Question

我希望增强潜在的fingure打印图像，因为我已经做到了图像规范化我先写了代码 fingureprint图像的标准化，但它完全给出输出黑色图片我想知道下面代码中的错误是java 类。

package com.quality;
import java.awt.Color;
import java.awt.Graphics2D;
import java.awt.image.BufferedImage;
import java.awt.image.WritableRaster;
import java.io.File;
import java.io.IOException;

import javax.imageio.ImageIO;

class ImageArray
{
    double M;
    double VAR;
    BufferedImage img;
    int w;
    int h;
  double[][] imagedata; 
  double [][] outputdata;


public ImageArray()
        {
            try {
                img = ImageIO.read(new File("Sample7.jpg"));
                w = img.getWidth();
                h = img.getHeight();
                imagedata = new double[w][h];
                outputdata = new double[w][h];
            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }

        public void Mean()
        {

    for(int x=0;x<imagedata.length;x++)
    {
        for(int y=0;y<imagedata[x].length;y++)
        {
            Color color = new Color(img.getRGB(x, y));

            imagedata[x][y] = color.getRed();
        }
    }


    for(int x=0;x<imagedata.length;x++)
    {
        for(int y=0;y<imagedata[x].length;y++)
        {
            M+=(imagedata[x][y]);
        }
    }

    M =   M/(w*h);
    System.out.println("mean"+M);

    }



public void Variance()
        {

            for(int x=0;x<imagedata.length;x++)
            {
                for(int y=0;y<imagedata[x].length;y++)
                {
                    VAR+=Math.pow(imagedata[x][y]-M,2);
                }
            }

        VAR = VAR/(w*h);
            System.out.println("varience"+VAR);
        }



public void normalization(double mean,double varience)
        {
            int M0 = 100;
            int VAR0 = 100;

            for(int x=0;x<imagedata.length;x++)
            {
                for(int y=0;y<imagedata[x].length;y++)
                {
                    if(imagedata[x][y]>mean)
                    {
                        outputdata[x][y]=M0+(Math.sqrt(VAR0*Math.pow(imagedata[x][y]-M, 2)))/VAR;
                    }else
                    {
                        outputdata[x][y]=M0-(Math.sqrt(VAR0*Math.pow(imagedata[x][y]-M, 2)))/VAR;
                    }
                }
            }

        }




public void DrawImage() throws IOException
        {
            BufferedImage outputImage = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_GRAY);

            for(int x=0;x<outputdata.length;x++)
            {
                for(int y=0;y<outputdata[x].length;y++)
                {
                    outputImage.setRGB(x, y, (int) outputdata[x][y]);
                }
            }



            Graphics2D g2 = outputImage.createGraphics();
            g2.drawImage(outputImage, null, null);

            File imageFile = new File("output.jpg");
            ImageIO.write(outputImage, "jpg", imageFile);

        }







 public static void main(String args[])
            {

                try {
                    ImageArray abc = new ImageArray();
                    abc.Mean();
                    abc.Variance();
                            abc.normalization();
                    abc.DrawImage();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
}

Answer 1

您可能想要将表达式括起来：

M =   M/w*h;

作为

M =   M/(w*h);

图像的标准化给出了黑色图像

1 个答案: