def create_one_3D_file():
'''
Create one big file which contains all 3D Images (not slices).
'''
class HP:
DATASET = "HCP"
RESOLUTION = "1.25mm"
FEATURES_FILENAME = "270g_125mm_peaks"
LABELS_TYPE = np.int16
DATASET_FOLDER = "HCP"
data_all = []
seg_all = []
print("\n\nProcessing Data...")
for s in get_all_subjects():
print("processing data subject {}".format(s))
data = nib.load(join(C.HOME, HP.DATASET_FOLDER, s, HP.FEATURES_FILENAME + ".nii.gz")).get_data()
data = np.nan_to_num(data)
data = DatasetUtils.scale_input_to_unet_shape(data, HP.DATASET, HP.RESOLUTION)
data_all.append(np.array(data))
np.save("data.npy", data_all)
del data_all # free memory
print("\n\nProcessing Segs...")
for s in get_all_subjects():
print("processing seg subject {}".format(s))
seg = ImgUtils.create_multilabel_mask(HP, s, labels_type=HP.LABELS_TYPE)
if HP.RESOLUTION == "2.5mm":
seg = ImgUtils.resize_first_three_dims(seg, order=0, zoom=0.5)
seg = DatasetUtils.scale_input_to_unet_shape(seg, HP.DATASET, HP.RESOLUTION)
seg_all.append(np.array(seg))
print("SEG TYPE: {}".format(seg_all.dtype))
np.save("seg.npy", seg_all)
def _create_prob_slices_file(HP, subjects, filename, bundle, shuffle=True):
mask_dir = join(C.HOME, HP.DATASET_FOLDER)
input_dir = HP.MULTI_PARENT_PATH
combined_slices = []
mask_slices = []
for s in subjects:
print("processing subject {}".format(s))
probs_x = nib.load(join(input_dir, "UNet_x_" + str(HP.CV_FOLD), "probmaps", s + "_probmap.nii.gz")).get_data()
probs_y = nib.load(join(input_dir, "UNet_y_" + str(HP.CV_FOLD), "probmaps", s + "_probmap.nii.gz")).get_data()
probs_z = nib.load(join(input_dir, "UNet_z_" + str(HP.CV_FOLD), "probmaps", s + "_probmap.nii.gz")).get_data()
# probs_x = DatasetUtils.scale_input_to_unet_shape(probs_x, HP.DATASET, HP.RESOLUTION)
# probs_y = DatasetUtils.scale_input_to_unet_shape(probs_y, HP.DATASET, HP.RESOLUTION)
# probs_z = DatasetUtils.scale_input_to_unet_shape(probs_z, HP.DATASET, HP.RESOLUTION)
combined = np.stack((probs_x, probs_y, probs_z), axis=4) # (73, 87, 73, 18, 3) #not working alone: one dim too much for UNet -> reshape
combined = np.reshape(combined, (combined.shape[0], combined.shape[1], combined.shape[2],
combined.shape[3] * combined.shape[4])) # (73, 87, 73, 3*18)
# print("combined shape after", combined.shape)
mask_data = ImgUtils.create_multilabel_mask(HP, s, labels_type=HP.LABELS_TYPE)
if HP.DATASET == "HCP_2mm":
#use "HCP" because for mask we need downscaling
mask_data = DatasetUtils.scale_input_to_unet_shape(mask_data, "HCP", HP.RESOLUTION)
elif HP.DATASET == "HCP_2.5mm":
# use "HCP" because for mask we need downscaling
mask_data = DatasetUtils.scale_input_to_unet_shape(mask_data, "HCP", HP.RESOLUTION)
else:
# Mask has same resolution as probmaps -> we can use same resizing
mask_data = DatasetUtils.scale_input_to_unet_shape(mask_data, HP.DATASET, HP.RESOLUTION)
# Save as Img
img = nib.Nifti1Image(combined, ImgUtils.get_dwi_affine(HP.DATASET, HP.RESOLUTION))
nib.save(img, join(HP.EXP_PATH, "combined", s + "_combinded_probmap.nii.gz"))
combined = DatasetUtils.scale_input_to_unet_shape(combined, HP.DATASET, HP.RESOLUTION)
assert (combined.shape[2] == mask_data.shape[2])
#Save as Slices
for z in range(combined.shape[2]):
combined_slices.append(combined[:, :, z, :])
mask_slices.append(mask_data[:, :, z, :])
if shuffle:
combined_slices, mask_slices = sk_shuffle(combined_slices, mask_slices, random_state=9)
if HP.TRAIN:
np.save(filename + "_data.npy", combined_slices)
np.save(filename + "_seg.npy", mask_slices)
def create_one_3D_file():
'''
Create one big file which contains all 3D Images (not slices).
