这是我之前提出的一个问题的延伸。
我正在使用最新的MLKit对象检测版本（不需要firebase）。我使用了一个自定义模型和CameraX来检测对象并对其进行标记/获取信息。
目前，根据我现有的代码，它可以检测到区域内有对象存在，但存在以下问题：
- 没有显示标签或边界框；
- 一次只能检测一个对象；
- 一旦检测到一个对象，应用就不会“改变”（即当我移动手机，试图检测另一个对象时，显示屏上没有任何变化）。
这是我的代码：
package com.example.mlkitobjecttest;import androidx.annotation.NonNull;import androidx.appcompat.app.AppCompatActivity;import androidx.camera.core.Camera;import androidx.camera.core.CameraSelector;import androidx.camera.core.CameraX;import androidx.camera.core.ImageAnalysis;import androidx.camera.core.ImageProxy;import androidx.camera.core.Preview;import androidx.camera.core.impl.PreviewConfig;import androidx.camera.lifecycle.ProcessCameraProvider;import androidx.camera.view.PreviewView;import androidx.core.app.ActivityCompat;import androidx.core.content.ContextCompat;import androidx.lifecycle.LifecycleOwner;import android.content.pm.PackageManager;import android.graphics.Rect;import android.media.Image;import android.os.Bundle;import android.text.Layout;import android.util.Rational;import android.util.Size;import android.view.View;import android.widget.TextView;import android.widget.Toast;import com.google.android.gms.tasks.OnFailureListener;import com.google.android.gms.tasks.OnSuccessListener;import com.google.common.util.concurrent.ListenableFuture;import com.google.mlkit.common.model.LocalModel;import com.google.mlkit.vision.common.InputImage;import com.google.mlkit.vision.objects.DetectedObject;import com.google.mlkit.vision.objects.ObjectDetection;import com.google.mlkit.vision.objects.ObjectDetector;import com.google.mlkit.vision.objects.custom.CustomObjectDetectorOptions;import org.w3c.dom.Text;import java.util.List;import java.util.concurrent.ExecutionException;import java.util.concurrent.ExecutorService;import java.util.concurrent.Executors;public class MainActivity extends AppCompatActivity { private class YourAnalyzer implements ImageAnalysis.Analyzer { @Override @androidx.camera.core.ExperimentalGetImage public void analyze(ImageProxy imageProxy) { Image mediaImage = imageProxy.getImage(); if (mediaImage != null) { InputImage image = InputImage.fromMediaImage(mediaImage, imageProxy.getImageInfo().getRotationDegrees()); // Pass image to an ML Kit Vision API // ... LocalModel localModel = new LocalModel.Builder() .setAssetFilePath("mobilenet_v1_1.0_128_quantized_1_default_1.tflite") // or .setAbsoluteFilePath(absolute file path to tflite model) .build(); CustomObjectDetectorOptions customObjectDetectorOptions = new CustomObjectDetectorOptions.Builder(localModel) .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE) .enableMultipleObjects() .enableClassification() .setClassificationConfidenceThreshold(0.5f) .setMaxPerObjectLabelCount(3) .build(); ObjectDetector objectDetector = ObjectDetection.getClient(customObjectDetectorOptions); objectDetector .process(image) .addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { //Toast.makeText(getApplicationContext(), "Fail. Sad!", Toast.LENGTH_SHORT).show(); //textView.setText("Fail. Sad!"); imageProxy.close(); } }) .addOnSuccessListener(new OnSuccessListener<List<DetectedObject>>() { @Override public void onSuccess(List<DetectedObject> results) { for (DetectedObject detectedObject : results) { Rect box = detectedObject.getBoundingBox(); for (DetectedObject.Label label : detectedObject.getLabels()) { String text = label.getText(); int index = label.getIndex(); float confidence = label.getConfidence(); textView.setText(text); }} imageProxy.close(); } }); } //ImageAnalysis.Builder.fromConfig(new ImageAnalysisConfig).setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST); } } PreviewView prevView; private ListenableFuture<ProcessCameraProvider> cameraProviderFuture; private ExecutorService executor = Executors.newSingleThreadExecutor(); TextView textView; private int REQUEST_CODE_PERMISSIONS = 101; private String[] REQUIRED_PERMISSIONS = new String[]{"android.permission.CAMERA"}; /* @NonNull @Override public CameraXConfig getCameraXConfig() { return CameraXConfig.Builder.fromConfig(Camera2Config.defaultConfig()) .setCameraExecutor(ContextCompat.getMainExecutor(this)) .build(); }*/ @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); prevView = findViewById(R.id.viewFinder); textView = findViewById(R.id.scan_button); if(allPermissionsGranted()){ startCamera(); }else{ ActivityCompat.requestPermissions(this, REQUIRED_PERMISSIONS, REQUEST_CODE_PERMISSIONS); } } private void startCamera() { cameraProviderFuture = ProcessCameraProvider.getInstance(this); cameraProviderFuture.addListener(new Runnable() { @Override public void run() { try { ProcessCameraProvider cameraProvider = cameraProviderFuture.get(); bindPreview(cameraProvider); } catch (ExecutionException | InterruptedException e) { // No errors need to be handled for this Future. // This should never be reached. } } }, ContextCompat.getMainExecutor(this)); } void bindPreview(@NonNull ProcessCameraProvider cameraProvider) { Preview preview = new Preview.Builder() .build(); CameraSelector cameraSelector = new CameraSelector.Builder() .requireLensFacing(CameraSelector.LENS_FACING_BACK) .build(); preview.setSurfaceProvider(prevView.createSurfaceProvider()); ImageAnalysis imageAnalysis = new ImageAnalysis.Builder() .setTargetResolution(new Size(1280, 720)) .setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST) .build(); imageAnalysis.setAnalyzer(ContextCompat.getMainExecutor(this), new YourAnalyzer()); Camera camera = cameraProvider.bindToLifecycle((LifecycleOwner)this, cameraSelector, preview, imageAnalysis); } private boolean allPermissionsGranted() { for(String permission: REQUIRED_PERMISSIONS){ if(ContextCompat.checkSelfPermission(this, permission) != PackageManager.PERMISSION_GRANTED){ return false; } } return true; } @Override public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) { if(requestCode == REQUEST_CODE_PERMISSIONS){ if(allPermissionsGranted()){ startCamera(); } else{ Toast.makeText(this, "Permissions not granted by the user.", Toast.LENGTH_SHORT).show(); this.finish(); } } }}回答：
所以我解决了这个问题。当添加TensorFlow模型来帮助对象检测时，显然它必须包含元数据（这样，当你想调用“getLabels()”及其相关方法时，它实际上会返回一个标签。否则，它将返回空值并导致错误。
我使用并成功的模型是：mobilenet_v1_0.50_192_quantized_1_metadata_1.tflite
