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refactor cpp files naming
This commit is contained in:
marina.kolpakova
parent
0211843062
commit
318257f3a3
@ -48,4 +48,548 @@ cv::softcascade::Detection::Detection(const cv::Rect& b, const float c, int k)
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cv::Rect cv::softcascade::Detection::bb() const
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{
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return cv::Rect(x, y, w, h);
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}
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namespace {
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struct SOctave
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{
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SOctave(const int i, const cv::Size& origObjSize, const cv::FileNode& fn)
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: index(i), weaks((int)fn[SC_OCT_WEAKS]), scale((float)std::pow(2,(float)fn[SC_OCT_SCALE])),
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size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)) {}
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int index;
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int weaks;
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float scale;
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cv::Size size;
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static const char *const SC_OCT_SCALE;
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static const char *const SC_OCT_WEAKS;
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static const char *const SC_OCT_SHRINKAGE;
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};
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struct Weak
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{
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Weak(){}
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Weak(const cv::FileNode& fn) : threshold((float)fn[SC_WEAK_THRESHOLD]) {}
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float threshold;
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static const char *const SC_WEAK_THRESHOLD;
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};
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struct Node
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{
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Node(){}
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Node(const int offset, cv::FileNodeIterator& fIt)
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: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))) {}
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int feature;
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float threshold;
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};
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struct Feature
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{
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Feature() {}
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Feature(const cv::FileNode& fn, bool useBoxes = false) : channel((int)fn[SC_F_CHANNEL])
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{
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cv::FileNode rn = fn[SC_F_RECT];
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cv::FileNodeIterator r_it = rn.begin();
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int x = *r_it++;
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int y = *r_it++;
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int w = *r_it++;
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int h = *r_it++;
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// ToDo: fix me
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if (useBoxes)
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rect = cv::Rect(x, y, w, h);
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else
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rect = cv::Rect(x, y, w + x, h + y);
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// 1 / area
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rarea = 1.f / ((rect.width - rect.x) * (rect.height - rect.y));
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}
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int channel;
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cv::Rect rect;
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float rarea;
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static const char *const SC_F_CHANNEL;
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static const char *const SC_F_RECT;
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};
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const char *const SOctave::SC_OCT_SCALE = "scale";
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const char *const SOctave::SC_OCT_WEAKS = "weaks";
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const char *const SOctave::SC_OCT_SHRINKAGE = "shrinkingFactor";
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const char *const Weak::SC_WEAK_THRESHOLD = "treeThreshold";
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const char *const Feature::SC_F_CHANNEL = "channel";
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const char *const Feature::SC_F_RECT = "rect";
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struct Level
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{
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const SOctave* octave;
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float origScale;
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float relScale;
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int scaleshift;
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cv::Size workRect;
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cv::Size objSize;
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float scaling[2]; // 0-th for channels <= 6, 1-st otherwise
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Level(const SOctave& oct, const float scale, const int shrinkage, const int w, const int h)
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: octave(&oct), origScale(scale), relScale(scale / oct.scale),
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workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
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objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
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{
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scaling[0] = ((relScale >= 1.f)? 1.f : (0.89f * std::pow(relScale, 1.099f / std::log(2.f)))) / (relScale * relScale);
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scaling[1] = 1.f;
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scaleshift = static_cast<int>(relScale * (1 << 16));
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}
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void addDetection(const int x, const int y, float confidence, std::vector<cv::softcascade::Detection>& detections) const
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{
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// fix me
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int shrinkage = 4;//(*octave).shrinkage;
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cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
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detections.push_back(cv::softcascade::Detection(rect, confidence));
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}
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float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
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{
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#define SSHIFT(a) ((a) + (1 << 15)) >> 16
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// rescale
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scaledRect.x = SSHIFT(scaleshift * scaledRect.x);
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scaledRect.y = SSHIFT(scaleshift * scaledRect.y);
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scaledRect.width = SSHIFT(scaleshift * scaledRect.width);
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scaledRect.height = SSHIFT(scaleshift * scaledRect.height);
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#undef SSHIFT
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float sarea = static_cast<float>((scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y));
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// compensation areas rounding
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return (sarea == 0.0f)? threshold : (threshold * scaling[idx] * sarea);
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}
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};
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struct ChannelStorage
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{
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cv::Mat hog;
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int shrinkage;
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int offset;
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size_t step;
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int model_height;
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cv::Ptr<cv::softcascade::ChannelFeatureBuilder> builder;
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enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
