Opencv--resize函数五种插值算法实现
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。
- cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 3);
- cv::imwrite("area_2.jpg", matDst2);
- 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 = j * scale_y;
- for (int i = 0; i < matDst1.cols; ++i)
- {
- int sx = i * scale_x;
- matDst1.at<cv::Vec3b>(j, i) = matSrc.at<cv::Vec3b>(sy, sx);
- }
- }
- cv::imwrite("area_1.jpg", matDst1);
- 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 - 1);
- 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 - 1);
- 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]);
- }
- }
- }
- cv::imwrite("area_1.jpg", matDst1);
- 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);
- 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;
- }
- }
- }
- cv::imwrite("area_1.jpg", matDst1);
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。
转载自:https://blog.csdn.net/fengbingchun/article/details/17335477
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