tighten scope of conform function

Author: kaczmarj

nibabel/processing.py

@@ -25,6 +25,7 @@
 from .funcs import as_closest_canonical
 from .spaces import vox2out_vox
 from .nifti1 import Nifti1Header, Nifti1Image
+from .orientations import axcodes2ornt
 from .imageclasses import spatial_axes_first
 
 SIGMA2FWHM = np.sqrt(8 * np.log(2))
@@ -313,36 +314,12 @@ def smooth_image(img,
     return out_class(sm_data, img.affine, img.header)
 
 
-def _transform_range(x, new_min, new_max):
-    """ Transform data to a new range, while maintaining ratios.
-
-    Parameters
-    ----------
-    x : array-like
-        The data to transform.
-    new_min, new_max : scalar
-        The minimum and maximum of the output array.
-
-    Returns
-    -------
-    transformed : array-like
-        A copy of the transformed data.
-
-    Examples
-    --------
-    >>> _transform_range([2, 4, 6], -1, 1)
-    array([-1.,  0.,  1.])
-    """
-    x = np.asarray(x)
-    x_min, x_max = x.min(), x.max()
-    return (x - x_min) * (new_max - new_min) / (x_max - x_min) + new_min
-
-
 def conform(from_img,
             out_shape=(256, 256, 256),
             voxel_size=(1.0, 1.0, 1.0),
             order=3,
             cval=0.0,
+            orientation='RAS',
             out_class=Nifti1Image):
     """ Resample image to ``out_shape`` with voxels of size ``voxel_size``.
 
@@ -351,10 +328,12 @@ def conform(from_img,
     function:
         - Resamples data to ``output_shape``
         - Resamples voxel sizes to ``voxel_size``
-        - Transforms data to range [0, 255] (while maintaining ratios)
-        - Casts to unsigned eight-bit integer
         - Reorients to RAS (``mri_convert --conform`` reorients to LIA)
 
+    Unlike ``mri_convert --conform``, this command does not:
+        - Transform data to range [0, 255]
+        - Cast to unsigned eight-bit integer
+
     Parameters
     ----------
     from_img : object
@@ -373,6 +352,8 @@ def conform(from_img,
         Value used for points outside the boundaries of the input if
         ``mode='constant'``. Default is 0.0 (see
         ``scipy.ndimage.affine_transform``)
+    orientation : str, optional
+        Orientation of output image. Default is "RAS".
     out_class : None or SpatialImage class, optional
         Class of output image.  If None, use ``from_img.__class__``.
 
@@ -383,24 +364,29 @@ def conform(from_img,
         resampling `from_img` into axes aligned to the output space of
         ``from_img.affine``
     """
-    if from_img.ndim != 3:
+    # Only support 3D images. This can be made more general in the future, once tests
+    # are written.
+    required_ndim = 3
+    if from_img.ndim != required_ndim:
         raise ValueError("Only 3D images are supported.")
+    elif len(out_shape) != required_ndim:
+        raise ValueError("`out_shape` must have {} values".format(required_ndim))
+    elif len(voxel_size) != required_ndim:
+        raise ValueError("`voxel_size` must have {} values".format(required_ndim))
+
     # Create fake image of the image we want to resample to.
     hdr = Nifti1Header()
     hdr.set_data_shape(out_shape)
     hdr.set_zooms(voxel_size)
     dst_aff = hdr.get_best_affine()
     to_img = Nifti1Image(np.empty(out_shape), affine=dst_aff, header=hdr)
+
     # Resample input image.
     out_img = resample_from_to(
         from_img=from_img, to_vox_map=to_img, order=order, mode="constant",
         cval=cval, out_class=out_class)
-    # Cast to uint8.
-    data = out_img.get_fdata()
-    data = _transform_range(data, new_min=0.0, new_max=255.0)
-    data = data.round(out=data).astype(np.uint8)
-    out_img._dataobj = data
-    out_img.set_data_dtype(data.dtype)
-    # Reorient to RAS.
-    out_img = as_closest_canonical(out_img)
-    return out_img
+
+    labels =list(zip('LPI', 'RAS'))
+    # list(zip('RPI', 'LAS')
+    # Reorient to desired orientation.
+    return out_img.as_reoriented(axcodes2ornt(orientation, labels=labels))

nibabel/tests/test_processing.py

@@ -20,7 +20,7 @@
 import nibabel as nib
 from nibabel.processing import (sigma2fwhm, fwhm2sigma, adapt_affine,
                                 resample_from_to, resample_to_output, smooth_image,
-                                _transform_range, conform)
+                                conform)
 from nibabel.nifti1 import Nifti1Image
 from nibabel.nifti2 import Nifti2Image
 from nibabel.orientations import aff2axcodes, flip_axis, inv_ornt_aff
@@ -429,30 +429,30 @@ def test_against_spm_resample():
     assert_spm_resampling_close(moved_anat, moved2output, spm2output);
 
 
-def test__transform_range():
-    assert_array_equal(_transform_range([2, 4, 6], -1, 1), [-1, 0, 1])
-    assert_array_equal(_transform_range([-1, 0, 1], 2, 6), [2, 4, 6])
-    assert_array_equal(_transform_range(np.arange(11), 0, 5),
-                       np.arange(0, 5.5, 0.5))
-    assert_array_equal(_transform_range(np.arange(-100, 101), 0, 200),
-                       np.arange(201))
-
-
 @needs_scipy
 def test_conform():
     anat = nib.load(pjoin(DATA_DIR, 'anatomical.nii'))
 
+    # Test with default arguments.
     c = conform(anat)
     assert c.shape == (256, 256, 256)
     assert c.header.get_zooms() == (1, 1, 1)
-    assert c.dataobj.dtype == np.dtype(np.uint8)
+    assert c.dataobj.dtype.type == anat.dataobj.dtype.type
     assert aff2axcodes(c.affine) == ('R', 'A', 'S')
+    assert isinstance(c, Nifti1Image)
 
-    c = conform(anat, out_shape=(100, 100, 200), voxel_size=(2, 2, 1.5))
+    # Test with non-default arguments.
+    c = conform(anat, out_shape=(100, 100, 200), voxel_size=(2, 2, 1.5),
+        orientation="LPI", out_class=Nifti2Image)
     assert c.shape == (100, 100, 200)
     assert c.header.get_zooms() == (2, 2, 1.5)
-    assert c.dataobj.dtype == np.dtype(np.uint8)
-    assert aff2axcodes(c.affine) == ('R', 'A', 'S')
+    assert c.dataobj.dtype.type == anat.dataobj.dtype.type
+    assert aff2axcodes(c.affine) == ('L', 'P', 'I')
+    assert isinstance(c, Nifti2Image)
+
+    # Error on non-3D arguments.
+    assert_raises(ValueError, conform, anat, out_shape=(100, 100))
+    assert_raises(ValueError, conform, anat, voxel_size=(2, 2))
 
     # Error on non-3D images.
     func = nib.load(pjoin(DATA_DIR, 'functional.nii'))