def __init__(self, params, indexlist, phase, proc_id):
super(BatchLoader, self).__init__()
self.indexlist = indexlist
self.proc_id = proc_id
self.batch_size = params['batch_size']
self.im_shape = params['im_shape']
self.phase = phase
self.queue = Queue(_QSIZE)
#rec_conn, send_conn = Pipe()
# self.rec_conn = rec_conn
# self.send_conn = send_conn
## Dividing with rest the batch size for the jobs we have
self.batch_ck_size = self.batch_size // _nJobs
## in case of the last jobs adding the rest
if self.proc_id == (_nJobs - 1):
self.batch_ck_size += self.batch_size % _nJobs
## Opening LMDB
lmdb_output_pose_env = lmdb.Environment(params['source'] +
'/pose_lmdb/',
readonly=True,
lock=False)
self.cur_pose = lmdb_output_pose_env.begin().cursor()
lmdb_output_flip_env = lmdb.Environment(params['source'] +
'/flip_lmdb/',
readonly=True,
lock=False)
self.cur_flip = lmdb_output_flip_env.begin().cursor()
lmdb_output_land_env = lmdb.Environment(params['source'] +
'/land_lmdb/',
readonly=True,
lock=False)
self.cur_land = lmdb_output_land_env.begin().cursor()
################
self.Nimgs = len(self.indexlist)
# this class does some simple data-manipulations
#proto_data = open(params['mean_file'], "rb").read()
#a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
#mean = caffe.io.blobproto_to_array(a)[0]
## mean is read BGR and c,h,w; we convert it to h,w,c.
## BGR is OK since OpenCV and caffe are BGR
## Then MySimpleTransformer will remove mean after that the image
## has been changed to BGR as well. So apple-to-apple.
self.transformer = MySimpleTransformer()
self.aug_tr = aug_tracker.AugmentationTracker()
if params['mean_file'] is not None:
mean = np.load(params['mean_file'])
mean = mean.transpose(1, 2, 0)
mean = np.float32(mean)
self.transformer.set_mean(mean)
if self.phase == 1:
util.myprint("BatchLoader_valid" + str(self.proc_id) +
" initialized with " + str(self.Nimgs) + " images")
else:
util.myprint("BatchLoader_train" + str(self.proc_id) +
" initialized with " + str(self.Nimgs) + " images")
util.myprint("This will process: " + str(self.batch_ck_size) +
'/' + str(self.batch_size))
def make_database(db_name, files, map):
im_db_name = db_name + '_im_db'
label_db_name = db_name + '_label_db'
if os.path.isdir(im_db_name):
raise Exception(im_db_name + ' already exists. Delete it')
if os.path.isdir(label_db_name):
raise Exception(label_db_name + ' already exists. Delete it')
# open the database for writing
im_db = lmdb.Environment(im_db_name, map_size=1000000000000)
label_db = lmdb.Environment(label_db_name, map_size=1000000000000)
# output image file size
sz = (256, 256)
with im_db.begin(write=True) as im_db_txn:
with label_db.begin(write=True) as label_db_txn:
for idx, file in enumerate(files):
# get the label
label = np.asarray(map[file], dtype=np.float)
# make the label N x 1 x 1
label = label.reshape(label.shape + (1,1))
# load the image (RGB)
im = caffe.io.load_image(BASE_DIR + 'imageAlignedLD/' + file[6:])
im = caffe.io.resize_image(im, sz)
# channel swap for pre-trained (RGB -> BGR)
im = im[:, :, [2,1,0]]
# make channels x height x width
im = im.swapaxes(0,2).swapaxes(1,2)
# convert to uint8
im = (255*im).astype(np.uint8, copy=False)
# image to datum
im_datum = caffe.io.array_to_datum(im)
im_datum.ClearField('label')
im_str = im_datum.SerializeToString()
# label to datum
label_datum = caffe.io.array_to_datum(label)
label_datum.ClearField('label')
label_str = label_datum.SerializeToString()
# insert into the database
im_db_txn.put(file, im_str)
