def get_pair_data(self):
"""Return the tuple (input, ground truth) with the data content in numpy array."""
cache_mode = 'fill' if self.cache else 'unchanged'
input_data = []
for handle in self.input_handle:
hwd_oriented = imed_loader_utils.orient_img_hwd(
handle.get_fdata(cache_mode, dtype=np.float32),
self.slice_axis)
input_data.append(hwd_oriented)
gt_data = []
# Handle unlabeled data
if self.gt_handle is None:
gt_data = None
for gt in self.gt_handle:
if gt is not None:
hwd_oriented = imed_loader_utils.orient_img_hwd(
gt.get_fdata(cache_mode, dtype=np.float32),
self.slice_axis)
data_type = np.float32 if self.soft_gt else np.uint8
gt_data.append(hwd_oriented.astype(data_type))
else:
gt_data.append(
np.zeros(imed_loader_utils.orient_shapes_hwd(
self.input_handle[0].shape, self.slice_axis),
dtype=np.float32).astype(np.uint8))
return input_data, gt_data
def get_pair_data(self):
"""Return the tuple (input, ground truth) with the data content in numpy array."""
cache_mode = 'fill' if self.cache else 'unchanged'
input_data = []
for handle in self.input_handle:
hwd_oriented = imed_loader_utils.orient_img_hwd(handle.get_fdata(cache_mode, dtype=np.float32),
self.slice_axis)
input_data.append(hwd_oriented)
gt_data = []
# Handle unlabeled data
if self.gt_handle is None:
gt_data = None
for gt in self.gt_handle:
if gt is not None:
if not isinstance(gt, list): # this tissue has annotation from only one rater
hwd_oriented = imed_loader_utils.orient_img_hwd(gt.get_fdata(cache_mode, dtype=np.float32),
self.slice_axis)
gt_data.append(hwd_oriented)
else: # this tissue has annotation from several raters
hwd_oriented_list = [
imed_loader_utils.orient_img_hwd(gt_rater.get_fdata(cache_mode, dtype=np.float32),
self.slice_axis) for gt_rater in gt]
gt_data.append([hwd_oriented.astype(data_type) for hwd_oriented in hwd_oriented_list])
else:
gt_data.append(
np.zeros(imed_loader_utils.orient_shapes_hwd(self.input_handle[0].shape, self.slice_axis),
dtype=np.float32).astype(np.uint8))
return input_data, gt_data
def get_pair_shapes(self):
"""Return the tuple (input, ground truth) representing both the input and ground truth shapes."""
input_shape = []
for handle in self.input_handle:
shape = imed_loader_utils.orient_shapes_hwd(
handle.header.get_data_shape(), self.slice_axis)
input_shape.append(tuple(shape))
if not len(set(input_shape)):
raise RuntimeError('Inputs have different dimensions.')
gt_shape = []
for gt in self.gt_handle:
if gt is not None:
shape = imed_loader_utils.orient_shapes_hwd(
gt.header.get_data_shape(), self.slice_axis)
gt_shape.append(tuple(shape))
if not len(set(gt_shape)):
raise RuntimeError('Labels have different dimensions.')
return input_shape[0], gt_shape[0] if len(gt_shape) else None
def run_visualization(input, config, number, output, roi):
"""Utility function to visualize Data Augmentation transformations.
Data augmentation is a key part of the Deep Learning training scheme. This script aims at facilitating the
fine-tuning of data augmentation parameters. To do so, this script provides a step-by-step visualization of the
transformations that are applied on data.
This function applies a series of transformations (defined in a configuration file
``-c``) to ``-n`` 2D slices randomly extracted from an input image (``-i``), and save as png the resulting sample
after each transform.
For example::
ivadomed_visualize_transforms -i t2s.nii.gz -n 1 -c config.json -r t2s_seg.nii.gz
Provides a visualization of a series of three transformation on a randomly selected slice:
.. image:: https://raw.githubusercontent.com/ivadomed/doc-figures/main/scripts/transforms_im.png
:width: 600px
:align: center
And on a binary mask::
ivadomed_visualize_transforms -i t2s_gmseg.nii.gz -n 1 -c config.json -r t2s_seg.nii.gz
Gives:
.. image:: https://raw.githubusercontent.com/ivadomed/doc-figures/main/scripts/transforms_gt.png
:width: 600px
:align: center
Args:
input (string): Image filename. Flag: ``--input``, ``-i``
config (string): Configuration file filename. Flag: ``--config``, ``-c``
number (int): Number of slices randomly extracted. Flag: ``--number``, ``-n``
output (string): Folder path where the results are saved. Flag: ``--ofolder``, ``-o``
roi (string): Filename of the region of interest. Only needed if ROICrop is part of the transformations.
