008-OpenCV（C++）直方图绘制、直方图均衡化以及直方图比较

一、直方图绘制

代码如下：

calcHist(&mv[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRanges, true, false);绘制直方图

normalize(b_hist, b_hist, 0, nm, NORM_MINMAX, -1, Mat());进行归一化

#include
#include

using namespace cv;
using namespace std;

int main(int argc, char**argv) {

	Mat src = imread("D:/images/flower.png");
	if (src.empty()) {
		printf("Could not read image");
		return -1;
	}
	vectormv;
	split(src, mv);
	//绘制直方图
	int histSize = 256;
	Mat b_hist, g_hist, r_hist;
	float range[] = { 0,255 };
	const float*histRanges = {range};
	calcHist(&mv[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRanges, true, false);
	calcHist(&mv[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRanges, true, false);
	calcHist(&mv[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRanges, true, false);

	Mat result = Mat::zeros(Size(600, 400), CV_8UC3);
	int margin = 50;
	int nm = result.rows-2*margin;
	normalize(b_hist, b_hist, 0, nm, NORM_MINMAX, -1, Mat());
	normalize(g_hist, g_hist, 0, nm, NORM_MINMAX, -1, Mat());
	normalize(r_hist, r_hist, 0, nm, NORM_MINMAX, -1, Mat());
	float step = 500.0 / 256.0;

	for (int i = 0; i < 255; i++) {
		line(result, Point(step*i+50, 50 + (nm - b_hist.at(i, 0))), Point(step*(i + 1)+50, 50 + ( nm - b_hist.at(i + 1, 0))), Scalar(255, 0, 0), 2, 8, 0);
		line(result, Point(step*i+50, 50 + (nm - g_hist.at(i, 0))), Point(step*(i + 1)+50, 50 + (nm - g_hist.at(i + 1, 0))), Scalar(0, 255, 0), 2, 8, 0);
		line(result, Point(step*i+50, 50 + (nm - r_hist.at(i, 0))), Point(step*(i + 1)+50, 50 + (nm - r_hist.at(i + 1, 0))), Scalar(0, 0, 255), 2, 8, 0);

	}
	imshow("result", result);
	imshow("input", src);

	waitKey(0);
	destroyAllWindows;
	return 0;
}

 运行结果：

二、直方图均衡化

代码如下：

equalizeHist(gray, dst);

void  eh_demo() {
	Mat src = imread("D:/images/flower.png");
	Mat gray, dst;
	cvtColor(src, gray, COLOR_BGR2GRAY);
	imshow("gray", gray);
	equalizeHist(gray, dst);
	imshow("eh_demo", dst);
	//绘制直方图
	int histSize = 256;
	Mat b_hist, g_hist, r_hist;
	float range[] = { 0,255 };
	const float*histRanges = { range };
	calcHist(&gray, 1, 0, Mat(), b_hist, 1, &histSize, &histRanges, true, false);
	calcHist(&dst, 1, 0, Mat(), g_hist, 1, &histSize, &histRanges, true, false);

	Mat result = Mat::zeros(Size(600, 400), CV_8UC3);
	int margin = 50;
	int nm = result.rows - 2 * margin;
	normalize(b_hist, b_hist, 0, nm, NORM_MINMAX, -1, Mat());
	normalize(g_hist, g_hist, 0, nm, NORM_MINMAX, -1, Mat());
	normalize(r_hist, r_hist, 0, nm, NORM_MINMAX, -1, Mat());
	float step = 500.0 / 256.0;

	for (int i = 0; i < 255; i++) {
		line(result, Point(step*i + 50, 50 + (nm - b_hist.at(i, 0))), Point(step*(i + 1) + 50, 50 + (nm - b_hist.at(i + 1, 0))), Scalar(255, 0, 0), 2, 8, 0);
		line(result, Point(step*i + 50, 50 + (nm - g_hist.at(i, 0))), Point(step*(i + 1) + 50, 50 + (nm - g_hist.at(i + 1, 0))), Scalar(0, 255, 0), 2, 8, 0);

	}
	imshow("result", result);

演示结果：

 三、直方图比较：

double  h12 = compareHist(hist1, hist2, HISTCMP_BHATTACHARYYA);

四种比较方法

 代码演示巴氏距离与相关性直方图比较：

void hist_compare() {
	Mat src1 = imread("D:/images/flower.png");
	Mat src2 = imread("D:/images/objects.jpg");
	int histSize[] = { 256,256,256 };
	int channels[] = { 0,1,2 };
	Mat hist1, hist2;
	float  c1[] = { 0,255 };
	float  c2[] = { 0,255 };
	float  c3[] = { 0,255 };
	const float*histRanges[] = { c1,c2,c3 };
	calcHist(&src1, 1, channels, Mat(), hist1, 3, histSize, histRanges, true, false);
	calcHist(&src2, 1, channels, Mat(), hist2, 3, histSize, histRanges, true, false);
	//归一化
	normalize(hist1, hist1, 0, 1, NORM_MINMAX, -1, Mat());
	normalize(hist2, hist2, 0, 1, NORM_MINMAX, -1, Mat());
	//比较巴氏距离
	double  h12 = compareHist(hist1, hist2, HISTCMP_BHATTACHARYYA);
	double h11 = compareHist(hist1, hist2, HISTCMP_BHATTACHARYYA);
	//相关性比较
	double c12 = compareHist(hist1, hist2, HISTCMP_CORREL);
	double c11 = compareHist(hist1, hist2, HISTCMP_CORREL);
	std::cout << "h12"  <<"  " << fixed << setprecision(2) << h12 <<"  " << "h11" << "   " << fixed << setprecision(2) <