compute scales pyramid
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marina.kolpakova
parent
a54d456ad0
commit
f01c5d9033
@ -75,27 +75,93 @@ namespace {
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{printf(" stage: %f %f\n",threshold, weight);}
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};
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// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
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struct CascadeIntrinsics
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{
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static const float lambda = 1.099f, a = 0.89f;
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static const float intrinsics[10][4];
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static float getFor(int channel, float scaling)
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{
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CV_Assert(channel < 10);
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if ((scaling - 1.f) < FLT_EPSILON)
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return 1.f;
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int ud = (int)(scaling < 1.f);
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return intrinsics[channel][(ud << 1)] * pow(scaling, intrinsics[channel][(ud << 1) + 1]);
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}
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};
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const float CascadeIntrinsics::intrinsics[10][4] =
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{ //da, db, ua, ub
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// hog-like orientation bins
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{a, lambda / log(2), 1, 2},
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{a, lambda / log(2), 1, 2},
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{a, lambda / log(2), 1, 2},
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{a, lambda / log(2), 1, 2},
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{a, lambda / log(2), 1, 2},
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{a, lambda / log(2), 1, 2},
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// gradient magnitude
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{a, lambda / log(2), 1, 2},
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// luv color channels
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{1, 2, 1, 2},
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{1, 2, 1, 2},
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{1, 2, 1, 2}
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};
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static const char *SC_F_THRESHOLD = "threshold";
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static const char *SC_F_DIRECTION = "direction";
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static const char *SC_F_CHANNEL = "chennel";
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static const char *SC_F_CHANNEL = "channel";
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static const char *SC_F_RECT = "rect";
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struct Feature
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{
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float threshold;
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int direction;
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int chennel;
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int channel;
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cv::Rect rect;
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Feature() {}
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Feature(const cv::FileNode& fn)
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: threshold((float)fn[SC_F_THRESHOLD]), direction((int)fn[SC_F_DIRECTION]),
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chennel((int)fn[SC_F_CHANNEL])
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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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rect = cv::Rect(*(r_it++), *(r_it++), *(r_it++), *(r_it++));
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printf(" feature: %f %d %d [%d %d %d %d]\n",threshold, direction, chennel, rect.x, rect.y, rect.width, rect.height);}
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printf(" feature: %f %d %d [%d %d %d %d]\n",threshold, direction, channel, rect.x, rect.y, rect.width, rect.height);}
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Feature rescale(float relScale)
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{
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Feature res(*this);
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res.rect = cv::Rect (cvRound(rect.x * relScale), cvRound(rect.y * relScale),
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cvRound(rect.width * relScale), cvRound(rect.height * relScale));
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res.threshold = threshold * CascadeIntrinsics::getFor(channel, relScale);
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return res;
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}
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};
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struct Level
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{
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int index;
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float factor;
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float logFactor;
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int width;
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int height;
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float octave;
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cv::Size objSize;
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Level(int i,float f, float lf, int w, int h): index(i), factor(f), logFactor(lf), width(w), height(h), octave(0.f) {}
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void assign(float o, int detW, int detH)
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{
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octave = o;
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objSize = cv::Size(cv::saturate_cast<int>(detW * o), cv::saturate_cast<int>(detH * o));
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}
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float relScale() {return (factor / octave); }
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};
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}
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@ -112,6 +178,43 @@ struct cv::SoftCascade::Filds
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std::vector<Octave> octaves;
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std::vector<Stage> stages;
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std::vector<Feature> features;
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std::vector<Level> levels;
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// compute levels of full pyramid
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void calcLevels(int frameW, int frameH, int scales)
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{
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CV_Assert(scales > 1);
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levels.clear();
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float logFactor = (log(maxScale) - 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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Level level(sc, scale, log(scale) + logFactor,
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std::max(0.0f, frameW - (origObjWidth * scale)), std::max(0.0f, frameH - (origObjHeight * scale)));
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if (!level.width || !level.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(log(scale) + logFactor));
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}
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for (std::vector<Level>::iterator level = levels.begin(); level < levels.end(); ++level)
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{
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float minAbsLog = FLT_MAX;
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for (std::vector<Octave>::iterator oct = octaves.begin(); oct < octaves.end(); ++oct)
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{
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const Octave& octave =*oct;
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float logOctave = log(octave.scale);
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float logAbsScale = fabs((*level).logFactor - logOctave);
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if(logAbsScale < minAbsLog)
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(*level).assign(octave.scale, ORIG_OBJECT_WIDTH, ORIG_OBJECT_HEIGHT);
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}
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}
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}
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bool fill(const FileNode &root, const float mins, const float maxs)
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{
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@ -193,110 +296,6 @@ struct cv::SoftCascade::Filds
