opencv/modules/features2d/src/calonder.cpp

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#include "precomp.hpp"

#include <opencv2/core/wimage.hpp>
#include <vector>
#include <iostream>
#include <cmath>
#include <cassert>
#include <fstream>
#include <cstring>

using namespace cv;

#if defined _MSC_VER && _MSC_VER >= 1400
#pragma warning(disable: 4244 4267)
#endif

/****************************************************************************************\
The code below is implementation of Calonder Descriptor and RTree Classifier
originally introduced by Michael Calonder.

The code was integrated into OpenCV by Alexey Latyshev
\****************************************************************************************/

namespace cv {


    //----------------------------
    //randomized_tree.cpp

    inline uchar* getData(IplImage* image)
    {
        return reinterpret_cast<uchar*>(image->imageData);
    }

    inline float* RandomizedTree::getPosteriorByIndex(int index)
    {
        return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex(index));
    }

    inline const float* RandomizedTree::getPosteriorByIndex(int index) const
    {
        return posteriors_[index].p();
    }

    inline uchar* RandomizedTree::getPosteriorByIndex2(int index)
    {
        return posteriors2_[index].p();
    }


    template < typename PointT >
    cv::WImageView1_b extractPatch(cv::WImageView1_b const& image, PointT pt, int patch_sz = PATCH_SIZE)
    {
        const int offset = patch_sz / 2;

        // TODO: WImage{C}.View really should have const version
        cv::WImageView1_b &img_ref = const_cast< cv::WImageView1_b& >(image);
        return img_ref.View(pt.x - offset, pt.y - offset, patch_sz, patch_sz);
    }

    template < typename PointT >
    cv::WImageView3_b extractPatch3(cv::WImageView3_b const& image, PointT pt)
    {
        static const int offset = PATCH_SIZE / 2;

        // TODO: WImage{C}.View really should have const version
        cv::WImageView3_b &img_ref = const_cast< cv::WImageView3_b& >(image);
        return img_ref.View(pt.x - offset, pt.y - offset,
            PATCH_SIZE, PATCH_SIZE);
    }

    float *CSMatrixGenerator::cs_phi_   = NULL;
    int    CSMatrixGenerator::cs_phi_m_ = 0;
    int    CSMatrixGenerator::cs_phi_n_ = 0;

    RandomizedTree::RandomizedTree()
        : posteriors_(NULL), posteriors2_(NULL)
    {
    }

    RandomizedTree::~RandomizedTree()
    {
        freePosteriors(3);
    }

    void RandomizedTree::createNodes(int num_nodes, cv::RNG &rng)
    {
        nodes_.reserve(num_nodes);
        for (int i = 0; i < num_nodes; ++i) {
            nodes_.push_back( RTreeNode(rng(PATCH_SIZE),
                rng(PATCH_SIZE),
                rng(PATCH_SIZE),
                rng(PATCH_SIZE)) );
        }
    }

    int RandomizedTree::getIndex(uchar* patch_data) const
    {
        int index = 0;
        for (int d = 0; d < depth_; ++d) {
            int child_offset = nodes_[index](patch_data);
            index = 2*index + 1 + child_offset;
        }
        return index - nodes_.size();
    }

    void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
        cv::RNG &rng, int depth, int views, size_t reduced_num_dim,
        int num_quant_bits)
    {

        //CalonderPatchGenerator make_patch(NULL, rng);
        PatchGenerator make_patch = PatchGenerator();
        train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits);
    }

    void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
        cv::RNG &rng, PatchGenerator &make_patch,
        int depth, int views, size_t reduced_num_dim,
        int num_quant_bits)
    {
        init(base_set.size(), depth, rng);

        Mat patch;

        // Estimate posterior probabilities using random affine views
        std::vector<BaseKeypoint>::const_iterator keypt_it;
        int class_id = 0;
        for (keypt_it = base_set.begin(); keypt_it != base_set.end(); ++keypt_it, ++class_id) {
            for (int i = 0; i < views; ++i) {


                make_patch(keypt_it->image, Point2f(keypt_it->x,keypt_it->y) ,patch, Size(PATCH_SIZE,PATCH_SIZE),rng);

