add ImageClassification.java

Count/ImageClassification.java

+package com.example.count;
+
+import android.app.Activity;
+import android.content.DialogInterface;
+import android.content.Intent;
+import android.graphics.Bitmap;
+import android.net.Uri;
+import android.os.Bundle;
+import android.util.Log;
+import android.widget.Toast;
+
+import androidx.annotation.NonNull;
+import androidx.appcompat.app.AlertDialog;
+
+import com.example.count.imageclsicifiaction.Classifier;
+import com.example.count.imageclsicifiaction.TensorFlowImageClassifier;
+
+import java.io.ByteArrayOutputStream;
+import java.text.SimpleDateFormat;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Calendar;
+import java.util.Date;
+import java.util.List;
+import java.util.Random;
+import java.util.TimeZone;
+import java.util.concurrent.Executor;
+import java.util.concurrent.Executors;
+
+public class ImageClassification extends Activity {
+    private static final String MODEL_PATH = "model.tflite";
+    private static final boolean QUANT = false;
+    private static final String LABEL_PATH = "labels.txt";
+    private static final int INPUT_SIZE = 128;
+    private Classifier classifier;
+    private Executor executor = Executors.newSingleThreadExecutor();
+    public ArrayList<Bitmap> byteArrayPicture = new ArrayList<Bitmap>();
+    public Boolean Othertype=false;
+    public int OtherImagePosition;
+    public Boolean DetectOrNot=false;
+    public Boolean DetectOrNotmodel=false;
+    public Boolean freeOrNot=false;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.clasifiation_layout);
+        backgroundCacification newbackGP = new backgroundCacification();
+        newbackGP.start();
+        freeOrNot=false;
+
+    }
+    class backgroundCacification extends Thread{
+        List<Integer> listDitect= new ArrayList<Integer>();
+        List<Integer> listMarkOnly= new ArrayList<Integer>();
+        @Override
+        public void run(){
+            initTensorFlowAndLoadModel();
+            while(true){
+                if (DetectOrNotmodel){
+                    //GlobleVariable.bitmapArrayPicture.size()
+                    for (int i = 0; i <GlobleVariable.bitmapArrayPicture.size() ; i++) {
+                        final Bitmap bmpimageDetected = Bitmap.createScaledBitmap(GlobleVariable.bitmapArrayPicture.get(i), INPUT_SIZE, INPUT_SIZE, false);
+                        final List<Classifier.Recognition> results = classifier.recognizeImage(bmpimageDetected);
+                        if(results.get(0).getTitle().equals("Y")){
+                            listMarkOnly.add(i);
+                        }
+                        final int k=i;
+                        try {
+                            // code runs in a thread
+                            runOnUiThread(new Runnable() {
+                                @Override
+                                public void run() {
+                                    Toast.makeText(ImageClassification.this, Integer.toString(k), Toast.LENGTH_SHORT).show();
+
+
+                                }
+                            });
+                        } catch (final Exception ex) {
+                            Log.i("---","Exception in thread");
+                        }
+
+                    }
+                    break;
+                }
+            }
+            int Right=0;
+            int Wrong=0;
+            for(int x=0;x<listMarkOnly.size();x++){
+                if(GlobleVariable.listOriginal.get(listMarkOnly.get(x))==3 || GlobleVariable.listOriginal.get(listMarkOnly.get(x))==4){
+                    Right=Right+1;
+                }
+                else{
+                    Wrong=Wrong+1;
+                }
+            }
+            GlobleVariable.RightGloble=Right;
+            GlobleVariable.WrongGloble=Wrong;
+            GlobleVariable.resultList.add(Right-Wrong);
+            startActivity(new Intent(ImageClassification.this, ResultActivity.class));
+        }
+
+
+        private void initTensorFlowAndLoadModel() {
+            executor.execute(new Runnable() {
+                @Override
+                public void run() {
+                    try {
+                        classifier = TensorFlowImageClassifier.create(
+                                getAssets(),
+                                MODEL_PATH,
+                                LABEL_PATH,
+                                INPUT_SIZE,
+                                QUANT);
+                        DetectOrNotmodel=true;
+
+                    } catch (final Exception e) {
+                        throw new RuntimeException("Error initializing TensorFlow!", e);
+                    }
+                }
+            });
+        }
+    }
+
+
+}