def preprocess_with_augmentation(patient_data,
result,
index,
augment=True,
metadata=None,
testaug=False):
"""
Load the resulting data, augment it if needed, and put it in result at the correct index
:param patient_data:
:param result:
:param index:
:return:
"""
if augment:
augmentation_parameters = sample_augmentation_parameters()
else:
augmentation_parameters = None
for tag, data in patient_data.iteritems():
metadata_tag = metadata[tag]
desired_shape = result[tag][index].shape
# try to fit data into the desired shape
if tag.startswith("sliced:data:singleslice"):
cleaning_processes = getattr(config(), 'cleaning_processes', [])
data = clean_images([patient_data[tag]],
metadata=metadata_tag,
cleaning_processes=cleaning_processes)
patient_4d_tensor, zoom_ratios = resize_and_augment(
data,
output_shape=desired_shape[-2:],
augment=augmentation_parameters)[0]
if "area_per_pixel:sax" in result:
result["area_per_pixel:sax"][index] = zoom_ratios[0] * np.prod(
metadata_tag["PixelSpacing"])
put_in_the_middle(result[tag][index], patient_4d_tensor)
elif tag.startswith("sliced:data"):
# put time dimension first, then axis dimension
data = clean_images(patient_data[tag], metadata=metadata_tag)
patient_4d_tensor, zoom_ratios = resize_and_augment(
data,
output_shape=desired_shape[-2:],
augment=augmentation_parameters)
if "area_per_pixel:sax" in result:
result["area_per_pixel:sax"][index] = zoom_ratios[0] * np.prod(
metadata_tag[0]["PixelSpacing"])
if "noswitch" not in tag:
patient_4d_tensor = np.swapaxes(patient_4d_tensor, 1, 0)
put_in_the_middle(result[tag][index], patient_4d_tensor)
if tag.startswith("sliced:data:shape"):
result[tag][index] = patient_data[tag]
if tag.startswith("sliced:meta:"):
# TODO: this probably doesn't work very well yet
result[tag][index] = patient_data[tag]
return
def preprocess_with_augmentation(patient_data, result, index, augment=True, metadata=None, testaug=False):
"""
Load the resulting data, augment it if needed, and put it in result at the correct index
:param patient_data:
:param result:
:param index:
:return:
"""
if augment:
augmentation_parameters = sample_augmentation_parameters()
else:
augmentation_parameters = None
for tag, data in patient_data.iteritems():
metadata_tag = metadata[tag]
desired_shape = result[tag][index].shape
# try to fit data into the desired shape
if tag.startswith("sliced:data:singleslice"):
cleaning_processes = getattr(config(), 'cleaning_processes', [])
data = clean_images(
[patient_data[tag]], metadata=metadata_tag,
cleaning_processes=cleaning_processes)
patient_4d_tensor, zoom_ratios = resize_and_augment(data, output_shape=desired_shape[-2:], augment=augmentation_parameters)[0]
if "area_per_pixel:sax" in result:
result["area_per_pixel:sax"][index] = zoom_ratios[0] * np.prod(metadata_tag["PixelSpacing"])
put_in_the_middle(result[tag][index], patient_4d_tensor)
elif tag.startswith("sliced:data"):
# put time dimension first, then axis dimension
data = clean_images(patient_data[tag], metadata=metadata_tag)
patient_4d_tensor, zoom_ratios = resize_and_augment(data, output_shape=desired_shape[-2:], augment=augmentation_parameters)
if "area_per_pixel:sax" in result:
result["area_per_pixel:sax"][index] = zoom_ratios[0] * np.prod(metadata_tag[0]["PixelSpacing"])
if "noswitch" not in tag:
patient_4d_tensor = np.swapaxes(patient_4d_tensor,1,0)
put_in_the_middle(result[tag][index], patient_4d_tensor)
if tag.startswith("sliced:data:shape"):
result[tag][index] = patient_data[tag]
if tag.startswith("sliced:meta:"):
# TODO: this probably doesn't work very well yet
result[tag][index] = patient_data[tag]
return
def preprocess_normscale(
patient_data,
result,
index,
augment=True,
metadata=None,
normscale_resize_and_augment_function=normscale_resize_and_augment,
testaug=False):
"""Normalizes scale and augments the data.
Args:
patient_data: the data to be preprocessed.
result: dict to store the result in.
index: index indicating in which slot the result dict the data
should go.
augment: flag indicating wheter augmentation is needed.
metadata: metadata belonging to the patient data.
