def test_random_transforms():
x = np.random.random((2, 28, 28))
assert affine_transformations.random_rotation(x, 45).shape == (2, 28, 28)
assert affine_transformations.random_shift(x, 1, 1).shape == (2, 28, 28)
assert affine_transformations.random_shear(x, 20).shape == (2, 28, 28)
assert affine_transformations.random_channel_shift(x,
20).shape == (2, 28, 28)
def transfo_imgs(
image, args,
mode): # Types of transformations and range inspired by Sujit 2019
"""
Takes as input an image and transforms it (currently rotation and translation available)
"""
img = image
if len(img.shape) == 3:
if np.random.rand(1)[0] < args[0]:
angle = 10
img = at.random_rotation(img,
angle,
row_axis=0,
col_axis=1,
channel_axis=2)
if np.random.rand(1)[0] < args[1]:
axs_0 = 21
axs_1 = 6
img = at.random_shift(img,
axs_0,
axs_1,
row_axis=0,
col_axis=1,
channel_axis=2)
elif len(img.shape) == 4:
img2 = np.zeros((*img.shape[:-1], 0))
if np.random.rand(1)[0] < args[0]:
angle = 10
axes = tuple(np.random.choice(range(3), 2))
for k in range(img.shape[3]):
img2 = np.concatenate(
(img2, ndi.rotate(img[k], angle, axes=axes,
reshape=False)),
axis=3)
img = img2
if np.random.rand(1)[0] < args[1]:
axs_0 = np.random.randint(0, 21)
axs_1 = np.random.randint(-5, 6)
axs_2 = np.random.randint(-5, 5)
img = shift(img, [axs_0, axs_1, axs_2])
return img
def _transfo_img(
self, image,
args): # Types of transformations and range inspired by Sujit 2019
"""
Takes as input an image and transforms it (currently rotation and translation available)
"""
img = image
if np.random.rand(1)[0] < args[0]:
angle = 10
img = at.random_rotation(img,
angle,
row_axis=0,
col_axis=1,
channel_axis=2)
if np.random.rand(1)[0] < args[1]:
axs_0 = 21
axs_1 = 6
img = at.random_shift(img,
axs_0,
axs_1,
row_axis=0,
col_axis=1,
channel_axis=2)
return img
