圖像處理之直方圖計算

直方圖就是用來統計圖像的像素信息的，每個顏色級像素的數量是多少，佔整幅圖像的比率。

所以就首先有了直方圖計算；

opencv2有現成的函數calchist，其實原理是一樣的，就是統計每個顏色值級別的像素一共有多少

1，灰度直方圖計算

首先來看下灰度圖的直方圖計算

hist1d.h

#ifndef HIST1D_H
#define HIST1D_H

#include <opencv2/opencv.hpp>

class Hist1D{
private:
	int histsize[1];
	float histrange[2];
	const float* ranges[1];
	int chanels[1];

public:
	Hist1D(){
		histsize[0] = 256;
		histrange[0] = 0.0;
		histrange[1] = 255.0;
		ranges[0] = histrange;
		chanels[0] = 0;
	}

	cv::MatND calcHist1D(const cv::Mat& img){
		cv::MatND hist;

		cv::calcHist(&img,1,chanels,cv::Mat(),hist,1,histsize,ranges);

		return hist;
	}

	cv::Mat getHist1DImg(const cv::Mat& img){
		cv::MatND hist;
		hist = calcHist1D(img);

		double max_hist,min_hist;
		cv::minMaxLoc(hist,&min_hist,&max_hist,0,0);

		cv::Mat histimg(histsize[0],histsize[0],CV_8U,cv::Scalar(255));

		int hpt = static_cast<int>(0.9*histsize[0]);

		for (int k=0;k<histsize[0];k++){
			float binval = hist.at<float>(k);
			int intensity = static_cast<int>(binval * hpt /max_hist);
			cv::line(histimg,cv::Point(k,histsize[0]),cv::Point(k,histsize[0]-intensity),cv::Scalar(0));
		}

		return histimg;
	}
};


#endif

hist1d.cpp

#include "hist1d.h"
#include <iostream>
using namespace cv;
using namespace std;

int main(){

	Mat img = imread("group.jpg",0);
	imshow("src",img);
	Hist1D hist1d;
	Mat histimg = hist1d.getHist1DImg(img);
	imshow("histimg",histimg);
	waitKey(0);
}