Bug merge inference

src/neuron_proofreader/merge_proofreading/merge_inference.py

@@ -347,7 +347,6 @@ def read_superchunk(self, nodes):
         center = (start + shape // 2).astype(int)
         superchunk = self.img_reader.read(center, shape)
         superchunk = np.minimum(superchunk, self.brightness_clip)
-        #superchunk = img_util.normalize(superchunk)
         return superchunk, start.astype(int)
 
     def is_near_leaf(self, node, threshold=20):
@@ -502,8 +501,20 @@ def _get_batch(self, nodes, img, offset):
         batch = np.empty((len(nodes), 2,) + self.patch_shape)
         for i, center in enumerate(patch_centers):
             s = img_util.get_slices(center, self.patch_shape)
-            batch[i, 0, ...] = img[s]
+<<<<<<< HEAD
+            batch[i, 0, ...] = img_util.normalize(img[s])
             batch[i, 1, ...] = label_mask[s]
+=======
+            try:
+                batch[i, 0, ...] = img[s]
+                batch[i, 1, ...] = label_mask[s]
+            except:
+                print("center:", center)
+                print("img.shape:", img.shape)
+                print("\noffset:", offset)
+                print("patch_centers:", patch_centers)
+                stop
+>>>>>>> 6c98e4d1a99e00129c798e43efaf78a9563a4de7
         return nodes, torch.tensor(batch, dtype=torch.float)
 
     def _get_multimodal_batch(self, nodes, img, offset):
@@ -516,7 +527,11 @@ def _get_multimodal_batch(self, nodes, img, offset):
         point_clouds = np.empty((len(nodes), 3, 3600), dtype=np.float32)
         for i, (node, center) in enumerate(zip(nodes, patch_centers)):
             s = img_util.get_slices(center, self.patch_shape)
-            patches[i, 0, ...] = np.normalize(img[s])
+<<<<<<< HEAD
+            patches[i, 0, ...] = img_util.normalize(img[s])
+=======
+            patches[i, 0, ...] = img[s]
+>>>>>>> 6c98e4d1a99e00129c798e43efaf78a9563a4de7
             patches[i, 1, ...] = label_mask[s]
 
             subgraph = self.graph.get_rooted_subgraph(