選擇需要提取特徵的影象，並將其路徑匯入txt

./example/_temp

# 建立臨時目錄

mkdir examples/_temp

# 生成影象路徑列表檔案

find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt

# 每個影象路徑最後都有一個分類標籤，因此在每條路徑最後加上0代表結束

sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt

下載bvlc_reference_caffenet.caffemodel，並製作網路結構檔案

./models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
./examples/_temp/imagenet_val.prototxt

./data/ilsvrc12/get_ilsvrc_aux.sh

# 匯入網路結構檔案

cp examples/feature_extraction/imagenet_val.prototxt examples/_temp

使用extract_features.bin提取特徵，並以lmdb格式儲存。執行引數為extract_features.bin $MODEL $PROTOTXT $LAYER $LMDB_OUTPUT_PATH $BATCHSIZE

./models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
./examples/_temp/imagenet_val.prototxt
./examples/_temp/features

./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb GPU

將特徵轉化為.mat檔案
安裝CAFFE的python依賴庫，並使用以下兩個輔助檔案把lmdb轉換為mat。

1. ./feat_helper_pb2.py

   
   # Generated by the protocol buffer compiler.  DO NOT EDIT!
   
   
   from google.protobuf import descriptor
   from google.protobuf import message
   from google.protobuf import reflection
   from google.protobuf import descriptor_pb2
   
   
   # @@protoc_insertion_point(imports)
   
   
   DESCRIPTOR = descriptor.FileDescriptor(
     name='datum.proto',
     package='feat_extract',
     serialized_pb='\n\x0b\x64\x61tum.proto\x12\x0c\x66\x65\x61t_extract\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02')
   
   
   _DATUM = descriptor.Descriptor(
     name='Datum',
     full_name='feat_extract.Datum',
     filename=None,
     file=DESCRIPTOR,
     containing_type=None,
     fields=[
       descriptor.FieldDescriptor(
         name='channels', full_name='feat_extract.Datum.channels', index=0,
         number=1, type=5, cpp_type=1, label=1,
         has_default_value=False, default_value=0,
         message_type=None, enum_type=None, containing_type=None,
         is_extension=False, extension_scope=None,
         options=None),
       descriptor.FieldDescriptor(
         name='height', full_name='feat_extract.Datum.height', index=1,
         number=2, type=5, cpp_type=1, label=1,
         has_default_value=False, default_value=0,
         message_type=None, enum_type=None, containing_type=None,
         is_extension=False, extension_scope=None,
         options=None),
       descriptor.FieldDescriptor(
         name='width', full_name='feat_extract.Datum.width', index=2,
         number=3, type=5, cpp_type=1, label=1,
         has_default_value=False, default_value=0,
         message_type=None, enum_type=None, containing_type=None,
         is_extension=False, extension_scope=None,
         options=None),
       descriptor.FieldDescriptor(
         name='data', full_name='feat_extract.Datum.data', index=3,
         number=4, type=12, cpp_type=9, label=1,
         has_default_value=False, default_value="",
         message_type=None, enum_type=None, containing_type=None,
         is_extension=False, extension_scope=None,
         options=None),
       descriptor.FieldDescriptor(
         name='label', full_name='feat_extract.Datum.label', index=4,
         number=5, type=5, cpp_type=1, label=1,
         has_default_value=False, default_value=0,
         message_type=None, enum_type=None, containing_type=None,
         is_extension=False, extension_scope=None,
         options=None),
       descriptor.FieldDescriptor(
         name='float_data', full_name='feat_extract.Datum.float_data', index=5,
         number=6, type=2, cpp_type=6, label=3,
         has_default_value=False, default_value=[],
         message_type=None, enum_type=None, containing_type=None,
         is_extension=False, extension_scope=None,
         options=None),
     ],
     extensions=[
     ],
     nested_types=[],
     enum_types=[
     ],
     options=None,
     is_extendable=False,
     extension_ranges=[],
     serialized_start=29,
     serialized_end=134,
   )
   
   DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
   
   class Datum(message.Message):
     __metaclass__ = reflection.GeneratedProtocolMessageType
     DESCRIPTOR = _DATUM
   
     # @@protoc_insertion_point(class_scope:feat_extract.Datum)
   
   # @@protoc_insertion_point(module_scope)
   

2. ./lmdb2mat.py

   import lmdb
   import feat_helper_pb2
   import numpy as np
   import scipy.io as sio
   import time
   
   def main(argv):
       lmdb_name = sys.argv[1]
       print "%s" % sys.argv[1]
       batch_num = int(sys.argv[2]);
       batch_size = int(sys.argv[3]);
       window_num = batch_num*batch_size;
   
       start = time.time()
       if 'db' not in locals().keys():
           db = lmdb.open(lmdb_name)
           txn= db.begin()
           cursor = txn.cursor()
           cursor.iternext()
           datum = feat_helper_pb2.Datum()
   
           keys = []
           values = []
           for key, value in enumerate( cursor.iternext_nodup()):
               keys.append(key)
               values.append(cursor.value())
   
       ft = np.zeros((window_num, int(sys.argv[4])))
       for im_idx in range(window_num):
           datum.ParseFromString(values[im_idx])
           ft[im_idx, :] = datum.float_data
   
       print 'time 1: %f' %(time.time() - start)
       sio.savemat(sys.argv[5], {'feats':ft})
       print 'time 2: %f' %(time.time() - start)
       print 'done!'
   
   if __name__ == '__main__':
       import sys
       main(sys.argv)

3. 執行bash輸出.mat檔案

   
   #!/usr/bin/env sh
   
   LMDB=./examples/_temp/features_fc7 # lmdb檔案路徑
   BATCHNUM=1
   BATCHSIZE=10
   
   # DIM=290400 # feature長度，conv1
   
   
   # DIM=43264 # conv5
   
   
   DIM=4096
   OUT=./examples/_temp/features_fc7.mat #mat檔案儲存路徑
   python ./lmdb2mat.py $LMDB $BATCHNUM $BATCHSIZE $DIM $OUT