最新版opencv2.4.7中，cv::resize函数有五种插值算法：最近邻、双线性、双三次、基于像素区域关系、兰索斯插值。下面用for循环代替cv::resize函数来说明其详细的插值实现过程，其中部分代码摘自于cv::resize函数中的源代码。
每种插值算法的前部分代码是相同的，如下：
cv::mat matsrc, matdst1, matdst2;
matsrc = cv::imread("lena.jpg", 2 | 4);
matdst1 = cv::mat(cv::size(800, 1000), matsrc.type(), cv::scalar::all(0));
matdst2 = cv::mat(matdst1.size(), matsrc.type(), cv::scalar::all(0));
double scale_x = (double)matsrc.cols / matdst1.cols;
double scale_y = (double)matsrc.rows / matdst1.rows;
1、最近邻：公式，
for (int i = 0; i < matdst1.cols; ++i)
{
int sx = cvfloor(i * scale_x);
sx = std::min(sx, matsrc.cols - 1);
for (int j = 0; j < matdst1.rows; ++j)
{
int sy = cvfloor(j * scale_y);
sy = std::min(sy, matsrc.rows - 1);
matdst1.at<cv::vec3b>(j, i) = matsrc.at<cv::vec3b>(sy, sx);
}
}
cv::imwrite("nearest_1.jpg", matdst1);
cv::resize(matsrc, matdst2, matdst1.size(), 0, 0, 0);
cv::imwrite("nearest_2.jpg", matdst2);
2、双线性：由相邻的四像素(2*2)计算得出，公式，
uchar* datadst = matdst1.data;
int stepdst = matdst1.step;
uchar* datasrc = matsrc.data;
int stepsrc = matsrc.step;
int iwidthsrc = matsrc.cols;
int ihiehgtsrc = matsrc.rows;
for (int j = 0; j < matdst1.rows; ++j)
{
float fy = (float)((j + 0.5) * scale_y - 0.5);
int sy = cvfloor(fy);
fy -= sy;
sy = std::min(sy, ihiehgtsrc - 2);
sy = std::max(0, sy);
short cbufy[2];
cbufy[0] = cv::saturate_cast<short>((1.f - fy) * 2048);
cbufy[1] = 2048 - cbufy[0];
for (int i = 0; i < matdst1.cols; ++i)
{
float fx = (float)((i + 0.5) * scale_x - 0.5);
int sx = cvfloor(fx);
fx -= sx;
if (sx < 0) {
fx = 0, sx = 0;
}
if (sx >= iwidthsrc - 1) {
fx = 0, sx = iwidthsrc - 2;
}
short cbufx[2];
cbufx[0] = cv::saturate_cast<short>((1.f - fx) * 2048);
cbufx[1] = 2048 - cbufx[0];
for (int k = 0; k < matsrc.channels(); ++k)
{
*(datadst+ j*stepdst + 3*i + k) = (*(datasrc + sy*stepsrc + 3*sx + k) * cbufx[0] * cbufy[0] +
*(datasrc + (sy+1)*stepsrc + 3*sx + k) * cbufx[0] * cbufy[1] +
*(datasrc + sy*stepsrc + 3*(sx+1) + k) * cbufx[1] * cbufy[0] +
*(datasrc + (sy+1)*stepsrc + 3*(sx+1) + k) * cbufx[1] * cbufy[1]) >> 22;
}
}
}
cv::imwrite("linear_1.jpg", matdst1);
cv::resize(matsrc, matdst2, matdst1.size(), 0, 0, 1);
cv::imwrite("linear_2.jpg", matdst2);
3、双三次：由相邻的4*4像素计算得出，公式类似于双线性
int iscale_x = cv::saturate_cast<int>(scale_x);
int iscale_y = cv::saturate_cast<int>(scale_y);
for (int j = 0; j < matdst1.rows; ++j)
{
float fy = (float)((j + 0.5) * scale_y - 0.5);
int sy = cvfloor(fy);
fy -= sy;
sy = std::min(sy, matsrc.rows - 3);
sy = std::max(1, sy);
const float a = -0.75f;
float coeffsy[4];
coeffsy[0] = ((a*(fy + 1) - 5*a)*(fy + 1) + 8*a)*(fy + 1) - 4*a;
coeffsy[1] = ((a + 2)*fy - (a + 3))*fy*fy + 1;
coeffsy[2] = ((a + 2)*(1 - fy) - (a + 3))*(1 - fy)*(1 - fy) + 1;
coeffsy[3] = 1.f - coeffsy[0] - coeffsy[1] - coeffsy[2];
short cbufy[4];
cbufy[0] = cv::saturate_cast<short>(coeffsy[0] * 2048);
cbufy[1] = cv::saturate_cast<short>(coeffsy[1] * 2048);
cbufy[2] = cv::saturate_cast<short>(coeffsy[2] * 2048);
cbufy[3] = cv::saturate_cast<short>(coeffsy[3] * 2048);
for (int i = 0; i < matdst1.cols; ++i)
{
float fx = (float)((i + 0.5) * scale_x - 0.5);
int sx = cvfloor(fx);
fx -= sx;
if (sx < 1) {
fx = 0, sx = 1;
}
if (sx >= matsrc.cols - 3) {
fx = 0, sx = matsrc.cols - 3;
}
float coeffsx[4];
