caffe下用AlexNet模型提取图像特征并从指定层输出特征向量

caffe自带的工具extract_features.bin可用于提取输入图像的特征并选择fc层进行输出

1. 选择需要提取特征的图像，并将其路径导入txt

   ./example/_temp

   
   # 建立临时目录
   
   mkdir examples/_temp
   
   # 生成图像路径列表文件
   
   find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt
   
   # 每个图像路径最后都有一个分类标签，因此在每条路径最后加上0代表结束
   
   sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt

2. 下载bvlc_reference_caffenet.caffemodel，并制作网络结构文件

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

   ./data/ilsvrc12/get_ilsvrc_aux.sh
   
   # 导入网络结构文件
   
   cp examples/feature_extraction/imagenet_val.prototxt examples/_temp

3. 使用extract_features.bin提取特征，并以lmdb格式保存。运行参数为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

4. 将特征转化为.mat文件
   安装CAFFE的python依赖库，并使用以下两个辅助文件把lmdb转换为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. 运行bash输出.mat文件

      
      #!/usr/bin/env sh
      
      LMDB=./examples/_temp/features_fc7 # lmdb文件路径
      BATCHNUM=1
      BATCHSIZE=10
      
      # DIM=290400 # feature长度，conv1
      
      
      # DIM=43264 # conv5
      
      
      DIM=4096
      OUT=./examples/_temp/features_fc7.mat #mat文件保存路径
      python ./lmdb2mat.py $LMDB $BATCHNUM $BATCHSIZE $DIM $OUT

参考：
https://blog.csdn.net/lijiancheng0614/article/details/48180331