void CreateTestSet(int numTestFile, string categoryName = "")
{
string pathRaw = "Data/Raws/" + categoryName + "/";
vector<string> _listRawName = ReadFileList(pathRaw.c_str());
string pathTest = "Data/Test_images/" + categoryName + "/";
if (CreateDirectoryA(pathTest.c_str() , NULL) || ERROR_ALREADY_EXISTS == GetLastError())
{
int numFilePerVideo = numTestFile / _listRawName.size();
for (int i = 0; i < _listRawName.size(); i++)
{
string rawVideoPath = pathRaw + _listRawName[i];
string videoName = _listRawName[i].substr(0, _listRawName[i].size() - 4);
string testVideoPath = pathTest + videoName;
cout << "Extract test frame from video " << videoName << endl;
if (CreateDirectoryA(testVideoPath.c_str(), NULL) || ERROR_ALREADY_EXISTS == GetLastError())
{
RandomizeFrameTest(rawVideoPath, categoryName, videoName, numFilePerVideo);
}
else
{
cout << "Cannot find and create " << testVideoPath << " directory" << endl;
}
}
}
else
{
cout << "Something is wrong..." << endl;
}
}
// create data package --------------------------------------------------------
//
PRIVATE
int CreatePackage( const char* packname, const char *listname )
{
ASSERT( packname != NULL );
ASSERT( listname != NULL );
#define MAX_DATA_FILES 4096
list_item_s *listitems = (list_item_s *) ALLOCMEM( MAX_DATA_FILES * sizeof( list_item_s ) );
if ( listitems == NULL ) {
return FALSE;
}
int numitems = ReadFileList( listname, listitems, MAX_DATA_FILES );
if ( numitems == -1 ) {
FREEMEM( listitems );
MakepackError();
}
if ( !WritePackageFile( packname, listitems, numitems ) ) {
FREEMEM( listitems );
MakepackError();
}
FREEMEM( listitems );
return TRUE;
}
vector<string> SetupModel(string categoryName, vector<Mat> &trainData, vector<Mat> &trainLabels,
vector<float> &videoFPS, int database_type)
{
cout << "Loading training data..." << endl << endl;
//Get data stored in database
string path;
if (database_type == 1)
path = "Data/Training_Data/" + categoryName + "/BOW/";
else
path = "Data/Training_Data/" + categoryName + "/MPEG7/";
vector<string> _listTrainingData = ReadFileList(path);
for (int i = 0; i < (int)_listTrainingData.size(); i++)
{
string pathData = path + _listTrainingData[i];
Mat trainingData, trainingLabel;
float fps;
LoadDataFromFile(pathData, trainingData, trainingLabel, fps);
trainData.push_back(trainingData);
trainLabels.push_back(trainingLabel);
videoFPS.push_back(fps);
}
return _listTrainingData;
}
Interface::Interface(int argc, const char* argv[]){
if (argc>1){
const char* modestr = argv[1];
if (strcmp(modestr,"Eval")){
mode = 1;
} else {
mode = 0;
}
filelist = argv[2];
varlist = argv[3];
setlist = argv[4];
} else {
mode=0;
std::string pPath = getenv ("HOME");
std::string basename = pPath+"/Dropbox/CubeClassifier";
filelist=basename+"/DefaultFileList.txt";
varlist=basename+"/DefaultVarList.txt";
setlist=basename+"/DefaultSettings.txt";
}
ReadFileList();
ReadSettingsList();
ReadVariableList();
}
void ExtractAndSaveKeyFrame(string videoName)
{
//Extract key frames from a list of shots
string filepath = "Data/Video shots/" + videoName + "/";
string destPath = "Data/Key frames/" + videoName + "/";
vector<string> listFileName = ReadFileList(filepath);
int j = 0;
int i = 0;
int n = (int)listFileName.size();
int numFrame = 0;
int count = 0;
// #pragma omp parallel shared(n) private(i,j,numFrame,count)
{
// #pragma omp for
for (j = 0; j < n; j++)
{
string filename = filepath + listFileName[j];
VideoCapture cap(filename);
//vector<int> keyFrames = KeyframeMomentExtractor(cap);
vector<int> keyFrames = KeyframeCurvatureExtractor(cap);
//RemoveRedundantKeyframes(cap, keyFrames);
count = 0;
numFrame = (int)keyFrames.size();
for (i = 0; i < numFrame; i++)
