-
Notifications
You must be signed in to change notification settings - Fork 34
/
random_rain.py
73 lines (67 loc) · 3.35 KB
/
random_rain.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
# 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, Optional, Tuple, Union
from albumentations.augmentations.transforms import RandomRain as RandomRainAlb
from fastestimator.op.numpyop.univariate.univariate import ImageOnlyAlbumentation
from fastestimator.util.traceability_util import traceable
@traceable()
class RandomRain(ImageOnlyAlbumentation):
"""Add rain to an image
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".
slant_lower: Should be in range [-20, 20].
slant_upper: Should be in range [-20, 20].
drop_length: Should be in range [0, 100].
drop_width: Should be in range [1, 5].
drop_color: Rain lines color (r, g, b).
blur_value: How blurry to make the rain.
brightness_coefficient: Rainy days are usually shady. Should be in range [0, 1].
rain_type: One of [None, "drizzle", "heavy", "torrential"].
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,
slant_lower: int = -10,
slant_upper: int = 10,
drop_length: int = 20,
drop_width: int = 1,
drop_color: Tuple[int, int, int] = (200, 200, 200),
blur_value: int = 7,
brightness_coefficient: float = 0.7,
rain_type: Optional[str] = None):
super().__init__(
RandomRainAlb(slant_lower=slant_lower,
slant_upper=slant_upper,
drop_length=drop_length,
drop_width=drop_width,
drop_color=drop_color, # Their docstring type hint doesn't match the real code
blur_value=blur_value,
brightness_coefficient=brightness_coefficient,
rain_type=rain_type,
always_apply=True),
inputs=inputs,
outputs=outputs,
mode=mode,
ds_id=ds_id)