'''
class HP:
DATASET = "HCP"
RESOLUTION = "1.25mm"
FEATURES_FILENAME = "270g_125mm_peaks"
LABELS_TYPE = np.int16
DATASET_FOLDER = "HCP"
data_all = []
seg_all = []
print("\n\nProcessing Data...")
for s in get_all_subjects():
print("processing data subject {}".format(s))
data = nib.load(
join(C.HOME, HP.DATASET_FOLDER, s,
HP.FEATURES_FILENAME + ".nii.gz")).get_data()
data = np.nan_to_num(data)
data = DatasetUtils.scale_input_to_unet_shape(
data, HP.DATASET, HP.RESOLUTION)
data_all.append(np.array(data))
np.save("data.npy", data_all)
del data_all # free memory
print("\n\nProcessing Segs...")
for s in get_all_subjects():
print("processing seg subject {}".format(s))
seg = ImgUtils.create_multilabel_mask(HP,
s,
labels_type=HP.LABELS_TYPE)
if HP.RESOLUTION == "2.5mm":
seg = ImgUtils.resize_first_three_dims(seg, order=0, zoom=0.5)
seg = DatasetUtils.scale_input_to_unet_shape(
seg, HP.DATASET, HP.RESOLUTION)
seg_all.append(np.array(seg))
print("SEG TYPE: {}".format(seg_all.dtype))
np.save("seg.npy", seg_all)
def _create_slices_file(HP, subjects, filename, slice, shuffle=True):
data_dir = join(C.HOME, HP.DATASET_FOLDER)
dwi_slices = []
mask_slices = []
print("\n\nProcessing Data...")
for s in subjects:
print("processing dwi subject {}".format(s))
dwi = nib.load(join(data_dir, s, HP.FEATURES_FILENAME + ".nii.gz"))
dwi_data = dwi.get_data()
dwi_data = np.nan_to_num(dwi_data)
dwi_data = DatasetUtils.scale_input_to_unet_shape(dwi_data, HP.DATASET, HP.RESOLUTION)
# if slice == "x":
# for z in range(dwi_data.shape[0]):
# dwi_slices.append(dwi_data[z, :, :, :])
#
# if slice == "y":
# for z in range(dwi_data.shape[1]):
# dwi_slices.append(dwi_data[:, z, :, :])
#
# if slice == "z":
# for z in range(dwi_data.shape[2]):
# dwi_slices.append(dwi_data[:, :, z, :])
#Use slices from all directions in one dataset
for z in range(dwi_data.shape[0]):
dwi_slices.append(dwi_data[z, :, :, :])
for z in range(dwi_data.shape[1]):
dwi_slices.append(dwi_data[:, z, :, :])
for z in range(dwi_data.shape[2]):
dwi_slices.append(dwi_data[:, :, z, :])
dwi_slices = np.array(dwi_slices)
random_idxs = None
if shuffle:
random_idxs = np.random.choice(len(dwi_slices), len(dwi_slices))
dwi_slices = dwi_slices[random_idxs]
np.save(filename + "_data.npy", dwi_slices)
del dwi_slices #free memory
print("\n\nProcessing Segs...")
