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Commit dd8de0c4 authored by marina.kolpakova's avatar marina.kolpakova
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move soft cascade octave to ml module

parent a0e93d04
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Showing with 497 additions and 409 deletions
apps/sft/include/sft/octave.hpp
View file @ dd8de0c4
......@@ -155,6 +155,9 @@ private:
void write(cv::FileStorage& fs, const std::string&, const ICF& f);
std::ostream& operator<<(std::ostream& out, const ICF& m);
using cv::FeaturePool;
using cv::Dataset;
class ICFFeaturePool : public cv::FeaturePool
{
public:
......@@ -184,79 +187,20 @@ private:
};
using cv::FeaturePool;
class Dataset
class ScaledDataset : public Dataset
{
public:
typedef enum {POSITIVE = 1, NEGATIVE = 2} SampleType;
Dataset(const sft::string& path, const int octave);
ScaledDataset(const sft::string& path, const int octave);
cv::Mat get(SampleType type, int idx) const;
int available(SampleType type) const;
virtual cv::Mat get(SampleType type, int idx) const;
virtual int available(SampleType type) const;
virtual ~ScaledDataset();
private:
svector pos;
svector neg;
};
// used for traning single octave scale
class Octave : cv::Boost
{
public:
enum
{
// Direct backward pruning. (Cha Zhang and Paul Viola)
DBP = 1,
// Multiple instance pruning. (Cha Zhang and Paul Viola)
MIP = 2,
// Originally proposed by L. bourdev and J. brandt
HEURISTIC = 4
};
Octave(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);
virtual ~Octave();
virtual bool train(const Dataset& dataset, const FeaturePool* pool, int weaks, int treeDepth);
virtual float predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const;
virtual void setRejectThresholds(cv::Mat& thresholds);
virtual void write( CvFileStorage* fs, string name) const;
virtual void write( cv::FileStorage &fs, const FeaturePool* pool, const Mat& thresholds) const;
int logScale;
protected:
virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat());
void processPositives(const Dataset& dataset, const FeaturePool* pool);
void generateNegatives(const Dataset& dataset, const FeaturePool* pool);
float predict( const Mat& _sample, const cv::Range range) const;
private:
void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const;
virtual void initial_weights(double (&p)[2]);
cv::Rect boundingBox;
int npositives;
int nnegatives;
int shrinkage;
Mat integrals;
Mat responses;
CvBoostParams params;
Mat trainData;
};
}
#endif
\ No newline at end of file
apps/sft/octave.cpp
View file @ dd8de0c4
......@@ -46,343 +46,6 @@
#include <glob.h>
#include <queue>
// ============ Octave ============ //
sft::Octave::Octave(cv::Rect bb, int np, int nn, int ls, int shr)
: logScale(ls), boundingBox(bb), npositives(np), nnegatives(nn), shrinkage(shr)
{
int maxSample = npositives + nnegatives;
responses.create(maxSample, 1, CV_32FC1);
CvBoostParams _params;
{
// tree params
_params.max_categories = 10;
_params.max_depth = 2;
_params.cv_folds = 0;
_params.truncate_pruned_tree = false;
_params.use_surrogates = false;
_params.use_1se_rule = false;
_params.regression_accuracy = 1.0e-6;
// boost params
_params.boost_type = CvBoost::GENTLE;
