Revert but add assert_headers_compatible everywhere

scilpy/image/volume_operations.py

@@ -308,7 +308,7 @@ def compute_snr(dwi, bval, bvec, b0_thr, mask,
     """
     data = dwi.get_fdata(dtype=np.float32)
     affine = dwi.affine
-    mask = get_data_as_mask(mask, dtype=bool, ref_shape=data.shape)
+    mask = get_data_as_mask(mask, dtype=bool)
 
     if split_shells:
         centroids, shell_indices = identify_shells(bval, tol=40.0,
@@ -343,8 +343,7 @@ def compute_snr(dwi, bval, bvec, b0_thr, mask,
         nib.save(nib.Nifti1Image(noise_mask, affine),
                  basename + '_noise_mask.nii.gz')
     elif noise_mask:
-        noise_mask = get_data_as_mask(noise_mask, dtype=bool,
-                                      ref_shape=data.shape).squeeze()
+        noise_mask = get_data_as_mask(noise_mask, dtype=bool).squeeze()
     elif noise_map:
         data_noisemap = noise_map.get_fdata(dtype=np.float32)
 

scilpy/io/image.py

@@ -88,7 +88,7 @@ def assert_same_resolution(images):
             raise Exception("Images are not of the same resolution/affine")
 
 
-def get_data_as_mask(mask_img, dtype=np.uint8, ref_img=None, ref_shape=None):
+def get_data_as_mask(mask_img, dtype=np.uint8):
     """
     Get data as mask (force type np.uint8 or bool), check data type before
     casting.
@@ -99,11 +99,6 @@ def get_data_as_mask(mask_img, dtype=np.uint8, ref_img=None, ref_shape=None):
         Mask image.
     dtype: type or str
         Data type for the output data (default: uint8)
-    ref_img: nibabel.nitfi1.Nifti1Image
-        Reference image. If given, mask must be compatible.
-    ref_shape: shape
-        Alternative to ref_image. The shape of the associated data. If given,
-        verifies that the mask shape fits with the ref_shape.
 
     Return
     ------
@@ -116,16 +111,6 @@ def get_data_as_mask(mask_img, dtype=np.uint8, ref_img=None, ref_shape=None):
         raise IOError('Output data type must be uint8 or bool. '
                       'Current data type is {}.'.format(dtype))
 
-    # Verify that shape is ok
-    if ref_img is not None:
-        if not is_header_compatible(mask_img, ref_img):
-            raise IOError("Mask is not of the same resolution/affine as data.")
-    elif ref_shape is not None:
-        if not np.array_equal(mask_img.shape, ref_shape[0:3]):
-            raise IOError("Mask is not the same shape as data. Got {}, and "
-                          "data is of shape {}"
-                          .format(mask_img.shape, ref_shape))
-
     # Verify that loaded datatype is ok
     curr_type = mask_img.get_data_dtype().type
     basename = os.path.basename(mask_img.get_filename())

scilpy/io/utils.py

@@ -702,9 +702,8 @@ def assert_roi_radii_format(parser):
     return roi_radii
 
 
-def is_header_compatible_multiple_files(parser, list_files,
-                                        verbose_all_compatible=False,
-                                        reference=None):
+def assert_headers_compatible(parser, required, optional=None,
+                              verbose_all_compatible=False, reference=None):
     """
     Verifies the compatibility between the first item in list_files
     and the remaining files in list.
@@ -713,17 +712,32 @@ def is_header_compatible_multiple_files(parser, list_files,
     ---------
     parser: argument parser
         Will raise an error if a file is not compatible.
-    list_files: List[str]
+    required: List[str]
         List of files to test
+    optional: List[str or None]
+        List of files. May contain None, they will be discarted.
     verbose_all_compatible: bool
         If true will print a message when everything is okay
     reference: str
         Reference for any .tck passed in `list_files`
     """
     all_valid = True
 
