OpenCV中图像旋转(warpAffine)算法的实现过程
在OpenCV中，目前并没有现成的函数直接用来实现图像旋转，它是用仿射变换函数cv::warpAffine来实现的，此函数目前支持4种插值算法，最近邻、双线性、双三次、兰索斯插值，如果传进去的参数为基于像素区域关系插值算法(INTER_AREA)，则按双线性插值。
通常使用2*3矩阵来表示仿射变换：
其中，T相当于变换前的原始图像，x,y为变换后的图像坐标。
对于cv::getRotationMatrix2D函数的实现公式为：
其中scale为缩放因子(x、y方向保持一致)，angle为旋转角度(弧长)，centerx，centery为旋转中心。
以lena.jpg图像旋转45度为例：采用最近邻插值算法的实现代码为：
cv::Mat matSrc = cv::imread("lena.jpg", 2 | 4);
if (matSrc.empty()) return;
const double degree = 45;
double angle = degree * CV_PI / 180.;
double alpha = cos(angle);
double beta = sin(angle);
int iWidth = matSrc.cols;
int iHeight = matSrc.rows;
int iNewWidth = cvRound(iWidth * fabs(alpha) + iHeight * fabs(beta));
int iNewHeight = cvRound(iHeight * fabs(alpha) + iWidth * fabs(beta));
double m[6];
m[0] = alpha;
m[1] = beta;
m[2] = (1 - alpha) * iWidth / 2. - beta * iHeight / 2.;
m[3] = -m[1];
m[4] = m[0];
m[5] = beta * iWidth / 2. + (1 - alpha) * iHeight / 2.;
cv::Mat M = cv::Mat(2, 3, CV_64F, m);
cv::Mat matDst1 = cv::Mat(cv::Size(iNewWidth, iNewHeight), matSrc.type(), cv::Scalar::all(0));
double D = m[0]*m[4] - m[1]*m[3];
D = D != 0 ? 1./D : 0;
double A11 = m[4]*D, A22 = m[0]*D;
m[0] = A11; m[1] *= -D;
m[3] *= -D; m[4] = A22;
double b1 = -m[0]*m[2] - m[1]*m[5];
double b2 = -m[3]*m[2] - m[4]*m[5];
m[2] = b1; m[5] = b2;
int round_delta = 512;//nearest
for (int y=0; y(m[0] * x * 1024);
int bdelta = cv::saturate_cast(m[3] * x * 1024);
int X0 = cv::saturate_cast((m[1] * y + m[2]) * 1024) + round_delta;
int Y0 = cv::saturate_cast((m[4] * y + m[5]) * 1024) + round_delta;
int X = (X0 + adelta) >> 10;
int Y = (Y0 + bdelta) >> 10;
if ((unsigned)X < iWidth && (unsigned)Y < iHeight)
{
matDst1.at(y, x) = matSrc.at(Y, X);
}
}
}
cv::imwrite("rotate_nearest_1.jpg", matDst1);
M = cv::getRotationMatrix2D(cv::Point2f(iWidth / 2., iHeight / 2.), degree, 1);
cv::Mat matDst2;
cv::warpAffine(matSrc, matDst2, M, cv::Size(iNewWidth, iNewHeight), 0, 0, 0);
cv::imwrite("rotate_nearest_2.jpg", matDst2); cv::Mat matSrc = cv::imread("lena.jpg", 2 | 4);
if (matSrc.empty()) return;
const double degree = 45;
double angle = degree * CV_PI / 180.;
double alpha = cos(angle);
double beta = sin(angle);
int iWidth = matSrc.cols;
int iHeight = matSrc.rows;
int iNewWidth = cvRound(iWidth * fabs(alpha) + iHeight * fabs(beta));
int iNewHeight = cvRound(iHeight * fabs(alpha) + iWidth * fabs(beta));
double m[6];
m[0] = alpha;
m[1] = beta;
m[2] = (1 - alpha) * iWidth / 2. - beta * iHeight / 2.;
m[3] = -m[1];
m[4] = m[0];
m[5] = beta * iWidth / 2. + (1 - alpha) * iHeight / 2.;
cv::Mat M = cv::Mat(2, 3, CV_64F, m);
cv::Mat matDst1 = cv::Mat(cv::Size(iNewWidth, iNewHeight), matSrc.type(), cv::Scalar::all(0));
double D = m[0]*m[4] - m[1]*m[3];
D = D != 0 ? 1./D : 0;
double A11 = m[4]*D, A22 = m[0]*D;
m[0] = A11; m[1] *= -D;
m[3] *= -D; m[4] = A22;
double b1 = -m[0]*m[2] - m[1]*m[5];
double b2 = -m[3]*m[2] - m[4]*m[5];
m[2] = b1; m[5] = b2;
for (int y=0; y= iHeight) continue;
short cbufy[2];
cbufy[0] = cv::saturate_cast((1.f - fy) * 2048);
cbufy[1] = 2048 - cbufy[0];
int sx = cvFloor(fx);
fx -= sx;
//if (sx < 0) {
// fx = 0, sx = 0;
//}
//if (sx >= iWidth - 1) {
// fx = 0, sx = iWidth - 2;
//}
if (sx < 0 || sx >= iWidth) continue;
short cbufx[2];
cbufx[0] = cv::saturate_cast((1.f - fx) * 2048);
cbufx[1] = 2048 - cbufx[0];
for (int k=0; k(y, x)[k] = (matSrc.at(sy, sx)[k] * cbufx[0] * cbufy[0] +
matSrc.at(sy+1, sx)[k] * cbufx[0] * cbufy[1] +
matSrc.at(sy, sx+1)[k] * cbufx[1] * cbufy[0] +
matSrc.at(sy+1, sx+1)[k] * cbufx[1] * cbufy[1]) >> 22;
}
}
}
}
cv::imwrite("rotate_bilinear_1.jpg", matDst1);
M = cv::getRotationMatrix2D(cv::Point2f(iWidth / 2., iHeight / 2.), degree, 1);
cv::Mat matDst2;
cv::warpAffine(matSrc, matDst2, M, cv::Size(iNewWidth, iNewHeight), 1, 0, 0);
cv::imwrite("rotate_bilinear_2.jpg", matDst2); 其它插值算法的实现代码与双线性类似，可参考 http://blog.csdn.net/fengbingchun/article/details/17335477