# rl_coach.filters.observation.observation_rescale_to_size_filter — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.filters.observation.observation_rescale_to_size_filter

    #
    # 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

    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