[FIXED] Android: How to expand dimension of image using tensorflow lite in Android

Issue

The question itself is self-explanatory. In Python, its quite simple to do that with tf.expand_dims(image, 0). How can I do the same thing in Android? I'm getting error on running the tensorflow model I prepared. It says,

Cannot copy to a TensorFlowLite tensor (input_3) with X bytes from a Java Buffer with Y bytes.

I'm guessing it comes from one less dimension of image. I've run another model which is working fine. So I need to know how to do that. My code snippet:

val contentArray =
        ImageUtils.bitmapToByteBuffer(
            scaledBitmap,
            imageSize,
            imageSize,
            IMAGE_MEAN,
            IMAGE_STD
        )

    val tfliteOptions = Interpreter.Options()
    tfliteOptions.setNumThreads(4)
    val tflite = Interpreter(tfliteModel, tfliteOptions)
    tflite.run(contentArray, segmentationMasks)

fun bitmapToByteBuffer(
  bitmapIn: Bitmap,
  width: Int,
  height: Int,
  mean: Float = 0.0f,
  std: Float = 255.0f
): ByteBuffer {
  val bitmap = scaleBitmapAndKeepRatio(bitmapIn, width, height)
  val inputImage = ByteBuffer.allocateDirect(1 * width * height * 3 * 4)
  inputImage.order(ByteOrder.nativeOrder())
  inputImage.rewind()

  val intValues = IntArray(width * height)
  bitmap.getPixels(intValues, 0, width, 0, 0, width, height)
  var pixel = 0
  for (y in 0 until height) {
    for (x in 0 until width) {
      val value = intValues[pixel++]

      // Normalize channel values to [-1.0, 1.0]. This requirement varies by
      // model. For example, some models might require values to be normalized
      // to the range [0.0, 1.0] instead.
      inputImage.putFloat(((value shr 16 and 0xFF) - mean) / std)
      inputImage.putFloat(((value shr 8 and 0xFF) - mean) / std)
      inputImage.putFloat(((value and 0xFF) - mean) / std)
    }
  }

  inputImage.rewind()
  return inputImage
}

Solution

There is JVM/Android equivalent op in the TensorFlow API: https://www.tensorflow.org/jvm/api_docs/java/org/tensorflow/op/core/ExpandDims.

However, if you are using TfLite Interpreter API to run inference on a pre-trained model, then you will most likely want to deal with the array dimensions when you construct and save the model (i.e. using Python) instead of when you call the interpreter from the Android code.

Answered By - Jared Junyoung Lim