C++ opencv之图像直方图比较（compareHist）

这篇博客主要来学习图像直方图比较。

一、主要内容

1.1 目的

图像直方图比较，就是计算两幅图像的直方图数据，比较两组数据的相似性，从而得到两幅图像之间的相似程度。

1.2 函数原型

void cv::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 
    ) 

1.3 参数介绍

参数：
images： 源图像，注意这里的格式是const Mat*，也就是说，你要传入一个地址，输入的数组(图片)或者数组集(一堆图片)需要为相同的深度和相同的尺寸。每个都能够用任意数量的通道。

Nimages:输入图像个数

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.

mask：可选的掩码，如果不为空的话，那么它必须是8位且和image[i]尺寸相同。

hist：输出的直方图，二维数组。

dims：需要统计的直方图维度（特征数目）,必须是正数，且不大于CV_MAX_DIMS(这个版本opencv3.1里面是32)

histSize：存放每个维度的直方图尺寸的数组。

ranges：每一维数值的取值范围。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 L0 of the 0-th histogram bin and the upper (exclusive) boundary UhistSize[i]−1 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: L0,U0=L1,U1=L2,...,UhistSize[i]−2=LhistSize[i]−1,UhistSize[i]−1 . The array elements, that are not between L0 and UhistSize[i]−1 , are not counted in the histogram.

uniform：表示直方图是否均匀

accumulate：累计标识符，若为ture，直方图再配置阶段不会被清零。

二、代码演示

#include 
#include 

using namespace cv;
using namespace std;

int main(int artc, char** argv) {
	Mat src1 = imread("C:/Users/Dell/Desktop/picture/basketball1.png");
	Mat src2 = imread("C:/Users/Dell/Desktop/picture/basketball2.png");
	Mat src3 = imread("C:/Users/Dell/Desktop/picture/helloworld.png");
	Mat src4 = imread("C:/Users/Dell/Desktop/picture/7.jpg");

	imshow("input1", src1);
	imshow("input2", src2);
	imshow("input3", src3);
	imshow("input4", src4);

	Mat hsv1, hsv2, hsv3, hsv4;
	cvtColor(src1, hsv1, COLOR_BGR2HSV);
	cvtColor(src2, hsv2, COLOR_BGR2HSV);
	cvtColor(src3, hsv3, COLOR_BGR2HSV);
	cvtColor(src4, hsv4, COLOR_BGR2HSV);

	int h_bins = 60; int s_bins = 64;
	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 };
	//参数1 指针引用
	//参数2 数组中有几个图片
	//参数3 通道（0-1）
	//参数4 输出
	//参数5 计算的两个通道直方图
	//参数6 存放每个维度的直方图尺寸的数组。
	//参数7 通道像素的取值范围
	Mat hist1, hist2, hist3, hist4;
	calcHist(&hsv1, 1, channels, Mat(), hist1, 2, histSize, ranges, true, false);
	calcHist(&hsv2, 1, channels, Mat(), hist2, 2, histSize, ranges, true, false);
	calcHist(&hsv3, 1, channels, Mat(), hist3, 2, histSize, ranges, true, false);
	calcHist(&hsv4, 1, channels, Mat(), hist4, 2, histSize, ranges, true, false);
	//归一化
	normalize(hist1, hist1, 0, 1, NORM_MINMAX, -1, Mat());
	normalize(hist2, hist2, 0, 1, NORM_MINMAX, -1, Mat());
	normalize(hist3, hist3, 0, 1, NORM_MINMAX, -1, Mat());
	normalize(hist4, hist4, 0, 1, NORM_MINMAX, -1, Mat());

	for (int i = 0; i < 4; i++)
	{
		int compare_method = i;
		double src1_src2 = compareHist(hist1, hist2, compare_method);
		double src3_src4 = compareHist(hist3, hist4, compare_method);
		printf(" Method [%d]  : src1_src2 : %f, src3_src4: %f,  \n", i, src1_src2, src3_src4);
	}

	waitKey(0);
	return 0;
}

三、输出结果

加油吧 阿超没有蛀牙！