caffe特征提取/C++数据格式转换

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Caffe生成的数据分为2种格式：Lmdb 和 Leveldb

它们都是键/值对（Key/Value Pair）嵌入式数据库管理系统编程库。
虽然lmdb的内存消耗是leveldb的1.1倍，但是lmdb的速度比leveldb快10%至15%，更重要的是lmdb允许多种训练模型同时读取同一组数据集。
因此lmdb取代了leveldb成为Caffe默认的数据集生成格式。

create_babyface.sh调用的convertData的源代码如下：

[cpp] view plain copy

#include<sys/types.h>
#include<sys/stat.h>
#include<dirent.h>
#include <stdio.h>
#include<string.h>
#include <fstream> // NOLINT(readability/streams)
#include <string>
#include <vector>
#include "boost/scoped_ptr.hpp"
#include "glog/logging.h"
#include "google/protobuf/text_format.h"
#include "stdint.h"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
#include <opencv/cv.h>
#include <opencv/highgui.h>
using caffe::Datum;
using boost::scoped_ptr;
using std::string;
namespace db = caffe::db;
using namespace std;
const int kCIFARSize = 32;
const int kCIFARChannelBytes = 1024;
const int kCIFARImageNBytes = 3072;
const int kCIFARBatchSize = 1000;//1000 for a batch!
const int kCIFARTrainBatches = 5;
void read_image(std::ifstream* file, int* label, char* buffer) {
char label_char;
file->read(&label_char, 1);
*label = label_char;
file->read(buffer, kCIFARImageNBytes);
return;
}
//Read IPLimage to the buffer
void read_image(
IplImage* out, char* buffer,
char* RC, char* GC, char* BC)
{
int x,y;
int idx =0;
for(y = 0; y<out->height; y++){
char *ptr= out->imageData + y * out->widthStep;
for( x = 0;x< out->width;x++){
idx =y*out->height + x;
BC[idx]= ptr[3*x];
GC[idx]= ptr[3*x+1];
RC[idx]= ptr[3*x+2]; //这样就可以添加自己的操作，这里我使三通道颜色一样，就彩色图转黑白图了
}
}
memcpy( buffer ,RC, kCIFARChannelBytes*sizeof(char) );
memcpy( buffer+ kCIFARChannelBytes*sizeof(char) , GC,kCIFARChannelBytes*sizeof(char) );
memcpy( buffer+ kCIFARChannelBytes*sizeof(char) *2, BC,kCIFARChannelBytes*sizeof(char) );
return;
}
//Travel the folder and load the filelist!
//使用linux dirent遍历目录
int traveldir(char* path ,int depth, vector<string > &FileList)
{
DIR* d;// a
struct dirent *file; struct stat sb;
if( !(d=opendir(path ) ) ){
printf("Read path %s error,wishchin! ", path);
return -1;
}
while( (file= readdir(d ) ) != NULL ) {
if(0== strncmp(file->d_name, ".", 1 ) ) continue;
char filename[256];
strcpy( filename , file->d_name );
string Sfilename(filename);string Spath(path);
Spath.append(Sfilename);
FileList.push_back(Spath);
}
if( stat(file->d_name, &sb)>=0 && S_ISDIR(sb.st_mode) && depth <=4 )
traveldir(file->d_name,depth+1,FileList);
closedir(d);
return 1;
}
// convert the data to the lmdb format !
void convert_dataset(
const string& input_folder,
const string& output_folder,
const string& db_type) {
scoped_ptr<db::DB> train_db(db::GetDB(db_type));
train_db->Open(output_folder + "/babyface_train_" + db_type, db::NEW);
scoped_ptr<db::Transaction> txn(train_db->NewTransaction());
char* path=new char[256];
int depth=2;
vector<string > FileList(0);
// Data buffer
int label;
IplImage* ImageS;
char str_buffer[kCIFARImageNBytes];
char* RC=new char[kCIFARChannelBytes];
char* GC=new char[kCIFARChannelBytes];
char* BC=new char[kCIFARChannelBytes];
Datum datum;
datum.set_channels(3);
datum.set_height(kCIFARSize);
datum.set_width(kCIFARSize);
//"Writing Training data"//载入训练数据
LOG(INFO) << "Writing Training data";
strcpy(path,( input_folder+(string)("train1") ).c_str() );
traveldir( path , depth, FileList);
for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
// Open files
LOG(INFO) << "Training Batch " << fileid + 1;
snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
//CHECK(data_file) << "Unable to open train file #" << fileid + 1;
label=1;//The Batch has 10000 pics!
for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
ImageS =cvLoadImage( (FileList[ fileid*kCIFARTrainBatches + itemid] ).c_str() );
read_image( ImageS, str_buffer, RC, GC, BC);
datum.set_label(label);//datum.set_label(label);
