Lines Matching defs:Data
35 // loss of use, data, or profits; or business interruption) however caused
480 uchar *src = in.data;
928 return !oldCascade && data.stages.empty();
934 data = Data();
958 (data.featureType == FeatureEvaluator::HAAR ||
959 data.featureType == FeatureEvaluator::LBP ||
960 data.featureType == FeatureEvaluator::HOG) );
964 if( data.maxNodesPerTree == 1 )
966 if( data.featureType == FeatureEvaluator::HAAR )
968 else if( data.featureType == FeatureEvaluator::LBP )
975 if( data.featureType == FeatureEvaluator::HAAR )
977 else if( data.featureType == FeatureEvaluator::LBP )
1027 Size origWinSize = classifier->data.origWinSize;
1049 result = -(int)classifier->data.stages.size();
1050 if( classifier->data.stages.size() + result == 0 )
1114 copyVectorToUMat(data.stages, ustages);
1115 if (!data.stumps.empty())
1116 copyVectorToUMat(data.stumps, unodes);
1118 copyVectorToUMat(data.nodes, unodes);
1119 copyVectorToUMat(data.leaves, uleaves);
1120 if( !data.subsets.empty() )
1121 copyVectorToUMat(data.subsets, usubsets);
1124 int nstages = (int)data.stages.size();
1138 localsz.width, localsz.height, lbufSize.area(), lbufSize.width, data.maxNodesPerTree, splitstage_ocl, nstages, MAX_FACES);
1141 localsz.width, localsz.height, data.maxNodesPerTree, splitstage_ocl, nstages, MAX_FACES);
1161 normrect, sqofs, data.origWinSize);
1166 if (data.maxNodesPerTree > 1)
1187 int subsetSize = (data.ncategories + 31)/32;
1200 data.origWinSize);
1217 cvRound(data.origWinSize.width*s.scale),
1218 cvRound(data.origWinSize.height*s.scale)));
1236 return data.origWinSize;
1284 (data.minNodesPerTree == data.maxNodesPerTree) &&
1345 Size szw = s->getWorkingSize(data.origWinSize);
1349 szw = s[i].getWorkingSize(data.origWinSize);
1367 Size origWinSize = data.origWinSize;
1374 unsigned char* inData = heval.sbuf.data;
1417 Size origWinSize = data.origWinSize;
1445 int nstages = (int) data.stages.size();
1449 st.first = data.stages[j].first;
1450 st.ntrees = data.stages[j].ntrees;
1451 st.threshold = data.stages[j].threshold;
1456 hf.nStumps = (int) data.stumps.size();
1458 int nstumps = data.stumps.size();
1462 st.featureIdx = data.stumps[j].featureIdx;
1463 st.threshold = data.stumps[j].threshold;
1464 st.left = data.stumps[j].left;
1465 st.right = data.stumps[j].right;
1561 CascadeClassifierImpl::Data::Data()
1566 bool CascadeClassifierImpl::Data::read(const FileNode &root)
1709 if( !data.read(root) )
1713 featureEvaluator = FeatureEvaluator::create(data.featureType);
1718 return featureEvaluator->read(fn, data.origWinSize);
