前言

将图片学习的模型加载到手机应用中，让学习模型发挥实际用处。这里以SqueezeNet模型（典型轻量化模型）为参考，记录如何将轻量化模型应用到手机app上。

一、使用工具

采用pycharm和android studio分别作为python和android开发的IDE。

二、使用步骤

1.模型格式的转换

Keras可以作为初学者入手深度学习模型不错的模型库，其语言简单且易于理解。Keras一般将训练好的模型和参数以h5格式保存，这里需要转换成tflite格式，以供android调用。

from keras_squeezenet import SqueezeNet
model = SqueezeNet(weights=None)
model.load_weights("./squeezenet_weights_tf_dim_ordering_tf_kernels.h5") ###从网上下载SequeezeNet网络模型及参数converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("./squeezenet_model.tflite", "wb").write(tflite_model)

这里生成的tflite文件能够根据输入图片产生预测结果的概率值。

将训练的模型加载到android上不仅需要tflite模型，还需要label文件。

在github中下载的模型相配套的label文件。

时候不好下载，可以从此处下载：
分类标签文件

接下来通过Android Studio建立项目，该过程这里不详细描述。

2.配置文件修改

在新建的项目中，在build.gradle中增加

android {......aaptOptions {noCompress "tflite"noCompress "lite"
}
}dependencies {.......
implementation 'org.tensorflow:tensorflow-lite:2.2.0'
}

大家可以根据自己的要求写界面文件，这里附上写的界面文件（AndroidManifest.xml）：

<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"package="gdut.bsx.tensorflowtraining"><uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/><uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/><uses-permission android:name="android.permission.CAMERA"/><applicationandroid:allowBackup="true"android:icon="@mipmap/ic_launcher"android:label="@string/app_name"android:roundIcon="@mipmap/ic_launcher_round"android:supportsRtl="true"android:screenOrientation="portrait"android:theme="@style/AppTheme"><activity android:name=".MainActivity" android:screenOrientation="portrait"android:launchMode="singleTask"><intent-filter><action android:name="android.intent.action.MAIN"/><category android:name="android.intent.category.LAUNCHER"/></intent-filter></activity><providerandroid:name="android.support.v4.content.FileProvider"android:authorities="gdut.bsx.tensorflowtraining.fileprovider"android:exported="false"android:grantUriPermissions="true"><meta-dataandroid:name="android.support.FILE_PROVIDER_PATHS"android:resource="@xml/file_paths" /></provider></application></manifest>

