MLKit对象检测未检测到对象 [英] MLKit Object Detection isn't not detecting objects
问题描述
MLKit(没有Firebase)是新的，所以我遇到了麻烦.我尝试在此遵循以下示例: https://developers.google.com/ml-kit/vision/object-detection/custom-models/android
MLKit by Google (without Firebase) is new, so I'm having trouble. I'm trying to follow this example here: https://developers.google.com/ml-kit/vision/object-detection/custom-models/android
该应用可以正常打开，&相机正常工作(就像我看到的一样).但是实际的检测似乎不起作用.
The app opens fine, & the camera works (As in, I can see things). But the actual detection doesn't seem to work.
我是否缺少部分代码来实际检测到对象?还是CameraX或ImageInput的实现有问题?
Am I missing part of the code to actually detect the object? Or is it a issue with the implementation of CameraX or ImageInput?
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();
}
}
}
}
推荐答案
未检测到任何内容，因为您为tflite模型文件定义了错误的路径.您的仿真器或物理设备无法解析给定路径，因为该路径在移动设备上不存在:C:\\Users\\dude\\Documents\\mlkitobjecttest\\app\\src\\main\\assets\\mobilenet_v1_1.0_128_quantized_1_default_1.tflite
Nothing is detected because you defined the wrong path to tflite model file. You emulator or physical device cannot resolve given path as it doesn't exists on mobile device: C:\\Users\\dude\\Documents\\mlkitobjecttest\\app\\src\\main\\assets\\mobilenet_v1_1.0_128_quantized_1_default_1.tflite
将模型mobilenet_v1_1.0_128_quantized_1_default_1.tflite复制到应用程序项目src/main目录下的assets目录中.
Copy your model mobilenet_v1_1.0_128_quantized_1_default_1.tflite into assets directory under you app's project src/main directory.
如果没有该目录，只需创建一个名为assets的新目录.
If you do not have that directory just create a new one named assets.
最后应该看起来像这样:
At the end it should look like this:
此修复程序LocalModel初始化代码之后:
After that fix LocalModel initialization code:
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();
更新:发现了另外一个问题
ImageAnalysis实例未绑定到CameraProvider:
...
ImageAnalysis imageAnalysis = ...
Camera camera = cameraProvider.bindToLifecycle((LifecycleOwner)this, cameraSelector, preview); // imageAnalysis is not used
要解决此问题，只需将最后一个参数imageAnalysis变量传递给bindToLifecycle方法:
To fix it just pass as the last argument imageAnalysis variable into bindToLifecycle method:
Camera camera = cameraProvider.bindToLifecycle((LifecycleOwner)this, cameraSelector, preview, imageAnalysis);
第二次更新:发现了另一个问题
MLKit无法处理图像，因为它在处理过程中或在处理开始之前就已关闭.我说的是在public void analyze(ImageProxy imageProxy)内部声明的imageProxy.close()行代码.
Second update: another one issue found
MLKit cannot process an image because it was closed while it was processing or right before processing started. I'm talking about imageProxy.close() line of code declared inside of public void analyze(ImageProxy imageProxy).
close()方法的Java文档:
Java documentation of close() method:
/**
* 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>
*/
要解决将imageProxy.close()转移到失败和成功侦听器中的问题，
To fix that move imageProxy.close() into failure and success listeners:
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();
}
});
固定解决方案已使用来自以下产品的图像分类模型进行了测试Tensorflow和测试成功.
The fixed solution was tested with image classification model from Tensorflow and test was successful.
这篇关于MLKit对象检测未检测到对象的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
