void RunObjectness(CStr &resName, double base, int W, int NSS, int numPerSz)
{
srand((unsigned int)time(NULL));
DataSetVOC voc("../VOC2007/");
voc.loadAnnotations();
//voc2007.loadDataGenericOverCls();
printf("Dataset:`%s' with %d training and %d testing\n", _S(voc.wkDir), voc.trainNum, voc.testNum);
printf("%s Base = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, W, NSS, numPerSz);
Objectness objNess(voc, base, W, NSS);
vector<vector<Vec4i> > boxesTests;
//objNess.getObjBndBoxesForTests(boxesTests, 250);
//If running this for the first time, set preloadModel to false.
//Although it is not tested, ensure that the model is generated by
//the getObjBndBoxesForTestsFast function and not
//the getObjBndBoxesForTests function which uses colour information too.
//To avoid running out of memory, you can load images only during
//prediction by setting preloadImages to false.
bool preloadModel = true, preloadImages = false;
objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz, preloadModel, preloadImages);
objNess.getRandomBoxes(boxesTests);
objNess.evaluatePerClassRecall(boxesTests, resName, numPerSz);
objNess.illuTestReults(boxesTests);
}
void RunObjectness(CStr &resName, double base, int W, int NSS, int numPerSz)
{
srand((unsigned int) time(NULL));
// DataSetVOC voc2007("/Datasets/VOC2007/");
// voc2007.loadAnnotations();
//voc2007.loadDataGenericOverCls();
// printf("Dataset:`%s' with %d training and %d testing\n", _S(voc2007.wkDir), voc2007.trainNum, voc2007.testNum);
// printf("%s Base = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, W, NSS, numPerSz);
Objectness objNess(base, W, NSS);
objNess.loadTrainedModel("/Datasets/VOC2007/model");
// printf("Model\n");
for(int i = 1; i < 10; i++)
{
string filename = format("/Datasets/VOC2007/JPEGImages/00001%d.jpg", i);
printf("%s\n", filename.c_str());
Mat img3u = imread(filename);
ValStructVec<float, Vec4i> boxesTests;
boxesTests.reserve(10000);
objNess.getObjBndBoxes(img3u, boxesTests, numPerSz);
int xmin, ymin, xmax, ymax;
int num = boxesTests.size();
printf(" %d\n", num);
for (int j = 0; j < num; j++){
Vec4i bb = boxesTests[j];
xmin = bb[0];
ymin = bb[1];
xmax = bb[2];
ymax = bb[3];
// printf(" (%d, %d) -> (%d, %d)\n", xmin, ymin, xmax, ymax);
rectangle(img3u, cvPoint(xmin, ymin), cvPoint(xmax, ymax), cvScalar(0, 0, 255), 3);
}
imshow(filename, img3u);
waitKey(0);
}
// Objectness objNess(voc2007, base, W, NSS);
// vector<vector<Vec4i> > boxesTests;
// objNess.getObjBndBoxesForTests(boxesTests, 250);
// objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz);
//objNess.getRandomBoxes(boxesTests);
//objNess.evaluatePerClassRecall(boxesTests, resName, 1000);
//objNess.illuTestReults(boxesTests);
//objNess.evaluatePAMI12();
//objNess.evaluateIJCV13();
}
int main(int argc, char* argv[])
{
int W = 2, NSS = 8;
double base = 2.0;
if (argc < 2)
{
fprintf(stderr, "usage: work/directory/\n");
return -1;
}
srand((unsigned int)time(NULL));
DataSetVOC voc2007(argv[1]);
