论文的caffemodel转化为tensorflow模型过程中越坑无数,最后索性直接用caffe提特征。
caffe提取倒数第二层,pool5的输出,fc1000层的输入,2048维的特征
1 #coding=utf-8 2 3 import caffe 4 import os 5 import numpy as np 6 import scipy.io as sio 7 8 #路径设置 9 OUTPUT='E:/caffemodel/'#输出txt文件夹 10 root='E:/caffemodel/' #根目录 11 deploy=root + 'ResNet-101-deploy.prototxt' #deploy文件 12 caffe_model=root + 'ResNet-101-model.caffemodel' #训练好的 caffemodel 13 imgroot = 'E:/bjfu-cv-project/img_35/' #随机找的一张待测图片 14 #labels_filename = 'E:/bjfu-cv-project/CUB_200_2011/CUB_200_2011/classes.txt' #类别名称文件,将数字标签转换回类别名称 15 net = caffe.Net(deploy,caffe_model,caffe.TEST) #加载model和network 16 mean_file='mean.npy' 17 18 #容器初始化 19 dict = {} 20 21 fea = [] 22 out_array = np.zeros(shape=(2048,)) 23 24 #文件读取 25 26 count = 0 27 for root, dirs, files in os.walk(imgroot): 28 for dir in dirs: 29 print(dir) 30 for root, dirs, files in os.walk(imgroot+dir): 31 i = 0 32 for img in files: 33 img = imgroot+dir + '/' + img 34 #图片预处理设置 35 transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) #设定图片的shape格式(1,3,224,224) 36 transformer.set_transpose('data', (2,0,1)) #改变维度的顺序,由原始图片(224,224,3)变为(3,224,224) 37 transformer.set_mean('data', np.load(mean_file).mean(1).mean(1)) #减去均值,前面训练模型时没有减均值,这儿就不用 38 transformer.set_raw_scale('data', 255) # 缩放到【0,255】之间 39 transformer.set_channel_swap('data', (2,1,0)) #交换通道,将图片由RGB变为BGR 40 try: 41 im=caffe.io.load_image(img) #加载图片 42 except: 43 continue 44 net.blobs['data'].data[...] = transformer.preprocess('data',im) #执行上面设置的图片预处理操作,并将图片载入到blob中 45 46 #执行测试 47 out = net.forward() 48 fea.append(net.blobs['pool5'].data) # 提取某层数据(特征) 49 print(dir, i, img) 50 out_array = np.column_stack((fea[i][0,:,0,0], out_array)) 51 i = i + 1 52 #结果输出 53 dict['array'] = out_array 54 save_matFile = 'fearture_of_35.mat' 55 sio.savemat(save_matFile, dict)
均值文件ResNet_mean.binaryproto转化mean.npy
1 #coding=utf-8 2 import caffe 3 import numpy as np 4 5 MEAN_PROTO_PATH = 'ResNet_mean.binaryproto' # 待转换的pb格式图像均值文件路径 6 7 MEAN_NPY_PATH = 'mean.npy' # 转换后的numpy格式图像均值文件路径 8 9 blob = caffe.proto.caffe_pb2.BlobProto() # 创建protobuf blob 10 data = open(MEAN_PROTO_PATH, 'rb' ).read() # 读入mean.binaryproto文件内容 11 blob.ParseFromString(data) # 解析文件内容到blob 12 13 array = np.array(caffe.io.blobproto_to_array(blob))# 将blob中的均值转换成numpy格式,array的shape (mean_number,channel, hight, width) 14 mean_npy = array[0] # 一个array中可以有多组均值存在,故需要通过下标选择其中一组均值 15 np.save(MEAN_NPY_PATH ,mean_npy)
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