拷贝convert_imageset，生成新工程convert_imageset_multi_label
修改源码
std::ifstream infile(argv[2]); std::vector<std::pair<std::string, std::vector<float>> > lines; std::string filename; std::string label_count_string = argv[5]; int label_count = std::atoi(label_count_string.c_str()); std::vector<float> label(label_count); while (infile >> filename) { for (int i = 0; i < label_count; i++) infile >> label[i]; lines.push_back(std::make_pair(filename, label)); } if (FLAGS_shuffle) { // randomly shuffle data LOG(INFO) << “Shuffling data”; shuffle(lines.begin(), lines.end()); } LOG(INFO) << “A total of ” << lines.size() << ” images.”; if (encode_type.size() && !encoded) LOG(INFO) << “encode_type specified, assuming encoded=true.”; int resize_height = std::max<int>(0, FLAGS_resize_height); int resize_width = std::max<int>(0, FLAGS_resize_width); // Create new DB scoped_ptr<db::DB> db_image(db::GetDB(FLAGS_backend)); scoped_ptr<db::DB> db_label(db::GetDB(FLAGS_backend)); db_image->Open(argv[3], db::NEW); db_label->Open(argv[4], db::NEW); scoped_ptr<db::Transaction> txn_image(db_image->NewTransaction()); scoped_ptr<db::Transaction> txn_label(db_label->NewTransaction());// // Create new DB// scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));// db->Open(argv[3], db::NEW);// scoped_ptr<db::Transaction> txn(db->NewTransaction()); // Storing to db std::string root_folder(argv[1]); Datum datum_image; Datum datum_label; int count = 0; int data_size = 0; bool data_size_initialized = false; for (int line_id = 0; line_id < lines.size(); ++line_id) { bool status; std::string enc = encode_type; if (encoded && !enc.size()) { // Guess the encoding type from the file name string fn = lines[line_id].first; size_t p = fn.rfind(‘.’); if ( p == fn.npos ) LOG(WARNING) << “Failed to guess the encoding of ‘” << fn << “‘”; enc = fn.substr(p); std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower); } status = ReadImageToDatum(root_folder + lines[line_id].first, lines[line_id].second[0], resize_height, resize_width, is_color, enc, &datum_image); if (status == false) continue; if (check_size) { if (!data_size_initialized) { data_size = datum_image.channels() * datum_image.height() * datum_image.width(); data_size_initialized = true; } else { const std::string& data = datum_image.data(); CHECK_EQ(data.size(), data_size) << “Incorrect data field size ” << data.size(); } } // sequential string key_str = caffe::format_int(line_id, 8) + “_” + lines[line_id].first; // Put in db string out; CHECK(datum_image.SerializeToString(&out)); txn_image->Put(key_str, out); ////////////////////////////////////////////////////////////////////////// datum_label.set_channels(label_count); datum_label.set_height(1); datum_label.set_width(1); datum_label.clear_data(); datum_label.clear_float_data(); datum_label.set_encoded(false); std::vector<float> label_vec = lines[line_id].second; for (int i = 0; i < label_vec.size();i++) { datum_label.add_float_data(label_vec[i]); } string out_label; CHECK(datum_label.SerializeToString(&out_label)); txn_label->Put(key_str, out_label); ////////////////////////////////////////////////////////////////////////// if (++count % 1000 == 0) { // Commit db txn_image->Commit(); txn_image.reset(db_image->NewTransaction()); txn_label->Commit(); txn_label.reset(db_label->NewTransaction()); LOG(INFO) << “Processed ” << count << ” files.”; } } // write the last batch if (count % 1000 != 0) { txn_image->Commit(); txn_label->Commit(); LOG(INFO) << “Processed ” << count << ” files.”; }上述方式使用了二个data层，编译之后，使用如下方式生成：
Build\x64\Release>convert_imageset_multi_label.exe ./ train.txt data/train_image_lmdb data/train_label_lmdb 4
train.txt文件格式如下：
data/00A03AF5-41C7-4966-8EF3-8B2C90DCF75C_cgfn.jpg 1 2 3 6data/00A15FBD-9637-44C5-B2E7-81611263C88C_tmph.jpg 2 5 6 4
网络配置文件需要加入slice层将标签分割开来
layer { name: “slice” type: “Slice” bottom: “label” top: “label_1” top: “label_2” top: “label_3” top: “label_4” slice_param { axis: 1 slice_point: 1 slice_point: 2 slice_point: 3 }}
也可以通过python直接生成lmdb格式，其方式如下：
# -*- coding: utf-8 -*-“””Created on Sat Dec 24 20:57:28 2016 @author: zhouly”””import lmdbfrom skimage import ioimport numpy as np import syscaffe_root = ‘../../’sys.path.insert(0, caffe_root + ‘/python’)import caffeimport cv2root = ‘../../’file_input=open(root+’data/train.txt’,’r’) in_image_db=lmdb.open(root+’examples/99/train_image_lmdb’, map_size=int(1e12)) in_label_db=lmdb.open(root+’examples/99/train_label_lmdb’, map_size=int(1e12)) in_image_txn = in_image_db.begin(write=True) in_label_txn = in_label_db.begin(write=True)for in_idx, in_ in enumerate(file_input): content = in_.strip() content = content.split(‘ ‘) im_file = root + ‘data/verification/’ + content[0] try: im = io.imread(im_file) except: print ‘————————-‘, im_file continue im = im[:,:, 3] im = cv2.resize(im, (224, 224), interpolation=cv2.INTER_LINEAR) data = np.zeros((3, 224, 224), np.uint8) data[0, :, :] = im[:, :] data[1, :, :] = im[:, :] data[2, :, :] = im[:, :] im_dat = caffe.io.array_to_datum(data) in_image_txn.put(‘{:0>10d}’.format(in_idx), im_dat.SerializeToString()) print ‘data train: {} [{}]’.format(content[0], in_idx + 1) del im_file, im, im_dat, data target_label = np.zeros((4, 1, 1)) target_label[0, 0, 0] = int(content[1]) target_label[1, 0, 0] = int(content[2]) target_label[2, 0, 0] = int(content[3]) target_label[3, 0, 0] = int(content[4]) label_data = caffe.io.array_to_datum(target_label) in_label_txn.put(‘{:0>10d}’.format(in_idx), label_data.SerializeToString()) del target_label, label_data in_image_txn.commit() in_label_txn.commit() in_image_db.close() in_label_db.close() file_input.close()js代码 css样式，css header 布局，js和css动画，css 发光宝袋 特效，css下拉选择框大小，css两张图片背景，css被访问过才链接的状态