# ============================================================================================= #
# Author: Filip Lux #
# Copyright: Filip Lux : lux.filip@gmail.com #
# #
# MIT License. #
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# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
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# The above copyright notice and this permission notice shall be included in all #
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
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# SOFTWARE. #
# ============================================================================================= #
import unittest
from bio_volumentations.augmentations.transforms import (
GaussianNoise, PoissonNoise, Resize, Pad, Scale, Flip, CenterCrop, AffineTransform,
RandomScale, RandomRotate90, RandomFlip, RandomCrop, RandomAffineTransform, RandomGamma,
NormalizeMeanStd, GaussianBlur, Normalize, HistogramEqualization, RandomBrightnessContrast)
from bio_volumentations.core.composition import Compose
import numpy as np
class TestGaussianNoise(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(GaussianNoise, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestPoissonNoise(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(PoissonNoise, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestScale(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(Scale, (31, 32, 33), params={'scales': 1.5})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
tests = get_shape_tests(Scale, (31, 32, 33), params={'scales': 0.8})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestRandomScale(unittest.TestCase):
def test_shape(self):
limits = [0.2,
(0.8, 1.2),
(0.2, 0.3, 0.1),
(0.8, 1.2, 0.9, 1.1, 0.7, 1.)]
for scaling_limit in limits:
tests = get_shape_tests(RandomScale, (31, 32, 33),
params={'scaling_limit': scaling_limit})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestRandomRotate90(unittest.TestCase):
def test_shape(self):
axes_list = [None,
[1],
[1, 2],
[1, 2, 3]]
for axes in axes_list:
tests = get_shape_tests(RandomRotate90, (30, 30, 30),
params={'axes': axes})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestFlip(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(Flip, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestRandomFlip(unittest.TestCase):
def test_shape(self):
axes_list = [None,
[],
[1],
[1, 2],
[1, 2, 3]]
for axes in axes_list:
tests = get_shape_tests(RandomFlip, (30, 30, 30),
params={'axes_to_choose': axes})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestCenterCrop(unittest.TestCase):
def test_inflate(self):
in_shape = (32, 31, 30)
shape_tests = get_shape_tests(CenterCrop, in_shape, {'shape': (40, 41, 42)})
for tr_img, expected_shape in shape_tests:
self.assertTupleEqual(tr_img.shape, expected_shape)
def test_deflate(self):
in_shape = (32, 31, 30)
shape_tests = get_shape_tests(CenterCrop, in_shape, {'shape': (20, 21, 22)})
for tr_img, expected_shape in shape_tests:
self.assertTupleEqual(tr_img.shape, expected_shape)
class TestRandomCrop(unittest.TestCase):
def test_inflate(self):
in_shape = (32, 31, 30)
shape_tests = get_shape_tests(RandomCrop, in_shape, {'shape': (40, 41, 42)})
for tr_img, expected_shape in shape_tests:
self.assertTupleEqual(tr_img.shape, expected_shape)
def test_deflate(self):
in_shape = (32, 31, 30)
shape_tests = get_shape_tests(RandomCrop, in_shape, {'shape': (20, 21, 22)})
for tr_img, expected_shape in shape_tests:
self.assertTupleEqual(tr_img.shape, expected_shape)
def get_shape_tests(transform, in_shape: tuple, params={}):
"""
Iterates over all the possibilities, hot the array can passed throught the transform
Args:
transform: biovol transform,
in_shape: spatial dimension of the input image
params: optional, params of the biovol transform
Returns:
list of outputs and expected shapes
"""
w, h, d = in_shape
w_, h_, d_ = params['shape'] if 'shape' in params.keys() else (w, h, d)
res = []
tr = Compose([transform(**params, p=1)])
# img (W, H, D), mask (W, H, D)
img = np.ones((w, h, d))
mask = np.ones((w, h, d))
tr_img = tr(image=img, mask=mask)
res.append((tr_img['image'], (1, w_, h_, d_)))
