def setUp(self) -> None:
paths = extract_file_paths(self.PATH)
self._dataset = MRBrainSSegmentationFactory.create(
natural_sort(paths), None, modalities=Modality.T1, dataset_id=0)
self._reconstructor = ImageReconstructor([256, 256, 192],
[1, 32, 32, 32], [1, 8, 8, 8])
transforms = Compose(
[ToNumpyArray(),
PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])
self._full_image = transforms(self.FULL_IMAGE_PATH)
class ImageReconstructorABIDETest(unittest.TestCase):
PATH = "/home/pierre-luc-delisle/ABIDE/5.1/Stanford_0051160/mri/patches/image"
# TARGET_PATH = "/home/pierre-luc-delisle/ABIDE/5.1/Stanford_0051160/mri/patches/labels"
FULL_IMAGE_PATH = "/home/pierre-luc-delisle/ABIDE/5.1/Stanford_0051160/mri/real_brainmask.nii.gz"
def setUp(self) -> None:
paths = extract_file_paths(self.PATH)
self._dataset = ABIDESegmentationFactory.create(natural_sort(paths),
None,
modalities=Modality.T1,
dataset_id=0)
self._reconstructor = ImageReconstructor([224, 224, 192],
[1, 32, 32, 32], [1, 8, 8, 8])
transforms = Compose(
[ToNumpyArray(),
PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])
self._full_image = transforms(self.FULL_IMAGE_PATH)
def test_should_output_reconstructed_image(self):
all_patches = []
all_labels = []
for current_batch, input in enumerate(self._dataset):
all_patches.append(input.x)
all_labels.append(input.y)
img = self._reconstructor.reconstruct_from_patches_3d(all_patches)
plt.imshow(img[112, :, :], cmap="gray")
plt.show()
np.testing.assert_array_almost_equal(img, self._full_image.squeeze(0),
6)
plt.imshow(self._full_image.squeeze(0)[112, :, :], cmap="gray")
plt.show()
class ImageReconstructorMRBrainSTest(unittest.TestCase):
PATH = "/mnt/md0/Data/Preprocessed_4/MRBrainS/DataNii/TrainingData/1/T1"
# TARGET_PATH = "/mnt/md0/Data/Preprocessed/iSEG/Patches/Aligned/label/6"
FULL_IMAGE_PATH = "/mnt/md0/Data/Preprocessed/MRBrainS/DataNii/TrainingData/1/T1/T1.nii.gz"
def setUp(self) -> None:
paths = extract_file_paths(self.PATH)
self._dataset = MRBrainSSegmentationFactory.create(
natural_sort(paths), None, modalities=Modality.T1, dataset_id=0)
self._reconstructor = ImageReconstructor([256, 256, 192],
[1, 32, 32, 32], [1, 8, 8, 8])
transforms = Compose(
[ToNumpyArray(),
PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])
self._full_image = transforms(self.FULL_IMAGE_PATH)
def test_should_output_reconstructed_image(self):
all_patches = []
all_labels = []
for current_batch, input in enumerate(self._dataset):
all_patches.append(input.x)
all_labels.append(input.y)
img = self._reconstructor.reconstruct_from_patches_3d(all_patches)
plt.imshow(img[64, :, :], cmap="gray")
plt.show()
np.testing.assert_array_almost_equal(img, self._full_image.squeeze(0),
6)
class SlicedImageReconstructorTest(unittest.TestCase):
FULL_IMAGE_PATH = "/mnt/md0/Data/Preprocessed/iSEG/Training/1/T1/T1.nii.gz"
TARGET_PATH = "/mnt/md0/Data/Preprocessed/iSEG/Training/1/Labels/Labels.nii.gz"
def setUp(self) -> None:
transforms = Compose(
[ToNumpyArray(),
PadToPatchShape((1, 32, 32, 32), (1, 8, 8, 8))])
self._image = transforms(self.FULL_IMAGE_PATH)
self._target = transforms(self.TARGET_PATH)
patches = iSEGSliceDatasetFactory.get_patches([self._image],
[self._target],
(1, 32, 32, 32),
(1, 16, 16, 16))
self._dataset = iSEGSliceDatasetFactory.create(
[self._image], [self._target],
patches,
Modality.T1,
0,
transforms=[ToNDTensor()])
self._reconstructor = ImageReconstructor([256, 192, 160],
[1, 32, 32, 32],
[1, 16, 16, 16],
models=None,
dataset=self._dataset,
test_image=self._image)
def test_should_output_reconstructed_image(self):
all_patches = list(
map(lambda dataset: [patch.slice for patch in dataset._patches],
[self._dataset]))
img = self._reconstructor.reconstruct_from_patches_3d(all_patches[0])
plt.imshow(img[150, :, :], cmap="gray")
plt.show()
np.testing.assert_array_almost_equal(img, self._image.squeeze(0), 6)
def setUp(self) -> None:
transforms = Compose(
[ToNumpyArray(),
PadToPatchShape((1, 32, 32, 32), (1, 8, 8, 8))])
self._image = transforms(self.FULL_IMAGE_PATH)
self._target = transforms(self.TARGET_PATH)