                IplImage _patch = patch;
                addExample(class_id, getData(&_patch));
            }
        }

        finalize(reduced_num_dim, num_quant_bits);

    }

    void RandomizedTree::allocPosteriorsAligned(int num_leaves, int num_classes)
    {
printf("alloc posteriors aligned\n");
      freePosteriors(3);

        posteriors_ = new FloatSignature[num_leaves];
      for (int i=0; i<num_leaves; ++i)
         posteriors_[i].alloc(num_classes, 16);
      //(float**) malloc(num_leaves*sizeof(float*));
        //for (int i=0; i<num_leaves; ++i) {
        //  //added
        //  /* err_cnt += posix_memalign((void**)&posteriors_[i], 16, num_classes*sizeof(float));*/
      //   posteriors_[i] = (float*)malloc(num_classes*sizeof(float));
        //  memset(posteriors_[i], 0, num_classes*sizeof(float));
        //}

        posteriors2_ = new Signature[num_leaves];
        for (int i=0; i<num_leaves; ++i)
         posteriors2_[i].alloc(num_classes, 16);
      //for (int i=0; i<num_leaves; ++i) {
        //  //added
        //  /*  err_cnt += posix_memalign((void**)&posteriors2_[i], 16, num_classes*sizeof(uchar)); */
      //   posteriors2_[i] = (uchar*)malloc(num_classes*sizeof(uchar));
        //  memset(posteriors2_[i], 0, num_classes*sizeof(uchar));
        //}

        //if (err_cnt) {
        //  printf("Something went wrong in posix_memalign()! err_cnt=%i\n", err_cnt);
        //  exit(0);
        //}

        classes_ = num_classes;
    }

    void RandomizedTree::freePosteriors(int which)
    {
        if (posteriors_ && (which&1)) {
            //for (int i=0; i<num_leaves_; ++i) {
            //  if (posteriors_[i]) {
            //      free(posteriors_[i]); //delete [] posteriors_[i];
            //      posteriors_[i] = NULL;
            //  }
            //}
            delete [] posteriors_;
            posteriors_ = NULL;
        }

        if (posteriors2_ && (which&2)) {
            //for (int i=0; i<num_leaves_; ++i)
            //  free(posteriors2_[i]);
            delete [] posteriors2_;
            posteriors2_ = NULL;
        }

        classes_ = -1;
    }

    void RandomizedTree::init(int num_classes, int depth, cv::RNG &rng)
    {
        depth_ = depth;
        num_leaves_ = 1 << depth;        // 2**d
        int num_nodes = num_leaves_ - 1; // 2**d - 1

        // Initialize probabilities and counts to 0
        allocPosteriorsAligned(num_leaves_, num_classes);      // will set classes_ correctly
        for (int i = 0; i < num_leaves_; ++i)
            memset((void*)posteriors_[i].p(), 0, num_classes*sizeof(float));
        leaf_counts_.resize(num_leaves_);

        for (int i = 0; i < num_leaves_; ++i)
            memset((void*)posteriors2_[i].p(), 0, num_classes*sizeof(uchar));

        createNodes(num_nodes, rng);
    }

    void RandomizedTree::addExample(int class_id, uchar* patch_data)
    {
        int index = getIndex(patch_data);
        float* posterior = getPosteriorByIndex(index);
        ++leaf_counts_[index];
        ++posterior[class_id];
    }

    void RandomizedTree::finalize(size_t reduced_num_dim, int num_quant_bits)
    {
        // Normalize by number of patches to reach each leaf
        for (int index = 0; index < num_leaves_; ++index) {
            float* posterior = posteriors_[index].p();
            assert(posterior != NULL);
            int count = leaf_counts_[index];
            if (count != 0) {
                float normalizer = 1.0f / count;
                for (int c = 0; c < classes_; ++c) {
                    *posterior *= normalizer;
                    ++posterior;
                }
            }
        }
        leaf_counts_.clear();

        // apply compressive sensing
        if ((int)reduced_num_dim != classes_)
            compressLeaves(reduced_num_dim);
        else {
            static bool notified = false;
            //if (!notified)
            //  printf("\n[OK] NO compression to leaves applied, dim=%i\n", reduced_num_dim);
            notified = true;
        }

