a bit refactored soft cascade
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@ -223,7 +223,7 @@ struct cv::SCascade::Fields
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int shrinkage;
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std::vector<Octave> octaves;
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std::vector<Weak> stages;
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std::vector<Weak> weaks;
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std::vector<Node> nodes;
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std::vector<float> leaves;
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std::vector<Feature> features;
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@ -233,47 +233,46 @@ struct cv::SCascade::Fields
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cv::Size frameSize;
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typedef std::vector<Octave>::iterator octIt_t;
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typedef std::vector<Detection> dvector;
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void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage,
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std::vector<Detection>& detections) const
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void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, dvector& detections) const
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{
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float detectionScore = 0.f;
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const Octave& octave = *(level.octave);
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int stBegin = octave.index * octave.weaks, stEnd = stBegin + ((octave.index)? 1024 : 416);
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int stBegin = octave.index * octave.weaks, stEnd = stBegin + octave.weaks;
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int st = stBegin;
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int offset = (octave.index)? -2: 0;
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for(; st < stEnd; ++st)
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for(int st = stBegin; st < stEnd; ++st)
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{
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const Weak& stage = stages[st];
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{
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int nId = st * 3 + offset;
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const Weak& weak = weaks[st];
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// work with root node
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const Node& node = nodes[nId];
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const Feature& feature = features[node.feature + offset];
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cv::Rect scaledRect(feature.rect);
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int nId = st * 3;
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float threshold = level.rescale(scaledRect, node.threshold,(int)(feature.channel > 6)) * feature.rarea;
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float sum = storage.get(feature.channel, scaledRect);
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int next = (sum >= threshold)? 2 : 1;
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// work with root node
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const Node& node = nodes[nId];
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const Feature& feature = features[node.feature];
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// leaves
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const Node& leaf = nodes[nId + next];
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const Feature& fLeaf = features[leaf.feature + offset];
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cv::Rect scaledRect(feature.rect);
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scaledRect = fLeaf.rect;
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threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
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sum = storage.get(fLeaf.channel, scaledRect);
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float threshold = level.rescale(scaledRect, node.threshold, (int)(feature.channel > 6)) * feature.rarea;
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float sum = storage.get(feature.channel, scaledRect);
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int next = (sum >= threshold)? 2 : 1;
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int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
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float impact = leaves[(st * 4 + offset) + lShift];
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// leaves
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const Node& leaf = nodes[nId + next];
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const Feature& fLeaf = features[leaf.feature];
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detectionScore += impact;
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}
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if (detectionScore <= stage.threshold) return;
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scaledRect = fLeaf.rect;
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threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
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sum = storage.get(fLeaf.channel, scaledRect);
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int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
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float impact = leaves[(st * 4) + lShift];
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detectionScore += impact;
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if (detectionScore <= weak.threshold) return;
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}
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if (detectionScore > 0)
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@ -346,13 +345,14 @@ struct cv::SCascade::Fields
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static const char *const SC_ORIG_H = "height";
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static const char *const SC_OCTAVES = "octaves";
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static const char *const SC_STAGES = "stages";
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static const char *const SC_TREES = "trees";
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static const char *const SC_FEATURES = "features";
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static const char *const SC_WEEK = "weakClassifiers";
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static const char *const SC_INTERNAL = "internalNodes";
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static const char *const SC_LEAF = "leafValues";
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static const char *const SC_SHRINKAGE = "shrinkage";
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// only Ada Boost supported
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std::string stageTypeStr = (string)root[SC_STAGE_TYPE];
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CV_Assert(stageTypeStr == SC_BOOST);
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@ -364,17 +364,14 @@ struct cv::SCascade::Fields
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origObjWidth = (int)root[SC_ORIG_W];
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origObjHeight = (int)root[SC_ORIG_H];
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shrinkage = (int)root["shrinkage"];
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shrinkage = (int)root[SC_SHRINKAGE];
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FileNode fn = root[SC_OCTAVES];
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if (fn.empty()) return false;
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FileNodeIterator it = fn.begin(), it_end = fn.end();
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int feature_offset = 0;
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int octIndex = 0;
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// for each octave
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for (; it != it_end; ++it)
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FileNodeIterator it = fn.begin(), it_end = fn.end();
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for (int octIndex = 0; it != it_end; ++it, ++octIndex)
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{
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FileNode fns = *it;
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Octave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns);
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@ -384,19 +381,18 @@ struct cv::SCascade::Fields
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FileNode ffs = fns[SC_FEATURES];
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if (ffs.empty()) return false;
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fns = fns["trees"];
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fns = fns[SC_TREES];
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if (fn.empty()) return false;
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// for each tree (~ decision tree with H = 2)
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FileNodeIterator st = fns.begin(), st_end = fns.end();
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for (; st != st_end; ++st )
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{
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stages.push_back(Weak(*st));
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weaks.push_back(Weak(*st));
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fns = (*st)[SC_INTERNAL];
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FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
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for (; inIt != inIt_end;)
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nodes.push_back(Node(feature_offset, inIt));
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nodes.push_back(Node(features.size(), inIt));
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fns = (*st)[SC_LEAF];
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inIt = fns.begin(), inIt_end = fns.end();
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@ -408,10 +404,8 @@ struct cv::SCascade::Fields
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st = ffs.begin(), st_end = ffs.end();
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for (; st != st_end; ++st )
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features.push_back(Feature(*st));
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feature_offset += octave.weaks * 3;
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++octIndex;
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}
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return true;
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}
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};
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