-    # Gather "headers" for all files to compare against
-    # eachother later
+    # Format required and optional to lists if a single filename was sent.
+    if isinstance(required, str):
+        required = [required]
+    if optional is None:
+        optional = []
+    elif isinstance(optional, str):
+        optional = [optional]
+    else:
+        optional = [f for f in optional if f is not None]
+    list_files = required + optional
+
+    if len(list_files) <= 1:
+        return
+
+    # Gather "headers" for all files to compare against each other later
     headers = []
     for filepath in list_files:
         _, in_extension = split_name_with_nii(filepath)

scilpy/reconst/mti.py

@@ -191,7 +191,7 @@ def threshold_map(computed_map,  in_mask,
     # Load and apply sum of T1 probability maps on myelin maps
     if in_mask is not None:
         mask_image = nib.load(in_mask)
-        mask_data = get_data_as_mask(mask_image, ref_shape=computed_map.shape)
+        mask_data = get_data_as_mask(mask_image)
         computed_map[np.where(mask_data == 0)] = 0
 
     # Apply threshold based on combination of specific contrast maps

scilpy/segment/tractogram_from_roi.py

@@ -353,7 +353,7 @@ def _extract_vb_one_bundle(
     mask_1_img = nib.load(head_filename)
     mask_2_img = nib.load(tail_filename)
     mask_1 = get_data_as_mask(mask_1_img)
-    mask_2 = get_data_as_mask(mask_2_img, ref_img=mask_1_img)
+    mask_2 = get_data_as_mask(mask_2_img)
 
     if dilate_endpoints:
         mask_1 = binary_dilation(mask_1, iterations=dilate_endpoints)
@@ -501,7 +501,7 @@ def _extract_ib_one_bundle(sft, mask_1_filename, mask_2_filename,
     mask_1_img = nib.load(mask_1_filename)
     mask_2_img = nib.load(mask_2_filename)
     mask_1 = get_data_as_mask(mask_1_img)
-    mask_2 = get_data_as_mask(mask_2_img, ref_img=mask_1_img)
+    mask_2 = get_data_as_mask(mask_2_img)
 
     if dilate_endpoints:
         mask_1 = binary_dilation(mask_1, iterations=dilate_endpoints)

scripts/scil_aodf_metrics.py

@@ -43,6 +43,7 @@
                              add_sh_basis_args,
                              add_verbose_arg,
                              assert_inputs_exist,
+                             assert_headers_compatible,
                              assert_outputs_exist,
                              add_overwrite_arg,
                              parse_sh_basis_arg)
@@ -142,6 +143,8 @@ def main():
 
     assert_inputs_exist(parser, inputs)
     assert_outputs_exist(parser, args, arglist)
+    if args.mask:
+        assert_headers_compatible(parser, inputs)
 
     # Loading
     sh_img = nib.load(args.in_sh)
@@ -155,8 +158,7 @@ def main():
         parser.error('Invalid SH image. A full SH basis is expected.')
 
     if args.mask:
-        mask = get_data_as_mask(nib.load(args.mask), dtype=bool,
-                                ref_img=sh_img)
+        mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
     else:
         mask = np.sum(np.abs(sh), axis=-1) > 0
 

scripts/scil_bingham_metrics.py

@@ -23,11 +23,12 @@
 import time
 import argparse
 import logging
-from scilpy.io.image import get_data_as_mask
 
+from scilpy.io.image import get_data_as_mask
 from scilpy.io.utils import (add_overwrite_arg, add_processes_arg,
                              add_verbose_arg, assert_inputs_exist,
-                             assert_outputs_exist, validate_nbr_processes)
+                             assert_outputs_exist, validate_nbr_processes,
+                             assert_headers_compatible)
 from scilpy.reconst.bingham import (compute_fiber_density,
                                     compute_fiber_spread,
                                     compute_fiber_fraction)
@@ -94,11 +95,12 @@ def main():
     outputs = [args.out_fd, args.out_fs, args.out_ff]
     assert_inputs_exist(parser, args.in_bingham, args.mask)
     assert_outputs_exist(parser, args, [], optional=outputs)
+    assert_headers_compatible(parser, args.in_bingham, args.mask)
 
     bingham_im = nib.load(args.in_bingham)
     bingham = bingham_im.get_fdata()
-    mask = get_data_as_mask(nib.load(args.mask), dtype=bool,
-                            ref_img=bingham_im) if args.mask else None
+    mask = get_data_as_mask(nib.load(args.mask),
+                            dtype=bool) if args.mask else None
 
     nbr_processes = validate_nbr_processes(parser, args)
 

scripts/scil_btensor_metrics.py

@@ -52,7 +52,8 @@
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist, add_processes_arg,
                              add_verbose_arg, add_skip_b0_check_arg,
-                             add_tolerance_arg)
+                             add_tolerance_arg,
+                             assert_headers_compatible)
 from scilpy.reconst.divide import fit_gamma, gamma_fit2metrics
 
 
@@ -158,9 +159,10 @@ def main():
         parser.error('When using --not_all, you need to specify at least '
                      'one file to output.')
 
