上下文：我正在尝试将tensorflow lite用于摄像机捕获
启动我的应用程序时，我的应用程序崩溃并且我的手机上显示了此错误消息（构建步骤中没有错误）：
java.lang.NoClassDefFoundError：无法解决以下问题：
Lcom / google / android / things / pio / GpioCallback; 在http://com.google.android处。things.contrib.driver.button.ButtonInputDriver。（http://ButtonInputDriver.java:44） 在http：// com.google.android .things.contrib.driver.rainbowhat.RainbowHat.createButtonInputDriver（htt ps：// t.co/pCxFVemJoJ） 在http：//com.google.android.things.contrib.driver.rainbowhat.RainbowHat.createButtonCInputDriver（htt ps：// t.co/L5GdT2osCC） 在com.example.androidthings.imageclassifier.ImageClassifierActivity.initButton（http：// ImageClassifierActivity.java:186） 在com.example.androidthings.imageclassifier.ImageClassifierActivity.onCreate（http://ImageClassifierActivity.java:172） 在http://android.app。Activity.performCreate（http s：// t.co/iAgPPJxDqN） 在http://android.app处。Activity.performCreate（http s：// t.co/x1jCqk83Vz） 在http://android.app处。Instrumentation.callActivityOnCreate（http s：// t.co/sH4ZVEXClz） 在http://android.app处。ActivityThread.performLaunchActivity（http s：// t.co/7ivkHUx5ol） 在http://android.app处。ActivityThread.handleLaunchActivity（http s：// t.co/JrImVgSukO） 在http://android.app处。servertransaction.LaunchActivityItem.execute（http s：// t.co/xJzsqMgH1S） 在http://android.app处.servertransaction.TransactionExecutor.executeCallbacks（htt ps：// t.co/pmDJcEYPAe） 在http://android.app处.servertransaction.TransactionExecutor.execute（htt ps：// t.co/B56pWorl1W） 在http://android.app处。ActivityThread$ H.handleMessage（htt ps：// t.co/3pIiwfbaZj） 在android.os.Handler.dispatchMessage（ht tp：//Handler.java：106） 在android.os.Looper.loop（ht tp：//Looper.java：201） 位于http://android.app。ActivityThread.main（ht tps：// t.co/X8nXQTeKz7） 在java.lang.reflect.Method.invoke（本机方法） 在http://com.android处。internal.os.RuntimeInit$ MethodAndArgsCaller.run（ht tps：// t.co/5IltBH9HjT） 在http://com.android处.internal.os.ZygoteInit.main（ht tps：//t.co/ UDaAWnMnAC） 原因：java.lang.ClassNotFoundException：在路径：DexPathList [[zip file“ /system/framework/org.apache]上找不到类” h ttp：//com.google.android .things.pio.GpioCallback“。 http.legacy.boot.jar”，zip文件“ /data/app/com.example.androidthings.imageclassifier-n33NGwKNRyCZU-UICaS1Qw==/base.apk”]、nativeLibraryDirectories=[/data/app/com.example.androidthings .imageclassifier-n33NGwKNRyCZU-UICaS1Qw == / lib / arm64，/data/app/com.example.androidthings.imageclassifier-n33NGwKNRyCZU-UICaS1Qw==/base.apk!/lib/arm64-v8a，/ system / lib64，/ vendor / lib64]] 在dalvik.system.BaseDexClassLoader.findClass（h tp：//BaseDexClassLoader.java：171） 在java.lang.ClassLoader.loadClass（ht tp：// ClassLoader.java:379） 在java.lang.ClassLoader.loadClass（ht tp：// ClassLoader.java:312）
这是 ImageClassifierActivity ：
package com.example.androidthings.imageclassifier;
import android.app.Activity;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.media.ImageReader;
import android.os.Bundle;
import android.util.Log;
import android.view.KeyEvent;
import android.view.WindowManager;
import android.widget.ImageView;
import android.widget.TextView;
import com.example.androidthings.imageclassifier.classifier.Recognition;
import com.example.androidthings.imageclassifier.classifier.TensorFlowHelper;
