我正在尝试匹配此图片
在此图片中
但是,我找不到一个以上的老板敌人。找到其他人需要做些什么?图片加载
struct XYposition{
float X;
float Y;
};
std::vector<cv::Mat> bossList;
std::string bossStrings[1] = { "sprites\\boss\\bossUp.png" };
for (int i = 0; i < 1; i++){
cv::Mat pic = cv::imread(bossStrings[i], CV_LOAD_IMAGE_GRAYSCALE);
bossList.push_back(pic);
}
multipleTemplateMatch(screenImage, bossList);
模板匹配
std::vector<XYposition> multipleTemplateMatch(cv::Mat &img, std::vector<cv::Mat> tplList){
std::vector<XYposition> matches;
cv::Mat convertImg(img.rows, img.cols, CV_8UC3);
cv::cvtColor(img, convertImg, CV_BGRA2GRAY);
double threshold = 0.8;
int imgint = convertImg.type();
for(cv::Mat tpl : tplList){
int tplint = tpl.type();
cv::Mat result(convertImg.rows - tpl.rows + 1, convertImg.cols - tpl.cols + 1,
CV_32FC1); //must be this result type
cv::matchTemplate(convertImg, tpl, result, CV_TM_CCOEFF_NORMED);
cv::threshold(result, result, threshold, 1., CV_THRESH_TOZERO);
while (true)
{
double minval, maxval;
cv::Point minloc, maxloc;
cv::minMaxLoc(result, &minval, &maxval, &minloc, &maxloc);
if (maxval >= threshold)
{
rectangle(result, maxloc, cv::Point(maxloc.x - tpl.cols, maxloc.y - tpl.rows),
cv::Scalar(0, 0, 255), 4, 8, 0);
cv::floodFill(result, maxloc, cv::Scalar(0), 0, cv::Scalar(.1), cv::Scalar(1.));
XYposition info = {
maxloc.x - ceil(tpl.cols / 2), maxloc.y - ceil(tpl.rows / 2)
};
matches.push_back(info);
}
else
break;
}
}
return matches;
}
答案 0 :(得分:3)
我没有调试你的代码,但由于它不起作用(可能floodfill
搞乱你的结果矩阵),这是一个简单的工作样本。
迭代结果矩阵中的最大点,查找值超过阈值的blob,并找到每个blob中的最高值(用作掩码以检索结果矩阵中的实际值)。
#include <opencv2\opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
Mat3b img = imread("path_to_image");
Mat3b templ = imread("path_to_template");
Mat1b img_gray;
Mat1b templ_gray;
cvtColor(img, img_gray, COLOR_BGR2GRAY);
cvtColor(templ, templ_gray, COLOR_BGR2GRAY);
Mat1f result;
matchTemplate(img, templ, result, TM_CCOEFF_NORMED);
double thresh = 0.7;
threshold(result, result, thresh, 1., THRESH_BINARY);
Mat1b resb;
result.convertTo(resb, CV_8U, 255);
vector<vector<Point>> contours;
findContours(resb, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);
for (int i=0; i<contours.size(); ++i)
{
Mat1b mask(result.rows, result.cols, uchar(0));
drawContours(mask, contours, i, Scalar(255), CV_FILLED);
Point max_point;
double max_val;
minMaxLoc(result, NULL, &max_val, NULL, &max_point, mask);
rectangle(img, Rect(max_point.x, max_point.y, templ.cols, templ.rows), Scalar(0,255,0), 2);
}
return 0;
}
结果: