File size: 1,395 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 | import scipy
from scipy import signal
import os
import lmdb
import pickle
root_dir = '/data/datasets/shu_datasets/mat'
files = [file for file in os.listdir(root_dir)]
files = sorted(files)
# print(files)
files_dict = {
'train':files[:75],
'val':files[75:100],
'test':files[100:],
}
dataset = {
'train': list(),
'val': list(),
'test': list(),
}
db = lmdb.open('/data/datasets/shu_datasets/processed', map_size=110612736)
for files_key in files_dict.keys():
for file in files_dict[files_key]:
data = scipy.io.loadmat(os.path.join(root_dir, file))
eeg = data['data']
labels = data['labels'][0]
bz, ch_num, points = eeg.shape
print(eeg.shape)
eeg_resample = signal.resample(eeg, 800, axis=2)
eeg_ = eeg_resample.reshape(bz, ch_num, 4, 200)
print(eeg_.shape, labels.shape)
for i, (sample, label) in enumerate(zip(eeg_, labels)):
sample_key = f'{file[:-4]}-{i}'
# print(sample_key)
data_dict = {
'sample':sample, 'label':label-1
}
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() |