本文參考如下：

關於如何批量提取特徵，本文的框架如下：
１. 準備資料及相應準備工作
２. 初始化網路
３.讀取影象列表
４.提取影象特徵，並儲存為特定格式

Python方法一
主要有三個函式：
initialize () 初始化網路的相關
readlist() 讀取抽取影象列表
extractFeatre() 抽取影象的特徵，儲存為指定的格式

其中在transformer那裡需要根據自己的需求設定

#encoding:utf-8
#詳情請檢視http://www.cnblogs.com/louyihang-loves-baiyan/p/5078746.html
 

import numpy as np
import matplotlib.pyplot as plt
import os
import caffe
import sys
import pickle
import struct
import sys,cv2
caffe_root = '../'  
# 執行模型的prototxt
deployPrototxt =  '/home/bids/caffe/caffe-master/changmiao/model/deploy.prototxt'
# 相應載入的modelfile
modelFile = '/home/bids/caffe/caffe-master/changmiao/model/bvlc_reference_caffenet.caffemodel'
 

# meanfile 也可以用自己生成的
meanFile = 'python/caffe/imagenet/ilsvrc_2012_mean.npy'
# 需要提取的影象列表
imageListFile = '/home/bids/caffe/caffe-master/changmiao/data/temp.txt'
imageBasePath = '/home/bids/caffe/caffe-master/changmiao/data/cat'
#gpuID = 4　＃根據你自己電腦的ＧＰＵ情況而定
postfix = '.classify_allCar1716_fc6'

# 初始化函式的相關操作
def initilize
 
():
    print 'initilize ... '

    sys.path.insert(0, caffe_root + 'python')
    caffe.set_mode_gpu()
#    caffe.set_device(gpuID)
    net = caffe.Net(deployPrototxt, modelFile,caffe.TEST)
    return net  
# 提取特徵並儲存為相應地檔案
def extractFeature(imageList, net):
    # 對輸入資料做相應地調整如通道、尺寸等等
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
    transformer.set_transpose('data', (2,0,1))
    transformer.set_mean('data', np.load(caffe_root + meanFile).mean(1).mean(1)) # mean pixel
    transformer.set_raw_scale('data', 255)  
    transformer.set_channel_swap('data', (2,1,0))  
    # set net to batch size of 1 如果圖片較多就設定合適的batchsize 
    net.blobs['data'].reshape(1,3,227,227)      #這裡根據需要設定，如果網路中不一致，需要調整
    num=0

#imageList = os.listdir(imageBasePath)



    for imagefile in imageList:
        imagefile_abs = os.path.join(imageBasePath, imagefile)
        print imagefile_abs
        net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(imagefile_abs))
        out = net.forward()
        fea_file = imagefile_abs.replace('.jpg',postfix)
        num +=1
        print 'Num ',num,' extract feature ',fea_file
        with  open(fea_file,'wb') as f:
            for x in xrange(0, net.blobs['fc6'].data.shape[0]):
                for y in xrange(0, net.blobs['fc6'].data.shape[1]):
                    f.write(struct.pack('f', net.blobs['fc6'].data[x,y]))

# 讀取檔案列表
def readImageList(imageListFile):
    imageList = []
    with open(imageListFile,'r') as fi:
        while(True):
            line = fi.readline().strip().split()# every line is a image file name
            if not line:
                break
            imageList.append(line[0]) 
    print 'read imageList done image num ', len(imageList)
    return imageList

if __name__ == "__main__":
    net = initilize()
    imageList = readImageList(imageListFile) 
    extractFeature(imageList, net)