for s in subjects:
print("processing seg subject {}".format(s))
mask_data = ImgUtils.create_multilabel_mask(HP, s, labels_type=HP.LABELS_TYPE)
if HP.RESOLUTION == "2.5mm":
mask_data = ImgUtils.resize_first_three_dims(mask_data, order=0, zoom=0.5)
mask_data = DatasetUtils.scale_input_to_unet_shape(mask_data, HP.DATASET, HP.RESOLUTION)
# if slice == "x":
# for z in range(dwi_data.shape[0]):
# mask_slices.append(mask_data[z, :, :, :])
#
# if slice == "y":
# for z in range(dwi_data.shape[1]):
# mask_slices.append(mask_data[:, z, :, :])
#
# if slice == "z":
# for z in range(dwi_data.shape[2]):
# mask_slices.append(mask_data[:, :, z, :])
# Use slices from all directions in one dataset
for z in range(dwi_data.shape[0]):
mask_slices.append(mask_data[z, :, :, :])
for z in range(dwi_data.shape[1]):
mask_slices.append(mask_data[:, z, :, :])
for z in range(dwi_data.shape[2]):
mask_slices.append(mask_data[:, :, z, :])
mask_slices = np.array(mask_slices)
print("SEG TYPE: {}".format(mask_slices.dtype))
if shuffle:
mask_slices = mask_slices[random_idxs]
np.save(filename + "_seg.npy", mask_slices)
def _create_slices_file(HP, subjects, filename, slice, shuffle=True):
data_dir = join(C.HOME, HP.DATASET_FOLDER)
dwi_slices = []
mask_slices = []
print("\n\nProcessing Data...")
for s in subjects:
print("processing dwi subject {}".format(s))
dwi = nib.load(join(data_dir, s, HP.FEATURES_FILENAME + ".nii.gz"))
dwi_data = dwi.get_data()
dwi_data = np.nan_to_num(dwi_data)
dwi_data = DatasetUtils.scale_input_to_unet_shape(
dwi_data, HP.DATASET, HP.RESOLUTION)
# if slice == "x":
# for z in range(dwi_data.shape[0]):
# dwi_slices.append(dwi_data[z, :, :, :])
#
# if slice == "y":
# for z in range(dwi_data.shape[1]):
# dwi_slices.append(dwi_data[:, z, :, :])
#
# if slice == "z":
# for z in range(dwi_data.shape[2]):
# dwi_slices.append(dwi_data[:, :, z, :])
#Use slices from all directions in one dataset
for z in range(dwi_data.shape[0]):
dwi_slices.append(dwi_data[z, :, :, :])
for z in range(dwi_data.shape[1]):
dwi_slices.append(dwi_data[:, z, :, :])
for z in range(dwi_data.shape[2]):
dwi_slices.append(dwi_data[:, :, z, :])
dwi_slices = np.array(dwi_slices)
random_idxs = None
if shuffle:
random_idxs = np.random.choice(len(dwi_slices), len(dwi_slices))
dwi_slices = dwi_slices[random_idxs]
np.save(filename + "_data.npy", dwi_slices)
del dwi_slices #free memory
print("\n\nProcessing Segs...")