voc2007.loadAnnotations();
Objectness objNess(voc2007, base, W, NSS);
objNess.evaluateIJCV13();
}
void RunObjectness(CStr &resName, double base, int W, int NSS, int numPerSz)
{
srand((unsigned int)time(NULL));
DataSetVOC voc2007("C:/WkDir/DetectionProposals/VOC2007/");
voc2007.loadAnnotations();
//voc2007.loadDataGenericOverCls();
cout << "Dataset:'" << _S(voc2007.wkDir) << "' with " << voc2007.trainNum << " training and " << voc2007.testNum << " testing" << endl;
cout << _S(resName) << " Base = " << base << ", W = " << W << ", NSS = " << NSS << ", perSz = " << numPerSz << endl;
Objectness objNess(voc2007, base, W, NSS);
vector<vector<Vec4i>> boxes;
//objNess.getObjBndBoxesForTests(boxes, 250);
objNess.getObjBndBoxesForTestFast(boxes, numPerSz);
//objNess.getRandomBoxes(boxes);
//objNess.evaluatePerClassRecall(boxes, resName, 2000);
//objNess.illuTestReults(boxes);
}
void RunObjectness(CStr &resName, double base, double intUionThr, int W, int NSS, int numPerSz)
{
srand((unsigned int)time(NULL));
//DataSetVOC voc2007("D:/WkDir/DetectionProposals/VOC2007/");
DataSetVOC voc2007("./VOC2007/");
voc2007.loadAnnotations();
//voc2007.loadDataGenericOverCls();
printf("Dataset:`%s' with %d training and %d testing\n", _S(voc2007.wkDir), voc2007.trainNum, voc2007.testNum);
printf("%s Base = %g, intUionThr = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, intUionThr, W, NSS, numPerSz);
Objectness objNess(voc2007, base, intUionThr, W, NSS);
vector<vector<Vec4i>> boxesTests;
//objNess.getObjBndBoxesForTests(boxesTests, 250);
objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz);
//objNess.getRandomBoxes(boxesTests);
//objNess.evaluatePerClassRecall(boxesTests, resName, 1000);
//objNess.illuTestReults(boxesTests);
}
void RunObjectness(CStr &resName, double base, double intUionThr, int W, int NSS, int numPerSz)
{
srand((unsigned int)time(NULL));
//DataSetVOC voc("../../DataSet/VOC2007/"); // BING Training
DataSetVOC voc("../../DataSet/VOC2007/", true, true); // Personness Training
//DataSetVOC voc("../../DataSet/VOC2012/", true, true); // Personness Test
voc.loadAnnotations();
//voc.loadDataGenericOverCls();
printf("Dataset:`%s' with %d training and %d testing\n", _S(voc.wkDir), voc.trainNum, voc.testNum);
printf("%s Base = %g, intUionThr = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, intUionThr, W, NSS, numPerSz);
Objectness objNess(voc, base, intUionThr, W, NSS);
vector<vector<Vec4i>> boxesTests;
//objNess.getObjBndBoxesForTests(boxesTests, 250);
objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz);
//objNess.getRandomBoxes(boxesTests);
//objNess.evaluatePerClassRecall(boxesTests, resName, 1000);
objNess.illuTestReults(boxesTests);
//objNess.chammoru_test();
}
void bingQdpmRocTest(vector<string> &dirs,
int windowLimit = -1, double timeLimitMs = -1, float ratioThreshold = -1)
{
size_t imageCount = 0;
size_t personCount = 0;
size_t matchCount = 0;
vector<ScoreTp> pScores;
TickMeter tm;
vector<std::string>::const_iterator it = dirs.begin();
