Merge f62bc99ab2 into 2100a3e483

batdetect2/api.py

@@ -123,6 +123,8 @@ from batdetect2.utils.detector_utils import list_audio_files, load_model
 import audioread
 import os 
 import soundfile as sf
+import requests
+import io
 
 # Remove warnings from torch
 warnings.filterwarnings("ignore", category=UserWarning, module="torch")
@@ -279,6 +281,49 @@ def process_file(
         file_id
     )
 
+def process_url(
+    url: str,
+    model: DetectionModel = MODEL,
+    config: Optional[ProcessingConfiguration] = None,
+    device: torch.device = DEVICE,
+    file_id: str | None = None
+) -> du.RunResults:
+    """Process audio file with model.
+
+    Parameters
+    ----------
+    url : str
+        HTTP URL to load the audio data from
+    model : DetectionModel, optional
+        Detection model. Uses default model if not specified.
+    config : Optional[ProcessingConfiguration], optional
+        Processing configuration, by default None (uses default parameters).
+    device : torch.device, optional
+        Device to use, by default tries to use GPU if available.
+    file_id: Optional[str],
+        Give the data an id. Defaults to the URL
+    """
+    if config is None:
+        config = CONFIG
+
+    if file_id is None:
+        file_id = url
+
+    response = requests.get(url)
+
+    # Raise exception on HTTP error
+    response.raise_for_status()
+
+    # Retrieve body as raw bytes
+    raw_audio_data = response.content
+    
+    return du.process_file(
+        io.BytesIO(raw_audio_data),
+        model,
+        config,
+        device,
+        file_id
+    )
 
 def process_spectrogram(
     spec: torch.Tensor,

batdetect2/utils/audio_utils.py

@@ -9,6 +9,7 @@ import torch
 import audioread
 import os 
 import soundfile as sf
+import io
 
 from batdetect2.detector import parameters
 
@@ -147,7 +148,10 @@ def generate_spectrogram(
 def get_samplerate(
     path:  Union[
         str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
-    ]):
+    ]):       
+    if isinstance(path, (BinaryIO, io.BytesIO)):
+        path.seek(0)
+    
     with sf.SoundFile(path) as f:
         return f.samplerate
 

batdetect2/utils/detector_utils.py

@@ -33,8 +33,10 @@ from batdetect2.types import (
 
 import audioread
 import os 
+import io
 import soundfile as sf
-
+import hashlib
+import uuid
 
 __all__ = [
     "load_model",
@@ -832,7 +834,7 @@ def process_file(
 
     _file_id = file_id
     if _file_id is None:
-        _file_id = os.path.basename(path) if isinstance(path, str) else "unknown"
+        _file_id = _generate_id(path)
 
     # convert results to a dictionary in the right format
     results = convert_results(
@@ -856,6 +858,24 @@ def process_file(
 
     return results
 
+def _generate_id(path:  Union[
+        str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
+    ]) -> str:
+    """ Generate an id based on the path.
+    
+    If the path is a str or PathLike it will parsed as the basename. 
+    This should ensure backwards compatibility with previous versions.     
+    """
+    if isinstance(path, str) or isinstance(path, os.PathLike):
+        return os.path.basename(path)
+    elif isinstance(path, (BinaryIO, io.BytesIO)):
+        path.seek(0)
+        md5 = hashlib.md5(path.read()).hexdigest()
+        path.seek(0)
+        return md5
+    else:
+        return str(uuid.uuid4())
+
 
 def summarize_results(results, predictions, config):
     """Print summary of results."""

tests/test_api.py

@@ -10,11 +10,13 @@ import torch
 from torch import nn
 
 from batdetect2 import api
+import io 
 
 PKG_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 TEST_DATA_DIR = os.path.join(PKG_DIR, "example_data", "audio")
 TEST_DATA = glob(os.path.join(TEST_DATA_DIR, "*.wav"))
 
+DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
 
 def test_load_model_with_default_params():
     """Test loading model with default parameters."""
@@ -280,3 +282,28 @@ def test_process_file_with_empty_predictions_does_not_fail(
 
     assert results is not None
     assert len(results["pred_dict"]["annotation"]) == 0
+
+def test_process_file_file_id_defaults_to_basename():
+    """Test that no detections are made above the nyquist frequency."""
+    # Recording donated by @@kdarras
+    basename = "20230322_172000_selec2.wav"
+    path = os.path.join(DATA_DIR, basename)
+
+    output = api.process_file(path)
+    predictions = output["pred_dict"]
+    id = predictions["id"]
+    assert id == basename
+
+def test_bytesio_file_id_defaults_to_md5():
+    """Test that no detections are made above the nyquist frequency."""
+    # Recording donated by @@kdarras
+    basename = "20230322_172000_selec2.wav"
+    path = os.path.join(DATA_DIR, basename)
+
+    with open(path, "rb") as f:
+        data = io.BytesIO(f.read())
+
+    output = api.process_file(data)
+    predictions = output["pred_dict"]
+    id = predictions["id"]
+    assert id == "7ade9ebf1a9fe5477ff3a2dc57001929"

tests/test_audio_utils.py

@@ -7,7 +7,9 @@ from hypothesis import strategies as st
 from batdetect2.detector import parameters
 from batdetect2.utils import audio_utils, detector_utils
 import io
-import requests
+import os
+
+DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
 
 @given(duration=st.floats(min_value=0.1, max_value=2))
 def test_can_compute_correct_spectrogram_width(duration: float):
@@ -144,3 +146,48 @@ def test_get_samplerate_using_bytesio():
 
     expected_sample_rate = 500000
     assert expected_sample_rate == sample_rate
+
+    
+
+def test_load_audio_using_bytes():
+    filename = "example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav"
+    
+    with open(filename, "rb") as f:
+        audio_bytes = io.BytesIO(f.read())
+    
+    sample_rate, audio_data = audio_utils.load_audio(audio_bytes, time_exp_fact=1, target_samp_rate=parameters.TARGET_SAMPLERATE_HZ)
+
+    expected_sample_rate, expected_audio_data = audio_utils.load_audio(filename, time_exp_fact=1, target_samp_rate=parameters.TARGET_SAMPLERATE_HZ)
+
+    assert expected_sample_rate == sample_rate
+
+    assert np.array_equal(audio_data, expected_audio_data)
+
+
+
+def test_get_samplerate_using_bytesio_2():
+    basename = "20230322_172000_selec2.wav"
+    path = os.path.join(DATA_DIR, basename)
+
+    with open(path, "rb") as f:
+        audio_bytes = io.BytesIO(f.read())
+    
+    sample_rate = audio_utils.get_samplerate(audio_bytes)
+
+    expected_sample_rate = 192_000
+    assert expected_sample_rate == sample_rate
+
+def test_load_audio_using_bytes_2():
+    basename = "20230322_172000_selec2.wav"
+    path = os.path.join(DATA_DIR, basename)
+
+    with open(path, "rb") as f:
+        data = io.BytesIO(f.read())
+    
+    sample_rate, audio_data = audio_utils.load_audio(data, time_exp_fact=1, target_samp_rate=parameters.TARGET_SAMPLERATE_HZ)
+
+    expected_sample_rate, expected_audio_data = audio_utils.load_audio(path, time_exp_fact=1, target_samp_rate=parameters.TARGET_SAMPLERATE_HZ)
+
+    assert expected_sample_rate == sample_rate
+
+    assert np.array_equal(audio_data, expected_audio_data)