{
if (keyFrames[i] != -1)
{
string resultPath = destPath;
string name = listFileName[j].substr(0, listFileName[j].size() - 4);
resultPath.append(name);
resultPath.append("_");
char *buffer = new char[255];
resultPath.append(_itoa(keyFrames[i], buffer, 10));
resultPath.append(".jpg");
delete buffer;
Mat frame = ExtractFrameFromVideo(cap, keyFrames[i]);
if (!frame.empty())
imwrite(resultPath, frame);
else
cout << "Cannot extract keyframe " << i << endl;
count++;
}
}
cout << "There're " << count << " key frames in " << listFileName[j] << endl;
cout << endl;
}
}
}
void DoFileList(const char *filelist, const char *environment)
{
// read config files
if (!ReadFileList(filelist)) {
printf("No Files to test!\r\n");
return;
}
ReadGlobalEnvironment(environment);
DoThreads();
}
int APIENTRY WinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPSTR lpCmdLine, int nCmdShow)
{
char szPath[MAX_PATH];
CmyStringArray astrFileList;
GetModuleFilePath (szPath, sizeof (szPath));
QuitModule ("スクラップブックオンライン ランチャー");
ReadFileList (astrFileList);
CopyFileList (astrFileList);
ExecuteLauncher ();
return 0;
}
InstData *ReadOldInstData(HANDLE fin, char *product, char *installdir)
{
InstData *idata;
idata = bmalloc(sizeof(InstData));
idata->product = product;
idata->installdir = installdir;
idata->startmenudir = read_line0(fin);
idata->instfiles = ReadFileList(fin);
idata->extrafiles = ReadFileList(fin);
idata->keepfiles = NULL;
idata->startmenu = ReadLinkList(fin);
idata->desktop = ReadLinkList(fin);
idata->service = ReadServiceDetails(fin);
idata->flags = ReadInstFlags(fin);
return idata;
}
/**************************************************************************************************
函数用途：main函数，程序入口；读取txt列表，并分割选中的文件
输入参数：无
返回参数：无
*/
void main(void)
{
FILE *fpSrc,*fpDec;
unsigned char* buff;
int i,j,select,FileNum = 0,singleFileSize;
long size;
char fileName_Suffix[100],fileName[100],fileNameTmp[100];
Show();//显示一些无关紧要的信息
FileNum = ReadFileList();//读取txt文件列表
PrintFileList(FileNum);//打印txt文件列表
printf("\n如果列表显示无误，请输入需要分割的文本文档（txt文件）前的序号: "); //菜单显示
select = ReScanf(1,FileNum);//输入序号
/* 下面五行内容，用于分别去除字符串中最后一个回车和后缀名 */
strcpy(fileName_Suffix,List[select].Name);
j = strlen(fileName_Suffix)-1;
fileName_Suffix[j] = '\0';//字符用单引号，去掉最后一个回车
strcpy(fileName,fileName_Suffix);
fileName[j-4] = '\0';//将文件名去除后缀
printf("\n你选择的是:“%s”，",fileName_Suffix);
if( ( fpSrc = fopen(fileName_Suffix,"r" ) ) == 0 )//判断是否有文件
{
printf("\n\n对不起，文件木有找到或打不开啊！程序退出！\n");
system("pause");//任意键继续
exit(1);
}
fseek(fpSrc, 0L,SEEK_END);//将指针移动到末位
size = ftell(fpSrc);//计算文件长度，目前指针的位置既是文件的长度
if( size < 10000)
{
printf("这个文件长度为：“%ld”字节。\n连10000字节都不到，分个毛线啊，自己手动分割去。程序退出！\n",size);
system("pause");//任意键继续
exit(0);
}
printf("这个文件长度为：%ld字节。\n期望平均分割成几个文件？请输入小于99的数值：",size);
select = ReScanf(2,99);//输入分割成几段的数值
singleFileSize = size/select;//计算每个小文件大小
printf("\n下面将会把“%s”分割成%d个大小为：%d字节（%dKB）的文件。\n\n",fileName_Suffix,select,singleFileSize,singleFileSize/1024);
system("pause");//任意键继续
fseek(fpSrc, 0L,SEEK_SET);//将指针移动到首位
for(i = 1 ; i<=select ; i++ )
{
buff = (unsigned char*)malloc(singleFileSize);//动态申请单个小文件大小所需的内存
sprintf(fileNameTmp,"%s - 第%02d段.txt",fileName,i);//文件名拼接
fread(buff,singleFileSize,1,fpSrc);//从fpSrc中读取数据到buff
fpDec = fopen(fileNameTmp,"w");//新建小文件
fwrite(buff,singleFileSize,1,fpDec);//将buff数据写入到fpDec
free(buff);//释放内存
fclose(fpDec);//关闭目标文件
printf("\n“%s”已成功生成！",fileNameTmp);
}
fclose(fpSrc);//关闭源文件
printf("\n分割完毕！程序退出！");
system("pause");//任意键继续
exit(0);
}
int main(int argc, char **argv) {
char *list_in;
char *file_out;
double ratio;
if (argc != 3 && argc != 4) {
printf("Usage: %s <list.txt> <outfile> [window_radius]\n", argv[0]);
return EXIT_FAILURE;
