Input pixels normalization for image recognition app

[JuliaZ]

Hello! I have started my Android app with my custom neural network model saved in .lite format. How can I do a input pixel normalization from 0 to 1 (not from 0 to 255)?
This is my modified ImageClassifier.java from repo: https://github.com/googlecodelabs/tensorflow-for-poets-2

/** Classifies images with Tensorflow Lite. */
public class ImageClassifier {

  /** Tag for the {@link Log}. */
  private static final String TAG = "TfLiteCameraDemo";

  /** Name of the model file stored in Assets. */
  private static final String MODEL_PATH = "graph.lite";

  /** Name of the label file stored in Assets. */
  private static final String LABEL_PATH = "labels.txt";

  /** Number of results to show in the UI. */
  private static final int RESULTS_TO_SHOW = 1;

  /** Dimensions of inputs. */
  private static final int DIM_BATCH_SIZE = 1;

  private static final int DIM_PIXEL_SIZE = 3;

  static final int DIM_IMG_SIZE_X = 256;
  static final int DIM_IMG_SIZE_Y = 256;

  private static final int IMAGE_MEAN = 128;
  private static final float IMAGE_STD = 128.0f;


  /* Preallocated buffers for storing image data in. */
  private int[] intValues = new int[4*DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y];

  /** An instance of the driver class to run model inference with Tensorflow Lite. */
  private Interpreter tflite;

  /** Labels corresponding to the output of the vision model. */
  private List<String> labelList;

  /** A ByteBuffer to hold image data, to be feed into Tensorflow Lite as inputs. */
  private ByteBuffer imgData = null;

  /** An array to hold inference results, to be feed into Tensorflow Lite as outputs. */
  private float[][] labelProbArray = null;
  /** multi-stage low pass filter **/
  private float[][] filterLabelProbArray = null;
  private static final int FILTER_STAGES = 3;
  private static final float FILTER_FACTOR = 0.4f;

  private PriorityQueue<Map.Entry<String, Float>> sortedLabels =
      new PriorityQueue<>(
          RESULTS_TO_SHOW,
          new Comparator<Map.Entry<String, Float>>() {
            @Override
            public int compare(Map.Entry<String, Float> o1, Map.Entry<String, Float> o2) {
              return (o1.getValue()).compareTo(o2.getValue());
            }
          });

  /** Initializes an {@code ImageClassifier}. */
  ImageClassifier(Activity activity) throws IOException {
    tflite = new Interpreter(loadModelFile(activity));
    labelList = loadLabelList(activity);
    imgData =
        ByteBuffer.allocateDirect(
            4 * DIM_BATCH_SIZE * DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE);
    imgData.order(ByteOrder.nativeOrder());
    labelProbArray = new float[1][labelList.size()];
    filterLabelProbArray = new float[FILTER_STAGES][labelList.size()];
    Log.d(TAG, "Created a Tensorflow Lite Image Classifier.");
  }

  /** Classifies a frame from the preview stream. */
  String classifyFrame(Bitmap bitmap) {
    if (tflite == null) {
      Log.e(TAG, "Image classifier has not been initialized; Skipped.");
      return "Uninitialized Classifier.";
    }
    convertBitmapToByteBuffer(bitmap);
    // Here's where the magic happens!!!
    long startTime = SystemClock.uptimeMillis();
    tflite.run(imgData, labelProbArray);
    long endTime = SystemClock.uptimeMillis();
    Log.d(TAG, "Timecost to run model inference: " + Long.toString(endTime - startTime));

    // smooth the results
    applyFilter();

    // print the results
    String textToShow = printTopKLabels();
    textToShow = Long.toString(endTime - startTime) + "ms" + textToShow;
    return textToShow;
  }

  void applyFilter(){
    int num_labels =  labelList.size();

    // Low pass filter `labelProbArray` into the first stage of the filter.
    for(int j=0; j<num_labels; ++j){
      filterLabelProbArray[0][j] += FILTER_FACTOR*(labelProbArray[0][j] -
                                                   filterLabelProbArray[0][j]);
    }
    // Low pass filter each stage into the next.
    for (int i=1; i<FILTER_STAGES; ++i){
      for(int j=0; j<num_labels; ++j){
        filterLabelProbArray[i][j] += FILTER_FACTOR*(
                filterLabelProbArray[i-1][j] -
                filterLabelProbArray[i][j]);

      }
    }

    // Copy the last stage filter output back to `labelProbArray`.
    for(int j=0; j<num_labels; ++j){
      labelProbArray[0][j] = filterLabelProbArray[FILTER_STAGES-1][j];
    }
  }

  /** Closes tflite to release resources. */
  public void close() {
    tflite.close();
    tflite = null;
  }

  /** Reads label list from Assets. */
  private List<String> loadLabelList(Activity activity) throws IOException {
    List<String> labelList = new ArrayList<String>();
    BufferedReader reader =
        new BufferedReader(new InputStreamReader(activity.getAssets().open(LABEL_PATH)));
    String line;
    while ((line = reader.readLine()) != null) {
      labelList.add(line);
    }
    reader.close();
    return labelList;
  }

  /** Memory-map the model file in Assets. */
  private MappedByteBuffer loadModelFile(Activity activity) throws IOException {
    AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_PATH);
    FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
    FileChannel fileChannel = inputStream.getChannel();
    long startOffset = fileDescriptor.getStartOffset();
    long declaredLength = fileDescriptor.getDeclaredLength();
    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
  }

  /** Writes Image data into a {@code ByteBuffer}. */
  private void convertBitmapToByteBuffer(Bitmap bitmap) {
    if (imgData == null) {
      return;
    }
    imgData.rewind();
    bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
    // Convert the image to floating point.
    int pixel = 0;
    long startTime = SystemClock.uptimeMillis();
    for (int i = 0; i < DIM_IMG_SIZE_X; ++i) {
      for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) {
        final int val = intValues[pixel++];
        imgData.putFloat((((val >> 16) & 0xFF)-IMAGE_MEAN)/IMAGE_STD);
        imgData.putFloat((((val >> 8) & 0xFF)-IMAGE_MEAN)/IMAGE_STD);
        imgData.putFloat((((val) & 0xFF)-IMAGE_MEAN)/IMAGE_STD);
      }
    }
    long endTime = SystemClock.uptimeMillis();
    Log.d(TAG, "Timecost to put values into ByteBuffer: " + Long.toString(endTime - startTime));
  }

  /** Prints top-K labels, to be shown in UI as the results. */
  private String printTopKLabels() {
    for (int i = 0; i < labelList.size(); ++i) {
      sortedLabels.add(
          new AbstractMap.SimpleEntry<>(labelList.get(i), labelProbArray[0][i]));
      if (sortedLabels.size() > RESULTS_TO_SHOW) {
        sortedLabels.poll();
      }
    }
    String textToShow = "";
    final int size = sortedLabels.size();
    for (int i = 0; i < size; ++i) {
      Map.Entry<String, Float> label = sortedLabels.poll();
      textToShow = String.format("\n%s: %4.2f",label.getKey(),label.getValue()) + textToShow;
    }
    return textToShow;
  }
}

Where do I need to add normalization option?

 

[bcrichster]

Welcome to Android Forums and nice job!
There are many devices with differing amount of pixel capability.. Is this related to that or strictly an image size reference?

 

[JuliaZ]

Thank you for kind invitation!
It is important for neural network model, because pixels are input data for it. My custom network can do class predictions using inputs as numbers in range of (0..1). So, I need to divide every pixel in RGB format by 255 to get pixels value in range (0..1). Then pixels would be forwarded as input data to neural network for subsequent recognition.
Device isn't important.