如何在屏幕上绘制标签:MLKit对象检测 [英] How to draw labels on a screen: MLKit Object Detection
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问题描述
这是我之前提出的问题的延伸.
This is an extension of a question I asked before.
我正在使用最新版本的MLKit对象检测(不需要firebase).我正在使用自定义模型和CameraX来检测对象并标记它们/获取信息.
I'm using the recent version of MLKit Object Detection (which doesn't require firebase). I'm using a custom model, and CameraX, to detect objects and label them/get info.
现在，使用我拥有的代码，它可以检测到该区域中存在对象，但是:
Right now, with the code I have, it can detect that objects are present in the area, but:
- 没有标签或边框出现；
- 一次不能检测多个物体；
- 一旦检测到对象，该应用就不会更改"该对象.(即，当我移动手机时，尝试检测另一个物体时，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();
}
}
}
}
```
推荐答案
所以我知道了.当添加TensorFlow模型以帮助对象检测时，显然它必须包含元数据(这样，当您要调用"getLabels()"及其适当的方法时，它实际上将返回一个标签.否则，它将返回一个标签.不会返回任何内容，并且显然会导致错误.
So I figured it out. When adding a TensorFlow model to help with the object detection, apparently it has to contain metadata (so that that way, when you want to call "getLabels()" and its appropriate methods, it will actually return a label. Otherwise it will return nothing and cause errors apparently.
这是我用过的一个:mobilenet_v1_0.50_192_quantized_1_metadata_1.tflite
This is the one I used that worked: mobilenet_v1_0.50_192_quantized_1_metadata_1.tflite
这篇关于如何在屏幕上绘制标签:MLKit对象检测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！
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