype='f4')
# This are the visualized streamlines.
# (Note: maybe a copy is not strictly necessary here)
self.streamlines_visualized_first = self.streamlines_first.copy()
self.streamlines_visualized_count = self.streamlines_count.copy()
# Clustering:
self.clustering_parameter = clustering_parameter
self.clustering_parameter_max = clustering_parameter_max
self.full_dissimilarity_matrix = full_dissimilarity_matrix
self.cantroi = 0
