void PatchTracking::setPatchRegion(RealImage::RegionType region) {
_patchRegion[0] = region;
_patchRegion[1] = region;
extractPatch(0);
extractPatch(1);
transformPatchRegion();
}
cv::Point2f pyramidIteration(char& status, const cv::Point2f& pointI, const cv::Point2f& pointJ, const cv::Mat& I,
const cv::Mat& J, const int patchSize = 5)
{
try
{
cv::Point2f result;
// Extract a patch around the image
std::vector<std::vector<float>> patch(patchSize + 2, std::vector<float>(patchSize + 2));
std::vector<std::vector<float>> patchIt(patchSize, std::vector<float>(patchSize));
status = extractPatch(patch, (int)pointI.x,(int)pointI.y, patchSize + 2, I);
// if (status)
// return result;
status = extractPatchIt(patchIt, pointI.x, pointI.y, pointJ.x, pointJ.y, I, J, patchSize);
// if (status)
// return result;
// Get the Ix, Iy and It vectors
std::vector<float> ix, iy, it;
getVectors(ix, iy, it, patch, patchIt, patchSize);
// Calculate optical flow
cv::Point2f delta;
status = computeLK(delta, ix, iy, it);
// if (status)
// return result;
result = pointJ + delta;
return result;
}
catch (const std::exception& e)
{
error(e.what(), __LINE__, __FUNCTION__, __FILE__);
return cv::Point2f{};
}
}
//Do this when current trajectory is valid
void TLD::learn()
{
if(!learningEnabled || !valid || !detectorEnabled)
{
learning = false;
return;
}
learning = true;
DetectionResult *detectionResult = detectorCascade->detectionResult;
if(!detectionResult->containsValidData)
{
#ifndef USE_HTLD
detectorCascade->detect(currImg);
#else
detectorCascade->detect(currImg,
hTLDMaster->getTLDObject(tldObjId)->getFastDetStr(),
false);
#endif
}
//This is the positive patch
NormalizedPatch patch;
tldExtractNormalizedPatchRect(currImg, currBB, patch.values);
float *overlap = new float[detectorCascade->numWindows];
tldOverlapRect(detectorCascade->windows, detectorCascade->numWindows, currBB, overlap);
//Add all bounding boxes with high overlap
vector<std::pair<int, float> > positiveIndices;
vector<int> negativeIndices;
vector<int> negativeIndicesForNN;
//First: Find overlapping positive and negative patches
for(int i = 0; i < detectorCascade->numWindows; i++)
{
if(overlap[i] > 0.6)
{
positiveIndices.push_back(std::pair<int, float>(i, overlap[i]));
}
if(overlap[i] < 0.2)
{
if(!detectorCascade->ensembleClassifier->enabled || detectionResult->posteriors[i] > 0.5) //Should be 0.5 according to the paper
{
negativeIndices.push_back(i);
negativeIndicesForNN.push_back(i);
}
}
}
sort(positiveIndices.begin(), positiveIndices.end(), tldSortByOverlapDesc);
vector<NormalizedPatch> patches;
patch.positive = 1;
patches.push_back(patch);
//TODO: Flip
int numIterations = std::min<size_t>(positiveIndices.size(), 10); //Take at most 10 bounding boxes (sorted by overlap)
for(size_t i = 0; i < negativeIndices.size(); i++)
{
int idx = negativeIndices.at(i);
//TODO: Somewhere here image warping might be possible
detectorCascade->ensembleClassifier->learn(&detectorCascade->windows[TLD_WINDOW_SIZE * idx], false, &detectionResult->featureVectors[detectorCascade->numTrees * idx]);
}
//***********************************************************************************
//**************Warping Positive Patches & Traininng the Classifier...***************
//***********************************************************************************
int bbW;
int bbH;
std::vector<int> indices(numIterations);
for(int i = 0; i<numIterations; i++)
indices[i] = positiveIndices.at(i).first;
bbHull(detectorCascade->windowOffsets,
indices,
currImg.cols,
hull);
bbW = hull[2] - hull[0] + 1;
bbH = hull[3] - hull[1] + 1;
cv::Rect roi(hull[0], hull[1], bbW, bbH);
#ifdef USE_HTLD
//Move Blurred Image to CPU...
cudaMemcpy((void*)imBlurred->data,
(void*)memMgr->getDevBlurredCurFrame(),
sizeof(Npp8u) * currImg.rows * currImg.cols,
cudaMemcpyDeviceToHost);
#else
gaussianFilter->apply(currImg,
*imBlurred);
#endif
cv::Mat noise(bbH, bbW, CV_64FC1);
cv::Mat result(bbH, bbW, CV_8UC1);
//TODO: Make All Patch Related Params Configurable...
for(int i = 0; i < 20; i++) { //Here 20 is equal to # of Warped-Images...
if(i > 0) {
//TODO: GPU is a Better Prospect for This Sort of Operations(Next Step for Parallelization)???!!!!
extractPatch(*imBlurred,
hull,
bbW,
bbH,
(unsigned char)0,
rng,
5.0,
20.0,
0.02,
0.02,
noise,
result);
result.copyTo((*ppHolder)(roi));
}//End of if-Block...
for(int j = 0; j<numIterations; j++) {
int idx = positiveIndices.at(j).first;
detectorCascade->ensembleClassifier->calcFeatureVectorPatch(ppHolder->data,
idx,
&detectionResult->featureVectors[detectorCascade->numTrees * idx]);
detectorCascade->ensembleClassifier->learn(&detectorCascade->windows[TLD_WINDOW_SIZE * idx],
true,
&detectionResult->featureVectors[detectorCascade->numTrees * idx]);
}
}
for(size_t i = 0; i < negativeIndicesForNN.size(); i++)
{
int idx = negativeIndicesForNN.at(i);
NormalizedPatch patch;
tldExtractNormalizedPatchBB(currImg, &detectorCascade->windows[TLD_WINDOW_SIZE * idx], patch.values);
patch.positive = 0;
patches.push_back(patch);
}
detectorCascade->nnClassifier->learn(patches);
//cout << "NN has now " << detectorCascade->nnClassifier->truePositives->size() << " positives and " << detectorCascade->nnClassifier->falsePositives->size() << " negatives.\n";
delete[] overlap;
}
void PatchTracking::translatePatchRegion(RealImage::RegionType region) {
_patchRegion[0] = region;
transformPatchRegion();
extractPatch(0, false);
resamplePatch();
}
void NNUtilities::extractPatch(const Vector2i& center, const Vector2i& inSize, const Vector2i& outSize, const GrayscaledImage& src, GrayscaledImage& dest, const ExtractionMode mode) {
dest.setResolution(static_cast<unsigned int>(outSize(0)), static_cast<unsigned int>(outSize(0)));
extractPatch(center, inSize, outSize, src, dest[0], mode);
}
