如何将 RGBA_888 转换为 bytebuffer 以将其提供给 tf lite model

Question

I am using camera x to imageAnalysis use case to run tf lite model, I am getting output image format RGBA_8888. How to convert it to bytebuffer to feed it to my ml model.

This is the code generated by the android studio for the ml model:

// Creates inputs for reference.
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 224, 224, 3), DataType.FLOAT32)
inputFeature0.loadBuffer(byteBuffer)

// Runs model inference and gets result.
val outputs = model.process(inputFeature0)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer

// Releases model resources if no longer used.
model.close()

The code I have written for converting rgba_8888 to bytebuffer, but is giving same output data(confidences):

  imageAnalysis.setAnalyzer(ContextCompat.getMainExecutor(this)) { imageProxy ->

                val bitmap = Bitmap.createBitmap(
                    imageProxy.width,
                    imageProxy.height,
                    Bitmap.Config.ARGB_8888
                )
                val img = Bitmap.createScaledBitmap(bitmap, 224, 224, false)

                val model = ModelFull.newInstance(context)
                val byteBuffer: ByteBuffer = ByteBuffer.allocate(4 * 224 * 224 * 3)
                byteBuffer.order(ByteOrder.nativeOrder())

                // get 1D array of 224 * 224 pixels in image
                val intValues = IntArray(224 * 224)
                img.getPixels(
                    intValues,
                    0,
                    img.width,
                    0,
                    0,
                    img.width,
                    img.height
                )
                // iterate over pixels and extract R, G, and B values. Add to bytebuffer.

                var pixel = 0
                for (i in 0 until 224) {
                    for (j in 0 until 224) {
                        val `val` = intValues[pixel++] // RGB
                        byteBuffer.putFloat((`val` shr 16 and 0xFF) * (1f / 255f))
                        byteBuffer.putFloat((`val` shr 8 and 0xFF) * (1f / 255f))
                        byteBuffer.putFloat((`val` and 0xFF) * (1f / 255f))
                    }
                }

                val inputFeature0 =
                    TensorBuffer.createFixedSize(intArrayOf(1, 224, 224, 3), DataType.FLOAT32)
                inputFeature0.loadBuffer(byteBuffer)

//                Runs model inference and gets result .
                val outputs = model.process(inputFeature0)
                val outputFeature0 = outputs.outputFeature0AsTensorBuffer

                val confidences = outputFeature0.floatArray
                Log.d("this is my array", "arr: " + Arrays.toString(confidences))
//                Releases model resources if no longer used.
                model.close()
                imageProxy.close()
            } ```

Answer 1

Try this, works for me.

       TensorBuffer inputFeature0 = TensorBuffer.createFixedSize(new int[]{1, 
       400, 600, 3}, DataType.FLOAT32);

        Bitmap input=Bitmap.createScaledBitmap(bitmap,400,600,true);
        TensorImage image=new TensorImage(DataType.FLOAT32);
        image.load(input);

        ByteBuffer byteBuffer=image.getBuffer();
        inputFeature0.loadBuffer(byteBuffer);

        Seeinthedark.Outputs outputs = model.process(inputFeature0);
        TensorBuffer outputFeature0 = outputs.getOutputFeature0AsTensorBuffer()