Caffe of the Neural network framework (convert picture to LEVELDB format)

Caffe is responsible for the entire network input datalayer is read from the LEVELDB data, is a Google implementation of a very efficient KV database.

Therefore, we must first turn the data into LEVELDB format for training the network.

What I do here is to turn all the pictures of a directory into LEVELDB format.

Tool using the command grid format: Convert_imagedata src_dir dst_dir attach_dir channel width height

Example:./convert_imagedata.bin/home/linger/imdata/collar_train//home/linger/linger/testfile/crop_train_db//home/ LINGER/LINGER/TESTFILE/CROP_TRAIN_ATTACHMENT/3 50 50

Source:

#include <google/protobuf/text_format.h> #include <glog/logging.h> #include <leveldb/db.h># Include <stdint.h> #include <fstream>//Nolint (readability/streams) #include <string> #include < set> #include <stdio.h> #include <string.h> #include <stdlib.h> #include <dirent.h> #include <sys/stat.h> #include <unistd.h> #include <sys/types.h> #include "caffe/proto/caffe.pb.h" #include <opencv2/highgui/highgui.hpp> #include <opencv2/highgui/highgui_c.h> #include <opencv2/imgproc/ Imgproc.hpp>using std::string;using namespace Std;set<string> all_class_name;map<string,int> class2id;/** * Path: folder * files: vector for saving file name * R: Whether you need to traverse subfolders * Return: file name, not including path */void list_dir (const char *path,vector <string>& Files,bool r = False) {DIR *pdir;struct dirent *ent;char childpath[512];pD ir = opendir (path); Memset ( Childpath, 0, sizeof (Childpath)), while (ent = Readdir (pdir)) = NULL) {if (Ent->d_type &Amp Dt_dir) {if (strcmp (Ent->d_name, ".") = = 0 | | strcmp (Ent->d_name, "..") = = 0) {continue;} if (r)//assume the need to traverse subfolders {sprintf (Childpath, "%s/%s", Path, ent->d_name); List_dir (childpath,files);}} Else{files.push_back (Ent->d_name);}} Sort (Files.begin (), Files.end ());//Sort}string get_classname (string path) {int index = path.find_last_of ('_'); return PATH.SUBSTR (0, index);} int Get_labelid (string fileName) {string class_name_tmp = Get_classname (fileName); All_class_name.insert (class_name_ TMP); Map<string,int>::iterator name_iter_tmp = Class2id.find (class_name_tmp); if (name_iter_tmp = = Class2id.end ()) {int id = class2id.size (); Class2id.insert (Name_iter_tmp, Std::make_pair (class_name_tmp, id)); return ID;} Else{return Name_iter_tmp->second;}} void loadimg (string path,char* buffer) {cv::mat img = cv::imread (path, cv_load_image_color); string Val;int rows = img.rows ; int cols = Img.cols;int pos=0;for (int c = 0; c < 3; C + +) {for (int row = 0; row < rows; row++) {for (int col = 0; co L < cols; col++) {buffer[pos++]=img.at<cv::vec3b> (Row,col) [C];}}} void Convert (String imgdir,string outputdb,string attachdir,int channel,int width,int height) {leveldb::D b* db;leveldb: : Options options;options.create_if_missing = true;options.error_if_exists = True;caffe::D atum datum;datum.set_ Channels (channel);d atum.set_height (height);d atum.set_width (width); int image_size = Channel*width*height;char Buffer[image_size];string value; CHECK (leveldb::D b::open (Options, Outputdb, &db). OK ());vector<string> Filenames;list_dir (Imgdir.c_str (), filenames); string img_log = attachdir+ "Image_filename"; Ofstream WriteFile (Img_log.c_str ()); for (int i=0;i< Filenames.size (); i++) {string path= imgdir;path.append (Filenames[i]);//Calculate absolute path loadimg (path,buffer); int labelid = get_ Labelid (Filenames[i]);d Atum.add_label (labelid);d atum.set_data (buffer,image_size);d atum. Serializetostring (&value); snprintf (buffer, image_size, "%05d", i);p rintf ("\nclassid:%d classname:%s abspath:%s" , Labelid,get_classname (filenameS[i]). C_STR (), Path.c_str ());d B->put (Leveldb::writeoptions (), string (buffer), value),//printf ("%d%s\n", I, Filenames[i].c_str ()); Assert (Writefile.is_open ());writefile<<i<< "" <<filenames[i]<< "\ n" ;} Delete Db;writefile.close (); img_log = attachdir+ "Image_classname"; Writefile.open (Img_log.c_str ()); set<string >::iterator iter = All_class_name.begin (); while (iter! = All_class_name.end ()) {assert (Writefile.is_open ()); writefile<< (*iter) << "\ n";//printf ("%s\n", (*iter). C_STR ()); iter++;} Writefile.close ();} int main (int argc, char** argv) {if (ARGC < 6) {LOG (ERROR) << "Convert_imagedata src_dir dst_dir attach_dir Cha    Nnel width height "; return 0;} ./convert_imagedata.bin/home/linger/imdata/collartest//home/linger/linger/testfile/dbtest//home/linger/ LINGER/TESTFILE/TEST_ATTACHMENT/3 250//./convert_imagedata.bin/home/linger/imdata/collar_train//home/linger/ linger/testfile/crop_train_db//home/linger/linger/testfile/crop_train_attachment/3 50google::initgooglelogging (Argv[0]); string src_dir = argv[1];string Src_dst = argv[2];string Attach_dir = argv[3]; int channel = Atoi (argv[4]), int width = atoi (argv[5]), int height = atoi (argv[6]);//for test/*src_dir = "/home/linger/imdat a/collartest/"; src_dst ="/home/linger/linger/testfile/dbtest/"; attach_dir ="/home/linger/linger/testfile/"; Channel = 3;width = 250;height = 250; */convert (src_dir,src_dst,attach_dir,channel,width,height);}

Caffe of the Neural network framework (convert picture to LEVELDB format)