        // convert float-posteriors to char-posteriors (quantization step)
        makePosteriors2(num_quant_bits);
    }

    void RandomizedTree::compressLeaves(size_t reduced_num_dim)
    {
        static bool warned = false;
        if (!warned) {
            printf("\n[OK] compressing leaves with phi %i x %i\n", (int)reduced_num_dim, classes_);
            warned = true;
        }

        static bool warned2 = false;
        if ((int)reduced_num_dim == classes_) {
            if (!warned2)
                printf("[WARNING] RandomizedTree::compressLeaves:  not compressing because reduced_dim == classes()\n");
            warned2 = true;
            return;
        }

        // DO NOT FREE RETURNED POINTER
        float *cs_phi = CSMatrixGenerator::getCSMatrix(reduced_num_dim, classes_, CSMatrixGenerator::PDT_BERNOULLI);

        float *cs_posteriors = new float[num_leaves_ * reduced_num_dim];         // temp, num_leaves_ x reduced_num_dim

        for (int i=0; i<num_leaves_; ++i)
        {
            //added (inside cycle)
            //float *post = getPosteriorByIndex(i);
            //   float *prod = &cs_posteriors[i*reduced_num_dim];
            //   cblas_sgemv(CblasRowMajor, CblasNoTrans, reduced_num_dim, classes_, 1.f, cs_phi,
            //               classes_, post, 1, 0.f, prod, 1);
            float *post = getPosteriorByIndex(i);
            //Matrix multiplication
            for (int idx = 0; idx < (int)reduced_num_dim; idx++)
            {
                cs_posteriors[i*reduced_num_dim+idx] = 0.0f;
                for (int col = 0; col < classes_; col++)
                {
                    cs_posteriors[i*reduced_num_dim+idx] += cs_phi[idx*reduced_num_dim + col] * post[col];
                }
            }
        }

        // copy new posteriors
        freePosteriors(3);
        allocPosteriorsAligned(num_leaves_, reduced_num_dim);
        for (int i=0; i<num_leaves_; ++i)
            memcpy(posteriors_[i].p(), &cs_posteriors[i*reduced_num_dim], reduced_num_dim*sizeof(float));
        classes_ = reduced_num_dim;

        delete [] cs_posteriors;
    }

    void RandomizedTree::makePosteriors2(int num_quant_bits)
    {
        int N = (1<<num_quant_bits) - 1;

        float perc[2];
        estimateQuantPercForPosteriors(perc);

        assert(posteriors_ != NULL);
        for (int i=0; i<num_leaves_; ++i)
            quantizeVector(posteriors_[i].p(), classes_, N, perc, posteriors2_[i].p());

        // printf("makePosteriors2 quantization bounds: %.3e, %.3e (num_leaves=%i, N=%i)\n",
        //        perc[0], perc[1], num_leaves_, N);
    }


    float* RandomizedTree::getPosterior(uchar* patch_data)
    {
        return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosterior(patch_data));
    }

    const float* RandomizedTree::getPosterior(uchar* patch_data) const
    {
        return getPosteriorByIndex( getIndex(patch_data) );
    }

    uchar* RandomizedTree::getPosterior2(uchar* patch_data)
    {
        return getPosteriorByIndex2( getIndex(patch_data) );
    }

    void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode)
    {
        float map_bnd[2] = {0.f,(float)N};          // bounds of quantized target interval we're mapping to
        for (int k=0; k<dim; ++k, ++vec) {
            *vec = float(int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]));
            // 0: clamp both, lower and upper values
            if (clamp_mode == 0)      *vec = (*vec<map_bnd[0]) ? map_bnd[0] : ((*vec>map_bnd[1]) ? map_bnd[1] : *vec);
            // 1: clamp lower values only
            else if (clamp_mode == 1) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : *vec;
            // 2: clamp upper values only
            else if (clamp_mode == 2) *vec = (*vec>map_bnd[1]) ? map_bnd[1] : *vec;
            // 4: no clamping
            else if (clamp_mode == 4) ; // yep, nothing
            else {
                printf("clamp_mode == %i is not valid (%s:%i).\n", clamp_mode, __FILE__, __LINE__);
                exit(1);
            }
        }