label_db_txn.put(file, label_str)
if idx % 500 == 0:
print "processed %d of %d (%s)" % (idx, len(files), db_name)
def main():
readEnv = lmdb.Environment(input_fpath, readonly=True)
writeEnv = lmdb.Environment(output_fpath,
readonly=False,
map_size=1000000000000) # 1 TB
data = readPairs(fpath)
with readEnv.begin() as readTx:
for pair in data:
f = readTx.get(pair[0])
with writeEnv.begin(write=True) as writeTxn:
writeTxn.put(pair[1], f)
tic_toc_print('Done for %s' % pair)
def read_test():
img_db_fn = 'data/image_train.lmdb'
img_env = lmdb.Environment(img_db_fn, map_size=1099511627776)
img_txn = img_env.begin(write=True, buffers=True)
img_cur = img_txn.cursor()
jnt_db_fn = 'data/joint_train.lmdb'
jnt_env = lmdb.Environment(jnt_db_fn, map_size=1099511627776)
jnt_txn = jnt_env.begin(write=True, buffers=True)
jnt_cur = jnt_txn.cursor()
for _ in range(10000):
img_cur.next()
jnt_cur.next()
img_datum = caffe.io.caffe_pb2.Datum()
jnt_datum = caffe.io.caffe_pb2.Datum()
if not os.path.exists('data/test'):
os.makedirs('data/test')
for i in range(100):
img_key, img_value = img_cur.item()
jnt_key, jnt_value = jnt_cur.item()
if img_key != jnt_key:
sys.exit('img_key and jnt_key should be same')
img_datum.ParseFromString(img_value)
jnt_datum.ParseFromString(jnt_value)
img_data = [struct.unpack('B', d) for d in img_datum.data]
img_data = np.asarray(img_data, dtype=np.uint8)
img_data = img_data.reshape(
(img_datum.channels, img_datum.height, img_datum.width))
img = np.array(img_data.swapaxes(0, 2).swapaxes(0, 1))
cv.imwrite('data/test/%d.jpg' % i, img)
img = cv.imread('data/test/%d.jpg' % i)
jnt_data = np.asarray(jnt_datum.float_data).reshape((7, 2))
for j in jnt_data:
jt = (int(j[0] * img.shape[1]), int(j[1] * img.shape[0]))
print jt
cv.circle(img, jt, 5, (0, 0, 255), -1)
cv.imwrite('data/test/%d.jpg' % i, img)
img_cur.next()
jnt_cur.next()
print i
img_env.close()
jnt_env.close()
def storeSTRs(subfolder, key, val):
with lmdb.Environment.begin(lmdb.Environment(
'/home/alex/workspace-noneclipse/crycsv/' + subfolder + '/',
max_dbs=0),
write=True,
buffers=True) as txn:
txn.put(str(key).encode(), str(val).encode())
def test_lmdb(name='train_QP36_QP40'):
# env_db = lmdb.Environment(name)
dir = '/data/disk2/SHVC_SNR/data/rec_data/lmdb/train_QP36_QP39_part8' # /train_QP36_QP40 dir
env_db = lmdb.Environment(dir)
# env_db = lmdb.open("./trainC")
txn = env_db.begin()
# get函数通过键值查询数据,如果要查询的键值没有对应数据，则输出None
buf = txn.get(str('GT_001_088').encode('ascii'))
print(type(buf))
# value = np.frombuffer(buf, dtype=np.uint8)
# value = value.reshape(720, 1280)
# cv2.imwrite('./test.png', value)
# k = 1
# for key, value in txn.cursor(): #遍历
# print (key)
# value = np.frombuffer(value, dtype=np.uint8)
# value = value.reshape(720, 1280)
# # print(value.shape)
# # cv2.imshow(str(k), value)
# # cv2.waitKey()
# # cv2.imwrite('./test_'+ str(k) + '.png', value)
# k+=1
# if k >= 10:
# break
env_db.close()
def load_data_into_lmdb(path, features, labels=None):
env = lmdb.Environment(path, map_size=features.nbytes * 2, subdir=True)
#env = lmdb.open(lmdb_name, )
features = features[:, :, None, None]
with env.begin(write=True) as txn:
for i in range(features.shape[0]):
datum = caffe.proto.caffe_pb2.Datum()
datum.channels = features.shape[1]
datum.height = 1
datum.width = 1
if features.dtype == np.int:
datum.data = features[i].tostring()
elif features.dtype == np.float:
datum.float_data.extend(features[i].flat)
else:
raise Exception("features.dtype unknown.")
