在我的android应用程序中输入的每一个图像，由AI模型输出的全部结果是0.0039+{一些随机数

Question

我正在为我的大学项目“AI肺炎检测器”建立一个android应用程序，它从用户数据库中获取输入图像，并预测这个人患肺炎的可能性。

在我的应用程序中，我导入了模型，从图库中获取图像的位图，然后将其大小调整为(224，224)，这是模型的输入大小，然后将其转换为张量图像，然后从张量图像中获取bytebuffer，然后将bytebuffer作为输入提供给模型

但毕竟，对于每个图像，这个模型的值总是从0.0039开始（即使是在正图像上，它应该大于0.5）（模型在python上工作得很好）。

我的应用程序代码是：

package com.shekhardwivedi.aipneumoniadetector;

import androidx.appcompat.app.AppCompatActivity;

import android.app.Activity;
import android.content.Intent;
import android.graphics.Bitmap;
import android.graphics.Matrix;
import android.net.Uri;
import android.os.Bundle;
import android.os.Debug;
import android.provider.MediaStore;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;

import com.shekhardwivedi.aipneumoniadetector.ml.AiModel;
import com.shekhardwivedi.aipneumoniadetector.ml.Stacked;
import com.squareup.picasso.Picasso;

import org.tensorflow.lite.DataType;
import org.tensorflow.lite.support.common.ops.DequantizeOp;
import org.tensorflow.lite.support.common.ops.QuantizeOp;
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.tensorbuffer.TensorBuffer;

import java.io.ByteArrayOutputStream;
import java.io.Console;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.nio.ByteBuffer;

public class predictionscreen extends AppCompatActivity {

    Button upload;
    TextView prediction;
    ImageView imageView;
    boolean debuFlag = false;
    String debuGlobal = "";


    public static final int GET_FROM_GALLERY = 3;
    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_predictionscreen);

        upload = findViewById(R.id.button);
        prediction = findViewById(R.id.textView);
        imageView = findViewById(R.id.imageView);

        prediction.setText("Result: Unavailable\nUpload Image");

        Picasso.get().load(R.drawable.tittle).into(imageView);



        upload.setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View v) {

                startActivityForResult(new Intent(Intent.ACTION_PICK, android.provider.MediaStore.Images.Media.INTERNAL_CONTENT_URI), GET_FROM_GALLERY);

                prediction.setText("Upload Image");

            }
        });
    }

    @Override
    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
        super.onActivityResult(requestCode, resultCode, data);

        //Detects request codes
        if(requestCode==GET_FROM_GALLERY && resultCode == Activity.RESULT_OK) {
            Uri selectedImage = data.getData();
            Bitmap bitmap = null;
            try {
                bitmap = MediaStore.Images.Media.getBitmap(this.getContentResolver(), selectedImage);
                //Picasso.get().load(data).centerCrop().into(imageView);
                imageView.setImageBitmap(bitmap);

                int width = bitmap.getWidth();
                int height = bitmap.getHeight();

                int size = bitmap.getRowBytes() * bitmap.getHeight();
                ByteBuffer imageByteBuffer = preProcessImage(bitmap);

                prediction.setText("Processing");

                String pred1 = stackedModel(imageByteBuffer);
                //String pred2 = aiModel(bitmap);

                prediction.setText(String.format("%s", pred1));


            } catch (FileNotFoundException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
    }

    public ByteBuffer preProcessImage(Bitmap imgBitmap){
        int width = imgBitmap.getWidth();
        int height = imgBitmap.getHeight();
        int newHeight = 224;
        int newWidth = 224;
        float scaleWidth = ((float) newWidth) / width;
        float scaleHeight = ((float) newHeight) / height;

        // Create a matrix for the manipulation
        Matrix matrix = new Matrix();

        // Resize the bit map
        matrix.postScale(scaleWidth, scaleHeight);

        // Recreate the new Bitmap
        Bitmap resizedBitmap = Bitmap.createBitmap(imgBitmap, 0, 0, width, height, matrix, false);

        imageView.setImageBitmap(resizedBitmap);

        // Initialization code
        // Create an ImageProcessor with all ops required. For more ops, please
        // refer to the ImageProcessor Architecture section in this README.
        ImageProcessor imageProcessor = new ImageProcessor.Builder()
                        .add(new ResizeOp(224, 224, ResizeOp.ResizeMethod.BILINEAR)).add(new DequantizeOp(0, 1/255.0f)).build();
        // Create a TensorImage object. This creates the tensor of the corresponding
        // tensor type (uint8 in this case) that the TensorFlow Lite interpreter needs.
        TensorImage tensorImage = new TensorImage(DataType.FLOAT32);
        // Preprocess the image
        tensorImage.load(resizedBitmap);
        tensorImage = imageProcessor.process(tensorImage);

