59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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"""
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@author:Marques
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@file:my_augment.py
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@email:admin@marques22.com
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@email:2021022362@m.scnu.edu.cn
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@time:2022/07/26
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"""
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import torch.cuda
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import yaml
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from utils.Preprocessing import BCG_Operation
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import numpy as np
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from scipy.signal import stft
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from torch import from_numpy
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with open("./settings.yaml") as f:
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hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps
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apply_samplerate = hyp["apply_samplerate"]
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dataset_samplerate = hyp["dataset_samplerate"]
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preprocessing = BCG_Operation()
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preprocessing.sample_rate = dataset_samplerate
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def my_augment(dataset, config):
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# dataset = preprocessing.Butterworth(dataset, "lowpass", low_cut=20, order=6)
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# dataset = (dataset - dataset.mean()) / dataset.std()
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# dataset_low = preprocessing.Butterworth(dataset, "lowpass", low_cut=0.7, order=6)
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# dataset_high = preprocessing.Butterworth(dataset, "highpass", high_cut=1, order=6)
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print(f"dataset sample_rate is {config['dataset_samplerate']} down_ratio is {config['dataset_samplerate'] // config['apply_samplerate']}")
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dataset = dataset[::config['dataset_samplerate'] // config['apply_samplerate']]
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gpu = torch.cuda.is_available()
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dataset = {"raw": from_numpy(dataset).float().cuda() if gpu else from_numpy(dataset).float(),
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# "low": dataset_low,
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# "high": dataset_high
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}
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return dataset
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def get_stft(x, fs, n):
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print(len(x))
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f, t, amp = stft(x, fs, nperseg=n)
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z = np.abs(amp.copy())
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return f, t, z
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def my_segment_augment(dataset, SP, EP):
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# dataset_segment1 = dataset["low"][int(SP) * 100:int(EP) * 100].copy()
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# dataset_segment2 = dataset["high"][int(SP) * 100:int(EP) * 100].copy()
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# dataset_segment = np.concatenate(([dataset_segment1], [dataset_segment2]), axis=0)
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dataset_segment = dataset["raw"][int(SP):int(EP)].unsqueeze(dim=0)
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return [dataset_segment]
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if __name__ == '__main__':
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pass
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