if labels is not None:
datum.label = int(labels[i])
str_id = '{:08}'.format(i)
txn.put(str_id, datum.SerializeToString())
def open_env(version, name):
path = os.path.join(version, name)
if os.path.exists(os.path.join(path, 'data.mdb')):
raise LMDBExistsError
if not os.path.exists(path):
os.makedirs(path)
return lmdb.Environment(path=path, max_dbs=5, create=True)
def getSTRs(subfolder, key):
with lmdb.Environment.begin(lmdb.Environment(
'/home/alex/workspace-noneclipse/crycsv/' + subfolder + '/',
max_dbs=0),
write=False,
buffers=True) as txn:
return bytes(txn.get(key.encode())).decode('UTF-8')
def write_lmdb(img_to_class_path, lmdb_path):
img_to_class_fo = open(img_to_class_path, "r")
lmdb_map_size = 1 << 40
env = lmdb.Environment(lmdb_path, map_size=lmdb_map_size)
with env.begin(write=True) as txn: # txn is Transaction object
count = 0
for line in img_to_class_fo.readlines():
line = line.rstrip()
img_path = line.split()[0]
img_class = int(line.split()[1])
img_data = imageio.imread(img_path).astype(
np.float32) # shape is (n, n)
img_data = img_data.reshape(img_data.shape[0],
img_data.shape[1],
1) # shape is (n, n, 1)
# convert from height-width-channel(HWC) to channel-height-width (CHW)
img_data = np.transpose(img_data, (2, 0, 1))
tensor_protos = caffe2_pb2.TensorProtos()
img_tensor = tensor_protos.protos.add()
img_tensor.dims.extend(img_data.shape)
img_tensor.data_type = 1
flatten_img = img_data.reshape(np.prod(img_data.shape))
img_tensor.float_data.extend(flatten_img)
img_class_tensor = tensor_protos.protos.add()
img_class_tensor.data_type = 2
img_class_tensor.int32_data.append(img_class)
txn.put('{}'.format(count).encode('ascii'),
tensor_protos.SerializeToString())
if count % 10 == 0:
print("Inserted {} rows".format(count))
count += 1
print("Inserted {} rows".format(count))
print("\nLMDB saved at " + lmdb_path + "\n\n")
img_to_class_fo.close()
def test_update_targets():
variant_ids = "/s/project/kipoi-cadd/data/raw/v1.3/training_data/sample_variant_ids.pkl"
varids = load_variant_ids(variant_ids)
lmdb_dir = "/s/project/kipoi-cadd/data/tests/lmdb_3/"
num_vars = 0
inputfile = \
get_data_dir() + "/raw/v1.3/training_data/training_data.imputed.csv"
row_example = pd.read_csv(inputfile,
sep=',',
nrows=1,
skiprows=1,
header=None)
map_size = cadd_serialize_numpy_row(row_example.values[0], varids[0],
np.float16, 0).to_buffer().size
map_size = map_size * (varids.shape[0] + 1) * 5
env = lmdb.Environment(lmdb_dir,
lock=False,
map_size=map_size,
writemap=True)
with env.begin(write=True, buffers=True) as txn:
for var in varids:
row = bytes(txn.get(var.encode('ascii')))
np_row = pa.deserialize(row)
if np_row['targets'] == -1:
np_row['targets'] = 0
ser_data = pa.serialize(np_row)
buf = ser_data.to_buffer()
txn.replace(var.encode('ascii'), buf)
num_vars += 1
print("Finished changing", num_vars, "rows.")
def write_images_to_db(self, db_name, images, callback=lambda a: 0):
'''
Writes images to a lmdb database inside this model's folder. db_name is the name of the
database. If a database by that name already exists, it will be overwritten. images is a
list of 2-element tuples. The first elemnt of each tuple should be a path to an image, and
the second element should be the label of that image.
