Opencv-图像直方图比较

知识点

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，直方图比较在早期的CBIR中是应用很常见的技术手段，通常会结合边缘处理、词袋等技术一起使用。

OpenCV中直方图比较的API很简单
compareHist(hist1, hist2, method)
-常见比较方法有
相关性
卡方
交叉
巴氏

python代码

import cv2 as cv
import numpy as np

src1 = cv.imread("C:/Users/qqxd/Desktop/opencvcode/images/m1.png")
src2 = cv.imread("C:/Users/qqxd/Desktop/opencvcode/images/m2.png")
src3 = cv.imread("C:/Users/qqxd/Desktop/opencvcode/images/flower.png")
src4 = cv.imread("C:/Users/qqxd/Desktop/opencvcode/images/test.png")

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

hsv1 = cv.cvtColor(src1, cv.COLOR_BGR2HSV)
hsv2 = cv.cvtColor(src2, cv.COLOR_BGR2HSV)
hsv3 = cv.cvtColor(src3, cv.COLOR_BGR2HSV)
hsv4 = cv.cvtColor(src4, cv.COLOR_BGR2HSV)

hist1 = cv.calcHist([hsv1], [0, 1], None, [60, 64], [0, 180, 0, 256])
hist2 = cv.calcHist([hsv2], [0, 1], None, [60, 64], [0, 180, 0, 256])
hist3 = cv.calcHist([hsv3], [0, 1], None, [60, 64], [0, 180, 0, 256])
hist4 = cv.calcHist([hsv4], [0, 1], None, [60, 64], [0, 180, 0, 256])

cv.normalize(hist1, hist1, 0, 1.0, cv.NORM_MINMAX)
cv.normalize(hist2, hist2, 0, 1.0, cv.NORM_MINMAX)
cv.normalize(hist3, hist3, 0, 1.0, cv.NORM_MINMAX)
cv.normalize(hist4, hist4, 0, 1.0, cv.NORM_MINMAX)

methods = [cv.HISTCMP_CORREL, cv.HISTCMP_CHISQR,
           cv.HISTCMP_INTERSECT, cv.HISTCMP_BHATTACHARYYA]
str_method = ""
for method in methods:
    src1_src2 = cv.compareHist(hist1, hist2, method)
    src3_src4 = cv.compareHist(hist3, hist4, method)
    if method == cv.HISTCMP_CORREL:
        str_method = "Correlation"
    if method == cv.HISTCMP_CHISQR:
        str_method = "Chi-square"
    if method == cv.HISTCMP_INTERSECT:
        str_method = "Intersection"
    if method == cv.HISTCMP_BHATTACHARYYA:
        str_method = "Bhattacharyya"

    print("%s src1_src2 = %.2f, src3_src4 = %.2f"%(str_method, src1_src2, src3_src4))


cv.waitKey(0)
cv.destroyAllWindows()

c++代码

#include 
#include 

using namespace cv;
using namespace std;

int main(int artc, char** argv) {
	Mat src1 = imread("C:/Users/qqxd/Desktop/opencvcode/images/m1.png");
	Mat src2 = imread("C:/Users/qqxd/Desktop/opencvcode/images/m2.png");
	Mat src3 = imread("C:/Users/qqxd/Desktop/opencvcode/images/flower.png");
	Mat src4 = imread("C:/Users/qqxd/Desktop/opencvcode/images/test.png");

	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 };
	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);

	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;
}

运行结果如下：