-    assert_inputs_exist(parser, [],
-                        optional=args.in_dwis + args.in_bvals + args.in_bvecs)
+    assert_inputs_exist(parser, args.in_dwis + args.in_bvals + args.in_bvecs,
+                        args.mask)
     assert_outputs_exist(parser, args, arglist)
+    assert_headers_compatible(parser, args.in_dwis, args.mask)
 
     if args.op and not args.fa:
         parser.error('Computation of the OP requires a precomputed '
@@ -199,8 +201,7 @@ def main():
             'No mask provided. The fit might not converge due to noise. '
             'Please provide a mask if it is the case.')
     else:
-        mask = get_data_as_mask(nib.load(args.mask), dtype=bool,
-                                ref_shape=data.shape)
+        mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
 
     if args.fa is not None:
         vol = nib.load(args.fa)

scripts/scil_bundle_generate_priors.py

@@ -26,7 +26,8 @@
                              add_verbose_arg,
                              assert_inputs_exist,
                              assert_outputs_exist,
-                             parse_sh_basis_arg)
+                             parse_sh_basis_arg,
+                             assert_headers_compatible)
 from scilpy.reconst.utils import find_order_from_nb_coeff
 from scilpy.tractanalysis.todi import TrackOrientationDensityImaging
 
@@ -78,6 +79,7 @@ def main():
 
     required = [args.in_bundle, args.in_fodf, args.in_mask]
     assert_inputs_exist(parser, required)
+    assert_headers_compatible(parser, required)
 
     out_efod = os.path.join(args.out_dir,
                             '{0}efod.nii.gz'.format(args.out_prefix))
@@ -100,7 +102,7 @@ def main():
     sh_order = find_order_from_nb_coeff(sh_shape)
     sh_basis, is_legacy = parse_sh_basis_arg(args)
     img_mask = nib.load(args.in_mask)
-    mask_data = get_data_as_mask(img_mask, ref_img=img_sh)
+    mask_data = get_data_as_mask(img_mask)
 
     sft = load_tractogram_with_reference(parser, args, args.in_bundle)
     sft.to_vox()

scripts/scil_bundle_score_same_bundle_many_segmentations.py

@@ -218,7 +218,7 @@ def main():
         gs_binary_3d = get_data_as_mask(nib.load(args.voxels_measures[0]))
         gs_binary_3d[gs_binary_3d > 0] = 1
         tracking_mask_data = get_data_as_mask(
-            nib.load(args.voxels_measures[1]), ref_shape=gs_binary_3d.shape)
+            nib.load(args.voxels_measures[1]))
 
     if nbr_cpu == 1:
         voxels_dict = []

scripts/scil_denoising_nlmeans.py

@@ -21,7 +21,8 @@
                              add_overwrite_arg,
                              add_verbose_arg,
                              assert_inputs_exist,
-                             assert_outputs_exist)
+                             assert_outputs_exist,
+                             assert_headers_compatible)
 
 
 def _build_arg_parser():
@@ -77,8 +78,9 @@ def main():
     args = parser.parse_args()
     logging.getLogger().setLevel(logging.getLevelName(args.verbose))
 
-    assert_inputs_exist(parser, args.in_image)
+    assert_inputs_exist(parser, args.in_image, args.mask)
     assert_outputs_exist(parser, args, args.out_image, args.logfile)
+    assert_headers_compatible(parser, args.in_image, args.mask)
 
     if args.logfile is not None:
         logging.getLogger().addHandler(logging.FileHandler(args.logfile,
@@ -93,7 +95,7 @@ def main():
         else:
             mask[data > 0] = 1
     else:
-        mask = get_data_as_mask(nib.load(args.mask), dtype=bool, ref_img=vol)
+        mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
 