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ChannelStorage(const cv::Mat& colored, int shr, std::string featureTypeStr) : shrinkage(shr)
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{
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model_height = cvRound(colored.rows / (float)shrinkage);
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if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
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builder = cv::softcascade::ChannelFeatureBuilder::create(featureTypeStr);
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(*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height));
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step = hog.step1();
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}
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float get(const int channel, const cv::Rect& area) const
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{
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const int *ptr = hog.ptr<const int>(0) + model_height * channel * step + offset;
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int a = ptr[area.y * step + area.x];
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int b = ptr[area.y * step + area.width];
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int c = ptr[area.height * step + area.width];
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int d = ptr[area.height * step + area.x];
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return static_cast<float>(a - b + c - d);
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}
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};
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}
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struct cv::softcascade::Detector::Fields
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{
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float minScale;
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float maxScale;
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int scales;
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int origObjWidth;
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int origObjHeight;
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int shrinkage;
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std::vector<SOctave> octaves;
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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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std::vector<Level> levels;
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cv::Size frameSize;
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typedef std::vector<SOctave>::iterator octIt_t;
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typedef std::vector<Detection> dvector;
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std::string featureTypeStr;
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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 SOctave& octave = *(level.octave);
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int stBegin = octave.index * octave.weaks, stEnd = stBegin + octave.weaks;
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for(int st = stBegin; st < stEnd; ++st)
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{
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const Weak& weak = weaks[st];
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int nId = st * 3;
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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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cv::Rect scaledRect(feature.rect);
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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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// 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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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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level.addDetection(dx, dy, detectionScore, detections);
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}
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octIt_t fitOctave(const float& logFactor)
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{
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float minAbsLog = FLT_MAX;
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octIt_t res = octaves.begin();
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for (octIt_t oct = octaves.begin(); oct < octaves.end(); ++oct)
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{
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const SOctave& octave =*oct;
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float logOctave = std::log(octave.scale);
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float logAbsScale = fabs(logFactor - logOctave);
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if(logAbsScale < minAbsLog)
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{
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res = oct;
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minAbsLog = logAbsScale;
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}
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}
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return res;
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}
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// compute levels of full pyramid
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void calcLevels(const cv::Size& curr, float mins, float maxs, int total)
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{
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if (frameSize == curr && maxs == maxScale && mins == minScale && total == scales) return;
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frameSize = curr;
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maxScale = maxs; minScale = mins; scales = total;
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CV_Assert(scales > 1);
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levels.clear();
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float logFactor = (std::log(maxScale) - std::log(minScale)) / (scales -1);
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float scale = minScale;
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for (int sc = 0; sc < scales; ++sc)
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{
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int width = static_cast<int>(std::max(0.0f, frameSize.width - (origObjWidth * scale)));
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int height = static_cast<int>(std::max(0.0f, frameSize.height - (origObjHeight * scale)));
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float logScale = std::log(scale);
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octIt_t fit = fitOctave(logScale);
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Level level(*fit, scale, shrinkage, width, height);
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if (!width || !height)
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break;
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else
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levels.push_back(level);
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if (fabs(scale - maxScale) < FLT_EPSILON) break;
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scale = std::min(maxScale, expf(std::log(scale) + logFactor));
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}
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}
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bool fill(const FileNode &root)
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{
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// cascade properties
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static const char *const SC_STAGE_TYPE = "stageType";
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static const char *const SC_BOOST = "BOOST";
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static const char *const SC_FEATURE_TYPE = "featureType";
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static const char *const SC_HOG6_MAG_LUV = "HOG6MagLuv";
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static const char *const SC_ICF = "ICF";
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static const char *const SC_ORIG_W = "width";
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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_TREES = "trees";
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static const char *const SC_FEATURES = "features";
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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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static const char *const FEATURE_FORMAT = "featureFormat";
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// only Ada Boost supported
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std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
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CV_Assert(stageTypeStr == SC_BOOST);
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std::string fformat = (std::string)root[FEATURE_FORMAT];
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bool useBoxes = (fformat == "BOX");
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// only HOG-like integral channel features supported
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featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
