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color.py
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color.py
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# Copyright 2021 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.
# ==============================================================================
import random
from typing import Any, Dict, Iterable, List, Union
import numpy as np
from PIL import Image, ImageEnhance
from fastestimator.op.numpyop.numpyop import NumpyOp
from fastestimator.util.traceability_util import traceable
from fastestimator.util.base_util import param_to_range
@traceable()
class Color(NumpyOp):
"""Randomly change the color balance of an image.
This is a wrapper for functionality provided by the PIL library:
https://github.com/python-pillow/Pillow/tree/master/src/PIL.
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".
limit: Factor range for changing color balance. If limit is a single float, the range will be (-limit, limit).
A factor of 0.0 gives a black and white image and a factor of 1.0 gives the original image.
Image types:
uint8
"""
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,
limit: float = 0.54):
super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
self.limit = param_to_range(limit)
self.in_list, self.out_list = True, True
def set_rua_level(self, magnitude_coef: float) -> None:
"""Set the augmentation intensity based on the magnitude_coef.
This method is specifically designed to be invoked by the RUA Op.
Args:
magnitude_coef: The desired augmentation intensity (range [0-1]).
"""
param_mid = (self.limit[1] + self.limit[0]) / 2
param_extent = magnitude_coef * ((self.limit[1] - self.limit[0]) / 2)
self.limit = (param_mid - param_extent, param_mid + param_extent)
def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> List[np.ndarray]:
factor = 1.0 + random.uniform(self.limit[0], self.limit[1])
return [Color._apply_color(elem, factor) for elem in data]
@staticmethod
def _apply_color(data: np.ndarray, factor: float) -> np.ndarray:
im = Image.fromarray(data)
im = ImageEnhance.Color(im).enhance(factor)
return np.array(im)