Harmonise napari plugin and CLI entry points

brainreg/napari/register.py

@@ -127,9 +127,9 @@ def brainreg_register():
         freeform_n_steps=6,
         freeform_use_n_steps=4,
         bending_energy_weight=0.95,
-        grid_spacing=10,
-        smoothing_sigma_reference=1,
-        smoothing_sigma_floating=1,
+        grid_spacing=-10,
+        smoothing_sigma_reference=-1.0,
+        smoothing_sigma_floating=-1.0,
         histogram_n_bins_floating=128,
         histogram_n_bins_reference=128,
         debug=False,
@@ -239,10 +239,10 @@ def widget(
         freeform_use_n_steps: int,
         bending_energy_weight: float,
         grid_spacing: int,
-        smoothing_sigma_reference: int,
+        smoothing_sigma_reference: float,
         smoothing_sigma_floating: float,
-        histogram_n_bins_floating: float,
-        histogram_n_bins_reference: float,
+        histogram_n_bins_floating: int,
+        histogram_n_bins_reference: int,
         debug: bool,
         reset_button,
         check_orientation_button,
@@ -314,7 +314,9 @@ def widget(
         grid_spacing: int
             Sets the control point grid spacing in x, y & z.
             Smaller grid spacing allows for more local deformations
-             but increases the risk of over-fitting.
+             but increases the risk of over-fitting. Positive
+            values are interpreted as real values in mm, negative values
+            are interpreted as distance in voxels.
         smoothing_sigma_reference: int
             Adds a Gaussian smoothing to the reference image (the one being
             registered), with the sigma defined by the number. Positive
@@ -325,11 +327,11 @@ def widget(
             registered), with the sigma defined by the number. Positive
             values are interpreted as real values in mm, negative values
             are interpreted as distance in voxels.
-        histogram_n_bins_floating: float
+        histogram_n_bins_floating: int
             Number of bins used for the generation of the histograms used
             for the calculation of Normalized Mutual Information on the
             floating image
-        histogram_n_bins_reference: float
+        histogram_n_bins_reference: int
              Number of bins used for the generation of the histograms used
              for the calculation of Normalized Mutual Information on the
              reference image
@@ -404,9 +406,9 @@ def run():
                 freeform_n_steps,
                 freeform_use_n_steps,
                 bending_energy_weight,
-                -grid_spacing,
-                -smoothing_sigma_reference,
-                -smoothing_sigma_floating,
+                grid_spacing,
+                smoothing_sigma_reference,
+                smoothing_sigma_floating,
                 histogram_n_bins_floating,
                 histogram_n_bins_reference,
                 debug=False,
@@ -463,6 +465,7 @@ def run():
                 n_free_cpus,
                 save_original_orientation=save_original_orientation,
                 brain_geometry=brain_geometry.value,
+                debug=debug,
             )
 
             logging.info("Calculating volumes of each brain area")

brainreg/napari/util.py

@@ -40,30 +40,52 @@
     )
 
 
-def downsample_and_save_brain(img_layer, scaling):
+def downsample_and_save_brain(
+    img_layer,
+    scaling,
+    anti_aliasing=True,
+    preserve_range=True,
+    mode="constant",
+):
     first_frame_shape = skimage.transform.rescale(
-        img_layer.data[0], scaling[1:2], anti_aliasing=True
+        img_layer.data[0],
+        scaling[1:2],
+        anti_aliasing=anti_aliasing,
+        preserve_range=preserve_range,
+        mode=mode,
     ).shape
     preallocated_array = np.empty(
         (img_layer.data.shape[0], first_frame_shape[0], first_frame_shape[1])
     )
-    print("downsampling data in x, y")
+    print("Downsampling data in x, y")
     for i, img in tqdm(enumerate(img_layer.data)):
         down_xy = skimage.transform.rescale(
-            img, scaling[1:2], anti_aliasing=True
+            img,
+            scaling[1:2],
+            anti_aliasing=anti_aliasing,
+            preserve_range=preserve_range,
+            mode=mode,
         )
         preallocated_array[i] = down_xy
 
     first_ds_frame_shape = skimage.transform.rescale(
-        preallocated_array[:, :, 0], [scaling[0], 1], anti_aliasing=True
+        preallocated_array[:, :, 0],
+        [scaling[0], 1],
+        anti_aliasing=anti_aliasing,
+        preserve_range=preserve_range,
+        mode=mode,
     ).shape
     downsampled_array = np.empty(
         (first_ds_frame_shape[0], first_frame_shape[0], first_frame_shape[1])
     )
-    print("downsampling data in z")
+    print("Downsampling data in z")
     for i, img in tqdm(enumerate(preallocated_array.T)):
         down_xyz = skimage.transform.rescale(
-            img, [1, scaling[0]], anti_aliasing=True
+            img,
+            [1, scaling[0]],
+            anti_aliasing=anti_aliasing,
+            preserve_range=preserve_range,
+            mode=mode,
         )
         downsampled_array[:, :, i] = down_xyz.T
     return downsampled_array