TESTS: expand tests for ops and plotting

ants/ops/iMath.py

@@ -115,10 +115,6 @@ def iMath_ops():
 def iMath_canny(image, sigma, lower, upper):
     return iMath(image, 'Canny', sigma, lower, upper)
 
-@image_method
-def iMath_distance_map(image, use_spacing=True):
-    return iMath(image, 'DistanceMap', use_spacing)
-
 @image_method
 def iMath_fill_holes(image, hole_type=2):
     return iMath(image, 'FillHoles', hole_type)

ants/ops/reorient_image.py

@@ -62,6 +62,9 @@ def reorient_image2(image, orientation='RAS'):
      >>> mni = ants.image_read(ants.get_data('mni'))
      >>> mni2 = mni.reorient_image2()
      """
+     if image.has_components:
+         return ants.merge_channels([img.reorient_image2(orientation) for img in ants.split_channels(image)])
+
      if image.dimension != 3:
          raise ValueError('image must have 3 dimensions')
 

ants/plotting/plot.py

@@ -205,9 +205,6 @@ def rotate180_matrix(x):
     def rotate90_matrix(x):
         return x.T
 
-    def flip_matrix(x):
-        return mirror_matrix(rotate180_matrix(x))
-
     def reorient_slice(x, axis):
         if axis != 2:
             x = rotate90_matrix(x)
@@ -235,12 +232,12 @@ def reorient_slice(x, axis):
 
     # handle `overlay` argument
     if overlay is not None:
+        if isinstance(overlay, str):
+            overlay = ants.image_read(overlay)
         if vminol is None:
             vminol = overlay.min()
         if vmaxol is None:
             vmaxol = overlay.max()
-        if isinstance(overlay, str):
-            overlay = ants.image_read(overlay)
         if not ants.is_image(overlay):
             raise ValueError("overlay argument must be an ANTsImage")
         if overlay.components > 1:
@@ -258,32 +255,16 @@ def reorient_slice(x, axis):
 
     # handle `domain_image_map` argument
     if domain_image_map is not None:
-        if ants.is_image(domain_image_map):
-            tx = ants.new_ants_transform(
-                precision="float",
-                transform_type="AffineTransform",
-                dimension=image.dimension,
+        tx = ants.new_ants_transform(
+            precision="float",
+            transform_type="AffineTransform",
+            dimension=image.dimension,
+        )
+        image = ants.apply_ants_transform_to_image(tx, image, domain_image_map)
+        if overlay is not None:
+            overlay = ants.apply_ants_transform_to_image(
+                tx, overlay, domain_image_map, interpolation="nearestNeighbor"
             )
-            image = ants.apply_ants_transform_to_image(tx, image, domain_image_map)
-            if overlay is not None:
-                overlay = ants.apply_ants_transform_to_image(
-                    tx, overlay, domain_image_map, interpolation="nearestNeighbor"
-                )
-        elif isinstance(domain_image_map, (list, tuple)):
-            # expect an image and transformation
-            if len(domain_image_map) != 2:
-                raise ValueError("domain_image_map list or tuple must have length == 2")
-
-            dimg = domain_image_map[0]
-            if not ants.is_image(dimg):
-                raise ValueError("domain_image_map first entry should be ANTsImage")
-
-            tx = domain_image_map[1]
-            image = ants.apply_transforms(dimg, image, transform_list=tx)
-            if overlay is not None:
-                overlay = ants.apply_transforms(
-                    dimg, overlay, transform_list=tx, interpolator="linear"
-                )
 
     ## single-channel images ##
     if image.components == 1:
@@ -489,24 +470,8 @@ def reorient_slice(x, axis):
                 fig.colorbar(im, cax=cax, orientation=cbar_orient)
 
     ## multi-channel images ##
-    elif image.components > 1:
-        if not image.is_rgb:
-            if not image.components == 3:
-                raise ValueError("Multi-component images only supported if they have 3 components")
-
-        img_arr = image.numpy()
-        img_arr = img_arr / img_arr.max()
-        img_arr = np.stack(
-            [rotate90_matrix(img_arr[:, :, i]) for i in range(3)], axis=-1
-        )
-
-        fig = plt.figure()
-        ax = plt.subplot(111)
-
-        # plot main image
-        ax.imshow(img_arr, alpha=alpha)
-
-        plt.axis("off")
+    elif image.has_components:
+        raise Exception('Plotting images with components is not currently supported.')
 