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}
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};
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// namespace {
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// struct Cascade {
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// int logOctave;
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// float octave;
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// cv::Size objSize;
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// };
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// struct Level {
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// int index;
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// float factor;
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// float logFactor;
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// int width;
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// int height;
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// float octave;
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// cv::Size objSize;
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// Level(int i,float f, float lf, int w, int h) : index(i), factor(f), logFactor(lf), width(w), height(h), octave(0.f) {}
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// void assign(float o, int detW, int detH)
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// {
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// octave = o;
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// objSize = cv::Size(cv::saturate_cast<int>(detW * o), cv::saturate_cast<int>(detH * o));
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// }
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// float relScale() {return (factor / octave); }
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// };
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// // compute levels of full pyramid
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// void pyrLevels(int frameW, int frameH, int detW, int detH, int scales, float minScale, float maxScale, std::vector<Level> levels)
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// {
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// CV_Assert(scales > 1);
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// levels.clear();
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// float logFactor = (log(maxScale) - 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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// Level level(sc, scale, log(scale) + logFactor, std::max(0.0f, frameW - (detW * scale)), std::max(0.0f, frameH - (detH * scale)));
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// if (!level.width || !level.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(log(scale) + logFactor));
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// }
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// }
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// // according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
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// struct CascadeIntrinsics {
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// static const float lambda = 1.099f, a = 0.89f;
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// static const float intrinsics[10][4];
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// static float getFor(int chennel, float scaling)
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// {
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// CV_Assert(chennel < 10);
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// if ((scaling - 1.f) < FLT_EPSILON)
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// return 1.f;
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// int ud = (int)(scaling < 1.f);
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// return intrinsics[chennel][(ud << 1)] * pow(scaling, intrinsics[chennel][(ud << 1) + 1]);
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// }
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// };
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// const float CascadeIntrinsics::intrinsics[10][4] =
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// { //da, db, ua, ub
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// // hog-like orientation bins
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// // gradient magnitude
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// {a, lambda / log(2), 1, 2},
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// // luv -color chennels
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// {1, 2, 1, 2},
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// {1, 2, 1, 2},
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// {1, 2, 1, 2}
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// };
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// struct Feature
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// {
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// cv::Rect rect;
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// int channel;
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// float threshold;
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// Feature(int x, int y, int w, int h, int c, float t) : rect(cv::Rect(x, y, w, h)), channel(c), threshold(t) {}
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// Feature(cv::Rect r, int c, float t) : rect(r), channel(c), threshold(t) {}
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// Feature rescale(float relScale)
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// {
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// cv::Rect r(cvRound(rect.x * relScale), cvRound(rect.y * relScale), cvRound(rect.width * relScale), cvRound(rect.height * relScale));
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// return Feature( r, channel, threshold * CascadeIntrinsics::getFor(channel, relScale));
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// }
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// };
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// }
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cv::SoftCascade::SoftCascade() : filds(0) {}
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cv::SoftCascade::SoftCascade( const string& filename, const float minScale, const float maxScale)
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@ -318,38 +317,9 @@ bool cv::SoftCascade::load( const string& filename, const float minScale, const
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if (!fs.isOpened()) return false;
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filds = new Filds;
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if (!(*filds).fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
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// ////////////////
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// // temp fixture
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// Filds& flds = *filds;
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// flds.octaves.push_back(0.5f);
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// flds.octaves.push_back(1.0f);
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// flds.octaves.push_back(2.0f);
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// flds.octaves.push_back(4.0f);
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// flds.octaves.push_back(8.0f);
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// // scales calculations
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// std::vector<Level> levels;
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// pyrLevels(FRAME_WIDTH, FRAME_HEIGHT, ORIG_OBJECT_WIDTH, ORIG_OBJECT_HEIGHT, TOTAL_SCALES, minScale, maxScale, levels);
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// for (std::vector<Level>::iterator level = levels.begin(); level < levels.end(); ++level)
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// {
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// float minAbsLog = FLT_MAX;
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// for (std::vector<float>::iterator oct = flds.octaves.begin(); oct < flds.octaves.end(); ++oct)
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// {
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// float logOctave = log(*oct);
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// float logAbsScale = fabs((*level).logFactor - logOctave);
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// if(logAbsScale < minAbsLog)
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// (*level).assign(*oct, ORIG_OBJECT_WIDTH, ORIG_OBJECT_HEIGHT);
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// }
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// }
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// load cascade from xml
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// read(const FileNode &root)
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Filds& flds = *filds;
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if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
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// flds.calcLevels(FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
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return true;
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}
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