"""
if augment:
if testaug:
augmentation_params = sample_test_augmentation_parameters()
else:
augmentation_params = sample_augmentation_parameters()
else:
augmentation_params = None
zoom_factor = None
# Iterate over different sorts of data
for tag, data in patient_data.iteritems():
if tag in metadata:
metadata_tag = metadata[tag]
desired_shape = result[tag][index].shape
cleaning_processes = getattr(config(), 'cleaning_processes', [])
cleaning_processes_post = getattr(config(), 'cleaning_processes_post',
[])
if tag.startswith("sliced:data:singleslice"):
# Cleaning data before extracting a patch
data = clean_images([patient_data[tag]],
metadata=metadata_tag,
cleaning_processes=cleaning_processes)
# Augment and extract patch
# Decide which roi to use.
shift_center = (None, None)
if getattr(config(), 'use_hough_roi', False):
shift_center = metadata_tag["hough_roi"]
patient_3d_tensor = normscale_resize_and_augment_function(
data,
output_shape=desired_shape[-2:],
augment=augmentation_params,
pixel_spacing=metadata_tag["PixelSpacing"],
shift_center=shift_center[::-1])[0]
if augmentation_params is not None:
zoom_factor = augmentation_params[
"zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
patient_3d_tensor = clean_images(
patient_3d_tensor,
metadata=metadata_tag,
cleaning_processes=cleaning_processes_post)
if "area_per_pixel:sax" in result:
raise NotImplementedError()
if augmentation_params and not augmentation_params.get(
"change_brightness", 0) == 0:
patient_3d_tensor = augment_brightness(
patient_3d_tensor,
augmentation_params["change_brightness"])
put_in_the_middle(result[tag][index], patient_3d_tensor, True)
elif tag.startswith("sliced:data:randomslices"):
# Clean each slice separately
data = [
clean_images([slicedata],
metadata=metadata,
cleaning_processes=cleaning_processes)[0]
for slicedata, metadata in zip(data, metadata_tag)
]
# Augment and extract patches
shift_centers = [(None, None)] * len(data)
if getattr(config(), 'use_hough_roi', False):
shift_centers = [m["hough_roi"] for m in metadata_tag]
patient_3d_tensors = [
normscale_resize_and_augment_function(
[slicedata],
output_shape=desired_shape[-2:],
augment=augmentation_params,
pixel_spacing=metadata["PixelSpacing"],
shift_center=shift_center[::-1])[0]
for slicedata, metadata, shift_center in zip(
data, metadata_tag, shift_centers)
]
if augmentation_params is not None:
zoom_factor = augmentation_params[
"zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
patient_3d_tensors = [
clean_images([patient_3d_tensor],
metadata=metadata,
cleaning_processes=cleaning_processes_post)[0]
for patient_3d_tensor, metadata in zip(patient_3d_tensors,
metadata_tag)
]
patient_4d_tensor = _make_4d_tensor(patient_3d_tensors)
if augmentation_params and not augmentation_params.get(
"change_brightness", 0) == 0:
patient_4d_tensor = augment_brightness(
patient_4d_tensor,
augmentation_params["change_brightness"])
if "area_per_pixel:sax" in result:
raise NotImplementedError()
put_in_the_middle(result[tag][index], patient_4d_tensor, True)
elif tag.startswith("sliced:data:sax:locations"):
pass # will be filled in by the next one
elif tag.startswith("sliced:data:sax:is_not_padded"):
pass # will be filled in by the next one
elif tag.startswith("sliced:data:sax"):
# step 1: sort (data, metadata_tag) with slice_location_finder
slice_locations, sorted_indices, sorted_distances = slice_location_finder(
{i: metadata
for i, metadata in enumerate(metadata_tag)})
data = [data[idx] for idx in sorted_indices]
metadata_tag = [metadata_tag[idx] for idx in sorted_indices]
slice_locations = np.array([
slice_locations[idx]["relative_position"]
for idx in sorted_indices