}
list_in = argv[1];
ratio = 0.6;
file_out = argv[2];
int window_radius = -1;
if (argc == 4) {
window_radius = atoi(argv[3]);
}
clock_t start = clock();
/* Read the list of files */
std::vector<std::string> key_files;
if (ReadFileList(list_in, key_files) != 0) return EXIT_FAILURE;
FILE *f;
if ((f = fopen(file_out, "w")) == NULL) {
printf("Could not open %s for writing.\n", file_out);
return EXIT_FAILURE;
}
int num_images = (int) key_files.size();
std::vector<unsigned char*> keys(num_images);
std::vector<int> num_keys(num_images);
/* Read all keys */
for (int i = 0; i < num_images; i++) {
keys[i] = NULL;
num_keys[i] = ReadKeyFile(key_files[i].c_str(), &keys[i]);
}
clock_t end = clock();
printf("[KeyMatchFull] Reading keys took %0.3fs\n",
(end - start) / ((double) CLOCKS_PER_SEC));
for (int i = 0; i < num_images; i++) {
if (num_keys[i] == 0)
continue;
printf("[KeyMatchFull] Matching to image %d\n", i);
start = clock();
/* Create a tree from the keys */
ANNkd_tree *tree = CreateSearchTree(num_keys[i], keys[i]);
/* Compute the start index */
int start_idx = 0;
if (window_radius > 0)
start_idx = std::max(i - window_radius, 0);
for (int j = start_idx; j < i; j++) {
if (num_keys[j] == 0)
continue;
/* Compute likely matches between two sets of keypoints */
std::vector<KeypointMatch> matches =
MatchKeys(num_keys[j], keys[j], tree, ratio);
int num_matches = (int) matches.size();
if (num_matches >= 16) {
/* Write the pair */
fprintf(f, "%d %d\n", j, i);
/* Write the number of matches */
fprintf(f, "%d\n", (int) matches.size());
for (int i = 0; i < num_matches; i++) {
fprintf(f, "%d %d\n",
matches[i].m_idx1, matches[i].m_idx2);
}
}
}
end = clock();
printf("[KeyMatchFull] Matching took %0.3fs\n",
(end - start) / ((double) CLOCKS_PER_SEC));
fflush(stdout);
delete tree;
}
/* Free keypoints */
for (int i = 0; i < num_images; i++) {
if (keys[i] != NULL)
delete [] keys[i];
}
fclose(f);
return EXIT_SUCCESS;
}
void TestShotRetrieval(string categoryName, int database_type, int num_retrieve)
{
string resultFile = "Data/Result_" + categoryName + "_ShotRetrieval.txt";
ofstream out(resultFile);
//Load database
vector<Mat> trainData, trainLabels;
vector<float> videoFPS;
vector<string> _listDataName = SetupModel(categoryName, trainData, trainLabels, videoFPS, database_type);
//Load dictionary for BOW model
if (database_type == 1)
bowDE.setVocabulary(LoadBOWDictionaryFromFile("Data/Training_Data/" + categoryName + "_BOW_dictionary.xml"));
//Get list of test frames
string pathTest = "Data/Test_images/" + categoryName + "/";
vector<string> _listTestClass = ReadFileList(pathTest);
vector<vector<float>> _listWeightScore;
for (int index = 0; index < _listTestClass.size(); index++)
{
vector<float> avgWeigthScore;
avgWeigthScore.resize(num_retrieve);
string pathClass = pathTest + _listTestClass[index] + "/";
vector<string> _listTestImage = ReadFileList(pathClass);
for (int i = 0; i < _listTestImage.size(); i++)
{
string testpath = pathClass + _listTestImage[i];
Mat testImage = imread(testpath);
//---Extract feature based on database_type
Mat queryFeature;
if (database_type == 1)
queryFeature = ExtractBOWFeature(bowDE, detector, testImage);
else
queryFeature = ExtractMPEGFeature(testImage);
//For each test image, retrieve list of shot and check if there's any correct shot in candidate list
int trueShotID = IdentifyShotFromKeyFrame(_listTestImage[i]);
cout << "Retrieve shot in video " << GetName(_listDataName[index]) << " using " << _listTestImage[i] << endl;
vector<int> _listShotID = RetrieveShot(videoFPS[index], trainData[index], trainLabels[index], queryFeature);
//Calculate precision and recall
