WIP split and reflect halves

brainglobe_template_builder/napari/align_widget.py

@@ -95,6 +95,14 @@ def _create_align_group(self):
         self.align_image_button.clicked.connect(self._on_align_button_click)
         self.align_groupbox.layout().addRow(self.align_image_button)
 
+        # Add button to split image down the midplane
+        self.split_image_button = QPushButton(
+            "Split and refect image", parent=self.align_groupbox
+        )
+        self.split_image_button.setEnabled(False)
+        self.split_image_button.clicked.connect(self._on_split_button_click)
+        self.align_groupbox.layout().addRow(self.split_image_button)
+
     def _get_layers_by_type(self, layer_type: Layer) -> list:
         """Return a list of napari layers of a given type."""
         return [
@@ -164,10 +172,32 @@ def _on_align_button_click(self):
         self.viewer.add_image(aligned_image, name="aligned_image")
         aligned_mask = aligner.transform_image(mask_data)
         self.viewer.add_labels(aligned_mask, name="aligned_mask", opacity=0.5)
+        split_mask = aligner.split_mask(aligned_mask)
+        self.viewer.add_labels(split_mask, name="split_mask", opacity=0.5)
         # Hide original image, mask, and points layers
         self.viewer.layers[image_name].visible = False
         self.viewer.layers[mask_name].visible = False
         self.viewer.layers[points_name].visible = False
+        # Enable the split button
+        self.split_image_button.setEnabled(True)
+        # Make aligner object accessible to other methods
+        self.aligner = aligner
+
+    def _on_split_button_click(self):
+        """Split the aligned image and its mask down the midplane and reflect
+        each half."""
+        aligned_image = self.viewer.layers["aligned_image"].data
+        split_mask = self.viewer.layers["split_mask"].data
+        hemi1_image, hemi2_image = self.aligner.split_and_reflect_image(
+            aligned_image
+        )
+        hemi1_mask, hemi2_mask = self.aligner.split_and_reflect_image(
+            split_mask
+        )
+        self.viewer.add_image(hemi1_image, name="hemi1_sym_image")
+        self.viewer.add_labels(hemi1_mask, name="hemi1_sym_mask", opacity=0.5)
+        self.viewer.add_image(hemi2_image, name="hemi2_sym_image")
+        self.viewer.add_labels(hemi2_mask, name="hemi2_sym_mask", opacity=0.5)
 
     def _on_dropdown_selection_change(self):
         # Enable estimate button if mask dropdown has a value

brainglobe_template_builder/preproc/alignment.py

@@ -195,3 +195,60 @@ def transform_image(self, image: np.ndarray = None):
             image = self.image
         self.transformed_image = apply_transform(image, self.transform)
         return self.transformed_image
+
+    def split_mask(self, mask: np.ndarray) -> np.ndarray:
+        """Label each half of the mask along the symmetry axis with different
+        integer values, to help diagnose issues with the splitting process.
+
+        Parameters
+        ----------
+        mask : np.ndarray
+            The mask to split. Must contain only 1 label
+
+        Returns
+        -------
+        np.ndarray
+            An array of the same shape as the input mask, with each half
+            labelled with a different integer value.
+        """
+        # Ensure mask is binary
+        mask = mask.astype(bool)
+        axi = self.symmetry_axis_idx
+        axis_len = mask.shape[axi]
+        half_len = axis_len // 2
+        split_mask = np.zeros_like(mask, dtype=int)
+
+        # Create slicing objects for each half
+        slicer_half1 = [slice(None)] * mask.ndim
+        slicer_half1[axi] = slice(0, half_len)
+        slicer_half2 = [slice(None)] * mask.ndim
+        slicer_half2[axi] = slice(half_len, axis_len)
+
+        # Apply new labels only to the regions labeled in the input mask
+        split_mask[tuple(slicer_half1)] = mask[tuple(slicer_half1)] * 2
+        split_mask[tuple(slicer_half2)] = mask[tuple(slicer_half2)] * 3
+
+        return split_mask
+
+    def split_and_reflect_image(
+        self, image: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Split the transformed image into two halves along the symmetry
+        axis and reflect each half to produce two full images.
+
+        Parameters
+        ----------
+        image : np.ndarray
+            The image to split and reflect.
+        """
+        axi = self.symmetry_axis_idx  # axis index
+        axis_len = image.shape[axi]
+        half_len = axis_len // 2
+        # take first half_len slices along the symmetry axis (first hald)
+        hemi1 = image.take(range(half_len), axis=axi)
+        # take last half_len slices along the symmetry axis (second half)
+        hemi2 = image.take(range(half_len, axis_len), axis=axi)
+        # reflect each half to produce two full images
+        full1 = np.concatenate([hemi1, np.flip(hemi1, axis=axi)], axis=axi)
+        full2 = np.concatenate([np.flip(hemi2, axis=axi), hemi2], axis=axi)
+        return full1, full2