coeffsx[0] = ((a*(fx + 1) - 5*a)*(fx + 1) + 8*a)*(fx + 1) - 4*a;
coeffsx[1] = ((a + 2)*fx - (a + 3))*fx*fx + 1;
coeffsx[2] = ((a + 2)*(1 - fx) - (a + 3))*(1 - fx)*(1 - fx) + 1;
coeffsx[3] = 1.f - coeffsx[0] - coeffsx[1] - coeffsx[2];
short cbufx[4];
cbufx[0] = cv::saturate_cast<short>(coeffsx[0] * 2048);
cbufx[1] = cv::saturate_cast<short>(coeffsx[1] * 2048);
cbufx[2] = cv::saturate_cast<short>(coeffsx[2] * 2048);
cbufx[3] = cv::saturate_cast<short>(coeffsx[3] * 2048);
for (int k = 0; k < matsrc.channels(); ++k)
{
matdst1.at<cv::vec3b>(j, i)[k] = abs((matsrc.at<cv::vec3b>(sy-1, sx-1)[k] * cbufx[0] * cbufy[0] + matsrc.at<cv::vec3b>(sy, sx-1)[k] * cbufx[0] * cbufy[1] +
matsrc.at<cv::vec3b>(sy+1, sx-1)[k] * cbufx[0] * cbufy[2] + matsrc.at<cv::vec3b>(sy+2, sx-1)[k] * cbufx[0] * cbufy[3] +
matsrc.at<cv::vec3b>(sy-1, sx)[k] * cbufx[1] * cbufy[0] + matsrc.at<cv::vec3b>(sy, sx)[k] * cbufx[1] * cbufy[1] +
matsrc.at<cv::vec3b>(sy+1, sx)[k] * cbufx[1] * cbufy[2] + matsrc.at<cv::vec3b>(sy+2, sx)[k] * cbufx[1] * cbufy[3] +
matsrc.at<cv::vec3b>(sy-1, sx+1)[k] * cbufx[2] * cbufy[0] + matsrc.at<cv::vec3b>(sy, sx+1)[k] * cbufx[2] * cbufy[1] +
matsrc.at<cv::vec3b>(sy+1, sx+1)[k] * cbufx[2] * cbufy[2] + matsrc.at<cv::vec3b>(sy+2, sx+1)[k] * cbufx[2] * cbufy[3] +
matsrc.at<cv::vec3b>(sy-1, sx+2)[k] * cbufx[3] * cbufy[0] + matsrc.at<cv::vec3b>(sy, sx+2)[k] * cbufx[3] * cbufy[1] +
matsrc.at<cv::vec3b>(sy+1, sx+2)[k] * cbufx[3] * cbufy[2] + matsrc.at<cv::vec3b>(sy+2, sx+2)[k] * cbufx[3] * cbufy[3] ) >> 22);
}
}
}
cv::imwrite("cubic_1.jpg", matdst1);
cv::resize(matsrc, matdst2, matdst1.size(), 0, 0, 2);
cv::imwrite("cubic_2.jpg", matdst2);
4、基于像素区域关系：共分三种情况，图像放大时类似于双线性插值，图像缩小(x轴、y轴同时缩小)又分两种情况，此情况下可以避免波纹出现。
#ifdef _msc_ver
cv::resize(matsrc, matdst2, matdst1.size(), 0, 0, 3);
cv::imwrite("e:/gitcode/opencv_test/test_images/area_2.jpg", matdst2);
#else
cv::resize(matsrc, matdst2, matdst1.size(), 0, 0, 3);
cv::imwrite("area_2.jpg", matdst2);
#endif
fprintf(stdout, "==== start area ====\n");
double inv_scale_x = 1. / scale_x;
double inv_scale_y = 1. / scale_y;
int iscale_x = cv::saturate_cast<int>(scale_x);
int iscale_y = cv::saturate_cast<int>(scale_y);
bool is_area_fast = std::abs(scale_x - iscale_x) < dbl_epsilon && std::abs(scale_y - iscale_y) < dbl_epsilon;
if (scale_x >= 1 && scale_y >= 1) { // zoom out
if (is_area_fast) { // integer multiples
for (int j = 0; j < matdst1.rows; ++j) {
int sy = std::min(cvfloor(j * scale_y), matsrc.rows - 1);
for (int i = 0; i < matdst1.cols; ++i) {
int sx = std::min(cvfloor(i * scale_x), matsrc.cols -1);
matdst1.at<cv::vec3b>(j, i) = matsrc.at<cv::vec3b>(sy, sx);
}
}
#ifdef _msc_ver
cv::imwrite("e:/gitcode/opencv_test/test_images/area_1.jpg", matdst1);
#else
cv::imwrite("area_1.jpg", matdst1);
#endif
return 0;
}
for (int j = 0; j < matdst1.rows; ++j) {
double fsy1 = j * scale_y;
double fsy2 = fsy1 + scale_y;
double cellheight = cv::min(scale_y, matsrc.rows - fsy1);
int sy1 = cvceil(fsy1), sy2 = cvfloor(fsy2);
sy2 = std::min(sy2, matsrc.rows - 2);
sy1 = std::min(sy1, sy2);
float cbufy[2];
cbufy[0] = (float)((sy1 - fsy1) / cellheight);
cbufy[1] = (float)(std::min(std::min(fsy2 - sy2, 1.), cellheight) / cellheight);
for (int i = 0; i < matdst1.cols; ++i) {
double fsx1 = i * scale_x;
