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

# Source code for rl_coach.filters.observation.observation_rescale_size_by_factor_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.
    #

    from skimage.transform import resize

    from rl_coach.core_types import ObservationType
    from rl_coach.filters.observation.observation_filter import ObservationFilter
    from rl_coach.spaces import ObservationSpace

    class ObservationRescaleSizeByFactorFilter(ObservationFilter):
        """
        Rescales an image observation by some factor. For example, the image size
        can be reduced by a factor of 2.
        """
        def __init__(self, rescale_factor: float):
            """
            :param rescale_factor: the factor by which the observation will be rescaled
            """
            super().__init__()
            self.rescale_factor = float(rescale_factor)
            # TODO: allow selecting the channels dim

        def validate_input_observation_space(self, input_observation_space: ObservationSpace):
            if not 2 <= input_observation_space.num_dimensions <= 3:
                raise ValueError("The rescale filter only applies to image observations where the number of dimensions is"
                                 "either 2 (grayscale) or 3 (RGB). The number of dimensions defined for the "
                                 "output observation was {}".format(input_observation_space.num_dimensions))
            if input_observation_space.num_dimensions == 3 and input_observation_space.shape[-1] != 3:
                raise ValueError("Observations with 3 dimensions must have 3 channels in the last axis (RGB)")

        def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
            observation = observation.astype('uint8')
            rescaled_output_size = tuple([int(self.rescale_factor * dim) for dim in observation.shape[:2]])

            if len(observation.shape) == 3:
                rescaled_output_size += (3,)

            # rescale
            observation = resize(observation, rescaled_output_size, anti_aliasing=False, preserve_range=True).astype('uint8')

            return observation

        def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
            input_observation_space.shape[:2] = (input_observation_space.shape[:2] * self.rescale_factor).astype('int')
            return input_observation_space