get_spectrogram.py

"""Extracts spectrograms from multiple recordings"""

import os
import argparse
import numpy as np
import soundfile
from p_tqdm import p_map
from tqdm import tqdm
import utils
import pandas as pd


def main(data, arguments):
    """
    Load the data and compute n spectrograms with a specific duration and save it into a folder.
    :param data (DataFrame): DataFrame containing the path of each file to process.
    :param arguments (args): Arguments for signal processing and directory.
    """
    _, (i) = data
    filename = str(i[0])  # Store the filename of the recording
    try:
        info = soundfile.info(filename)  # Collection recording information
        file_duration, fs = info.duration, info.samplerate
    except Exception as error:
        print(f'`{filename}` cannot be open : : {error}')
        return

    # Create the list of all possible offset to compute spectrogram
    offset_list = np.arange(0, file_duration, arguments.duration - arguments.overlap)

    for offset in offset_list:
        file = filename.replace('/', '_').split('.', maxsplit=1)[0]
        try:
            sig, fs = soundfile.read(filename, start=int(
                  offset*fs), stop=int((offset+arguments.duration)*fs),
                  always_2d=True)  # Load the signal
                  
            sig = sig[:, 0]  # Only take channel 0
            # Apply resample and low/high pass filter
            sig = utils.signal_processing(
                sig, rf=arguments.rf, fs=fs, high=arguments.high, low=arguments.low)

            # Check if empty .txt annotation file is needed
            if arguments.background:
                folder = 'background'
                utils.create_directory(os.path.join(arguments.directory, folder))
                name = os.path.join(arguments.directory, folder, f'{file}_{offset}')
                empty_dataframe = pd.DataFrame(columns=['specie', 'x', 'y', 'w', 'h'])
                empty_dataframe.to_csv(str(name+'.txt'), sep=' ', index=False, header=False)
            else:
                folder = 'spectrograms'
                utils.create_directory(os.path.join(arguments.directory, folder))
                name = os.path.join(arguments.directory, folder, f'{file}_{offset}')
            
            utils.create_spectrogram(
                sig, arguments.directory, name, arguments.cmap, arguments.vmin, window_size=int(arguments.window),
                overlap=arguments.hop)
            
        except Exception as error:
            folder = 'spectrograms'
            print(f'`{filename}` cannot be open : {error}')

    return folder

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='Extract spectrogram for each .wav file')
    parser.add_argument('path', type=utils.arg_directory,
                        help='Path of the folder/file that contains the recordings')
    parser.add_argument('directory', type=utils.arg_directory,
                        help='Directory to which spectrograms will be stored')
    parser.add_argument('--duration', type=int,
                        help='Duration for each spectrogram', default=8)
    parser.add_argument('--window', type=int, help='Window size for the Fourier Transform',
                        default=1024)
    parser.add_argument('--hop', type=float,
                        help='Ratio of hop in window : 50%% = 0.5', default=0.5)
    parser.add_argument('--cmap', type=str, help='Colormar of the spectrograms', 
                        choices=['jet', 'cividis', 'viridis'], default='viridis')
    parser.add_argument('--high', type=int,
                        help='High Pass Filter value in Hz', default=10)
    parser.add_argument('--low', type=int,
                        help='Low Pass Filter value in Hz', default=None)
    parser.add_argument('--overlap', type=int,
                        help='Overlap in secondes between 2 spectrograms', default=0)
    parser.add_argument('--rf', type=int, help='Resampling Frequency of the signal. If no argument,'
                        ' will be original frequency sampling of the recording', default=None)
    parser.add_argument('--vmin', type=str, help="If vmin == True, then the spectrogram's minimum color"
                        ' will be stft.mean(). If False stft.min()', default=True)
    parser.add_argument(
        '--cpu', type=int, help='To speed up the process, write 2 or more', default=1)
    parser.add_argument(
        '--background', action='store_const', help='If in arguments, will save an empty .txt file', 
                        const=1, default=None)
    args = parser.parse_args()

    # Load the data and put it into a DataFrame
    df = utils.open_file(args.path)

    if args.cpu == 1:  # If no multiprocessing, then loop processing
        for num, row in tqdm(df.iterrows(), total=len(df),
                             desc="Processing", ascii='░▒▓█'):
            dest = main([num, [row.Path]], args)
        directory = args.directory
    else:
        args = [args]*len(df.groupby('Path'))
        dest = p_map(main, enumerate(df.groupby('Path')), args,
              num_cpus=args[0].cpu, total=len(df.groupby('Path')))
        directory = args[0].directory

    final_dest = os.path.join(directory, dest[0] if len(dest) is list else dest)
    print(f'Saved to {final_dest}')