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)
READimport 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))