以下代码来自于caffe的examples/mnist目录，去掉一些google flags的内容，并且把命令行参数去掉了，适合入门阅读
改代码能将mnist数据的images和label转换成lmdb数据，目前只在ubuntu linux 下测试。
#include <gflags/gflags.h> #include <glog/logging.h> #include <google/protobuf/text_format.h> //#if defined(USE_LEVELDB) && defined(USE_LMDB) #include <leveldb/db.h> #include <leveldb/write_batch.h> #include <lmdb.h> //#endif #include <stdint.h> #include <sys/stat.h> #include <fstream> // NOLINT(readability/streams) #include <string> #include "boost/scoped_ptr.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/db.hpp" #include "caffe/util/format.hpp" using namespace caffe; // NOLINT(build/namespaces) using boost::scoped_ptr; using std::string; //DEFINE_string(backend, "lmdb", "The backend for storing the result"); uint32_t swap_endian(uint32_t val) { val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF); return (val << 16) | (val >> 16); } void convert_dataset(const char* image_filename, const char* label_filename, const char* db_path, const string& db_backend) { // Open files std::ifstream image_file(image_filename, std::ios::in | std::ios::binary); std::ifstream label_file(label_filename, std::ios::in | std::ios::binary); uint32_t magic; uint32_t num_items; uint32_t num_labels; uint32_t rows; uint32_t cols; image_file.read(reinterpret_cast<char*>(&magic), 4); magic = swap_endian(magic); label_file.read(reinterpret_cast<char*>(&magic), 4); magic = swap_endian(magic); image_file.read(reinterpret_cast<char*>(&num_items), 4); num_items = swap_endian(num_items); label_file.read(reinterpret_cast<char*>(&num_labels), 4); num_labels = swap_endian(num_labels); image_file.read(reinterpret_cast<char*>(&rows), 4); rows = swap_endian(rows); image_file.read(reinterpret_cast<char*>(&cols), 4); cols = swap_endian(cols); scoped_ptr<db::DB> db(db::GetDB(db_backend)); db->Open(db_path, db::NEW); scoped_ptr<db::Transaction> txn(db->NewTransaction()); // Storing to db char label; char* pixels = new char[rows * cols]; int count = 0; string value; Datum datum; datum.set_channels(1); datum.set_height(rows); datum.set_width(cols); for (int item_id = 0; item_id < num_items; ++item_id) { image_file.read(pixels, rows * cols); label_file.read(&label, 1); datum.set_data(pixels, rows*cols); datum.set_label(label); string key_str = caffe::format_int(item_id, 8); datum.SerializeToString(&value); txn->Put(key_str, value); if (++count % 1000 == 0) { txn->Commit(); } } // write the last batch if (count % 1000 != 0) { txn->Commit(); } //LOG(INFO) << "Processed " << count << " files."; delete[] pixels; db->Close(); } int main() { const string& db_backend = "lmdb"; const char* my_image_filename = "/mnt/e/ccc/ubuntu/lib/caffe_mnist/train-images.idx3-ubyte"; const char* my_label_filename = "/mnt/e/ccc/ubuntu/lib/caffe_mnist/train-labels.idx1-ubyte"; const char* my_db_path = "/mnt/e/ccc/ubuntu/lib/caffe_mnist/005"; //这个是创建lmdb数据库时候要保存的目录， //不能够提前在目录下创建，因为caffe会调用mkdir创建，不然就重名了 convert_dataset(my_image_filename, my_label_filename, my_db_path, db_backend); return 0; }