])
slice_locations = slice_locations - (slice_locations[-1] +
slice_locations[0]) / 2.0
data = [
clean_images([slicedata],
metadata=metadata,
cleaning_processes=cleaning_processes)[0]
for slicedata, metadata in zip(data, metadata_tag)
]
# Augment and extract patches
shift_centers = [(None, None)] * len(data)
if getattr(config(), 'use_hough_roi', False):
shift_centers = [m["hough_roi"] for m in metadata_tag]
patient_3d_tensors = [
normscale_resize_and_augment_function(
[slicedata],
output_shape=desired_shape[-2:],
augment=augmentation_params,
pixel_spacing=metadata["PixelSpacing"],
shift_center=shift_center[::-1])[0]
for slicedata, metadata, shift_center in zip(
data, metadata_tag, shift_centers)
]
if augmentation_params is not None:
zoom_factor = augmentation_params[
"zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
patient_3d_tensors = [
clean_images([patient_3d_tensor],
metadata=metadata,
cleaning_processes=cleaning_processes_post)[0]
for patient_3d_tensor, metadata in zip(patient_3d_tensors,
metadata_tag)
]
patient_4d_tensor = _make_4d_tensor(patient_3d_tensors)
if augmentation_params and not augmentation_params.get(
"change_brightness", 0) == 0:
patient_4d_tensor = augment_brightness(
patient_4d_tensor,
augmentation_params["change_brightness"])
# Augment sax order
if augmentation_params and augmentation_params.get("flip_sax",
0) > 0.5:
patient_4d_tensor = patient_4d_tensor[::-1]
slice_locations = slice_locations[::-1]
# Put data (images and metadata) in right location
put_in_the_middle(result[tag][index], patient_4d_tensor, True)
if "sliced:data:sax:locations" in result:
eps_location = 1e-7
is_padded = np.array(
[False] * len(result["sliced:data:sax:locations"][index]))
put_in_the_middle(result["sliced:data:sax:locations"][index],
slice_locations + eps_location, True,
is_padded)
if "sliced:data:sax:distances" in result:
eps_location = 1e-7
sorted_distances.append(0.0) # is easier for correct padding
is_padded = np.array(
[False] * len(result["sliced:data:sax:distances"][index]))
put_in_the_middle(result["sliced:data:sax:distances"][index],
np.array(sorted_distances) + eps_location,
True, is_padded)
if "sliced:data:sax:is_not_padded" in result:
result["sliced:data:sax:is_not_padded"][
index] = np.logical_not(is_padded)
elif tag.startswith("sliced:data:chanzoom:2ch"):
# step 1: sort (data, metadata_tag) with slice_location_finder
slice_locations, sorted_indices, sorted_distances = slice_location_finder(
{i: metadata
for i, metadata in enumerate(metadata_tag[2])})
top_slice_metadata = metadata_tag[2][sorted_indices[0]]
bottom_slice_metadata = metadata_tag[2][sorted_indices[-1]]
ch2_metadata = metadata_tag[1]
ch4_metadata = metadata_tag[0]
trf_2ch, trf_4ch = get_chan_transformations(
ch2_metadata=ch2_metadata,
ch4_metadata=ch4_metadata,
top_point_metadata=top_slice_metadata,
bottom_point_metadata=bottom_slice_metadata,
output_width=desired_shape[-1])
ch4_3d_patient_tensor, ch2_3d_patient_tensor = [], []
ch4_data = data[0]
ch2_data = data[1]
if ch4_data is None and ch2_data is not None:
ch4_data = ch2_data
ch4_metadata = ch2_metadata
if ch2_data is None and ch4_data is not None:
ch2_data = ch4_data
ch2_metadata = ch4_metadata
for ch, ch_result, transform, metadata in [
(ch4_data, ch4_3d_patient_tensor, trf_4ch, ch4_metadata),
(ch2_data, ch2_3d_patient_tensor, trf_2ch, ch2_metadata)
]:
tform_shift_center, tform_shift_uncenter = build_center_uncenter_transforms(
desired_shape[-2:])
zoom_factor = np.sqrt(
np.abs(np.linalg.det(transform.params[:2, :2])) *
np.prod(metadata["PixelSpacing"]))
normalise_zoom_transform = build_augmentation_transform(
zoom_x=zoom_factor, zoom_y=zoom_factor)