    }

    void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], uchar *dst)
    {
        int map_bnd[2] = {0, N};          // bounds of quantized target interval we're mapping to
        int tmp;
        for (int k=0; k<dim; ++k) {
            tmp = int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]);
            *dst = (uchar)((tmp<0) ? 0 : ((tmp>N) ? N : tmp));
            ++vec;
            ++dst;
        }
    }


    void RandomizedTree::read(const char* file_name, int num_quant_bits)
    {
        std::ifstream file(file_name, std::ifstream::binary);
        read(file, num_quant_bits);
        file.close();
    }

    void RandomizedTree::read(std::istream &is, int num_quant_bits)
    {
        is.read((char*)(&classes_), sizeof(classes_));
        is.read((char*)(&depth_), sizeof(depth_));

        num_leaves_ = 1 << depth_;
        int num_nodes = num_leaves_ - 1;

        nodes_.resize(num_nodes);
        is.read((char*)(&nodes_[0]), num_nodes * sizeof(nodes_[0]));

        //posteriors_.resize(classes_ * num_leaves_);
        //freePosteriors(3);
        //printf("[DEBUG] reading: %i leaves, %i classes\n", num_leaves_, classes_);
        allocPosteriorsAligned(num_leaves_, classes_);
        for (int i=0; i<num_leaves_; i++)
            is.read((char*)posteriors_[i].p(), classes_ * sizeof(*posteriors_[0].p()));

        // make char-posteriors from float-posteriors
        makePosteriors2(num_quant_bits);
    }

    void RandomizedTree::write(const char* file_name) const
    {
        std::ofstream file(file_name, std::ofstream::binary);
        write(file);
        file.close();
    }

    void RandomizedTree::write(std::ostream &os) const
    {
        if (!posteriors_) {
            printf("WARNING: Cannot write float posteriors cause posteriors_ == NULL\n");
            return;
        }

        os.write((char*)(&classes_), sizeof(classes_));
        os.write((char*)(&depth_), sizeof(depth_));

        os.write((char*)(&nodes_[0]), nodes_.size() * sizeof(nodes_[0]));
        for (int i=0; i<num_leaves_; i++) {
            os.write((char*)posteriors_[i].p(), classes_ * sizeof(*posteriors_[0].p()));
        }
    }


    void RandomizedTree::savePosteriors(std::string url, bool append)
    {
        std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
        for (int i=0; i<num_leaves_; i++) {
            float *post = posteriors_[i].p();
            char buf[20];
            for (int i=0; i<classes_; i++) {
                sprintf(buf, "%.10e", *post++);
                file << buf << ((i<classes_-1) ? " " : "");
            }
            file << std::endl;
        }
        file.close();
    }

    void RandomizedTree::savePosteriors2(std::string url, bool append)
    {
        std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
        for (int i=0; i<num_leaves_; i++) {
            uchar *post = posteriors2_[i].p();
            for (int i=0; i<classes_; i++)
                file << int(*post++) << (i<classes_-1?" ":"");
            file << std::endl;
        }
        file.close();
    }

    // returns the p% percentile of data (length n vector)
    static float percentile(float *data, int n, float p)
    {
        assert(n>0);
        assert(p>=0 && p<=1);
        std::vector<float> vec(data, data+n);
        sort(vec.begin(), vec.end());
        int ix = (int)(p*(n-1));
        return vec[ix];
    }

    void RandomizedTree::estimateQuantPercForPosteriors(float perc[2])
    {
        // _estimate_ percentiles for this tree
        // TODO: do this more accurately
        assert(posteriors_ != NULL);
        perc[0] = perc[1] = .0f;
        for (int i=0; i<num_leaves_; i++) {
            perc[0] += percentile(posteriors_[i].p(), classes_, LOWER_QUANT_PERC);
            perc[1] += percentile(posteriors_[i].p(), classes_, UPPER_QUANT_PERC);
        }
        perc[0] /= num_leaves_;
        perc[1] /= num_leaves_;
    }

    float* CSMatrixGenerator::getCSMatrix(int m, int n, PHI_DISTR_TYPE dt)
    {
        assert(m <= n);

        if (cs_phi_m_!=m || cs_phi_n_!=n || cs_phi_==NULL) {
            if (cs_phi_) delete [] cs_phi_;
            cs_phi_ = new float[m*n];
        }

