cv::Rect cv::softcascade::Detection::bb() const
{
return cv::Rect(x, y, w, h);
+}
+
+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<cv::softcascade::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(cv::softcascade::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<cv::softcascade::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 = cv::softcascade::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 cv::softcascade::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;
+ }
+};
+
+cv::softcascade::Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
+: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
+
+cv::softcascade::Detector::~Detector() { delete fields;}
+
+void cv::softcascade::Detector::read(const cv::FileNode& fn)
+{
+ Algorithm::read(fn);
+}
+
+bool cv::softcascade::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 == cv::softcascade::Detector::DOLLAR);
+ DollarNMS(objects);
+}
+
+}
+
+void cv::softcascade::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 cv::softcascade::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 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;
+ }
}
\ No newline at end of file
+++ /dev/null
-/*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;
- }
-}
\ No newline at end of file
projects / platform / upstream / opencv.git / commitdiff