CImg:图像二值化结果失败

因此，我下面代码中的问题是图像二值化的结果变得太暗。（我甚至有一个例子，它的二进制图像变成了全黑。）

很长一段时间以来，我一直在搜索代码中的任何错误，但没有发现任何对我来说似乎有问题的错误

下面是我想要二值化的图像：

二值化之前的图像-在我的代码中命名为："hildebrantmed.bmp"

下面是生成的二进制图像：

二值化后的图像

在我向您展示我的源代码之前，以下是图像二值化中的"规则"（因为这是我最近得到的一项任务）：

1. 除CImg外，我不允许使用任何其他图书馆
2. 要使用的编程语言是C/C++。没有其他的
3. 通常，大津的方法是选择。然而，如果更好的话，我可能会被允许使用其他算法

最后，这里是我的源代码：

#include <iostream>
#include <CImg.h>
using namespace std;
using namespace cimg_library;
/**
 * Generate histogram of the grayscale image
 */
int * generate_histogram(CImg<unsigned char> img)
{   
    int histogram[256];
    // initialize default values for histogram
    for (int i = 0; i < 256; i++) 
    {
        histogram[i] = 0;
    }
    // increment intensity for histogram
    for (int i = 0; i < img.height(); i++)
    {
        for (int j = 0; j < img.width(); j++)
        {
            int gray_value = img(j, i, 0, 0);
            histogram[gray_value]++;
        }
    }
    return histogram;
}
/**
 * Find threshold value from the grayscale image's histogram
 */
int otsu_threshold(CImg<unsigned char> img)
{
    int * histogram = generate_histogram(img); // image histogram
    int total = img.width() * img.height(); // total pixels
    double sum = 0;
    int i;
    for (i = 0; i < 256; i++)
    {
        sum += i * histogram[i];
    }
    double sumB = 0;
    int wB = 0;
    int wF = 0;
    double var_max = 0;
    int threshold = 0;
    for (i = 0; i < 256; i++)
    {
        wB += histogram[i];
        if (wB == 0) continue;
        wF = total - wB;
        if (wF == 0) continue;
        sumB += (double)(i * histogram[i]);
        double mB = sumB / wB;
        double mF = (sum - sumB) / wF;
        double var_between = (double)wB * (double)wF * (mB - mF) * (mB - mF);
        if (var_between > var_max)
        {
            var_max = var_between;
            threshold = i;
        }
    }
    return threshold;
}
/**
 * Main function
 */
int main(int argc, char * argv[])
{
    // retrieve image from its path
    CImg<unsigned char> img("hildebrantmed.bmp");
    const int width = img.width();
    const int height = img.height();
    // initialize a new image for img's grayscale
    CImg<unsigned char> gray_img(width, height, 1, 1, 0);
    // from RGB divided into three separate channels
    CImg<unsigned char> imgR(width, height, 1, 3, 0);
    CImg<unsigned char> imgG(width, height, 1, 3, 0);
    CImg<unsigned char> imgB(width, height, 1, 3, 0);
    // for all (x, y) pixels in image
    cimg_forXY(img, x, y)
    {
        imgR(x, y, 0, 0) = img(x, y, 0, 0),
        imgG(x, y, 0, 1) = img(x, y, 0, 1),
        imgB(x, y, 0, 2) = img(x, y, 0, 2);
        // separate the channels
        int R = (int)img(x, y, 0, 0);
        int G = (int)img(x, y, 0, 1);
        int B = (int)img(x, y, 0, 2);
        // obtain gray value from different weights of RGB channels
        int gray_value = (int)(0.299 * R + 0.587 * G + 0.114 * B);
        gray_img(x, y, 0, 0) = gray_value;
    }
    // find threshold of grayscale image
    int threshold = otsu_threshold(gray_img);
    // initialize a binary image version of img
    CImg<unsigned char> binary_img(width, height, 1, 1, 0);
    // for every (x, y) pixel in gray_img
    cimg_forXY(img, x, y)
    {
        int gray_value = gray_img(x, y, 0, 0);
        // COMPARE gray_value with threshold
        int binary_value;
        // gray_value > threshold: 255 (white)
        if (gray_value > threshold) binary_value = 255;
        // gray_value < threshold: 0 (black)
        else binary_value = 0;
        // assign binary_value to each of binary_img's pixels
        binary_img(x, y, 0, 0) = binary_value;
    }
    // display the images
    CImgDisplay src_disp(img, "Source image");
    CImgDisplay gray_disp(gray_img, "Grayscale image");
    CImgDisplay binary_disp(binary_img, "Binary image");
    while (!src_disp.is_closed() && !gray_disp.is_closed() && !binary_disp.is_closed())
    {
        src_disp.wait();
        gray_disp.wait();
    }
    return 0;
}

如果你发现另一种算法会更好地工作，请在你的答案中提供算法和源代码。感谢您的关注。