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from __future__ import annotations
import pickle
import random
def test_pickle():
from scitbx.array_family import flex
from dials.model.data import PixelList
size = (100, 100)
sf = 10
image = flex.double(flex.grid(size))
mask = flex.bool(flex.grid(size))
for i in range(len(image)):
image[i] = random.randint(0, 100)
mask[i] = bool(random.randint(0, 1))
pl = PixelList(sf, image, mask)
assert pl.size() == size
assert pl.frame() == sf
obj = pickle.dumps(pl)
pl2 = pickle.loads(obj)
assert pl2.size() == size
assert pl2.frame() == sf
assert len(pl2) == len(pl)
assert pl2.index().all_eq(pl.index())
assert pl2.value().all_eq(pl.value())
def test_add_image():
from scitbx.array_family import flex
from dials.model.data import PixelList, PixelListLabeller
size = (2000, 2000)
sf = 10
labeller = PixelListLabeller()
count = 0
for i in range(3):
image = flex.random_int_gaussian_distribution(size[0] * size[1], 100, 5)
mask = flex.random_bool(size[0] * size[1], 0.5)
image.reshape(flex.grid(size))
mask.reshape(flex.grid(size))
pl = PixelList(sf + i, image, mask)
count += len(mask.as_1d().select(mask.as_1d()))
labeller.add(pl)
assert len(labeller.values()) == count
def test_labels_3d():
from scitbx.array_family import flex
from dials.model.data import PixelList, PixelListLabeller
size = (500, 500)
sf = 0
labeller = PixelListLabeller()
count = 0
mask_list = []
for i in range(3):
image = flex.random_int_gaussian_distribution(size[0] * size[1], 100, 5)
mask = flex.random_bool(size[0] * size[1], 0.5)
image.reshape(flex.grid(size))
mask.reshape(flex.grid(size))
pl = PixelList(sf + i, image, mask)
count += len(mask.as_1d().select(mask.as_1d()))
labeller.add(pl)
mask_list.append(mask)
coords = labeller.coords()
labels = labeller.labels_3d()
# Create a map of labels
label_map = flex.int(flex.grid(3, size[0], size[1]))
for c, l in zip(coords, labels):
label_map[c] = l
# Ensure all labels are correct
vi = 0
for k in range(3):
for j in range(size[0]):
for i in range(size[1]):
if mask_list[k][j, i]:
l1 = labels[vi]
if k > 0 and mask_list[k - 1][j, i]:
l2 = label_map[k - 1, j, i]
assert l2 == l1
if j > 0 and mask_list[k][j - 1, i]:
l2 = label_map[k, j - 1, i]
assert l2 == l1
if i > 0 and mask_list[k][j, i - 1]:
l2 = label_map[k, j, i - 1]
assert l2 == l1
vi += 1
def test_labels_2d():
from scitbx.array_family import flex
from dials.model.data import PixelList, PixelListLabeller
size = (500, 500)
sf = 0
labeller = PixelListLabeller()
count = 0
mask_list = []
for i in range(3):
image = flex.random_int_gaussian_distribution(size[0] * size[1], 100, 5)
mask = flex.random_bool(size[0] * size[1], 0.5)
image.reshape(flex.grid(size))
mask.reshape(flex.grid(size))
pl = PixelList(sf + i, image, mask)
count += len(mask.as_1d().select(mask.as_1d()))
labeller.add(pl)
mask_list.append(mask)
coords = labeller.coords()
labels = labeller.labels_2d()
# Create a map of labels
label_map = flex.int(flex.grid(3, size[0], size[1]))
for c, l in zip(coords, labels):
label_map[c] = l
# Ensure all labels are correct
vi = 0
for k in range(3):
for j in range(size[0]):
for i in range(size[1]):
if mask_list[k][j, i]:
l1 = labels[vi]
if k > 0 and mask_list[k - 1][j, i]:
l2 = label_map[k - 1, j, i]
assert l2 != l1
if j > 0 and mask_list[k][j - 1, i]:
l2 = label_map[k, j - 1, i]
assert l2 == l1
if i > 0 and mask_list[k][j, i - 1]:
l2 = label_map[k, j, i - 1]
assert l2 == l1
vi += 1
def test_with_no_points():
from scitbx.array_family import flex
from dials.model.data import PixelList, PixelListLabeller
size = (500, 500)
sf = 0
labeller = PixelListLabeller()
count = 0
mask_list = []
for i in range(3):
image = flex.random_int_gaussian_distribution(size[0] * size[1], 100, 5)
mask = flex.bool(size[0] * size[0], False)
image.reshape(flex.grid(size))
mask.reshape(flex.grid(size))
pl = PixelList(sf + i, image, mask)
count += len(mask.as_1d().select(mask.as_1d()))
labeller.add(pl)
mask_list.append(mask)
coords = labeller.coords()
labels1 = labeller.labels_2d()
labels2 = labeller.labels_2d()
assert len(coords) == 0
assert len(labels1) == 0
assert len(labels2) == 0
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