update documentation for softcascade module
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marina.kolpakova
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@ -197,7 +197,7 @@ namespace {
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using namespace sft;
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void glob(const string& refRoot, const string& refExt, svector &refvecFiles)
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{
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std::string strFilePath; // Filepath
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std::string strFilePath; // File path
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std::string strExtension; // Extension
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std::string strPattern = refRoot + "\\*.*";
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@ -240,14 +240,14 @@ void glob(const string& refRoot, const string& refExt, svector &refvecFiles)
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#endif
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// in the default case data folders should be alligned as following:
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// in the default case data folders should be aligned as following:
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// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
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// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
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ScaledDataset::ScaledDataset(const string& path, const int oct)
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{
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dprintf("%s\n", "get dataset file names...");
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dprintf("%s\n", "Positives globbing...");
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dprintf("%s\n", "Positives globing...");
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#if !defined (_WIN32) && ! defined(__MINGW32__)
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glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);
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@ -255,7 +255,7 @@ ScaledDataset::ScaledDataset(const string& path, const int oct)
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glob(path + "/pos/octave_" + itoa(oct), "png", pos);
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#endif
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dprintf("%s\n", "Negatives globbing...");
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dprintf("%s\n", "Negatives globing...");
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#if !defined (_WIN32) && ! defined(__MINGW32__)
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glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);
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#else
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@ -93,7 +93,7 @@ struct Config
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// List of octaves for which have to be trained cascades (a list of powers of two)
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ivector octaves;
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// Maximum number of positives that should be ised during training
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// Maximum number of positives that should be used during training
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int positives;
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// Initial number of negatives used during training.
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@ -102,10 +102,10 @@ struct Config
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// Number of weak negatives to add each bootstrapping step.
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int btpNegatives;
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// Inverse of scale for feature resazing
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// Inverse of scale for feature resizing
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int shrinkage;
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// Depth on weak classifier's desition tree
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// Depth on weak classifier's decision tree
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int treeDepth;
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// Weak classifiers number in resulted cascade
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@ -120,10 +120,10 @@ struct Config
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// path to resulting cascade
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string outXmlPath;
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// seed for fandom generation
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// seed for random generation
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int seed;
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// // bounding retangle for actual exemple into example window
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// // bounding rectangle for actual example into example window
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// cv::Rect exampleWindow;
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};
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@ -40,7 +40,7 @@
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//
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//M*/
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// Trating application for Soft Cascades.
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// Training application for Soft Cascades.
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#include <sft/common.hpp>
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#include <iostream>
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@ -114,7 +114,7 @@ int main(int argc, char** argv)
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// 3. Train all octaves
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for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
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{
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// a. create rangom feature pool
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// a. create random feature pool
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int nfeatures = cfg.poolSize;
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cv::Size model = cfg.model(it);
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std::cout << "Model " << model << std::endl;
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@ -25,29 +25,37 @@ The sample has been rejected if it fall rejection threshold. So stageless cascad
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.. [BMTG12] Rodrigo Benenson, Markus Mathias, Radu Timofte and Luc Van Gool. Pedestrian detection at 100 frames per second. IEEE CVPR, 2012.
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SCascade
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----------------
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.. ocv:class:: SCascade
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SoftCascadeDetector
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-------------------
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.. ocv:class:: SoftCascadeDetector
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Implementation of soft (stageless) cascaded detector. ::
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class CV_EXPORTS SCascade : public Algorithm
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class CV_EXPORTS_W SoftCascadeDetector : public Algorithm
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{
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public:
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SCascade(const float minScale = 0.4f, const float maxScale = 5.f, const int scales = 55, const int rejfactor = 1);
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virtual ~SCascade();
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enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT};
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CV_WRAP SoftCascadeDetector(double minScale = 0.4, double maxScale = 5., int scales = 55, int rejCriteria = 1);
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CV_WRAP virtual ~SoftCascadeDetector();
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cv::AlgorithmInfo* info() const;
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virtual bool load(const FileNode& fn);
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CV_WRAP virtual bool load(const FileNode& fileNode);
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CV_WRAP virtual void read(const FileNode& fileNode);
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virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
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virtual void detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const;
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};
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CV_WRAP virtual void detect(InputArray image, InputArray rois, CV_OUT OutputArray rects, CV_OUT OutputArray confs) const;
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}
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SCascade::SCascade
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--------------------------
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SoftCascadeDetector::SoftCascadeDetector
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----------------------------------------
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An empty cascade will be created.
