Gymnasium/gym/wrappers/resize_observation.py

import numpy as np
import warnings
from gym.spaces import Box
from gym import ObservationWrapper


class ResizeObservation(ObservationWrapper):
    r"""Downsample the image observation to a square image."""

    def __init__(self, env, shape):
        super(ResizeObservation, self).__init__(env)
        if isinstance(shape, int):
            shape = (shape, shape)
        assert all(x > 0 for x in shape), shape
        warnings.warn(
            "Gym's internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit"
        )
        self.shape = tuple(shape)

        obs_shape = self.shape + self.observation_space.shape[2:]
        self.observation_space = Box(low=0, high=255, shape=obs_shape, dtype=np.uint8)

    def observation(self, observation):
        import cv2

        observation = cv2.resize(observation, self.shape[::-1], interpolation=cv2.INTER_AREA)
        if observation.ndim == 2:
            observation = np.expand_dims(observation, -1)
        return observation