_params.split_criteria = CvBoost::SQERR;
_params.weight_trim_rate = 0.95;
// simple defaults
_params.min_sample_count = 2;
_params.weak_count = 1;
}
params = _params;
}
sft::Octave::~Octave(){}
bool sft::Octave::train( const cv::Mat& _trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
{
bool update = false;
return cv::Boost::train(_trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params,
update);
}
void sft::Octave::setRejectThresholds(cv::Mat& thresholds)
{
dprintf("set thresholds according to DBP strategy\n");
// labels desided by classifier
cv::Mat desisions(responses.cols, responses.rows, responses.type());
float* dptr = desisions.ptr<float>(0);
// mask of samples satisfying the condition
cv::Mat ppmask(responses.cols, responses.rows, CV_8UC1);
uchar* mptr = ppmask.ptr<uchar>(0);
int nsamples = npositives + nnegatives;
cv::Mat stab;
for (int si = 0; si < nsamples; ++si)
{
float decision = dptr[si] = predict(trainData.col(si), stab, false, false);
mptr[si] = cv::saturate_cast<uchar>((uint)( (responses.ptr<float>(si)[0] == 1.f) && (decision == 1.f)));
}
int weaks = weak->total;
thresholds.create(1, weaks, CV_64FC1);
double* thptr = thresholds.ptr<double>(0);
cv::Mat traces(weaks, nsamples, CV_64FC1, cv::Scalar::all(FLT_MAX));
for (int w = 0; w < weaks; ++w)
{
double* rptr = traces.ptr<double>(w);
for (int si = 0; si < nsamples; ++si)
{
cv::Range curr(0, w + 1);
if (mptr[si])
{
float trace = predict(trainData.col(si), curr);
rptr[si] = trace;
}
}
double mintrace = 0.;
cv::minMaxLoc(traces.row(w), &mintrace);
thptr[w] = mintrace;
}
}
namespace {
using namespace sft;
}
void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool* pool)
{
int w = boundingBox.width;
int h = boundingBox.height;
integrals.create(pool->size(), (w / shrinkage + 1) * (h / shrinkage * 10 + 1), CV_32SC1);
int total = 0;
// for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it)
for (int curr = 0; curr < dataset.available( Dataset::POSITIVE); ++curr)
{
cv::Mat sample = dataset.get( Dataset::POSITIVE, curr);
cv::Mat channels = integrals.row(total).reshape(0, h / shrinkage * 10 + 1);
sample = sample(boundingBox);
pool->preprocess(sample, channels);
responses.ptr<float>(total)[0] = 1.f;
if (++total >= npositives) break;
}
dprintf("Processing positives finished:\n\trequested %d positives, collected %d samples.\n", npositives, total);
npositives = total;
nnegatives = cvRound(nnegatives * total / (double)npositives);
}
void sft::Octave::generateNegatives(const Dataset& dataset, const FeaturePool* pool)
{
// ToDo: set seed, use offsets
sft::Random::engine eng(65633343L);
sft::Random::engine idxEng(764224349868L);
// int w = boundingBox.width;
int h = boundingBox.height;
int nimages = dataset.available(Dataset::NEGATIVE);
sft::Random::uniform iRand(0, nimages - 1);
int total = 0;
Mat sum;
for (int i = npositives; i < nnegatives + npositives; ++total)
{
int curr = iRand(idxEng);
Mat frame = dataset.get(Dataset::NEGATIVE, curr);
int maxW = frame.cols - 2 * boundingBox.x - boundingBox.width;
int maxH = frame.rows - 2 * boundingBox.y - boundingBox.height;
sft::Random::uniform wRand(0, maxW -1);
sft::Random::uniform hRand(0, maxH -1);
int dx = wRand(eng);
int dy = hRand(eng);