import com.google.android.things.contrib.driver.button.ButtonInputDriver;
import com.google.android.things.contrib.driver.rainbowhat.RainbowHat;
import org.tensorflow.lite.Interpreter;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
public class ImageClassifierActivity extends Activity {
private static final String TAG = "ImageClassifierActivity";
/** Camera image capture size */
private static final int PREVIEW_IMAGE_WIDTH = 640;
private static final int PREVIEW_IMAGE_HEIGHT = 480;
/** Image dimensions required by TF model */
private static final int TF_INPUT_IMAGE_WIDTH = 224;
private static final int TF_INPUT_IMAGE_HEIGHT = 224;
/** Dimensions of model inputs. */
private static final int DIM_BATCH_SIZE = 1;
private static final int DIM_PIXEL_SIZE = 3;
/** TF model asset files */
private static final String LABELS_FILE = "labels.txt";
private static final String MODEL_FILE = "mobilenet_quant_v1_224.tflite";
private ButtonInputDriver mButtonDriver;
private boolean mProcessing;
private ImageView mImage;
private TextView mResultText;
private Interpreter mTensorFlowLite;
private List<String> mLabels;
private CameraHandler mCameraHandler;
private ImagePreprocessor mImagePreprocessor;
/**
* Initialize the classifier that will be used to process images.
*/
private void initClassifier() {
try {
mTensorFlowLite = new Interpreter(TensorFlowHelper.loadModelFile(this, MODEL_FILE));
mLabels = TensorFlowHelper.readLabels(this, LABELS_FILE);
} catch (IOException e) {
Log.w(TAG, "Unable to initialize TensorFlow Lite.", e);
}
}
/**
* Clean up the resources used by the classifier.
*/
private void destroyClassifier() {
mTensorFlowLite.close();
}
/**
* Process an image and identify what is in it. When done, the method
* {@link #onPhotoRecognitionReady(Collection)} must be called with the results of
* the image recognition process.
*
* @param image Bitmap containing the image to be classified. The image can be
* of any size, but preprocessing might occur to resize it to the
* format expected by the classification process, which can be time
* and power consuming.
*/
private void doRecognize(Bitmap image) {
// Allocate space for the inference results
byte[][] confidencePerLabel = new byte[1][mLabels.size()];
// Allocate buffer for image pixels.
int[] intValues = new int[TF_INPUT_IMAGE_WIDTH * TF_INPUT_IMAGE_HEIGHT];
ByteBuffer imgData = ByteBuffer.allocateDirect(
DIM_BATCH_SIZE * TF_INPUT_IMAGE_WIDTH * TF_INPUT_IMAGE_HEIGHT * DIM_PIXEL_SIZE);
imgData.order(ByteOrder.nativeOrder());
// Read image data into buffer formatted for the TensorFlow model
TensorFlowHelper.convertBitmapToByteBuffer(image, intValues, imgData);
// Run inference on the network with the image bytes in imgData as input,
// storing results on the confidencePerLabel array.
mTensorFlowLite.run(imgData, confidencePerLabel);
// Get the results with the highest confidence and map them to their labels
Collection<Recognition> results = TensorFlowHelper.getBestResults(confidencePerLabel, mLabels);
// Report the results with the highest confidence
onPhotoRecognitionReady(results);
}
/**
* Initialize the camera that will be used to capture images.
