如何改进自我实现的otsu算法的结果？ [英] How can I improve the result of my self implemented otsu algorithm?

问题描述

我为我的项目制作了自己的Otsu实现。我按照数学算法来做。我试图使用直方图，但我很难，所以我只是遵循它的逻辑。我怎样才能准确地将其二值化？我目前的输出只是全黑或全白？



我尝试过：

I made my own implementation of Otsu for my project. I followed the mathematical algorithm to do it. I was trying to use histogram but im having a hard time so i just followed the logic of it. How can I binarize it accurately? My current output is only full black or full white?

What I have tried:

I made my own implementation of Otsu for my project.

After hours of experimenting on how can I make the image to black-white after getting the within class variance. Here is my full working code and some output.

Here is my code:

Otsu.java

        Bitmap tempImg = (Bitmap) original;
        Bitmap OImg = Bitmap.createBitmap(tempImg.getWidth(), tempImg.getHeight(), tempImg.getConfig());

        int width = tempImg.getWidth();
        int height = tempImg.getHeight();
        int A, R, G, B,colorPixel;

        for (int x = 0; x < width; x++) { //original image to grayscale
            for (int y = 0; y < height; y++) {

                colorPixel = tempImg.getPixel(x, y);

                A = Color.alpha(colorPixel);
                R = Color.red(colorPixel);
                G = Color.green(colorPixel);
                B = Color.blue(colorPixel);

                R = (R + G + B) / 3;
                G = R;
                B = R;

                OImg.setPixel(x, y, Color.argb(A, R, G,B ));
            }
        }
        return OImg;
    }

    public static Bitmap Botsu(Bitmap gImg){

        Bitmap tempImg = (Bitmap) gImg;
        Bitmap BWimg = Bitmap.createBitmap(tempImg.getWidth(), tempImg.getHeight(), tempImg.getConfig());

        int width = tempImg.getWidth();
        int height = tempImg.getHeight();
        int A, R, G, B, colorPixel;

        // histo-thresh

        double Wcv = 0;
        int[] Bx = new int[256];
        int[] By = new int[256];
        int[] Fx = new int[256];
        int[] Fy = new int[256];
        double Bw =0, Bm =0, Bv =0, Bp = 0;
        double Fw =0, Fm =0, Fv =0, Fp = 0;
        int c = 0, ImgPix = 0, ImgPixB = 0, ImgPixF = 0, newPixel = 0;

            // pixel check for histogram

        for (int x = 0; x < width; x++) {
            for (int y = 0; y < height; y++) {

                colorPixel = tempImg.getPixel(x, y);

                A = Color.alpha(colorPixel);
                R = Color.red(colorPixel);
                G = Color.green(colorPixel);
                B = Color.blue(colorPixel);

                int gray = (int) (0.2989 * R + 0.5870 * G + 0.1140 * B);
                
                if (gray > 128) { // white - foreground
                    for (int z=0; z<Fx.length; z++){
                        if (Fx[z] == gray){
                            c++;
                        }
                    }
                    if (c==1){
                        Fy[gray] = Fy[gray]+1; //y axis - counter for pixels for each x
                    }
                    else{
                        Fx[x] = gray; //x axis - 0-255
                        Fy[gray] = Fy[gray]+1;
                    }
                }//By[Bx[x]]
                else{ // black - background
                    for (int z=0; z<Bx.length; z++){
                        if (Bx[z] == gray){
                            c++;
                        }
                    }
                    if (c==1){
                        By[gray] = By[gray]+1; //y axis - counter for pixels for each x
                    }
                    else{
                        Bx[x] = gray; //x axis - 0-255
                        By[gray] = By[gray]+1;
                    }
                }
            }
        }

        for (int b=0; b<By.length; b++){
            ImgPixB = ImgPixB + By[b];
        }
        for (int f=0; f<Fy.length; f++){
            ImgPixF = ImgPixF + Fy[f];
        }
        ImgPix = ImgPixB + ImgPixF;

        //bg part hist
        for (int i=0; i<By.length; i++){ //weight
            Bw = Bw + By[i];
        }
        Bw = Bw/ImgPix;
        for (int i=0; i<By.length; i++){ //pixel sum
            Bp = Bp + By[i];
        }
        for (int i = 0; i<Bx.length; i++){ //mean
            Bm = Bm + (Bx[i]*By[Bx[i]]);
        }
        Bm = Bm/Bp;
        for (int i=0; i<Bx.length; i++){ //variance
            Bv = Bv + (Math.pow((Bx[i]-Bm),2)*By[Bx[i]]); // (Bx[i]-Bm) * (Bx[i]-Bm)
        }
        Bv = Bv/Bp;