Flag: ``--roi``, ``-r``
"""
# Load context
context = imed_config_manager.ConfigurationManager(config).get_config()
# Create output folder
if not Path(output).is_dir():
Path(output).mkdir(parents=True)
# Slice extracted according to below axis
axis = imed_utils.AXIS_DCT[context[ConfigKW.LOADER_PARAMETERS][LoaderParamsKW.SLICE_AXIS]]
# Get data
input_img, input_data = get_data(input, axis)
# Image or Mask
is_mask = np.array_equal(input_data, input_data.astype(bool))
# Get zooms
zooms = imed_loader_utils.orient_shapes_hwd(input_img.header.get_zooms(), slice_axis=axis)
# Get indexes
indexes = random.sample(range(0, input_data.shape[2]), number)
# Get training transforms
training_transforms, _, _ = imed_transforms.get_subdatasets_transforms(context[ConfigKW.TRANSFORMATION])
if TransformationKW.ROICROP in training_transforms:
if roi and Path(roi).is_file():
roi_img, roi_data = get_data(roi, axis)
else:
raise ValueError("\nPlease provide ROI image (-r) in order to apply ROICrop transformation.")
# Compose transforms
dict_transforms = {}
stg_transforms = ""
for transform_name in training_transforms:
# We skip NumpyToTensor transform since that s only a change of data type
if transform_name == "NumpyToTensor":
continue
# Update stg_transforms
stg_transforms += transform_name + "_"
# Add new transform to Compose
dict_transforms.update({transform_name: training_transforms[transform_name]})
composed_transforms = imed_transforms.Compose(dict_transforms)
# Loop across slices
for i in indexes:
data = [input_data[:, :, i]]
# Init metadata
metadata = SampleMetadata({MetadataKW.ZOOMS: zooms, MetadataKW.DATA_TYPE: "gt" if is_mask else "im"})
# Apply transformations to ROI
if TransformationKW.CENTERCROP in training_transforms or \
(TransformationKW.ROICROP in training_transforms and Path(roi).is_file()):
metadata.__setitem__(MetadataKW.CROP_PARAMS, {})
# Apply transformations to image
stack_im, _ = composed_transforms(sample=data,
metadata=[metadata for _ in range(number)],
data_type="im")
# Plot before / after transformation
fname_out = str(Path(output, stg_transforms + "slice" + str(i) + ".png"))
logger.debug(f"Fname out: {fname_out}.")
logger.debug(f"\t{dict(metadata)}")
# rescale intensities
if len(stg_transforms[:-1].split("_")) == 1:
before = np.rot90(imed_maths.rescale_values_array(data[0], 0.0, 1.0))
else:
before = after
if isinstance(stack_im[0], torch.Tensor):
after = np.rot90(imed_maths.rescale_values_array(stack_im[0].numpy(), 0.0, 1.0))
else:
after = np.rot90(imed_maths.rescale_values_array(stack_im[0], 0.0, 1.0))
# Plot
imed_utils.plot_transformed_sample(before,
after,
list_title=["\n".join(stg_transforms[:-1].split("_")[:-1]),
"\n".join(stg_transforms[:-1].split("_"))],
fname_out=fname_out,
cmap="jet" if is_mask else "gray")
def get_pair_metadata(self, slice_index=0, coord=None):
"""Return dictionary containing input and gt metadata.
Args:
slice_index (int): Index of 2D slice if 2D model is used, else 0.
coord (tuple or list): Coordinates of subvolume in volume if 3D model is used, else None.
Returns:
dict: Input and gt metadata.
"""
gt_meta_dict = []
for gt in self.gt_handle:
if gt is not None:
gt_meta_dict.append(
imed_loader_utils.SampleMetadata({
"zooms":
imed_loader_utils.orient_shapes_hwd(
gt.header.get_zooms(), self.slice_axis),
"data_shape":
imed_loader_utils.orient_shapes_hwd(
gt.header.get_data_shape(), self.slice_axis),
"gt_filenames":
self.metadata[0]["gt_filenames"],
"bounding_box":
self.metadata[0]["bounding_box"]
if 'bounding_box' in self.metadata[0] else None,
"data_type":
'gt',
"crop_params": {}
}))
else:
# Temporarily append null metadata to null gt
gt_meta_dict.append(None)
# Replace null metadata with metadata from other existing classes of the same subject
for idx, gt_metadata in enumerate(gt_meta_dict):
if gt_metadata is None:
gt_meta_dict[idx] = list(filter(None, gt_meta_dict))[0]
input_meta_dict = []
for handle in self.input_handle:
input_meta_dict.append(
imed_loader_utils.SampleMetadata({
"zooms":
imed_loader_utils.orient_shapes_hwd(
handle.header.get_zooms(), self.slice_axis),
"data_shape":
imed_loader_utils.orient_shapes_hwd(
handle.header.get_data_shape(), self.slice_axis),
"data_type":
'im',
"crop_params": {}
}))
dreturn = {
"input_metadata": input_meta_dict,
"gt_metadata": gt_meta_dict,
}
for idx, metadata in enumerate(self.metadata): # loop across channels
metadata["slice_index"] = slice_index
metadata["coord"] = coord
self.metadata[idx] = metadata
for metadata_key in metadata.keys(): # loop across input metadata
dreturn["input_metadata"][idx][metadata_key] = metadata[
metadata_key]
return dreturn
def run_visualization(input, config, number, output, roi):
"""Utility function to visualize Data Augmentation transformations.
