Checked that returned features is a 2d numpy array

batdetect2/api.py

@@ -115,6 +115,7 @@ from batdetect2.types import (
     DetectionModel,
     ModelOutput,
     ProcessingConfiguration,
+    RunResults,
     SpectrogramParameters,
 )
 from batdetect2.utils.detector_utils import list_audio_files, load_model
@@ -134,6 +135,7 @@ __all__ = [
     "process_audio",
     "process_file",
     "process_spectrogram",
+    "print_summary",
 ]
 
 
@@ -150,11 +152,11 @@ def get_config(**kwargs) -> ProcessingConfiguration:
 
     Can be used to override default parameters by passing keyword arguments.
     """
-    return {**DEFAULT_PROCESSING_CONFIGURATIONS, **kwargs}  # type: ignore
+    return {**DEFAULT_PROCESSING_CONFIGURATIONS, **PARAMS, **kwargs}  # type: ignore
 
 
 # Default processing configuration
-CONFIG = get_config(**PARAMS)
+CONFIG = get_config()
 
 
 def load_audio(
@@ -270,7 +272,7 @@ def process_spectrogram(
     samp_rate: int = TARGET_SAMPLERATE_HZ,
     model: DetectionModel = MODEL,
     config: Optional[ProcessingConfiguration] = None,
-) -> Tuple[List[Annotation], List[np.ndarray]]:
+) -> Tuple[List[Annotation], np.ndarray]:
     """Process spectrogram with model.
 
     Parameters
@@ -289,7 +291,11 @@ def process_spectrogram(
 
     Returns
     -------
-    DetectionResult
+    detections : List[Annotation]
+        List of detections.
+    features: np.ndarray
+        An array of features. The array has shape (n_detections, n_features)
+        where each row is a feature vector for a detection.
     """
     if config is None:
         config = CONFIG
@@ -308,7 +314,7 @@ def process_audio(
     model: DetectionModel = MODEL,
     config: Optional[ProcessingConfiguration] = None,
     device: torch.device = DEVICE,
-) -> Tuple[List[Annotation], List[np.ndarray], torch.Tensor]:
+) -> Tuple[List[Annotation], np.ndarray, torch.Tensor]:
     """Process audio array with model.
 
     Parameters
@@ -329,10 +335,9 @@ def process_audio(
     -------
     annotations : List[Annotation]
         List of predicted annotations.
-
-    features: List[np.ndarray]
-        List of extracted features for each annotation.
-
+    features: np.ndarray
+        An array of features. The array has shape (n_detections, n_features)
+        where each row is a feature vector for a detection.
     spec : torch.Tensor
         Spectrogram of the audio used for prediction.
     """
@@ -395,3 +400,26 @@ model: DetectionModel = MODEL
 
 config: ProcessingConfiguration = CONFIG
 """Default processing configuration."""
+
+
+def print_summary(results: RunResults) -> None:
+    """Print summary of results.
+
+    Parameters
+    ----------
+    results : DetectionResult
+        Detection result.
+    """
+    print("Results for " + results["pred_dict"]["id"])
+    print("{} calls detected\n".format(len(results["pred_dict"]["annotation"])))
+
+    print("time\tprob\tlfreq\tspecies_name")
+    for ann in results["pred_dict"]["annotation"]:
+        print(
+            "{}\t{}\t{}\t{}".format(
+                ann["start_time"],
+                ann["class_prob"],
+                ann["low_freq"],
+                ann["class"],
+            )
+        )

batdetect2/utils/detector_utils.py

@@ -295,7 +295,6 @@ def save_results_to_file(results, op_path: str) -> None:
         op_path (str): Output path.
 