char buf[512];
for (; it != dirs.end(); it++) {
string dir = *it;
DataSetVOC voc(dir, true, true);
voc.loadAnnotations();
const size_t testNum = voc.testSet.size();
const char *imgPath =_S(voc.imgPathW);
// Objectness
double base = 2;
double intUionThr = 0.5;
int W = 8;
int NSS = 2;
#ifdef WINDOW_GUESS
Objectness objNess(voc, base, intUionThr, W, NSS);
objNess.loadTrainedModel(TRAIN_MODEL);
#endif
// LSVM DPM
string dpmPersonModel = "../ExtraData/latentsvmXml/person.xml";
vector<string> models;
models.push_back(dpmPersonModel);
QUniLsvmDetector detector(models);
float overlapThreshold = 0.2f;
if (ratioThreshold > 0)
detector.setRatioThreshold(ratioThreshold);
printf("%d: \n", testNum);
for (int i = 0; i < testNum; i++) {
const vector<Vec4i> &boxesGT = voc.gtTestBoxes[i];
const size_t gtNumCrnt = boxesGT.size();
if (gtNumCrnt <= 0)
continue;
imageCount++;
personCount += gtNumCrnt;
Mat image = imread(format(imgPath, _S(voc.testSet[i])));
if (image.ptr() == NULL) {
fprintf(stderr, "No JPG Image !\n");
exit(1);
}
int numPerSz = 130;
ValStructVec<float, Vec4i> boxes;
double preObj = tm.getTimeMilli();
double objTime = 0.;
#ifdef WINDOW_GUESS // window guess
tm.start();
objNess.getObjBndBoxes(image, boxes, numPerSz);
tm.stop();
objTime = tm.getTimeMilli() - preObj;
#endif
double localTimeLimitMs = timeLimitMs;
if (timeLimitMs > 0) {
localTimeLimitMs -= objTime;
if (localTimeLimitMs < 0.)
localTimeLimitMs = 0.;
}
vector<QRect> searchBoxes;
if (windowLimit > 0) {
for (int j = 0; j < (int)boxes.size() && j < windowLimit; j++) {
const Vec4i &bb = boxes[j];
QRect rt(bb[0], bb[1], bb[2], bb[3]);
searchBoxes.push_back(rt);
}
} else {
for (int j = 0; j < (int)boxes.size(); j++) {
const Vec4i &bb = boxes[j];
QRect rt(bb[0], bb[1], bb[2], bb[3]);
searchBoxes.push_back(rt);
}
}
tm.start();
detector.setup(image, overlapThreshold, localTimeLimitMs);
tm.stop();
vector<FeatureMapCoord> ftrMapCoords;
#ifdef WINDOW_GUESS
detector.cvtBox2FtrMapCoord(&searchBoxes, &ftrMapCoords);
#else
detector.genFullFtrMapCoord(&ftrMapCoords);
preObj = tm.getTimeMilli();
tm.start();
#ifdef SHUFFLE_WINDOW
random_shuffle(ftrMapCoords.begin(), ftrMapCoords.end());
#endif
tm.stop();
double randGenTime = tm.getTimeMilli() - preObj;
if (localTimeLimitMs > 0 && localTimeLimitMs - preObj >= 0.)
detector.setTimeLimit(localTimeLimitMs - preObj);
#endif
vector<QUniLsvmDetector::ObjectDetection> detections;
vector<vector<FeatureMapCoord> *> fmcss;
fmcss.push_back(&ftrMapCoords);
tm.start();
detector.detect(detections, fmcss);
tm.stop();
vector<DetectedInfo> di(detections.size());
vector<int> gtIdx(gtNumCrnt, -1);
int detectCount = 0;
for (size_t j = 0; j < detections.size(); j++) {
const QUniLsvmDetector::ObjectDetection& od = detections[j];
if (od.score < RECOMMENDABLE_THRESHOLD)
continue;
detectCount++;
Vec4i bb(od.rect.x + 1, od.rect.y + 1, od.rect.x + od.rect.width, od.rect.y + od.rect.height);
// score matchScore for the ROC curve
double maxMatchScore = 0;