        //fg part hist
        for (int i=0; i<Fy.length; i++){ //weight
            Fw = Fw + Fy[i];
        }
        Fw = Fw/ImgPix;
        for (int i=0; i<Fy.length; i++){ //pixel sum
            Fp = Fp + Fy[i];
        }
        for (int i = 0; i<Fx.length; i++){ //mean
            Fm = Fm + (Fx[i]*Fy[Fx[i]]);
        }
        Fm = Fm/Fp;
        for (int i=0; i<Fx.length; i++){ //variance
            Fv = Fv + (Math.pow((Fx[i]-Fm),2)*Fy[Fx[i]]); // (Bx[i]-Bm) * (Bx[i]-Bm)
        }
        Fv = Fv/Fp;

        // within class variance
        Wcv = (Bw * Bv) + (Fw * Fv);

        for (int x = 0; x < width; x++) {
            for (int y = 0; y < height; y++) {

                colorPixel = tempImg.getPixel(x, y);

                A = Color.alpha(colorPixel);
                R = Color.red(colorPixel);
                G = Color.green(colorPixel);
                B = Color.blue(colorPixel);

                //int gray = (int) (0.2989 * R + 0.5870 * G + 0.1140 * B);
                int gray2 = (int) (Wcv * R + Wcv * G + Wcv * B);
                if (gray2 > 128) {
                    gray2 = 255;
                }
                else if (gray2 <129){
                    gray2 = 0;
                }

                BWimg.setPixel(x, y, Color.argb(A, gray2, gray2, gray2));
            }
        }

        return BWimg;


`x[z]` is for x-axis and`y[gray] ` is for y-axis. I based this on the graph on [Lab Book][1]

    x = 0-255
    y = how many pixels is on a certain color shade

输出:(我添加了2个if-else值，我实验显示3个不同的输出。其他值只返回几个黑点或纯白色图片。）

OUTPUT: (I added 2 if-else values that i experimented that showed 3 different outputs. Other values will only return few black dots or just pure white image.)

if (gray2 > 128) {
        gray2 = 255;
    }
    else if (gray2 < 129){
        gray2 = 0;
    }

输出1

输出2

Output 1
Output 2

if (gray2 > 64 && gray2 < 129) {
        gray2 = 255;
    }
    else if (gray2 < 65){
        gray2 = 0;
    }

Output3

推荐答案

编译并不意味着您的代码是正确的！ ：笑：

将开发过程想象成编写电子邮件：成功编译意味着您使用正确的语言编写电子邮件 - 例如英语而不是德语 - 而不是电子邮件包含您的邮件想发送。



所以现在你进入第二阶段的发展（实际上它是第四或第五阶段，但你将在之后的阶段进入）：测试和调试。



首先查看它的作用，以及它与你想要的有何不同。这很重要，因为它可以为您提供有关其原因的信息。例如，如果程序旨在让用户输入一个数字并将其翻倍并打印答案，那么如果输入/输出是这样的：

Compiling does not mean your code is right! :laugh:
Think of the development process as writing an email: compiling successfully means that you wrote the email in the right language - English, rather than German for example - not that the email contained the message you wanted to send.

So now you enter the second stage of development (in reality it's the fourth or fifth, but you'll come to the earlier stages later): Testing and Debugging.

Start by looking at what it does do, and how that differs from what you wanted. This is important, because it give you information as to why it's doing it. For example, if a program is intended to let the user enter a number and it doubles it and prints the answer, then if the input / output was like this:

Input   Expected output    Actual output
  1            2                 1
  2            4                 4
  3            6                 9
  4            8                16

然后很明显问题出在将它加倍的位 - 它不会将自身加到自身上，或者将它乘以2，它会将它自身相乘并返回输入的平方。

所以，你可以查看代码和很明显，它在某处：

Then it's fairly obvious that the problem is with the bit which doubles it - it's not adding itself to itself, or multiplying it by 2, it's multiplying it by itself and returning the square of the input.
So with that, you can look at the code and it's obvious that it's somewhere here:

private int Double(int value)
   {
   return value * value;
   }

一旦你知道可能出现的问题，就开始使用调试器找出原因。在你的线上设一个断点：

Once you have an idea what might be going wrong, start using the debugger to find out why. Put a breakpoint on your line:

colorPixel = tempImg.getPixel(x, y);

和运行你的应用程序在执行代码之前，请考虑代码中的每一行应该做什么，并将其与使用Step over按钮依次执行每一行时实际执行的操作进行比较。它符合您的期望吗？如果是这样，请转到下一行。

如果没有，为什么不呢？它有何不同？



这是一项非常值得开发的技能，因为它可以帮助你在现实世界和发展中。和所有技能一样，它只能通过使用来改善！

and run your app. Think about what each line in the code should do before you execute it, and compare that to what it actually did when you use the "Step over" button to execute each line in turn. Did it do what you expect? If so, move on to the next line.
If not, why not? How does it differ?

This is a skill, and it's one which is well worth developing as it helps you in the real world as well as in development. And like all skills, it only improves by use!

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