OpenCV学习二十四： compareHist 基于直方图的图片相似度计算函数

版权声明：共享知识，欢迎转载 https://blog.csdn.net/kakiebu/article/details/79476737

函数的使用很简单，直接把 OpenCV 的官方文档截取过来吧，主要是。。。懒了。。。

#include <opencv2/opencv.hpp>  
#include <stdio.h>  
#include <stdlib.h>  
#include <iostream>

using namespace cv;  
using namespace std;  

char file[] = "1.jpg";
string convertToString(double d);
int main(int argc, char** argv)  
{  
	Mat img1 = imread(file, -1);
	pyrDown(img1, img1, Size(img1.cols/2, img1.rows/2));
	imshow("1",img1);

	Mat img20 = imread("img.jpg", -1);
	imshow("2", img20);
	
	//转到HSV
	cvtColor(img1, img1, CV_BGR2HSV);
	cvtColor(img20, img20, CV_BGR2HSV);
	Mat img2;
	img20.copyTo(img2);

	//
	int h_bins = 50;int s_bins = 60;
	int histSize[] = {h_bins, s_bins};
	
	float h_ranges[] = {0, 180};
	float s_ranges[] = {0, 256};
	const float* ranges[] = {h_ranges, s_ranges};

	int channels[] = {0, 1};
	MatND hist_base, hist_test1, hist_test2;
	
	calcHist(&img1, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false);
	normalize(hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat());

	calcHist(&img2, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false);
	normalize(hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat());

	double basetest1 = compareHist(hist_base, hist_base, CV_COMP_CORREL);
	double basetest2 = compareHist(hist_base, hist_test1, CV_COMP_CORREL);//相关性比较
	double basetest3 = compareHist(hist_base, hist_test1, CV_COMP_CHISQR);//卡方比较
	double basetest4 = compareHist(hist_base, hist_test1, CV_COMP_INTERSECT);//十字交叉性
	double basetest5 = compareHist(hist_base, hist_test1, CV_COMP_BHATTACHARYYA);//巴氏距离


	putText(img1, convertToString(basetest1), Point(50, 90), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	putText(img1, "Origional CORREL", Point(50, 40), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	imshow("3", img1);
	imwrite("13.jpg", img1);

	img20.copyTo(img2);
	putText(img2, convertToString(basetest2), Point(50, 90), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	putText(img2, "CORREL", Point(50, 40), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	imshow("相关性比较", img2);
	imwrite("14.jpg", img2);

	img20.copyTo(img2);
	putText(img2, convertToString(basetest3), Point(50, 90), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	putText(img2, "CHISQR", Point(50, 40), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	imshow("卡方比较", img2);
	imwrite("15.jpg", img2);

	img20.copyTo(img2);
	putText(img2, convertToString(basetest4), Point(50, 90), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	putText(img2, "INTERSECT", Point(50, 40), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	imshow("十字交叉", img2);
	imwrite("16.jpg", img2);

	img20.copyTo(img2);
	putText(img2, convertToString(basetest5), Point(50, 90), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	putText(img2, "BHATTACHARYYA", Point(50, 40), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0,0,0), 2 );
	imshow("巴氏距离", img2);
	imwrite("17.jpg", img2);

	waitKey();
	return 1;
} 

string convertToString(double d)
{
	ostringstream os;
	if (os << d)
	{
		return os.str();
	}
	return "invalid conversion";
}