switching from knn to svm (opencv 3.1) [closed]

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Hello !

I would like to switch from knn to svm in order to see how performance goes:
This is the "script" I use to generate a file call "raw-features.yml".

int main(int argc, char **argv)
{

// data to return
Mat features(nbrOfCells, normalizedSizeForCell * normalizedSizeForCell, CV_8UC1);
Mat labels(1, nbrOfCells, CV_8UC1);
Mat svm_labels(nbrOfCells, 1, CV_32S);


// Ptr<ml::KNearest> knn(ml::KNearest::create());
std::map<int, std::map<int, int>> knownCellValues(cellValues());
int value;

string fileName;
Mat raw, sudoku;
ExtractionInformation extractInfo;

string raw_features_path("./../assets/raw-features.yml");
cv::FileStorage raw_features(raw_features_path, cv::FileStorage::WRITE); // open the classifications file

int incrCell = 0; // --> 1184
Mat roi, normalized;
for (int i = 0; i <= lastTrainingPuzzle; i++)
{
    // cout << i << endl;
    stringstream ss;
    ss << "./../assets/puzzles/s";
    ss << i;
    ss << ".jpg";
    string fileName(ss.str());

    raw = imread(fileName, CV_LOAD_IMAGE_GRAYSCALE);

    vector<Point> biggestApprox = findBiggestBlob(raw);
    extractInfo = extractPuzzle(raw, biggestApprox);
    Mat sudoku = recursiveExtraction(extractInfo.image);


    for (int k = 0; k < 81; k++)
    {
        roi = extractRoiFromCell(sudoku, k);
        if (!roi.empty())
        {
            value = knownCellValues[i][k];
            Mat feat = roi.reshape(1, 1);

            feat.copyTo(features.row(incrCell));
            labels.at<unsigned char>(0, incrCell) = value;
            svm_labels.at<int>(incrCell, 0) = value;


            incrCell++;
        }
    }
}

features.convertTo(features, CV_32F);   
labels.convertTo(labels, CV_32F);

raw_features << "features" << features;
raw_features << "labels" << labels;
raw_features << "svm_labels" << svm_labels;
raw_features.release();

return 0;
}

Once "raw-features.yml" is ready, I can use it like that !

I use this the "getKnn" function from my project to train the knn algo.

Ptr<ml::KNearest> getKnn(cv::FileStorage raw_features)
{
int trainingNbr = nbrOfCells * 0.9;
int testingNbr = nbrOfCells - trainingNbr;
Mat features(nbrOfCells, normalizedSizeForCell * normalizedSizeForCell, CV_8UC1);
Mat labels(1, nbrOfCells, CV_8UC1);
Ptr<ml::KNearest> knn(ml::KNearest::create());

if (raw_features.isOpened() == false)
{
    throw std::logic_error("error, unable to open training classifications file, exiting program\n\n");
}

raw_features["features"] >> features;
raw_features["labels"] >> labels;
raw_features.release();

Mat sub_features = features(cv::Range(0, trainingNbr), cv::Range::all());
Mat sub_labels = labels(cv::Range::all(), cv::Range(0, trainingNbr));

knn->train(sub_features, ml::ROW_SAMPLE, sub_labels);

return knn;
}

I'm really happy of it, it works perfectly but I have a problem of performance so I would like to test other algo.

I created the same function for SVM:

Ptr<ml::SVM> getSvm(FileStorage raw_features)
{
Mat features(nbrOfCells, normalizedSizeForCell * normalizedSizeForCell, CV_32F);
Mat svm_labels(nbrOfCells, 1, CV_32S);
raw_features["features"] >> features;
raw_features["svm_labels"] >> svm_labels;
raw_features.release();

Ptr<ml::SVM> svm = ml::SVM::create();
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::POLY);
svm->setDegree(3); // I had to put it if not it fails
svm->setGamma(3);

Ptr<ml::TrainData> tData = ml::TrainData::create(features, ml::SampleTypes::ROW_SAMPLE, svm_labels);
svm->train(tData);
return svm;
}

I use it like that:

string grabNumbers(Mat extractedPuzzle, Ptr<ml::SVM> svm)
{
Mat roi, response, dist;
stringstream ss;
int K = 1;

for (int k = 0; k < 81; k++)
{
    roi = extractRoiFromCell(extractedPuzzle, k);
    if (!roi.empty())
    {
        roi.convertTo(roi, CV_32F);
        float res = svm->predict(roi.reshape(1, 1));
        ss << res;
    }
    else
    {
        ss << "0";
    }
}

return ss.str();
}

But I have this weird error I say weird because I already done the conversion !

    Gtk-Message: Failed ...