// Get potential bounding boxes for all test images
void Objectness::getObjBndBoxesForTests(vector<vector<Vec4i>> &_boxesTests, int numDetPerSize)
{
const int TestNum = _voc.testSet.size();
vecM imgs3u(TestNum);
vector<ValStructVec<float, Vec4i>> boxesTests;
boxesTests.resize(TestNum);
#pragma omp parallel for
for (int i = 0; i < TestNum; i++){
//imgs3u[i] = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i])));
imgs3u[i] = imread("C:/Users/TerryChen/Desktop/M.jpg");
boxesTests[i].reserve(10000);
}
int scales[3] = {1, 3, 5};
for (int clr = MAXBGR; clr <= G; clr++){
setColorSpace(clr);
trainObjectness(numDetPerSize);
loadTrainedModel();
CmTimer tm("Predict");
tm.Start();
#pragma omp parallel for
for (int i = 0; i < TestNum; i++){
ValStructVec<float, Vec4i> boxes;
getObjBndBoxes(imgs3u[i], boxes, numDetPerSize);
boxesTests[i].append(boxes, scales[clr]);
}
tm.Stop();
printf("Average time for predicting an image (%s) is %gs\n", _clrName[_Clr], tm.TimeInSeconds()/TestNum);
}
_boxesTests.resize(TestNum);
CmFile::MkDir(_bbResDir);
#pragma omp parallel for
for (int i = 0; i < TestNum; i++){
CStr fName = _bbResDir + _voc.testSet[i];
ValStructVec<float, Vec4i> &boxes = boxesTests[i];
FILE *f = fopen(_S(fName + ".txt"), "w");
fprintf(f, "%d\n", boxes.size());
for (size_t k = 0; k < boxes.size(); k++)
fprintf(f, "%g, %s\n", boxes(k), _S(strVec4i(boxes[k])));
fclose(f);
_boxesTests[i].resize(boxesTests[i].size());
for (int j = 0; j < boxesTests[i].size(); j++)
_boxesTests[i][j] = boxesTests[i][j];
}
evaluatePerImgRecall(_boxesTests, "PerImgAllNS.m", 5000);
#pragma omp parallel for
for (int i = 0; i < TestNum; i++){
boxesTests[i].sort(false);
for (int j = 0; j < boxesTests[i].size(); j++)
_boxesTests[i][j] = boxesTests[i][j];
}
evaluatePerImgRecall(_boxesTests, "PerImgAllS.m", 5000);
}
void Objectness::getRandomBoxes(vector<vector<Vec4i> > &boxesTests, int num)
{
const int TestNum = _voc.testSet.size();
boxesTests.resize(TestNum);
#pragma omp parallel for
for (int i = 0; i < TestNum; i++) {
Mat imgs3u = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i])));
int H = imgs3u.cols, W = imgs3u.rows;
boxesTests[i].reserve(num);
for (int k = 0; k < num; k++) {
int x1 = rand()%W + 1, x2 = rand()%W + 1;
int y1 = rand()%H + 1, y2 = rand()%H + 1;
boxesTests[i].push_back(Vec4i(min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)));
}
}
evaluatePerImgRecall(boxesTests, "PerImgAll.m", num);
}
void Objectness::evaluatePerClassRecall(vector<vector<Vec4i> > &boxesTests, CStr &saveName, const int WIN_NUM)
{
const int TEST_NUM = _voc.testSet.size(), CLS_NUM = _voc.classNames.size();
if (boxesTests.size() != TEST_NUM) {
boxesTests.resize(TEST_NUM);
for (int i = 0; i < TEST_NUM; i++) {
Mat boxes;
matRead(_voc.localDir + _voc.testSet[i] + ".dat", boxes);
Vec4i* d = (Vec4i*)boxes.data;
boxesTests[i].resize(boxes.rows, WIN_NUM);
memcpy(&boxesTests[i][0], boxes.data, sizeof(Vec4i)*boxes.rows);
}
}
for (int i = 0; i < TEST_NUM; i++)
if ((int)boxesTests[i].size() < WIN_NUM) {
printf("%s.dat: %d, %d\n", _S(_voc.testSet[i]), boxesTests[i].size(), WIN_NUM);
boxesTests[i].resize(WIN_NUM);
}
// #class by #win matrix for saving correct detection number and gt number
Mat crNum1i = Mat::zeros(CLS_NUM, WIN_NUM, CV_32S);
vecD gtNums(CLS_NUM);
{
for (int i = 0; i < TEST_NUM; i++) {
const vector<Vec4i> &boxes = boxesTests[i];
const vector<Vec4i> &boxesGT = _voc.gtTestBoxes[i];
const vecI &clsGT = _voc.gtTestClsIdx[i];
CV_Assert((int)boxes.size() >= WIN_NUM);
const int gtNumCrnt = boxesGT.size();
for (int j = 0; j < gtNumCrnt; j++) {
gtNums[clsGT[j]]++;
double maxIntUni = 0;
int* crNum = crNum1i.ptr<int>(clsGT[j]);
for (int k = 0; k < WIN_NUM; k++) {
double val = DataSetVOC::interUnio(boxes[k], boxesGT[j]);
