"""Wrapper that converts a color observation to grayscale."""
import numpy as np
import gymnasium as gym
from gymnasium.spaces import Box
[docs]class GrayScaleObservation(gym.ObservationWrapper, gym.utils.RecordConstructorArgs):
"""Convert the image observation from RGB to gray scale.
Example:
>>> import gymnasium as gym
>>> from gymnasium.wrappers import GrayScaleObservation
>>> env = gym.make("CarRacing-v2")
>>> env.observation_space
Box(0, 255, (96, 96, 3), uint8)
>>> env = GrayScaleObservation(gym.make("CarRacing-v2"))
>>> env.observation_space
Box(0, 255, (96, 96), uint8)
>>> env = GrayScaleObservation(gym.make("CarRacing-v2"), keep_dim=True)
>>> env.observation_space
Box(0, 255, (96, 96, 1), uint8)
"""
def __init__(self, env: gym.Env, keep_dim: bool = False):
"""Convert the image observation from RGB to gray scale.
Args:
env (Env): The environment to apply the wrapper
keep_dim (bool): If `True`, a singleton dimension will be added, i.e. observations are of the shape AxBx1.
Otherwise, they are of shape AxB.
"""
gym.utils.RecordConstructorArgs.__init__(self, keep_dim=keep_dim)
gym.ObservationWrapper.__init__(self, env)
self.keep_dim = keep_dim
assert (
isinstance(self.observation_space, Box)
and len(self.observation_space.shape) == 3
and self.observation_space.shape[-1] == 3
)
obs_shape = self.observation_space.shape[:2]
if self.keep_dim:
self.observation_space = Box(
low=0, high=255, shape=(obs_shape[0], obs_shape[1], 1), dtype=np.uint8
)
else:
self.observation_space = Box(
low=0, high=255, shape=obs_shape, dtype=np.uint8
)
def observation(self, observation):
"""Converts the colour observation to greyscale.
Args:
observation: Color observations
Returns:
Grayscale observations
"""
import cv2
observation = cv2.cvtColor(observation, cv2.COLOR_RGB2GRAY)
if self.keep_dim:
observation = np.expand_dims(observation, -1)
return observation