res.append((tr_img['mask'], (w_, h_, d_)))
# img (C, W, H, D), mask (W, H, D)
img = np.ones((4, w, h, d))
mask = np.ones((w, h, d))
tr_img = tr(image=img, mask=mask)
res.append((tr_img['image'], (4, w_, h_, d_)))
res.append((tr_img['mask'], (w_, h_, d_)))
# img (C, W, H, D, T), mask (W, H, D, T)
img = np.ones((4, w, h, d, 5))
mask = np.ones((w, h, d, 5))
tr_img = tr(image=img, mask=mask)
res.append((tr_img['image'], (4, w_, h_, d_, 5)))
res.append((tr_img['mask'], (w_, h_, d_, 5)))
return res
class TestResize(unittest.TestCase):
def test_inflate(self):
in_shape = (32, 31, 30)
shape_tests = get_shape_tests(Resize, in_shape, {'shape': (40, 41, 42)})
for tr_img, expected_shape in shape_tests:
self.assertTupleEqual(tr_img.shape, expected_shape)
def test_deflate(self):
in_shape = (32, 31, 30)
shape_tests = get_shape_tests(Resize, in_shape, {'shape': (20, 21, 22)})
for tr_img, expected_shape in shape_tests:
self.assertTupleEqual(tr_img.shape, expected_shape)
class TestPad(unittest.TestCase):
def test_1(self):
tr = Compose([Pad(2)])
img = np.empty((30, 30, 30))
tr_img = tr(image=img)['image']
self.assertTupleEqual(tr_img.shape, (1, 34, 34, 34))
img = np.empty((1, 30, 30, 30))
tr_img = tr(image=img)['image']
self.assertTupleEqual(tr_img.shape, (1, 34, 34, 34))
img = np.empty((4, 30, 30, 30))
tr_img = tr(image=img)['image']
self.assertTupleEqual(tr_img.shape, (4, 34, 34, 34))
img = np.empty((4, 30, 30, 30, 5))
tr_img = tr(image=img)['image']
self.assertTupleEqual(tr_img.shape, (4, 34, 34, 34, 5))
class TestRandomAffineTransform(unittest.TestCase):
def test_shape(self):
angle_limits = [10,
(-20, 20),
(12, 30, 0),
(-20, 20, -180, 180, 0, 0)]
for angle_limit in angle_limits:
tests = get_shape_tests(RandomAffineTransform, (31, 32, 33),
params={'angle_limit': angle_limit})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
scale_limits = [0.2,
(0.8, 1.2),
(0.2, 0.3, 0.1),
(0.8, 1.2, 0.9, 1.1, 0.7, 1.)]
for scale_limit in scale_limits:
tests = get_shape_tests(RandomAffineTransform, (31, 32, 33),
params={'scaling_limit': scale_limit})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
translation_limits = [10,
(0, 12),
(3, 5, 10),
(-3, 3, -5, 5, 0, 0)]
for translation in translation_limits:
tests = get_shape_tests(RandomAffineTransform, (31, 32, 33),
params={'translation_limit': translation})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestAffineTransform(unittest.TestCase):
def test_shape(self):
scale = (1.2, 0.8, 1)
translation = (0, 1, -40)
angles = (-20, 0, -0.5)
tests = get_shape_tests(AffineTransform, (31, 32, 33),
params={'translation': translation})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
tests = get_shape_tests(AffineTransform, (31, 32, 33),
params={'scale': scale})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
tests = get_shape_tests(AffineTransform, (31, 32, 33),
params={'angles': angles})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestNormalizeMeanStd(unittest.TestCase):
def test_shape(self):
mean = 1.2
std = 2
tests = get_shape_tests(NormalizeMeanStd, (31, 32, 33),
params={'mean': mean,
'std': std})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestGaussianBlur(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(GaussianBlur, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestRandomGamma(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(RandomGamma, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestRandomBrightnessContrast(unittest.TestCase):
def test_shape(self):
brightness_list = [3, (2, 5)]
contrast_list = [0.5, (.7, 1.1)]
for brightness in brightness_list:
for contrast in contrast_list:
tests = get_shape_tests(RandomBrightnessContrast, (30, 31, 32),
params={'brightness_limit': brightness,
'contrast_limit': contrast})
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestHistogramEqualization(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(HistogramEqualization, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
class TestNormalize(unittest.TestCase):
def test_shape(self):
tests = get_shape_tests(Normalize, (31, 32, 33))
for output, expected_shape in tests:
self.assertTupleEqual(output.shape, expected_shape)
if __name__ == '__main__':
unittest.main()