| 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() |
