local tests pass, but need a test for when there are both empty and n…

…on-empty mixed

src/deepforest/main.py

@@ -694,10 +694,23 @@ def validation_step(self, batch, batch_idx):
         for index, result in enumerate(preds):
             # Skip empty predictions
             if result["boxes"].shape[0] == 0:
-                continue
-            boxes = visualize.format_geometry(result)
-            boxes["image_path"] = path[index]
-            self.predictions.append(boxes)
+                self.predictions.append(
+                    pd.DataFrame(
+                        {
+                            "image_path": [path[index]],
+                            "xmin": [None],
+                            "ymin": [None],
+                            "xmax": [None],
+                            "ymax": [None],
+                            "label": [None],
+                            "score": [None]
+                        }
+                    )
+                )
+            else:
+                boxes = visualize.format_geometry(result)
+                boxes["image_path"] = path[index]
+                self.predictions.append(boxes)
 
         return losses
 
@@ -731,13 +744,14 @@ def calculate_empty_frame_accuracy(self, ground_df, predictions_df):
         if len(empty_images) == 0:
             return None
 
-        # Get predictions for empty images
-        empty_predictions = predictions_df.loc[predictions_df.image_path.isin(empty_images)]
+        # Get non-empty predictions for empty images
+        non_empty_predictions = predictions_df.loc[predictions_df.xmin.notnull()]
+        predictions_for_empty_images = non_empty_predictions.loc[non_empty_predictions.image_path.isin(empty_images)]
 
         # Create prediction tensor - 1 if model predicted objects, 0 if predicted empty
         predictions = torch.zeros(len(empty_images))
         for index, image in enumerate(empty_images):
-            if len(empty_predictions.loc[empty_predictions.image_path == image]) > 0:
+            if len(predictions_for_empty_images.loc[predictions_for_empty_images.image_path == image]) > 0:
                 predictions[index] = 1
 
         # Ground truth tensor - all zeros since these are empty frames