'''
random.shuffle(images) #Shuffle input data to improve training.
p = os.path.join(self.get_folder(), db_name)
s.call(['rm', '-r', p])
map_size = 256 * 256 * 3 * 2 * len(images)
env = lmdb.Environment(p, map_size=map_size)
write_to = env.begin(write=True, buffers=True)
i = 0
num_images = len(images)
update_interval = int(num_images / 100 + 1)
for image in images:
try:
resize_image(image[0])
input = np.transpose(
mp.imread('/tmp/resized.jpg'),
(2, 1, 0)) #Caffe wants CxHxW, not the standard WxHxC.
datum = array_to_datum(input, image[1])
write_to.put('{:08}'.format(i).encode('ascii'),
datum.SerializeToString())
i += 1
except:
pass
if (i % update_interval == 0):
callback([(i / num_images, '')])
write_to.commit()
env.close()
def __init__(self, filename, flag):
"""Constructor for the LMDBNoLockDatabase class.
Args:
filename (str): The filename of the database file.
flag (str): a flag indicating the mode for opening the database.
Refer to the documentation for anydbm.open().
"""
super().__init__()
create = bool(flag == 'c')
if flag == 'n':
if os.path.isfile(filename):
os.remove(filename)
create = True
self._lmdb = lmdb.Environment(path=filename,
map_size=1024**4,
map_async=True,
writemap=True,
readahead=False,
subdir=False,
create=create,
lock=True)
def write_images_to_lmdb(img_dir, db_name, labels):
for root, dirs, files in os.walk(img_dir, topdown = False):
if root != img_dir:
continue
#multiply 2 to make the map_size large enough
map_size = 2*IMAGE_WIDTH * IMAGE_HEIGHT *3*len(files)
env = lmdb.Environment(db_name, map_size=map_size)
txn = env.begin(write=True,buffers=True)
for idx, name in enumerate(files):
#print(img_dir + name)
img = cv2.imread(img_dir + name, cv2.IMREAD_COLOR)
img = transform_img(img, img_width=IMAGE_WIDTH, img_height=IMAGE_HEIGHT)
#print(img.shape)
#print(img)
img=img.transpose(2,0,1)
y = labels[name]
#print(name)
#print(y)
datum = array_to_datum(img,y)
#print(datum)
#lala
str_id = '{:08}'.format(idx)
txn.put(str_id.encode('ascii'), datum.SerializeToString())
if idx % 1000 == 1:
print("transforming" + str(idx) + "th image to sb")
txn.commit()
env.close()
print " ".join(["Writing to", db_name, "done!"])
def __init__(self,
storage_dir=None,
shared_key=get_shared_key_from_environ(),
client_password="******"):
self._shared_key = shared_key
self._client_password = client_password
if not storage_dir:
storage_dir = tempfile.mkdtemp()
self.file_dir = os.path.join(storage_dir, 'storage')
try:
os.makedirs(self.file_dir)
except:
pass # This is OK
self.db_env = lmdb.Environment(
os.path.join(storage_dir, 'db'),
map_size=1024**3, # 1GB - mostly metadata, so should be fine
max_dbs=5)
self.nonce_db = self.db_env.open_db(b'nonce')
self.inbox_db = self.db_env.open_db(b'inbox', dupsort=True)
self.metadata_db = self.db_env.open_db(b'metadata')
self.increment_db = self.db_env.open_db(b'increment')
self.tracking_db = self.db_env.open_db(b'tracking')
self.timestamp_source = MonotonicTimestampSource()
def get_one_batch(lmdb_batch_dir, idx):
env = lmdb.Environment(lmdb_batch_dir, readonly=True, lock=False)
with env.begin() as txn:
buff = bytes(txn.get(str(idx).encode('ascii')))
ser = blosc.decompress(buff)
batch = pa.deserialize(ser)
return batch
def createDB(name, contenido, funcion):
db = lmdb.Environment(name, map_size=int(1e12))
tx = db.begin(write=True)
for label, imagen in enumerate(contenido):
im = Image.open(imagen)
punt = im.fp
im = im.resize((IMAGE_WIDTH, IMAGE_HEIGHT))
if 'cat' in os.path.split(imagen)[1]:
y = 0
else:
y = 1
x = np.array(im.getdata()).reshape(im.size[1], im.size[0], 3)
datum = array_to_datum(np.transpose(x, (2, 0, 1)), y)
if funcion(label, SCORE):
print label
tx.put('{:08}'.format(label), datum.SerializeToString())
if not punt.closed:
punt.close()
if (label + 1) % 2500 == 0:
tx.commit()
tx = db.begin(write=True)
print '------- commit -------'
tx.commit()
db.close()
def start(self, ctx=None):
logger.debug("Starting data engine...")
