[英]MLKit Object Detection is not detecting objects
Google 的 MLKit(沒有 Firebase)是新的,所以我遇到了麻煩。 我試圖在這里遵循這個例子: https://developers.google.com/ml-kit/vision/object-detection/custom-models/android
該應用程序可以正常打開,並且相機可以正常工作(例如,我可以看到東西)。 但實際檢測似乎不起作用。
我是否缺少實際檢測 object 的部分代碼? 還是 CameraX 或 ImageInput 的實現有問題?
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();
}
}
}
}
未檢測到任何內容,因為您定義了tflite
model 文件的錯誤路徑。 您的模擬器或物理設備無法解析給定路徑,因為它在移動設備上不存在: C:\\Users\\dude\\Documents\\mlkitobjecttest\\app\\src\\main\\assets\\mobilenet_v1_1.0_128_quantized_1_default_1.tflite
將 model mobilenet_v1_1.0_128_quantized_1_default_1.tflite
復制到應用程序項目src/main
目錄下的assets
目錄中。
如果您沒有該目錄,只需創建一個名為assets
的新目錄。
最后它應該是這樣的:
之后修復LocalModel
初始化代碼:
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();
ImageAnalysis
實例未綁定到CameraProvider
:
...
ImageAnalysis imageAnalysis = ...
Camera camera = cameraProvider.bindToLifecycle((LifecycleOwner)this, cameraSelector, preview); // imageAnalysis is not used
要修復它,只需將imageAnalysis
變量作為最后一個參數傳遞給bindToLifecycle
方法:
Camera camera = cameraProvider.bindToLifecycle((LifecycleOwner)this, cameraSelector, preview, imageAnalysis);
MLKit 無法處理圖像,因為它在處理過程中或處理開始之前已關閉。 我說的是在public void analyze(ImageProxy imageProxy)
內部聲明的imageProxy.close()
代碼行。
Java close()
方法的文檔:
/**
* Free up this frame for reuse.
* <p>
* After calling this method, calling any methods on this {@code Image} will
* result in an {@link IllegalStateException}, and attempting to read from
* or write to {@link ByteBuffer ByteBuffers} returned by an earlier
* {@link Plane#getBuffer} call will have undefined behavior. If the image
* was obtained from {@link ImageWriter} via
* {@link ImageWriter#dequeueInputImage()}, after calling this method, any
* image data filled by the application will be lost and the image will be
* returned to {@link ImageWriter} for reuse. Images given to
* {@link ImageWriter#queueInputImage queueInputImage()} are automatically
* closed.
* </p>
*/
要修復將imageProxy.close()
移動到失敗和成功偵聽器中的問題:
objectDetector
.process(image)
.addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
Toast.makeText(getApplicationContext(), "Fail. Sad!", Toast.LENGTH_LONG).show();
...
imageProxy.close();
}
})
.addOnSuccessListener(new OnSuccessListener<List<DetectedObject>>() {
@Override
public void onSuccess(List<DetectedObject> results) {
Toast.makeText(getBaseContext(), "Success...", Toast.LENGTH_LONG).show();
...
imageProxy.close();
}
});
使用來自 Tensorflow 的圖像分類 model對固定解決方案進行測試,測試成功。
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