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.. ocv:function:: bool SCascade::SCascade(const float minScale = 0.4f, const float maxScale = 5.f, const int scales = 55, const int rejfactor = 1)
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.. ocv:function:: SoftCascadeDetector::SoftCascadeDetector(float minScale = 0.4f, float maxScale = 5.f, int scales = 55, int rejCriteria = 1)
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.. ocv:pyfunction:: cv2.SoftCascadeDetector.SoftCascadeDetector(minScale[, maxScale[, scales[, rejCriteria]]]) -> cascade
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:param minScale: a minimum scale relative to the original size of the image on which cascade will be applied.
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@ -55,35 +63,39 @@ An empty cascade will be created.
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:param scales: a number of scales from minScale to maxScale.
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:param rejfactor: used for non maximum suppression.
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:param rejCriteria: algorithm used for non maximum suppression.
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SCascade::~SCascade
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---------------------------
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Destructor for SCascade.
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SoftCascadeDetector::~SoftCascadeDetector
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-----------------------------------------
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Destructor for SoftCascadeDetector.
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.. ocv:function:: SCascade::~SCascade()
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.. ocv:function:: SoftCascadeDetector::~SoftCascadeDetector()
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SCascade::load
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SoftCascadeDetector::load
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--------------------------
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Load cascade from FileNode.
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.. ocv:function:: bool SCascade::load(const FileNode& fn)
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.. ocv:function:: bool SoftCascadeDetector::load(const FileNode& fileNode)
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:param fn: File node from which the soft cascade are read.
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.. ocv:pyfunction:: cv2.SoftCascadeDetector.load(fileNode)
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:param fileNode: File node from which the soft cascade are read.
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SCascade::detect
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--------------------------
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SoftCascadeDetector::detect
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---------------------------
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Apply cascade to an input frame and return the vector of Detection objects.
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.. ocv:function:: void SCascade::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
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.. ocv:function:: void SoftCascadeDetector::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
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.. ocv:function:: void SCascade::detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const
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.. ocv:function:: void SoftCascadeDetector::detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const
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.. ocv:pyfunction:: cv2.SoftCascadeDetector.detect(image, rois) -> (rects, confs)
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:param image: a frame on which detector will be applied.
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@ -93,4 +105,41 @@ Apply cascade to an input frame and return the vector of Detection objects.
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:param rects: an output array of bounding rectangles for detected objects.
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:param confs: an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th confidence.
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:param confs: an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th confidence.
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ChannelFeatureBuilder
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---------------------
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.. ocv:class:: ChannelFeatureBuilder
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Public interface for of soft (stageless) cascaded detector. ::
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class CV_EXPORTS_W ChannelFeatureBuilder : public Algorithm
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{
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public:
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virtual ~ChannelFeatureBuilder();
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CV_WRAP_AS(compute) virtual void operator()(InputArray src, CV_OUT OutputArray channels) const = 0;
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CV_WRAP static cv::Ptr<ChannelFeatureBuilder> create();
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};
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ChannelFeatureBuilder:~ChannelFeatureBuilder
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--------------------------------------------
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Destructor for ChannelFeatureBuilder.
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.. ocv:function:: ChannelFeatureBuilder::~ChannelFeatureBuilder()
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ChannelFeatureBuilder::operator()
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---------------------------------
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Create channel feature integrals for input image.
