caffe提取每一层中的特征，在matlab或python查看

参考博客：

http://blog.csdn.net/abc8730866/article/details/52522843

http://blog.csdn.net/lijiancheng0614/article/details/48180331

 

编译出extract_features.exe模块

在×64、Release模式下编译生成extract_features.exe

 

将某一层的特征向量生成lmdb文件

在caffe工程的examples下新建一个文件夹，命名为_temp

 

 将examples\images下的图片写成一个文本文档，命名为file_list.txt，放在_temp文件夹下

将examples/eature_extraction/imagenet_val.prototxt复制到之前新建的_temp文件夹。

 打开imagenet_val.prototxt，修改以下file_list.txt的路径，对应准确即可：

 

在models\bvlc_reference_caffenet目录中，下载bvlc_reference_caffenet.caffemodel文件

 

在caffe根目录下，新建bat脚本，

Build\x64\Release\extract_features.exe models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb
pause

可以在examples/_temp/features中生成提取的lmdb文件

 

将lmdb文件转化为mat文件

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)

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