// 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);
}
// 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);
}