opencv - 多 otsu(多阈值)与 openCV

Question

我正在尝试使用 otsu 进行多阈值处理。我目前使用的方法实际上是通过最大化类间方差，我已经设法获得与 OpenCV 库给出的阈值相同的阈值。然而，这只是通过运行一次 otsu 方法。

关于如何进行多级阈值处理或递归阈值处理的文档相当有限。获得大津原值后去哪里？希望得到一些提示，我一直在研究代码，添加一个外部 for 循环，但对于任何给定图像，下一个计算值始终是 254:(

如果需要，我的代码是:

//compute histogram first
cv::Mat imageh; //image edited to grayscale for histogram purpose
//imageh=image; //to delete and uncomment below;
cv::cvtColor(image, imageh, CV_BGR2GRAY);

int histSize[1] = {256}; // number of bins
float hranges[2] = {0.0, 256.0}; // min andax pixel value
const float* ranges[1] = {hranges};
int channels[1] = {0}; // only 1 channel used

cv::MatND hist;
// Compute histogram
calcHist(&imageh, 1, channels, cv::Mat(), hist, 1, histSize, ranges);

IplImage* im = new IplImage(imageh);//assign the image to an IplImage pointer
IplImage* finalIm = cvCreateImage(cvSize(im->width, im->height), IPL_DEPTH_8U, 1);
double otsuThreshold= cvThreshold(im, finalIm, 0, 255, cv::THRESH_BINARY | cv::THRESH_OTSU );

cout<<"opencv otsu gives "<<otsuThreshold<<endl;

int totalNumberOfPixels= imageh.total();
cout<<"total number of Pixels is " <<totalNumberOfPixels<< endl;


float sum = 0;
for (int t=0 ; t<256 ; t++) 
{
    sum += t * hist.at<float>(t);
}
cout<<"sum is "<<sum<<endl;

float sumB = 0; //sum of background
int wB = 0; // weight of background
int wF = 0; //weight of foreground

float varMax = 0;
int threshold = 0;

//run an iteration to find the maximum value of the between class variance(as between class variance shld be maximise)
for (int t=0 ; t<256 ; t++) 
{
       wB += hist.at<float>(t);               // Weight Background
       if (wB == 0) continue;

       wF = totalNumberOfPixels - wB;                 // Weight Foreground
       if (wF == 0) break;

       sumB += (float) (t * hist.at<float>(t));

       float mB = sumB / wB;            // Mean Background
       float mF = (sum - sumB) / wF;    // Mean Foreground

       // Calculate Between Class Variance
       float varBetween = (float)wB * (float)wF * (mB - mF) * (mB - mF);

       // Check if new maximum found
       if (varBetween > varMax) {
          varMax = varBetween;
          threshold = t;
       }
}

       cout<<"threshold value is: "<<threshold;

Answer 1

为了将 Otsu 的阈值化方法扩展到多级阈值化，类间方差方程变为:

Please check out Deng-Yuan Huang, Ta-Wei Lin, Wu-Chih Hu, Automatic Multilevel Thresholding Based on Two-Stage Otsu's Method with Cluster Determination by Valley Estimation, Int. Journal of Innovative Computing, 2011, 7:5631-5644 for more information.

http://www.ijicic.org/ijicic-10-05033.pdf

这是我针对 2 个阈值的 Otsu Multi 的 C# 实现:

/* Otsu (1979) - multi */

Tuple < int, int > otsuMulti(object sender, EventArgs e) {
    //image histogram
    int[] histogram = new int[256];

    //total number of pixels
    int N = 0;

    //accumulate image histogram and total number of pixels
    foreach(int intensity in image.Data) {
        if (intensity != 0) {
            histogram[intensity] += 1;
            N++;
        }
    }

    double W0K, W1K, W2K, M0, M1, M2, currVarB, optimalThresh1, optimalThresh2, maxBetweenVar, M0K, M1K, M2K, MT;

    optimalThresh1 = 0;
    optimalThresh2 = 0;

    W0K = 0;
    W1K = 0;

    M0K = 0;
    M1K = 0;

    MT = 0;
    maxBetweenVar = 0;
    for (int k = 0; k <= 255; k++) {
        MT += k * (histogram[k] / (double) N);
    }


    for (int t1 = 0; t1 <= 255; t1++) {
        W0K += histogram[t1] / (double) N; //Pi
        M0K += t1 * (histogram[t1] / (double) N); //i * Pi
        M0 = M0K / W0K; //(i * Pi)/Pi

        W1K = 0;
        M1K = 0;

        for (int t2 = t1 + 1; t2 <= 255; t2++) {
            W1K += histogram[t2] / (double) N; //Pi
            M1K += t2 * (histogram[t2] / (double) N); //i * Pi
            M1 = M1K / W1K; //(i * Pi)/Pi

            W2K = 1 - (W0K + W1K);
            M2K = MT - (M0K + M1K);

            if (W2K <= 0) break;

            M2 = M2K / W2K;

            currVarB = W0K * (M0 - MT) * (M0 - MT) + W1K * (M1 - MT) * (M1 - MT) + W2K * (M2 - MT) * (M2 - MT);

            if (maxBetweenVar < currVarB) {
                maxBetweenVar = currVarB;
                optimalThresh1 = t1;
                optimalThresh2 = t2;
            }
        }
    }

    return new Tuple(optimalThresh1, optimalThresh2);
}

这是我用上面的代码对土壤图像扫描进行阈值化得到的结果:

(T1 = 110，T2 = 147)。

Otsu's original paper: "Nobuyuki Otsu, A Threshold Selection Method from Gray-Level Histogram, IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9:62-66" also briefly mentions the extension to Multithresholding.

https://engineering.purdue.edu/kak/computervision/ECE661.08/OTSU_paper.pdf

希望这对您有所帮助。