def update(self, time):
if self._destroyed:
return False
if self._t0 is None:
self._t0 = time
if self._follow is not None and not self._follow.closing() :
d = self._follow._pos - self._pos
T.unit_vector(d,d)
self._dir = self._dir * (1-self._thrust) + d * self._thrust
if time - self._t0 > 3000 or self._follow._destroyed:
print "Not following anymore"
self._follow = None
Entity.moveTo(self, self._pos + self._dir * self._speed) # also updates _pos
else:
# too simple to use Verlet
Entity.moveTo(self, self._pos + self._dir * self._speed) # also updates _pos
self._dir += self._gravity
self._xform = mmult(self._pos_m,self._scaling_m, T.quaternion_matrix(self._rotation))
self._rotation = T.quaternion_multiply(self._d_rotation, self._rotation)
# hit the floor
if self._map(self._pos[0], self._pos[1], False) > self._pos[2]:
self.explode()
return False # tell the scene the missile is toast
return True
def update(self, time):
if self._destroyed:
return False
if self._closing:
self._scale -= 0.01
self._scale = max(self._scale, 0)
self._scale_m = T.scale_matrix(self._scale)
if self._scale == 0:
self._destroyed = True
self._rot = T.quaternion_multiply(self._rot_speed, self._rot)
self._anim_m = T.quaternion_matrix(self._rot)
self._beacon_xform = self._pos_m
self._geom_xform = mmult(self._pos_m, self._anim_m, self._scale_m)
return True
pred_r, pred_t, pred_c, colorEmb = estimator(img, cloud, pointIndex, idx)
bs, num_p, _ = pred_c.size()
pred_r = pred_r / (torch.norm(pred_r, dim=2).view(1, num_p, 1))
pred_c = pred_c.view(batchSize, num_p)
how_max, which_max = torch.max(pred_c, 1)
pred_t = pred_t.view(batchSize * num_p, 1, 3)
my_r = pred_r[0][which_max[0]].view(-1).cpu().data.numpy()
# my_t = (cloud.view(batchSize * num_p, 1, 3) + pred_t)[which_max[0]].view(-1).cpu().data.numpy()
my_t = pred_t[which_max[0]].view(-1).cpu().data.numpy()
my_pred = np.append(my_r, my_t)
modelPoints = modelPoints[0].cpu().detach().numpy()
my_r = quaternion_matrix(my_r)[:3, :3]
pred = np.dot(modelPoints, my_r.T) + my_t
tarPoints = tarPoints[0].cpu().detach().numpy()
if idx[0].item() in symList:
pred = torch.from_numpy(pred.astype(np.float32)).cuda().transpose(
1, 0).contiguous()
tarPoints = torch.from_numpy(tarPoints.astype(
np.float32)).cuda().transpose(1, 0).contiguous()
inds = knn(tarPoints.unsqueeze(0), pred.unsqueeze(0))
tarPoints = torch.index_select(tarPoints, 1, inds.view(-1) - 1)
dis = torch.mean(torch.norm(
(pred.transpose(1, 0) - tarPoints.transpose(1, 0)), dim=1),
dim=0).item()
else:
dis = np.mean(np.linalg.norm(pred - tarPoints, axis=1))
def draw(self, scene):
self._graphics.draw(scene, mmult(self._pos_m, T.quaternion_matrix(self._rotation), self._scaling))