fix: the case of no detections is now handled better

batdetect2/utils/detector_utils.py

@@ -143,7 +143,19 @@ def load_model(
 
 
 def _merge_results(predictions, spec_feats, cnn_feats, spec_slices):
-    predictions_m = {}
+    predictions_m = {
+        "det_probs": np.array([]),
+        "x_pos": np.array([]),
+        "y_pos": np.array([]),
+        "bb_widths": np.array([]),
+        "bb_heights": np.array([]),
+        "start_times": np.array([]),
+        "end_times": np.array([]),
+        "low_freqs": np.array([]),
+        "high_freqs": np.array([]),
+        "class_probs": np.array([]),
+    }
+
     num_preds = np.sum([len(pp["det_probs"]) for pp in predictions])
 
     if num_preds > 0:
@@ -151,10 +163,6 @@ def _merge_results(predictions, spec_feats, cnn_feats, spec_slices):
             predictions_m[key] = np.hstack(
                 [pp[key] for pp in predictions if pp["det_probs"].shape[0] > 0]
             )
-    else:
-        # hack in case where no detected calls as we need some of the key
-        # names in dict
-        predictions_m = predictions[0]
 
     if len(spec_feats) > 0:
         spec_feats = np.vstack(spec_feats)
@@ -226,11 +234,19 @@ def format_single_result(
     Returns:
         dict: Results in the format expected by the annotation tool.
     """
-    # Get a single class prediction for the file
+    try:
-    class_overall = pp.overall_class_pred(
+        # Get a single class prediction for the file
-        predictions["det_probs"],
+        class_overall = pp.overall_class_pred(
-        predictions["class_probs"],
+            predictions["det_probs"],
-    )
+            predictions["class_probs"],
+        )
+        class_name = class_names[np.argmax(class_overall)]
+        annotations = get_annotations_from_preds(predictions, class_names)
+    except (np.AxisError, ValueError):
+        # No detections
+        class_overall = np.zeros(len(class_names))
+        class_name = "None"
+        annotations = []
 
     return {
         "id": file_id,
@@ -239,8 +255,8 @@ def format_single_result(
         "notes": "Automatically generated.",
         "time_exp": time_exp,
         "duration": round(float(duration), 4),
-        "annotation": get_annotations_from_preds(predictions, class_names),
+        "annotation": annotations,
-        "class_name": class_names[np.argmax(class_overall)],
+        "class_name": class_name,
     }
 
 

tests/test_api.py

@@ -1,11 +1,14 @@
 """Test bat detect module API."""
 
+from pathlib import Path
+
 import os
 from glob import glob
 
 import numpy as np
 import torch
 from torch import nn
+import soundfile as sf
 
 from batdetect2 import api
 
@@ -262,3 +265,20 @@ def test_process_file_with_spec_slices():
     assert "spec_slices" in results
     assert isinstance(results["spec_slices"], list)
     assert len(results["spec_slices"]) == len(detections)
+
+
+
+def test_process_file_with_empty_predictions_does_not_fail(
+    tmp_path: Path,
+):
+    """Test process file with empty predictions does not fail."""
+    # Create empty file
+    empty_file = tmp_path / "empty.wav"
+    empty_wav = np.zeros((0, 1), dtype=np.float32)
+    sf.write(empty_file, empty_wav, 256000)
+
+    # Process file
+    results = api.process_file(str(empty_file))
+
+    assert results is not None
+    assert len(results["pred_dict"]["annotation"]) == 0