#if 0 // debug - load the random matrix from a file (for reproducability of results)
        //assert(m == 176);
        //assert(n == 500);
        //const char *phi = "/u/calonder/temp/dim_red/kpca_phi.txt";
        const char *phi = "/u/calonder/temp/dim_red/debug_phi.txt";
        std::ifstream ifs(phi);
        for (size_t i=0; i<m*n; ++i) {
            if (!ifs.good()) {
                printf("[ERROR] RandomizedTree::makeRandomMeasMatrix: problem reading '%s'\n", phi);
                exit(0);
            }
            ifs >> cs_phi[i];
        }
        ifs.close();

        static bool warned=false;
        if (!warned) {
            printf("[NOTE] RT: reading %ix%i PHI matrix from '%s'...\n", m, n, phi);
            warned=true;
        }

        return;
#endif

        float *cs_phi = cs_phi_;

        if (m == n) {
            // special case - set to 0 for safety
            memset(cs_phi, 0, m*n*sizeof(float));
            printf("[WARNING] %s:%i: square CS matrix (-> no reduction)\n", __FILE__, __LINE__);
        }
        else {
            cv::RNG rng(23);

            // par is distr param, cf 'Favorable JL Distributions' (Baraniuk et al, 2006)
            if (dt == PDT_GAUSS) {
                float par = (float)(1./m);
                //modified
                cv::RNG _rng;
                for (int i=0; i<m*n; ++i)
                {
                    *cs_phi++ = (float)_rng.gaussian((double)par);//sample_normal<float>(0., par);
                }
            }
            else if (dt == PDT_BERNOULLI) {
                float par = (float)(1./sqrt((float)m));
                for (int i=0; i<m*n; ++i)
                    *cs_phi++ = (rng(2)==0 ? par : -par);
            }
            else if (dt == PDT_DBFRIENDLY) {
                float par = (float)sqrt(3./m);
                for (int i=0; i<m*n; ++i) {
                    //added
                    int _i = rng(6);
                    *cs_phi++ = (_i==0 ? par : (_i==1 ? -par : 0.f));
                }
            }
            else
                throw("PHI_DISTR_TYPE not implemented.");
        }

        return cs_phi_;
    }

    CSMatrixGenerator::~CSMatrixGenerator()
    {
        if (cs_phi_) delete [] cs_phi_;
        cs_phi_ = NULL;
    }


    //} // namespace features

    //----------------------------
    //rtree_classifier.cpp
    //namespace features {

    // Returns 16-byte aligned signatures that can be passed to getSignature().
    // Release by calling free() - NOT delete!
    //
    // note: 1) for num_sig>1 all signatures will still be 16-byte aligned, as
    //          long as sig_len%16 == 0 holds.
    //       2) casting necessary, otherwise it breaks gcc's strict aliasing rules
    inline void RTreeClassifier::safeSignatureAlloc(uchar **sig, int num_sig, int sig_len)
    {
        assert(sig_len == 176);
        void *p_sig;
        //added
        // posix_memalign(&p_sig, 16, num_sig*sig_len*sizeof(uchar));
        p_sig = malloc(num_sig*sig_len*sizeof(uchar));
        *sig = reinterpret_cast<uchar*>(p_sig);
    }

    inline uchar* RTreeClassifier::safeSignatureAlloc(int num_sig, int sig_len)
    {
        uchar *sig;
        safeSignatureAlloc(&sig, num_sig, sig_len);
        return sig;
    }

    inline void add(int size, const float* src1, const float* src2, float* dst)
    {
        while(--size >= 0) {
            *dst = *src1 + *src2;
            ++dst; ++src1; ++src2;
        }
    }

    inline void add(int size, const ushort* src1, const uchar* src2, ushort* dst)
    {
        while(--size >= 0) {
            *dst = *src1 + *src2;
            ++dst; ++src1; ++src2;
        }
    }