     if filename is not None:
         filename = os.path.expanduser(filename)

ants/plotting/plot_ortho.py

@@ -220,10 +220,10 @@ def reorient_slice(x, axis):
 
     # handle `overlay` argument
     if overlay is not None:
-        vminol = overlay.min()
-        vmaxol = overlay.max()
         if isinstance(overlay, str):
             overlay = ants.image_read(overlay)
+        vminol = overlay.min()
+        vmaxol = overlay.max()
         if not ants.is_image(overlay):
             raise ValueError("overlay argument must be an ANTsImage")
         if overlay.components > 1:
@@ -302,21 +302,8 @@ def reorient_slice(x, axis):
                 overlay = ants.apply_ants_transform_to_image(
                     tx, overlay, domain_image_map, interpolation="linear"
                 )
-        elif isinstance(domain_image_map, (list, tuple)):
-            # expect an image and transformation
-            if len(domain_image_map) != 2:
-                raise ValueError("domain_image_map list or tuple must have length == 2")
-
-            dimg = domain_image_map[0]
-            if not ants.is_image(dimg):
-                raise ValueError("domain_image_map first entry should be ANTsImage")
-
-            tx = domain_image_map[1]
-            image = ants.apply_transforms(dimg, image, transform_list=tx)
-            if overlay is not None:
-                overlay = ants.apply_transforms(
-                    dimg, overlay, transform_list=tx, interpolator="linear"
-                )
+        else:
+            raise Exception('The domain_image_map must be an image.')
 
     ## single-channel images ##
     if image.components == 1:

ants/plotting/plot_ortho_stack.py

@@ -201,22 +201,8 @@ def reorient_slice(x, axis):
                     overlays[i] = ants.apply_ants_transform_to_image(
                         tx, overlays[i], domain_image_map, interpolation="linear"
                     )
-        elif isinstance(domain_image_map, (list, tuple)):
-            # expect an image and transformation
-            if len(domain_image_map) != 2:
-                raise ValueError("domain_image_map list or tuple must have length == 2")
-
-            dimg = domain_image_map[0]
-            if not ants.is_image(dimg):
-                raise ValueError("domain_image_map first entry should be ANTsImage")
-
-            tx = domain_image_map[1]
-            for i in range(n_images):
-                images[i] = ants.apply_transforms(dimg, images[i], transform_list=tx)
-                if overlays[i] is not None:
-                    overlays[i] = ants.apply_transforms(
-                        dimg, overlays[i], transform_list=tx, interpolator="linear"
-                    )
+        else:
+            raise Exception('The domain_image_map must be an ants image.')
 
     # potentially find dynamic range
     if scale == True:

ants/utils/scalar_rgb_vector.py

@@ -13,7 +13,6 @@
 from ants.internal import get_lib_fn, process_arguments
 from ants.decorators import image_method
 
-@image_method
 def scalar_to_rgb(image, mask=None, filename=None, cmap='red', custom_colormap_file=None, 
                   min_input=None, max_input=None, min_rgb_output=None, max_rgb_output=None,
                   vtk_lookup_table=None):
@@ -27,54 +26,9 @@ def scalar_to_rgb(image, mask=None, filename=None, cmap='red', custom_colormap_f
     -------
     >>> import ants
     >>> img = ants.image_read(ants.get_data('r16'))
-    >>> img_color = img.scalar_to_rgb(cmap='jet')
+    >>> img_color = ants.scalar_to_rgb(img, cmap='jet')
     """
-
-    if filename is None:
-        file_is_temp = True
-        filename = mktemp(suffix='.png')
-    else:
-        file_is_temp = False
-
-    args = 'imageDimension inputImage outputImage mask colormap'.split(' ')
-    args[0] = image.dimension
-
-    if ants.is_image(image):
-        tmpimgfile = mktemp(suffix='.nii.gz')
-        image.to_file(tmpimgfile)
-    elif isinstance(image, str):
-        tmpimgfile = image
-    args[1] = tmpimgfile
-    args[2] = filename
-    args[3] = mask if mask is not None else image.new_image_like(np.ones(image.shape))
-    args[4] = cmap
-    if custom_colormap_file is not None:
-        args.append('customColormapFile=%s' % custom_colormap_file)
-    if min_input is not None:
-        args.append('minimumInput=%f' % min_input)
-    if max_input is not None:
-        args.append('maximumInput=%f' % max_input)
-    if min_rgb_output is not None:
-        args.append('minRGBOutput=%f' % min_rgb_output)
-    if max_rgb_output is not None:
-        args.append('maxRGBOutput=%f' % min_rgb_output)
-    if vtk_lookup_table is not None:
-        vtk_lookup_table = mktemp(suffix='.csv')
-        args.append('vtkLookupTable=%s' % vtk_lookup_table)
-
-    processed_args = process_arguments(args)
-    libfn = get_lib_fn('ConvertScalarImageToRGB')
-    libfn(processed_args)
-
-    if file_is_temp:
-        outimg = ants.image_read(filename, pixeltype=None)
-        # clean up temp files
-        os.remove(filename)
-        os.remove(tmpimgfile)
-
-        return outimg
-    else:
-        os.remove(tmpimgfile)
+    raise Exception('This function is currently not supported.')
 