将生成的模型文件 sequeezenet.tflite和Class_Label.txt 放入文件夹assets中。
注意：一定要用分类标签文件。

3. 应用程序

这里我们实现的代码主要功能包括摄像头拍照，图片管理、图像分类。
包含代码包括：
ImageClassifier.java

package gdut.bsx.tensorflowtraining;import android.content.Context;
import android.content.res.AssetFileDescriptor;
import android.graphics.Bitmap;
import android.os.SystemClock;
import android.util.Log;import org.tensorflow.lite.Interpreter;import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.channels.FileChannel;
import java.util.AbstractMap;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.Map;
import java.util.PriorityQueue;public class ImageClassifier {private static volatile ImageClassifier INSTANCE;/*** Tag for the {@link Log}.*/private static final String TAG = "TfLiteCameraDemo";/*** Name of the model file stored in Assets.*/private static final String MODEL_PATH = "squeezenet.tflite";//private static final String MODEL_PATH = "mobilenet_v1_0.25_128_quant.tflite";/*** Name of the label file stored in Assets.*/private static final String LABEL_PATH = "Class_Label.txt";/** Number of results to show in the UI. */private static final int RESULTS_TO_SHOW = 3;/** Dimensions of inputs. */private static final int DIM_BATCH_SIZE = 1;private static final int DIM_PIXEL_SIZE = 3;static final int DIM_IMG_SIZE_X = 224;static final int DIM_IMG_SIZE_Y = 224;private static final int IMAGE_MEAN = 128;private static final float IMAGE_STD = 128.0f;/* Preallocated buffers for storing image data in. */private int[] intValues = new int[DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y];/*** An instance of the driver class to run model inference with Tensorflow Lite.*/private Interpreter tflite;/*** Labels corresponding to the output of the vision model.*/private List<String> labelList;/*** A ByteBuffer to hold image data, to be feed into Tensorflow Lite as inputs.*/private ByteBuffer imgData ;/*** An array to hold inference results, to be feed into Tensorflow Lite as outputs.*/private float[][] labelProbArray ;/*** multi-stage low pass filter**/private float[][] filterLabelProbArray = null;private static final int FILTER_STAGES = 3;private static final float FILTER_FACTOR = 0.4f;/*** Initializes an {@code ImageClassifier}.*/private PriorityQueue<Map.Entry<String, Float>> sortedLabels =new PriorityQueue<>(RESULTS_TO_SHOW,new Comparator<Map.Entry<String, Float>>() {@Overridepublic int compare(Map.Entry<String, Float> o1, Map.Entry<String, Float> o2) {return (o1.getValue()).compareTo(o2.getValue());}});private ImageClassifier(Context activity) throws IOException {tflite = new Interpreter(loadModelFile(activity));labelList = loadLabelList(activity);imgData = ByteBuffer.allocateDirect(4 * DIM_BATCH_SIZE * DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE);imgData.order(ByteOrder.nativeOrder());labelProbArray = new float[1][labelList.size()];filterLabelProbArray = new float[FILTER_STAGES][labelList.size()];Log.d(TAG, "Created a Tensorflow Lite Image Classifier.");}public static ImageClassifier getInstance(Context context) {if (INSTANCE == null) {try {INSTANCE = new ImageClassifier(context);} catch (IOException e) {e.printStackTrace();}}return INSTANCE;}/*** Classifies a frame from the preview stream.*/public String classifyFrame(Bitmap bitmap) {if (tflite == null) {Log.e(TAG, "Image classifier has not been initialized; Skipped.");return "Uninitialized Classifier.";}convertBitmapToByteBuffer(bitmap);long startTime = SystemClock.uptimeMillis();tflite.run(imgData, labelProbArray);long endTime = SystemClock.uptimeMillis();Log.d(TAG, "Timecost to run model inference: " + Long.toString(endTime - startTime));// Smooth the results across frames.//applyFilter();return printTopKLabels();}/*** Smooth the results across frames.*//*void applyFilter() {int numLabels = labelList.size();// Low pass filter `labelProbArray` into the first stage of the filter.for (int j = 0; j < numLabels; ++j) {filterLabelProbArray[0][j] +=FILTER_FACTOR * (labelProbArray[0][j] - filterLabelProbArray[0][j]);}// Low pass filter each stage into the next.for (int i = 1; i < FILTER_STAGES; ++i) {for (int j = 0; j < numLabels; ++j) {filterLabelProbArray[i][j] +=FILTER_FACTOR * (filterLabelProbArray[i - 1][j] - filterLabelProbArray[i][j]);}}// Copy the last stage filter output back to `labelProbArray`.for (int j = 0; j < numLabels; ++j) {labelProbArray[0][j] = filterLabelProbArray[FILTER_STAGES - 1][j];}}*//** Closes tflite to release resources. */public void close() {tflite.close();tflite = null;}/*** Memory-map the model file in Assets.*/private ByteBuffer loadModelFile(Context activity) throws IOException {AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_PATH);FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());FileChannel fileChannel = inputStream.getChannel();long startOffset = fileDescriptor.getStartOffset();long declaredLength = fileDescriptor.getDeclaredLength();return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength).asReadOnlyBuffer();}/*** Reads label list from Assets.*/private List<String> loadLabelList(Context activity) throws IOException {List<String> labelList = new ArrayList<String>();BufferedReader reader =new BufferedReader(new InputStreamReader(activity.getAssets().open(LABEL_PATH)));String line;while ((line = reader.readLine()) != null) {labelList.add(line);}reader.close();return labelList;}/*** Get Label.txt.*/public List<String> getLabelList() throws IOException {return labelList;}/*** Writes Image data into a {@code ByteBuffer}.*/private void convertBitmapToByteBuffer(Bitmap bitmap) {if (imgData == null) {return;}imgData.rewind();bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());// Convert the image to floating point.int pixel = 0;long startTime = SystemClock.uptimeMillis();for (int i = 0; i < DIM_IMG_SIZE_X; ++i) {for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) {final int val = intValues[pixel++];imgData.putFloat((((val >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);imgData.putFloat((((val >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);imgData.putFloat((((val) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);// imgData.putFloat(((val >> 16) & 0xFF) / IMAGE_STD);//  imgData.putFloat(((val >> 8) & 0xFF)/ IMAGE_STD );// imgData.putFloat(((val) & 0xFF)/ IMAGE_STD );}}long endTime = SystemClock.uptimeMillis();Log.d(TAG, "Timecost to put values into ByteBuffer: " + Long.toString(endTime - startTime));}/** Prints top-K labels, to be shown in UI as the results. */private String printTopKLabels() {for (int i = 0; i < labelList.size(); ++i) {sortedLabels.add(new AbstractMap.SimpleEntry<>(labelList.get(i), labelProbArray[0][i]));if (sortedLabels.size() > RESULTS_TO_SHOW) {sortedLabels.poll();}}String textToShow = "";final int size = sortedLabels.size();for (int i = 0; i < size; ++i) {Map.Entry<String, Float> label = sortedLabels.poll();textToShow = String.format("\n%s: %4.2f", label.getKey(), label.getValue()) + textToShow;}return textToShow;}}

