raplase error output on CV_Error
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@ -221,7 +221,6 @@ void cv::Octave::processPositives(const Dataset* dataset, const FeaturePool* poo
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integrals.create(pool->size(), (w / shrinkage + 1) * (h / shrinkage * 10 + 1), CV_32SC1);
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int total = 0;
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// for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it)
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for (int curr = 0; curr < dataset->available( Dataset::POSITIVE); ++curr)
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{
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cv::Mat sample = dataset->get( Dataset::POSITIVE, curr);
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@ -247,7 +246,6 @@ void cv::Octave::generateNegatives(const Dataset* dataset, const FeaturePool* po
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sft::Random::engine eng(65633343L);
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sft::Random::engine idxEng(764224349868L);
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// int w = boundingBox.width;
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int h = boundingBox.height;
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int nimages = dataset->available(Dataset::NEGATIVE);
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@ -276,7 +274,6 @@ void cv::Octave::generateNegatives(const Dataset* dataset, const FeaturePool* po
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pool->preprocess(frame, channels);
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dprintf("generated %d %d\n", dx, dy);
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// // if (predict(sum))
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{
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responses.ptr<float>(i)[0] = 0.f;
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@ -436,7 +433,7 @@ bool cv::Octave::train(const Dataset* dataset, const FeaturePool* pool, int weak
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bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
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if (!ok)
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std::cout << "ERROR: tree can not be trained " << std::endl;
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CV_Error(CV_StsInternal, "ERROR: tree can not be trained");
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return ok;
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}
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