因此,我以下代码中的问题是图像二值化的结果变得太暗。 (我什至有一个示例图像,其二进制图像变成全黑。)
很长一段时间以来,我一直在寻找我的代码中的任何错误,但没有发现任何对我来说看起来有问题的错误。
下面是我要二值化的图像:
Image before binarized - in my code is named: "hildebrantmed.bmp"
下面是生成的二进制图像:
在我向您展示我的源代码之前,这里是图像二值化中的“规则”(因为这是我最近收到的作业):
- 除 CImg 外,我不得使用任何其他库。
- 要使用的编程语言是 C/C++。没有别的。
- 通常情况下,Otsu 的方法是首选。但是,如果更好的话,我可能会被允许使用其他算法。
最后,这是我的源代码:
#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;
}
如果您发现另一种算法效果更好,请在您的回答中提供算法和源代码。感谢您的关注。
最佳答案
第一个错误:您试图返回
一个数组的指针,该指针实际上在generate_histogram
函数结束后立即被销毁。
为了使其正常工作,您应该从调用函数提供指向数组的指针,例如:
{
//[....]
int histogram[256];
generate_histogram(img, histogram);
//[....]
}
int * generate_histogram(CImg<unsigned char> img, int* arHistogram)
{
//[....]
}
关于c++ - CImg : Image binarization result fails,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33693378/