Converting pytorch tensor / floatArray to Android Bitmap

convert.kt

fun floatArrayToGrayscaleBitmap (
    floatArray: FloatArray,
    width: Int,
    height: Int,
    alpha :Byte = (255).toByte(),
    reverseScale :Boolean = false
) : Bitmap {

    // Create empty bitmap in RGBA format (even though it says ARGB but channels are RGBA)
    val bmp = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888)
    val byteBuffer = ByteBuffer.allocate(width*height*4)

    // mapping smallest value to 0 and largest value to 255
    val maxValue = floatArray.maxOrNull() ?: 1.0f
    val minValue = floatArray.minOrNull() ?: 0.0f
    val delta = maxValue-minValue
    var tempValue :Byte

    // Define if float min..max will be mapped to 0..255 or 255..0
    val conversion = when(reverseScale) {
        false -> { v: Float -> ((v-minValue)/delta*255).toInt().toByte() }
        true -> { v: Float -> (255-(v-minValue)/delta*255).toInt().toByte() }
    }

    // copy each value from float array to RGB channels and set alpha channel
    floatArray.forEachIndexed { i, value ->
        tempValue = conversion(value)
        byteBuffer.put(4*i, tempValue)
        byteBuffer.put(4*i+1, tempValue)
        byteBuffer.put(4*i+2, tempValue)
        byteBuffer.put(4*i+3, alpha)
    }

    bmp.copyPixelsFromBuffer(byteBuffer)

    return bmp
}

The function maps the smallest value in your floatArray to 0 (black) and the largest value to 255 (white). If you want to have it the other way round, so the smallest value is white and the largest are black, then reverseScale should be true.
I assume that your object detection function delivers a float array where background is 0 and detected objects are >0, so if you want to have the detected objects in black and the background in white set reverseScale=true then calling the function.