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CV_Assert(featureTypeStr == SC_ICF || featureTypeStr == SC_HOG6_MAG_LUV);
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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[SC_SHRINKAGE];
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FileNode fn = root[SC_OCTAVES];
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if (fn.empty()) return false;
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// for each octave
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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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SOctave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns);
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CV_Assert(octave.weaks > 0);
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octaves.push_back(octave);
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FileNode ffs = fns[SC_FEATURES];
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if (ffs.empty()) return false;
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fns = fns[SC_TREES];
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if (fn.empty()) return false;
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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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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((int)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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for (; inIt != inIt_end; ++inIt)
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leaves.push_back((float)(*inIt));
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}
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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, useBoxes));
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}
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return true;
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}
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};
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cv::softcascade::Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
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: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
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cv::softcascade::Detector::~Detector() { delete fields;}
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void cv::softcascade::Detector::read(const cv::FileNode& fn)
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{
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Algorithm::read(fn);
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}
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bool cv::softcascade::Detector::load(const cv::FileNode& fn)
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{
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if (fields) delete fields;
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fields = new Fields;
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return fields->fill(fn);
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}
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namespace {
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using cv::softcascade::Detection;
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typedef std::vector<Detection> dvector;
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struct ConfidenceGt
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{
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bool operator()(const Detection& a, const Detection& b) const
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{
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return a.confidence > b.confidence;
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}
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};
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static float overlap(const cv::Rect &a, const cv::Rect &b)
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{
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int w = std::min(a.x + a.width, b.x + b.width) - std::max(a.x, b.x);
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int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y);
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return (w < 0 || h < 0)? 0.f : (float)(w * h);
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}
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void DollarNMS(dvector& objects)
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{
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static const float DollarThreshold = 0.65f;
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std::sort(objects.begin(), objects.end(), ConfidenceGt());
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for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt)
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{
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const Detection &a = *dIt;
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for (dvector::iterator next = dIt + 1; next != objects.end(); )
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{
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const Detection &b = *next;
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const float ovl = overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area());
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if (ovl > DollarThreshold)
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next = objects.erase(next);
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else
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++next;
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}
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}
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}
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static void suppress(int type, std::vector<Detection>& objects)
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{
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CV_Assert(type == cv::softcascade::Detector::DOLLAR);
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DollarNMS(objects);
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}
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}
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void cv::softcascade::Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
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{
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Fields& fld = *fields;
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// create integrals
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ChannelStorage storage(image, fld.shrinkage, fld.featureTypeStr);
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typedef std::vector<Level>::const_iterator lIt;
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for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it)
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{
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const Level& level = *it;
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// we train only 3 scales.
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if (level.origScale > 2.5) break;
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for (int dy = 0; dy < level.workRect.height; ++dy)
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{
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for (int dx = 0; dx < level.workRect.width; ++dx)
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{
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storage.offset = (int)(dy * storage.step + dx);
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fld.detectAt(dx, dy, level, storage, objects);
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}
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}
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}
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if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
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}
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void cv::softcascade::Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
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{
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// only color images are suppered
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cv::Mat image = _image.getMat();
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CV_Assert(image.type() == CV_8UC3);
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Fields& fld = *fields;
|
||||
fld.calcLevels(image.size(),(float) minScale, (float)maxScale, scales);
|
||||
|
||||
objects.clear();
|
||||
|
||||
if (_rois.empty())
|
||||
return detectNoRoi(image, objects);
|
||||
|
||||
int shr = fld.shrinkage;
|
||||
|
||||
cv::Mat roi = _rois.getMat();
|
||||
cv::Mat mask(image.rows / shr, image.cols / shr, CV_8UC1);
|
||||
|
||||
mask.setTo(cv::Scalar::all(0));
|
||||
cv::Rect* r = roi.ptr<cv::Rect>(0);
|
||||
for (int i = 0; i < (int)roi.cols; ++i)
|
||||
cv::Mat(mask, cv::Rect(r[i].x / shr, r[i].y / shr, r[i].width / shr , r[i].height / shr)).setTo(cv::Scalar::all(1));
|
||||
|
||||
// create integrals
|
||||
ChannelStorage storage(image, shr, fld.featureTypeStr);
|
||||
|
||||
typedef std::vector<Level>::const_iterator lIt;
|
||||
for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it)
|
||||
{
|
||||
const Level& level = *it;
|
||||
|
||||
// we train only 3 scales.