        ByteBuffer imageBuffer = tensorImage.getBuffer();


        return imageBuffer;

    }


    public String stackedModel(ByteBuffer byteBuffer){
        String debu1 = Float.toString(byteBuffer.getFloat(100));
        String debu2 = Float.toString(byteBuffer.getFloat(200));
        String debu3 = Float.toString(byteBuffer.getFloat(300));
        String debu4 = Float.toString(byteBuffer.getFloat(400));

        String debu = "("+debu1+","+ debu2 +","+ debu3+","+ debu4+")";
        debuGlobal += debu;



        try {
            Stacked model = Stacked.newInstance(this);

            // Creates inputs for reference.
            TensorBuffer inputFeature0 = TensorBuffer.createFixedSize(new int[]{1, 224, 224, 3}, DataType.FLOAT32);
            inputFeature0.loadBuffer(byteBuffer);

            // Runs model inference and gets result.
            Stacked.Outputs outputs = model.process(inputFeature0);
            TensorBuffer outputFeature0 = outputs.getOutputFeature0AsTensorBuffer();

            float [] out = outputFeature0.getFloatArray();
            int leng = out.length;
            String pred = String.format("Probability: %s", Double.toString(out[0] / 255.0));


            // Releases model resources if no longer used.
            model.close();
            return pred+"::"+debuGlobal;
        } catch (IOException e) {
            // TODO Handle the exception
        }
        return "errored";

    }

    public String aiModel(Bitmap imgBitmap){

        int width = imgBitmap.getWidth();
        int height = imgBitmap.getHeight();
        int newHeight = 150;
        int newWidth = 150;
        float scaleWidth = ((float) newWidth) / width;
        float scaleHeight = ((float) newHeight) / height;

        // Create a matrix for the manipulation
        Matrix matrix = new Matrix();

        // Resize the bit map
        matrix.postScale(scaleWidth, scaleHeight);

        // Recreate the new Bitmap
        Bitmap resizedBitmap = Bitmap.createBitmap(imgBitmap, 0, 0, width, height, matrix, false);

        imageView.setImageBitmap(resizedBitmap);

        // Initialization code
        // Create an ImageProcessor with all ops required. For more ops, please
        // refer to the ImageProcessor Architecture section in this README.
        ImageProcessor imageProcessor = new ImageProcessor.Builder()
                .add(new ResizeOp(150, 150, ResizeOp.ResizeMethod.BILINEAR))
                .build();
        // Create a TensorImage object. This creates the tensor of the corresponding
        // tensor type (uint8 in this case) that the TensorFlow Lite interpreter needs.
        TensorImage tensorImage = new TensorImage(DataType.FLOAT32);
        // Preprocess the image
        tensorImage.load(resizedBitmap);
        tensorImage = imageProcessor.process(tensorImage);

        ByteBuffer imageBuffer = tensorImage.getBuffer();

//        String debu1 = Float.toString(imageBuffer.getFloat(100));
//        String debu2 = Float.toString(imageBuffer.getFloat(200));
//        String debu3 = Float.toString(imageBuffer.getFloat(300));
//        String debu4 = Float.toString(imageBuffer.getFloat(400));
//
//        String debu = debu1 + debu2 + debu3 + debu4;


        try {
            AiModel model = AiModel.newInstance(this);

            // Creates inputs for reference.
            TensorBuffer inputFeature0 = TensorBuffer.createFixedSize(new int[]{1, 150, 150, 3}, DataType.FLOAT32);
            inputFeature0.loadBuffer(imageBuffer);

            // Runs model inference and gets result.
            AiModel.Outputs outputs = model.process(inputFeature0);
            TensorBuffer outputFeature0 = outputs.getOutputFeature0AsTensorBuffer();

            float [] out = outputFeature0.getFloatArray();
            int leng = out.length;
            String pred = String.format("Probability: %s", Double.toString(out[0] / 255.0));

            // Releases model resources if no longer used.
            model.close();

            return pred;
        } catch (IOException e) {
            // TODO Handle the exception
        }
        return "none returned";
    }


}

Answer 1

在责怪模型之前，我们需要考虑在预处理数据（在您的情况下是图像）时会出现什么问题。

所以我建议您检查和测试以下内容：

null