Predict

// Predict function
public static int[][] toIntArrayArray(BufferedImage image) {
        int w = image.getWidth();
        int h = image.getHeight();
        int[][] ret = new int[h][w];
        int j;
        if(image.getRaster().getNumDataElements() == 1) {
            WritableRaster i = image.getRaster();

            for(j = 0; j < h; ++j) {
                for(int j1 = 0; j1 < w; ++j1) {
                    ret[j][j1] = i.getSample(j1, j, 0);
                }
            }
        } else {
            for(int var8 = 0; var8 < h; ++var8) {
                for(j = 0; j < w; ++j) {
                    ret[var8][j] = image.getRGB(j, var8);
                }
            }
        }

        return ret;
    }
    
    
    //Steps which appy data before train and then apply for predict
    public static BufferedImage preprocess(BufferedImage img) {
        opencv_core.Mat original = ImageOpenCvUtils.toMat(img);
        opencv_core.Mat grayScale = ImageOpenCvUtils.toGray(original);
        opencv_core.Mat maximizeContrast = ImageOpenCvUtils.maximizeContrastMat(grayScale, 1);
        opencv_core.Mat output = new opencv_core.Mat(maximizeContrast.size(),maximizeContrast.type());
        opencv_imgproc.resize(maximizeContrast,output,new opencv_core.Size(23,43));
        return ImageOpenCvUtils.toBufferedImage(output);
    }
    
    public static double predict(BufferedImage img,MultiLayerNetwork model) {
        img = preprocess(img);
        int[][] ret = toIntArrayArray(img);
        ImageLoader loader = new ImageLoader();

        INDArray input = ArrayUtil.toNDArray(ArrayUtil.flatten(ret));
        INDArray output = model.output(input);
        output = Nd4j.argMax(output, 1);
        return output.getDouble(0);
    }