lmdb数据读写
WRITE:
import lmdb
import numpy as np
import cv2
import caffe
from caffe.proto import caffe_pb2
#basic setting
lmdb_file = 'lmdb_data'
batch_size = 256
# create the leveldb file
lmdb_env = lmdb.open(lmdb_file, map_size=int(1e12))
lmdb_txn = lmdb_env.begin(write=True)
datum = caffe_pb2.Datum()
item_id = -1
for x in range(1000):
item_id += 1
#prepare the data and label
data = np.ones((3,64,64), np.uint8) * (item_id%128 + 64) #CxHxW array, uint8 or float
label = item_id%128 + 64
# save in datum
datum = caffe.io.array_to_datum(data, label)
keystr = '{:0>8d}'.format(item_id)
lmdb_txn.put( keystr, datum.SerializeToString() )
# write batch
if(item_id + 1) % batch_size == 0:
lmdb_txn.commit()
lmdb_txn = lmdb_env.begin(write=True)
print (item_id + 1)
# write last batch
if (item_id+1) % batch_size != 0:
lmdb_txn.commit()
print 'last batch'
print (item_id + 1)
READ
import caffe
import lmdb
import numpy as np
import cv2
from caffe.proto import caffe_pb2
lmdb_env = lmdb.open('lmdb_data')
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
datum = caffe_pb2.Datum()
for key, value in lmdb_cursor:
datum.ParseFromString(value)
label = datum.label
data = caffe.io.datum_to_array(datum)
#CxHxW to HxWxC in cv2
image = np.transpose(data, (1,2,0))
cv2.imshow('cv2', image)
cv2.waitKey(1)
print('{},{}'.format(key, label))