Source code for rl_coach.filters.observation.observation_rescale_size_by_factor_filter

#
# Copyright (c) 2017 Intel Corporation
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# 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
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#      http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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from enum import Enum

import scipy.ndimage

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


# imresize interpolation types as defined by scipy here:
# https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.misc.imresize.html
class RescaleInterpolationType(Enum):
    NEAREST = 'nearest'
    LANCZOS = 'lanczos'
    BILINEAR = 'bilinear'
    BICUBIC = 'bicubic'
    CUBIC = 'cubic'


[docs]class ObservationRescaleSizeByFactorFilter(ObservationFilter): """ Rescales an image observation by some factor. For example, the image size can be reduced by a factor of 2. Warning: this requires the input observation to be of type uint8 due to scipy requirements! """ def __init__(self, rescale_factor: float, rescaling_interpolation_type: RescaleInterpolationType): """ :param rescale_factor: the factor by which the observation will be rescaled :param rescaling_interpolation_type: the interpolation type for rescaling """ super().__init__() self.rescale_factor = float(rescale_factor) # scipy requires float scale factors self.rescaling_interpolation_type = rescaling_interpolation_type # 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: # scipy works only with uint8 observation = observation.astype('uint8') # rescale observation = scipy.misc.imresize(observation, self.rescale_factor, interp=self.rescaling_interpolation_type.value) 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