    RTreeClassifier::RTreeClassifier()
        : classes_(0)
    {
        posteriors_ = NULL;
    }

    void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
        cv::RNG &rng, int num_trees, int depth,
        int views, size_t reduced_num_dim,
        int num_quant_bits, bool print_status)
    {
        PatchGenerator make_patch = PatchGenerator();
        train(base_set, rng, make_patch, num_trees, depth, views, reduced_num_dim, num_quant_bits, print_status);
    }

    // Single-threaded version of train(), with progress output
    void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
        cv::RNG &rng, PatchGenerator &make_patch, int num_trees,
        int depth, int views, size_t reduced_num_dim,
        int num_quant_bits, bool print_status)
    {
        if (reduced_num_dim > base_set.size()) {
            if (print_status)
            {
                printf("INVALID PARAMS in RTreeClassifier::train: reduced_num_dim{%i} > base_set.size(){%i}\n",
                    (int)reduced_num_dim, (int)base_set.size());
            }
            return;
        }

        num_quant_bits_ = num_quant_bits;
        classes_ = reduced_num_dim; // base_set.size();
        original_num_classes_ = base_set.size();
        trees_.resize(num_trees);
        if (print_status)
        {
            printf("[OK] Training trees: base size=%i, reduced size=%i\n", (int)base_set.size(), (int)reduced_num_dim);
        }

        int count = 1;
        if (print_status)
        {
            printf("[OK] Trained 0 / %i trees", num_trees);  fflush(stdout);
        }
        //added
        //BOOST_FOREACH( RandomizedTree &tree, trees_ ) {
        //tree.train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits_);
        //printf("\r[OK] Trained %i / %i trees", count++, num_trees);
        //fflush(stdout);
        for (int i=0; i<(int)trees_.size(); i++)
        {
            trees_[i].train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits_);
            if (print_status)
            {
                printf("\r[OK] Trained %i / %i trees", count++, num_trees);
                fflush(stdout);
            }
        }

        if (print_status)
        {
            printf("\n");
            countZeroElements();
            printf("\n\n");
        }
    }

    void RTreeClassifier::getSignature(IplImage* patch, float *sig)
    {
        // Need pointer to 32x32 patch data
        uchar buffer[PATCH_SIZE * PATCH_SIZE];
        uchar* patch_data;
        if (patch->widthStep != PATCH_SIZE) {
            //printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
            //       patch->widthStep, PATCH_SIZE);
            uchar* data = getData(patch);
            patch_data = buffer;
            for (int i = 0; i < PATCH_SIZE; ++i) {
                memcpy((void*)patch_data, (void*)data, PATCH_SIZE);
                data += patch->widthStep;
                patch_data += PATCH_SIZE;
            }
            patch_data = buffer;
        }
        else {
            patch_data = getData(patch);
        }

        memset((void*)sig, 0, classes_ * sizeof(float));
        std::vector<RandomizedTree>::iterator tree_it;

        // get posteriors
        float **posteriors = new float*[trees_.size()];  // TODO: move alloc outside this func
        float **pp = posteriors;
        for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++) {
            *pp = tree_it->getPosterior(patch_data);
            assert(*pp != NULL);
        }

        // sum them up
        pp = posteriors;
        for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
            add(classes_, sig, *pp, sig);

        delete [] posteriors;
        posteriors = NULL;

        // full quantization (experimental)
#if 0
        int n_max = 1<<8 - 1;
        int sum_max = (1<<4 - 1)*trees_.size();
        int shift = 0;
        while ((sum_max>>shift) > n_max) shift++;

        for (int i = 0; i < classes_; ++i) {
            sig[i] = int(sig[i] + .5) >> shift;
            if (sig[i]>n_max) sig[i] = n_max;
        }

        static bool warned = false;
        if (!warned) {
            printf("[WARNING] Using full quantization (RTreeClassifier::getSignature)! shift=%i\n", shift);
            warned = true;
        }
#else
        // TODO: get rid of this multiply (-> number of trees is known at train
        // time, exploit it in RandomizedTree::finalize())
        float normalizer = 1.0f / trees_.size();
        for (int i = 0; i < classes_; ++i)
            sig[i] *= normalizer;
#endif
    }