# register with the context
if ctx:
ctx.bind('dataengine', self)
if not self.datapath:
self.datapath = os.path.join(environ.data_dir(), 'stores')
if not os.path.exists(self.datapath):
os.mkdir(self.datapath)
logger.debug("Data path: %s", self.datapath)
try:
self.database = lmdb.Environment(self.datapath,
map_size=2000000000,
max_dbs=1024)
with self.database.begin(write=False) as txn:
cur = txn.cursor()
for k, v in iter(cur):
logger.debug("Found existing store: %s", k)
_db = self.database.open_db(k, create=False)
self.stores[k] = Store(k, _db, self)
except lmdb.Error:
logger.exception("Failed to open database.", exc_info=True)
raise
logger.debug("Data engine started.")
def create_lmdb(db_name, labels):
map_size = SEQUENCE_LENGTH * ALPHA_LENGTH * 27 * len(
labels) # 27x is just for safety
env = lmdb.Environment(db_name, map_size=map_size)
txn = env.begin(write=True, buffers=True)
X_copy = np.ndarray((3, SEQUENCE_LENGTH, ALPHA_LENGTH))
for i, label in enumerate(labels):
print('writing img-{}'.format(i))
X = mp.imread(IMG_BASE_NAME.format(i)) * 255
X_rev = np.transpose(X)
X_copy[0, :, :] = X_rev
X_copy[1, :, :] = X_rev
X_copy[2, :, :] = X_rev
if label < 5:
class_label = 0
else:
class_label = 1
datum = array_to_datum(X_copy.astype(np.uint8), class_label)
str_id = '{:08}'.format(i)
txn.put(str_id.encode('ascii'), datum.SerializeToString())
txn.commit()
env.close()
print('Done creating {}!'.format(db_name))
def main(json_path=None, lmdb_path=None):
assert json_path is not None, 'json_path is needed'
if lmdb_path is None:
lmdb_path = json_path
meta = os.path.join(json_path, 'meta.json')
data_ids = []
value = {}
env = lmdb.Environment(lmdb_path,
subdir=True,
map_size=int(1e9),
max_dbs=2,
lock=False)
db_extra = env.open_db('extra'.encode(), create=True)
db_image = env.open_db('image'.encode(), create=True)
with open(meta, 'r') as meta_reader:
for line in tqdm(meta_reader):
single_meta = json.loads(line)
data_id = os.path.join(json_path, single_meta['filename'])
data_id = str(data_id.encode('utf-8').decode('utf-8'))
with open(data_id.encode(), 'rb') as file_reader:
image = file_reader.read()
value['extra'] = {}
for key in single_meta['extra']:
value['extra'][key] = single_meta['extra'][key]
with env.begin(write=True) as lmdb_writer:
lmdb_writer.put(data_id.encode(),
pickle.dumps(value),
db=db_extra)
with env.begin(write=True) as image_writer:
image_writer.put(data_id.encode(), image, db=db_image)
env.close()
def __init__(self, data_dir, split, alphabet, line_height, transforms):
logger.info("Loading IAM [%s] dataset..." % split)
self.data_dir = data_dir
self.split = split
self.alphabet = alphabet
self.preprocess = transforms
# Read Dataset Description
with open(os.path.join(data_dir, 'desc.json'), 'r') as fh:
self.data_desc = json.load(fh)
