OpenCV绘制图像中RGB三个通道的直方图

一开始是看《OpenCV计算机视觉编程攻略（第2版）》这本书学做直方图，但是书本里说直方图的部分只详细说了黑白图像（单通道）的直方图绘制方法，RGB图像的直方图只说了如何计算，没有说计算完之后如何绘制，自己想了很久也没想到正确的绘制方法。

去查OpenCV的官方文档，里面的例子只说了如何绘制H和S两通道的直方图，很多函数的用法也没搞清楚。

后来在网上看别人的程序，找到有绘制HSV三通道直方图的程序，花了一点时间一行一行地看，并且结合自己已经学过的知识把程序改成绘制RGB三通道的直方图的程序。

histogram.h：

#ifndef HISTOGRAM_H
#define HISTOGRAM_H

#include <opencv2\opencv.hpp>

#include <iostream>
#include <string>

class Histogram
{
private:
int histSize[3];        //直方图中箱子的数量
float hranges[2];    //值范围
const float * ranges[3];        //值范围的指针
int channels[3];        //要检查的通道数量

public:
Histogram();
cv::Mat getHistogram(const cv::Mat & image);
std::vector<cv::Mat> getHistogramImage(const cv::Mat & image, int zoom = 1);
static std::vector<cv::Mat> getImageOfHistogram(const cv::Mat & hist, int zoom);
};

#endif

histogram.cpp：

#include "histogram.h"

Histogram::Histogram()
{
histSize[0] = 256;
histSize[1] = 256;
histSize[2] = 256;
hranges[0] = 0.0;
hranges[1] = 256.0;
ranges[0] = hranges;
ranges[1] = hranges;
ranges[2] = hranges;
channels[0] = 0;
channels[1] = 1;
channels[2] = 2;
}

cv::Mat Histogram::getHistogram(const cv::Mat & image)
{
cv::Mat hist;

hranges[0] = 0.0;
hranges[1] = 256.0;
channels[0] = 0;
channels[1] = 1;
channels[2] = 2;

cv::calcHist(&image, 1, channels, cv::Mat(), hist, 3, histSize, ranges);

return hist;
}

std::vector<cv::Mat> Histogram::getHistogramImage(const cv::Mat & image, int zoom)
{
cv::Mat hist = getHistogram(image);

return Histogram::getImageOfHistogram(hist, zoom);
}

std::vector<cv::Mat> Histogram::getImageOfHistogram(const cv::Mat & hist, int zoom)
{
int scale = 2;

float hist_b[256];
float hist_g[256];
float hist_r[256];  

memset(hist_b, 0, 256 * sizeof(float));
memset(hist_g, 0, 256 * sizeof(float));
memset(hist_r, 0, 256 * sizeof(float));

//计算三个通道的直方图
for(int b = 0; b < 256; b ++ )
{
for(int g = 0; g < 256; g ++)
{
for(int r = 0; r < 256; r ++)
{
float binVal = hist.at<float>(b, g, r);
hist_b[b] += binVal;
hist_g[g] += binVal;
hist_r[r] += binVal;
}
}
}

//获得三个通道直方图中的最大值
double max_b = 0.0, max_g = 0.0,max_r = 0.0;
for(int i = 0; i < 256; i ++)
{
if(hist_b[i] > max_b)
{
max_b = hist_b[i];
}
}
for(int i = 0; i < 256; i ++)
{
if(hist_g[i] > max_g)
{
max_g = hist_g[i];
}
}
for(int i = 0; i < 256; i ++)
{
if(hist_r[i] > max_r)
{
max_r = hist_r[i];
}
}

//初始化空的图
cv::Mat b_img = cv::Mat::zeros(256, 256 * scale, CV_8UC3);
cv::Mat g_img = cv::Mat::zeros(256, 256 * scale, CV_8UC3);
cv::Mat r_img = cv::Mat::zeros(256, 256 * scale, CV_8UC3);

//绘制三个通道的直方图
for(int i = 0; i < 256; i ++)
{
int intensity = cvRound(hist_b[i] * b_img.rows / max_b);
cv::rectangle(b_img, cv::Point(i * scale, b_img.rows - intensity), cv::Point((i + 1) * scale - 1, b_img.rows - 1), cv::Scalar(255, 0 ,0), 1);
}
for(int i = 0; i < 256; i ++)
{
int intensity = cvRound(hist_g[i] * g_img.rows / max_g);
cv::rectangle(g_img, cv::Point(i * scale, g_img.rows - intensity), cv::Point((i + 1) * scale - 1, g_img.rows - 1), cv::Scalar(0, 255, 0), 1);
}
for(int i = 0; i < 256; i ++)
{
int intensity = cvRound(hist_r[i] * r_img.rows / max_r);
cv::rectangle(r_img, cv::Point(i * scale, r_img.rows - intensity), cv::Point((i + 1) * scale - 1, r_img.rows - 1), cv::Scalar(0, 0, 255), 1);
}

std::vector<cv::Mat> imgs;
imgs.push_back(b_img);
imgs.push_back(g_img);
imgs.push_back(r_img);

return imgs;
}

main.cpp：

#include <opencv2\opencv.hpp>

#include <iostream>
#include <string>

#include "histogram.h"

using namespace std;

int main()
{
cv::Mat image = cv::imread("animal.jpg");
cv::imshow("image", image);

Histogram h;
std::vector<cv::Mat> imgs = h.getHistogramImage(image);

cv::namedWindow("B");
cv::imshow("B", imgs[0]);
cv::namedWindow("G");
cv::imshow("G", imgs[1]);
cv::namedWindow("R");
cv::imshow("R", imgs[2]);

cv::waitKey();

return 0;
}

运行结果：

参考资料：

http://blog.csdn.net/ljbkiss/article/details/7420429