From 34de2a3262ad072bdbc11d1b76bb32759dcfbf21 Mon Sep 17 00:00:00 2001
From: Stephane Chavin <stephane.chavin@lis-lab.fr>
Date: Sun, 21 Jul 2024 20:41:39 +0200
Subject: [PATCH] correct get_time_freq
---
get_time_freq_detection.py | 11 +++++++----
utils.py | 18 ++++++++++--------
2 files changed, 17 insertions(+), 12 deletions(-)
diff --git a/get_time_freq_detection.py b/get_time_freq_detection.py
index 29655be..803ed72 100755
--- a/get_time_freq_detection.py
+++ b/get_time_freq_detection.py
@@ -20,7 +20,7 @@ def main(arguments):
df, dir_path = utils.detection2time_freq(annotations_folder=arguments.path_to_data,
duration=arguments.duration,
outdir=arguments.directory,
- sr=arguments.sr,
+ rf=arguments.rf,
names=names,
wav=args.path_to_wav,
raven=args.raven)
@@ -35,7 +35,10 @@ def main(arguments):
'"begin time (s)"; "end time (s)" and the species detected'
' in the given files.')
ds.attrs['date_created'] = pd.Timestamp.now().isoformat()
- ds.attrs['creator_name'] = os.getlogin()
+ try:
+ ds.attrs['creator_name'] = os.getlogin()
+ except Exception:
+ ds.attrs['creator_name'] = input('Your name : ')
# Save Dataset to NetCDF file
ds.to_netcdf(dir_path)
@@ -51,8 +54,8 @@ if __name__ == "__main__":
help='Directory where the dataframe will be stored')
parser.add_argument('names', type=str,
help='path to YOLOv5 custom_data.yaml file')
- parser.add_argument('-s', '--sr', type=int,
- help='Sampling Rate of the spectrogram', required=True)
+ parser.add_argument('--rf', type=int,
+ help='Resample frequency', required=True)
parser.add_argument('--duration', type=int,
help='Duration of the spectrogram', default=8)
parser.add_argument('--path_to_wav', type=utils.arg_directory,
diff --git a/utils.py b/utils.py
index 080aa8f..bab268f 100755
--- a/utils.py
+++ b/utils.py
@@ -355,17 +355,19 @@ def prepare_dataframe(df, args):
return df, species_list
-def detection2time_freq(annotations_folder, duration, outdir, sr, names, wav, raven):
+def detection2time_freq(annotations_folder, duration, outdir, rf, names, wav, raven):
"""
Collect all .txt detection and get time and frequency informations
- :param annotations_folder (str): Path to the .json files
+ :param annotations_folder (str): Path to the .txt files
:param duration (int): Directory to save the .txt files
- :param outfir (str): Directory to save the .txt files
- :param sr (int): Directory to save the .txt files
- :param names (str): Directory to save the .txt files
+ :param outdir (str): Directory to save the .txt files
+ :param rf (int): Resampling freq.
+ :param names (str): names of the classes
+ :param wav (str): Path to the wav
+ :param raven (int): Save into Raven format or not
"""
today = date.today()
- out_file = f'YOLO_detection_{today.day}_{today.month}_freq_{sr}_duration_{duration}.nc'
+ out_file = f'YOLO_detection_{today.day}_{today.month}_freq_{rf}_duration_{duration}.nc'
# Load and process data
df = pd.concat({f: pd.read_csv(os.path.join(annotations_folder, f),
@@ -390,8 +392,8 @@ def detection2time_freq(annotations_folder, duration, outdir, sr, names, wav, ra
df['species'] = df['class'].apply(lambda x: names[int(x)])
df['pos'] = (df['x'] * duration) + df['class'].astype(int)
- df['Low Freq (Hz)'] = (1 - df['y']) * (sr / 2) - (df['h'] * (sr / 2)) / 2
- df['High Freq (Hz)'] = (1 - df['y']) * (sr / 2) + (df['h'] * (sr / 2)) / 2
+ df['Low Freq (Hz)'] = (1 - df['y']) * (rf / 2) - (df['h'] * (rf / 2)) / 2
+ df['High Freq (Hz)'] = (1 - df['y']) * (rf / 2) + (df['h'] * (rf / 2)) / 2
df['Begin Time (s)'] = df['pos'] - (df['w'] * duration) / 2
df['End Time (s)'] = df['pos'] + (df['w'] * duration) / 2
df['duration'] = df['End Time (s)'] - df['Begin Time (s)']
--
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