def test_converter(self, data_shape, filenames, expected_shape, meta_shape):
test_image = np.random.randint(0, 256, size=data_shape)
with tempfile.TemporaryDirectory() as tempdir:
for i, name in enumerate(filenames):
filenames[i] = os.path.join(tempdir, name)
Image.fromarray(test_image.astype("uint8")).save(filenames[i])
reader = PILReader(converter=lambda image: image.convert("LA"))
result = reader.get_data(reader.read(filenames, mode="r"))
# load image by PIL and compare the result
test_image = np.asarray(Image.open(filenames[0]).convert("LA"))
self.assertTupleEqual(tuple(result[1]["spatial_shape"]), meta_shape)
self.assertTupleEqual(result[0].shape, expected_shape)
np.testing.assert_allclose(result[0], test_image)
def test_shape_value(self, data_shape, filenames, expected_shape, meta_shape):
test_image = np.random.randint(0, 256, size=data_shape)
with tempfile.TemporaryDirectory() as tempdir:
for i, name in enumerate(filenames):
filenames[i] = os.path.join(tempdir, name)
Image.fromarray(test_image.astype("uint8")).save(filenames[i])
reader = PILReader(mode="r")
result = reader.get_data(reader.read(filenames))
# load image by PIL and compare the result
test_image = np.asarray(Image.open(filenames[0]))
self.assertTupleEqual(tuple(result[1]["spatial_shape"]), meta_shape)
self.assertTupleEqual(result[0].shape, expected_shape)
test_image = np.moveaxis(test_image, 0, 1)
if result[0].shape == test_image.shape:
np.testing.assert_allclose(result[0], test_image)
else:
np.testing.assert_allclose(result[0], np.tile(test_image, [result[0].shape[0], 1, 1]))
def test_readers(self):
inst = ITKReader()
self.assertIsInstance(inst, ITKReader)
inst = NibabelReader()
self.assertIsInstance(inst, NibabelReader)
inst = NibabelReader(as_closest_canonical=True)
self.assertIsInstance(inst, NibabelReader)
inst = NumpyReader()
self.assertIsInstance(inst, NumpyReader)
inst = NumpyReader(npz_keys="test")
self.assertIsInstance(inst, NumpyReader)
inst = PILReader()
self.assertIsInstance(inst, PILReader)
inputZ01, inputZ01_val = split_train_val(inputZ01_path)
inputZ02, inputZ02_val = split_train_val(inputZ02_path)
inputZ03, inputZ03_val = split_train_val(inputZ03_path)
inputZ04, inputZ04_val = split_train_val(inputZ04_path)
inputZ05, inputZ05_val = split_train_val(inputZ05_path)
inputZ06, inputZ06_val = split_train_val(inputZ06_path)
inputZ07, inputZ07_val = split_train_val(inputZ07_path)
targetC01, targetC01_val = split_train_val(targetC01_path)
targetC02, targetC02_val = split_train_val(targetC02_path)
targetC03, targetC03_val = split_train_val(targetC03_path)
# data preprocessing/augmentation
trans_train = Compose([
#LoadPNG(image_only=True),
LoadImage(PILReader(), image_only=True),
AddChannel(),
CenterSpatialCrop(roi_size=2154), # 2154
#ScaleIntensity(),
#RandRotate(range_x=15, prob=aug_prob, keep_size=True),
#RandRotate90(prob=aug_prob, spatial_axes=(0, 1)),
#RandFlip(spatial_axis=0, prob=aug_prob),
#RandScaleIntensity(factors=0.5, prob=aug_prob)
ToTensor()
])
trans_val = Compose([
#LoadPNG(image_only=True),
LoadImage(PILReader(), image_only=True),
AddChannel(),
#CenterSpatialCrop(roi_size=2154),
RandFlip(spatial_axis=(1, 2), prob=aug_prob),
ToTensor()
])
trans_val = MozartTheComposer([
# LoadImage(PILReader(), image_only=True),
#ScaleIntensity(),
# AddChannel(),
# RandSpatialCrop(roi_size=256, random_size=False),
#CenterSpatialCrop(roi_size=2154),
ToTensor()
])
# create dataset class
train_dataset = OurDataset(data=train_split,
data_reader=PILReader(),
transform=trans_train,
roi_size=256,
samples_per_image=8)
val_dataset = OurGridyDataset(data=val_split,
data_reader=PILReader(),
patch_size=256)
# now create data loader ( MONAI DataLoader)
training_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=8 #multiprocessing.cpu_count(),