【OpenCV学习笔记】二十二、直方图计算及绘制（二）

直方图计算及绘制（二）

1.直方图均衡化——equalizeHist()

2.直方图对比——compareHist()

3.完成了几个应用：灰度图像直方图均衡化、彩色图像直方图均衡化、直方图对比、反向投影（待补）。

先上ppt：























代码：

1.灰度图像直方图均衡化

///灰度图像直方图均衡化
#include "opencv2/opencv.hpp"
using namespace cv;
#include <iostream>
using namespace std;
int main()
{
//1.灰度直方图均衡化
Mat srcImg = imread("06.jpg",CV_LOAD_IMAGE_GRAYSCALE);//直方图均衡化需为8位单通道图像
Mat dstImg;
equalizeHist(srcImg,dstImg);
imshow("srcImg",srcImg);
imshow("dstImg",dstImg);
//2.画出源图srcImg和目标图像dstImg的直方图
//2.1计算直方图
int nimages = 1;//图像的个数
int channels = 0;//需要统计通道的索引
Mat mask = Mat();
Mat histImg_src;//存放srcImg输出的直方图
Mat histImg_dst;//存放dstImg输出的直方图
int dims = 1;//需要计算的直方图的维度
int histSize = 256;//计算的直方图的分组数
float range[] = { 0, 256 };//表示直方图每一维度的取值范围[0,256)
const float* ranges[] = { range };//参数形式需要,表示每一维度数值的取值范围
calcHist(&srcImg, nimages, &channels, mask, histImg_src, dims, &histSize, ranges);//计算srcImg直方图
calcHist(&dstImg, nimages, &channels, mask, histImg_dst, dims, &
4000
;histSize, ranges);//计算dstImg直方图
//2.2绘制直方图
//2.2.1绘制srcImg的直方图
double minValue = 0;
double maxValue = 0;
minMaxLoc(histImg_src, &minValue, &maxValue);//得到计算出的直方图中的最小值和最大值
int width = histSize;//定义绘制直方图的宽度，令其等于histSize
int height = 400;//定义绘制直方图的高度
Mat histShow_src = Mat::zeros(Size(width, height), CV_8UC3);//宽为histSize，高为height
for (int i = 0; i < histSize; i++)//遍历histImg
{
float binValue = histImg_src.at<float>(i);//得到histImg中每一分组的值
cout << "i: " << i << " ,binValue: " << binValue << endl;
float realValue = (binValue / maxValue)*height;//归一化数据，缩放到图像的height之内
cout << "i: " << i << " ,realValue: " << realValue << endl;
//用直线方法绘制直方图，注意两端点坐标的计算
line(histShow_src, Point(i, height - 1), Point(i, height - 1 - realValue), Scalar(255, 0, 0), 1);
}
namedWindow("srcHist", CV_WINDOW_NORMAL);
imshow("srcHist", histShow_src);
//2.2.2绘制dstImg的直方图
double minValue_dst = 0;
double maxValue_dst = 0;
minMaxLoc(histImg_dst, &minValue_dst, &maxValue_dst);//得到计算出的直方图中的最小值和最大值
Mat histShow_dst = Mat::zeros(Size(width, height), CV_8UC3);//宽为histSize，高为height
for (int i = 0; i < histSize; i++)//遍历histImg
{
float binValue = histImg_dst.at<float>(i);//得到histImg中每一分组的值
cout << "i: " << i << " ,binValue: " << binValue << endl;
float realValue = (binValue / maxValue_dst)*height;//归一化数据，缩放到图像的height之内
cout << "i: " << i << " ,realValue: " << realValue << endl;
//用直线方法绘制直方图，注意两端点坐标的计算
line(histShow_dst, Point(i, height - 1), Point(i, height - 1 - realValue), Scalar(255, 0, 0), 1);
}
namedWindow("dstHist",CV_WINDOW_NORMAL);
imshow("dstHist", histShow_dst);
waitKey(0);
return 0;
}

运行结果：



2.彩色图像直方图均衡化

///彩色图像直方图均衡化
#include "opencv2/opencv.hpp"
using namespace cv;
#include <iostream>
using namespace std;
int main()
{
Mat srcImg = imread("horse.png",CV_LOAD_IMAGE_COLOR);
Mat dstImg;
//1.BGR通道分离——split()
vector<Mat> channels;
split(srcImg,channels);
Mat channelBlue = channels.at(0);
Mat channelGreen = channels.at(1);
Mat channelRed = channels.at(2);
//2.对BGR通道分别进行直方图均衡化——equalizeHist()
equalizeHist(channelBlue, channelBlue);
equalizeHist(channelGreen, channelGreen);
equalizeHist(channelRed, channelRed);
//3.BGR通道融合——merge()
merge(channels,dstImg);
namedWindow("srcImg",CV_WINDOW_NORMAL);
imshow("srcImg", srcImg);
namedWindow("dstImg", CV_WINDOW_NORMAL);
imshow("dstImg", dstImg);
waitKey(0);
return 0;
}