*/
private void initCamera() {
mImagePreprocessor = new ImagePreprocessor(PREVIEW_IMAGE_WIDTH, PREVIEW_IMAGE_HEIGHT,
TF_INPUT_IMAGE_WIDTH, TF_INPUT_IMAGE_HEIGHT);
mCameraHandler = CameraHandler.getInstance();
mCameraHandler.initializeCamera(this,
PREVIEW_IMAGE_WIDTH, PREVIEW_IMAGE_HEIGHT, null,
new ImageReader.OnImageAvailableListener() {
@Override
public void onImageAvailable(ImageReader imageReader) {
Bitmap bitmap = mImagePreprocessor.preprocessImage(imageReader.acquireNextImage());
onPhotoReady(bitmap);
}
});
}
/**
* Clean up resources used by the camera.
*/
private void closeCamera() {
mCameraHandler.shutDown();
}
/**
* Load the image that will be used in the classification process.
* When done, the method {@link #onPhotoReady(Bitmap)} must be called with the image.
*/
private void loadPhoto() {
mCameraHandler.takePicture();
}
// --------------------------------------------------------------------------------------
// NOTE: The normal codelab flow won't require you to change anything below this line,
// although you are encouraged to read and understand it.
@Override
protected void onCreate(final Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
setContentView(R.layout.activity_camera);
mImage = findViewById(R.id.imageView);
mResultText = findViewById(R.id.resultText);
updateStatus(getString(R.string.initializing));
initCamera();
initClassifier();
initButton();
updateStatus(getString(R.string.help_message));
}
/**
* Register a GPIO button that, when clicked, will generate the {@link KeyEvent#KEYCODE_ENTER}
* key, to be handled by {@link #onKeyUp(int, KeyEvent)} just like any regular keyboard
* event.
*
* If there's no button connected to the board, the doRecognize can still be triggered by
* sending key events using a USB keyboard or `adb shell input keyevent 66`.
*/
private void initButton() {
try {
mButtonDriver = RainbowHat.createButtonCInputDriver(KeyEvent.KEYCODE_ENTER);
mButtonDriver.register();
} catch (IOException e) {
Log.w(TAG, "Cannot find button. Ignoring push button. Use a keyboard instead.", e);
}
}
private Bitmap getStaticBitmap() {
Log.d(TAG, "Using sample photo in res/drawable/sampledog_224x224.png");
return BitmapFactory.decodeResource(this.getResources(), R.drawable.sampledog_224x224);
}
@Override
public boolean onKeyUp(int keyCode, KeyEvent event) {
if (keyCode == KeyEvent.KEYCODE_ENTER) {
if (mProcessing) {
updateStatus("Still processing, please wait");
return true;
}
updateStatus("Running photo recognition");
mProcessing = true;
loadPhoto();
return true;
}
return super.onKeyUp(keyCode, event);
}
/**
* Image capture process complete
*/
private void onPhotoReady(Bitmap bitmap) {
mImage.setImageBitmap(bitmap);
doRecognize(bitmap);
}
/**
* Image classification process complete
*/
private void onPhotoRecognitionReady(Collection<Recognition> results) {
updateStatus(formatResults(results));
mProcessing = false;
}
/**
* Format results list for display
*/
private String formatResults(Collection<Recognition> results) {
if (results == null || results.isEmpty()) {
return getString(R.string.empty_result);
} else {
StringBuilder sb = new StringBuilder();
Iterator<Recognition> it = results.iterator();
int counter = 0;
while (it.hasNext()) {
Recognition r = it.next();
sb.append(r.getTitle());
counter++;
if (counter < results.size() - 1) {
sb.append(", ");
} else if (counter == results.size() - 1) {
sb.append(" or ");
}
}
return sb.toString();
}
}
/**
* Report updates to the display and log output
*/
private void updateStatus(String status) {
Log.d(TAG, status);
mResultText.setText(status);
}
@Override
protected void onDestroy() {
super.onDestroy();
try {
destroyClassifier();
} catch (Throwable t) {
// close quietly
}
try {
closeCamera();
} catch (Throwable t) {
// close quietly
}
try {
if (mButtonDriver != null) mButtonDriver.close();
} catch (Throwable t) {
// close quietly
}
}
}