File size: 1,444 Bytes
a0fd507 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import scipy
from scipy import signal
import os
import lmdb
import pickle
import numpy as np
labels = np.array([0,0,0,1,1,1,2,2,2,3,3,3,4,4,4,4,5,5,5,6,6,6,7,7,7,8,8,8])
root_dir = '/data/cyn/FACED/Processed_data'
files = [file for file in os.listdir(root_dir)]
files = sorted(files)
files_dict = {
'train':files[:80],
'val':files[80:100],
'test':files[100:],
}
dataset = {
'train': list(),
'val': list(),
'test': list(),
}
db = lmdb.open('/data/datasets/BigDownstream/Faced/processed', map_size=6612500172)
for files_key in files_dict.keys():
for file in files_dict[files_key]:
f = open(os.path.join(root_dir, file), 'rb')
array = pickle.load(f)
eeg = signal.resample(array, 6000, axis=2)
eeg_ = eeg.reshape(28, 32, 30, 200)
for i, (samples, label) in enumerate(zip(eeg_, labels)):
for j in range(3):
sample = samples[:, 10*j:10*(j+1), :]
sample_key = f'{file}-{i}-{j}'
print(sample_key)
data_dict = {
'sample': sample, 'label': label
}
txn = db.begin(write=True)
txn.put(key=sample_key.encode(), value=pickle.dumps(data_dict))
txn.commit()
dataset[files_key].append(sample_key)
txn = db.begin(write=True)
txn.put(key='__keys__'.encode(), value=pickle.dumps(dataset))
txn.commit()
db.close() |