Data augmentation is a key part of the Deep Learning training scheme. This script aims at facilitating the
fine-tuning of data augmentation parameters. To do so, this script provides a step-by-step visualization of the
transformations that are applied on data.
This function applies a series of transformations (defined in a configuration file
``-c``) to ``-n`` 2D slices randomly extracted from an input image (``-i``), and save as png the resulting sample
after each transform.
For example::
ivadomed_visualize_transforms -i t2s.nii.gz -n 1 -c config.json -r t2s_seg.nii.gz
Provides a visualization of a series of three transformation on a randomly selected slice:
.. image:: ../../images/transforms_im.png
:width: 600px
:align: center
And on a binary mask::
ivadomed_visualize_transforms -i t2s_gmseg.nii.gz -n 1 -c config.json -r t2s_seg.nii.gz
Gives:
.. image:: ../../images/transforms_gt.png
:width: 600px
:align: center
Args:
input (string): Image filename. Flag: ``--input``, ``-i``
config (string): Configuration file filename. Flag: ``--config``, ``-c``
number (int): Number of slices randomly extracted. Flag: ``--number``, ``-n``
output (string): Folder path where the results are saved. Flag: ``--ofolder``, ``-o``
roi (string): Filename of the region of interest. Only needed if ROICrop is part of the transformations.
Flag: ``--roi``, ``-r``
"""
# Load context
with open(config, "r") as fhandle:
context = json.load(fhandle)
# Create output folder
if not os.path.isdir(output):
os.makedirs(output)
# Slice extracted according to below axis
axis = imed_utils.AXIS_DCT[context["loader_parameters"]["slice_axis"]]
# Get data
input_img, input_data = get_data(input, axis)
# Image or Mask
is_mask = np.array_equal(input_data, input_data.astype(bool))
# Get zooms
zooms = imed_loader_utils.orient_shapes_hwd(input_img.header.get_zooms(),
slice_axis=axis)
# Get indexes
indexes = random.sample(range(0, input_data.shape[2]), number)
# Get training transforms
training_transforms, _, _ = imed_transforms.get_subdatasets_transforms(
context["transformation"])
if "ROICrop" in training_transforms:
if roi and os.path.isfile(roi):
roi_img, roi_data = get_data(roi, axis)
else:
print(
"\nPlease provide ROI image (-r) in order to apply ROICrop transformation."
)
exit()
# Compose transforms
dict_transforms = {}
stg_transforms = ""
for transform_name in training_transforms:
# We skip NumpyToTensor transform since that s only a change of data type
if transform_name == "NumpyToTensor":
continue
# Update stg_transforms
stg_transforms += transform_name + "_"
# Add new transform to Compose
dict_transforms.update(
{transform_name: training_transforms[transform_name]})
composed_transforms = imed_transforms.Compose(dict_transforms)
# Loop across slices
for i in indexes:
data = [input_data[:, :, i]]
# Init metadata
metadata = imed_loader_utils.SampleMetadata({
"zooms":
zooms,
"data_type":
"gt" if is_mask else "im"
})
# Apply transformations to ROI
if "CenterCrop" in training_transforms or (
"ROICrop" in training_transforms and os.path.isfile(roi)):
metadata.__setitem__('crop_params', {})
# Apply transformations to image
stack_im, _ = composed_transforms(
sample=data,
metadata=[metadata for _ in range(number)],
data_type="im")
# Plot before / after transformation
fname_out = os.path.join(
output, stg_transforms + "slice" + str(i) + ".png")
print("Fname out: {}.".format(fname_out))
print("\t{}".format(dict(metadata)))
# rescale intensities
if len(stg_transforms[:-1].split("_")) == 1:
before = np.rot90(
imed_maths.rescale_values_array(data[0], 0.0, 1.0))
else:
before = after
after = np.rot90(
imed_maths.rescale_values_array(stack_im[0], 0.0, 1.0))
# Plot
imed_utils.plot_transformed_sample(
before,
after,
list_title=[
"\n".join(stg_transforms[:-1].split("_")[:-1]),
"\n".join(stg_transforms[:-1].split("_"))
],
fname_out=fname_out,
cmap="jet" if is_mask else "gray")