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import gym
import numpy as np
from gym import ObservationWrapper
from gym.spaces import Box
class TransposeImageObservation(ObservationWrapper):
def __init__(self, env: gym.Env) -> None:
super().__init__(env)
assert isinstance(env.observation_space, Box)
obs_space = env.observation_space
axes = tuple(i for i in range(len(obs_space.shape)))
self._transpose_axes = axes[:-3] + (axes[-1],) + axes[-3:-1]
self.observation_space = Box(
low=np.transpose(obs_space.low, axes=self._transpose_axes),
high=np.transpose(obs_space.high, axes=self._transpose_axes),
shape=[obs_space.shape[idx] for idx in self._transpose_axes],
dtype=obs_space.dtype,
)
def observation(self, obs: np.ndarray) -> np.ndarray:
full_shape = obs.shape
obs_shape = self.observation_space.shape
addl_dims = len(full_shape) - len(obs_shape)
if addl_dims > 0:
transpose_axes = list(range(addl_dims))
transpose_axes.extend(a + addl_dims for a in self._transpose_axes)
else:
transpose_axes = self._transpose_axes
return np.transpose(obs, axes=transpose_axes)
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