 @image_method
 def rgb_to_vector(image):
@@ -94,7 +48,7 @@ def rgb_to_vector(image):
     -------
     >>> import ants
     >>> mni = ants.image_read(ants.get_data('mni'))
-    >>> mni_rgb = mni.scalar_to_rgb()
+    >>> mni_rgb = ants.scalar_to_rgb(mni)
     >>> mni_vector = mni.rgb_to_vector()
     >>> mni_rgb2 = mni.vector_to_rgb()
     """
@@ -124,7 +78,7 @@ def vector_to_rgb(image):
     -------
     >>> import ants
     >>> img = ants.image_read(ants.get_data('r16'), pixeltype='unsigned char')
-    >>> img_rgb = img.clone().scalar_to_rgb()
+    >>> img_rgb = ants.scalar_to_rgb(img.clone())
     >>> img_vec = img_rgb.rgb_to_vector()
     >>> img_rgb2 = img_vec.vector_to_rgb()
     """

tests/test_bugs.py

@@ -99,6 +99,13 @@ def test_bspline_image_with_2d_weights(self):
                                                                 number_of_fitting_levels=number_of_fitting_levels,
                                                                 mesh_size=1)
 
+
+    def test_scalar_rgb_missing(self):
+        import ants
+        img = ants.image_read(ants.get_data('r16'))
+        with self.assertRaises(Exception):
+            img_color = ants.scalar_to_rgb(img, cmap='jet')
+
     def test_bspline_zeros(self):
         import ants
         import numpy as np

tests/test_ops.py

@@ -8,6 +8,39 @@
 import numpy.testing as nptest
 
 
+class Test_iMath(unittest.TestCase):
+
+
+    def setUp(self):
+        pass
+
+    def tearDown(self):
+        pass
+
+    def test_functions(self):
+        image = ants.image_read(ants.get_data('r16'))
+        ants.iMath_canny(image, sigma=2, lower=0, upper=1)
+        ants.iMath_fill_holes(image, hole_type=2)
+        ants.iMath_GC(image, radius=1)
+        ants.iMath_GD(image, radius=1)
+        ants.iMath_GE(image, radius=1)
+        ants.iMath_GO(image, radius=1)
+        ants.iMath_get_largest_component(image, min_size=50)
+        ants.iMath_grad(image, sigma=0.5, normalize=False)
+        ants.iMath_histogram_equalization(image, alpha=0.5, beta=0.5)
+        ants.iMath_laplacian(image, sigma=0.5, normalize=False)
+        ants.iMath_MC(image, radius=1, value=1, shape=1, parametric=False, lines=3, thickness=1, include_center=False)
+        ants.iMath_MD(image, radius=1, value=1, shape=1, parametric=False, lines=3, thickness=1, include_center=False)
+        ants.iMath_ME(image, radius=1, value=1, shape=1, parametric=False, lines=3, thickness=1, include_center=False)
+        ants.iMath_MO(image, radius=1, value=1, shape=1, parametric=False, lines=3, thickness=1, include_center=False)
+        ants.iMath_maurer_distance(image, foreground=1)
+        ants.iMath_normalize(image)
+        ants.iMath_pad(image, padding=2)
+        ants.iMath_perona_malik(image, conductance=0.25, n_iterations=1)
+        ants.iMath_sharpen(image)
+        ants.iMath_truncate_intensity(image, lower_q=0.05, upper_q=0.95, n_bins=64)
+
+
 class Test_slice_image(unittest.TestCase):
     """
     Test ants.ANTsImage class