for (int numRetrieve = 0; numRetrieve < num_retrieve; numRetrieve++)
{
float weightScore = ShotRetrievalPerformance(_listShotID, trueShotID, numRetrieve+1);
avgWeigthScore[numRetrieve] += weightScore;
}
cout << endl;
}
for (int i = 0; i < num_retrieve; i++)
{
avgWeigthScore[i] /= _listTestImage.size();
}
_listWeightScore.push_back(avgWeigthScore);
}
vector<float> avgWeightScorePerNumRetrieval;
avgWeightScorePerNumRetrieval.resize(num_retrieve);
for (int i = 0; i < _listWeightScore.size(); i++)
{
float avgPrecision = 0.0f;
out << _listTestClass[i].c_str() << endl;
for (int j = 0; j < num_retrieve; j++)
{
out << j+1 << ". " << _listWeightScore[i][j] << endl;
avgWeightScorePerNumRetrieval[j] += _listWeightScore[i][j];
}
out << endl;
}
out << endl << "Average weight score: " << endl;
for (int i = 0; i < num_retrieve; i++)
{
avgWeightScorePerNumRetrieval[i] /= (float)_listWeightScore.size();
out << i + 1 << ". " << avgWeightScorePerNumRetrieval[i] << endl;
}
out.close();
}
void TestVideoRetrieval(string categoryName, int database_type)
{
//Load database
vector<Mat> trainData, trainLabels;
vector<float> videoFPS;
vector<string> _listDataName = SetupModel(categoryName, trainData, trainLabels, videoFPS, database_type);
//Load dictionary for BOW model
if (database_type == 1)
bowDE.setVocabulary(LoadBOWDictionaryFromFile("Data/Training_Data/" + categoryName + "_BOW_dictionary.xml"));
float avgReciprocalRank = 0.0f;
float avgPrecision = 0.0f;
float avgRecall = 0.0f;
string pathTest = "Data/Test_images/" + categoryName + "/";
vector<string> _listTestClass = ReadFileList(pathTest);
int countTotal = 0;
for (int index = 0; index < _listTestClass.size(); index++)
{
float avgClassRR = 0.0f;
string pathClass = pathTest + _listTestClass[index] + "/";
vector<string> _listTestImage = ReadFileList(pathClass);
for (int i = 0; i < _listTestImage.size(); i++)
{
string testpath = pathClass + _listTestImage[i];
Mat testImage = imread(testpath);
//---Extract feature based on database_type
Mat queryFeature;
if (database_type == 1)
queryFeature = ExtractBOWFeature(bowDE, detector, testImage);
else
queryFeature = ExtractMPEGFeature(testImage);
//For each test image, retrieve list of video and check if there's any correct video in candidate list
int countMatch = 0;
int countMatchRank = 0;
int countRetrievedMatch = 0;
int numMatchRetrieve = 3;
vector<int> predictLabels = RetrieveVideo(_listTestClass.size(), queryFeature, trainData);
//Calculate precision and recall from retrieved list
if (predictLabels.size() > 0)
{
cout << _listTestImage[i] << " in video " << _listTestClass[index] << " can belong to: " << endl;
for (int j = 0; j < predictLabels.size(); j++)
{
int predictLabel = predictLabels[j];
cout << _listTestClass[predictLabel] << endl;
countMatchRank++;
if (predictLabel == index)
{
if (j < numMatchRetrieve)
{
countRetrievedMatch++;
}
countMatch++;
break;
}
}
cout << endl;
}
else
{
cout << "No video match the query image" << endl << endl;
}
float reciprocalrank = 0.0f;
float precision = 0.0f;
float recall = 0.0f;
if (predictLabels.size() > 0)
{
reciprocalrank = (float)countMatch / (float)countMatchRank;
precision = (float)countRetrievedMatch / (float)numMatchRetrieve;
recall = countRetrievedMatch;
}
avgClassRR += reciprocalrank;
avgPrecision += precision;
avgRecall += recall;
}
countTotal += _listTestImage.size();
avgReciprocalRank += avgClassRR;
}
avgReciprocalRank = avgReciprocalRank / countTotal * 100.0f;
avgPrecision = avgPrecision / countTotal * 100.0f;
avgRecall = avgRecall / countTotal * 100.0f;
cout << "MAP = " << avgReciprocalRank << endl;
cout << "Average Precision = " << avgPrecision << endl;
cout << "Average Recall = " << avgRecall << endl;
}
