Fixed errors with command

bat_detect/command.py

@@ -71,7 +71,7 @@ def parse_args():
     parser.add_argument(
         "--model_path",
         type=str,
-        default=os.path.join(CURRENT_DIR, "models/Net2DFast_UK_same.pth.tar"),
+        default=du.DEFAULT_MODEL_PATH,
         help="Path to trained BatDetect2 model",
     )
     args = vars(parser.parse_args())
@@ -97,8 +97,11 @@ def main():
     print(f"Number of audio files: {len(files)}")
     print("\nSaving results to: " + args["ann_dir"])
 
+    default_config = du.get_default_config()
+
     # set up run config
     run_config = {
+        **default_config,
         **args,
         **params,
     }
@@ -119,6 +122,7 @@ def main():
         except (RuntimeError, ValueError, LookupError) as err:
             error_files.append(audio_file)
             print(f"Error processing file!: {err}")
+            raise err
 
     print("\nResults saved to: " + args["ann_dir"])
 

bat_detect/detector/parameters.py

@@ -13,6 +13,9 @@ SCALE_RAW_AUDIO = False
 DETECTION_THRESHOLD = 0.01
 NMS_KERNEL_SIZE = 9
 NMS_TOP_K_PER_SEC = 200
+SPEC_SCALE = "pcen"
+DENOISE_SPEC_AVG = True
+MAX_SCALE_SPEC = False
 
 
 def mk_dir(path):
@@ -70,14 +73,14 @@ def get_params(make_dirs=False, exps_dir="../../experiments/"):
     # spec processing params
     params[
         "denoise_spec_avg"
-    ] = True  # removes the mean for each frequency band
+    ] = DENOISE_SPEC_AVG  # removes the mean for each frequency band
     params[
         "scale_raw_audio"
     ] = SCALE_RAW_AUDIO  # scales the raw audio to [-1, 1]
     params[
         "max_scale_spec"
-    ] = False  # scales the spectrogram so that it is max 1
+    ] = MAX_SCALE_SPEC  # scales the spectrogram so that it is max 1
-    params["spec_scale"] = "pcen"  # 'log', 'pcen', 'none'
+    params["spec_scale"] = SPEC_SCALE  # 'log', 'pcen', 'none'
 
     # detection params
     params[

bat_detect/utils/detector_utils.py

@@ -12,10 +12,12 @@ import bat_detect.detector.post_process as pp
 import bat_detect.utils.audio_utils as au
 from bat_detect.detector import models
 from bat_detect.detector.parameters import (
+    DENOISE_SPEC_AVG,
     DETECTION_THRESHOLD,
     FFT_OVERLAP,
     FFT_WIN_LENGTH_S,
     MAX_FREQ_HZ,
+    MAX_SCALE_SPEC,
     MIN_FREQ_HZ,
     NMS_KERNEL_SIZE,
     NMS_TOP_K_PER_SEC,
@@ -23,6 +25,7 @@ from bat_detect.detector.parameters import (
     SCALE_RAW_AUDIO,
     SPEC_DIVIDE_FACTOR,
     SPEC_HEIGHT,
+    SPEC_SCALE,
     TARGET_SAMPLERATE_HZ,
 )
 
@@ -35,12 +38,13 @@ except ImportError:
 DEFAULT_MODEL_PATH = os.path.join(
     os.path.dirname(os.path.dirname(__file__)),
     "models",
-    "model.pth",
+    "Net2DFast_UK_same.pth.tar",
 )
 