MainActivity.java

package gdut.bsx.tensorflowtraining;import android.Manifest;
import android.content.Context;
import android.content.DialogInterface;
import android.content.Intent;
import android.content.pm.PackageManager;
import android.graphics.Bitmap;
import android.graphics.Matrix;
import android.net.Uri;
import android.os.Build;
import android.os.Bundle;
import android.os.Looper;
import android.os.MessageQueue;
import android.provider.MediaStore;
import android.provider.Settings;
import android.support.annotation.NonNull;
import android.support.annotation.Nullable;
import android.support.v4.app.ActivityCompat;
import android.support.v4.content.ContextCompat;
import android.support.v4.content.FileProvider;
import android.support.v7.app.AlertDialog;
import android.support.v7.app.AppCompatActivity;
import android.support.v7.app.AppCompatDelegate;
import android.util.Log;
import android.view.View;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;import com.bumptech.glide.load.DataSource;
import com.bumptech.glide.load.engine.GlideException;
import com.bumptech.glide.request.RequestListener;
import com.bumptech.glide.request.target.Target;import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.Executor;
import java.util.concurrent.ScheduledThreadPoolExecutor;
import java.util.concurrent.ThreadFactory;blic class MainActivity extends AppCompatActivity implements View.OnClickListener {public static final String TAG = MainActivity.class.getSimpleName();private static final int OPEN_SETTING_REQUEST_COED = 110;private static final int TAKE_PHOTO_REQUEST_CODE = 120;private static final int PICTURE_REQUEST_CODE = 911;private static final int PERMISSIONS_REQUEST = 108;private static final int CAMERA_PERMISSIONS_REQUEST_CODE = 119;private static final String CURRENT_TAKE_PHOTO_URI = "currentTakePhotoUri";private gdut.bsx.tensorflowtraining.ImageClassifier imageClassifier;static final int dstWidth = 224;static final int dstHeight = 224;private Executor executor;private Uri currentTakePhotoUri;private TextView result;private ImageView ivPicture;static {AppCompatDelegate.setCompatVectorFromResourcesEnabled(true);}@Overrideprotected void onCreate(Bundle savedInstanceState) {super.onCreate(savedInstanceState);if (!isTaskRoot()) {finish();}setContentView(R.layout.activity_main);findViewById(R.id.iv_choose_picture).setOnClickListener(this);findViewById(R.id.iv_take_photo).setOnClickListener(this);ivPicture = findViewById(R.id.iv_picture);result = findViewById(R.id.tv_classifier_info);initialiseImageClassifier(this);// 避免耗时任务占用 CPU 时间片造成UI绘制卡顿，提升启动页面加载速度Looper.myQueue().addIdleHandler(idleHandler);}@Overridepublic void onSaveInstanceState(Bundle savedInstanceState){// 防止拍照后无法返回当前 activity 时数据丢失savedInstanceState.putParcelable(CURRENT_TAKE_PHOTO_URI, currentTakePhotoUri);super.onSaveInstanceState(savedInstanceState);}@Overrideprotected void onRestoreInstanceState(Bundle savedInstanceState) {super.onRestoreInstanceState(savedInstanceState);if (savedInstanceState != null) {currentTakePhotoUri = savedInstanceState.getParcelable(CURRENT_TAKE_PHOTO_URI);}}/***  主线程消息队列空闲时（视图第一帧绘制完成时）处理耗时事件*/MessageQueue.IdleHandler idleHandler = new MessageQueue.IdleHandler() {@Overridepublic boolean queueIdle() {// 初始化线程池executor = new ScheduledThreadPoolExecutor(1, new ThreadFactory() {@Overridepublic Thread newThread(@NonNull Runnable r) {Thread thread = new Thread(r);thread.setDaemon(true);thread.setName("ThreadPool-ImageClassifier");return thread;}});// 请求权限requestMultiplePermissions();return false;}};/*** 请求存储和相机权限*/private void requestMultiplePermissions() {String storagePermission = Manifest.permission.WRITE_EXTERNAL_STORAGE;String cameraPermission = Manifest.permission.CAMERA;int hasStoragePermission = ActivityCompat.checkSelfPermission(this, storagePermission);int