1.上一篇博客講了直方圖的概念

• 直方圖不只是對圖像灰度值的統計，也可以對圖像的梯度，像素角度等屬性進行統計。
• API
• 首先要拆分通道，將BGR圖像拆分，每個通道范圍{0-255}

split(
//不多通道的圖像拆分為多個單通道
Mat 輸入圖像，
Mat 輸出多通道數組
)

• 訪問不同通道輸入數組指針 bgr_split[int]

vector<Mat> bgr_split;
	split(src, bgr_split);

計算直方圖

calcHist(
Mat*輸入圖像指針，
int 圖像數目,
int 通道數,  （img.channels-1）
Mat（）,
輸出直方圖Mat,
int 維數，//灰度圖維度為1
binsize ,//直方圖級數[0-255]
const *range//直方圖值域范圍
)

	int histSize = 256;
	float range[]= { 0,256 };
	const float *histRange = { range };
	Mat b_hist, g_hist, r_hist;
	calcHist(&bgr_split[0], 1, 0,Mat(), b_hist, 1, &histSize, &histRange, true, false);
	calcHist(&bgr_split[1], 1, 0, Mat(),g_hist, 1, &histSize, &histRange, true, false);
	calcHist(&bgr_split[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, true, false);

• 歸一化

1.在通一個灰度值可能出現多個像素點，超過直方圖的高

normalize(
輸入直方圖，
輸出直方圖Mat,
最小值，
最大值，
 NORM_MINMAX，
 -1,
 Mat()
)

normalize(b_hist, b_hist, 0, hist_h, NORM_MINMAX, -1,Mat());//將圖像的高歸一化到0-400之間
	normalize(g_hist, g_hist, 0, hist_h, NORM_MINMAX, -1, Mat());//將圖像的高歸一化到0-400之間
	normalize(r_hist, r_hist, 0, hist_h, NORM_MINMAX, -1, Mat());//將圖像的高歸一化到0-400之間

• 繪制直方圖

line(
待繪制的圖像矩陣，
線段起點，
線段終點，
顏色，
線寬，
線型
)

• 連接直方圖每個bins的左角點

for (int i = 1; i < histSize; i++)
	{
		line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(b_hist.at<float>(i - 1))),
			Point((i)*bin_w, hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0), 2, LINE_AA);
		line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(g_hist.at<float>(i - 1))),
			Point((i)*bin_w, hist_h - cvRound(g_hist.at<float>(i))), Scalar(0, 255, 0), 2, LINE_AA);
		line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(r_hist.at<float>(i - 1))),
			Point((i)*bin_w, hist_h - cvRound(r_hist.at<float>(i))), Scalar(0, 0, 255), 2, LINE_AA);
	}

#include<opencv2/opencv.hpp>
#include<iostream>
#include<math.h>

using namespace cv;
using namespace std;
int main()
{
	Mat src = imread("C:\\Users\\Administrator\\Pictures\\Saved Pictures\\timg6ZAGKYHR.jpg");
	if (src.empty())
	{
		printf("ould not find image \n");
		return -1;
	}
	namedWindow("opencv set up demo", WINDOW_AUTOSIZE);
	imshow("opencv set up demo", src);
	vector<Mat> bgr_split;
	split(src, bgr_split);
	//計算直方圖
	int histSize = 256;
	float range[]= { 0,256 };
	const float *histRange = { range };
	Mat b_hist, g_hist, r_hist;
	calcHist(&bgr_split[0], 1, 0,Mat(), b_hist, 1, &histSize, &histRange, true, false);
	calcHist(&bgr_split[1], 1, 0, Mat(),g_hist, 1, &histSize, &histRange, true, false);
	calcHist(&bgr_split[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, true, false);
	int hist_h = 400;
	int hist_w = 512;
	int bin_w = hist_w / histSize;
	//構建直方圖圖像
	Mat histImage(hist_w, hist_h, CV_8UC3, Scalar(0, 0, 0));
	//歸一化
	normalize(b_hist, b_hist, 0, hist_h, NORM_MINMAX, -1,Mat());//將圖像的高歸一化到0-400之間
	normalize(g_hist, g_hist, 0, hist_h, NORM_MINMAX, -1, Mat());//將圖像的高歸一化到0-400之間
	normalize(r_hist, r_hist, 0, hist_h, NORM_MINMAX, -1, Mat());//將圖像的高歸一化到0-400之間

	//繪制直方圖
	for (int i = 1; i < histSize; i++)
	{
		line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(b_hist.at<float>(i - 1))),
			Point((i)*bin_w, hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0), 2, LINE_AA);
		line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(g_hist.at<float>(i - 1))),
			Point((i)*bin_w, hist_h - cvRound(g_hist.at<float>(i))), Scalar(0, 255, 0), 2, LINE_AA);
		line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(r_hist.at<float>(i - 1))),
			Point((i)*bin_w, hist_h - cvRound(r_hist.at<float>(i))), Scalar(0, 0, 255), 2, LINE_AA);
	}
	imshow("split", histImage);
	waitKey(0);
	return 1;
}