def test_translation(self):
image = np.array([[0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [1, 1, 1, 1, 1],
[0, 0, 1, 0, 0], [0, 0, 1, 0, 0]],
dtype=DTYPE)
intp = gryds.Interpolator(image, mode='mirror')
trf1 = gryds.TranslationTransformation([0.1, 0])
trf2 = gryds.TranslationTransformation([-0.1, 0])
trf = gryds.ComposedTransformation(trf2, trf1)
new_image = intp.transform(trf)
np.testing.assert_almost_equal(image, new_image)
def test_rotation_translation(self):
image = np.array([[0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [1, 1, 1, 1, 1],
[0, 0, 1, 0, 0], [0, 0, 1, 0, 0]],
dtype=DTYPE)
intp = gryds.Interpolator(image, mode='mirror')
trf1 = gryds.TranslationTransformation([0.1, 0])
trf2 = gryds.AffineTransformation(ndim=2, angles=[0.1])
trf3 = gryds.AffineTransformation(ndim=2, angles=[-0.1])
trf4 = gryds.TranslationTransformation([-0.1, 0])
trf = gryds.ComposedTransformation(trf1, trf2, trf3, trf4)
new_image = intp.transform(trf)
np.testing.assert_almost_equal(image, new_image, decimal=6)
def test_2d_translation(self):
trf = gryds.TranslationTransformation(
[0.1, 0]) # move grid 10% downwards (moves image 10% upwards)
grid = gryds.Grid((10, 20))
new_grid = grid.transform(trf)
# The original grid runs from 0 to 0.9 for the i-coordinates
# The transformed grid should run from 0.1 to 1
np.testing.assert_equal(new_grid.grid[0, 0], np.array(0.1, DTYPE))
np.testing.assert_equal(new_grid.grid[0, 9], np.array(1.0, DTYPE))
# The jacobian of this transformation should be 1 everywhere, i.e. no
# scaling should have happened
np.testing.assert_almost_equal(grid.jacobian_det(trf),
np.array(1, DTYPE),
decimal=4)
def test_5d_translation(self):
trf = gryds.TranslationTransformation([0, 0, 0.1, 0, 0])
grid = gryds.Grid((10, 10, 10, 10, 10))
new_grid = grid.transform(trf)
# The original grid runs from 0 to 0.9
# The transformed grid should run from 0.1 to 1
self.assertTrue(np.all(new_grid.grid[2, :, :, 0] == np.array(0.1, DTYPE)))
self.assertTrue(np.all(new_grid.grid[2, :, :, 9] == np.array(1.0, DTYPE)))
# The jacobian of this transformation should be 1 everywhere, i.e. no
# scaling should have happened
np.testing.assert_almost_equal(
grid.jacobian_det(trf),
np.array(1, DTYPE),
decimal=4)
def test_repr(self):
self.assertEqual(str(gryds.TranslationTransformation([3, 4])),
'TranslationTransformation(2D, t=[3 4])')
def test_base_interpolator_transform(self):
trf = gryds.TranslationTransformation([1, 2, 3, 4, 5])
im = np.random.rand(10, 10, 10, 10, 10)
intp = gryds.base.Interpolator(im)
self.assertRaises(NotImplementedError, intp.transform, trf)
import os
import sys
sys.path.append(os.path.abspath('..'))
import gryds
import numpy as np
from cProfile import Profile
from pstats import Stats
import time
import matplotlib.pyplot as plt
import seaborn as sns
# bsp = gryds.BSplineTransformation(np.random.rand(3, 32, 32, 32), order=1)
bsp = gryds.TranslationTransformation([0.1, 0.2, 0.3])
N = 1
Ns = range(0, 151, 1)
M = 10
image = np.random.rand(N, 128, 128)
intp = gryds.BSplineInterpolatorCuda(image)
intp.transform(bsp)
times = []
for i in range(M):
ts = []
for N in Ns:
print(i, N)
image = np.random.rand(N, 128, 128)
intp = gryds.Interpolator(image, order=1)
t0 = time.time()
def test_incompatible_error(self):
trf1 = gryds.TranslationTransformation([0.1, 0, 0])
trf2 = gryds.TranslationTransformation([-0.1, 0])
self.assertRaises(ValueError, gryds.ComposedTransformation, trf2, trf1)
def augment(imA, imB):
"""
Input day0 and day4 image of the same rat
An augmented version of both is returned
"""
#"3D" to 2D
imA = imA.reshape((256, 256))
imB = imB.reshape((256, 256))
# Define random deformation matrix
random_grid = np.random.rand(2, 3, 3)
random_grid -= 0.5
random_grid /= 10
# Define B-Spline transformation matrix
bspline = gryds.BSplineTransformation(random_grid)
# Define random translation matrix
a_translation = gryds.TranslationTransformation(
[random.uniform(-0.03, 0.03),
random.uniform(-0.03, 0.03)])
# Define random rotation matrix
a_rotation = gryds.AffineTransformation(
ndim=2,
angles=[random.uniform(-np.pi / 24, np.pi / 24)], # ± 7 degrees
center=[0.5, 0.5])
# Define an image interpolater
an_image_interpolatorA = gryds.Interpolator(imA)
an_image_interpolatorB = gryds.Interpolator(imB)
# Combine all operations and apply the same augmentation to day0 and day4
composed = gryds.ComposedTransformation(bspline, a_rotation, a_translation)
transformed_imageA = an_image_interpolatorA.transform(composed)
transformed_imageB = an_image_interpolatorB.transform(composed)
# Define noise augmentation
mu = 0.0
sigma = random.uniform(0., 0.05)
noise_mapA = np.random.normal(mu, sigma, size=np.size(imA)).reshape(
(256, 256))
noise_mapB = np.random.normal(mu, sigma, size=np.size(imB)).reshape(
(256, 256))
noise_mapA[transformed_imageA < 1e-2] = 0.
noise_mapB[transformed_imageB < 1e-2] = 0.
# Add noise to augmented image
transformed_imageA = transformed_imageA + noise_mapA
transformed_imageB = transformed_imageB + noise_mapB
# Flip L/R (half of the time)
perform_flip = random.choice([False, True])
if perform_flip:
transformed_imageA = np.fliplr(transformed_imageA)
transformed_imageB = np.fliplr(transformed_imageB)
return [
transformed_imageA.reshape((256, 256, 1)),
transformed_imageB.reshape((256, 256, 1))
]