import numpy as np
from autolab_core import RigidTransform

# 寫上用四元數(shù)表示的orientation和xyz表示的position
orientation = {'y': -0.6971278819736084, 'x': -0.716556549511624, 'z': -0.010016582945017661, 'w': 0.02142651612120239}
position = {'y': -0.26022684372145516, 'x': 0.6453529828252734, 'z': 1.179122068068349}

rotation_quaternion = np.asarray([orientation['w'], orientation['x'], orientation['y'], orientation['z']])
translation = np.asarray([position['x'], position['y'], position['z']])
# 這里用的是UC Berkeley的autolab_core，比較方便吧，當(dāng)然可以自己寫一個fuction來計算，計算公式在https://www.cnblogs.com/flyinggod/p/8144100.html
T_qua2rota = RigidTransform(rotation_quaternion, translation)

print(T_qua2rota)
 
# 以下是打印的結(jié)果
Tra: [ 0.64535298 -0.26022684 1.17912207]
   Rot: [[ 0.02782477 0.99949234 -0.01551915]
   [ 0.99863386 -0.02710724 0.0446723 ]
   [ 0.04422894 -0.01674094 -0.99888114]]
   Qtn: [-0.02142652 0.71655655 0.69712788 0.01001658]
   from unassigned to world

def quaternion_to_rotation_matrix(quat):
  q = quat.copy()
  n = np.dot(q, q)
  if n < np.finfo(q.dtype).eps:
    return np.identity(4)
  q = q * np.sqrt(2.0 / n)
  q = np.outer(q, q)
  rot_matrix = np.array(
    [[1.0 - q[2, 2] - q[3, 3], q[1, 2] + q[3, 0], q[1, 3] - q[2, 0], 0.0],
     [q[1, 2] - q[3, 0], 1.0 - q[1, 1] - q[3, 3], q[2, 3] + q[1, 0], 0.0],
     [q[1, 3] + q[2, 0], q[2, 3] - q[1, 0], 1.0 - q[1, 1] - q[2, 2], 0.0],
     [0.0, 0.0, 0.0, 1.0]],
    dtype=q.dtype)
  return rot_matrix