平台：Windows 10 20H2
Visual Studio 2015
OpenCV 4.5.3

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本文算法改进自图形算法与实战：6.图像运动专题（5）图像旋转-基于近邻插值的图像旋转 —— 进击的CV

原理

       将旋转后图像的像素点映射回原图像，找到它的采样点，即旋转的逆变换。映射的结果不会都是整数像素点，那么旋转后的点的像素值由与采样点最邻近的像素值表示，这就是最近邻插值。

改变尺寸的图像旋转
这种旋转是将旋转后的图像内容完全显示出来，所以要确定新的图像的尺寸。

源码

RotateImage

Mat RotateImage(Mat src, double angle)
{int x0, y0, x1, y1;angle = angle * 3.1415926535897932384626433832795 / 180;int dx = abs((int)src.cols*cos(angle)) + abs((int)src.rows*sin(angle));int dy = abs((int)src.cols*sin(angle)) + abs((int)src.rows*cos(angle));Mat dst(dy, dx, CV_8UC3, Scalar(0));  //创建新图像for (x1 = 0; x1 < dst.cols; x1++){for (y1 = 0; y1 < dst.rows; y1++){double fx0, fy0;double fx1, fy1;double R;double sita, sita0, sita1;//将图片中点设为坐标原点fx1 = x1 - dst.cols / 2;fy1 = y1 - dst.rows / 2;R = sqrt(fx1 * fx1 + fy1 * fy1);	//极径sita = angle;sita1 = atan2(fy1, fx1);			//新点极角sita0 = sita1 + sita;				//旧点极角//旧点直角坐标(中点为坐标原点)fx0 = R * cos(sita0);				fy0 = R * sin(sita0);//旧点直角坐标(坐标原点在角上)x0 = fx0 + src.cols / 2 + 0.5;y0 = fy0 + src.rows / 2 + 0.5;if (x0 >= 0 && x0 < src.cols && y0 >= 0 && y0 < src.rows){dst.at<Vec3b>(Point(x1, y1)) = src.at<Vec3b>(Point(x0, y0));}elsedst.at<Vec3b>(Point(x1, y1)) = 0;}}return dst;
}

主函数

int main(int argc, char * argv[])
{Mat src;src = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\4.jpg");imshow("原图", src);for (short i = -360; i <= 360; ++i){imshow("输出", RotateImage(src, i));waitKey(1);}waitKey(0);return 0;
}

效果

完整源码

#include <opencv2\opencv.hpp>
#include <iostream>using namespace cv;
using namespace std;Mat RotateImage(Mat src, double angle)
{int x0, y0, x1, y1;angle = angle * 3.1415926535897932384626433832795 / 180;int dx = abs((int)src.cols*cos(angle)) + abs((int)src.rows*sin(angle));int dy = abs((int)src.cols*sin(angle)) + abs((int)src.rows*cos(angle));Mat dst(dy, dx, CV_8UC3, Scalar(0));  //创建新图像for (x1 = 0; x1 < dst.cols; x1++){for (y1 = 0; y1 < dst.rows; y1++){double fx0, fy0;double fx1, fy1;double R;double sita, sita0, sita1;//将图片中点设为坐标原点fx1 = x1 - dst.cols / 2;fy1 = y1 - dst.rows / 2;R = sqrt(fx1 * fx1 + fy1 * fy1);	//极径sita = angle;sita1 = atan2(fy1, fx1);			//新点极角sita0 = sita1 + sita;				//旧点极角//旧点直角坐标(中点为坐标原点)fx0 = R * cos(sita0);fy0 = R * sin(sita0);//旧点直角坐标(坐标原点在角上)x0 = fx0 + src.cols / 2 + 0.5;y0 = fy0 + src.rows / 2 + 0.5;if (x0 >= 0 && x0 < src.cols && y0 >= 0 && y0 < src.rows){dst.at<Vec3b>(Point(x1, y1)) = src.at<Vec3b>(Point(x0, y0));}elsedst.at<Vec3b>(Point(x1, y1)) = 0;}}return dst;
}int main(int argc, char * argv[])
{Mat src;src = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\4.jpg");imshow("原图", src);for (short i = -360; i <= 360; ++i){imshow("输出", RotateImage(src, i));waitKey(1);}waitKey(0);return 0;
}

速度优化

源码

Mat RotateImage(Mat src, float angle)
{int x0, y0, x1, y1;angle = angle * 3.1415926535897932384626433832795 / 180;float sin_sita = sin(angle), cos_sita = cos(angle);Mat dst(abs((int)src.cols*sin_sita) + abs((int)src.rows*cos_sita), abs((int)src.cols*cos_sita) + abs((int)src.rows*sin_sita), CV_8UC3, Scalar(0));  //创建新图像for (x1 = 0; x1 < dst.cols; ++x1){for (y1 = 0; y1 < dst.rows; ++y1){float fx1, fy1;//将图片中点设为坐标原点fx1 = x1 - dst.cols / 2;fy1 = y1 - dst.rows / 2;//旧点直角坐标(坐标原点在角上)x0 = fx1*cos_sita - fy1*sin_sita + src.cols / 2 + 0.5;y0 = fx1*sin_sita + fy1*cos_sita + src.rows / 2 + 0.5;if (x0 >= 0 && x0 < src.cols && y0 >= 0 && y0 < src.rows){dst.at<Vec3b>(Point(x1, y1)) = src.at<Vec3b>(Point(x0, y0));}elsedst.at<Vec3b>(Point(x1, y1)) = 0;}}return dst;
}

优化效果

旋转一幅1200×562的图像


用时几乎是原来的1/2