if augmentation_params:
augment_tform = build_augmentation_transform(
**augmentation_params)
total_tform = tform_shift_uncenter + augment_tform + normalise_zoom_transform + tform_shift_center + transform
else:
total_tform = tform_shift_uncenter + normalise_zoom_transform + tform_shift_center + transform
ch_result[:] = [
fast_warp(c, total_tform, output_shape=desired_shape[-2:])
for c in ch
]
# print "zoom factor:", zoom_factor
if augmentation_params is not None:
zoom_factor = augmentation_params[
"zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
ch4_3d_patient_tensor = clean_images(
np.array([ch4_3d_patient_tensor]),
metadata=ch4_metadata,
cleaning_processes=cleaning_processes_post)[0]
ch2_3d_patient_tensor = clean_images(
np.array([ch2_3d_patient_tensor]),
metadata=ch2_metadata,
cleaning_processes=cleaning_processes_post)[0]
# Put data (images and metadata) in right location
put_in_the_middle(result["sliced:data:chanzoom:2ch"][index],
ch2_3d_patient_tensor, True)
put_in_the_middle(result["sliced:data:chanzoom:4ch"][index],
ch4_3d_patient_tensor, True)
elif tag.startswith("sliced:data:shape"):
raise NotImplementedError()
elif tag.startswith("sliced:data"):
# put time dimension first, then axis dimension
data = clean_images(patient_data[tag], metadata=metadata_tag)
patient_4d_tensor, zoom_ratios = resize_and_augment(
data,
output_shape=desired_shape[-2:],
augment=augmentation_parameters)
if "area_per_pixel:sax" in result:
result["area_per_pixel:sax"][index] = zoom_ratios[0] * np.prod(
metadata_tag[0]["PixelSpacing"])
if "noswitch" not in tag:
patient_4d_tensor = np.swapaxes(patient_4d_tensor, 1, 0)
put_in_the_middle(result[tag][index], patient_4d_tensor)
elif tag.startswith("sliced:meta:all"):
# TODO: this probably doesn't work very well yet
result[tag][index] = patient_data[tag]
elif tag.startswith("sliced:meta:PatientSex"):
result[tag][index][0] = -1. if patient_data[tag] == 'M' else 1.
elif tag.startswith("sliced:meta:PatientAge"):
number, letter = patient_data[tag][:3], patient_data[tag][-1]
letter_rescale_factors = {
'D': 365.25,
'W': 52.1429,
'M': 12.,
'Y': 1.
}
result[tag][index][0] = float(
patient_data[tag][:3]) / letter_rescale_factors[letter]
if augmentation_params and zoom_factor:
label_correction_function = lambda x: x * zoom_factor
classification_correction_function = lambda x: utils.zoom_array(
x, 1. / zoom_factor)
return label_correction_function, classification_correction_function
else:
return lambda x: x, lambda x: x
def preprocess_normscale(patient_data, result, index, augment=True,
metadata=None,
normscale_resize_and_augment_function=normscale_resize_and_augment,
testaug=False):
"""Normalizes scale and augments the data.
Args:
patient_data: the data to be preprocessed.
result: dict to store the result in.
index: index indicating in which slot the result dict the data
should go.
augment: flag indicating wheter augmentation is needed.
metadata: metadata belonging to the patient data.
"""
if augment:
if testaug:
augmentation_params = sample_test_augmentation_parameters()
else:
augmentation_params = sample_augmentation_parameters()
else:
augmentation_params = None
zoom_factor = None
# Iterate over different sorts of data
for tag, data in patient_data.iteritems():
if tag in metadata:
metadata_tag = metadata[tag]
desired_shape = result[tag][index].shape
cleaning_processes = getattr(config(), 'cleaning_processes', [])
cleaning_processes_post = getattr(config(), 'cleaning_processes_post', [])
if tag.startswith("sliced:data:singleslice"):
# Cleaning data before extracting a patch
data = clean_images(
[patient_data[tag]], metadata=metadata_tag,
cleaning_processes=cleaning_processes)