double fsx2 = fsx1 + scale_x;
double cellwidth = std::min(scale_x, matsrc.cols - fsx1);
int sx1 = cvceil(fsx1), sx2 = cvfloor(fsx2);
sx2 = std::min(sx2, matsrc.cols - 2);
sx1 = std::min(sx1, sx2);
float cbufx[2];
cbufx[0] = (float)((sx1 - fsx1) / cellwidth);
cbufx[1] = (float)(std::min(std::min(fsx2 - sx2, 1.), cellwidth) / cellwidth);
for (int k = 0; k < matsrc.channels(); ++k) {
matdst1.at<cv::vec3b>(j, i)[k] = (uchar)(matsrc.at<cv::vec3b>(sy1, sx1)[k] * cbufx[0] * cbufy[0] +
matsrc.at<cv::vec3b>(sy1 + 1, sx1)[k] * cbufx[0] * cbufy[1] +
matsrc.at<cv::vec3b>(sy1, sx1 + 1)[k] * cbufx[1] * cbufy[0] +
matsrc.at<cv::vec3b>(sy1 + 1, sx1 + 1)[k] * cbufx[1] * cbufy[1]);
}
}
}
#ifdef _msc_ver
cv::imwrite("e:/gitcode/opencv_test/test_images/area_1.jpg", matdst1);
#else
cv::imwrite("area_1.jpg", matdst1);
#endif
return 0;
}
//zoom in,it is emulated using some variant of bilinear interpolation
for (int j = 0; j < matdst1.rows; ++j) {
int sy = cvfloor(j * scale_y);
float fy = (float)((j + 1) - (sy + 1) * inv_scale_y);
fy = fy <= 0 ? 0.f : fy - cvfloor(fy);
sy = std::min(sy, matsrc.rows - 2);
short cbufy[2];
cbufy[0] = cv::saturate_cast<short>((1.f - fy) * 2048);
cbufy[1] = 2048 - cbufy[0];
for (int i = 0; i < matdst1.cols; ++i) {
int sx = cvfloor(i * scale_x);
float fx = (float)((i + 1) - (sx + 1) * inv_scale_x);
fx = fx < 0 ? 0.f : fx - cvfloor(fx);
if (sx < 0) {
fx = 0, sx = 0;
}
if (sx >= matsrc.cols - 1) {
fx = 0, sx = matsrc.cols - 2;
}
short cbufx[2];
cbufx[0] = cv::saturate_cast<short>((1.f - fx) * 2048);
cbufx[1] = 2048 - cbufx[0];
for (int k = 0; k < matsrc.channels(); ++k) {
matdst1.at<cv::vec3b>(j, i)[k] = (matsrc.at<cv::vec3b>(sy, sx)[k] * cbufx[0] * cbufy[0] +
matsrc.at<cv::vec3b>(sy + 1, sx)[k] * cbufx[0] * cbufy[1] +
matsrc.at<cv::vec3b>(sy, sx + 1)[k] * cbufx[1] * cbufy[0] +
matsrc.at<cv::vec3b>(sy + 1, sx + 1)[k] * cbufx[1] * cbufy[1]) >> 22;
}
}
}
fprintf(stdout, "==== end area ====\n");
#ifdef _msc_ver
cv::imwrite("e:/gitcode/opencv_test/test_images/area_1.jpg", matdst1);
#else
cv::imwrite("area_1.jpg", matdst1);
#endif
注：以上基于area进行图像缩小的代码有问题，具体实现代码可以参考https://github.com/fengbingchun/opencv_test/blob/master/src/fbc_cv/include/resize.hpp，用法如下：
fbc::mat3bgr src(matsrc.rows, matsrc.cols, matsrc.data); fbc::mat3bgr dst(matdst1.rows, matdst1.cols, matdst1.data); fbc::resize(src, dst, 3);
5、兰索斯插值：由相邻的8*8像素计算得出，公式类似于双线性
int iscale_x = cv::saturate_cast<int>(scale_x);
int iscale_y = cv::saturate_cast<int>(scale_y);
for (int j = 0; j < matdst1.rows; ++j)
{
float fy = (float)((j + 0.5) * scale_y - 0.5);
int sy = cvfloor(fy);
fy -= sy;
sy = std::min(sy, matsrc.rows - 5);
sy = std::max(3, sy);
const double s45 = 0.70710678118654752440084436210485;
const double cs[][2] = {{1, 0}, {-s45, -s45}, {0, 1}, {s45, -s45}, {-1, 0}, {s45, s45}, {0, -1}, {-s45, s45}};
float coeffsy[8];
if (fy < flt_epsilon) {
for (int t = 0; t < 8; t++)
coeffsy[t] = 0;
coeffsy[3] = 1;
} else {
float sum = 0;
double y0 = -(fy + 3) * cv_pi * 0.25, s0 = sin(y0), c0 = cos(y0);
for (int t = 0; t < 8; ++t)
{
double dy = -(fy + 3 -t) * cv_pi * 0.25;
coeffsy[t] = (float)((cs[t][0] * s0 + cs[t][1] * c0) / (dy * dy));
sum += coeffsy[t];
}
sum = 1.f / sum;
for (int t = 0; t < 8; ++t)
coeffsy[t] *= sum;
}
short cbufy[8];