    // sum up 50 byte vectors of length 176
    // assume 5 bits max for input vector values
    // final shift is 3 bits right
    //void sum_50c_176c(uchar **pp, uchar *sig)
    //{

    //}

    void RTreeClassifier::getSignature(IplImage* patch, uchar *sig)
    {
        // Need pointer to 32x32 patch data
        uchar buffer[PATCH_SIZE * PATCH_SIZE];
        uchar* patch_data;
        if (patch->widthStep != PATCH_SIZE) {
            //printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
            //       patch->widthStep, PATCH_SIZE);
            uchar* data = getData(patch);
            patch_data = buffer;
            for (int i = 0; i < PATCH_SIZE; ++i) {
                memcpy((void*)patch_data, (void*)data, PATCH_SIZE);
                data += patch->widthStep;
                patch_data += PATCH_SIZE;
            }
            patch_data = buffer;
        } else {
            patch_data = getData(patch);
        }

        std::vector<RandomizedTree>::iterator tree_it;

        // get posteriors
        if (posteriors_ == NULL)
        {
            posteriors_ = new uchar*[trees_.size()];
            //aadded
            //  posix_memalign((void **)&ptemp_, 16, classes_*sizeof(ushort));
            ptemp_ = (ushort*)malloc(classes_*sizeof(ushort));
        }
        uchar **pp = posteriors_;
        for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
            *pp = tree_it->getPosterior2(patch_data);
        pp = posteriors_;

#if 0    // SSE2 optimized code
        sum_50t_176c(pp, sig, ptemp_);    // sum them up
#else
        static bool warned = false;

        memset((void*)sig, 0, classes_ * sizeof(sig[0]));
        ushort *sig16 = new ushort[classes_];           // TODO: make member, no alloc here
        memset((void*)sig16, 0, classes_ * sizeof(sig16[0]));
        for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
            add(classes_, sig16, *pp, sig16);

        // squeeze signatures into an uchar
        const bool full_shifting = true;
        int shift;
        if (full_shifting) {
            float num_add_bits_f = log((float)trees_.size())/log(2.f);     // # additional bits required due to summation
            int num_add_bits = int(num_add_bits_f);
            if (num_add_bits_f != float(num_add_bits)) ++num_add_bits;
            shift = num_quant_bits_ + num_add_bits - 8*sizeof(uchar);
            //shift = num_quant_bits_ + num_add_bits - 2;
            //shift = 6;
            if (shift>0)
                for (int i = 0; i < classes_; ++i)
                    sig[i] = (sig16[i] >> shift);      // &3 cut off all but lowest 2 bits, 3(dec) = 11(bin)

            if (!warned)
                printf("[OK] RTC: quantizing by FULL RIGHT SHIFT, shift = %i\n", shift);
        }
        else {
            printf("[ERROR] RTC: not implemented!\n");
            exit(0);
        }

        if (!warned)
            printf("[WARNING] RTC: unoptimized signature computation\n");
        warned = true;
#endif
    }


    void RTreeClassifier::getSparseSignature(IplImage *patch, float *sig, float thresh)
    {
        getFloatSignature(patch, sig);
        for (int i=0; i<classes_; ++i, sig++)
            if (*sig < thresh) *sig = 0.f;
    }

    int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol)
    {
        int res = 0;
        while (n-- > 0)
            res += (fabs(*vec++) > tol);
        return res;
    }

    void RTreeClassifier::read(const char* file_name)
    {
        std::ifstream file(file_name, std::ifstream::binary);
        read(file);
        file.close();
    }

    void RTreeClassifier::read(std::istream &is)
    {
        int num_trees = 0;
        is.read((char*)(&num_trees), sizeof(num_trees));
        is.read((char*)(&classes_), sizeof(classes_));
        is.read((char*)(&original_num_classes_), sizeof(original_num_classes_));
        is.read((char*)(&num_quant_bits_), sizeof(num_quant_bits_));

        if (num_quant_bits_<1 || num_quant_bits_>8) {
            printf("[WARNING] RTC: suspicious value num_quant_bits_=%i found; setting to %i.\n",
                num_quant_bits_, (int)DEFAULT_NUM_QUANT_BITS);
            num_quant_bits_ = DEFAULT_NUM_QUANT_BITS;
        }

