DrGraph原理示教 - OpenCV 4 功能 - 直方图

OpenCV直方图是一种可以对整幅图的灰度分布进行整体了解的图示。它是带有像素值（从0到255，不总是）的图在X轴上，在y轴上的图像对应的像素个数。通过观察图像的直方图，我们可以直观的了解图像的对比度、亮度、亮度分布等。
在直方图中，横坐标表示图像中各个像素点的灰度级，纵坐标表示具有该灰度级的像素个数。直方图的左边部分显示了图像中较暗像素的数量，右边区域显示了更明亮的像素。
直方图是非常常用的图像处理方法，有时在很多图像预处理中能起到特别好的效果。

一维直方图

OpenCV中，直方图是调用calxHist函数，该函数的参数比较多，不太好理解

The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used
to increment a histogram bin are taken from the corresponding input arrays at the same location. The
sample below shows how to compute a 2D Hue-Saturation histogram for a color image. :
@include snippets/imgproc_calcHist.cpp

@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
size. Each of them can have an arbitrary number of channels.
@param nimages Number of source images.
@param channels List of the dims channels used to compute the histogram. The first array channels
are numerated from 0 to images[0].channels()-1 , the second array channels are counted from
images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size
as images[i] . The non-zero mask elements mark the array elements counted in the histogram.
@param hist Output histogram, which is a dense or sparse dims -dimensional array.
@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
(equal to 32 in the current OpenCV version).
@param histSize Array of histogram sizes in each dimension.
@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the
histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower
(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary
\f$U_{
   
   \texttt{
   
   histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a
uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform (
uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
\f$L_0, U_0=L_1, U_1=L_2, ..., U_{
   
   \texttt{
   
   histSize[i]}-2}=L_{
   
   \texttt{
   
   histSize[i]}-1}, U_{
   
   \texttt{
   
   histSize[i]}-1}\f$
. The array elements, that are not between \f$L_0\f$ and \f$U_{
   
   \texttt{
   
   histSize[i]}-1}\f$ , are not
counted in the histogram.
@param uniform Flag indicating whether the histogram is uniform or not (see above).
@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning
when it is allocated. This feature enables you to compute a single histogram from several sets of
arrays, or to update the histogram in time.
*/
CV_EXPORTS void calcHist( const Mat* images, int nimages,
                          const int* channels, InputArray mask,
                          OutputArray hist, int dims, const int* histSize,
                          const float** ranges, bool uniform = true, bool accumulate = false );

简单化理解，那就是hist参数之前的为输入参数，其余的为输出参数。
dims指定直方图的维数，可以先理解1维的。
因为calcHist函数可以用于多维，所以它的参数也要支持多维。其实嘛，简单点多好，多用几个函数分别实现相应多维，用得多的一二维直方图就用得轻松了。
正常使用，calcHist就针对一维处理。所以要用的时候，先需要将彩色图像灰度化，或者split取得各通首图像分别应用calcHist，然后相应算后再合并处理。如果只是想看直方图，那就直接画出来。

  		std::vector<Mat> mv;
   		split(dstMat, mv);  
        int histSize[] = {
   
    bins };
        float rgb_ranges[] = {
   
    0, r };
        const