opencv/apps/sft/octave.cpp

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#include <sft/octave.hpp>
#include <sft/random.hpp>

#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)
{
    CV_Assert(m != cv::Size() && n > 0);
    fill(nfeatures);
}

void sft::ICFFeaturePool::preprocess(const Mat& frame, Mat& integrals) const
{
    preprocessor.apply(frame, integrals);
}

float sft::ICFFeaturePool::apply(int fi, int si, const Mat& integrals) const
{
    return pool[fi](integrals.row(si), model);
}

void sft::ICFFeaturePool::write( cv::FileStorage& fs, int index) const
{
    CV_Assert((index > 0) && (index < (int)pool.size()));
    fs << pool[index];
}

void sft::write(cv::FileStorage& fs, const string&, const ICF& f)
{
    fs << "{" << "channel" << f.channel << "rect" << f.bb << "}";
}

sft::ICFFeaturePool::~ICFFeaturePool(){}

void sft::ICFFeaturePool::fill(int desired)
{
    int mw = model.width;
    int mh = model.height;

    int maxPoolSize = (mw -1) * mw / 2 * (mh - 1) * mh / 2 * N_CHANNELS;

    nfeatures = std::min(desired, maxPoolSize);
    dprintf("Requeste feature pool %d max %d suggested %d\n", desired, maxPoolSize, nfeatures);

    pool.reserve(nfeatures);

    sft::Random::engine eng(8854342234L);
    sft::Random::engine eng_ch(314152314L);

    sft::Random::uniform chRand(0, N_CHANNELS - 1);

    sft::Random::uniform xRand(0, model.width  - 2);
    sft::Random::uniform yRand(0, model.height - 2);

    sft::Random::uniform wRand(1, model.width  - 1);
    sft::Random::uniform hRand(1, model.height - 1);

    while (pool.size() < size_t(nfeatures))
    {
        int x = xRand(eng);
        int y = yRand(eng);

        int w = 1 + wRand(eng, model.width  - x - 1);
        int h = 1 + hRand(eng, model.height - y - 1);

        CV_Assert(w > 0);
        CV_Assert(h > 0);

        CV_Assert(w + x < model.width);
        CV_Assert(h + y < model.height);

        int ch = chRand(eng_ch);

        sft::ICF f(x, y, w, h, ch);

        if (std::find(pool.begin(), pool.end(),f) == pool.end())
        {
            pool.push_back(f);
        }
    }
}

std::ostream& sft::operator<<(std::ostream& out, const sft::ICF& m)
{
    out << m.channel << " " << m.bb;
    return out;
}

// ============ Dataset ============ //
namespace {
using namespace sft;

string itoa(long i)
{
    char s[65];
    sprintf(s, "%ld", i);
    return std::string(s);
}

void glob(const string& path, svector& ret)
{
    glob_t glob_result;
    glob(path.c_str(), GLOB_TILDE, 0, &glob_result);

    ret.clear();
    ret.reserve(glob_result.gl_pathc);

    for(uint i = 0; i < glob_result.gl_pathc; ++i)
    {
        ret.push_back(std::string(glob_result.gl_pathv[i]));
        dprintf("%s\n", ret[i].c_str());
    }

    globfree(&glob_result);
}
}
// 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)
{
    dprintf("%s\n", "get dataset file names...");

    dprintf("%s\n", "Positives globbing...");
    glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);

    dprintf("%s\n", "Negatives globbing...");
    glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);

    // Check: files not empty
    CV_Assert(pos.size() != size_t(0));
    CV_Assert(neg.size() != size_t(0));
}

cv::Mat Dataset::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
{
    return (int)((type == POSITIVE)? pos.size():neg.size());
}