     """
-
     # make directory if it does not exist
     if not os.path.isdir(os.path.dirname(op_path)):
         os.makedirs(os.path.dirname(op_path))
@@ -474,7 +473,7 @@ def _process_spectrogram(
     samplerate: int,
     model: DetectionModel,
     config: ProcessingConfiguration,
-) -> Tuple[PredictionResults, List[np.ndarray]]:
+) -> Tuple[PredictionResults, np.ndarray]:
     # evaluate model
     with torch.no_grad():
         outputs = model(spec)
@@ -504,7 +503,7 @@ def _process_spectrogram(
     ):
         pred_nms["class_probs"] = class_probs[:-1, :]
 
-    return pred_nms, features
+    return pred_nms, np.concatenate(features, axis=0)
 
 
 def postprocess_model_outputs(
@@ -550,7 +549,7 @@ def process_spectrogram(
     samplerate: int,
     model: DetectionModel,
     config: ProcessingConfiguration,
-) -> Tuple[List[Annotation], List[np.ndarray]]:
+) -> Tuple[List[Annotation], np.ndarray]:
     """Process a spectrogram with detection model.
 
     Will run non-maximum suppression on the output of the model.
@@ -569,10 +568,11 @@ def process_spectrogram(
 
     Returns
     -------
-    annotations : List[Annotation]
-        List of annotations predicted by the model.
-    features : List[np.ndarray]
-        List of CNN features associated with each annotation.
+    detections: List[Annotation]
+        List of detections predicted by the model.
+    features : np.ndarray
+        An array of CNN features associated with each annotation.
+        The array is of shape (num_detections, num_features).
         Is empty if `config["cnn_features"]` is False.
     """
     pred_nms, features = _process_spectrogram(
@@ -582,12 +582,12 @@ def process_spectrogram(
         config,
     )
 
-    annotations = get_annotations_from_preds(
+    detections = get_annotations_from_preds(
         pred_nms,
         config["class_names"],
     )
 
-    return annotations, features
+    return detections, features
 
 
 def _process_audio_array(
@@ -596,7 +596,7 @@ def _process_audio_array(
     model: DetectionModel,
     config: ProcessingConfiguration,
     device: torch.device,
-) -> Tuple[PredictionResults, List[np.ndarray], torch.Tensor]:
+) -> Tuple[PredictionResults, np.ndarray, torch.Tensor]:
     # load audio file and compute spectrogram
     _, spec, _ = compute_spectrogram(
         audio,
@@ -634,7 +634,7 @@ def process_audio_array(
     model: DetectionModel,
     config: ProcessingConfiguration,
     device: torch.device,
-) -> Tuple[List[Annotation], List[np.ndarray], torch.Tensor]:
+) -> Tuple[List[Annotation], np.ndarray, torch.Tensor]:
     """Process a single audio array with detection model.
 
     Parameters
@@ -656,10 +656,9 @@ def process_audio_array(
     -------
     annotations : List[Annotation]
         List of annotations predicted by the model.
-
-    features : List[np.ndarray]
-        List of CNN features associated with each annotation.
-
+    features : np.ndarray
+        Array of CNN features associated with each annotation.
+        The array is of shape (num_detections, num_features).
     spec : torch.Tensor
         Spectrogram of the audio used as input.
 

tests/test_api.py

@@ -81,7 +81,7 @@ def test_get_default_config():
     assert config["denoise_spec_avg"] is True
     assert config["max_scale_spec"] is False
     assert config["scale_raw_audio"] is False
-    assert len(config["class_names"]) == 0
+    assert len(config["class_names"]) == 17
     assert config["detection_threshold"] == 0.01
     assert config["time_expansion"] == 1
     assert config["top_n"] == 3
@@ -193,8 +193,8 @@ def test_process_spectrogram_with_default_model():
     assert "high_freq" in sample_pred
 
     assert features is not None
-    assert isinstance(features, list)
-    assert len(features) == 1
+    assert isinstance(features, np.ndarray)
+    assert len(features) == len(predictions)
 
 
 def test_process_audio_with_default_model():
@@ -216,8 +216,8 @@ def test_process_audio_with_default_model():
     assert "high_freq" in sample_pred
 
     assert features is not None
-    assert isinstance(features, list)
-    assert len(features) == 1
+    assert isinstance(features, np.ndarray)
+    assert len(features) == len(predictions)
 
     assert spec is not None
     assert isinstance(spec, torch.Tensor)