frame = frame(cv::Rect(dx, dy, boundingBox.width, boundingBox.height));
cv::Mat channels = integrals.row(i).reshape(0, h / shrinkage * 10 + 1);
pool->preprocess(frame, channels);
dprintf("generated %d %d\n", dx, dy);
// // if (predict(sum))
{
responses.ptr<float>(i)[0] = 0.f;
++i;
}
}
dprintf("Processing negatives finished:\n\trequested %d negatives, viewed %d samples.\n", nnegatives, total);
}
template <typename T> int sgn(T val) {
return (T(0) < val) - (val < T(0));
}
void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const
{
std::queue<const CvDTreeNode*> nodes;
nodes.push( tree->get_root());
const CvDTreeNode* tempNode;
int leafValIdx = 0;
int internalNodeIdx = 1;
float* leafs = new float[(int)pow(2.f, get_params().max_depth)];
fs << "{";
fs << "treeThreshold" << *th;
fs << "internalNodes" << "[";
while (!nodes.empty())
{
tempNode = nodes.front();
CV_Assert( tempNode->left );
if ( !tempNode->left->left && !tempNode->left->right)
{
leafs[-leafValIdx] = (float)tempNode->left->value;
fs << leafValIdx-- ;
}
else
{
nodes.push( tempNode->left );
fs << internalNodeIdx++;
}
CV_Assert( tempNode->right );
if ( !tempNode->right->left && !tempNode->right->right)
{
leafs[-leafValIdx] = (float)tempNode->right->value;
fs << leafValIdx--;
}
else
{
nodes.push( tempNode->right );
fs << internalNodeIdx++;
}
int fidx = tempNode->split->var_idx;
fs << nfeatures;
used[nfeatures++] = fidx;
fs << tempNode->split->ord.c;
nodes.pop();
}
fs << "]";
fs << "leafValues" << "[";
for (int ni = 0; ni < -leafValIdx; ni++)
fs << leafs[ni];
fs << "]";
fs << "}";
}
void sft::Octave::write( cv::FileStorage &fso, const FeaturePool* pool, const Mat& thresholds) const
{
CV_Assert(!thresholds.empty());
cv::Mat used( 1, weak->total * (pow(2, params.max_depth) - 1), CV_32SC1);
int* usedPtr = used.ptr<int>(0);
int nfeatures = 0;
fso << "{"
<< "scale" << logScale
<< "weaks" << weak->total
<< "trees" << "[";
// should be replased with the H.L. one
CvSeqReader reader;
cvStartReadSeq( weak, &reader);
for(int i = 0; i < weak->total; i++ )
{
CvBoostTree* tree;
CV_READ_SEQ_ELEM( tree, reader );
traverse(tree, fso, nfeatures, usedPtr, thresholds.ptr<double>(0) + i);
}
fso << "]";
// features
fso << "features" << "[";
for (int i = 0; i < nfeatures; ++i)
pool->write(fso, usedPtr[i]);
fso << "]"
<< "}";
}
void sft::Octave::initial_weights(double (&p)[2])
{
double n = data->sample_count;
p[0] = n / (2. * (double)(nnegatives));
p[1] = n / (2. * (double)(npositives));
}
bool sft::Octave::train(const Dataset& dataset, const FeaturePool* pool, int weaks, int treeDepth)
{
CV_Assert(treeDepth == 2);
CV_Assert(weaks > 0);
params.max_depth = treeDepth;
params.weak_count = weaks;
// 1. fill integrals and classes
processPositives(dataset, pool);
generateNegatives(dataset, pool);
// 2. only sumple case (all features used)
int nfeatures = pool->size();
cv::Mat varIdx(1, nfeatures, CV_32SC1);
int* ptr = varIdx.ptr<int>(0);
for (int x = 0; x < nfeatures; ++x)
ptr[x] = x;
// 3. only sumple case (all samples used)
int nsamples = npositives + nnegatives;
cv::Mat sampleIdx(1, nsamples, CV_32SC1);
ptr = sampleIdx.ptr<int>(0);
for (int x = 0; x < nsamples; ++x)
ptr[x] = x;
// 4. ICF has an orderable responce.
