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

# Source code for rl_coach.filters.observation.observation_rgb_to_y_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 rl_coach.core_types import ObservationType
    from rl_coach.filters.observation.observation_filter import ObservationFilter
    from rl_coach.spaces import ObservationSpace

    class ObservationRGBToYFilter(ObservationFilter):
        """
        Converts a color image observation specified using the RGB encoding into a grayscale
        image observation, by keeping only the luminance (Y) channel of the YUV encoding. This can be useful if the colors
        in the original image are not relevant for solving the task at hand.
        The channels axis is assumed to be the last axis
        """
        def __init__(self):
            super().__init__()

        def validate_input_observation_space(self, input_observation_space: ObservationSpace):
            if input_observation_space.num_dimensions != 3:
                raise ValueError("The rescale filter only applies to image observations where the number of dimensions is"
                                 "3 (RGB). The number of dimensions defined for the input observation was {}"
                                 .format(input_observation_space.num_dimensions))
            if input_observation_space.shape[-1] != 3:
                raise ValueError("The observation space is expected to have 3 channels in the 1st dimension. The number of "
                                 "dimensions received is {}".format(input_observation_space.shape[-1]))

        def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:

            # rgb to y
            r, g, b = observation[:, :, 0], observation[:, :, 1], observation[:, :, 2]
            observation = 0.2989 * r + 0.5870 * g + 0.1140 * b

            return observation

        def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
            input_observation_space.shape = input_observation_space.shape[:-1]
            return input_observation_space