Fix TFLite project, expand the Android project materials

ILT6-deploying-models/TFLite/SimpleImageClassification/app/.gitignore

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+/build

ILT6-deploying-models/TFLite/SimpleImageClassification/app/build.gradle

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+plugins {
+    id 'com.android.application'
+    id 'org.jetbrains.kotlin.android'
+}
+
+android {
+    compileSdk 34
+
+    defaultConfig {
+        applicationId "com.example.travelokaocr"
+        minSdk 21
+        targetSdk 34
+        versionCode 1
+        versionName "1.0"
+
+        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
+    }
+
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+
+    buildFeatures{
+        viewBinding true
+    }
+
+    compileOptions {
+        sourceCompatibility JavaVersion.VERSION_1_8
+        targetCompatibility JavaVersion.VERSION_1_8
+    }
+    kotlinOptions {
+        jvmTarget = '1.8'
+    }
+
+    aaptOptions {
+        noCompress "tflite"
+    }
+    namespace 'com.example.imageclassification'
+}
+
+dependencies {
+    implementation 'androidx.core:core-ktx:1.7.0'
+    implementation 'androidx.appcompat:appcompat:1.5.1'
+    implementation 'com.google.android.material:material:1.7.0'
+    implementation 'androidx.constraintlayout:constraintlayout:2.1.4'
+    testImplementation 'junit:junit:4.13.2'
+    androidTestImplementation 'androidx.test.ext:junit:1.1.5'
+    androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
+
+    implementation('org.tensorflow:tensorflow-lite-support:0.4.4')
+    implementation('org.tensorflow:tensorflow-lite-gpu:2.9.0')
+}

ILT6-deploying-models/TFLite/SimpleImageClassification/app/proguard-rules.pro

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+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

ILT6-deploying-models/TFLite/SimpleImageClassification/app/src/androidTest/java/com/example/imageclassification/ExampleInstrumentedTest.java

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+package com.example.imageclassification;
+
+import android.content.Context;
+
+import androidx.test.platform.app.InstrumentationRegistry;
+import androidx.test.ext.junit.runners.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
+        assertEquals("com.example.imageclassification", appContext.getPackageName());
+    }
+}

ILT6-deploying-models/TFLite/SimpleImageClassification/app/src/main/AndroidManifest.xml

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+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android">
+
+    <application
+        android:allowBackup="true"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:roundIcon="@mipmap/ic_launcher_round"
+        android:supportsRtl="true"
+        android:theme="@style/AppTheme">
+        <activity android:name="com.example.imageclassification.MainActivity"
+            android:exported="true">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+        </activity>
+    </application>
+
+</manifest>

ILT6-deploying-models/TFLite/SimpleImageClassification/app/src/main/assets/vegs.txt

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+Broccoli
+Carrot
+Cheese
+Potato
+Tomato

ILT6-deploying-models/TFLite/SimpleImageClassification/app/src/main/java/com/example/imageclassification/MainActivity.java

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+package com.example.imageclassification;
+
+import android.content.Intent;
+import android.graphics.Bitmap;
+import android.net.Uri;
+import android.os.Bundle;
+import android.provider.MediaStore;
+import android.view.View;
+import android.widget.Button;
+import android.widget.ImageView;
+import android.widget.TextView;
+
+import androidx.annotation.Nullable;
+import androidx.appcompat.app.AppCompatActivity;
+
+import java.io.IOException;
+
+public class MainActivity extends AppCompatActivity {
+
+    // ---- Tambahkan contoh kodingan disini ----
+    ImageView imageView;
+    Uri imageuri;
+    Button buclassify;
+    TextView classitext;
+
+    TFLiteHelper tfLiteHelper;
+    private Bitmap bitmap;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+        imageView = (ImageView) findViewById(R.id.image);
+        buclassify = (Button) findViewById(R.id.classify);
+        classitext = (TextView) findViewById(R.id.classifytext);
+
+        tfLiteHelper = new TFLiteHelper(this);
+        tfLiteHelper.init();
+
+        imageView.setOnClickListener(selectImageListener);
+        buclassify.setOnClickListener(classifyImageListtener);
+
+    }
+
+    @Override
+    protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {
+        super.onActivityResult(requestCode, resultCode, data);
+        if (requestCode == 12 && resultCode == RESULT_OK && data != null) {
+            imageuri = data.getData();
+            try {
+                bitmap = MediaStore.Images.Media.getBitmap(getContentResolver(), imageuri);
+                imageView.setImageBitmap(bitmap);
+            } catch (IOException e) {
+                e.printStackTrace();
+            }
+        }
+    }
+
+    View.OnClickListener selectImageListener = new View.OnClickListener() {
+        @Override
+        public void onClick(View view) {
+            String SELECT_TYPE = "image/*";
+            String SELECT_PICTURE = "Select Picture";
+
+            Intent intent = new Intent();
+            intent.setType(SELECT_TYPE);
+            intent.setAction(Intent.ACTION_GET_CONTENT);
+            startActivityForResult(Intent.createChooser(intent, SELECT_PICTURE), 12);
+        }
+    };
+
+    View.OnClickListener classifyImageListtener = new View.OnClickListener() {
+        @Override
+        public void onClick(View v) {
+            if (bitmap != null) {
+                tfLiteHelper.classifyImage(bitmap);
+                setLabel(tfLiteHelper.showresult());
+            }
+        }
+    };
+
+    void setLabel(String label) {
+        classitext.setText(label);
+    }
+    // ----------------------------------------------------
+}

ILT6-deploying-models/TFLite/SimpleImageClassification/app/src/main/java/com/example/imageclassification/TFLiteHelper.java