|
||||
if (level.origScale > 2.5) break;
|
||||
|
||||
for (int dy = 0; dy < level.workRect.height; ++dy)
|
||||
{
|
||||
uchar* m = mask.ptr<uchar>(dy);
|
||||
for (int dx = 0; dx < level.workRect.width; ++dx)
|
||||
{
|
||||
if (m[dx])
|
||||
{
|
||||
storage.offset = (int)(dy * storage.step + dx);
|
||||
fld.detectAt(dx, dy, level, storage, objects);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
|
||||
}
|
||||
|
||||
void cv::softcascade::Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects, OutputArray _confs) const
|
||||
{
|
||||
std::vector<Detection> objects;
|
||||
detect( _image, _rois, objects);
|
||||
|
||||
_rects.create(1, (int)objects.size(), CV_32SC4);
|
||||
cv::Mat_<cv::Rect> rects = (cv::Mat_<cv::Rect>)_rects.getMat();
|
||||
cv::Rect* rectPtr = rects.ptr<cv::Rect>(0);
|
||||
|
||||
_confs.create(1, (int)objects.size(), CV_32F);
|
||||
cv::Mat confs = _confs.getMat();
|
||||
float* confPtr = confs.ptr<float>(0);
|
||||
|
||||
typedef std::vector<Detection>::const_iterator IDet;
|
||||
|
||||
int i = 0;
|
||||
for (IDet it = objects.begin(); it != objects.end(); ++it, ++i)
|
||||
{
|
||||
rectPtr[i] = (*it).bb();
|
||||
confPtr[i] = (*it).confidence;
|
||||
}
|
||||
}
|
||||
@ -1,594 +0,0 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2008-2013, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and / or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using cv::softcascade::Detection;
|
||||
using cv::softcascade::Detector;
|
||||
using cv::softcascade::ChannelFeatureBuilder;
|
||||
|
||||
using namespace cv;
|
||||
|
||||
namespace {
|
||||
|
||||
struct SOctave
|
||||
{
|
||||
SOctave(const int i, const cv::Size& origObjSize, const cv::FileNode& fn)
|
||||
: index(i), weaks((int)fn[SC_OCT_WEAKS]), scale((float)std::pow(2,(float)fn[SC_OCT_SCALE])),
|
||||
size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)) {}
|
||||
|
||||
int index;
|
||||
int weaks;
|
||||
|
||||
float scale;
|
||||
|
||||
cv::Size size;
|
||||
|
||||
static const char *const SC_OCT_SCALE;
|
||||
static const char *const SC_OCT_WEAKS;
|
||||
static const char *const SC_OCT_SHRINKAGE;
|
||||
};
|
||||
|
||||
|
||||
struct Weak
|
||||
{
|
||||
Weak(){}
|
||||
Weak(const cv::FileNode& fn) : threshold((float)fn[SC_WEAK_THRESHOLD]) {}
|
||||
|
||||
float threshold;
|
||||
|
||||
static const char *const SC_WEAK_THRESHOLD;
|
||||
};
|
||||
|
||||
|
||||
struct Node
|
||||
{
|
||||
Node(){}
|
||||
Node(const int offset, cv::FileNodeIterator& fIt)
|
||||
: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))) {}
|
||||
|
||||
int feature;
|
||||
float threshold;
|
||||
};
|
||||
|
||||
struct Feature
|
||||
{
|
||||
Feature() {}
|
||||
Feature(const cv::FileNode& fn, bool useBoxes = false) : channel((int)fn[SC_F_CHANNEL])
|
||||
{
|
||||
cv::FileNode rn = fn[SC_F_RECT];
|
||||
cv::FileNodeIterator r_it = rn.begin();
|
||||
|
||||
int x = *r_it++;
|
||||
int y = *r_it++;
|
||||
int w = *r_it++;
|
||||
int h = *r_it++;
|
||||
|
||||
// ToDo: fix me
|
||||
if (useBoxes)
|
||||
rect = cv::Rect(x, y, w, h);
|
||||
else
|
||||
rect = cv::Rect(x, y, w + x, h + y);
|
||||
|
||||
// 1 / area
|
||||
rarea = 1.f / ((rect.width - rect.x) * (rect.height - rect.y));
|
||||
}
|
||||
|
||||
int channel;
|
||||
cv::Rect rect;
|
||||
float rarea;
|
||||
|
||||
static const char *const SC_F_CHANNEL;
|
||||
static const char *const SC_F_RECT;
|
||||
};
|
||||
|
||||
const char *const SOctave::SC_OCT_SCALE = "scale";
|
||||
const char *const SOctave::SC_OCT_WEAKS = "weaks";
|
||||
const char *const SOctave::SC_OCT_SHRINKAGE = "shrinkingFactor";
|
||||
const char *const Weak::SC_WEAK_THRESHOLD = "treeThreshold";
|
||||
const char *const Feature::SC_F_CHANNEL = "channel";
|
||||
const char *const Feature::SC_F_RECT = "rect";
|
||||
|
||||
struct Level
|
||||
{
|
||||
const SOctave* octave;
|
||||
|
||||
float origScale;
|
||||
