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I tried to implement fourier descriptor to use it in machine learning algorithm but i got this error OpenCV Error: Assertion failed (type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2) in void cv::dft(cv::InputArray, cv::OutputArray, int, int), file /home/maksim/workspace/android-pack/opencv/modules/core/src/dxt.cpp, line 2506

This is my code:

            Mat trainData = new Mat();
    Mat train_labels = new Mat();


    String Newligne=System.getProperty("line.separator");


    for (int i = 0; i <48; i++) {
        String path1 = Environment.getExternalStorageDirectory().toString()
                + "/Pictures/images/" + "a"+i + ".jpg";


        Mat img = Imgcodecs.imread(path1);
        Log.i(TAG, "error" + i + img.empty());
        img.convertTo(img, CvType.CV_32FC1);
        Mat secondImage = new Mat(img.rows(), img.cols(), CvType.CV_64FC1);
        img.convertTo(secondImage, CvType.CV_64FC1);

        int m = Core.getOptimalDFTSize(img.rows());
        int n = Core.getOptimalDFTSize(img.cols()); // on the border
        // add zero values Imgproc.copyMakeBorder(image1, padded, 0, m - image1.rows(), 0, n

        Mat padded = new Mat(new Size(n, m), CvType.CV_64FC1); // expand input
        // image to
        // optimal size

        Core.copyMakeBorder(secondImage, padded, 0, m - secondImage.rows(), 0,
                n - secondImage.cols(), Core.BORDER_CONSTANT);

        List<Mat> planes = new ArrayList<Mat>();
        planes.add(padded);
        planes.add(Mat.zeros(padded.rows(), padded.cols(), CvType.CV_64FC1));

        Mat complexI = Mat.zeros(padded.rows(), padded.cols(), CvType.CV_64FC2);

        Mat complexI2 = Mat
                .zeros(padded.rows(), padded.cols(), CvType.CV_64FC2);

        Core.merge(planes, complexI); // Add to the expanded another plane with
        // zeros

        Core.dft(complexI, complexI2); // this way the result may fit in the
        // source matrix

        // compute the magnitude and switch to logarithmic scale
        // => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
        Core.split(complexI2, planes); // planes[0] = Re(DFT(I), planes[1] =
        // Im(DFT(I))

        Mat mag = new Mat(planes.get(0).size(), planes.get(0).type());

        Core.magnitude(planes.get(0), planes.get(1), mag);// planes[0]
        // =
        // magnitude

        Mat magI = mag;
        Mat magI2 = new Mat(magI.size(), magI.type());
        Mat magI3 = new Mat(magI.size(), magI.type());
        Mat magI4 = new Mat(magI.size(), magI.type());
        Mat magI5 = new Mat(magI.size(), magI.type());

        Core.add(magI, Mat.ones(padded.rows(), padded.cols(), CvType.CV_64FC1),
                magI2); // switch to logarithmic scale
        Core.log(magI2, magI3);

        Mat crop = new Mat(magI3, new Rect(0, 0, magI3.cols() & -2,
                magI3.rows() & -2));

        magI4 = crop.clone();

        // rearrange the quadrants of Fourier image so that the origin is at the
        // image center
        int cx = magI4.cols() / 2;
        int cy = magI4.rows() / 2;

        Rect q0Rect = new Rect(0, 0, cx, cy);
        Rect q1Rect = new Rect(cx, 0, cx, cy);
        Rect q2Rect = new Rect(0, cy, cx, cy);
        Rect q3Rect = new Rect(cx, cy, cx, cy);

        Mat q0 = new Mat(magI4, q0Rect); // Top-Left - Create a ROI per quadrant
        Mat q1 = new Mat(magI4, q1Rect); // Top-Right
        Mat q2 = new Mat(magI4, q2Rect); // Bottom-Left
        Mat q3 = new Mat(magI4, q3Rect); // Bottom-Right

        Mat tmp = new Mat(); // swap quadrants (Top-Left with Bottom-Right)
        q0.copyTo(tmp);
        q3.copyTo(q0);
        tmp.copyTo(q3);

        q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left)
        q2.copyTo(q1);
        tmp.copyTo(q2);

        Core.normalize(magI4, magI5, 0, 255, Core.NORM_MINMAX);

        Mat realResult = new Mat(magI5.size(), CvType.CV_8UC1);

        magI5.convertTo(realResult, CvType.CV_8UC1);
    Mat imgResized = realResult.reshape(1, 1);
        trainData.push_back( imgResized);

        train_labels
                .push_back(new Mat(1, 1, CvType.CV_32SC1, new Scalar(i)));

    }