在 C++ 的 OpenCV 中是否有任何相当于 Matlab 的 imadjust 的函数？

Question

我习惯于在 Matlab 中使用imadjust 进行对比度增强。 OpenCV中是否有任何等效功能？

谷歌搜索给出了关于亮度和对比度增强的OpenCV documentation，但它使用可能效率低下的 for 循环。即使我们通过使用Matrix expressions 使其高效，它也不等同于imadjust 所做的。

OpenCV 中是否有任何内置函数或任何有效的任务方法？

我看到了相关的帖子，但不是我上面提到的they link to the OpenCV doc 或they suggest Histogram Equalization and thresholding。我更喜欢imadjust 到直方图均衡，并且阈值处理似乎并没有像这样执行对比度增强。

对此的任何帮助表示赞赏。

Answer 1

OpenCV 中没有内置的解决方案来执行直方图拉伸，但您可以在循环中轻松完成。

imadjust 允许选择上限和下限的容差，或者直接选择边界，因此您需要比简单的 for 循环更多的逻辑。

您可以在实现自己的示例时使用下面的示例作为参考：

#include <opencv2\opencv.hpp>
#include <vector>
#include <algorithm>

using namespace std;
using namespace cv;

void imadjust(const Mat1b& src, Mat1b& dst, int tol = 1, Vec2i in = Vec2i(0, 255), Vec2i out = Vec2i(0, 255))
{
    // src : input CV_8UC1 image
    // dst : output CV_8UC1 imge
    // tol : tolerance, from 0 to 100.
    // in  : src image bounds
    // out : dst image buonds

    dst = src.clone();

    tol = max(0, min(100, tol));

    if (tol > 0)
    {
        // Compute in and out limits

        // Histogram
        vector<int> hist(256, 0);
        for (int r = 0; r < src.rows; ++r) {
            for (int c = 0; c < src.cols; ++c) {
                hist[src(r,c)]++;
            }
        }

        // Cumulative histogram
        vector<int> cum = hist;
        for (int i = 1; i < hist.size(); ++i) {
            cum[i] = cum[i - 1] + hist[i];
        }

        // Compute bounds
        int total = src.rows * src.cols;
        int low_bound = total * tol / 100;
        int upp_bound = total * (100-tol) / 100;
        in[0] = distance(cum.begin(), lower_bound(cum.begin(), cum.end(), low_bound));
        in[1] = distance(cum.begin(), lower_bound(cum.begin(), cum.end(), upp_bound));

    }

    // Stretching
    float scale = float(out[1] - out[0]) / float(in[1] - in[0]);
    for (int r = 0; r < dst.rows; ++r)
    {
        for (int c = 0; c < dst.cols; ++c)
        {
            int vs = max(src(r, c) - in[0], 0);
            int vd = min(int(vs * scale + 0.5f) + out[0], out[1]);
            dst(r, c) = saturate_cast<uchar>(vd);
        }
    }
}

int main()
{
    Mat3b img = imread("path_to_image");

    Mat1b gray;
    cvtColor(img, gray, COLOR_RGB2GRAY);

    Mat1b adjusted;
    imadjust(gray, adjusted);

    // int low_in, high_in, low_out, high_out
    // imadjust(gray, adjusted, 0, Vec2i(low_in, high_in), Vec2i(low_out, high_out));

    return 0;
}

输入图片：

输出调整后的图片：

Answer 2

这里有imadjust和stretchlim的实现：

https://github.com/joshdoe/opencv/blob/1d319f683f6b9a8b0c7cbe2abdc9664f0dac919f/modules/imgproc/src/imadjust.cpp

Answer 3

你可以试着问这里的人： http://opencv-users.1802565.n2.nabble.com/imadjust-matlab-function-with-stretchlim-OpenCV-implementation-td6253242.html

他的实现基于此： http://www.mathworks.com/matlabcentral/fileexchange/12191-bilateral-filtering

该文件应如下所示，但我不完全确定它是否有效：

void
getOptimalImgAdjustParamsFromHist (IplImage* p_img,unsigned int* p_optminmaxidx, int p_count)
{
  int numBins = 256;
  CvMat* bins = cvCreateMat(1,numBins,CV_8UC1);
  calcHistogram(p_img,bins,numBins);
  int sumlow = 0, sumhigh = 0;
  int low_idx = 0, high_idx = 0;
  for (unsigned int i = 0; i < numBins; i++) {
    float curval = (float) cvGetReal1D (bins, (i));
    sumlow += curval;
    if (sumlow >= p_count) {
      low_idx = i;
      break;
    }
  }
  for (unsigned int i = numBins - 1 ; i >= 0; i--) {
    float curval = (float) cvGetReal1D (bins, (i));
    sumhigh += curval;
    if (sumhigh >= p_count) {
      high_idx = i;
      break;
    }
  }
  cvReleaseMat(&bins);
  p_optminmaxidx[OPTMINIDX] = low_idx;
  p_optminmaxidx[OPTMAXIDX] = high_idx;
}

IplImage *
imageAdjust (IplImage * p_img)
{
  CvSize framesize = cvGetSize (p_img);
  int low_count = round (framesize.width * framesize.height * 0.01);
  unsigned int *optminmaxidx = new unsigned int [2];
  getOptimalImgAdjustParamsFromHist (p_img, optminmaxidx,low_count);
  int range = optminmaxidx[OPTMAXIDX] - optminmaxidx[OPTMINIDX];
  IplImage *adjustedImg = p_img;
  for (int i = 0; i < framesize.height; i++)
    for (int j = 0; j < framesize.width; j++) {
      unsigned int val = (unsigned int) getData (p_img, i, j);
      unsigned int newval = 0;
      if (val <= optminmaxidx[OPTMINIDX]) {
        newval = 0;
        setData (adjustedImg, i, j, (uchar) newval);
      } else if (val >= optminmaxidx[OPTMAXIDX]) {
        newval = 255;
        setData (adjustedImg, i, j, (uchar) newval);
      } else {
        newval =
            (unsigned int) round ((double) (((double) val -
                    (double) optminmaxidx[OPTMINIDX]) * (double) (255.0 /
                    (double) range)));
        setData (adjustedImg, i, j, (uchar) newval);
      }
    }
  delete[]optminmaxidx;
  return adjustedImg;
}

希望对你有帮助。 棒棒哒。