float relScale;
|
||||
int scaleshift;
|
||||
|
||||
cv::Size workRect;
|
||||
cv::Size objSize;
|
||||
|
||||
float scaling[2]; // 0-th for channels <= 6, 1-st otherwise
|
||||
|
||||
Level(const SOctave& oct, const float scale, const int shrinkage, const int w, const int h)
|
||||
: octave(&oct), origScale(scale), relScale(scale / oct.scale),
|
||||
workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
|
||||
objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
|
||||
{
|
||||
scaling[0] = ((relScale >= 1.f)? 1.f : (0.89f * std::pow(relScale, 1.099f / std::log(2.f)))) / (relScale * relScale);
|
||||
scaling[1] = 1.f;
|
||||
scaleshift = static_cast<int>(relScale * (1 << 16));
|
||||
}
|
||||
|
||||
void addDetection(const int x, const int y, float confidence, std::vector<Detection>& detections) const
|
||||
{
|
||||
// fix me
|
||||
int shrinkage = 4;//(*octave).shrinkage;
|
||||
cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
|
||||
|
||||
detections.push_back(Detection(rect, confidence));
|
||||
}
|
||||
|
||||
float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
|
||||
{
|
||||
#define SSHIFT(a) ((a) + (1 << 15)) >> 16
|
||||
// rescale
|
||||
scaledRect.x = SSHIFT(scaleshift * scaledRect.x);
|
||||
scaledRect.y = SSHIFT(scaleshift * scaledRect.y);
|
||||
scaledRect.width = SSHIFT(scaleshift * scaledRect.width);
|
||||
scaledRect.height = SSHIFT(scaleshift * scaledRect.height);
|
||||
#undef SSHIFT
|
||||
float sarea = static_cast<float>((scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y));
|
||||
|
||||
// compensation areas rounding
|
||||
return (sarea == 0.0f)? threshold : (threshold * scaling[idx] * sarea);
|
||||
}
|
||||
};
|
||||
struct ChannelStorage
|
||||
{
|
||||
cv::Mat hog;
|
||||
int shrinkage;
|
||||
int offset;
|
||||
size_t step;
|
||||
int model_height;
|
||||
|
||||
cv::Ptr<ChannelFeatureBuilder> builder;
|
||||
|
||||
enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
|
||||
|
||||
ChannelStorage(const cv::Mat& colored, int shr, std::string featureTypeStr) : shrinkage(shr)
|
||||
{
|
||||
model_height = cvRound(colored.rows / (float)shrinkage);
|
||||
if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
|
||||
|
||||
builder = ChannelFeatureBuilder::create(featureTypeStr);
|
||||
(*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height));
|
||||
|
||||
step = hog.step1();
|
||||
}
|
||||
|
||||
float get(const int channel, const cv::Rect& area) const
|
||||
{
|
||||
const int *ptr = hog.ptr<const int>(0) + model_height * channel * step + offset;
|
||||
|
||||
int a = ptr[area.y * step + area.x];
|
||||
int b = ptr[area.y * step + area.width];
|
||||
int c = ptr[area.height * step + area.width];
|
||||
int d = ptr[area.height * step + area.x];
|
||||
|
||||
return static_cast<float>(a - b + c - d);
|
||||
}
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
|
||||
struct Detector::Fields
|
||||
{
|
||||
float minScale;
|
||||
float maxScale;
|
||||
int scales;
|
||||
|
||||
int origObjWidth;
|
||||
int origObjHeight;
|
||||
|
||||
int shrinkage;
|
||||
|
||||
std::vector<SOctave> octaves;
|
||||
std::vector<Weak> weaks;
|
||||
std::vector<Node> nodes;
|
||||
std::vector<float> leaves;
|
||||
std::vector<Feature> features;
|
||||
|
||||
std::vector<Level> levels;
|
||||
|
||||
cv::Size frameSize;
|
||||
|
||||
typedef std::vector<SOctave>::iterator octIt_t;
|
||||
typedef std::vector<Detection> dvector;
|
||||
|
||||
std::string featureTypeStr;
|
||||
|
||||
void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, dvector& detections) const
|
||||
{
|
||||
float detectionScore = 0.f;
|
||||
|
||||
const SOctave& octave = *(level.octave);
|
||||
|
||||
int stBegin = octave.index * octave.weaks, stEnd = stBegin + octave.weaks;
|
||||
|
||||
for(int st = stBegin; st < stEnd; ++st)
|
||||
{
|
||||
const Weak& weak = weaks[st];
|
||||
|
||||
int nId = st * 3;
|
||||
|
