OpenCV學習筆記（七）

一、直方圖的計算與繪制

1.計算直方圖：calcHist()函數

calcHist()函數用于計算一個或多個陣列的直方圖

void calcHist(const Mat* images, //輸入的數組需為相同的深度（CV_8U或CV_32F）和相同的尺寸
int nimages,//輸入數組的個數，也就是第一個參數中存放了多少張“圖像”，有幾個原數組
const int* channels,//需要統計的通道(dim)索引
InputArray mask,//可選的操作掩碼，若不為空需為8位，且與images[i]尺寸相同
OutputArray hist,//輸出的模闆直方圖，一個二維數組
 int dims, //需要計算的直方圖的次元，一必須為正數，且不大于CV_MAX_DIMS
 const int* histSize,//存放每個次元的直方圖的取值範圍
 const float**ranges,//表示每一個數組的每一維的邊界陣列，可以了解為每一維數值的取值範圍
 bool uniform = true,//訓示直方圖是否均勻的辨別符，預設true
 bool accumulate = false)//累計辨別符，預設false
           

2.尋找最值：minMaxLoc()函數

void minMaxLoc(InputArray src,//輸入的單通道陣列
double* minVal,//傳回最小值的指針，若無傳回，此值為NULL
double*maxVal = 0,//傳回的最大值的指針，若無傳回，為NULL
Point* minLoc = 0,//傳回最小值的指針（二維情況），若無傳回，此值為NULL
Point*maxLoc = 0,//傳回的最大值的指針（二維情況），若無傳回，為NULL
InputArray mask = noArray())//用于選擇子陣列的可選掩膜
           

繪制H-S直方圖

#include<opencv2/opencv.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;

 
int main()
{
	Mat srcImage, hasvImage;
	srcImage = imread("1.jpg");
	if (!srcImage.data) {
		printf("讀取圖檔失敗");
		return -1;
	}
	cvtColor(srcImage, hasvImage, COLOR_BGR2HSV);
	//将色調量化為30個等級，将飽和度量化為32個等級
	int hueBinNum = 30;//色調的直方圖條數量
	int saturationBinNum = 32;//飽和度的直方圖直條數量
	int histSize[] = { hueBinNum, saturationBinNum };
	//定義色調的變化範圍為0到179
	float hueRanges[] = { 0,180 };
	//定義飽和度的變化範圍0（黑白灰）到255（純光譜顔色）
	float saturationRanges[] = { 0,256 };
	const float* ranges[] = { hueRanges,saturationRanges };
	MatND dstHist;
	//參數準備，calcHist函數中将計算第0通道和第1通道的直方圖
	int channels[] = { 0,1 };
	//正式調用calcHist函數，進行直方圖計算
	calcHist(&hasvImage,//輸入的數組
		1,//數組個數為1
		channels,//通道索引
		Mat(),//不使用掩膜
		dstHist,//輸出目标直方圖
		2,//需要計算的直方圖次元為2
		histSize,//存放每個次元的直方圖尺寸的數組
		ranges,//每一維數值的取值範圍數組
		true,//訓示直方圖是否均勻的辨別符，true表示均勻的直方圖
		false);//累計辨別符，false表示直方圖在配置階段會被置零
	//繪制直方圖準備參數
	double maxValue = 0;//最大值
	minMaxLoc(dstHist, 0, &maxValue, 0, 0);//查找數組和子數組的全局最小值
	int scale = 10;
	Mat histImg = Mat::zeros(saturationBinNum*scale, hueBinNum * 10, CV_8UC3);
	//雙層循環進行直方圖繪制
	for(int hue = 0; hue < hueBinNum;hue++)
		for (int saturation = 0; saturation < saturationBinNum; saturation++) {
			//直方圖直條的值
			float binValue = dstHist.at<float>(hue, saturation);
			int intensity = cvRound(binValue * 255 / maxValue);//強度
			rectangle(histImg, Point(hue*scale, saturation*scale), 
				Point((hue + 1)*scale - 1, (saturation + 1)*scale - 1), Scalar::all(intensity), FILLED);

		}
	imshow("原圖", srcImage);
	imshow("H-S直方圖", histImg);

	waitKey(0);
	return 0;
}
 

           

