Merge pull request #34 from jnolan14/master

Added 'reshape_on_bbox' method to the FramedImage class

surfa/image/framed.py

@@ -13,6 +13,7 @@
 from surfa.core.array import check_array
 from surfa.core.slicing import sane_slicing
 from surfa.core.slicing import slicing_parameters
+from surfa.core.slicing import fit_slicing_to_shape
 from surfa.transform import cast_space
 from surfa.transform.geometry import ImageGeometry
 from surfa.transform.geometry import cast_image_geometry
@@ -503,14 +504,16 @@ def reorient(self, orientation, copy=True):
             voxsize=voxsize)
         return self.new(data, target_geom)
 
-    def reshape(self, shape, copy=True):
+    def reshape(self, shape, center='image', copy=True):
         """
-        Returns a volume fit to a given shape. Image will be centered in the conformed volume.
+        Returns a volume fit to a given shape. Image will be centered in the conformed volume, or bbox of the original image.
 
         Parameters
         ----------
         shape : tuple of int
             Target shape.
+        center : str
+            Center the reshaped image on image center or bbox: ['image', 'bbox']
         copy : bool
             Return copy of image even if target shape is already satisfied.
 
@@ -528,6 +531,19 @@ def reshape(self, shape, copy=True):
         if np.array_equal(self.baseshape, shape):
             return self.copy() if copy else self
 
+        # validate 'center'
+        valid_centers = ['image', 'bbox']
+        if center not in valid_centers:
+            raise ValueError(f'Unsupported argument for "center", must be in {valid_centers}')
+
+        if center == 'bbox':
+            # get the bbox
+            c_bbox = self.bbox()
+            # reshape the bbox to the target size
+            bbox_centered_cropping = fit_slicing_to_shape(c_bbox, self.baseshape, shape)
+            # return the bbox centered cropping
+            return self[bbox_centered_cropping]
+
         delta = (np.array(shape) - np.array(self.baseshape)) / 2
         low = np.floor(delta).astype(int)
         high = np.ceil(delta).astype(int)
@@ -579,9 +595,7 @@ def fit_to_shape(self, shape, center=None, copy=True):
         arr : !class
             Reshaped image.
         """
-        if center is not None:
-            warnings.warn('fit_to_shape center argument no longer has any effect')
-        return self.reshape(shape, copy)
+        return self.reshape(shape, center, copy)
 
     def conform(self, shape=None, voxsize=None, orientation=None, dtype=None, method='linear', copy=True):
         """