在Android中运行tflite分类器(Kotlin)

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:2.7.0' // 请检查最新版本
}

import android.content.Context
import org.tensorflow.lite.Interpreter
import java.nio.ByteBuffer
import java.nio.ByteOrder
import java.nio.file.Files
import java.nio.file.Paths

class Classifier(context: Context) {
    private val interpreter: Interpreter
    private val inputSize = 224 // 假设模型需要的输入大小是224x224
    private val batchSize = 1 // 批处理大小
    private val inputBuffer: ByteBuffer

    init {
        val assetFileDescriptor = context.assets.openFd("your_model.tflite")
        val fileInputStream = assetFileDescriptor.createInputStream()
        val fileChannel = fileInputStream.channel
        val startOffset = assetFileDescriptor.startOffset
        val declaredLength = assetFileDescriptor.declaredLength
        val buffer = ByteArray(declaredLength.toInt())
        fileChannel.read(ByteBuffer.wrap(buffer), startOffset)
        fileChannel.close()
        fileInputStream.close()

        interpreter = Interpreter(buffer)

        inputBuffer = ByteBuffer.allocateDirect(batchSize * inputSize * inputSize * 3 * 4)
        inputBuffer.order(ByteOrder.nativeOrder())
    }

    fun classify(image: Bitmap): String {
        // 将Bitmap转换为适合模型的ByteBuffer
        val pixels = IntArray(image.width * image.height)
        image.getPixels(pixels, 0, image.width, 0, 0, image.width, image.height)
        val byteBuffer = ByteBuffer.allocateDirect(pixels.size * 4)
        byteBuffer.order(ByteOrder.nativeOrder())
        for (pixel in pixels) {
            val r = (pixel shr 16 and 0xff)
            val g = (pixel shr 8 and 0xff)
            val b = (pixel and 0xff)
            byteBuffer.putInt((r shl 16) or (g shl 8) or b)
        }
        byteBuffer.position(0)
        inputBuffer.clear()
        inputBuffer.put(byteBuffer)

        // 运行模型
        val outputBuffer = Array(1) { FloatArray(1000) } // 假设模型有1000个类别
        interpreter.run(inputBuffer, outputBuffer)

        // 获取最可能的类别
        val result = outputBuffer[0]
        val bestLabelIdx = result.indexOf(result.max()!!)
        return "Class: $bestLabelIdx"
    }
}