public void init() {
	try {
		if (Objects.isNull(modelPath)) {

			yoloModel = (ComputationGraph) YOLO2.builder().build().initPretrained();
			setModelClasses(COCO_CLASSES);
		} else {
			yoloModel = ModelSerializer.restoreComputationGraph(modelPath);
			if (!(yoloModel.getOutputLayer(0) instanceof Yolo2OutputLayer)) {
				throw new Error("The model is not an YOLO model (output layer is not Yolo2OutputLayer)");
			}
			setModelClasses(classes.split("\\,"));
		}
		imageLoader = new NativeImageLoader(getInputWidth(), getInputHeight(), getInputChannels(),
				new ColorConversionTransform(COLOR_BGR2RGB));
		loadInputParameters();
	} catch (IOException e) {
		throw new Error("Not able to init the model", e);
	}
}
 

/**
 * Preprocess and store cifar based on successful Torch approach by Sergey Zagoruyko
 * Reference: https://github.com/szagoruyko/cifar.torch
 */
public opencv_core.Mat convertCifar(Mat orgImage) {
    numExamples++;
    Mat resImage = new Mat();
    OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
    //        ImageTransform yuvTransform = new ColorConversionTransform(new Random(seed), COLOR_BGR2Luv);
    //        ImageTransform histEqualization = new EqualizeHistTransform(new Random(seed), COLOR_BGR2Luv);
    ImageTransform yuvTransform = new ColorConversionTransform(new Random(seed), COLOR_BGR2YCrCb);
    ImageTransform histEqualization = new EqualizeHistTransform(new Random(seed), COLOR_BGR2YCrCb);

    if (converter != null) {
        ImageWritable writable = new ImageWritable(converter.convert(orgImage));
        // TODO determine if need to normalize y before transform - opencv docs rec but currently doing after
        writable = yuvTransform.transform(writable); // Converts to chrome color to help emphasize image objects
        writable = histEqualization.transform(writable); // Normalizes values to further clarify object of interest
        resImage = converter.convert(writable.getFrame());
    }

    return resImage;
}
 

/**
 * Preprocess and store cifar based on successful Torch approach by Sergey Zagoruyko
 * Reference: <a href="https://github.com/szagoruyko/cifar.torch">https://github.com/szagoruyko/cifar.torch</a>
 */
public Mat convertCifar(Mat orgImage) {
    numExamples++;
    Mat resImage = new Mat();
    OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
    //        ImageTransform yuvTransform = new ColorConversionTransform(new Random(seed), COLOR_BGR2Luv);
    //        ImageTransform histEqualization = new EqualizeHistTransform(new Random(seed), COLOR_BGR2Luv);
    ImageTransform yuvTransform = new ColorConversionTransform(new Random(seed), COLOR_BGR2YCrCb);
    ImageTransform histEqualization = new EqualizeHistTransform(new Random(seed), COLOR_BGR2YCrCb);

    if (converter != null) {
        ImageWritable writable = new ImageWritable(converter.convert(orgImage));
        // TODO determine if need to normalize y before transform - opencv docs rec but currently doing after
        writable = yuvTransform.transform(writable); // Converts to chrome color to help emphasize image objects
        writable = histEqualization.transform(writable); // Normalizes values to further clarify object of interest
        resImage = converter.convert(writable.getFrame());
    }

    return resImage;
}
 

private void createTransformProcess() {
    IHistoryProcessor historyProcessor = getHistoryProcessor();

    if(historyProcessor != null && shape.length == 3) {
        int skipFrame = historyProcessor.getConf().getSkipFrame();
        int frameStackLength = historyProcessor.getConf().getHistoryLength();

        int height = shape[1];
        int width = shape[2];

        int cropBottom = height - historyProcessor.getConf().getCroppingHeight();
        int cropRight = width - historyProcessor.getConf().getCroppingWidth();

        transformProcess = TransformProcess.builder()
                .filter(new UniformSkippingFilter(skipFrame))
                .transform("data", new EncodableToImageWritableTransform())
                .transform("data", new MultiImageTransform(
                        new CropImageTransform(historyProcessor.getConf().getOffsetY(), historyProcessor.getConf().getOffsetX(), cropBottom, cropRight),
                        new ResizeImageTransform(historyProcessor.getConf().getRescaledWidth(), historyProcessor.getConf().getRescaledHeight()),
                        new ColorConversionTransform(COLOR_BGR2GRAY)
                        //new ShowImageTransform("crop + resize + greyscale")
                ))
                .transform("data", new ImageWritableToINDArrayTransform())
                .transform("data", new SimpleNormalizationTransform(0.0, 255.0))
                .transform("data", HistoryMergeTransform.builder()
                        .isFirstDimenstionBatch(true)
                        .build(frameStackLength))
                .build("data");
    }
    else {
        transformProcess = TransformProcess.builder()
                .transform("data", new EncodableToINDArrayTransform())
                .build("data");
    }
}