运行结果：



3.直方图对比

///直方图对比
#include "opencv2/opencv.hpp"
using namespace cv;
#include <iostream>
using namespace std;
int main()
{
Mat srcImg1 = imread("A.JPG",CV_LOAD_IMAGE_COLOR);
Mat srcImg2 = imread("B.JPG", CV_LOAD_IMAGE_COLOR);
//1.计算srcImg1和srcImg2的直方图
int nimages = 1;//图像的个数
int channels = 0;//需要统计通道的索引
Mat mask = Mat();
Mat histImg1;//存放srcImg1输出的直方图
Mat histImg2;//存放srcImg2输出的直方图
int dims = 1;//需要计算的直方图的维度
int histSize = 256;//计算的直方图的分组数
float range[] = { 0, 256 };//表示直方图每一维度的取值范围[0,256)
const float* ranges[] = { range };//参数形式需要,表示每一维度数值的取值范围
calcHist(&srcImg1, nimages, &channels, mask, histImg1, dims, &histSize, ranges);//计算srcImg1直方图
calcHist(&srcImg2, nimages, &channels, mask, histImg2, dims, &histSize, ranges);//计算srcImg2直方图
//2.直方图对比，注意是依据两源图像所计算出的直方图进行相似度对比.
double num1 = compareHist(histImg1, histImg2, CV_COMP_CORREL);//相关性方法(值越大匹配度越高)
double num2 = compareHist(histImg1, histImg2, CV_COMP_CHISQR);//卡方测量法(值越小匹配度越高)
double num3 = compareHist(histImg1, histImg2, CV_COMP_INTERSECT);//直方图相交法(值越大匹配度越高)
double num4 = compareHist(histImg1, histImg2, CV_COMP_BHATTACHARYYA);//Bhattacharyya测量法(小)
cout << "CV_COMP_CORREL(max_best): " << num1 << endl;
cout << "CV_COMP_CHISQR(min_best): " << num2 << endl;
cout << "CV_COMP_INTERSECT(max_best): " << num3 << endl;
cout << "CV_COMP_BHATTACHARYYA(min_best): " << num4 << endl;
//3.绘制srcImg1和srcImg2的直方图
//3.1绘制srcImg1的直方图
double minValue = 0;
double maxValue = 0;
minMaxLoc(histImg1, &minValue, &maxValue);//得到计算出的直方图中的最小值和最大值
int width = histSize;//定义绘制直方图的宽度，令其等于histSize
int height = 400;//定义绘制直方图的高度
Mat histShow1 = Mat::zeros(Size(width, height), CV_8UC3);//宽为histSize，高为height
for (int i = 0; i < histSize; i++)//遍历histImg
{
float binValue = histImg1.at<float>(i);//得到histImg中每一分组的值
float realValue = (binValue / maxValue)*height;//归一化数据，缩放到图像的height之内
//用直线方法绘制直方图，注意两端点坐标的计算
line(histShow1, Point(i, height - 1), Point(i, height - 1 - realValue), Scalar(255, 0, 0), 1);
}
namedWindow("srcHist1", CV_WINDOW_NORMAL);
imshow("srcHist1", histShow1);
//3.2绘制srcImg2的直方图
double minValue2 = 0;
double maxValue2 = 0;
minMaxLoc(histImg2, &minValue2, &maxValue2);//得到计算出的直方图中的最小值和最大值
Mat histShow2 = Mat::zeros(Size(width, height), CV_8UC3);//宽为histSize，高为height
for (int i = 0; i < histSize; i++)//遍历histImg
{
float binValue = histImg2.at<float>(i);//得到histImg中每一分组的值
float realValue = (binValue / maxValue2)*height;//归一化数据，缩放到图像的height之内
//用直线方法绘制直方图，注意两端点坐标的计算
line(histShow2, Point(i, height - 1), Point(i, height - 1 - realValue), Scalar(255, 0, 0), 1);
}
namedWindow("srcHist2",CV_WINDOW_NORMAL);
imshow("srcHist2", histShow2);

namedWindow("srcImg1", CV_WINDOW_NORMAL);
imshow("srcImg1", srcImg1);
namedWindow("srcImg2", CV_WINDOW_NORMAL);
imshow("srcImg2", srcImg2);
waitKey(0);
return 0;
}

运行结果：



4.反向投影（未理解，以后补）