||||
// work with root node
|
||||
const Node& node = nodes[nId];
|
||||
const Feature& feature = features[node.feature];
|
||||
|
||||
cv::Rect scaledRect(feature.rect);
|
||||
|
||||
float threshold = level.rescale(scaledRect, node.threshold, (int)(feature.channel > 6)) * feature.rarea;
|
||||
float sum = storage.get(feature.channel, scaledRect);
|
||||
int next = (sum >= threshold)? 2 : 1;
|
||||
|
||||
// leaves
|
||||
const Node& leaf = nodes[nId + next];
|
||||
const Feature& fLeaf = features[leaf.feature];
|
||||
|
||||
scaledRect = fLeaf.rect;
|
||||
threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
|
||||
sum = storage.get(fLeaf.channel, scaledRect);
|
||||
|
||||
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
|
||||
float impact = leaves[(st * 4) + lShift];
|
||||
|
||||
detectionScore += impact;
|
||||
|
||||
if (detectionScore <= weak.threshold) return;
|
||||
}
|
||||
|
||||
if (detectionScore > 0)
|
||||
level.addDetection(dx, dy, detectionScore, detections);
|
||||
}
|
||||
|
||||
octIt_t fitOctave(const float& logFactor)
|
||||
{
|
||||
float minAbsLog = FLT_MAX;
|
||||
octIt_t res = octaves.begin();
|
||||
for (octIt_t oct = octaves.begin(); oct < octaves.end(); ++oct)
|
||||
{
|
||||
const SOctave& octave =*oct;
|
||||
float logOctave = std::log(octave.scale);
|
||||
float logAbsScale = fabs(logFactor - logOctave);
|
||||
|
||||
if(logAbsScale < minAbsLog)
|
||||
{
|
||||
res = oct;
|
||||
minAbsLog = logAbsScale;
|
||||
}
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
// compute levels of full pyramid
|
||||
void calcLevels(const cv::Size& curr, float mins, float maxs, int total)
|
||||
{
|
||||
if (frameSize == curr && maxs == maxScale && mins == minScale && total == scales) return;
|
||||
|
||||
frameSize = curr;
|
||||
maxScale = maxs; minScale = mins; scales = total;
|
||||
CV_Assert(scales > 1);
|
||||
|
||||
levels.clear();
|
||||
float logFactor = (std::log(maxScale) - std::log(minScale)) / (scales -1);
|
||||
|
||||
float scale = minScale;
|
||||
for (int sc = 0; sc < scales; ++sc)
|
||||
{
|
||||
int width = static_cast<int>(std::max(0.0f, frameSize.width - (origObjWidth * scale)));
|
||||
int height = static_cast<int>(std::max(0.0f, frameSize.height - (origObjHeight * scale)));
|
||||
|
||||
float logScale = std::log(scale);
|
||||
octIt_t fit = fitOctave(logScale);
|
||||
|
||||
|
||||
Level level(*fit, scale, shrinkage, width, height);
|
||||
|
||||
if (!width || !height)
|
||||
break;
|
||||
else
|
||||
levels.push_back(level);
|
||||
|
||||
if (fabs(scale - maxScale) < FLT_EPSILON) break;
|
||||
scale = std::min(maxScale, expf(std::log(scale) + logFactor));
|
||||
}
|
||||
}
|
||||
|
||||
bool fill(const FileNode &root)
|
||||
{
|
||||
// cascade properties
|
||||
static const char *const SC_STAGE_TYPE = "stageType";
|
||||
static const char *const SC_BOOST = "BOOST";
|
||||
|
||||
static const char *const SC_FEATURE_TYPE = "featureType";
|
||||
static const char *const SC_HOG6_MAG_LUV = "HOG6MagLuv";
|
||||
static const char *const SC_ICF = "ICF";
|
||||
|
||||
static const char *const SC_ORIG_W = "width";
|
||||
static const char *const SC_ORIG_H = "height";
|
||||
|
||||
static const char *const SC_OCTAVES = "octaves";
|
||||
static const char *const SC_TREES = "trees";
|
||||
static const char *const SC_FEATURES = "features";
|
||||
|
||||
static const char *const SC_INTERNAL = "internalNodes";
|
||||
static const char *const SC_LEAF = "leafValues";
|
||||
|
||||
static const char *const SC_SHRINKAGE = "shrinkage";
|
||||
|
||||
static const char *const FEATURE_FORMAT = "featureFormat";
|
||||
|
||||
// only Ada Boost supported
|
||||
std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
|
||||
CV_Assert(stageTypeStr == SC_BOOST);
|
||||
|
||||
std::string fformat = (std::string)root[FEATURE_FORMAT];
|
||||
bool useBoxes = (fformat == "BOX");
|
||||
|
||||
