More rough-in to reconcile object classifier result with object labels

extensions/chaeo/workflows.py

@@ -7,8 +7,9 @@ import pandas as pd
 from skimage.morphology import dilation
 from sklearn.model_selection import train_test_split
 
-from extensions.ilastik.models import IlastikPixelClassifierModel
+from extensions.chaeo.accessors import MonoPatchStack
 from extensions.chaeo.annotators import draw_boxes_on_3d_image
+from extensions.chaeo.models import PatchStackObjectClassifier
 from extensions.chaeo.products import export_patches_from_zstack, export_patch_masks_from_zstack, export_multichannel_patches_from_zstack, get_patches_from_zmask_meta, get_patch_masks_from_zmask_meta
 from extensions.chaeo.zmask import build_zmask_from_object_mask, project_stack_from_focal_points
 from extensions.ilastik.models import IlastikPixelClassifierModel
@@ -224,7 +225,11 @@ def get_object_map_from_zstack(
         zmask_expand_box_by: int = None,
         **kwargs,
 ) -> Dict:
+    assert len(models) == 2
     pixel_classifier = models[0]
+    assert isinstance(pixel_classifier, IlastikPixelClassifierModel)
+    object_classifier = models[1]
+    assert isinstance(object_classifier, PatchStackObjectClassifier)
 
     ti, stack, fstem, obmask, pxmap, zmask, zmask_meta, df, interm = get_zmask_meta(
         input_file_path,
@@ -256,14 +261,25 @@ def get_object_map_from_zstack(
     )
 
     # send patches and mask stacks to object classifier
-    mod = PatchStackObjectClassifier({'project_file': where_patch_stack / ilp})
-    result_acc, _ = mod.infer(raw, mask)
-    # write_accessor_data_to_file(where_patch_stack / f'zstack_train_result_{suffix}.tif', result_acc)
+    result_acc, _ = MonoPatchStack(
+        object_classifier.infer(patches_acc, patch_masks_acc)
+    )
+
+    object_labels_map = np.copy(interm['label_map'])
+    assert object_labels_map.shape == interm['label_map'].shape
+    assert object_labels_map.dtype == interm['label_map'].dtype
 
     # assign labels to object map:
-    for ii, mi in enumerate(zmask_meta):
-        obj = mi['info']
-        la = obj.label
+    for ii in range(0, len(zmask_meta)):
+        mi = zmask_meta[ii]
+        object_label_id = mi['info'].label
+        result_label_map = result_acc.iat(ii)
+        unique_values = np.unique(result_label_map)
+        assert len(unique_values) == 2
+        assert unique_values[0] == 0
+        inferred_class = result_acc.iat(ii)
+        ii_mask = object_labels_map == object_label_id
+        object_labels_map[ii_mask] = unique_values[1]
         patch = patches_acc.iat(ii)