計算并繪制圖像一維直方圖

#include <opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;

int main()
{
	Mat srcImage = imread("1.jpg", 0);
	imshow("原圖", srcImage);
	if (!srcImage.data) {
		printf( "fail to load image" );
		return -1;
	}
	//定義變量
	MatND dstHist;//在cv中用cvHistogram * hist = cvCreateHist
	int dims = 1;
	float hranges[] = { 0,255 };
	const float* ranges[] = { hranges };
	int size = 256;
	int channels = 0;
	//計算圖像的直方圖
	calcHist(&srcImage, 1, &channels, Mat(), dstHist, dims, &size, ranges);
	//cv中是cvCalcHist
	int scale = 1;
	Mat dstImage(size * scale, size, CV_8U, Scalar(0));
	//擷取最大值和最小值
	double minValue = 0;
	double maxValue = 0;
	minMaxLoc(dstHist, &minValue, 0, 0);//在cv中用cvGetMinMaxHistValue
	//繪制直方圖
	int hpt = saturate_cast<int>(0.9 * size);
	for (int i = 0; i < 256; i++) {
		float binValue = dstHist.at<float>(i);
		int realValue = saturate_cast<int>(binValue * hpt / maxValue);
		rectangle(dstImage, Point(i * scale, size - 1), 
			Point((i + 1) * scale - 1,size - realValue), Scalar(255));
	}
	imshow("一維直方圖", dstImage);

	waitKey(0);
	return 0;
}
           

繪制RGB三色直方圖

#include <opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;

int main()
{
	Mat srcImage = imread("1.jpg");
	imshow("原圖", srcImage);
	if (!srcImage.data) {
		printf( "fail to load image" );
		return -1;
	}
	//參數準備
	int bins = 256;
	int hist_size[] = { bins };
	float range[] = { 0,256 };
	const float* ranges[] = { range };
	MatND redHist, grayHist, blueHist;
	int channels_r[] = { 0 };
	//計算圖像的直方圖紅色部分
	calcHist(&srcImage, 1, channels_r, Mat(), //不使用掩膜
		redHist, 1, hist_size, ranges,true,false);
	//計算圖像的直方圖綠色部分
	int channels_g[] = { 1 };
	calcHist(&srcImage, 1, channels_g, Mat(), //不使用掩膜
		grayHist, 1, hist_size, ranges, true, false);
	//計算圖像的直方圖藍色部分
	int channels_b[] = { 2 };
	calcHist(&srcImage, 1, channels_b, Mat(), //不使用掩膜
		blueHist, 1, hist_size, ranges, true, false);

	//繪制三色直方圖
	double maxValue_red, maxValue_green, maxValue_blue;
	minMaxLoc(redHist, 0, &maxValue_red, 0, 0);
	minMaxLoc(grayHist, 0, &maxValue_green, 0, 0);
	minMaxLoc(blueHist, 0, &maxValue_blue, 0, 0);
	int scale = 1;
	int histHeight = 256;
	Mat histImage = Mat::zeros(histHeight, bins * 3, CV_8UC3);
	//開始繪制
	for (int i = 0; i < bins; i++) {
		//參數準備
		float binValue_red = redHist.at<float>(i);
		float binValue_green = grayHist.at<float>(i);
		float binValue_blue = blueHist.at<float>(i);
		int intensity_red = cvRound(binValue_red * histHeight / maxValue_red);
		int intensity_green= cvRound(binValue_green * histHeight / maxValue_green);
		int intensity_blue = cvRound(binValue_blue * histHeight / maxValue_blue);
		//繪制紅色分量
		rectangle(histImage,Point(i*scale,histHeight-1),
			Point((i+1)*scale-1,histHeight-intensity_red),Scalar(255,0,0));
		//繪制綠色分量
		rectangle(histImage, Point((i * +bins) * scale, histHeight - 1),
			Point((i + bins + 1) * scale - 1, histHeight - intensity_green), Scalar(0, 255, 0));
		//繪制藍色分量
		rectangle(histImage, Point((i + scale*2)*scale, histHeight - 1),
			Point((i +bins*2+ 1) * scale - 1, histHeight - intensity_blue), Scalar(0, 0, 255));
	}
	imshow("圖像的RGB直方圖", histImage);

	waitKey(0);
	return 0;
}
           

直方圖的對比：compareHist()函數

函數原型

//版本一
double compareHist(InputArray H1,InputArray H2,int method)
//版本二
double compareHist(const SparseMat&H1,const SparseMat&H2,int method)
           