// only HOG-like integral channel features supported
|
||||
featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
|
||||
CV_Assert(featureTypeStr == SC_ICF || featureTypeStr == SC_HOG6_MAG_LUV);
|
||||
|
||||
origObjWidth = (int)root[SC_ORIG_W];
|
||||
origObjHeight = (int)root[SC_ORIG_H];
|
||||
|
||||
shrinkage = (int)root[SC_SHRINKAGE];
|
||||
|
||||
FileNode fn = root[SC_OCTAVES];
|
||||
if (fn.empty()) return false;
|
||||
|
||||
// for each octave
|
||||
FileNodeIterator it = fn.begin(), it_end = fn.end();
|
||||
for (int octIndex = 0; it != it_end; ++it, ++octIndex)
|
||||
{
|
||||
FileNode fns = *it;
|
||||
SOctave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns);
|
||||
CV_Assert(octave.weaks > 0);
|
||||
octaves.push_back(octave);
|
||||
|
||||
FileNode ffs = fns[SC_FEATURES];
|
||||
if (ffs.empty()) return false;
|
||||
|
||||
fns = fns[SC_TREES];
|
||||
if (fn.empty()) return false;
|
||||
|
||||
FileNodeIterator st = fns.begin(), st_end = fns.end();
|
||||
for (; st != st_end; ++st )
|
||||
{
|
||||
weaks.push_back(Weak(*st));
|
||||
|
||||
fns = (*st)[SC_INTERNAL];
|
||||
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
|
||||
for (; inIt != inIt_end;)
|
||||
nodes.push_back(Node((int)features.size(), inIt));
|
||||
|
||||
fns = (*st)[SC_LEAF];
|
||||
inIt = fns.begin(), inIt_end = fns.end();
|
||||
|
||||
for (; inIt != inIt_end; ++inIt)
|
||||
leaves.push_back((float)(*inIt));
|
||||
}
|
||||
|
||||
st = ffs.begin(), st_end = ffs.end();
|
||||
for (; st != st_end; ++st )
|
||||
features.push_back(Feature(*st, useBoxes));
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
};
|
||||
|
||||
Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
|
||||
: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
|
||||
|
||||
Detector::~Detector() { delete fields;}
|
||||
|
||||
void Detector::read(const cv::FileNode& fn)
|
||||
{
|
||||
Algorithm::read(fn);
|
||||
}
|
||||
|
||||
bool Detector::load(const cv::FileNode& fn)
|
||||
{
|
||||
if (fields) delete fields;
|
||||
|
||||
fields = new Fields;
|
||||
return fields->fill(fn);
|
||||
}
|
||||
|
||||
namespace {
|
||||
|
||||
using cv::softcascade::Detection;
|
||||
typedef std::vector<Detection> dvector;
|
||||
|
||||
|
||||
struct ConfidenceGt
|
||||
{
|
||||
bool operator()(const Detection& a, const Detection& b) const
|
||||
{
|
||||
return a.confidence > b.confidence;
|
||||
}
|
||||
};
|
||||
|
||||
static float overlap(const cv::Rect &a, const cv::Rect &b)
|
||||
{
|
||||
int w = std::min(a.x + a.width, b.x + b.width) - std::max(a.x, b.x);
|
||||
int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y);
|
||||
|
||||
return (w < 0 || h < 0)? 0.f : (float)(w * h);
|
||||
}
|
||||
|
||||
void DollarNMS(dvector& objects)
|
||||
{
|
||||
static const float DollarThreshold = 0.65f;
|
||||
std::sort(objects.begin(), objects.end(), ConfidenceGt());
|
||||
|
||||
for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt)
|
||||
{
|
||||
const Detection &a = *dIt;
|
||||
for (dvector::iterator next = dIt + 1; next != objects.end(); )
|
||||
{
|
||||
const Detection &b = *next;
|
||||
|
||||
const float ovl = overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area());
|
||||
|
||||
if (ovl > DollarThreshold)
|
||||
next = objects.erase(next);
|
||||
else
|
||||
++next;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void suppress(int type, std::vector<Detection>& objects)
|
||||
{
|
||||
CV_Assert(type == Detector::DOLLAR);
|
||||
DollarNMS(objects);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
|
||||
{
|
||||
Fields& fld = *fields;
|
||||
// create integrals
|
||||
ChannelStorage storage(image, fld.shrinkage, fld.featureTypeStr);
|
||||
|
||||
typedef std::vector<Level>::const_iterator lIt;
|
||||
for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it)
|
||||
{
|
||||
const Level& level = *it;
|
||||
|
||||
// we train only 3 scales.
