直方图规范化的opencv的实现

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直方图规范化也叫规定化。通过直方图的规定，能够把两幅图像的色调拉成一样。

数学原理在网上很多可以找到。这里说一下实现过程步骤和opencv的C++实现。

上表一行行看下来，大部分应该没什么问题。SML映射看的是原始累计直方图的值最相近的规定累计直方图的位置，（比如0.14在下面找最接近的是0.19，是就是3）.

然后下面就很简单了，灰度0映射到灰度3，灰度1映射到灰度4。。。

代码还是比较简单的，就是网上搜了一下也没有，OPENCV本身也没有封装。就自己写了一个，和大家共享。

  
  
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;


bool Cal_Hist(Mat Gray_img, MatND &hist){
int bins = 256;
int hist_size[] = { bins };
float range[] = { 0, 256 };
const float* ranges[] = { range };

int channels[] = { 0 };
//计算直方图
calcHist(&Gray_img, 1, channels, Mat(), // do not use mask
hist, 1, hist_size, ranges,
true, // the histogram is uniform
false);
if (hist.data == 0) return false;
return true;
}
void DrawGrayHist(const char* pTitle, MatND& hist)
{
int hist_height = 256;
int bins = 256;
double max_val; //直方图的最大值
int scale = 2; //直方图的宽度
minMaxLoc(hist, 0, &max_val, 0, 0); //计算直方图最大值

Mat hist_img = Mat::zeros(hist_height, bins*scale, CV_8UC3); //创建一个直方图图像并初始化为0
for ( int i = 0; i<bins; i++)
{
float bin_val = hist.at< float>(i); // 第i灰度级上的数
int intensity = cvRound(bin_val*hist_height / max_val); //要绘制的高度
//填充第i灰度级的数据
rectangle(hist_img, Point(i*scale, hist_height - 1),
Point((i + 1)*scale - 1, hist_height - intensity),
CV_RGB( 255, 255, 255));
}
imshow(pTitle, hist_img);
}

void one_channel_hist_specify(Mat input_img, Mat dst_img, Mat &output_img)//单通道
{
int i,j;

//计算输入，规定图像的直方图
MatND input_hist, dst_hist;
Cal_Hist(input_img, input_hist);
Cal_Hist(dst_img, dst_hist);

//计算概率直方图
MatND input_p_hist, dst_p_hist;
input_p_hist = MatND::zeros(input_hist.size[ 0], input_hist.size[ 1], CV_32FC1); //原始概率直方图
dst_p_hist = MatND::zeros(dst_hist.size[ 0], dst_hist.size[ 1], CV_32FC1); //规定概率直方图
float input_totalnum = 0;
float dst_totalnum = 0;
for (i = 0; i < input_hist.rows; i++)
input_totalnum += input_hist.at< float>(i);
for (i = 0; i < dst_hist.rows; i++)
dst_totalnum += dst_hist.at< float>(i);
for (i = 0; i < input_hist.rows; i++)
input_p_hist.at< float>(i) = input_hist.at< float>(i) / input_totalnum;
for (i = 0; i < dst_hist.rows; i++)
dst_p_hist.at< float>(i) = dst_hist.at< float>(i) / dst_totalnum;

//计算累计直方图
MatND input_c_hist, dst_c_hist;
input_c_hist = MatND::zeros(input_hist.size[ 0], input_hist.size[ 1], CV_32FC1); //原始累计直方图
dst_c_hist = MatND::zeros(dst_hist.size[ 0], dst_hist.size[ 1], CV_32FC1); //规定累计直方图
float input_accum_p = 0;
float dst_accum_p = 0;
for (i = 0; i < input_hist.rows; i++)
{
input_accum_p += input_p_hist.at< float>(i);
input_c_hist.at< float>(i) = input_accum_p;
}
for (i = 0; i < dst_hist.rows; i++)
{
dst_accum_p += dst_p_hist.at< float>(i);
dst_c_hist.at< float>(i) = dst_accum_p;
}

//计算单映射规则
MatND SML = MatND::zeros(input_hist.size[ 0], input_hist.size[ 1], CV_32FC1); //SML单映射规则
for (i = 0; i < input_c_hist.rows; i++)
{
int minind = 0;
float minval = 1;
for (j = 0; j < dst_c_hist.rows; j++)
{
float abssub = abs(input_c_hist.at< float>(i)-dst_c_hist.at< float>(j));
if (abssub < minval)
{
minval = abssub;
minind = j;
}
}
SML.at< float>(i) = minind;
}

//计算输出图像
Mat outimg = Mat::zeros(input_img.size[ 0], input_img.size[ 1], CV_8U);
for (i = 0; i < input_img.rows; i++)
{
for (j = 0; j < input_img.cols; j++)
{
outimg.at<uchar>(i, j) = SML.at< float>(input_img.at<uchar>(i, j));
}
}
outimg.copyTo(output_img);

//计算输出图像直方图
//MatND output_hist;
//Cal_Hist(output_img, output_hist);

//DrawGrayHist("input_hist", input_hist);
//DrawGrayHist("dst_hist", dst_hist);
//DrawGrayHist("output_hist", output_hist);
}

void three_channel_hist_specify(Mat input_img, Mat dst_img, Mat &output_img)//三通道
{
//Mat src = imread（"path", 1）; //读入目标图像
Mat out_img(input_img.rows, input_img.cols, CV_8UC3); //用来存储目的图片的矩阵
//Mat数组来存车分离后的三个通道，每个通道都初始化为0;
Mat input_planes[] = { Mat::zeros(input_img.size(), CV_8UC1), Mat::zeros(input_img.size(), CV_8UC1), Mat::zeros(input_img.size(), CV_8UC1) };
Mat dst_planes[] = { Mat::zeros(dst_img.size(), CV_8UC1), Mat::zeros(dst_img.size(), CV_8UC1), Mat::zeros(dst_img.size(), CV_8UC1) };
//多通道分成3个单通道,BGR
split(input_img, input_planes);
split(dst_img, dst_planes);
Mat B_output_img, G_output_img, R_output_img;
one_channel_hist_specify(input_planes[ 0], dst_planes[ 0], B_output_img);
one_channel_hist_specify(input_planes[ 1], dst_planes[ 1], G_output_img);
one_channel_hist_specify(input_planes[ 2], dst_planes[ 2], R_output_img);
Mat output_planes[ 3];
output_planes[ 0] = B_output_img;
output_planes[ 1] = G_output_img;
output_planes[ 2] = R_output_img;
merge(output_planes, 3, out_img); //通道合并
out_img.copyTo(output_img);
}

int main()
{

Mat src, gray, src2, gray2;
//src=imread("D://input//buti.jpg");
src = imread( "F:\\大海.jpg");
cvtColor(src, gray, CV_RGB2GRAY); //转换成灰度图

src2 = imread( "F:\\沙漠.jpg");
cvtColor(src2, gray2, CV_RGB2GRAY); //转换成灰度图
MatND hist2;

imshow( "Source", src);
//imshow( "Gray Histogram", hist_img );
imshow( "Source2", src2);

Mat output_img;
three_channel_hist_specify(src, src2, output_img);

imshow( "5", output_img);
//DrawGrayHist("2", hist2);

waitKey();
return 0;

}
  
  

原图：

规定图：

转化结果：

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