# Augment and extract patch
# Decide which roi to use.
shift_center = (None, None)
if getattr(config(), 'use_hough_roi', False):
shift_center = metadata_tag["hough_roi"]
patient_3d_tensor = normscale_resize_and_augment_function(
data, output_shape=desired_shape[-2:],
augment=augmentation_params,
pixel_spacing=metadata_tag["PixelSpacing"],
shift_center=shift_center[::-1])[0]
if augmentation_params is not None:
zoom_factor = augmentation_params["zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
patient_3d_tensor = clean_images(
patient_3d_tensor, metadata=metadata_tag,
cleaning_processes=cleaning_processes_post)
if "area_per_pixel:sax" in result:
raise NotImplementedError()
if augmentation_params and not augmentation_params.get("change_brightness", 0) == 0:
patient_3d_tensor = augment_brightness(patient_3d_tensor, augmentation_params["change_brightness"])
put_in_the_middle(result[tag][index], patient_3d_tensor, True)
elif tag.startswith("sliced:data:randomslices"):
# Clean each slice separately
data = [
clean_images([slicedata], metadata=metadata, cleaning_processes=cleaning_processes)[0]
for slicedata, metadata in zip(data, metadata_tag)]
# Augment and extract patches
shift_centers = [(None, None)] * len(data)
if getattr(config(), 'use_hough_roi', False):
shift_centers = [m["hough_roi"] for m in metadata_tag]
patient_3d_tensors = [
normscale_resize_and_augment_function(
[slicedata], output_shape=desired_shape[-2:],
augment=augmentation_params,
pixel_spacing=metadata["PixelSpacing"],
shift_center=shift_center[::-1])[0]
for slicedata, metadata, shift_center in zip(data, metadata_tag, shift_centers)]
if augmentation_params is not None:
zoom_factor = augmentation_params["zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
patient_3d_tensors = [
clean_images([patient_3d_tensor], metadata=metadata, cleaning_processes=cleaning_processes_post)[0]
for patient_3d_tensor, metadata in zip(patient_3d_tensors, metadata_tag)]
patient_4d_tensor = _make_4d_tensor(patient_3d_tensors)
if augmentation_params and not augmentation_params.get("change_brightness", 0) == 0:
patient_4d_tensor = augment_brightness(patient_4d_tensor, augmentation_params["change_brightness"])
if "area_per_pixel:sax" in result:
raise NotImplementedError()
put_in_the_middle(result[tag][index], patient_4d_tensor, True)
elif tag.startswith("sliced:data:sax:locations"):
pass # will be filled in by the next one
elif tag.startswith("sliced:data:sax:is_not_padded"):
pass # will be filled in by the next one
elif tag.startswith("sliced:data:sax"):
# step 1: sort (data, metadata_tag) with slice_location_finder
slice_locations, sorted_indices, sorted_distances = slice_location_finder({i: metadata for i,metadata in enumerate(metadata_tag)})
data = [data[idx] for idx in sorted_indices]
metadata_tag = [metadata_tag[idx] for idx in sorted_indices]
slice_locations = np.array([slice_locations[idx]["relative_position"] for idx in sorted_indices])
slice_locations = slice_locations - (slice_locations[-1] + slice_locations[0])/2.0
data = [
clean_images([slicedata], metadata=metadata, cleaning_processes=cleaning_processes)[0]
for slicedata, metadata in zip(data, metadata_tag)]
# Augment and extract patches
shift_centers = [(None, None)] * len(data)
if getattr(config(), 'use_hough_roi', False):
shift_centers = [m["hough_roi"] for m in metadata_tag]
patient_3d_tensors = [
normscale_resize_and_augment_function(
[slicedata], output_shape=desired_shape[-2:],
augment=augmentation_params,
pixel_spacing=metadata["PixelSpacing"],
shift_center=shift_center[::-1])[0]
for slicedata, metadata, shift_center in zip(data, metadata_tag, shift_centers)]
if augmentation_params is not None:
zoom_factor = augmentation_params["zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
patient_3d_tensors = [
clean_images([patient_3d_tensor], metadata=metadata, cleaning_processes=cleaning_processes_post)[0]
for patient_3d_tensor, metadata in zip(patient_3d_tensors, metadata_tag)]
patient_4d_tensor = _make_4d_tensor(patient_3d_tensors)
if augmentation_params and not augmentation_params.get("change_brightness", 0) == 0:
patient_4d_tensor = augment_brightness(patient_4d_tensor, augmentation_params["change_brightness"])
# Augment sax order
if augmentation_params and augmentation_params.get("flip_sax", 0) > 0.5:
patient_4d_tensor = patient_4d_tensor[::-1]
slice_locations = slice_locations[::-1]
# Put data (images and metadata) in right location
put_in_the_middle(result[tag][index], patient_4d_tensor, True)
if "sliced:data:sax:locations" in result:
