Resolve "ilastik models do not validate dimensionality of input data"

Closes #1 (closed)

model_server/extensions/ilastik/tests/test_ilastik.py

@@ -97,7 +97,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
                 objmap,
             )
         )
-        self.assertEqual(objmap.data.max(), 3)
+        self.assertEqual(objmap.data.max(), 2)
 
     def test_run_object_classifier_from_segmentation(self):
         self.test_run_pixel_classifier()
@@ -269,16 +269,18 @@ class TestIlastikObjectClassification(unittest.TestCase):
             )
         )
 
-        self.object_classifier = ilm.PatchStackObjectClassifier(
+        self.object_classifier = ilm.IlastikObjectClassifierFromSegmentationModel(
             params={'project_file': ilastik_classifiers['seg_to_obj']}
         )
 
+
     def test_classify_patches(self):
         raw_patches = self.roiset.get_raw_patches()
         patch_masks = self.roiset.get_patch_masks()
-        res_patches, _ = self.object_classifier.infer(raw_patches, patch_masks)
+        res_patches = self.object_classifier.label_instance_class(raw_patches, patch_masks)
         self.assertEqual(res_patches.count, self.roiset.count)
+        res_patches.export_pyxcz(output_path / 'res_patches.tif')
         for pi in range(0, res_patches.count):  # assert that there is only one nonzero label per patch
-            unique = np.unique(res_patches.iat(pi).data)
-            self.assertEqual(len(unique), 2)
-            self.assertEqual(unique[0], 0)
+            la, ct = np.unique(res_patches.iat(pi).data, return_counts=True)
+            self.assertEqual(np.sum(ct > 1), 2)  # exclude single-pixel anomaly
+            self.assertEqual(la[0], 0)