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downscale.py
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downscale.py
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# Copyright 2019 The FastEstimator Authors. All Rights Reserved.
#
# 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 Iterable, Union
import cv2
from albumentations.augmentations.transforms import Downscale as DownscaleAlb
from fastestimator.op.numpyop.univariate.univariate import ImageOnlyAlbumentation
from fastestimator.util.traceability_util import traceable
@traceable()
class Downscale(ImageOnlyAlbumentation):
"""Decrease image quality by downscaling and then upscaling.
Args:
inputs: Key(s) of images to be modified.
outputs: Key(s) into which to write the modified images.
mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
like "!infer" or "!train".
ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
ds_ids except for a particular one, you can pass an argument like "!ds1".
scale_min: Lower bound on the image scale. Should be < 1.
scale_max: Upper bound on the image scale. Should be >= scale_min.
interpolation: cv2 interpolation method.
Image types:
uint8, float32
"""
def __init__(self,
inputs: Union[str, Iterable[str]],
outputs: Union[str, Iterable[str]],
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None,
scale_min: float = 0.25,
scale_max: float = 0.25,
interpolation: int = cv2.INTER_NEAREST):
super().__init__(
DownscaleAlb(scale_min=scale_min, scale_max=scale_max, interpolation=interpolation, always_apply=True),
inputs=inputs,
outputs=outputs,
mode=mode,
ds_id=ds_id)