|
||||
if (level.origScale > 2.5) break;
|
||||
|
||||
for (int dy = 0; dy < level.workRect.height; ++dy)
|
||||
{
|
||||
for (int dx = 0; dx < level.workRect.width; ++dx)
|
||||
{
|
||||
storage.offset = (int)(dy * storage.step + dx);
|
||||
fld.detectAt(dx, dy, level, storage, objects);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
|
||||
}
|
||||
|
||||
void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
|
||||
{
|
||||
// only color images are suppered
|
||||
cv::Mat image = _image.getMat();
|
||||
CV_Assert(image.type() == CV_8UC3);
|
||||
|
||||
Fields& fld = *fields;
|
||||
fld.calcLevels(image.size(),(float) minScale, (float)maxScale, scales);
|
||||
|
||||
objects.clear();
|
||||
|
||||
if (_rois.empty())
|
||||
return detectNoRoi(image, objects);
|
||||
|
||||
int shr = fld.shrinkage;
|
||||
|
||||
cv::Mat roi = _rois.getMat();
|
||||
cv::Mat mask(image.rows / shr, image.cols / shr, CV_8UC1);
|
||||
|
||||
mask.setTo(cv::Scalar::all(0));
|
||||
cv::Rect* r = roi.ptr<cv::Rect>(0);
|
||||
for (int i = 0; i < (int)roi.cols; ++i)
|
||||
cv::Mat(mask, cv::Rect(r[i].x / shr, r[i].y / shr, r[i].width / shr , r[i].height / shr)).setTo(cv::Scalar::all(1));
|
||||
|
||||
// create integrals
|
||||
ChannelStorage storage(image, shr, fld.featureTypeStr);
|
||||
|
||||
typedef std::vector<Level>::const_iterator lIt;
|
||||
for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it)
|
||||
{
|
||||
const Level& level = *it;
|
||||
|
||||
// we train only 3 scales.
|
||||
if (level.origScale > 2.5) break;
|
||||
|
||||
for (int dy = 0; dy < level.workRect.height; ++dy)
|
||||
{
|
||||
uchar* m = mask.ptr<uchar>(dy);
|
||||
for (int dx = 0; dx < level.workRect.width; ++dx)
|
||||
{
|
||||
if (m[dx])
|
||||
{
|
||||
storage.offset = (int)(dy * storage.step + dx);
|
||||
fld.detectAt(dx, dy, level, storage, objects);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
|
||||
}
|
||||
|
||||
void Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects, OutputArray _confs) const
|
||||
{
|
||||
std::vector<Detection> objects;
|
||||
detect( _image, _rois, objects);
|
||||
|
||||
_rects.create(1, (int)objects.size(), CV_32SC4);
|
||||
cv::Mat_<cv::Rect> rects = (cv::Mat_<cv::Rect>)_rects.getMat();
|
||||
cv::Rect* rectPtr = rects.ptr<cv::Rect>(0);
|
||||
|
||||
_confs.create(1, (int)objects.size(), CV_32F);
|
||||
cv::Mat confs = _confs.getMat();
|
||||
float* confPtr = confs.ptr<float>(0);
|
||||
|
||||
typedef std::vector<Detection>::const_iterator IDet;
|
||||
|
||||
int i = 0;
|
||||
for (IDet it = objects.begin(); it != objects.end(); ++it, ++i)
|
||||
{
|
||||
rectPtr[i] = (*it).bb();
|
||||
confPtr[i] = (*it).confidence;
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user