integrals.create(pool->size(), (w / shrinkage + 1) * (h / shrinkage * 10 + 1), CV_32SC1);
int total = 0;
- // for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it)
for (int curr = 0; curr < dataset->available( Dataset::POSITIVE); ++curr)
{
cv::Mat sample = dataset->get( Dataset::POSITIVE, curr);
sft::Random::engine eng(65633343L);
sft::Random::engine idxEng(764224349868L);
- // int w = boundingBox.width;
int h = boundingBox.height;
int nimages = dataset->available(Dataset::NEGATIVE);
pool->preprocess(frame, channels);
dprintf("generated %d %d\n", dx, dy);
-
// // if (predict(sum))
{
responses.ptr<float>(i)[0] = 0.f;
bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
if (!ok)
- std::cout << "ERROR: tree can not be trained " << std::endl;
+ CV_Error(CV_StsInternal, "ERROR: tree can not be trained");
return ok;
}