muhwyndhamhp

private fun cosineSim(x1: FloatArray, x2: FloatArray): Float {
        var dotProduct = 0.0f
        var normA = 0.0f
        var normB = 0.0f
        for (i in x1.indices) {
            dotProduct += x1[i] * x2[i]
            normA += x1[i].pow(2)
            normB += x2[i].pow(2)
        }
        return dotProduct / (sqrt(normA) * sqrt(normB))

muhwyndhamhp

private fun l2Norm(x1: FloatArray, x2: FloatArray): Float {
  var sum = 0.0f
  val mag1 = sqrt(x1.map { xi -> xi.pow(2) }.sum())
  val mag2 = sqrt(x2.map { xi -> xi.pow(2) }.sum())
  for (i in x1.indices) {
      sum += ((x1[i] / mag1) - (x2[i] / mag2)).pow(2)
  }
  return sqrt(sum)
}

muhwyndhamhp

val interpreterOptions = Interpreter.Options().apply {
    setNumThreads(4)
}

val interpreter = Interpreter(FileUtil.loadMappedFile(context, "model.tflite"), interpreterOptions)

private fun runFaceNet(inputs: Any): Array<FloatArray> {
  val faceNetModelOutputs: MutableMap<Int, Any> = HashMap()
  faceNetModelOutputs[0] = Array(1) { FloatArray(embeddingDim) }
  

muhwyndhamhp

private val imgSize = 
// Set this image size according to our choice of DNN Model (160 for FaceNet, 112 for MobileFaceNet)

private val imageTensorProcessor = ImageProcessor.Builder()
        .add(ResizeOp(imgSize, imgSize, ResizeOp.ResizeMethod.BILINEAR))
        .add(CastOp( DataType.FLOAT32 ))
        .build()
        
private fun convertBitmapToBuffer(image: Bitmap): ByteBuffer {
  return imageTensorProcessor.process(TensorImage.fromBitmap(image)).buffer

muhwyndhamhp

implementation 'org.tensorflow:tensorflow-lite:2.4.0'
implementation 'org.tensorflow:tensorflow-lite-gpu:2.4.0'

implementation 'org.tensorflow:tensorflow-lite-support:0.1.0'

muhwyndhamhp

arrayOfLikeness : [
    (
      name: "Wyndham",
      likeness: [ // This is the result form DNN model of each images that contains this person's face
        [1.2,1.4, -10.3, .... ],
        [1.2,1.4, -10.3, .... ],
        [1.2,1.4, -10.3, .... ],
        ...,
        [1.2,1.4, -10.3, .... ]]
    ),

muhwyndhamhp

fun Bitmap.bitmapToNV21ByteArray(): ByteArray {
        val argb = IntArray(this.width * this.height)
        this.getPixels(argb, 0, this.width, 0, 0, this.width, this.height)
        val yuv = ByteArray(
            this.height * this.width + 2 * ceil(this.height / 2.0).toInt()
                    * ceil(this.width / 2.0).toInt()
        )
        encodeYUV420SP(yuv, argb, this.width, this.height)
        return yuv
    }

muhwyndhamhp

val image = InputImage.fromByteArray(
                bitmap?.bitmapToNV21ByteArray()!!,
                bitmap!!.width,
                bitmap!!.height,
                screenOrientation,
                InputImage.IMAGE_FORMAT_NV21
            )
faceDetector.process(image).addOnSuccessListener { image -> 
  // Continue processing here, 
  // including Cropping image from BoundingBox (which available inside `image` Object above)

muhwyndhamhp

val options = FaceDetectorOptions.Builder()
            .setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_FAST) 
            .setContourMode(FaceDetectorOptions.LANDMARK_MODE_NONE) // skips landmark mapping
            .setClassificationMode(FaceDetectorOptions.CLASSIFICATION_MODE_NONE) // skips facial expressions and other classification such as wink
            .build()

val faceDetector = FaceDetection.getClient(options)

muhwyndhamhp

class PageDotDecorator(
    private val itemCount: Int,
    private val context: Context?
) : RecyclerView.ItemDecoration() {

    companion object {
        /**
         * Draw Parameters
         */
        const val DOT_RADIUS = 3f