cbufy[0] = cv::saturate_cast<short>(coeffsy[0] * 2048);
cbufy[1] = cv::saturate_cast<short>(coeffsy[1] * 2048);
cbufy[2] = cv::saturate_cast<short>(coeffsy[2] * 2048);
cbufy[3] = cv::saturate_cast<short>(coeffsy[3] * 2048);
cbufy[4] = cv::saturate_cast<short>(coeffsy[4] * 2048);
cbufy[5] = cv::saturate_cast<short>(coeffsy[5] * 2048);
cbufy[6] = cv::saturate_cast<short>(coeffsy[6] * 2048);
cbufy[7] = cv::saturate_cast<short>(coeffsy[7] * 2048);
for (int i = 0; i < matdst1.cols; ++i)
{
float fx = (float)((i + 0.5) * scale_x - 0.5);
int sx = cvfloor(fx);
fx -= sx;
if (sx < 3) {
fx = 0, sx = 3;
}
if (sx >= matsrc.cols - 5) {
fx = 0, sx = matsrc.cols - 5;
}
float coeffsx[8];
if (fx < flt_epsilon) {
for ( int t = 0; t < 8; t++ )
coeffsx[t] = 0;
coeffsx[3] = 1;
} else {
float sum = 0;
double x0 = -(fx + 3) * cv_pi * 0.25, s0 = sin(x0), c0 = cos(x0);
for (int t = 0; t < 8; ++t)
{
double dx = -(fx + 3 -t) * cv_pi * 0.25;
coeffsx[t] = (float)((cs[t][0] * s0 + cs[t][1] * c0) / (dx * dx));
sum += coeffsx[t];
}
sum = 1.f / sum;
for (int t = 0; t < 8; ++t)
coeffsx[t] *= sum;
}
short cbufx[8];
cbufx[0] = cv::saturate_cast<short>(coeffsx[0] * 2048);
cbufx[1] = cv::saturate_cast<short>(coeffsx[1] * 2048);
cbufx[2] = cv::saturate_cast<short>(coeffsx[2] * 2048);
cbufx[3] = cv::saturate_cast<short>(coeffsx[3] * 2048);
cbufx[4] = cv::saturate_cast<short>(coeffsx[4] * 2048);
cbufx[5] = cv::saturate_cast<short>(coeffsx[5] * 2048);
cbufx[6] = cv::saturate_cast<short>(coeffsx[6] * 2048);
cbufx[7] = cv::saturate_cast<short>(coeffsx[7] * 2048);
for (int k = 0; k < matsrc.channels(); ++k)
{
matdst1.at<cv::vec3b>(j, i)[k] = abs((matsrc.at<cv::vec3b>(sy-3, sx-3)[k] * cbufx[0] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx-3)[k] * cbufx[0] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx-3)[k] * cbufx[0] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx-3)[k] * cbufx[0] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx-3)[k] * cbufx[0] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx-3)[k] * cbufx[0] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx-3)[k] * cbufx[0] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx-3)[k] * cbufx[0] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx-2)[k] * cbufx[1] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx-2)[k] * cbufx[1] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx-2)[k] * cbufx[1] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx-2)[k] * cbufx[1] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx-2)[k] * cbufx[1] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx-2)[k] * cbufx[1] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx-2)[k] * cbufx[1] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx-2)[k] * cbufx[1] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx-1)[k] * cbufx[2] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx-1)[k] * cbufx[2] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx-1)[k] * cbufx[2] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx-1)[k] * cbufx[2] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx-1)[k] * cbufx[2] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx-1)[k] * cbufx[2] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx-1)[k] * cbufx[2] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx-1)[k] * cbufx[2] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx)[k] * cbufx[3] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx)[k] * cbufx[3] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx)[k] * cbufx[3] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx)[k] * cbufx[3] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx)[k] * cbufx[3] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx)[k] * cbufx[3] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx)[k] * cbufx[3] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx)[k] * cbufx[3] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx+1)[k] * cbufx[4] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx+1)[k] * cbufx[4] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx+1)[k] * cbufx[4] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx+1)[k] * cbufx[4] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx+1)[k] * cbufx[4] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx+1)[k] * cbufx[4] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx+1)[k] * cbufx[4] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx+1)[k] * cbufx[4] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx+2)[k] * cbufx[5] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx+2)[k] * cbufx[5] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx+2)[k] * cbufx[5] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx+2)[k] * cbufx[5] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx+2)[k] * cbufx[5] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx+2)[k] * cbufx[5] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx+2)[k] * cbufx[5] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx+2)[k] * cbufx[5] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx+3)[k] * cbufx[6] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx+3)[k] * cbufx[6] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx+3)[k] * cbufx[6] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx+3)[k] * cbufx[6] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx+3)[k] * cbufx[6] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx+3)[k] * cbufx[6] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx+3)[k] * cbufx[6] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx+3)[k] * cbufx[6] * cbufy[7] +
matsrc.at<cv::vec3b>(sy-3, sx+4)[k] * cbufx[7] * cbufy[0] + matsrc.at<cv::vec3b>(sy-2, sx+4)[k] * cbufx[7] * cbufy[1] +
matsrc.at<cv::vec3b>(sy-1, sx+4)[k] * cbufx[7] * cbufy[2] + matsrc.at<cv::vec3b>(sy, sx+4)[k] * cbufx[7] * cbufy[3] +
matsrc.at<cv::vec3b>(sy+1, sx+4)[k] * cbufx[7] * cbufy[4] + matsrc.at<cv::vec3b>(sy+2, sx+4)[k] * cbufx[7] * cbufy[5] +
matsrc.at<cv::vec3b>(sy+3, sx+4)[k] * cbufx[7] * cbufy[6] + matsrc.at<cv::vec3b>(sy+4, sx+4)[k] * cbufx[7] * cbufy[7] ) >> 22);// 4194304
}
}
}
cv::imwrite("lanczos_1.jpg", matdst1);
cv::resize(matsrc, matdst2, matdst1.size(), 0, 0, 4);
cv::imwrite("lanczos_2.jpg", matdst2);
以上代码的实现结果与cv::resize函数相同，但是执行效率非常低，只是为了详细说明插值过程。opencv中默认采用c++ concurrency进行优化加速，你也可以采用tbb、openmp等进行优化加速。
github：https://github.com/fengbingchun/opencv_test/blob/master/demo/opencv_test/test_opencv_funset.cpp
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