cv::Mat varType(1, nfeatures + 1, CV_8UC1);
uchar* uptr = varType.ptr<uchar>(0);
for (int x = 0; x < nfeatures; ++x)
uptr[x] = CV_VAR_ORDERED;
uptr[nfeatures] = CV_VAR_CATEGORICAL;
trainData.create(nfeatures, nsamples, CV_32FC1);
for (int fi = 0; fi < nfeatures; ++fi)
{
float* dptr = trainData.ptr<float>(fi);
for (int si = 0; si < nsamples; ++si)
{
dptr[si] = pool->apply(fi, si, integrals);
}
}
cv::Mat missingMask;
bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
if (!ok)
std::cout << "ERROR: tree can not be trained " << std::endl;
return ok;
}
float sft::Octave::predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const
{
CvMat sample = _sample, votes = _votes;
return CvBoost::predict(&sample, 0, (_votes.empty())? 0 : &votes, CV_WHOLE_SEQ, raw_mode, return_sum);
}
float sft::Octave::predict( const Mat& _sample, const cv::Range range) const
{
CvMat sample = _sample;
return CvBoost::predict(&sample, 0, 0, range, false, true);
}
void sft::Octave::write( CvFileStorage* fs, string name) const
{
CvBoost::write(fs, name.c_str());
}
// ========= FeaturePool ========= //
sft::ICFFeaturePool::ICFFeaturePool(cv::Size m, int n) : FeaturePool(), model(m), nfeatures(n)
......@@ -499,7 +162,7 @@ void glob(const string& path, svector& ret)
// in the default case data folders should be alligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
Dataset::Dataset(const string& path, const int oct)
ScaledDataset::ScaledDataset(const string& path, const int oct)
{
dprintf("%s\n", "get dataset file names...");
......@@ -514,13 +177,15 @@ Dataset::Dataset(const string& path, const int oct)
CV_Assert(neg.size() != size_t(0));
}
cv::Mat Dataset::get(SampleType type, int idx) const
cv::Mat ScaledDataset::get(SampleType type, int idx) const
{
const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
return cv::imread(src);
}
int Dataset::available(SampleType type) const
int ScaledDataset::available(SampleType type) const
{
return (int)((type == POSITIVE)? pos.size():neg.size());
}
ScaledDataset::~ScaledDataset(){}
\ No newline at end of file
apps/sft/sft.cpp
View file @ dd8de0c4
......@@ -127,12 +127,12 @@ int main(int argc, char** argv)
cv::Rect boundingBox = cfg.bbox(it);
std::cout << "Object bounding box" << boundingBox << std::endl;
sft::Octave boost(boundingBox, npositives, nnegatives, *it, shrinkage);
cv::Octave boost(boundingBox, npositives, nnegatives, *it, shrinkage);
std::string path = cfg.trainPath;
sft::Dataset dataset(path, boost.logScale);
sft::ScaledDataset dataset(path, boost.logScale);
if (boost.train(dataset, &pool, cfg.weaks, cfg.treeDepth))
if (boost.train(&dataset, &pool, cfg.weaks, cfg.treeDepth))
{
CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
boost.write(fout, cfg.cascadeName);
......
modules/ml/include/opencv2/ml/ml.hpp
View file @ dd8de0c4
......@@ -2142,7 +2142,72 @@ public:
virtual void preprocess(const Mat& frame, Mat& integrals) const = 0;
virtual ~FeaturePool() = 0;
virtual ~FeaturePool();
};
class Dataset
{
public:
typedef enum {POSITIVE = 1, NEGATIVE = 2} SampleType;
virtual cv::Mat get(SampleType type, int idx) const = 0;
virtual int available(SampleType type) const = 0;
virtual ~Dataset();
};
// used for traning single octave scale
class Octave : cv::Boost
{
public:
enum
{
// Direct backward pruning. (Cha Zhang and Paul Viola)
DBP = 1,
// Multiple instance pruning. (Cha Zhang and Paul Viola)
MIP = 2,
// Originally proposed by L. bourdev and J. brandt
HEURISTIC = 4
};
Octave(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);
virtual ~Octave();
virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth);
virtual float predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const;
virtual void setRejectThresholds(cv::Mat& thresholds);
virtual void write( CvFileStorage* fs, string name) const;
virtual void write( cv::FileStorage &fs, const FeaturePool* pool, const Mat& thresholds) const;
int logScale;
protected:
virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat());
void processPositives(const Dataset* dataset, const FeaturePool* pool);
void generateNegatives(const Dataset* dataset, const FeaturePool* pool);
float predict( const Mat& _sample, const cv::Range range) const;
private:
void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const;
virtual void initial_weights(double (&p)[2]);
cv::Rect boundingBox;
int npositives;
int nnegatives;
int shrinkage;
Mat integrals;
Mat responses;
CvBoostParams params;
Mat trainData;
};
}
......
