opencv学习笔记(六)直方图比较图片相似度
opencv提供了API来比较图片的相似程度,使我们很简单的就能对2个图片进行比较,这就是直方图的比较,直方图英文是histogram, 原理就是就是将图片转换成直方图,然后对直方图进行比较,在某些程度,真实地反映了图片的相似度。
代码如下:
1 #include <iostream>
2 #include <cv.h>
3 #include <highgui.h>
4 using namespace std;
5 using namespace cv;
6
7 int main(void)
8 {
9 Mat pic1 = imread("pic1.jpg");
10 Mat pic2 = imread("pic2.jpg");
11 //计算相似度
12 if (pic2.channels() == 1) {//单通道时,
13 int histSize = 256;
14 float range[] = { 0, 256 };
15 const float* histRange = { range };
16 bool uniform = true;
17 bool accumulate = false;
18
19 cv::Mat hist1, hist2;
20
21 cv::calcHist(&pic2, 1, 0, cv::Mat(), hist1, 1, &histSize, &histRange, uniform, accumulate);
22 cv::normalize(hist1, hist1, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
23
24 cv::calcHist(&pic1, 1, 0, cv::Mat(), hist2, 1, &histSize, &histRange, uniform, accumulate);
25 cv::normalize(hist2, hist2, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
26
27 double dSimilarity = cv::compareHist(hist1, hist2, CV_COMP_CORREL);//,CV_COMP_CHISQR,CV_COMP_INTERSECT,CV_COMP_BHATTACHARYYA CV_COMP_CORREL
28
29 cout << "similarity = " << dSimilarity << endl;
30 }
31 else {//三通道时
32 cv::cvtColor(pic2, pic2, cv::COLOR_BGR2HSV);
33 cv::cvtColor(pic1, pic1, cv::COLOR_BGR2HSV);
34
35 int h_bins = 50, s_bins = 60;
36 int histSize[] = { h_bins, s_bins };
37 float h_ranges[] = { 0, 180 };
38 float s_ranges[] = { 0, 256 };
39 const float* ranges[] = { h_ranges, s_ranges };
40 int channels[] = { 0, 1 };
41
42 cv::MatND hist1, hist2;
43
44 cv::calcHist(&pic2, 1, channels, cv::Mat(), hist1, 2, histSize, ranges, true, false);
45 cv::normalize(hist1, hist1, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
46
47 cv::calcHist(&pic1, 1, channels, cv::Mat(), hist2, 2, histSize, ranges, true, false);
48 cv::normalize(hist2, hist2, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
49
50 double dSimilarity = cv::compareHist(hist1, hist2, CV_COMP_CORREL); //,CV_COMP_CHISQR,CV_COMP_INTERSECT,CV_COMP_BHATTACHARYYA CV_COMP_CORREL
51
52 cout << "similarity = " << dSimilarity << endl;
53 }
54 waitKey(0);
55 return 1;
56
57 }
pic1:
![](https://imgconvert.csdnimg.cn/aHR0cHM6Ly9pbWFnZXMyMDE1LmNuYmxvZ3MuY29tL2Jsb2cvNzYzOTQzLzIwMTYwNi83NjM5NDMtMjAxNjA2MjIyMzQwNDI4OTEtMTUzNjU5OTM0Ni5qcGc?x-oss-process=image/format,png)
pic2:
![](https://imgconvert.csdnimg.cn/aHR0cHM6Ly9pbWFnZXMyMDE1LmNuYmxvZ3MuY29tL2Jsb2cvNzYzOTQzLzIwMTYwNi83NjM5NDMtMjAxNjA2MjIyMzQxMTUwNzgtMTkwOTcwMDg3Mi5qcGc?x-oss-process=image/format,png)
使用相关系数法(CV_COMP_CORREL)进行图片相似度比较时,取值范围为[-1,1];越接近1说明两幅图片越相似;
比较pic1与pic2得到的结果为:
similarity =0.926247
pic与本身进行比较时,
similarity =1
转载于:https://www.cnblogs.com/codingmengmeng/p/5609036.html
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