     sigma = args.sigma
 

scripts/scil_dki_metrics.py

@@ -59,7 +59,8 @@
 from scilpy.io.image import get_data_as_mask
 from scilpy.io.utils import (add_overwrite_arg, add_skip_b0_check_arg,
                              add_verbose_arg, assert_inputs_exist,
-                             assert_outputs_exist, add_tolerance_arg, )
+                             assert_outputs_exist, add_tolerance_arg,
+                             assert_headers_compatible, )
 from scilpy.gradients.bvec_bval_tools import (check_b0_threshold,
                                               is_normalized_bvecs,
                                               identify_shells,
@@ -174,13 +175,14 @@ def main():
     assert_inputs_exist(
         parser, [args.in_dwi, args.in_bval, args.in_bvec], args.mask)
     assert_outputs_exist(parser, args, outputs)
+    assert_headers_compatible(parser, args.in_dwi, args.mask)
 
     # Loading
     img = nib.load(args.in_dwi)
     data = img.get_fdata(dtype=np.float32)
     affine = img.affine
-    mask = get_data_as_mask(nib.load(args.mask), dtype=bool,
-                            ref_img=img) if args.mask else None
+    mask = get_data_as_mask(nib.load(args.mask),
+                            dtype=bool) if args.mask else None
 
     bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)
     if not is_normalized_bvecs(bvecs):

scripts/scil_dti_metrics.py

@@ -34,6 +34,7 @@
 from dipy.core.gradients import gradient_table
 import dipy.denoise.noise_estimate as ne
 from dipy.io.gradients import read_bvals_bvecs
+from dipy.io.utils import is_header_compatible
 from dipy.reconst.dti import (TensorModel, color_fa, fractional_anisotropy,
                               geodesic_anisotropy, mean_diffusivity,
                               axial_diffusivity, norm,
@@ -46,7 +47,8 @@
 from scilpy.io.image import get_data_as_mask
 from scilpy.io.utils import (add_b0_thresh_arg, add_overwrite_arg,
                              add_skip_b0_check_arg, add_verbose_arg,
-                             assert_inputs_exist, assert_outputs_exist)
+                             assert_inputs_exist, assert_outputs_exist,
+                             assert_headers_compatible)
 from scilpy.io.tensor import convert_tensor_from_dipy_format, \
     supported_tensor_formats, tensor_format_description
 from scilpy.gradients.bvec_bval_tools import (check_b0_threshold,
@@ -250,13 +252,14 @@ def main():
     assert_inputs_exist(
         parser, [args.in_dwi, args.in_bval, args.in_bvec], args.mask)
     assert_outputs_exist(parser, args, outputs)
+    assert_headers_compatible(parser, args.in_dwi, args.mask)
 
     # Loading
     img = nib.load(args.in_dwi)
     data = img.get_fdata(dtype=np.float32)
     affine = img.affine
-    mask = get_data_as_mask(nib.load(args.mask), dtype=bool,
-                            ref_img=img) if args.mask else None
+    mask = get_data_as_mask(nib.load(args.mask),
+                            dtype=bool) if args.mask else None
 
     logging.info('Tensor estimation with the {} method...'.format(args.method))
     bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)

scripts/scil_dwi_apply_bias_field.py

@@ -20,7 +20,7 @@
 from scilpy.io.utils import (add_overwrite_arg,
                              add_verbose_arg,
                              assert_inputs_exist,
-                             assert_outputs_exist)
+                             assert_outputs_exist, assert_headers_compatible)
 
 
 def _build_arg_parser():
@@ -53,6 +53,8 @@ def main():
 
     assert_inputs_exist(parser, [args.in_dwi, args.in_bias_field], args.mask)
     assert_outputs_exist(parser, args, args.out_name)
+    assert_headers_compatible(parser, [args.in_dwi, args.in_bias_field],
+                              args.mask)
 
     dwi_img = nib.load(args.in_dwi)
     dwi_data = dwi_img.get_fdata(dtype=np.float32)
@@ -61,7 +63,7 @@ def main():
     bias_field_data = bias_field_img.get_fdata(dtype=np.float32)
 
     if args.mask:
-        mask_data = get_data_as_mask(nib.load(args.mask), ref_img=dwi_img)
+        mask_data = get_data_as_mask(nib.load(args.mask))
     else:
         mask_data = np.average(dwi_data, axis=-1) != 0