# Read LMDB image database
self.lmdb_env = lmdb.Environment(os.path.join(data_dir,
'line-images.lmdb'),
map_size=1e12,
readonly=True)
self.lmdb_txn = self.lmdb_env.begin(buffers=True)
# Divide dataset into classes by width of images images for two purposes:
# (1) It is more efficient to group images of roughly the samze size in a minibatch,
# because that results in less padding, and thus less wasted computation, per minibatch
# (2) It is probably true (although we haven't shown this conclusively) that the system can
# learn faster by starting of on smaller images and working its way up to longer images
#
# We emprically measure that for IAM data, the breakdown in line-widths is roughly:
# Width Range Cumualtive Percent of Data
# 0-300 10%
# 300-350 20%
# 350-400 40%
# 400-500 70%
# 500-600 90%
# 600+ 100%
self.size_group_limits = [400, 600, 999999999]
self.size_group_keys = self.size_group_limits
self.size_groups = dict()
self.size_groups_dict = dict()
for cur_limit in self.size_group_limits:
self.size_groups[cur_limit] = []
self.size_groups_dict[cur_limit] = dict()
self.writer_id_map = dict()
for idx, entry in enumerate(self.data_desc[self.split]):
# First handle writer id
if not entry['writer'] in self.writer_id_map:
self.writer_id_map[entry['writer']] = len(self.writer_id_map)
# Now figure out which size-group it belongs in
for cur_limit in self.size_group_limits:
if entry['width'] < cur_limit:
self.size_groups[cur_limit].append(idx)
self.size_groups_dict[cur_limit][idx] = 1
break
logger.info("Done.")
def __init__(self, filename, name): ''' Create a lmdb-backed VStore using a cached environment ''' if filename not in self._allenvs: self._allenvs[filename] = lmdb.Environment(filename, map_size=100<<30, max_dbs=100) self._env = self._allenvs[filename] self._db = self._env.open_db(name);
def __init__(self,db_dir,transform=None,zfill_len=8,name_suffix='_INTEGRATED_'):
self.db_dir = db_dir
self.transform = transform
self.zfill_len = zfill_len
self.name_suffix = name_suffix
self.env = lmdb.Environment(self.db_dir,readonly=True)
self._len = self.env.stat()['entries']
def __init__(self,
filename,
serializer,
deserializer,
indexes=None,
flag=None,
_size=DEFAULT_SIZE):
"""Constructor for the IndexedDatabase class.
Args:
filename (str): The filename in that case Database name - <host>:<port>/<db name>.
serializer (function): converts entries to bytes
deserializer (function): restores items from bytes
indexes (dict:(str,function):optional): dict of index names to key
functions. The key functions use the deserialized value and
produce n index keys, that will reference the items primary
key. Defaults to None
flag (str:optional): a flag indicating the mode for opening the
database. Refer to the documentation for anydbm.open().
Defaults to None.
