def __init__(self, actionfile, action_list=[]):
self.src_ctx = SrcContext()
self.src_ctx.read_h5(actionfile)
self.tgt_cld = None
self.tgt_ctx = TgtContext(self.src_ctx)
self.name2ind = dict([(s, i)
for i, s in enumerate(self.src_ctx.seg_names)])
self.costs = np.zeros(self.src_ctx.N)
self.N = len(self.src_ctx.seg_names)
self.indicators = np.eye(self.N)
self.weights = np.r_[-1, np.zeros(self.N)]
self.w0 = 0
def batch_cost(self, test_scene_state):
if not(self.actionfile):
raise ValueError('No actionfile provided for gpu context')
tgt_ctx = TgtContext(self.src_ctx)
cloud = test_scene_state.cloud
cloud = self._clip_cloud(cloud)
tgt_ctx.set_cld(cloud)
cost_array = batch_tps_rpm_bij(self.src_ctx, tgt_ctx,
T_init=self.rad_init, T_final=self.rad_final,
outlierfrac=self.outlierfrac, outlierprior=self.outlierprior,
outliercutoff=settings.OUTLIER_CUTOFF,
em_iter=self.em_iter,
component_cost=True)
costs = dict(zip(self.src_ctx.seg_names, cost_array))
return costs
def set_landmark_file(self, landmarkf):
self.landmark_ctx = GPUContext()
self.landmark_ctx.read_h5(landmarkf)
self.landmark_targ_ctx = TgtContext(self.landmark_ctx)
self.weights = np.zeros(self.src_ctx.N + self.landmark_ctx.N +
MulFeats.N_costs)
