opencv绕图片中任意角度旋转任意角度
最近在做项目需要把把图片绕图片中任意点旋转任意角度，考虑到自己旋转需要编写插值算法，所以想到了用opencv，但是网上都是围绕图片中点旋转任意角度的，都是向下面这样写的：
绕图片中心旋转图片不裁剪
#include"opencv.hpp" #include<iostream> using namespace std; using namespace cv; int main() { Mat src = imread("timg.jpg"); Mat des,m; Point2f center = Point(src.cols / 2, src.rows / 2); double angle = 50,scale=0.5; int w = src.cols, h = src.rows; int bound_w = (h * fabs(sin(angle * CV_PI / 180)) + w * fabs(cos(angle * CV_PI / 180))) * scale; int bound_h = (h * fabs(cos(angle * CV_PI / 180)) + w * fabs(sin(angle * CV_PI / 180))) * scale; m = getRotationMatrix2D(center, angle, scale); m.at<double>(0, 2) += (bound_w - src.cols) / 2; m.at<double>(1, 2) += (bound_h - src.rows) / 2; warpAffine(src,des,m,Size2i(bound_h,bound_w)); imshow("image",des); waitKey(); return 0;旋转之后的效果:
但是遇到绕任意点旋转时，会产生问题，用这种方式还是会存在裁剪，如果要理解绕任意点旋转，需要先理解函数getRotationMatrix2D，这个函数处理过程如下面矩阵表示所示：
具体实现代码如下：
Mat src = imread("/home/sss/1111.jpg", IMREAD_GRAYSCALE); Mat des, m; //旋转的任意角度 double angle = 45; int w = src.cols, h = src.rows; Point2f rorate_center; //旋转的任意中心 rorate_center.x = w; rorate_center.y = h; //重新计算旋转后的宽和高 int bound_w = ceil(h * fabs(sin(angle * CV_PI / 180.0)) + w * fabs(cos(angle * CV_PI / 180.0))); int bound_h = ceil(h * fabs(cos(angle * CV_PI / 180.0)) + w * fabs(sin(angle * CV_PI / 180.0))); m = getRotationMatrix2D(rorate_center, angle, 1.0); //通过eigen计算旋转矩阵 Eigen::Matrix3d T1; T1 << 1, 0, -rorate_center.x, 0, 1, -rorate_center.y, 0, 0, 1; Eigen::Matrix3d T2; T2 << 1, 0, rorate_center.x, 0, 1, rorate_center.y, 0, 0, 1; Eigen::Matrix3d rorate; rorate << cos(angle * CV_PI / 180.0), sin(angle * CV_PI / 180.0), 0, -sin(angle * CV_PI / 180.0), cos(angle * CV_PI / 180.0), 0, 0, 0, 1; Eigen::Matrix3d T = T2 * rorate * T1; //计算原来矩阵的四个顶点经过变换后的顶点 Eigen::Matrix<double,3, 1> left_top_p, right_top_p, right_bottom_p, left_botoom_p; left_top_p << 0, 0, 1; right_top_p << w, 0, 1; right_bottom_p << w, h, 1; left_botoom_p << 0, h , 1; left_top_p = T * left_top_p; right_top_p = T * right_top_p; right_bottom_p = T * right_bottom_p; left_botoom_p = T * left_botoom_p; //找到经过变换过定位的最大最小值 double min_x = 10000, min_y = 10000; //min_x if(left_top_p[0] < min_x){ min_x = left_top_p[0]; } if(right_top_p[0] < min_x){ min_x = right_top_p[0]; } if(right_bottom_p[0] < min_x) { min_x = right_bottom_p[0]; } if(left_botoom_p[0] < min_x){ min_x = left_botoom_p[0]; } //min_y if(left_top_p[1] < min_y){ min_y = left_top_p[1]; } if(right_top_p[1] < min_y){ min_y = right_top_p[1]; } if(right_bottom_p[1] < min_y) { min_y = right_bottom_p[1]; } if(left_botoom_p[1] < min_y){ min_y = left_botoom_p[1]; } double max_x = -1000, max_y = -1000; //max_x if(left_top_p[0] > max_x){ max_x = left_top_p[0]; } if(right_top_p[0] > max_x){ max_x = right_top_p[0]; } if(right_bottom_p[0] > max_x) { max_x = right_bottom_p[0]; } if(left_botoom_p[0] > max_x){ max_x = left_botoom_p[0]; } //max_y if(left_top_p[1] > max_y){ max_y = left_top_p[1]; } if(right_top_p[1] > max_y){ max_y = right_top_p[1]; } if(right_bottom_p[1] > max_y) { max_y = right_bottom_p[1]; } if(left_botoom_p[1] > max_y){ max_y = left_botoom_p[1]; } //将偏置添加到矩阵中 m.at<double>(0, 2) += -min_x; m.at<double>(1, 2) += -min_y; //变换，最后不会存在裁剪 warpAffine(src, des , m , Size2i(bound_w , bound_h), INTER_LINEAR, 0, Scalar(100, 100, 100)); imwrite("/home/sss/222.jpg", des); return 0;经过变换过的图片不会存在裁剪：
