opencv/apps/sft/octave.cpp

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

#include <glob.h>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>

// ============ 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)
{

    std::cout << "WARNING: sampleIdx " << sampleIdx << std::endl;
    std::cout << "WARNING: trainData " << _trainData << std::endl;
    std::cout << "WARNING: _responses " << _responses << std::endl;
    std::cout << "WARNING: varIdx" << varIdx << std::endl;
    std::cout << "WARNING: varType" << varType << std::endl;

    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));
    }

    std::cout << "WARNING: responses " << responses << std::endl;
    std::cout << "WARNING: desisions " << desisions << std::endl;
    std::cout << "WARNING: ppmask "    << ppmask    << std::endl;

    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;
        std::cout << "mintrace " << mintrace << std::endl << traces.colRange(0, npositives) << std::endl;
    }
}

namespace {
using namespace sft;
class Preprocessor
{
public:
    Preprocessor(int shr) : shrinkage(shr) {}

    void apply(const Mat& frame, Mat& integrals)
    {
        CV_Assert(frame.type() == CV_8UC3);

        int h = frame.rows;
        int w = frame.cols;

        cv::Mat channels, gray;

        channels.create(h * BINS, w, CV_8UC1);
        channels.setTo(0);

        cvtColor(frame, gray, CV_BGR2GRAY);

        cv::Mat df_dx, df_dy, mag, angle;
        cv::Sobel(gray, df_dx, CV_32F, 1, 0);
        cv::Sobel(gray, df_dy, CV_32F, 0, 1);

        cv::cartToPolar(df_dx, df_dy, mag, angle, true);
        mag *= (1.f / (8 * sqrt(2.f)));

        cv::Mat nmag;
        mag.convertTo(nmag, CV_8UC1);

        angle *=  6 / 360.f;

        for (int y = 0; y < h; ++y)
        {
            uchar* magnitude = nmag.ptr<uchar>(y);
            float* ang = angle.ptr<float>(y);

            for (int x = 0; x < w; ++x)
            {
                channels.ptr<uchar>(y + (h * (int)ang[x]))[x] = magnitude[x];
            }
        }

        cv::Mat luv, shrunk;
        cv::cvtColor(frame, luv, CV_BGR2Luv);

        std::vector<cv::Mat> splited;
        for (int i = 0; i < 3; ++i)
            splited.push_back(channels(cv::Rect(0, h * (7 + i), w, h)));
        split(luv, splited);

        cv::resize(channels, shrunk, cv::Size(), 1.0 / shrinkage, 1.0 / shrinkage, CV_INTER_AREA);
        cv::integral(shrunk, integrals, cv::noArray(), CV_32S);
    }

    int shrinkage;
    enum {BINS = 10};
};
}

// ToDo: parallelize it, fix curring
// ToDo: sunch model size and shrinced model size usage/ Now model size mean already shrinked model
void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& pool)
{
    Preprocessor prepocessor(shrinkage);

    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)
    {
        const string& curr = *it;

        dprintf("Process candidate positive image %s\n", curr.c_str());

        cv::Mat sample = cv::imread(curr);

        cv::Mat channels = integrals.row(total).reshape(0, h / shrinkage * 10 + 1);
        sample = sample(boundingBox);

        prepocessor.apply(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)
{
    // ToDo: set seed, use offsets
    sft::Random::engine eng(65633343L);
    sft::Random::engine idxEng(764224349868L);

    // int w = boundingBox.width;
    int h = boundingBox.height;

    Preprocessor prepocessor(shrinkage);

    int nimages = (int)dataset.neg.size();
    sft::Random::uniform iRand(0, nimages - 1);

    int total = 0;
    Mat sum;
    for (int i = npositives; i < nnegatives + npositives; ++total)
    {
        int curr = iRand(idxEng);

        dprintf("View %d-th sample\n", curr);
        dprintf("Process %s\n", dataset.neg[curr].c_str());

        Mat frame = cv::imread(dataset.neg[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);
        prepocessor.apply(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);
}

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);
    // exit(0);

    // 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;

#if defined SELF_TEST
    cv::Mat a(1, nfeatures, CV_32FC1);
    cv::Mat votes(1, cvSliceLength( CV_WHOLE_SEQ, weak ), CV_32FC1, cv::Scalar::all(0));

    std::cout << a.cols << " " << a.rows << " !!!!!!!!!!! " << data->var_all << std::endl;
    for (int si = 0; si < nsamples; ++si)
    {
        // trainData.col(si).copyTo(a.reshape(0,trainData.rows));
        float desision = predict(trainData.col(si), votes, false, true);
        std::cout << "desision " << desision << " class " << responses.at<float>(si, 0) << votes <<std::endl;
    }
#endif
    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::FeaturePool::FeaturePool(cv::Size m, int n) : model(m), nfeatures(n)
{
    CV_Assert(m != cv::Size() && n > 0);
    fill(nfeatures);
}

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


void sft::FeaturePool::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())
        {
            // std::cout << f << std::endl;
            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));
}