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下面的例子是简单的使用opencv 实现的模板匹配流程,其中时间性能和精确度还需要调整,如果直接使用会出问题,所以这个只是例子,根据代码原理可以实现尺度变化的模板匹配和旋转尺度变化同时,具体根据实现的旋转代码进一步实现,但是就结果而言和halcon的模板匹配差距较大,性能更不行,因此仅供参考 ,同时本人整理了两个使用各种加速的高性能版本,有兴趣的可以下载看看
- struct MatchResult
- {
- std::vector<cv::Point> points;
- double angle;
- double score;
- MatchResult(std::vector<cv::Point> points, double angle, double score) :points(points), angle(angle), score(score) {};
- };
-
- //旋转图像
- cv::Mat ImageRotate(cv::Mat image, double angle)
- {
- cv::Mat newImg;
- cv::Point2f pt = cv::Point2f((float)image.cols / 2, (float)image.rows / 2);
- cv::Mat M = cv::getRotationMatrix2D(pt, angle, 1.0);
- cv::warpAffine(image, newImg, M, image.size());
- return newImg;
- }
- std::vector<cv::Point> GetRotatePoints(cv::Size size, double angle) {
- // 定义模板图像的中心点
- cv::Point2f center(size.width / 2.0, size.height / 2.0);
-
- // 计算旋转矩阵
- cv::Mat rotationMatrix = cv::getRotationMatrix2D(center, angle, 1.0);
-
- // 定义模板图像的四个顶点
- std::vector<cv::Point2f> srcPoints = {
- cv::Point2f(0, 0),
- cv::Point2f(size.width, 0),
- cv::Point2f(size.width, size.height),
- cv::Point2f(0, size.height)
- };
-
- // 存储旋转后的四个顶点
- std::vector<cv::Point2f> dstPoints(4);
-
- // 进行仿射变换
- cv::transform(srcPoints, dstPoints, rotationMatrix);
-
- // 将结果转换为cv::Point类型并返回
- std::vector<cv::Point> resultPoints(4);
- for (int i = 0; i < 4; ++i) {
- resultPoints[i] = cv::Point(cvRound(dstPoints[i].x), cvRound(dstPoints[i].y));
- }
-
- return resultPoints;
- }
-
-
- /*
- 旋转模板匹配函数(通过图像金字塔、增大旋转步长来提升匹配速度)
- Mat src:原图像
- Mat model:模板图
- double startAngle:旋转的最小角
- double endAngle:旋转的最大角
- double firstStep:角度旋转时的最大步长
- double secondStep:角度旋转时的最小步长
- int numLevels = 0:图像金字塔缩放次数
- */
- MatchResult rotateMatch(cv::Mat src, cv::Mat model, double startAngle, double endAngle, double firstStep, double secondStep, int numLevels = 0) {
- //对模板图像和待检测图像分别进行图像金字塔下采样
- for (int i = 0; i < numLevels; i++) {
- cv::pyrDown(src, src, cv::Size(src.cols / 2, src.rows / 2));
- cv::pyrDown(model, model, cv::Size(model.cols / 2, model.rows / 2));
- }
-
- cv::Mat rotatedImg, result;
- double score = -1;
- cv::Point location;
- double angle;
-
- bool isSecond = false;
- while (true) {
- for (double curAngle = startAngle; curAngle <= endAngle; curAngle += firstStep) {
- rotatedImg = ImageRotate(model, curAngle);
- //imshow("rotated", rotatedImg);
- //imshow("src-pyrDown", src);
- //waitKey();
-
- matchTemplate(src, rotatedImg, result, cv::TM_CCOEFF_NORMED);
- double minval, maxval;
- cv::Point minloc, maxloc;
- cv::minMaxLoc(result, &minval, &maxval, &minloc, &maxloc);
- if (maxval > score)
- {
- location = maxloc;
- score = maxval;
- angle = curAngle;
- }
- }
-
- if (isSecond && firstStep<= secondStep) break;
-
- startAngle = angle - firstStep;
- endAngle = angle + firstStep;
-
- if ((endAngle - startAngle) / 5 > secondStep) {
- firstStep = (endAngle - startAngle) / 5;
- }
- else {
- firstStep = secondStep;
- isSecond = true;
- }
- }
-
- cv::Point finalPoint = cv::Point(location.x * pow(2, numLevels), location.y * pow(2, numLevels));
- std::vector<cv::Point> points = GetRotatePoints(cv::Size(model.cols * pow(2, numLevels), model.rows * pow(2, numLevels)), angle);
-
- for (int j = 0; j < points.size(); j++)
- {
- points[j].x += finalPoint.x;
- points[j].y += finalPoint.y;
- }
-
- return MatchResult(points, angle, score);
- }
-
- int main() {
- //读取所有图像
- std::vector<cv::Mat> imgs;
- std::string imageName;
- std::string path = "E:\\prj\\shape_based_matching-master\\test\\board\\test";
- std::vector<std::string> img_paths;
- cv::glob(path, img_paths);
- for (auto& p : img_paths)
- {
- cv::Mat img = cv::imread(p);
- imgs.push_back(img);
- }
- cv::Mat templateImg = cv::imread("E:\\prj\\shape_based_matching-master\\test\\board\\train.png");
- cv::Rect box(cv::Point(135, 120), cv::Point(470, 365));
- //cv::rectangle(drawFrame, box, cv::Scalar(0, 255, 0), 2);
- templateImg = templateImg(box).clone();
- int i = 0;
- for (cv::Mat img : imgs)
- {
- i += 1;
- MatchResult matchResult = rotateMatch(img, templateImg, 0, 360, 30, 0.1, 0);
- std::vector<cv::Point> points = matchResult.points;
- std::cout << i << "- 角度:" << matchResult.angle << std::endl;
- std::cout << i << "- 得分:" << matchResult.score << std::endl;
-
- cv::line(img, points[0], points[1], cv::Scalar(255, 0, 0), 2);
- cv::line(img, points[1], points[2], cv::Scalar(255, 0, 0), 2);
- cv::line(img, points[2], points[3], cv::Scalar(255, 0, 0), 2);
- cv::line(img, points[3], points[0], cv::Scalar(255, 0, 0), 2);
-
- cv::Point pt1 = cv::Point((points[0].x + points[3].x) / 2, (points[0].y + points[3].y) / 2);
- cv::Point pt2 = cv::Point((points[1].x + points[2].x) / 2, (points[1].y + points[2].y) / 2);
- cv::arrowedLine(img, pt2, pt1, cv::Scalar(0, 0, 255), 2);
-
- cv::imshow("img_" + std::to_string(i), img);
- cv::waitKey(0);
- }
-
- return 0;
- }
-
结果如下:
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