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.. ocv:function:: void ChannelFeatureBuilder::operator()(InputArray src, OutputArray channels) const
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.. ocv:pyfunction:: cv2.ChannelFeatureBuilder.compute(src, channels) -> None
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:param src source frame
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:param channels in OutputArray of computed channels
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@ -1,2 +1,82 @@
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Soft Cascade Training
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=======================
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=======================
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.. highlight:: cpp
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Soft Cascade Detector Training
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--------------------------------------------
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SoftCascadeOctave
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-----------------
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.. ocv:class:: SoftCascadeOctave
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Public interface for soft cascade training algorithm
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class CV_EXPORTS SoftCascadeOctave : public Algorithm
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{
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public:
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enum {
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// Direct backward pruning. (Cha Zhang and Paul Viola)
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DBP = 1,
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// Multiple instance pruning. (Cha Zhang and Paul Viola)
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MIP = 2,
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// Originally proposed by L. Bourdev and J. Brandt
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HEURISTIC = 4 };
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virtual ~SoftCascadeOctave();
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static cv::Ptr<SoftCascadeOctave> create(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);
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virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth) = 0;
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virtual void setRejectThresholds(OutputArray thresholds) = 0;
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virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const = 0;
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virtual void write( CvFileStorage* fs, string name) const = 0;
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};
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SoftCascadeOctave::~SoftCascadeOctave
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---------------------------------------
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Destructor for SoftCascadeOctave.
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.. ocv:function:: SoftCascadeOctave::~SoftCascadeOctave()
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SoftCascadeOctave::train
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------------------------
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.. ocv:function:: bool SoftCascadeOctave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
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:param dataset an object that allows communicate for training set.
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:param pool an object that presents feature pool.
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:param weaks a number of weak trees should be trained.
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:param treeDepth a depth of resulting weak trees.
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SoftCascadeOctave::setRejectThresholds
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--------------------------------------
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.. ocv:function:: void SoftCascadeOctave::setRejectThresholds(OutputArray thresholds)
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:param thresholds an output array of resulted rejection vector. Have same size as number of trained stages.
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SoftCascadeOctave::write
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------------------------
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.. ocv:function:: write SoftCascadeOctave::train(cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const
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.. ocv:function:: write SoftCascadeOctave::train( CvFileStorage* fs, string name) const
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:param fs an output file storage to store trained detector.
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:param pool an object that presents feature pool.
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:param dataset a rejection vector that should be included in detector xml file.
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:param name a name of root node for trained detector.
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@ -11,7 +11,7 @@
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2008-2013, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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@ -22,7 +22,7 @@
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// and / or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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@ -216,7 +216,7 @@ void BoostedSoftCascadeOctave::setRejectThresholds(cv::OutputArray _thresholds)
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{
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dprintf("set thresholds according to DBP strategy\n");
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// labels desided by classifier
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// labels decided by classifier
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cv::Mat desisions(responses.cols, responses.rows, responses.type());
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float* dptr = desisions.ptr<float>(0);
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@ -423,7 +423,7 @@ void BoostedSoftCascadeOctave::write( cv::FileStorage &fso, const FeaturePool* p
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<< "scale" << logScale
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<< "weaks" << weak->total
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<< "trees" << "[";
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// should be replased with the H.L. one
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// should be replaced with the H.L. one
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CvSeqReader reader;
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cvStartReadSeq( weak, &reader);
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@ -463,7 +463,7 @@ bool BoostedSoftCascadeOctave::train(const Dataset* dataset, const FeaturePool*
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processPositives(dataset, pool);
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generateNegatives(dataset, pool);
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// 2. only sumple case (all features used)
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// 2. only simple case (all features used)
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int nfeatures = pool->size();
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cv::Mat varIdx(1, nfeatures, CV_32SC1);
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int* ptr = varIdx.ptr<int>(0);
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@ -471,7 +471,7 @@ bool BoostedSoftCascadeOctave::train(const Dataset* dataset, const FeaturePool*
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for (int x = 0; x < nfeatures; ++x)
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ptr[x] = x;
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// 3. only sumple case (all samples used)
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// 3. only simple case (all samples used)
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int nsamples = npositives + nnegatives;
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cv::Mat sampleIdx(1, nsamples, CV_32SC1);
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ptr = sampleIdx.ptr<int>(0);
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@ -479,7 +479,7 @@ bool BoostedSoftCascadeOctave::train(const Dataset* dataset, const FeaturePool*
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for (int x = 0; x < nsamples; ++x)
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ptr[x] = x;
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// 4. ICF has an orderable responce.
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// 4. ICF has an ordered response.
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cv::Mat varType(1, nfeatures + 1, CV_8UC1);
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uchar* uptr = varType.ptr<uchar>(0);
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for (int x = 0; x < nfeatures; ++x)
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