直方圖對比

#include <opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;

int main()
{
	//聲明存儲基準圖像和另外兩張對比圖像的矩陣(RGB,HSV)
	Mat srcImage_base, hsvImage_base;
	Mat srcImage_test1, hsvImage_test1;
	Mat srcImage_test2, hsvImage_test2;
	Mat hsvImage_halfDown;
	//載入基準圖像(srcImage_base)和兩張測試圖像srcImage_test1,srcImage_test2，并顯示
	srcImage_base = imread("1.jpg", 1);
	srcImage_test1 = imread("2.jpg", 1);
	srcImage_test2 = imread("3.jpg", 1);
	//顯示載入的3張圖像
	imshow("基準圖像", srcImage_base);
	imshow("測試圖像1", srcImage_test1);
	imshow("測試圖像2", srcImage_test2);
	//将圖像由BGR色彩空間轉換到HSV色彩空間
	cvtColor(srcImage_base, hsvImage_base, COLOR_BGR2HSV);
	cvtColor(srcImage_test1, hsvImage_test1, COLOR_BGR2HSV);
	cvtColor(srcImage_test2, hsvImage_test2, COLOR_BGR2HSV);
	//建立包含基準圖像下半部分的半身圖像（HSV格式）
	hsvImage_halfDown = hsvImage_base(Range(hsvImage_base.rows / 2, 
		hsvImage_base.rows - 1), Range(0, hsvImage_base.cols - 1));
	//初始化計算直方圖需要的實參
	//對hue通道使用30個bin，對saturation通道使用32個bin
	int h_bins = 50, s_bins = 60;
	int histSize[] = { h_bins,s_bins };
	//hue的取值範圍從0到256，saturation取值範圍0-180
	float h_ranges[] = { 0,256 };
	float s_ranges[] = { 0,180 };
	const float* ranges[] = { h_ranges,s_ranges };
	//使用第0和第1通道
	int channels[] = { 0,1 };
	//建立存儲直方圖的MatND類的執行個體
	MatND baseHist;
	MatND halfDownHist;
	MatND testHist1;
	MatND testHist2;
	//計算基準圖像，兩張測試圖像，半身基準圖像的HSV直方圖
	calcHist(&hsvImage_base, 1, channels, Mat(), baseHist, 2, histSize, ranges, true, false);
	normalize(baseHist, baseHist, 0, 1, NORM_MINMAX, -1, Mat());
	
	calcHist(&hsvImage_halfDown, 1, channels, Mat(), halfDownHist, 2, histSize, ranges, true, false);
	normalize(halfDownHist, halfDownHist, 0, 1, NORM_MINMAX, -1, Mat());

	calcHist(&hsvImage_test1, 1, channels, Mat(), testHist1, 2, histSize, ranges, true, false);
	normalize(testHist1, testHist1, 0, 1, NORM_MINMAX, -1, Mat());

	calcHist(&hsvImage_test2, 1, channels, Mat(), testHist2, 2, histSize, ranges, true, false);
	normalize(testHist2, testHist2, 0, 1, NORM_MINMAX, -1, Mat());
	//按順序使用4種對比标準将基準圖像的直方圖與其餘各直方圖進行對比
	for (int i = 0; i < 4; i++) {
		//進行圖像直方圖的對比
		int compare_method = i;
		double base_base = compareHist(baseHist, baseHist, compare_method);
		double base_half = compareHist(baseHist, halfDownHist, compare_method);
		double base_test1 = compareHist(baseHist, testHist1, compare_method);
		double base_test2 = compareHist(baseHist, testHist2, compare_method);
		//輸出結果
		printf("方法[%d]的比對結果如下：\n\n【基準圖-基準圖】：%f,【基準圖-半身圖】：%f,【基準圖-測試圖1】：%f,【基準圖-測試圖2】：%f\n===========\n",
			i,base_base,base_half,base_test1,base_test2);
	}
	printf("檢測結束");
	waitKey(0);
	return 0;
}