for s in subjects:
print("processing seg subject {}".format(s))
mask_data = ImgUtils.create_multilabel_mask(
HP, s, labels_type=HP.LABELS_TYPE)
if HP.RESOLUTION == "2.5mm":
mask_data = ImgUtils.resize_first_three_dims(mask_data,
order=0,
zoom=0.5)
mask_data = DatasetUtils.scale_input_to_unet_shape(
mask_data, HP.DATASET, HP.RESOLUTION)
# if slice == "x":
# for z in range(dwi_data.shape[0]):
# mask_slices.append(mask_data[z, :, :, :])
#
# if slice == "y":
# for z in range(dwi_data.shape[1]):
# mask_slices.append(mask_data[:, z, :, :])
#
# if slice == "z":
# for z in range(dwi_data.shape[2]):
# mask_slices.append(mask_data[:, :, z, :])
# Use slices from all directions in one dataset
for z in range(dwi_data.shape[0]):
mask_slices.append(mask_data[z, :, :, :])
for z in range(dwi_data.shape[1]):
mask_slices.append(mask_data[:, z, :, :])
for z in range(dwi_data.shape[2]):
mask_slices.append(mask_data[:, :, z, :])
mask_slices = np.array(mask_slices)
print("SEG TYPE: {}".format(mask_slices.dtype))
if shuffle:
mask_slices = mask_slices[random_idxs]
np.save(filename + "_seg.npy", mask_slices)
def _create_prob_slices_file(HP, subjects, filename, bundle, shuffle=True):
mask_dir = join(C.HOME, HP.DATASET_FOLDER)
input_dir = HP.MULTI_PARENT_PATH
combined_slices = []
mask_slices = []
for s in subjects:
print("processing subject {}".format(s))
probs_x = nib.load(
join(input_dir, "UNet_x_" + str(HP.CV_FOLD), "probmaps",
s + "_probmap.nii.gz")).get_data()
probs_y = nib.load(
join(input_dir, "UNet_y_" + str(HP.CV_FOLD), "probmaps",
s + "_probmap.nii.gz")).get_data()
probs_z = nib.load(
join(input_dir, "UNet_z_" + str(HP.CV_FOLD), "probmaps",
s + "_probmap.nii.gz")).get_data()
# probs_x = DatasetUtils.scale_input_to_unet_shape(probs_x, HP.DATASET, HP.RESOLUTION)
# probs_y = DatasetUtils.scale_input_to_unet_shape(probs_y, HP.DATASET, HP.RESOLUTION)
# probs_z = DatasetUtils.scale_input_to_unet_shape(probs_z, HP.DATASET, HP.RESOLUTION)
combined = np.stack(
(probs_x, probs_y, probs_z), axis=4
) # (73, 87, 73, 18, 3) #not working alone: one dim too much for UNet -> reshape
combined = np.reshape(
combined,
(combined.shape[0], combined.shape[1], combined.shape[2],
combined.shape[3] * combined.shape[4])) # (73, 87, 73, 3*18)
# print("combined shape after", combined.shape)
mask_data = ImgUtils.create_multilabel_mask(
HP, s, labels_type=HP.LABELS_TYPE)
if HP.DATASET == "HCP_2mm":
#use "HCP" because for mask we need downscaling
mask_data = DatasetUtils.scale_input_to_unet_shape(
mask_data, "HCP", HP.RESOLUTION)
elif HP.DATASET == "HCP_2.5mm":
# use "HCP" because for mask we need downscaling
mask_data = DatasetUtils.scale_input_to_unet_shape(
mask_data, "HCP", HP.RESOLUTION)
else:
# Mask has same resolution as probmaps -> we can use same resizing
mask_data = DatasetUtils.scale_input_to_unet_shape(
mask_data, HP.DATASET, HP.RESOLUTION)
# Save as Img
img = nib.Nifti1Image(
combined, ImgUtils.get_dwi_affine(HP.DATASET, HP.RESOLUTION))
nib.save(
img,
join(HP.EXP_PATH, "combined", s + "_combinded_probmap.nii.gz"))
combined = DatasetUtils.scale_input_to_unet_shape(
combined, HP.DATASET, HP.RESOLUTION)
assert (combined.shape[2] == mask_data.shape[2])
#Save as Slices
for z in range(combined.shape[2]):
combined_slices.append(combined[:, :, z, :])
mask_slices.append(mask_data[:, :, z, :])
if shuffle:
combined_slices, mask_slices = sk_shuffle(combined_slices,
mask_slices,
random_state=9)
if HP.TRAIN:
np.save(filename + "_data.npy", combined_slices)
np.save(filename + "_seg.npy", mask_slices)