modules/ml/src/octave.cpp
View file @ dd8de0c4
......@@ -41,5 +41,419 @@
//M*/
#include "precomp.hpp"
#include <queue>
#define WITH_DEBUG_OUT
#if defined WITH_DEBUG_OUT
# include <stdio.h>
# define dprintf(format, ...) \
do { printf(format, ##__VA_ARGS__); } while (0)
#else
# define dprintf(format, ...)
#endif
#if defined(_MSC_VER) && _MSC_VER >= 1600
# include <random>
namespace sft {
struct Random
{
typedef std::mt19937 engine;
typedef std::uniform_int<int> uniform;
};
}
#elif (__GNUC__) && __GNUC__ > 3 && __GNUC_MINOR__ > 1
# if defined (__cplusplus) && __cplusplus > 201100L
# include <random>
namespace sft {
struct Random
{
typedef std::mt19937 engine;
typedef std::uniform_int<int> uniform;
};
}
# else
# include <tr1/random>
namespace sft {
struct Random
{
typedef std::tr1::mt19937 engine;
typedef std::tr1::uniform_int<int> uniform;
};
}
# endif
#else
#include <opencv2/core/core.hpp>
namespace rnd {
typedef cv::RNG engine;
template<typename T>
struct uniform_int
{
uniform_int(const int _min, const int _max) : min(_min), max(_max) {}
T operator() (engine& eng, const int bound) const
{
return (T)eng.uniform(min, bound);
}
T operator() (engine& eng) const
{
return (T)eng.uniform(min, max);
}
private:
int min;
int max;
};
}
namespace sft {
struct Random
{
typedef rnd::engine engine;
typedef rnd::uniform_int<int> uniform;
};
}
#endif
cv::FeaturePool::~FeaturePool(){}
cv::Dataset::~Dataset(){}
cv::Octave::Octave(cv::Rect bb, int np, int nn, int ls, int shr)
: logScale(ls), boundingBox(bb), npositives(np), nnegatives(nn), shrinkage(shr)
{
int maxSample = npositives + nnegatives;
responses.create(maxSample, 1, CV_32FC1);
CvBoostParams _params;
{
// tree params
_params.max_categories = 10;
_params.max_depth = 2;
_params.cv_folds = 0;
_params.truncate_pruned_tree = false;
_params.use_surrogates = false;
_params.use_1se_rule = false;
_params.regression_accuracy = 1.0e-6;
// boost params
_params.boost_type = CvBoost::GENTLE;
_params.split_criteria = CvBoost::SQERR;
_params.weight_trim_rate = 0.95;
// simple defaults
_params.min_sample_count = 2;
_params.weak_count = 1;
}
params = _params;
}
cv::Octave::~Octave(){}
bool cv::Octave::train( const cv::Mat& _trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
{
bool update = false;
return cv::Boost::train(_trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params,
update);
}
void cv::Octave::setRejectThresholds(cv::Mat& thresholds)
{
dprintf("set thresholds according to DBP strategy\n");
// labels desided by classifier
cv::Mat desisions(responses.cols, responses.rows, responses.type());
float* dptr = desisions.ptr<float>(0);
// mask of samples satisfying the condition
cv::Mat ppmask(responses.cols, responses.rows, CV_8UC1);
uchar* mptr = ppmask.ptr<uchar>(0);
int nsamples = npositives + nnegatives;
cv::Mat stab;
for (int si = 0; si < nsamples; ++si)
{
float decision = dptr[si] = predict(trainData.col(si), stab, false, false);
mptr[si] = cv::saturate_cast<uchar>((uint)( (responses.ptr<float>(si)[0] == 1.f) && (decision == 1.f)));
}
int weaks = weak->total;
thresholds.create(1, weaks, CV_64FC1);
double* thptr = thresholds.ptr<double>(0);