 __all__ = [
     "load_model",
     "get_audio_files",
+    "get_default_config",
     "format_results",
     "save_results_to_file",
     "iterate_over_chunks",
@@ -313,7 +317,7 @@ def format_results(
     annotations: List[Annotation] = [
         {
             "start_time": round(float(start_time), 4),
-            "end_time": round(end_time, 4),
+            "end_time": round(float(end_time), 4),
             "low_freq": int(low_freq),
             "high_freq": int(high_freq),
             "class": str(class_names[class_index]),
@@ -331,7 +335,7 @@ def format_results(
             class_prob,
             det_prob,
         ) in zip(
-            predictions["start_time"],
+            predictions["start_times"],
             predictions["end_times"],
             predictions["low_freqs"],
             predictions["high_freqs"],
@@ -347,7 +351,7 @@ def format_results(
         "issues": False,
         "notes": "Automatically generated.",
         "time_exp": time_exp,
-        "duration": round(duration, 4),
+        "duration": round(float(duration), 4),
         "annotation": annotations,
         "class_name": class_names[np.argmax(class_overall)],
     }
@@ -458,7 +462,8 @@ def save_results_to_file(results, op_path: str) -> None:
     if "spec_feats" in results.keys():
         # create csv file with spectrogram features
         spec_feats_df = pd.DataFrame(
-            results["spec_feats"], columns=results["spec_feat_names"]
+            results["spec_feats"],
+            columns=results["spec_feat_names"],
         )
         spec_feats_df.to_csv(
             op_path + "_spec_features.csv",
@@ -506,6 +511,21 @@ class SpectrogramParameters(TypedDict):
     device: torch.device
     """Device to store the spectrogram on."""
 
+    max_freq: int
+    """Maximum frequency to display in the spectrogram."""
+
+    min_freq: int
+    """Minimum frequency to display in the spectrogram."""
+
+    spec_scale: str
+    """Scale to use for the spectrogram."""
+
+    denoise_spec_avg: bool
+    """Whether to denoise the spectrogram by averaging."""
+
+    max_scale_spec: bool
+    """Whether to scale the spectrogram so that its max is 1."""
+
 
 def compute_spectrogram(
     audio: np.ndarray,
@@ -640,6 +660,15 @@ class ProcessingConfiguration(TypedDict):
     spec_height: int
     """Height of the spectrogram in pixels."""
 
+    spec_scale: str
+    """Scale to use for the spectrogram."""
+
+    denoise_spec_avg: bool
+    """Whether to denoise the spectrogram by averaging."""
+
+    max_scale_spec: bool
+    """Whether to scale the spectrogram so that its max is 1."""
+
     scale_raw_audio: bool
     """Whether to scale the raw audio to be between -1 and 1."""
 
@@ -735,6 +764,7 @@ def process_spectrogram(
             "resize_factor": config["resize_factor"],
             "nms_top_k_per_sec": config["nms_top_k_per_sec"],
             "detection_threshold": config["detection_threshold"],
+            "max_scale_spec": config["max_scale_spec"],
         },
         np.array([float(samplerate)]),
     )
@@ -788,6 +818,11 @@ def process_audio_array(
             "resize_factor": config["resize_factor"],
             "spec_divide_factor": config["spec_divide_factor"],
             "device": config["device"],
+            "max_freq": config["max_freq"],
+            "min_freq": config["min_freq"],
+            "spec_scale": config["spec_scale"],
+            "denoise_spec_avg": config["denoise_spec_avg"],
+            "max_scale_spec": config["max_scale_spec"],
         },
         return_np=config["spec_features"] or config["spec_slices"],
     )
@@ -842,7 +877,7 @@ def process_file(
         time_exp_fact=config.get("time_expansion", 1) or 1,
         target_samp_rate=config["target_samp_rate"],
         scale=config["scale_raw_audio"],
-        max_duration=config["max_duration"],
+        max_duration=config.get("max_duration"),
     )
 
     # loop through larger file and split into chunks
@@ -930,7 +965,7 @@ def summarize_results(results, predictions, config):
             )
 