        trees_.resize(num_trees);
        std::vector<RandomizedTree>::iterator tree_it;

        for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it) {
            tree_it->read(is, num_quant_bits_);
        }

        printf("[OK] Loaded RTC, quantization=%i bits\n", num_quant_bits_);

        countZeroElements();
    }

    void RTreeClassifier::write(const char* file_name) const
    {
        std::ofstream file(file_name, std::ofstream::binary);
        write(file);
        file.close();
    }

    void RTreeClassifier::write(std::ostream &os) const
    {
        int num_trees = trees_.size();
        os.write((char*)(&num_trees), sizeof(num_trees));
        os.write((char*)(&classes_), sizeof(classes_));
        os.write((char*)(&original_num_classes_), sizeof(original_num_classes_));
        os.write((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
        printf("RTreeClassifier::write: num_quant_bits_=%i\n", num_quant_bits_);

        std::vector<RandomizedTree>::const_iterator tree_it;
        for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it)
            tree_it->write(os);
    }

    void RTreeClassifier::saveAllFloatPosteriors(std::string url)
    {
        printf("[DEBUG] writing all float posteriors to %s...\n", url.c_str());
        for (int i=0; i<(int)trees_.size(); ++i)
            trees_[i].savePosteriors(url, (i==0 ? false : true));
        printf("[DEBUG] done\n");
    }

    void RTreeClassifier::saveAllBytePosteriors(std::string url)
    {
        printf("[DEBUG] writing all byte posteriors to %s...\n", url.c_str());
        for (int i=0; i<(int)trees_.size(); ++i)
            trees_[i].savePosteriors2(url, (i==0 ? false : true));
        printf("[DEBUG] done\n");
    }


    void RTreeClassifier::setFloatPosteriorsFromTextfile_176(std::string url)
    {
        std::ifstream ifs(url.c_str());

        for (int i=0; i<(int)trees_.size(); ++i) {
            int num_classes = trees_[i].classes_;
            assert(num_classes == 176);     // TODO: remove this limitation (arose due to SSE2 optimizations)
            for (int k=0; k<trees_[i].num_leaves_; ++k) {
                float *post = trees_[i].getPosteriorByIndex(k);
                for (int j=0; j<num_classes; ++j, ++post)
                    ifs >> *post;
                assert(ifs.good());
            }
        }
        classes_ = 176;

        //setQuantization(num_quant_bits_);

        ifs.close();
        printf("[EXPERIMENTAL] read entire tree from '%s'\n", url.c_str());
    }


    float RTreeClassifier::countZeroElements()
    {
        int flt_zeros = 0;
        int ui8_zeros = 0;
        int num_elem = trees_[0].classes();
        for (int i=0; i<(int)trees_.size(); ++i)
            for (int k=0; k<(int)trees_[i].num_leaves_; ++k) {
                float *p = trees_[i].getPosteriorByIndex(k);
                uchar *p2 = trees_[i].getPosteriorByIndex2(k);
                assert(p); assert(p2);
                for (int j=0; j<num_elem; ++j, ++p, ++p2) {
                    if (*p == 0.f) flt_zeros++;
                    if (*p2 == 0) ui8_zeros++;
                }
            }
            num_elem = trees_.size()*trees_[0].num_leaves_*num_elem;
            float flt_perc = 100.*flt_zeros/num_elem;
            float ui8_perc = 100.*ui8_zeros/num_elem;
            printf("[OK] RTC: overall %i/%i (%.3f%%) zeros in float leaves\n", flt_zeros, num_elem, flt_perc);
            printf("          overall %i/%i (%.3f%%) zeros in uint8 leaves\n", ui8_zeros, num_elem, ui8_perc);

            return flt_perc;
    }

    void RTreeClassifier::setQuantization(int num_quant_bits)
    {
        for (int i=0; i<(int)trees_.size(); ++i)
            trees_[i].applyQuantization(num_quant_bits);

        printf("[OK] signature quantization is now %i bits (before: %i)\n", num_quant_bits, num_quant_bits_);
        num_quant_bits_ = num_quant_bits;
    }



    //} // namespace features
}