我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用caffe.proto.caffe_pb2.Datum()。
def loop_records(self, num_records=0, init_key=None): env = lmdb.open(self.fn, readonly=True) datum = Datum() with env.begin() as txn: cursor = txn.cursor() if init_key is not None: if not cursor.set_key(init_key): raise ValueError('key ' + init_key + ' not found in lmdb ' + self.fn + '.') num_read = 0 for key, value in cursor: datum.ParseFromString(value) label = datum.label data = datum_to_array(datum).squeeze() yield (data, label, key) num_read += 1 if num_records != 0 and num_read == num_records: break env.close()
def _add_record(self, data, label=None, key=None): data_dims = data.shape if data.ndim == 1: data_dims = np.array([data_dims[0], 1, 1], dtype=int) elif data.ndim == 2: data_dims = np.array([data_dims[0], data_dims[1], 1], dtype=int) datum = Datum() datum.channels, datum.height, datum.width = data_dims[0], data_dims[1], data_dims[2] if data.dtype == np.uint8: datum.data = data.tostring() else: datum.float_data.extend(data.tolist()) datum.label = int(label) if label is not None else -1 key = ('{:08}'.format(self.num) if key is None else key).encode('ascii') with self.env.begin(write=True) as txn: txn.put(key, datum.SerializeToString()) self.num += 1
def save_to_lmdb(images, labels, lmdb_file): if not os.path.exists(lmdb_file): batch_size = 256 lmdb_env = lmdb.open(lmdb_file, map_size=int(1e12)) lmdb_txn = lmdb_env.begin(write=True) item_id = 0 datum = caffe_pb2.Datum() for i in range(images.shape[0]): im = cv2.imread(images[i]) im = cv2.resize(im, (IM_HEIGHT, IM_WIDTH)) datum.channels = im.shape[2] datum.height = im.shape[0] datum.width = im.shape[1] datum.data = im.tobytes() datum.label = labels[i] 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('converted {} images'.format(item_id + 1)) item_id += 1 # write last batch if (item_id + 1) % batch_size != 0: lmdb_txn.commit() print('converted {} images'.format(item_id + 1)) print('Generated ' + lmdb_file) else: print(lmdb_file + ' already exists')
def save_to_lmdb(images, labels, lmdb_file): if not os.path.exists(lmdb_file): batch_size = 256 lmdb_env = lmdb.open(lmdb_file, map_size=int(1e12)) lmdb_txn = lmdb_env.begin(write=True) item_id = 0 datum = caffe_pb2.Datum() for i in range(images.shape[0]): im = cv2.imread(images[i]) if im is None: continue im = cv2.resize(im, (IM_HEIGHT, IM_WIDTH)) datum.channels = im.shape[2] datum.height = im.shape[0] datum.width = im.shape[1] datum.data = im.tobytes() datum.label = labels[i] 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('converted {} images'.format(item_id + 1)) item_id += 1 # write last batch if (item_id + 1) % batch_size != 0: lmdb_txn.commit() print('converted {} images'.format(item_id + 1)) print('Generated ' + lmdb_file) else: print(lmdb_file + ' already exists')
def make_datum(img, label): #image is numpy.ndarray format. BGR instead of RGB return caffe_pb2.Datum( channels=3, width=IMAGE_WIDTH, height=IMAGE_HEIGHT, label=label, data=np.rollaxis(img, 2).tostring())
def make_datum(img, label): #image is numpy.ndarray format. BGR instead of RGB return caffe_pb2.Datum( channels=3, width=IMAGE_WIDTH, height=IMAGE_HEIGHT, label=label, data=np.transpose(img, (2, 0, 1)).tostring()) # or .tobytes() if numpy < 1.9 # key = 0 # env = lmdb.open(img_lmdb_path, map_size=int(1e12)) # with env.begin(write=True) as txn: # for idx in xrange(numSample): # info = data[idx].split(" ") # OriImg = cv2.imread(datadir + info[0]) # img = cv2.resize(OriImg,(IMAGE_WIDTH,IMAGE_HEIGHT)) # label = int(info[1]) # img = np.transpose(img, (2, 0, 1)) # datum = caffe.io.array_to_datum(img, label) # key_str = '{:08}'.format(key) # # txn.put(key_str.encode('ascii'), datum.SerializeToString()) # txn.put(key_str, datum.SerializeToString()) # key += 1 # for idx in xrange(numSample): # info = data[idx].split(" ") # OriImg = cv2.imread(datadir + info[0]) # img = cv2.resize(OriImg,(IMAGE_WIDTH,IMAGE_HEIGHT)) # label = int(info[1]) # img = cv2.flip(img,1) # img = np.transpose(img, (2, 0, 1)) # datum = caffe.io.array_to_datum(img, label) # key_str = '{:08}'.format(key) # # txn.put(key_str.encode('ascii'), datum.SerializeToString()) # txn.put(key_str, datum.SerializeToString()) # key += 1 # print key
def main(args): datum = Datum() data = [] env = lmdb.open(args.input_lmdb) with env.begin() as txn: cursor = txn.cursor() for i, (key, value) in enumerate(cursor): if i >= args.truncate: break datum.ParseFromString(value) data.append(datum.float_data) data = np.squeeze(np.asarray(data)) np.save(args.output_npy, data)
def make_datum(img, label): #image is numpy.ndarray format. BGR instead of RGB return caffe_pb2.Datum( channels=1, # images are in black and white width=IMAGE_WIDTH, height=IMAGE_HEIGHT, label=label, data=img.tostring())
