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@@ -36,8 +36,6 @@ SemSegContextTree::SemSegContextTree (const Config *conf, const MultiDataset *md
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lfcw = new LFColorWeijer (conf);
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lfcw = new LFColorWeijer (conf);
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firstiteration = true;
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firstiteration = true;
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- grid = conf->gI (section, "grid", 10);
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-
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maxSamples = conf->gI (section, "max_samples", 2000);
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maxSamples = conf->gI (section, "max_samples", 2000);
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minFeats = conf->gI (section, "min_feats", 50);
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minFeats = conf->gI (section, "min_feats", 50);
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@@ -965,8 +963,6 @@ void SemSegContextTree::train (const MultiDataset *md)
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feat.rProbs = &lastRegionProbs[iCounter];
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feat.rProbs = &lastRegionProbs[iCounter];
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double val = splitfeat->getVal (feat, x, y);
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double val = splitfeat->getVal (feat, x, y);
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- //int subx = x / grid;
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- //int suby = y / grid;
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#pragma omp critical
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#pragma omp critical
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if (val < splitval)
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if (val < splitval)
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{
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{
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@@ -1495,8 +1491,6 @@ void SemSegContextTree::semanticseg (CachedExample *ce, NICE::Image & segresult,
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double val = forest[tree][t].feat->getVal (feat, x, y);
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double val = forest[tree][t].feat->getVal (feat, x, y);
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- //int subx = x / grid;
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- //int suby = y / grid;
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if (val < forest[tree][t].decision)
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if (val < forest[tree][t].decision)
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{
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{
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currentfeats.set (x, y, forest[tree][t].left, tree);
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currentfeats.set (x, y, forest[tree][t].left, tree);
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