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:
};
-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
#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)
// 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...");
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());
-}
\ No newline at end of file
+}
+
+ScaledDataset::~ScaledDataset(){}
\ No newline at end of file
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);
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;
};
}
//M*/
#include "precomp.hpp"
+#include <queue>
-cv::FeaturePool::~FeaturePool(){}
\ No newline at end of file
+#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());
+}
projects / profile / ivi / opencv.git / commitdiff