KNearest Java Assertion Error[SOLVED]

edit

I'm still new to OpenCV, but already learned a lot. I am however stuck on the K-NN Algorithm. I'm writing some software to read energy meters and I already succesfully wrote the image processing part. When the processing is finished, I end up with digits like this:

For the K-NN part (training and etc), I used almost all code from this OpenCV question. However, when running the following code (and doing the nescessary training in advance):

Mat one_feature = Imgcodecs.imread("/Users/jeremyk/Desktop/output/digits/digit0.png", 0);
Imgproc.resize(one_feature, one_feature, new Size(20, 20));
Mat res = new Mat();
one_feature.convertTo(one_feature, CvType.CV_32F);
one_feature.reshape(1,1);
float p = knn.findNearest(one_feature, 1, res);

I'm getting this nasty error:

OpenCV Error: Assertion failed (test_samples.type() == 5 && test_samples.cols == samples.cols) in cv::ml::BruteForceImpl::findNearest, file C:\build\master_winpack-bindings-win64-vc14-static\opencv\modules\ml\src\knearest.cpp, line 325
Exception in thread "main" CvException [org.opencv.core.CvException: cv::Exception: C:\build\master_winpack-bindings-win64-vc14-static\opencv\modules\ml\src\knearest.cpp:325: error: (-215) test_samples.type() == 5 && test_samples.cols == samples.cols in function cv::ml::BruteForceImpl::findNearest]

I can't seem to find any solution to this.. Anyone got a clue?

Thanks in advance!

Edit: import as grayscale and resize image added to one_feature in an attempt to solve the error.

Edit 2: Here's the training/testing code that I'm using

Edit 3: Here's the updated training/test code that I'm using

   for (int c = 0; c< numberOfTrainingDigits; c++) {
        // crop out 1 digit:
        digit = Imgcodecs.imread("/Users/jeremyk/Desktop/training/digit" + c + ".png");
        Mat num = new Mat();
        Imgproc.resize(digit, num, new Size(60, 110));
        // we need float data for knn:
        num.convertTo(num, CvType.CV_32F);
        // for opencv ml, each feature has to be a single row:
        trainData.push_back(num.reshape(1,1));
        trainLabs.add(c);
    }

    // Add test image
    Mat testDigit = Imgcodecs.imread("/Users/jeremyk/Desktop/testdigit.png", 0);
    Mat num = new Mat();
    Imgproc.resize(testDigit, num, new Size(60, 110));
    // we need float data for knn:
    num.convertTo(num, CvType.CV_32F);
    testData.push_back(num.reshape(1,1));
    testLabs.add(6);

    // make a Mat of the train labels, and train knn:
    KNearest knn = KNearest.create();
    knn.train(trainData, Ml.ROW_SAMPLE, Converters.vector_int_to_Mat(trainLabs));

    // now test predictions:
    for (int j=0; j<testData.rows(); j++)
    {
        Mat one_feature = testData.row(j);
        int testLabel = testLabs.get(j);

        Mat res = new Mat();
        float p = knn.findNearest(one_feature, 1, res);
        System.out.println(testLabel + " " + p + " " + res.dump());
    }