基于opencv的多目标模板匹配

2019独角兽企业重金招聘Python工程师标准>>>

利用opencv进行多目标模板匹配，只要是利用其matchTemplate函数，但在多目标（这里是讨论目标图片中不同大小模板的匹配），以下贴出代码和图片，供大家参考：

#include 
#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;

Point getNextMinLoc(Mat &result, Point minLoc, int maxValue, int templatW, int templatH);

int main(void)
{
	Mat src = imread("1_2.png");
	Mat srcCopy = src.clone();
	
	Mat temp = imread("1_4.png");
	Mat result;

	if (src.empty() || temp.empty())
	{
		cout << "打开图片失败" << endl;
		return 0;
	}

	vector templat;
	vector minV;
	vector minL;

	int srcW, srcH, templatW, templatH, resultH, resultW;
	srcW = src.cols;
	srcH = src.rows;
	templat.push_back(temp);
	double minValue, maxValue;
	Point minLoc, maxLoc;

	for (int i=0;i<10;i++)
	{
		cout << i << ": ";
		templatW = templat[i].cols;
		templatH = templat[i].rows;

		if (srcW < templatW || srcH < templatH)
		{
			cout << "模板不能比原图大" << endl;
			return 0;
		}

		resultW = srcW - templatW + 1;
		resultH = srcH - templatH + 1;

		result.create(Size(resultW, resultH), CV_32FC1);
		matchTemplate(src, templat[i], result, CV_TM_SQDIFF_NORMED);

		minMaxLoc(result, &minValue, &maxValue, &minLoc, &maxLoc);

		cout << "min1: " << minValue << endl;
		if (minValue<=0.070055)
		{
			rectangle(srcCopy, minLoc, Point(minLoc.x + templatW, minLoc.y + templatH), Scalar(0, 0, 255), 2, 8, 0);

			Point new_minLoc;
			new_minLoc = getNextMinLoc(result, minLoc, maxValue, templatW, templatH);
			
			float *data = result.ptr(new_minLoc.y);

			cout << "min2: " << data[new_minLoc.x] << " ";
			if (data[new_minLoc.x]<=0.5)
			{
				cout << "进这个函数了:" << i << "：" << new_minLoc.x;
				cout << " " << new_minLoc.y;
				rectangle(srcCopy, new_minLoc, Point(new_minLoc.x + templatW, new_minLoc.y + templatH),
					Scalar(0, 255, 0), 2, 8, 0);
				new_minLoc = getNextMinLoc(result, new_minLoc, maxValue, templatW, templatH);
			}

			float *data1 = result.ptr(new_minLoc.y);
			cout << "min3: " << data1[new_minLoc.x] << " " << endl;
			if (data1[new_minLoc.x] <= 0.4)
			{
				
				rectangle(srcCopy, new_minLoc, Point(new_minLoc.x + templatW, new_minLoc.y + templatH),
					Scalar(255, 0, 0), 2, 8, 0);
			}
		}
		cout << "#" << endl;
		Mat temp_templat;
		resize(templat[i], temp_templat, Size(templat[i].cols / 1.1, templat[i].rows / 1.1));
		templat.push_back(temp_templat);
	}

	imshow("结果", srcCopy);
	waitKey(0);
	return 0;
}

Point getNextMinLoc(Mat &result, Point minLoc, int maxValue, int templatW, int templatH)
{
	//imshow("result", result);
	//cout << "maxvalue: " << maxValue << endl;
	int startX = minLoc.x - templatW / 3;
	int startY = minLoc.y - templatH / 3;
	int endX = minLoc.x + templatW / 3;
	int endY = minLoc.y + templatH / 3;
	if (startX < 0 || startY < 0)
	{
		startX = 0;
		startY = 0;
	}
	if (endX > result.cols - 1 || endY > result.rows - 1)
	{
		endX = result.cols - 1;
		endY = result.rows - 1;
	}
	int y, x;
	for (y = startY; y < endY; y++)
	{
		for (x = startX; x < endX; x++)
		{
			float *data = result.ptr(y);
			
			data[x] = maxValue;
		}
	}
	double new_minValue, new_maxValue;
	Point new_minLoc, new_maxLoc;
	minMaxLoc(result, &new_minValue, &new_maxValue, &new_minLoc, &new_maxLoc);
	//imshow("result_end", result);
	return new_minLoc;
}

以下是结果图：

 

 

转载于:https://my.oschina.net/u/3397950/blog/1573076