maxIntUni = max(maxIntUni, val);
crNum[k] += maxIntUni >= 0.5 ? 1 : 0;
}
}
}
}
FILE* f = fopen(_S(_voc.resDir + saveName), "w");
{
CV_Assert(f != NULL);
fprintf(f, "figure(1);\nhold on;\n\n\n");
vecD val(WIN_NUM), recallObjs(WIN_NUM), recallClss(WIN_NUM);
for (int i = 0; i < WIN_NUM; i++)
val[i] = i;
PrintVector(f, gtNums, "GtNum");
PrintVector(f, val, "WinNum");
fprintf(f, "\n");
string leglendStr("legend(");
double sumObjs = 0;
for (int c = 0; c < CLS_NUM; c++) {
sumObjs += gtNums[c];
memset(&val[0], 0, sizeof(double)*WIN_NUM);
int* crNum = crNum1i.ptr<int>(c);
for (int i = 0; i < WIN_NUM; i++) {
val[i] = crNum[i]/(gtNums[c] + 1e-200);
recallClss[i] += val[i];
recallObjs[i] += crNum[i];
}
CStr className = _voc.classNames[c];
PrintVector(f, val, className);
fprintf(f, "plot(WinNum, %s, %s, 'linewidth', 2);\n", _S(className), COLORs[c % CN]);
leglendStr += format("'%s', ", _S(className));
}
for (int i = 0; i < WIN_NUM; i++) {
recallClss[i] /= CLS_NUM;
recallObjs[i] /= sumObjs;
}
PrintVector(f, recallClss, "class");
fprintf(f, "plot(WinNum, %s, %s, 'linewidth', 2);\n", "class", COLORs[CLS_NUM % CN]);
leglendStr += format("'%s', ", "class");
PrintVector(f, recallObjs, "objects");
fprintf(f, "plot(WinNum, %s, %s, 'linewidth', 2);\n", "objects", COLORs[(CLS_NUM+1) % CN]);
leglendStr += format("'%s', ", "objects");
leglendStr.resize(leglendStr.size() - 2);
leglendStr += ");";
fprintf(f, "%s\nhold off;\nxlabel('#WIN');\nylabel('Recall');\ngrid on;\naxis([0 %d 0 1]);\n", _S(leglendStr), WIN_NUM);
fprintf(f, "[class([1,10,100,1000]);objects([1,10,100,1000])]\ntitle('%s')\n", _S(saveName));
fclose(f);
printf("%-70s\r", "");
}
evaluatePerImgRecall(boxesTests, CmFile::GetNameNE(saveName) + "_PerI.m", WIN_NUM);
}
// Get potential bounding boxes for all test images
void Objectness::getObjBndBoxesForTestsFast(vector<vector<Vec4i> > &_boxesTests, int numDetPerSize, bool preloadModel, bool preloadImages)
{
//setColorSpace(HSV);
if (!preloadModel)
trainObjectness(numDetPerSize);
loadTrainedModel();
illustrate();
const int TestNum = _voc.testSet.size();
vecM imgs3u;
if (preloadImages)
imgs3u.resize(TestNum);
vector<ValStructVec<float, Vec4i> > boxesTests;
boxesTests.resize(TestNum);
//Reading all images beforehand needs a lot of memory.
//The timing results are affected based on when the image is loaded.
if (preloadImages) {
#pragma omp parallel for
for (int i = 0; i < TestNum; i++) {
imgs3u[i] = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i])));
boxesTests[i].reserve(10000);
}
}
printf("Start predicting\n");
CmTimer tm("Predict");
tm.Start();
#pragma omp parallel for
for (int i = 0; i < TestNum; i++) {
if (!preloadImages) {
Mat img = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i])));
getObjBndBoxes(img, boxesTests[i], numDetPerSize);
} else {
getObjBndBoxes(imgs3u[i], boxesTests[i], numDetPerSize);
}
}
tm.Stop();
if (!preloadImages) {
printf("Average time for loading and predicting an image (%s) is %gs\n", _clrName[_Clr], tm.TimeInSeconds()/TestNum);
} else {
printf("Average time for predicting an image (%s) is %gs\n", _clrName[_Clr], tm.TimeInSeconds()/TestNum);
}
_boxesTests.resize(TestNum);
CmFile::MkDir(_bbResDir);
#pragma omp parallel for
for (int i = 0; i < TestNum; i++) {
CStr fName = _bbResDir + _voc.testSet[i];
ValStructVec<float, Vec4i> &boxes = boxesTests[i];
FILE *f = fopen(_S(fName + ".txt"), "w");
fprintf(f, "%d\n", boxes.size());
for (size_t k = 0; k < boxes.size(); k++)
fprintf(f, "%g, %s\n", boxes(k), _S(strVec4i(boxes[k])));
fclose(f);
_boxesTests[i].resize(boxesTests[i].size());
for (int j = 0; j < boxesTests[i].size(); j++)
_boxesTests[i][j] = boxesTests[i][j];
}
evaluatePerImgRecall(_boxesTests, "PerImgAll.m", 5000);
}