hasCameraPermission = ActivityCompat.checkSelfPermission(this, cameraPermission);List<String> permissions = new ArrayList<>();if (hasStoragePermission != PackageManager.PERMISSION_GRANTED) {permissions.add(storagePermission);}if (hasCameraPermission != PackageManager.PERMISSION_GRANTED) {permissions.add(cameraPermission);}if (!permissions.isEmpty()) {String[] params = permissions.toArray(new String[permissions.size()]);ActivityCompat.requestPermissions(this, params, PERMISSIONS_REQUEST);}}@Overridepublic void onRequestPermissionsResult(int requestCode, String[] permissions, int[] grantResults){if (requestCode == PERMISSIONS_REQUEST) {if (Manifest.permission.WRITE_EXTERNAL_STORAGE.equals(permissions[0]) && grantResults[0] != PackageManager.PERMISSION_GRANTED) {//permission denied 显示对话框告知用户必须打开权限 (storagePermission )// Should we show an explanation?// 当app完全没有机会被授权的时候，调用shouldShowRequestPermissionRationale() 返回falseif (ActivityCompat.shouldShowRequestPermissionRationale(this,Manifest.permission.WRITE_EXTERNAL_STORAGE)) {// 系统弹窗提示授权showNeedStoragePermissionDialog();} else {// 已经被禁止的状态，比如用户在权限对话框中选择了"不再显示”，需要自己弹窗解释showMissingStoragePermissionDialog();}}} else if (requestCode == CAMERA_PERMISSIONS_REQUEST_CODE) {if (grantResults[0] != PackageManager.PERMISSION_GRANTED) {showNeedCameraPermissionDialog();} else {openSystemCamera();}}}/***  显示缺失权限提示，可再次请求动态权限*/private void showNeedStoragePermissionDialog() {new AlertDialog.Builder(this).setTitle("权限获取提示").setMessage("必须要有存储权限才能获取到图片").setNegativeButton("取消", new DialogInterface.OnClickListener() {@Override public void onClick(DialogInterface dialog, int which) {dialog.cancel();}}).setPositiveButton("确定", new DialogInterface.OnClickListener() {@Override public void onClick(DialogInterface dialog, int which) {ActivityCompat.requestPermissions(MainActivity.this,new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE}, PERMISSIONS_REQUEST);}}).setCancelable(false).show();}/***  显示权限被拒提示，只能进入设置手动改*/private void showMissingStoragePermissionDialog() {new AlertDialog.Builder(this).setTitle("权限获取失败").setMessage("必须要有存储权限才能正常运行").setNegativeButton("取消", new DialogInterface.OnClickListener() {@Override public void onClick(DialogInterface dialog, int which) {MainActivity.this.finish();}}).setPositiveButton("去设置", new DialogInterface.OnClickListener() {@Override public void onClick(DialogInterface dialog, int which) {startAppSettings();}}).setCancelable(false).show();}private void showNeedCameraPermissionDialog() {new AlertDialog.Builder(this).setMessage("摄像头权限被关闭，请开启权限后重试").setPositiveButton("确定", new DialogInterface.OnClickListener() {@Overridepublic void onClick(DialogInterface dialog, int which) {dialog.dismiss();}}).create().show();}private static final String PACKAGE_URL_SCHEME = "package:";/*** 启动应用的设置进行授权*/private void startAppSettings() {Intent intent = new Intent(Settings.ACTION_APPLICATION_DETAILS_SETTINGS);intent.setData(Uri.parse(PACKAGE_URL_SCHEME + getPackageName()));startActivityForResult(intent, OPEN_SETTING_REQUEST_COED);}@Overridepublic void onClick(View view) {switch (view.getId()) {case R.id.iv_choose_picture :choosePicture();break;case R.id.iv_take_photo :takePhoto();break;default:break;}}/*** 选择一张图片并裁剪获得一个小图*/private void choosePicture() {Intent intent = new Intent(Intent.ACTION_GET_CONTENT);intent.setType("image/*");startActivityForResult(intent, PICTURE_REQUEST_CODE);}/*** 使用系统相机拍照*/private void takePhoto() {if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.CAMERA}, CAMERA_PERMISSIONS_REQUEST_CODE);} else {openSystemCamera();}}/*** 打开系统相机*/private void openSystemCamera() {//调用系统相机Intent takePhotoIntent = new Intent();takePhotoIntent.setAction(MediaStore.ACTION_IMAGE_CAPTURE);//这句作用是如果没有相机则该应用不会闪退，要是不加这句则当系统没有相机应用的时候该应用会闪退if (takePhotoIntent.resolveActivity(getPackageManager()) == null) {Toast.makeText(this, "当前系统没有可用的相机应用", Toast.LENGTH_SHORT).show();return;}String fileName = "TF_" + System.currentTimeMillis() + ".jpg";File photoFile = new File(FileUtil.getPhotoCacheFolder(), fileName);if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.N) {//通过FileProvider创建一个content类型的UricurrentTakePhotoUri = FileProvider.getUriForFile(this, "gdut.bsx.tensorflowtraining.fileprovider", photoFile);//对目标应用临时授权该 Uri 所代表的文件takePhotoIntent.addFlags(Intent.FLAG_GRANT_READ_URI_PERMISSION);} else {currentTakePhotoUri = Uri.fromFile(photoFile);}//将拍照结果保存至 outputFile 的Uri中，不保留在相册中takePhotoIntent.putExtra(MediaStore.EXTRA_OUTPUT, currentTakePhotoUri);startActivityForResult(takePhotoIntent, TAKE_PHOTO_REQUEST_CODE);}@Overrideprotected void onActivityResult(int requestCode, int resultCode, Intent data) {super.onActivityResult(requestCode, resultCode, data);if (resultCode == RESULT_OK) {if (requestCode == PICTURE_REQUEST_CODE) {// 处理选择的图片handleInputPhoto(data.getData());} else if (requestCode == OPEN_SETTING_REQUEST_COED){requestMultiplePermissions();} else if (requestCode == TAKE_PHOTO_REQUEST_CODE) {// 如果拍照成功，加载图片并识别handleInputPhoto(currentTakePhotoUri);}}}/*** 处理图片* @param imageUri*/private void handleInputPhoto(Uri imageUri) {// 加载图片GlideApp.with(MainActivity.this).asBitmap().listener(new RequestListener<Bitmap>() {@Overridepublic boolean onLoadFailed(@Nullable GlideException e, Object model, Target<Bitmap> target, boolean isFirstResource) {Log.d(TAG,"handleInputPhoto onLoadFailed");Toast.makeText(MainActivity.this, "图片加载失败", Toast.LENGTH_SHORT).show();return false;}@Overridepublic boolean onResourceReady(Bitmap resource, Object model, Target<Bitmap> target, DataSource dataSource, boolean isFirstResource) {Log.d(TAG,"handleInputPhoto onResourceReady");startImageClassifier(resource);return false;}}).load(imageUri).into(ivPicture);//result.setText("Processing...");}/*** 开始图片识别匹配* @param bitmap*/private void startImageClassifier(final Bitmap bitmap) {executor.execute(new Runnable() {@Overridepublic void run() {Log.i(TAG, Thread.currentThread().getName() + " startImageClassifier");Bitmap reshapeBitmap = Bitmap.createScaledBitmap(bitmap, dstWidth, dstHeight, false);final String results = imageClassifier.classifyFrame(reshapeBitmap);Log.i(TAG, "startImageClassifier results: " + results);runOnUiThread(new Runnable() {@Overridepublic void run() {result.setText(results);}});}});}private void initialiseImageClassifier(Context app) {try {imageClassifier = gdut.bsx.tensorflowtraining.ImageClassifier.getInstance(app);} catch (Exception e) {Log.e(TAG, "Failed to initialize an image classifier.");}}}

FileUtil.java

package gdut.bsx.tensorflowtraining;import android.content.Context;
import android.media.MediaScannerConnection;
import android.net.Uri;
import android.os.Environment;
import android.text.TextUtils;
import android.util.Log;import java.io.File;public class FileUtil {/*** 删除或增加图片、视频等媒体资源文件时 通知系统更新媒体库，重新扫描* @param filePath 文件路径，包括后缀*/public static void notifyScanMediaFile(Context context, String filePath) {if (context == null || TextUtils.isEmpty(filePath)){Log.e("FileUtil", "notifyScanMediaFile context is null or filePath is empty.");return;}MediaScannerConnection.scanFile(context, new String[] {filePath}, null, new MediaScannerConnection.OnScanCompletedListener() {@Overridepublic void onScanCompleted(String path, Uri uri) {Log.i("FileUtil", "onScanCompleted");}});}public static File getPhotoCacheFolder() {File cacheFolder = new File(Environment.getExternalStorageDirectory(), "TensorFlowPhotos");if (!cacheFolder.exists()) {cacheFolder.mkdirs();}return cacheFolder;}
}

其中源代码可以通过以下链接获得。
全部源程序下载

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如何将深度学习模型加载到android环境中

文章目录

前言