patches = iSEGSliceDatasetFactory.get_patches([self._image],
[self._target],
(1, 32, 32, 32),
(1, 16, 16, 16))
self._dataset = iSEGSliceDatasetFactory.create(
[self._image], [self._target],
patches,
Modality.T1,
0,
transforms=[ToNDTensor()])
self._reconstructor = ImageReconstructor([256, 192, 160],
[1, 32, 32, 32],
[1, 16, 16, 16],
models=None,
test_image=self._image)
step=dataset_configs["ABIDE"].step,
test_patch_size=dataset_configs["ABIDE"].test_patch_size,
test_step=dataset_configs["ABIDE"].test_step,
data_augmentation_config=data_augmentation_config)
train_datasets.append(ABIDE_train)
valid_datasets.append(ABIDE_valid)
test_datasets.append(ABIDE_test)
reconstruction_datasets.append(ABIDE_reconstruction)
if len(list(dataset_configs.keys())) == 2:
normalized_reconstructor = ImageReconstructor(
[
iSEG_reconstruction._source_images[0],
MRBrainS_reconstruction._source_images[0]
],
patch_size=dataset_configs["iSEG"].test_patch_size,
reconstructed_image_size=(1, 256, 256, 192),
step=dataset_configs["iSEG"].test_step,
models=[model_trainers[GENERATOR]],
normalize=True,
batch_size=5)
segmentation_reconstructor = ImageReconstructor(
[
iSEG_reconstruction._source_images[0],
MRBrainS_reconstruction._source_images[0]
],
patch_size=dataset_configs["iSEG"].test_patch_size,
reconstructed_image_size=(1, 256, 256, 192),
step=dataset_configs["iSEG"].test_step,
models=[model_trainers[GENERATOR], model_trainers[SEGMENTER]],
normalize_and_segment=True,
max_subjects=dataset_configs["iSEG"].max_subjects,
max_num_patches=dataset_configs["iSEG"].max_num_patches,
augmentation_strategy=iSEG_augmentation_strategy,
patch_size=dataset_configs["iSEG"].patch_size,
step=dataset_configs["iSEG"].step,
augmented_path=dataset_configs["iSEG"].path_augmented,
test_patch_size=dataset_configs["iSEG"].test_patch_size,
test_step=dataset_configs["iSEG"].test_step)
train_datasets.append(iSEG_train)
valid_datasets.append(iSEG_valid)
test_datasets.append(iSEG_test)
reconstruction_datasets.append(iSEG_reconstruction)
normalized_reconstructors.append(ImageReconstructor(dataset_configs["iSEG"].reconstruction_size,
dataset_configs['iSEG'].test_patch_size,
dataset_configs["iSEG"].test_step,
[model_trainers[GENERATOR]],
normalize=True,
test_image=iSEG_reconstruction._augmented_images[
0] if iSEG_reconstruction._augmented_images is not None else
iSEG_reconstruction._source_images[0]))
segmentation_reconstructors.append(
ImageReconstructor(dataset_configs["iSEG"].reconstruction_size,
dataset_configs['iSEG'].test_patch_size,
dataset_configs["iSEG"].test_step,
[model_trainers[GENERATOR],
model_trainers[SEGMENTER]],
normalize_and_segment=True,
test_image=iSEG_reconstruction._augmented_images[
0] if iSEG_reconstruction._augmented_images is not None else
iSEG_reconstruction._source_images[0]))
input_reconstructors.append(ImageReconstructor(dataset_configs["iSEG"].reconstruction_size,
dataset_configs['iSEG'].test_patch_size,
patch_size=dataset_configs["iSEG"].patch_size,
step=dataset_configs["iSEG"].step,
augmented_path=dataset_configs["iSEG"].path_augmented,
test_patch_size=dataset_configs["iSEG"].test_patch_size,
test_step=dataset_configs["iSEG"].test_step)
train_datasets.append(iSEG_train)
valid_datasets.append(iSEG_valid)
test_datasets.append(iSEG_test)
reconstruction_datasets.append(iSEG_reconstruction)
segmentation_reconstructors.append(
ImageReconstructor(dataset_configs["iSEG"].reconstruction_size,
dataset_configs['iSEG'].test_patch_size,
dataset_configs["iSEG"].test_step,
[model_trainers[0]],
segment=True,
test_image=iSEG_augmentation_strategy(
iSEG_reconstruction._source_images[0])
if iSEG_augmentation_strategy is not None else
iSEG_reconstruction._source_images[0]))
input_reconstructors.append(
ImageReconstructor(dataset_configs["iSEG"].reconstruction_size,
dataset_configs['iSEG'].test_patch_size,
dataset_configs["iSEG"].test_step,
test_image=iSEG_augmentation_strategy(
iSEG_reconstruction._source_images[0])
if iSEG_augmentation_strategy is not None else
iSEG_reconstruction._source_images[0]))
gt_reconstructors.append(