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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from pathlib import Path
import cv2 as cv
from glob import glob
import pandas as pd
import numpy as np
from scipy.io import wavfile
from tqdm import tqdm
from project_settings import project_path
from toolbox.python_speech_features.misc import wave2spectrum_image
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--template_dir",
default=(project_path / "data/early_media/62/templates/").as_posix(),
type=str
)
parser.add_argument(
"--wav_dir",
default=(project_path / "data/early_media/62/wav/").as_posix(),
type=str
)
args = parser.parse_args()
return args
def main():
args = get_args()
template_dir = Path(args.template_dir)
wav_dir = Path(args.wav_dir)
max_wave_value = 32768.0
result = list()
for template_file in template_dir.glob("*/*.wav"):
template_label = template_file.parts[-2]
_, template = wavfile.read(template_file)
template = template / max_wave_value
template = wave2spectrum_image(
wave=template,
sample_rate=8000,
# n_low_freq=100
)
template = template.T
for wav_file in tqdm(wav_dir.glob("*/*.wav")):
wav_label = wav_file.parts[-2]
_, signal = wavfile.read(wav_file)
signal = signal[:12 * 8000]
signal = signal / max_wave_value
spectrum = wave2spectrum_image(
wave=signal,
sample_rate=8000,
# n_low_freq=100
)
spectrum = spectrum.T
sqdiff_normed = cv.matchTemplate(image=spectrum, templ=template, method=cv.TM_SQDIFF_NORMED)
min_val, _, min_loc, _ = cv.minMaxLoc(sqdiff_normed)
# msg = "label1: {}; label2: {}; min_val: {}".format(label1, label2, min_val)
# print(msg)
row = {
"template_label": template_label,
"template_file": template_file.as_posix(),
"wav_label": wav_label,
"wav_file": wav_file.as_posix(),
"min_val": min_val,
}
result.append(row)
result = pd.DataFrame(result)
result.to_excel("result.xlsx", index=False)
return
if __name__ == '__main__':
main()
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