cv::Mat traces(weaks, nsamples, CV_64FC1, cv::Scalar::all(FLT_MAX));
for (int w = 0; w < weaks; ++w)
{
double* rptr = traces.ptr<double>(w);
for (int si = 0; si < nsamples; ++si)
{
cv::Range curr(0, w + 1);
if (mptr[si])
{
float trace = predict(trainData.col(si), curr);
rptr[si] = trace;
}
}
double mintrace = 0.;
cv::minMaxLoc(traces.row(w), &mintrace);
thptr[w] = mintrace;
}
}
void cv::Octave::processPositives(const Dataset* dataset, const FeaturePool* pool)
{
int w = boundingBox.width;
int h = boundingBox.height;
integrals.create(pool->size(), (w / shrinkage + 1) * (h / shrinkage * 10 + 1), CV_32SC1);
int total = 0;
// for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it)
for (int curr = 0; curr < dataset->available( Dataset::POSITIVE); ++curr)
{
cv::Mat sample = dataset->get( Dataset::POSITIVE, curr);
cv::Mat channels = integrals.row(total).reshape(0, h / shrinkage * 10 + 1);
sample = sample(boundingBox);
pool->preprocess(sample, channels);
responses.ptr<float>(total)[0] = 1.f;
if (++total >= npositives) break;
}
dprintf("Processing positives finished:\n\trequested %d positives, collected %d samples.\n", npositives, total);
npositives = total;
nnegatives = cvRound(nnegatives * total / (double)npositives);
}
void cv::Octave::generateNegatives(const Dataset* dataset, const FeaturePool* pool)
{
// ToDo: set seed, use offsets
sft::Random::engine eng(65633343L);
sft::Random::engine idxEng(764224349868L);
// int w = boundingBox.width;
int h = boundingBox.height;
int nimages = dataset->available(Dataset::NEGATIVE);
sft::Random::uniform iRand(0, nimages - 1);
int total = 0;
Mat sum;
for (int i = npositives; i < nnegatives + npositives; ++total)
{
int curr = iRand(idxEng);
Mat frame = dataset->get(Dataset::NEGATIVE, curr);
int maxW = frame.cols - 2 * boundingBox.x - boundingBox.width;
int maxH = frame.rows - 2 * boundingBox.y - boundingBox.height;
sft::Random::uniform wRand(0, maxW -1);
sft::Random::uniform hRand(0, maxH -1);
int dx = wRand(eng);
int dy = hRand(eng);
frame = frame(cv::Rect(dx, dy, boundingBox.width, boundingBox.height));
cv::Mat channels = integrals.row(i).reshape(0, h / shrinkage * 10 + 1);
pool->preprocess(frame, channels);
dprintf("generated %d %d\n", dx, dy);
// // if (predict(sum))
{
responses.ptr<float>(i)[0] = 0.f;
++i;
}
}
dprintf("Processing negatives finished:\n\trequested %d negatives, viewed %d samples.\n", nnegatives, total);
}
template <typename T> int sgn(T val) {
return (T(0) < val) - (val < T(0));
}
void cv::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const
{
std::queue<const CvDTreeNode*> nodes;
nodes.push( tree->get_root());
const CvDTreeNode* tempNode;
int leafValIdx = 0;
int internalNodeIdx = 1;
float* leafs = new float[(int)pow(2.f, get_params().max_depth)];
fs << "{";
fs << "treeThreshold" << *th;
fs << "internalNodes" << "[";
while (!nodes.empty())
{
tempNode = nodes.front();
CV_Assert( tempNode->left );
if ( !tempNode->left->left && !tempNode->left->right)
{
leafs[-leafValIdx] = (float)tempNode->left->value;
fs << leafValIdx-- ;
}
else
{
nodes.push( tempNode->left );
fs << internalNodeIdx++;
}
CV_Assert( tempNode->right );