eps_location = 1e-7
is_padded = np.array([False]*len(result["sliced:data:sax:locations"][index]))
put_in_the_middle(result["sliced:data:sax:locations"][index], slice_locations + eps_location, True, is_padded)
if "sliced:data:sax:distances" in result:
eps_location = 1e-7
sorted_distances.append(0.0) # is easier for correct padding
is_padded = np.array([False]*len(result["sliced:data:sax:distances"][index]))
put_in_the_middle(result["sliced:data:sax:distances"][index], np.array(sorted_distances) + eps_location, True, is_padded)
if "sliced:data:sax:is_not_padded" in result:
result["sliced:data:sax:is_not_padded"][index] = np.logical_not(is_padded)
elif tag.startswith("sliced:data:chanzoom:2ch"):
# step 1: sort (data, metadata_tag) with slice_location_finder
slice_locations, sorted_indices, sorted_distances = slice_location_finder({i: metadata for i,metadata in enumerate(metadata_tag[2])})
top_slice_metadata = metadata_tag[2][sorted_indices[0]]
bottom_slice_metadata = metadata_tag[2][sorted_indices[-1]]
ch2_metadata = metadata_tag[1]
ch4_metadata = metadata_tag[0]
trf_2ch, trf_4ch = get_chan_transformations(
ch2_metadata=ch2_metadata,
ch4_metadata=ch4_metadata,
top_point_metadata = top_slice_metadata,
bottom_point_metadata = bottom_slice_metadata,
output_width=desired_shape[-1]
)
ch4_3d_patient_tensor, ch2_3d_patient_tensor = [], []
ch4_data = data[0]
ch2_data = data[1]
if ch4_data is None and ch2_data is not None:
ch4_data = ch2_data
ch4_metadata = ch2_metadata
if ch2_data is None and ch4_data is not None:
ch2_data = ch4_data
ch2_metadata = ch4_metadata
for ch, ch_result, transform, metadata in [(ch4_data, ch4_3d_patient_tensor, trf_4ch, ch4_metadata),
(ch2_data, ch2_3d_patient_tensor, trf_2ch, ch2_metadata)]:
tform_shift_center, tform_shift_uncenter = build_center_uncenter_transforms(desired_shape[-2:])
zoom_factor = np.sqrt(np.abs(np.linalg.det(transform.params[:2,:2])) * np.prod(metadata["PixelSpacing"]))
normalise_zoom_transform = build_augmentation_transform(zoom_x=zoom_factor, zoom_y=zoom_factor)
if augmentation_params:
augment_tform = build_augmentation_transform(**augmentation_params)
total_tform = tform_shift_uncenter + augment_tform + normalise_zoom_transform + tform_shift_center + transform
else:
total_tform = tform_shift_uncenter + normalise_zoom_transform + tform_shift_center + transform
ch_result[:] = [fast_warp(c, total_tform, output_shape=desired_shape[-2:]) for c in ch]
# print "zoom factor:", zoom_factor
if augmentation_params is not None:
zoom_factor = augmentation_params["zoom_x"] * augmentation_params["zoom_y"]
else:
zoom_factor = 1.0
# Clean data further
ch4_3d_patient_tensor = clean_images(np.array([ch4_3d_patient_tensor]), metadata=ch4_metadata, cleaning_processes=cleaning_processes_post)[0]
ch2_3d_patient_tensor = clean_images(np.array([ch2_3d_patient_tensor]), metadata=ch2_metadata, cleaning_processes=cleaning_processes_post)[0]
# Put data (images and metadata) in right location
put_in_the_middle(result["sliced:data:chanzoom:2ch"][index], ch2_3d_patient_tensor, True)
put_in_the_middle(result["sliced:data:chanzoom:4ch"][index], ch4_3d_patient_tensor, True)
elif tag.startswith("sliced:data:shape"):
raise NotImplementedError()
elif tag.startswith("sliced:data"):
# put time dimension first, then axis dimension
data = clean_images(patient_data[tag], metadata=metadata_tag)
patient_4d_tensor, zoom_ratios = resize_and_augment(data, output_shape=desired_shape[-2:], augment=augmentation_parameters)
if "area_per_pixel:sax" in result:
result["area_per_pixel:sax"][index] = zoom_ratios[0] * np.prod(metadata_tag[0]["PixelSpacing"])
if "noswitch" not in tag:
patient_4d_tensor = np.swapaxes(patient_4d_tensor,1,0)
put_in_the_middle(result[tag][index], patient_4d_tensor)
elif tag.startswith("sliced:meta:all"):
# TODO: this probably doesn't work very well yet
result[tag][index] = patient_data[tag]
elif tag.startswith("sliced:meta:PatientSex"):
result[tag][index][0] = -1. if patient_data[tag]=='M' else 1.
elif tag.startswith("sliced:meta:PatientAge"):
number, letter = patient_data[tag][:3], patient_data[tag][-1]
letter_rescale_factors = {'D': 365.25, 'W': 52.1429, 'M': 12., 'Y': 1.}
result[tag][index][0] = float(patient_data[tag][:3]) / letter_rescale_factors[letter]
if augmentation_params and zoom_factor:
label_correction_function = lambda x: x * zoom_factor
classification_correction_function = lambda x: utils.zoom_array(x, 1./zoom_factor)
return label_correction_function, classification_correction_function
else:
return lambda x: x, lambda x: x