"""
super(IndexedDatabase, self).__init__()
url = urlparse(filename)
LOGGER.debug("ORIENTDB url=%s", url)
create = bool(flag == 'c')
client = pyorient.OrientDB(ORIENTDB_HOST, 2424)
LOGGER.debug("TESTING ORIENTDB client=%s", client)
session_id = client.connect(DB_USER, DB_PASS)
LOGGER.debug("_ORIENTDB_ client=%s session_id=%s", client, session_id)
#db = client.db_create( DB_NAME, pyorient.DB_TYPE_GRAPH, pyorient.STORAGE_TYPE_PLOCAL )
is_db = client.db_exists(DB_NAME, pyorient.STORAGE_TYPE_PLOCAL)
if flag == 'n':
if os.path.isfile(filename):
os.remove(filename)
create = True
if indexes is None:
indexes = {}
self._serializer = serializer
self._deserializer = deserializer
self._lmdb = lmdb.Environment(path=filename,
map_size=_size,
map_async=True,
writemap=True,
readahead=False,
subdir=False,
create=create,
max_dbs=len(indexes) + 1,
lock=True)
self._main_db = self._lmdb.open_db('main'.encode())
self._indexes = \
{name: self._make_index_tuple(name, index_info)
for name, index_info in indexes.items()}
def storeSTRINT(subfolder, key, val):
if not type(key) is str or not type(val) is int:
raise TypeError('wrong Types for storeSTRINT')
with lmdb.Environment.begin(lmdb.Environment(
'/home/alex/workspace-noneclipse/crycsv/' + subfolder + '/',
max_dbs=0),
write=True,
buffers=True) as txn:
txn.put(str(key).encode(), val.to_bytes(10, 'little'))
def reset_lmdb_database(self) -> None:
"""
A function that needs to be called after each epoch, in case of using an lmdb dataset, to close the environment
and open a new one to kill active readers
"""
if self.save_buffer_on_disk:
if self._is_lmdb_env_created():
self._lmdb_env.close()
self._lmdb_env = lmdb.Environment(self.buffer_db_dir, map_size=self.map_size, max_spare_txns=6)
def __init__(self, db_file):
self._env = lmdb.Environment(db_file, map_size=1024**4, max_dbs=3)
with self._env.begin(write=True) as txn:
self._master_db = self._env.open_db(b'master', txn=txn)
self._attr_index_db = self._env.open_db(b'attr_index',
txn=txn,
dupsort=True)
self._attr_value_db = self._env.open_db(b'attr_value',
txn=txn,
dupsort=True)
def getExchMarkCurList(subfolder, key):
if not type(key) is int:
raise TypeError('wrong Types for getExchMarkCurList')
with lmdb.Environment.begin(lmdb.Environment(rootDatasDir + subfolder +
'/',
max_dbs=0),
write=False,
buffers=True) as txn:
bina = txn.get(type_IntToByte(key))
return type_ByteToFromJson(bina)
def create_dataset():
img_db_fn = 'data/image_train.lmdb'
del_and_create(img_db_fn)
img_env = lmdb.Environment(img_db_fn, map_size=1099511627776)
img_txn = img_env.begin(write=True, buffers=True)
jnt_db_fn = 'data/joint_train.lmdb'
del_and_create(jnt_db_fn)
jnt_env = lmdb.Environment(jnt_db_fn, map_size=1099511627776)
jnt_txn = jnt_env.begin(write=True, buffers=True)
keys = np.arange(100000)
np.random.shuffle(keys)
'''
img_fns = glob.glob('data/FLIC-full/crop/*.jpg')
img_fns += glob.glob('data/lspet_dataset/crop/*.jpg')
jnt_fns = glob.glob('data/FLIC-full/joint/*.npy')
jnt_fns += glob.glob('data/lspet_dataset/joint/*.npy')
'''
img_fns = glob.glob('data/lspet_dataset/mvnCrop/*.jpg')
jnt_fns = glob.glob('data/lspet_dataset/joint/*.npy')
for i, (img_fn, jnt_fn) in enumerate(zip(sorted(img_fns),
sorted(jnt_fns))):
img_datum = get_img_datum(img_fn)
jnt_datum = get_jnt_datum(jnt_fn)
key = '%010d' % keys[i]
img_txn.put(key, img_datum.SerializeToString())
jnt_txn.put(key, jnt_datum.SerializeToString())
if i % 10000 == 0:
img_txn.commit()
jnt_txn.commit()
jnt_txn = jnt_env.begin(write=True, buffers=True)
img_txn = img_env.begin(write=True, buffers=True)
print i, os.path.basename(img_fn), os.path.basename(jnt_fn)
img_txn.commit()
jnt_txn.commit()
img_env.close()
jnt_env.close()
def __init__(self, app):
assert app.config['data_dir']
BaseService.__init__(self, app)
db_directory = os.path.join(app.config['data_dir'], 'lmdb')
self.env = lmdb.Environment(db_directory, map_size=TB)
self.db_directory = db_directory
self.uncommitted = dict()
self.stop_event = Event()
