#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import copy
from skimage.transform import resize
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace, PlanarMapsObservationSpace, ImageObservationSpace
[docs]class ObservationRescaleToSizeFilter(ObservationFilter):
"""
Rescales an image observation to a given size. The target size does not
necessarily keep the aspect ratio of the original observation.
Warning: this requires the input observation to be of type uint8 due to scipy requirements!
"""
def __init__(self, output_observation_space: PlanarMapsObservationSpace):
"""
:param output_observation_space: the output observation space
"""
super().__init__()
self.output_observation_space = output_observation_space
if not isinstance(output_observation_space, PlanarMapsObservationSpace):
raise ValueError("The rescale filter only applies to observation spaces that inherit from "
"PlanarMapsObservationSpace. This includes observations which consist of a set of 2D "
"images or an RGB image. Instead the output observation space was defined as: {}"
.format(output_observation_space.__class__))
self.planar_map_output_shape = copy.copy(self.output_observation_space.shape)
self.planar_map_output_shape = np.delete(self.planar_map_output_shape,
self.output_observation_space.channels_axis)
def validate_input_observation_space(self, input_observation_space: ObservationSpace):
if not isinstance(input_observation_space, PlanarMapsObservationSpace):
raise ValueError("The rescale filter only applies to observation spaces that inherit from "
"PlanarMapsObservationSpace. This includes observations which consist of a set of 2D "
"images or an RGB image. Instead the input observation space was defined as: {}"
.format(input_observation_space.__class__))
if input_observation_space.shape[input_observation_space.channels_axis] \
!= self.output_observation_space.shape[self.output_observation_space.channels_axis]:
raise ValueError("The number of channels between the input and output observation spaces must match. "
"Instead the number of channels were: {}, {}"
.format(input_observation_space.shape[input_observation_space.channels_axis],
self.output_observation_space.shape[self.output_observation_space.channels_axis]))
def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
observation = observation.astype('uint8')
# rescale
if isinstance(self.output_observation_space, ImageObservationSpace):
observation = resize(observation, tuple(self.output_observation_space.shape), anti_aliasing=False,
preserve_range=True).astype('uint8')
else:
new_observation = []
for i in range(self.output_observation_space.shape[self.output_observation_space.channels_axis]):
new_observation.append(resize(observation.take(i, self.output_observation_space.channels_axis),
tuple(self.planar_map_output_shape),
preserve_range=True).astype('uint8'))
new_observation = np.array(new_observation)
observation = new_observation.swapaxes(0, self.output_observation_space.channels_axis)
return observation
def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
input_observation_space.shape = self.output_observation_space.shape
return input_observation_space