def __call__(self, data: Any):
if self.depth:
img, seg = create_test_image_3d(
self.height,
self.width,
self.depth,
self.num_objs,
self.rad_max,
self.rad_min,
self.noise_max,
self.num_seg_classes,
self.channel_dim,
self.random_state,
)
else:
img, seg = create_test_image_2d(
self.height,
self.width,
self.num_objs,
self.rad_max,
self.rad_min,
self.noise_max,
self.num_seg_classes,
self.channel_dim,
self.random_state,
)
return img, seg
def setUp(self):
im, msk = create_test_image_2d(self.im_shape[0], self.im_shape[1], 4,
20, 0, self.num_classes)
self.imt = im[None, None]
self.seg1 = (msk[None, None] > 0).astype(np.float32)
self.segn = msk[None, None]
def __getitem__(self, _unused_id):
im, seg = create_test_image_2d(128,
128,
noise_max=1,
num_objs=4,
num_seg_classes=1)
return im[None], seg[None].astype(np.float32)
def test_make_nifti(self, params):
im, _ = create_test_image_2d(100, 88)
created_file = make_nifti_image(im, verbose=True, **params)
self.assertTrue(os.path.isfile(created_file))
from unittest.case import skipUnless
import torch
from parameterized import parameterized
from monai.data.synthetic import create_test_image_2d, create_test_image_3d
from monai.transforms.utils_pytorch_numpy_unification import moveaxis
from monai.utils.module import optional_import
from monai.visualize.utils import blend_images
from tests.utils import TEST_NDARRAYS
plt, has_matplotlib = optional_import("matplotlib.pyplot")
TESTS = []
for p in TEST_NDARRAYS:
image, label = create_test_image_2d(100, 101)
TESTS.append((p(image), p(label)))
image, label = create_test_image_3d(100, 101, 102)
TESTS.append((p(image), p(label)))
@skipUnless(has_matplotlib, "Matplotlib required")
class TestBlendImages(unittest.TestCase):
@parameterized.expand(TESTS)
def test_blend(self, image, label):
blended = blend_images(image[None], label[None])
self.assertEqual(type(image), type(blended))
if isinstance(blended, torch.Tensor):
self.assertEqual(blended.device, image.device)
blended = blended.cpu().numpy()
from monai.data.synthetic import create_test_image_2d, create_test_image_3d
from monai.transforms.utils_pytorch_numpy_unification import moveaxis
from monai.utils.module import optional_import
from monai.visualize.utils import blend_images
from tests.utils import TEST_NDARRAYS
plt, has_matplotlib = optional_import("matplotlib.pyplot")
def get_alpha(img):
return 0.5 * np.arange(img.size).reshape(img.shape) / img.size
TESTS = []
for p in TEST_NDARRAYS:
image, label = create_test_image_2d(100, 101, channel_dim=0)
TESTS.append((p(image), p(label), 0.5))
TESTS.append((p(image), p(label), p(get_alpha(image))))
image, label = create_test_image_3d(100, 101, 102, channel_dim=0)
TESTS.append((p(image), p(label), 0.5))
TESTS.append((p(image), p(label), p(get_alpha(image))))
@skipUnless(has_matplotlib, "Matplotlib required")
class TestBlendImages(unittest.TestCase):
@parameterized.expand(TESTS)
def test_blend(self, image, label, alpha):
blended = blend_images(image, label, alpha)
self.assertEqual(type(image), type(blended))
if isinstance(blended, torch.Tensor):