def transform_observation_space(
self,
observation_space: spaces.Dict,
):
size = self._size
observation_space = copy.deepcopy(observation_space)
if size:
for key in observation_space.spaces:
if key in self.trans_keys:
# In the observation space dict, the channels are always last
h, w = get_image_height_width(
observation_space.spaces[key], channels_last=True
)
if size == min(h, w):
continue
scale = size / min(h, w)
new_h = int(h * scale)
new_w = int(w * scale)
new_size = (new_h, new_w)
logger.info(
"Resizing observation of %s: from %s to %s"
% (key, (h, w), new_size)
)
observation_space.spaces[key] = overwrite_gym_box_shape(
observation_space.spaces[key], new_size
)
return observation_space
def transform_observation_space(
self,
observation_space: spaces.Dict,
):
size = self._size
observation_space = copy.deepcopy(observation_space)
if size:
for key in observation_space.spaces:
if (key in self.trans_keys and
observation_space.spaces[key].shape[-3:-1] != size):
h, w = get_image_height_width(
observation_space.spaces[key], channels_last=True)
logger.info(
"Center cropping observation size of %s from %s to %s"
% (key, (h, w), size))
observation_space.spaces[key] = overwrite_gym_box_shape(
observation_space.spaces[key], size)
return observation_space
def transform_observation_space(
self,
observation_space: SpaceDict,
):
r"""Transforms the target UUID's sensor obs_space so it matches the new shape (EQ_H, EQ_W)"""
# Transforms the observation space to of the target UUID
observation_space = copy.deepcopy(observation_space)
for i, key in enumerate(self.target_uuids):
assert (
key in observation_space.spaces
), f"{key} not found in observation space: {observation_space.spaces}"
c = self.cubemap_length
logger.info(
f"Overwrite sensor: {key} from size of ({c}, {c}) to equirect image of {self.eq_shape} from sensors: {self.sensor_uuids[i*6:(i+1)*6]}"
)
if (c, c) != self.eq_shape:
observation_space.spaces[key] = overwrite_gym_box_shape(
observation_space.spaces[key], self.eq_shape)
return observation_space
def transform_observation_space(
self,
observation_space: spaces.Dict,
):
r"""Transforms the target UUID's sensor obs_space so it matches the new shape (H, W)"""
# Transforms the observation space to of the target UUID
for i, key in enumerate(self.target_uuids):
assert (
key in observation_space.spaces
), f"{key} not found in observation space: {observation_space.spaces}"
h, w = get_image_height_width(observation_space.spaces[key],
channels_last=True)
in_len = self.converter.input_len
logger.info(
f"Overwrite sensor: {key} from size of ({h}, {w}) to image of"
f" {self.img_shape} from sensors: {self.sensor_uuids[i*in_len:(i+1)*in_len]}"
)
if (h, w) != self.img_shape:
observation_space.spaces[key] = overwrite_gym_box_shape(
observation_space.spaces[key], self.img_shape)
return observation_space
def transform_observation_space(
self,
observation_space: spaces.Dict,
):
r"""Transforms the target UUID's sensor obs_space so it matches the new shape (FISH_H, FISH_W)"""
# Transforms the observation space to of the target UUID
for i, key in enumerate(self.target_uuids):
assert (
key in observation_space.spaces
), f"{key} not found in observation space: {observation_space.spaces}"
h, w = get_image_height_width(observation_space.spaces[key],
channels_last=True)
assert (
h == w
), f"cubemap height and width must be the same, but is {h} and {w}"
logger.info(
f"Overwrite sensor: {key} from size of ({h}, {w}) to fisheye image of {self.fish_shape} from sensors: {self.sensor_uuids[i*6:(i+1)*6]}"
)
if (h, w) != self.fish_shape:
observation_space.spaces[key] = overwrite_gym_box_shape(
observation_space.spaces[key], self.fish_shape)
return observation_space