def test_stardistdata():
from stardist.models import StarDistData2D
img, mask = real_image2d()
s = StarDistData2D([img, img], [mask, mask],
batch_size=1, patch_size=(30, 40), n_rays=32, length=1)
(img,), (prob, dist) = s[0]
return (img,), (prob, dist), s
def test_stardistdata_sequence():
from stardist.models import StarDistData2D
from csbdeep.utils.tf import keras_import
Sequence = keras_import('utils', 'Sequence')
x = np.zeros((100, 100), np.uint16)
x[10:-10, 10:-10] = 1
class MyData(Sequence):
def __init__(self, dtype):
self.dtype = dtype
def __getitem__(self, n):
return ((1 + n) * x).astype(self.dtype)
def __len__(self):
return 1000
X = MyData(np.float32)
Y = MyData(np.uint16)
s = StarDistData2D(X,
Y,
batch_size=1,
patch_size=(100, 100),
n_rays=32,
length=1)
(img, ), (prob, dist) = s[0]
return (img, ), (prob, dist), s
def test_stardistdata_sequence():
from stardist.models import StarDistData2D
X = np.zeros((2, 100, 100), np.uint16)
X[:, 10:-10, 10:-10] = 1
X, Y = NumpySequence(X), NumpySequence(X.astype(np.uint16))
s = StarDistData2D(X,
Y,
batch_size=1,
patch_size=(100, 100),
n_rays=32,
length=1)
(img, ), (prob, dist) = s[0]
return (img, ), (prob, dist), s
def test_stardistdata(shape_completion, n_classes, classes):
np.random.seed(42)
from stardist.models import StarDistData2D
img, mask = real_image2d()
s = StarDistData2D([img, img], [mask, mask],
grid=(2, 2),
n_classes=n_classes,
classes=classes,
shape_completion=shape_completion,
b=8,
batch_size=1,
patch_size=(30, 40),
n_rays=32,
length=1)
a, b = s[0]
return a, b, s
def test_stardistdata_multithreaded(workers=5):
np.random.seed(42)
from stardist.models import StarDistData2D
from scipy.ndimage import rotate
from concurrent.futures import ThreadPoolExecutor
from time import sleep
def augmenter(x, y):
deg = int(np.sum(y) % 117)
print(deg)
# return x,y
x = rotate(x, deg, reshape=False, order=0)
y = rotate(y, deg, reshape=False, order=0)
# sleep(np.abs(deg)/180)
return x, y
n_samples = 4
_, mask = real_image2d()
Y = np.stack([mask + i for i in range(n_samples)])
s = StarDistData2D(Y.astype(np.float32),
Y,
grid=(1, 1),
n_classes=None,
augmenter=augmenter,
batch_size=1,
patch_size=mask.shape,
n_rays=32,
length=len(Y))
a1, b1 = tuple(zip(*tuple(s[i] for i in range(n_samples))))
with ThreadPoolExecutor(max_workers=n_samples) as e:
a2, b2 = tuple(zip(*tuple(e.map(lambda i: s[i], range(n_samples)))))
assert all([np.allclose(_r1[0], _r2[0]) for _r1, _r2 in zip(a1, a2)])
assert all([np.allclose(_r1[0], _r2[0]) for _r1, _r2 in zip(b1, b2)])
assert all([np.allclose(_r1[1], _r2[1]) for _r1, _r2 in zip(b1, b2)])
return a2, b2, s