 
-def get_default_run_config(**kwargs) -> ProcessingConfiguration:
+def get_default_config(**kwargs) -> ProcessingConfiguration:
     """Get default configuration for running detection model."""
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -958,6 +993,9 @@ def get_default_run_config(**kwargs) -> ProcessingConfiguration:
         "max_freq": MAX_FREQ_HZ,
         "min_freq": MIN_FREQ_HZ,
         "nms_top_k_per_sec": NMS_TOP_K_PER_SEC,
+        "spec_scale": SPEC_SCALE,
+        "denoise_spec_avg": DENOISE_SPEC_AVG,
+        "max_scale_spec": MAX_SCALE_SPEC,
     }
     return {
         **args,

run_batdetect.py

@@ -1,112 +1,5 @@
-import argparse
+"""Run bat_detect.command.main() from the command line."""
-import os
+from bat_detect.command import main
-
-import bat_detect.utils.detector_utils as du
-
-
-def main(args):
-    print("Loading model: " + args["model_path"])
-    model, params = du.load_model(args["model_path"])
-
-    print("\nInput directory: " + args["audio_dir"])
-    files = du.get_audio_files(args["audio_dir"])
-    print("Number of audio files: {}".format(len(files)))
-    print("\nSaving results to: " + args["ann_dir"])
-
-    # process files
-    error_files = []
-    for ii, audio_file in enumerate(files):
-        print("\n" + str(ii).ljust(6) + os.path.basename(audio_file))
-        try:
-            results = du.process_file(audio_file, model, params, args)
-            if args["save_preds_if_empty"] or (
-                len(results["pred_dict"]["annotation"]) > 0
-            ):
-                results_path = audio_file.replace(
-                    args["audio_dir"], args["ann_dir"]
-                )
-                du.save_results_to_file(results, results_path)
-        except:
-            error_files.append(audio_file)
-            print("Error processing file!")
-
-    print("\nResults saved to: " + args["ann_dir"])
-
-    if len(error_files) > 0:
-        print("\nUnable to process the follow files:")
-        for err in error_files:
-            print("  " + err)
-
 
 if __name__ == "__main__":
-
+    main()
-    info_str = (
-        "\nBatDetect2 - Detection and Classification\n"
-        + "  Assumes audio files are mono, not stereo.\n"
-        + '  Spaces in the input paths will throw an error. Wrap in quotes "".\n'
-        + "  Input files should be short in duration e.g. < 30 seconds.\n"
-    )
-
-    print(info_str)
-    parser = argparse.ArgumentParser()
-    parser.add_argument("audio_dir", type=str, help="Input directory for audio")
-    parser.add_argument(
-        "ann_dir",
-        type=str,
-        help="Output directory for where the predictions will be stored",
-    )
-    parser.add_argument(
-        "detection_threshold",
-        type=float,
-        help="Cut-off probability for detector e.g. 0.1",
-    )
-    parser.add_argument(
-        "--cnn_features",
-        action="store_true",
-        default=False,
-        dest="cnn_features",
-        help="Extracts CNN call features",
-    )
-    parser.add_argument(
-        "--spec_features",
-        action="store_true",
-        default=False,
-        dest="spec_features",
-        help="Extracts low level call features",
-    )
-    parser.add_argument(
-        "--time_expansion_factor",
-        type=int,
-        default=1,
-        dest="time_expansion_factor",
-        help="The time expansion factor used for all files (default is 1)",
-    )
-    parser.add_argument(
-        "--quiet",
-        action="store_true",
-        default=False,
-        dest="quiet",
-        help="Minimize output printing",
-    )
-    parser.add_argument(
-        "--save_preds_if_empty",
-        action="store_true",
-        default=False,
-        dest="save_preds_if_empty",
-        help="Save empty annotation file if no detections made.",
-    )
-    parser.add_argument(
-        "--model_path",
-        type=str,
-        default="models/Net2DFast_UK_same.pth.tar",
-        help="Path to trained BatDetect2 model",
-    )
-    args = vars(parser.parse_args())
-
-    args["spec_slices"] = False  # used for visualization
-    args[
-        "chunk_size"
-    ] = 2  # if files greater than this amount (seconds) they will be broken down into small chunks
-    args["ann_dir"] = os.path.join(args["ann_dir"], "")
-
-    main(args)