datum.set_data(str_buffer, kCIFARImageNBytes);
int length = snprintf(str_buffer, kCIFARImageNBytes,
"%05d", fileid * kCIFARBatchSize + itemid);
string out;
CHECK(datum.SerializeToString( &out) ) ;
txn->Put(string(str_buffer, length), out);//The main sentence ,put data to the txn!
}
}
strcpy(path,( input_folder+(string)("train0") ).c_str() );
traveldir( path , depth, FileList);
for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
LOG(INFO) << "Training Batch " << fileid + 1;
snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
//CHECK(data_file) << "Unable to open train file #" << fileid + 1;
label=0;//The Batch has 10000 pics!
for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
ImageS =cvLoadImage( (FileList[ fileid*kCIFARTrainBatches + itemid] ).c_str() );
read_image( ImageS, str_buffer, RC, GC, BC);
datum.set_label(label);//datum.set_label(label);
datum.set_data(str_buffer, kCIFARImageNBytes);
int length = snprintf(str_buffer, kCIFARImageNBytes,
"%05d", fileid * kCIFARBatchSize + itemid);
string out;
CHECK(datum.SerializeToString( &out) ) ;
txn->Put(string(str_buffer, length), out);//The main sentence ,put data to the txn!
}
}
txn->Commit();
train_db->Close();
//写入测试数据！
LOG(INFO) << "Writing Testing data";
scoped_ptr<db::DB> test_db(db::GetDB(db_type));
test_db->Open(output_folder + "/babyface_test_" + db_type, db::NEW);
txn.reset(test_db->NewTransaction());
strcpy(path,( input_folder+(string)("test1") ).c_str() );
traveldir( path , depth, FileList);
for (int fileid = 0; fileid < 2; ++fileid) {
LOG(INFO) << "Training Batch " << fileid + 1;
snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
label=1;//The Batch has 10000 pics!
for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
ImageS =cvLoadImage( (FileList[ fileid*2 + itemid] ).c_str() );
read_image( ImageS, str_buffer, RC, GC, BC);
datum.set_label(label);//datum.set_label(label);
datum.set_data(str_buffer, kCIFARImageNBytes);
int length = snprintf(str_buffer, kCIFARImageNBytes,
"%05d", fileid * kCIFARBatchSize + itemid);
string out;
CHECK(datum.SerializeToString( &out) ) ;
txn->Put(string(str_buffer, length), out);//The main sentence ,put data to the txn!
}
}
strcpy(path,( input_folder+(string)("test0") ).c_str() );
traveldir( path , depth, FileList);
for (int fileid = 0; fileid < 2; ++fileid) {
LOG(INFO) << "Training Batch " << fileid + 1;
snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
label=0;//The Batch has 10000 pics!
for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
ImageS =cvLoadImage( (FileList[ fileid*2 + itemid] ).c_str() );
read_image( ImageS, str_buffer, RC, GC, BC);
datum.set_label(label);//datum.set_label(label);
datum.set_data(str_buffer, kCIFARImageNBytes);
int length = snprintf(str_buffer, kCIFARImageNBytes,
"%05d", fileid * kCIFARBatchSize + itemid);
string out;
CHECK(datum.SerializeToString( &out) ) ;
txn->Put(string(str_buffer, length), out);//The main sentence ,put data to the txn!
}
}
txn->Commit();
test_db->Close();
cvReleaseImage(&ImageS);
delete [] RC;delete [] GC;delete [] BC;
}
int main(int argc, char** argv) {
if (argc != 4) {
printf("This script converts the CIFAR dataset to the leveldb format used\n"
"by caffe to perform classification.\n"
"Usage:\n"
" convert_cifar_data input_folder output_folder db_type\n"
"Where the input folder should contain the binary batch files.\n"
"The CIFAR dataset could be downloaded at\n"
" http://www.cs.toronto.edu/~kriz/cifar.html\n"
"You should gunzip them after downloading.\n");
} else {
google::InitGoogleLogging(argv[0]);
convert_dataset(string(argv[1]), string(argv[2]), string(argv[3]));
}
return 0;
}

后记：目的是载入32×32的三通道图像，直接输入3072维的char向量进行训练，至于怎样训练网络，还得仔细查看一下。

后记：代码出现 coredump 问题，利用 gcc path/...bin -o coredemo -g ，出现caffe.pb.h 包含丢失现象，why？？？

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