# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from typing import Any
import gymnasium as gym
import numpy as np
from smarts.core.sensors import Observation
from smarts.core.utils.file import smarts_local_user_dir
from smarts.env.wrappers.utils.rendering import vis_sim_obs
Action = Any
Operation = Any
default_log_dir = smarts_local_user_dir()
[docs]class AgentCameraRGBRender:
"""Wraps the environment with `rgb_array` render mode capabilities."""
def __init__(self, env: gym.Env, max_agents: int = 1, enabled=True):
self._max_agents = max_agents
self._enabled = enabled
self._image_frame = []
self.is_vector_env = getattr(env, "is_vector_env", False)
[docs] def step(self, obs, rewards, dones, infos):
"""Record a step."""
single_env_obs = obs
if self.is_vector_env:
# For now only render one environment
single_env_obs = obs[0]
self._record_for_render(single_env_obs)
[docs] def render(self, mode="rgb_array", **kwargs):
"""Render the given camera image in this environment."""
if len(self._image_frame) > 0:
return self._image_frame
[docs] def reset(self, obs) -> Any:
"""Record the reset of the environment."""
self._record_for_render(obs)
return obs
def _record_for_render(self, obs) -> Any:
if not self._enabled:
return
if isinstance(obs, Observation):
obs = {"default_agent": obs}
values = list(vis_sim_obs(obs).values())
largest_image = max(values, key=lambda im: np.product(im.shape))
image = np.array([np.resize(im, largest_image.shape) for im in values])
if len(image.shape) > 2:
self._image_frame = np.reshape(
image,
(
image.shape[0] * image.shape[2],
image.shape[1] * image.shape[3],
image.shape[4],
),
)