-
Notifications
You must be signed in to change notification settings - Fork 299
/
transforms_unit_test.py
216 lines (168 loc) · 6.74 KB
/
transforms_unit_test.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
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import random
import unittest
from augly import image as imaugs
from augly.tests.image_tests.base_unit_test import BaseImageUnitTest
from augly.utils import EMOJI_PATH, IMAGE_METADATA_PATH, IMG_MASK_PATH
from PIL import Image
class TransformsImageUnitTest(BaseImageUnitTest):
@classmethod
def setUpClass(cls):
super().setUpClass()
with open(IMAGE_METADATA_PATH, "r") as f:
cls.metadata = json.load(f)
def test_ApplyLambda(self):
self.evaluate_class(imaugs.ApplyLambda(), fname="apply_lambda")
def test_ApplyPILFilter(self):
self.evaluate_class(imaugs.ApplyPILFilter(), fname="apply_pil_filter")
def test_Blur(self):
self.evaluate_class(imaugs.Blur(), fname="blur")
def test_Brightness(self):
self.evaluate_class(imaugs.Brightness(), fname="brightness")
def test_ChangeAspectRatio(self):
self.evaluate_class(imaugs.ChangeAspectRatio(), fname="change_aspect_ratio")
def test_ClipImageSize(self):
self.evaluate_class(
imaugs.ClipImageSize(max_resolution=1500000), fname="clip_image_size"
)
def test_ColorJitter(self):
self.evaluate_class(imaugs.ColorJitter(), fname="color_jitter")
def test_Compose(self):
random.seed(1)
self.evaluate_class(
imaugs.Compose(
[
imaugs.Blur(),
imaugs.ColorJitter(saturation_factor=1.5),
imaugs.OneOf(
[
imaugs.OverlayOntoScreenshot(),
imaugs.OverlayEmoji(),
imaugs.OverlayText(),
]
),
]
),
fname="compose",
)
def test_Contrast(self):
self.evaluate_class(imaugs.Contrast(), fname="contrast")
def test_ConvertColor(self):
self.evaluate_class(
imaugs.ConvertColor(mode="L"),
fname="convert_color",
check_mode=False,
)
def test_Crop(self):
self.evaluate_class(imaugs.Crop(), fname="crop")
def test_EncodingQuality(self):
self.evaluate_class(
imaugs.EncodingQuality(quality=30), fname="encoding_quality"
)
def test_Grayscale(self):
self.evaluate_class(imaugs.Grayscale(), fname="grayscale")
def test_HFlip(self):
self.evaluate_class(imaugs.HFlip(), fname="hflip")
def test_MaskedComposite(self):
self.evaluate_class(
imaugs.MaskedComposite(
mask=IMG_MASK_PATH,
transform_function=imaugs.Brightness(factor=0.1),
),
fname="masked_composite",
)
@unittest.skip("Failing on some envs, will fix")
def test_MemeFormat(self):
self.evaluate_class(imaugs.MemeFormat(), fname="meme_format")
def test_Opacity(self):
self.evaluate_class(imaugs.Opacity(), fname="opacity")
def test_OverlayEmoji(self):
self.evaluate_class(imaugs.OverlayEmoji(), fname="overlay_emoji")
def test_OverlayImage(self):
self.evaluate_class(
imaugs.OverlayImage(overlay=EMOJI_PATH, overlay_size=0.15, y_pos=0.8),
fname="overlay_image",
)
def test_OverlayOntoBackgroundImage(self):
self.evaluate_class(
imaugs.OverlayOntoBackgroundImage(
background_image=EMOJI_PATH, overlay_size=0.5, scale_bg=True
),
fname="overlay_onto_background_image",
)
def test_OverlayOntoScreenshot(self):
self.evaluate_class(
imaugs.OverlayOntoScreenshot(resize_src_to_match_template=False),
fname="overlay_onto_screenshot",
metadata_exclude_keys=[
"dst_bboxes",
"dst_height",
"dst_width",
"intensity",
"template_filepath",
],
)
def test_OverlayStripes(self):
self.evaluate_class(imaugs.OverlayStripes(), fname="overlay_stripes")
@unittest.skip("Failing on some envs, will fix")
def test_OverlayText(self):
text_indices = [5, 3, 1, 2, 1000, 221]
self.evaluate_class(imaugs.OverlayText(text=text_indices), fname="overlay_text")
def test_Pad(self):
self.evaluate_class(imaugs.Pad(), fname="pad")
def test_PadSquare(self):
self.evaluate_class(imaugs.PadSquare(), fname="pad_square")
def test_PerspectiveTransform(self):
self.evaluate_class(
imaugs.PerspectiveTransform(sigma=100.0), fname="perspective_transform"
)
def test_Pixelization(self):
self.evaluate_class(imaugs.Pixelization(), fname="pixelization")
def test_RandomAspectRatio(self):
random.seed(1)
self.evaluate_class(imaugs.RandomAspectRatio(), fname="RandomAspectRatio")
def test_RandomBlur(self):
random.seed(1)
self.evaluate_class(imaugs.RandomBlur(), fname="RandomBlur")
def test_RandomBrightness(self):
random.seed(1)
self.evaluate_class(imaugs.RandomBrightness(), fname="RandomBrightness")
@unittest.skip("Failing on some envs, will fix")
def test_RandomEmojiOverlay(self):
random.seed(1)
self.evaluate_class(
imaugs.RandomEmojiOverlay(emoji_size=(0.15, 0.3)),
fname="RandomEmojiOverlay",
)
def test_RandomNoise(self):
self.evaluate_class(imaugs.RandomNoise(), fname="random_noise")
def test_RandomPixelization(self):
random.seed(1)
self.evaluate_class(imaugs.RandomPixelization(), fname="RandomPixelization")
def test_RandomRotation(self):
random.seed(1)
self.evaluate_class(imaugs.RandomRotation(), fname="RandomRotation")
def test_Resize(self):
self.evaluate_class(imaugs.Resize(resample=Image.BICUBIC), fname="resize")
def test_Rotate(self):
self.evaluate_class(imaugs.Rotate(), fname="rotate")
def test_Saturation(self):
self.evaluate_class(imaugs.Saturation(factor=0.5), fname="saturation")
def test_Scale(self):
self.evaluate_class(imaugs.Scale(), fname="scale")
def test_Sharpen(self):
self.evaluate_class(imaugs.Sharpen(factor=2.0), fname="sharpen")
def test_ShufflePixels(self):
self.evaluate_class(imaugs.ShufflePixels(factor=0.5), fname="shuffle_pixels")
def test_Skew(self):
self.evaluate_class(imaugs.Skew(), fname="skew")
def test_VFlip(self):
self.evaluate_class(imaugs.VFlip(), fname="vflip")
if __name__ == "__main__":
unittest.main()