Made small optimization: for some processors using float accumulator gives speedup.
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@ -982,24 +982,47 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
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CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
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CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
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#ifdef HAVE_TEGRA_OPTIMIZATION
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float tmp; // float accumulator -- float operations are quicker
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#endif
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for( int si = 0; si < nstages; si++ )
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{
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CascadeClassifier::Data::Stage& stage = cascadeStages[si];
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int wi, ntrees = stage.ntrees;
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#ifdef HAVE_TEGRA_OPTIMIZATION
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tmp = 0;
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#else
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sum = 0;
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#endif
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for( wi = 0; wi < ntrees; wi++ )
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{
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CascadeClassifier::Data::DTreeNode& node = cascadeNodes[nodeOfs];
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int c = featureEvaluator(node.featureIdx);
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const int* subset = &cascadeSubsets[nodeOfs*subsetSize];
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#ifdef HAVE_TEGRA_OPTIMIZATION
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tmp += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
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#else
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sum += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
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#endif
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nodeOfs++;
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leafOfs += 2;
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}
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#ifdef HAVE_TEGRA_OPTIMIZATION
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if( tmp < stage.threshold ) {
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sum = (double)tmp;
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return -si;
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}
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#else
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if( sum < stage.threshold )
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return -si;
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#endif
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}
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#ifdef HAVE_TEGRA_OPTIMIZATION
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sum = (double)tmp;
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#endif
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return 1;
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}
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