int maxMatchId = -1;
for (int k = 0; k < gtNumCrnt; k++) {
double matchScore = DataSetVOC::interUnio(bb, boxesGT[k]);
if (matchScore > maxMatchScore) {
maxMatchScore = matchScore;
maxMatchId = k;
}
}
uchar match = maxMatchScore > 0.5 ? 1 : 0;
if (match) {
int preDetectedIdx = gtIdx[maxMatchId];
if (preDetectedIdx >= 0) {
if (maxMatchScore > di[preDetectedIdx].matchScore) {
di[preDetectedIdx].matched = false;
gtIdx[maxMatchId] = int(j);
di[j].matchScore = maxMatchScore;
di[j].matched = true;
}
} else {
gtIdx[maxMatchId] = int(j);
di[j].matchScore = maxMatchScore;
di[j].matched = true;
}
}
#ifdef SAVE_IMAGE_RESULT
// save the result image
char buf[256];
sprintf(buf, "%2f", od.score);
Point pt(max((bb[2] + bb[0] - 85) / 2, 0), (bb[1] + bb[3]) / 2);
putText(image, buf, pt, FONT_HERSHEY_SIMPLEX, 0.5, Scalar::all(255), 3, CV_AA);
putText(image, buf, pt, FONT_HERSHEY_SIMPLEX, 0.5, Scalar::all(0), 1, CV_AA);
rectangle(image, od.rect, cv::Scalar(0, 255, 0), 2);
#endif
}
for (size_t j = 0; j < detectCount; j++) { // detections are sorted in descending order
const QUniLsvmDetector::ObjectDetection& od = detections[j];
if (di[j].matched)
matchCount++;
pScores.push_back(ScoreTp(od.score, di[j].matched));
}
#ifdef SAVE_IMAGE_RESULT
imwrite((voc.testSet[i] + "_DpmResult.png").c_str(), image);
#endif
printf("%d ", i + 1);
}
printf("\n");
}
printf("BingQdpmRocTest time = %f sec\n", tm.getTimeSec());
printf("GT %d, Matched %d/%d \n", personCount, matchCount, pScores.size());
// Calculate log-average miss rate
stable_sort(begin(pScores), end(pScores),
[](const ScoreTp &p1, const ScoreTp &p2) { return p1.first > p2.first; });
vector<float> fp(pScores.size());
for (size_t i = 0; i < fp.size(); i++)
fp[i] = !pScores[i].second;
vector<float> tp(pScores.size());
tp[0] = pScores[0].second;
for (size_t i = 1; i < tp.size(); i++)
tp[i] = tp[i - 1] + pScores[i].second;
for (size_t i = 0; i < tp.size(); i++)
tp[i] /= personCount;
for (size_t i = 1; i < fp.size(); i++)
fp[i] += fp[i - 1];
for (size_t i = 0; i < fp.size(); i++)
fp[i] /= imageCount;
sprintf(buf, "%s%03d_%03.fms_6137gt_%04dtp_%04ddt_%.0fs.m",
METHOD_NAME, max(windowLimit, 0), timeLimitMs, matchCount, pScores.size(), tm.getTimeSec());
FILE *matlabFile = fopen(buf, "w");
printVector(matlabFile, tp, "tp");
printVector(matlabFile, fp, "fp");
char *matlabContent = "tp = tp'; fp = fp';\n"
"addpath(genpath('d:/81_dataset/03_Caltech/piotr_toolbox/')); savepath;\n"
"xs1=[-inf; fp];\n"
"ys1=[0; tp];\n"
"ref=10.^(-2:.25:0);\n"
"lims=[3.1e-4 1e1 .5 1];\n"
"m=length(ref);\n"
"for i=1:m\n"
"\tj=find(xs1<=ref(i));\n"
"\tmiss(i)=ys1(j(end));\n"
"end\n"
"miss=exp(mean(log(max(1e-10,1-miss))));\n"
"show=figure();\n"
"plotRoc([fp tp],'logx',1,'logy',1,'xLbl','fppi',...\n"
"\t'lims',lims,'color','g','smooth',1,'fpTarget',ref);\n"
"title(sprintf('log-average miss rate = %.2f%%',miss*100));\n"
"savefig(['MORU' 'Roc'],show,'png');\n";
fwrite(matlabContent, strlen(matlabContent), 1, matlabFile);
fclose(matlabFile);
}