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

# Source code for rl_coach.filters.observation.observation_crop_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 typing import Union, Tuple

    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

    class ObservationCropFilter(ObservationFilter):
        """
        Crops the size of the observation to a given crop window. For example, in Atari, the
        observations are images with a shape of 210x160. Usually, we will want to crop the size of the observation to a
        square of 160x160 before rescaling them.
        """
        def __init__(self, crop_low: np.ndarray=None, crop_high: np.ndarray=None):
            """
            :param crop_low: a vector where each dimension describes the start index for cropping the observation in the
                             corresponding dimension. a negative value of -1 will be mapped to the max size
            :param crop_high: a vector where each dimension describes the end index for cropping the observation in the
                              corresponding dimension. a negative value of -1 will be mapped to the max size
            """
            super().__init__()
            if crop_low is None and crop_high is None:
                raise ValueError("At least one of crop_low and crop_high should be set to a real value. ")
            if crop_low is None:
                crop_low = np.array([0] * len(crop_high))
            if crop_high is None:
                crop_high = np.array([-1] * len(crop_low))

            self.crop_low = crop_low
            self.crop_high = crop_high

            for h, l in zip(crop_high, crop_low):
                if h < l and h != -1:
                    raise ValueError("Some of the cropping low values are higher than cropping high values")
            if np.any(crop_high < -1) or np.any(crop_low < -1):
                raise ValueError("Cropping values cannot be negative")
            if crop_low.shape != crop_high.shape:
                raise ValueError("The low values and high values for cropping must have the same number of dimensions")
            if crop_low.dtype != int or crop_high.dtype != int:
                raise ValueError("The crop values should be int values, instead they are defined as: {} and {}"
                                 .format(crop_low.dtype, crop_high.dtype))

        def _replace_negative_one_in_crop_size(self, crop_size: np.ndarray, observation_shape: Union[Tuple, np.ndarray]):
            # replace -1 with the max size
            crop_size = crop_size.copy()
            for i in range(len(observation_shape)):
                if crop_size[i] == -1:
                    crop_size[i] = observation_shape[i]
            return crop_size

        def validate_input_observation_space(self, input_observation_space: ObservationSpace):
            crop_high = self._replace_negative_one_in_crop_size(self.crop_high, input_observation_space.shape)
            crop_low = self._replace_negative_one_in_crop_size(self.crop_low, input_observation_space.shape)
            if np.any(crop_high > input_observation_space.shape) or \
                    np.any(crop_low > input_observation_space.shape):
                raise ValueError("The cropping values are outside of the observation space")
            if not input_observation_space.is_valid_index(crop_low) or \
                    not input_observation_space.is_valid_index(crop_high - 1):
                raise ValueError("The cropping indices are outside of the observation space")

        def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
            # replace -1 with the max size
            crop_high = self._replace_negative_one_in_crop_size(self.crop_high, observation.shape)
            crop_low = self._replace_negative_one_in_crop_size(self.crop_low, observation.shape)

            # crop
            indices = [slice(i, j) for i, j in zip(crop_low, crop_high)]
            observation = observation[indices]
            return observation

        def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
            # replace -1 with the max size
            crop_high = self._replace_negative_one_in_crop_size(self.crop_high, input_observation_space.shape)
            crop_low = self._replace_negative_one_in_crop_size(self.crop_low, input_observation_space.shape)

            input_observation_space.shape = crop_high - crop_low
            return input_observation_space