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+package com.example.imageclassification;
+
+import android.app.Activity;
+import android.content.res.AssetFileDescriptor;
+import android.graphics.Bitmap;
+
+import org.tensorflow.lite.DataType;
+import org.tensorflow.lite.Interpreter;
+import org.tensorflow.lite.support.common.FileUtil;
+import org.tensorflow.lite.support.common.TensorOperator;
+import org.tensorflow.lite.support.common.TensorProcessor;
+import org.tensorflow.lite.support.common.ops.NormalizeOp;
+import org.tensorflow.lite.support.image.ImageProcessor;
+import org.tensorflow.lite.support.image.TensorImage;
+import org.tensorflow.lite.support.image.ops.ResizeOp;
+import org.tensorflow.lite.support.image.ops.ResizeWithCropOrPadOp;
+import org.tensorflow.lite.support.label.TensorLabel;
+import org.tensorflow.lite.support.tensorbuffer.TensorBuffer;
+
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.nio.MappedByteBuffer;
+import java.nio.channels.FileChannel;
+import java.util.Collections;
+import java.util.List;
+import java.util.Map;
+
+public class TFLiteHelper {
+
+    private int imageSizeX;
+    private int imageSizeY;
+
+    private List<String> labels;
+    private Interpreter tflite;
+
+    private MappedByteBuffer tfliteModel;
+    private TensorImage inputImageBuffer;
+    private TensorBuffer outputProbabilityBuffer;
+    private TensorProcessor probabilityProcessor;
+
+    private static final float IMAGE_MEAN = 0.0f;
+    private static final float IMAGE_STD = 1.0f;
+
+    private static final float PROBABILITY_MEAN = 0.0f;
+    private static final float PROBABILITY_STD = 255.0f;
+
+    private Activity context;
+
+    TFLiteHelper(Activity context) {
+        this.context = context;
+    }
+
+    // ---- Kolom inisiasi TensorFlow Lite Interpreter ----
+
+    void init() {
+        try {
+            Interpreter.Options opt = new Interpreter.Options();
+            tflite = new Interpreter(loadmodelfile(context), opt);
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+    // ----------------------------------------------------
+
+    // ---- Kolom preprocessing gambar ----
+    private TensorImage loadImage(final Bitmap bitmap) {
+        // Loads bitmap into a TensorImage.
+        inputImageBuffer.load(bitmap);
+
+        // Creates processor for the TensorImage.
+        int cropSize = Math.min(bitmap.getWidth(), bitmap.getHeight());
+        // TODO(b/143564309): Fuse ops inside ImageProcessor.
+        ImageProcessor imageProcessor =
+                new ImageProcessor.Builder()
+                        .add(new ResizeWithCropOrPadOp(cropSize, cropSize))
+                        .add(new ResizeOp(imageSizeX, imageSizeY, ResizeOp.ResizeMethod.NEAREST_NEIGHBOR))
+                        .add(getPreprocessNormalizeOp())
+                        .build();
+        return imageProcessor.process(inputImageBuffer);
+    }
+    // ----------------------------------------------------
+
+    // ---- Kolom pemanggilan model tflite ----
+    private MappedByteBuffer loadmodelfile(Activity activity) throws IOException {
+        String MODEL_NAME = "vegs.tflite";
+        AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_NAME);
+        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);
+    }
+
+    void classifyImage(Bitmap bitmap){
+        int imageTensorIndex = 0;
+        int[] imageShape = tflite.getInputTensor(imageTensorIndex).shape(); // {1, height, width, 3}
+        imageSizeY = imageShape[1];
+        imageSizeX = imageShape[2];
+        DataType imageDataType = tflite.getInputTensor(imageTensorIndex).dataType();
+
+        int probabilityTensorIndex = 0;
+        int[] probabilityShape =
+                tflite.getOutputTensor(probabilityTensorIndex).shape(); // {1, NUM_CLASSES}
+        DataType probabilityDataType = tflite.getOutputTensor(probabilityTensorIndex).dataType();
+
+        inputImageBuffer = new TensorImage(imageDataType);
+        outputProbabilityBuffer = TensorBuffer.createFixedSize(probabilityShape, probabilityDataType);
+        probabilityProcessor = new TensorProcessor.Builder().add(getPostprocessNormalizeOp()).build();
+
+        inputImageBuffer = loadImage(bitmap);
+
+        tflite.run(inputImageBuffer.getBuffer(),outputProbabilityBuffer.getBuffer().rewind());
+
+    }
+
+    private TensorOperator getPreprocessNormalizeOp() {
+        return new NormalizeOp(IMAGE_MEAN, IMAGE_STD);
+    }
+    // ----------------------------------------------------
+
+    // ---- Kolom postprocessing ----
+    public String showresult() {
+        try {
+            labels = FileUtil.loadLabels(context, "vegs.txt");
+        } catch (Exception e) {
+            e.printStackTrace();
+            return null;
+        }
+        Map<String, Float> labeledProbability =
+                new TensorLabel(labels, probabilityProcessor.process(outputProbabilityBuffer))
+                        .getMapWithFloatValue();
+        float maxValueInMap = (Collections.max(labeledProbability.values()));
+        String result = null;
+        for (Map.Entry<String, Float> entry : labeledProbability.entrySet()) {
+            if (entry.getValue() == maxValueInMap) {
+                result = entry.getKey();
+            }
+        }
+
+        return result;
+    }
+
+    private TensorOperator getPostprocessNormalizeOp() {
+        return new NormalizeOp(PROBABILITY_MEAN, PROBABILITY_STD);
+    }
+    // ----------------------------------------------------
+
+}
+