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);
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) <