if ( !tempNode->right->left && !tempNode->right->right)
{
leafs[-leafValIdx] = (float)tempNode->right->value;
fs << leafValIdx--;
}
else
{
nodes.push( tempNode->right );
fs << internalNodeIdx++;
}
int fidx = tempNode->split->var_idx;
fs << nfeatures;
used[nfeatures++] = fidx;
fs << tempNode->split->ord.c;
nodes.pop();
}
fs << "]";
fs << "leafValues" << "[";
for (int ni = 0; ni < -leafValIdx; ni++)
fs << leafs[ni];
fs << "]";
fs << "}";
}
void cv::Octave::write( cv::FileStorage &fso, const FeaturePool* pool, const Mat& thresholds) const
{
CV_Assert(!thresholds.empty());
cv::Mat used( 1, weak->total * (pow(2, params.max_depth) - 1), CV_32SC1);
int* usedPtr = used.ptr<int>(0);
int nfeatures = 0;
fso << "{"
<< "scale" << logScale
<< "weaks" << weak->total
<< "trees" << "[";
// should be replased with the H.L. one
CvSeqReader reader;
cvStartReadSeq( weak, &reader);
for(int i = 0; i < weak->total; i++ )
{
CvBoostTree* tree;
CV_READ_SEQ_ELEM( tree, reader );
traverse(tree, fso, nfeatures, usedPtr, thresholds.ptr<double>(0) + i);
}
fso << "]";
// features
fso << "features" << "[";
for (int i = 0; i < nfeatures; ++i)
pool->write(fso, usedPtr[i]);
fso << "]"
<< "}";
}
void cv::Octave::initial_weights(double (&p)[2])
{
double n = data->sample_count;
p[0] = n / (2. * (double)(nnegatives));
p[1] = n / (2. * (double)(npositives));
}
bool cv::Octave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
{
CV_Assert(treeDepth == 2);
CV_Assert(weaks > 0);
params.max_depth = treeDepth;
params.weak_count = weaks;
// 1. fill integrals and classes
processPositives(dataset, pool);
generateNegatives(dataset, pool);
// 2. only sumple case (all features used)
int nfeatures = pool->size();
cv::Mat varIdx(1, nfeatures, CV_32SC1);
int* ptr = varIdx.ptr<int>(0);
for (int x = 0; x < nfeatures; ++x)
ptr[x] = x;
// 3. only sumple case (all samples used)
int nsamples = npositives + nnegatives;
cv::Mat sampleIdx(1, nsamples, CV_32SC1);
ptr = sampleIdx.ptr<int>(0);
for (int x = 0; x < nsamples; ++x)
ptr[x] = x;
// 4. ICF has an orderable responce.
cv::Mat varType(1, nfeatures + 1, CV_8UC1);
uchar* uptr = varType.ptr<uchar>(0);
for (int x = 0; x < nfeatures; ++x)
uptr[x] = CV_VAR_ORDERED;
uptr[nfeatures] = CV_VAR_CATEGORICAL;
trainData.create(nfeatures, nsamples, CV_32FC1);
for (int fi = 0; fi < nfeatures; ++fi)
{
float* dptr = trainData.ptr<float>(fi);
for (int si = 0; si < nsamples; ++si)
{
dptr[si] = pool->apply(fi, si, integrals);
}
}
cv::Mat missingMask;
bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
if (!ok)
std::cout << "ERROR: tree can not be trained " << std::endl;
return ok;
}
float cv::Octave::predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const
{
CvMat sample = _sample, votes = _votes;
return CvBoost::predict(&sample, 0, (_votes.empty())? 0 : &votes, CV_WHOLE_SEQ, raw_mode, return_sum);
}
float cv::Octave::predict( const Mat& _sample, const cv::Range range) const
{
CvMat sample = _sample;
return CvBoost::predict(&sample, 0, 0, range, false, true);
}
void cv::Octave::write( CvFileStorage* fs, string name) const
{
CvBoost::write(fs, name.c_str());
}
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