| 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) |
| |
|
|
| 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}' |
| |
| 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() |
