Source code for rl_coach.filters.observation.observation_rescale_to_size_filter

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# 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
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import copy
from enum import Enum

import numpy as np
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, PlanarMapsObservationSpace, ImageObservationSpace


# 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 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, rescaling_interpolation_type: RescaleInterpolationType=RescaleInterpolationType.BILINEAR): """ :param output_observation_space: the output observation space :param rescaling_interpolation_type: the interpolation type for rescaling """ super().__init__() self.output_observation_space = output_observation_space self.rescaling_interpolation_type = rescaling_interpolation_type 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: # scipy works only with uint8 observation = observation.astype('uint8') # rescale if isinstance(self.output_observation_space, ImageObservationSpace): observation = scipy.misc.imresize(observation, tuple(self.output_observation_space.shape), interp=self.rescaling_interpolation_type.value) else: new_observation = [] for i in range(self.output_observation_space.shape[self.output_observation_space.channels_axis]): new_observation.append(scipy.misc.imresize(observation.take(i, self.output_observation_space.channels_axis), tuple(self.planar_map_output_shape), interp=self.rescaling_interpolation_type.value)) 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