我想将我的 TFLite 导出的 Yolov5s 模型加载到我的对象检测 Android 应用程序中。我遵循了本教程：https://www.youtube.com/watch?v=zs43IrWTzB0
但是，我的 TFLite Yolov5 模型输出形状为 [1, 25200, 9] 的数组。
同时，预期的输出签名是以下 4 个数组：Detection_boxes、Detection_Classes、Detection_Scores 和 num_Detections。根据https://www.tensorflow.org/lite/examples/object_detection/overview#output_signature。
我应该如何修改我的代码以使其可以在此应用程序中加载？
这是我的 TF lite 模型提供的示例代码：
val model = BestFp16.newInstance(context)
// Creates inputs for reference.
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 640, 640, 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()
这是我的 MainActivity.kt （注释部分是我尝试绘制边框的地方，但最终在屏幕的左上角绘制了静态边框，并且应用程序在几秒钟后崩溃）：
package com.example.sightfulkotlin
import android.annotation.SuppressLint
import android.content.Context
import android.content.pm.PackageManager
import android.graphics.*
import android.hardware.camera2.CameraCaptureSession
import android.hardware.camera2.CameraDevice
import android.hardware.camera2.CameraManager
import android.os.Bundle
import android.os.Handler
import android.os.HandlerThread
import android.view.Surface
import android.view.TextureView
import android.widget.ImageView
import androidx.appcompat.app.AppCompatActivity
import androidx.core.content.ContextCompat
import com.example.sightfulkotlin.ml.BestFp16
import org.tensorflow.lite.DataType
import org.tensorflow.lite.support.common.FileUtil
import org.tensorflow.lite.support.image.ImageProcessor
import org.tensorflow.lite.support.image.TensorImage
import org.tensorflow.lite.support.image.ops.ResizeOp
import org.tensorflow.lite.support.tensorbuffer.TensorBuffer
class MainActivity : AppCompatActivity() {
var colors = listOf(
Color.BLUE, Color.GREEN, Color.RED, Color.CYAN, Color.GRAY, Color.BLACK, Color.DKGRAY, Color.MAGENTA, Color.YELLOW, Color.LTGRAY, Color.WHITE)
val paint = Paint()
private lateinit var labels:List<String>
lateinit var bitmap: Bitmap
lateinit var imageView: ImageView
lateinit var cameraDevice: CameraDevice
lateinit var handler: Handler
private lateinit var cameraManager: CameraManager
lateinit var textureView: TextureView
lateinit var model: BestFp16
override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
setContentView(R.layout.activity_main)
getPermission()
labels = FileUtil.loadLabels(this, "labels.txt")
model = BestFp16.newInstance(this)
var imageProcessor = ImageProcessor.Builder().add(ResizeOp(640, 640, ResizeOp.ResizeMethod.BILINEAR)).build()
val handlerThread = HandlerThread("videoThread")
handlerThread.start()
handler = Handler(handlerThread.looper)
paint.color = Color.GREEN
imageView = findViewById(R.id.imageView)
textureView = findViewById(R.id.textureView)
textureView.surfaceTextureListener = object: TextureView.SurfaceTextureListener
{
override fun onSurfaceTextureAvailable(p0: SurfaceTexture, p1: Int, p2: Int) {
openCamera()
}
override fun onSurfaceTextureSizeChanged(p0: SurfaceTexture, p1: Int, p2: Int) {
}
override fun onSurfaceTextureDestroyed(p0: SurfaceTexture): Boolean {
return false
}
override fun onSurfaceTextureUpdated(p0: SurfaceTexture) {
bitmap = textureView.bitmap!!
var tensorImage = TensorImage(DataType.FLOAT32)
tensorImage.load(bitmap)
tensorImage = imageProcessor.process(tensorImage)
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 640, 640, 3), DataType.FLOAT32)
inputFeature0.loadBuffer(tensorImage.buffer)
val outputs = model.process(inputFeature0)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer
val mutable = bitmap.copy(Bitmap.Config.ARGB_8888, true)
val canvas = Canvas(mutable)
val h = bitmap.height
val w = bitmap.width
paint.textSize = h/15f
paint.strokeWidth = h/85f
/*val detection = outputFeature0.floatArray.copyOfRange(0, 9)
val numDetections = outputFeature0.shape[1]
for (i in 0 until numDetections) {
i * 9
val xCenter = detection[0]
val yCenter = detection[1]
val width = detection[2]
val height = detection[3]
val left = (xCenter - width/2) * w
val top = (yCenter - height/2) * h
val right = (xCenter + width/2) * w
val bottom = (yCenter + height/2) * h
canvas.drawRect(left, top, right, bottom, paint)
}*/
imageView.setImageBitmap(mutable)
}
}
cameraManager = getSystemService(Context.CAMERA_SERVICE) as CameraManager
}
override fun onDestroy() {
super.onDestroy()
model.close()
}
@SuppressLint("MissingPermission")
fun openCamera()
{
cameraManager.openCamera(cameraManager.cameraIdList[0], object: CameraDevice.StateCallback(){
@SuppressLint("MissingPermission")
override fun onOpened(p0: CameraDevice) {
cameraDevice = p0
var surfaceTexture = textureView.surfaceTexture
var surface = Surface(surfaceTexture)
var captureRequest = cameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW)
captureRequest.addTarget(surface)
cameraDevice.createCaptureSession(listOf(surface), object: CameraCaptureSession.StateCallback(){
override fun onConfigured(p0: CameraCaptureSession) {
p0.setRepeatingRequest(captureRequest.build(), null, null)
}
override fun onConfigureFailed(p0: CameraCaptureSession) {
}
}, handler)
}
override fun onDisconnected(p0: CameraDevice) {
}
@SuppressLint("MissingPermission")
override fun onError(p0: CameraDevice, p1: Int) {
}
},handler)
}
fun getPermission()
{
if(ContextCompat.checkSelfPermission(this, android.Manifest.permission.CAMERA)!=PackageManager.PERMISSION_GRANTED)
{
requestPermissions(arrayOf(android.Manifest.permission.CAMERA), 101)
}
}
override fun onRequestPermissionsResult(
requestCode: Int,
permissions: Array<out String>,
grantResults: IntArray
) {
super.onRequestPermissionsResult(requestCode, permissions, grantResults)
if (grantResults[0] != PackageManager.PERMISSION_GRANTED)
{
getPermission()
}
}
}