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：