【OpenCV學習筆記】二十三、模板匹配及應用

模板匹配及應用

1.模版匹配——matchTemplate()

2.實現了幾個小應用：圖像單目標模板匹配、視頻單目標模板匹配、多目標模板匹配

先上ppt：

代碼：

1.圖像單目標模板匹配

///圖像單目標模板匹配
#include "opencv2/opencv.hpp"
using namespace cv;
#include <iostream>
using namespace std;
int main()
{
	Mat srcImg = imread("j.jpg",CV_LOAD_IMAGE_COLOR);
	Mat tempImg = imread("template.jpg",CV_LOAD_IMAGE_COLOR);
	cout << "Size of template: "<<tempImg.size() << endl;
	//1.構建結果圖像resultImg(注意大小和類型)
	//如果原圖(待搜索圖像)尺寸爲W x H, 而模版尺寸爲 w x h, 則結果圖像尺寸一定是(W-w+1)x(H-h+1)
	//結果圖像必須爲單通道32位浮點型圖像
	int width = srcImg.cols - tempImg.cols + 1;
	int height = srcImg.rows - tempImg.rows + 1;
	Mat resultImg(Size(width,height),CV_32FC1);
	//2.模版匹配
	matchTemplate(srcImg, tempImg, resultImg, CV_TM_CCOEFF_NORMED);
	imshow("result",resultImg);
	//3.正則化(歸一化到0-1)
	normalize(resultImg,resultImg,0,1,NORM_MINMAX,-1);
	//4.找出resultImg中的最大值及其位置
	double minValue = 0;
	double maxValue = 0;
	Point minPosition;
	Point maxPosition;
	minMaxLoc(resultImg,&minValue,&maxValue,&minPosition,&maxPosition);
	cout << "minValue: " << minValue << endl;
	cout << "maxValue: " << maxValue << endl;
	cout << "minPosition: " << minPosition << endl;
	cout << "maxPosition: " << maxPosition << endl;
	//5.根據resultImg中的最大值位置在源圖上畫出矩形
	rectangle(srcImg,maxPosition,Point(maxPosition.x+tempImg.cols,maxPosition.y+tempImg.rows),Scalar(0,255,0),1,8);
	imshow("srcImg", srcImg);
	imshow("template", tempImg);
	waitKey(0);
	return 0;
}

運行結果：

2.視頻單目標模板匹配

///視頻單目標模板匹配
#include "opencv2/opencv.hpp"
using namespace cv;
#include <iostream>
using namespace std;
int main()
{
	//1.定義VideoCapture類對象video，讀取視頻
	VideoCapture video("1.mp4");
	//1.1.判斷視頻是否打開
	if (!video.isOpened())
	{
		cout << "video open error!" << endl;
		return 0;
	}
	//2.循環讀取視頻的每一幀，對每一幀進行模版匹配
	while (1)
	{
		//2.1.讀取幀
		Mat frame;
		video >> frame;
		//2.2.對幀進行異常檢測
		if (frame.empty())
		{
			cout << "frame empty" << endl;
			break;
		}
		//2.3.對幀進行模版匹配
		Mat tempImg = imread("green.JPG", CV_LOAD_IMAGE_COLOR);
		cout << "Size of template: " << tempImg.size() << endl;
		//2.3.1.構建結果圖像resultImg(注意大小和類型)
		//如果原圖(待搜索圖像)尺寸爲W x H, 而模版尺寸爲 w x h, 則結果圖像尺寸一定是(W-w+1)x(H-h+1)
		//結果圖像必須爲單通道32位浮點型圖像
		int width = frame.cols - tempImg.cols + 1;
		int height = frame.rows - tempImg.rows + 1;
		Mat resultImg(Size(width, height), CV_32FC1);
		//2.3.2.模版匹配
		matchTemplate(frame, tempImg, resultImg, CV_TM_CCOEFF_NORMED);
		imshow("result", resultImg);
		//2.3.3.正則化(歸一化到0-1)
		normalize(resultImg, resultImg, 0, 1, NORM_MINMAX, -1);
		//2.3.4.找出resultImg中的最大值及其位置
		double minValue = 0;
		double maxValue = 0;
		Point minPosition;
		Point maxPosition;
		minMaxLoc(resultImg, &minValue, &maxValue, &minPosition, &maxPosition);
		cout << "minValue: " << minValue << endl;
		cout << "maxValue: " << maxValue << endl;
		cout << "minPosition: " << minPosition << endl;
		cout << "maxPosition: " << maxPosition << endl;
		//2.3.5.根據resultImg中的最大值位置在源圖上畫出矩形
		rectangle(frame, maxPosition, Point(maxPosition.x + tempImg.cols, maxPosition.y + tempImg.rows), Scalar(0, 255, 0), 1, 8);
		imshow("srcImg", frame);
		imshow("template", tempImg);
		if (waitKey(10) == 27)
		{
			cout << "ESC退出" << endl;
			break;
		};
	}
	return 0;
}

運行結果：

3.多目標模板匹配

///多目標模板匹配
#include "opencv2/opencv.hpp"
using namespace cv;
#include <iostream>
using namespace std;
int main()
{
	Mat srcImg = imread("k.jpg", CV_LOAD_IMAGE_COLOR);
	Mat tempImg = imread("template.jpg", CV_LOAD_IMAGE_COLOR);
	//1.構建結果圖像resultImg(注意大小和類型)
	//如果原圖(待搜索圖像)尺寸爲W x H, 而模版尺寸爲 w x h, 則結果圖像尺寸一定是(W-w+1)x(H-h+1)
	//結果圖像必須爲單通道32位浮點型圖像
	int width = srcImg.cols - tempImg.cols + 1;
	int height = srcImg.rows - tempImg.rows + 1;
	Mat resultImg(Size(width, height), CV_32FC1);
	//2.模版匹配
	matchTemplate(srcImg, tempImg, resultImg, CV_TM_CCOEFF_NORMED);
	imshow("result", resultImg);
	//3.正則化(歸一化到0-1)
	normalize(resultImg, resultImg, 0, 1, NORM_MINMAX, -1);
	//4.遍歷resultImg，給定篩選條件，篩選出前幾個匹配位置
	int tempX = 0;
	int tempY = 0;
	char prob[10] = { 0 };
	//4.1遍歷resultImg
	for (int i = 0 ; i<resultImg.rows;i++)
	{
		for (int j = 0; j<resultImg.cols; j++)
		{
			//4.2獲得resultImg中(j,x)位置的匹配值matchValue
			double matchValue = resultImg.at<float>(i, j);
			sprintf(prob, "%.2f", matchValue);
			//4.3給定篩選條件
			//條件1:概率值大於0.9
			//條件2:任何選中的點在x方向和y方向上都要比上一個點大5(避免畫邊框重影的情況)
			if (matchValue > 0.9&& abs(i-tempY)>5&&abs(j-tempX)>5)
			{
				//5.給篩選出的點畫出邊框和文字
				rectangle(srcImg, Point(j,i), Point(j + tempImg.cols, i + tempImg.rows), Scalar(0, 255, 0), 1, 8);
				putText(srcImg, prob, Point(j, i+100),CV_FONT_BLACK,1,Scalar(0,0,255),1);
				tempX = j;
				tempY = i;
			}
		}
	}
	imshow("srcImg", srcImg);
	imshow("template", tempImg);
	waitKey(0);
	return 0;
}

運行結果：