jgsj
- 浏览: 957027 次
-
文章分类
最新评论
-
l67721363：
感谢分享，要是有各个函数性能比较就好了。
SQL优化 数据库优化 -
hanmiao：
此图片来自QQ空间，未经允许不可引用。
Hacking QQ空间
OpenCV中resize函数五种插值算法的实现过程
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);
分享到：
发表评论
相关推荐
Opencv中imwrite函数源代码，Opencv中imwrite函数源代码，Opencv中imwrite函数源代码
相关文章请在csdn的博客 It1995 查询
C++ OpenCV驱动程序，OpenCV beta工程环境。 项目代码可直接编译运行~
测试图片已经放到文件中，请自己配置vs2017 opencv 环境再进行测试 代码讲解见：https://blog.csdn.net/LZJSTUDY/article/details/103854590
OpenCV-Python图像处理：插值方法及使用resize函数进行图像缩放.rar
图像去雾算法opencv实现图像去雾算法opencv实现 暗原色的opencv实现
OPENCV函数中文手册
这是opencv的函数文档，pdf格式。方便大家查阅相关函数。
OPENCV函数手册（中文）
C++ OpenCV驱动程序，OpenCV beta工程环境。 项目代码可直接编译运行~
关于OPENCV的双线性插值算法源程序。
解决办法：在配置了opencv的电脑上（但是在环境变量中没有添加dll的路径），将调用相关函数的代码封装成函数，生成dll文件，拷贝到其他没配置opencv的电脑上，再解析该dll，将该dll中调用的一些opencv的函数涉及到的...
1. 基于python的opencv4.6.5内部函数库 2. opencv中所有函数的个人实际使用以及相关功能描述 3. 注释相对简单，具体描述需自己学习 4. 不了解的函数或者不懂的注释可以csdn中查找更详细的用法
用OpenCV C++实现Photoshop色阶调整算法, 包含Levels类和demo例程
opencv图像处理中常用函数汇总，包括显示图像，保存图像，图像灰度化，边缘检测等函数使用方式
主要介绍了python使用opencv resize图像不进行插值的操作，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
Lanczos插值的原理其实就是在原图像中取一个小窗口，通过计算权重映射到新的图像中的一个像素点中。
利用最近邻插值法实现图像的缩小与放大.....................................................................................
OpenCV是一个基于C/C++语言的开源图像处理函数库
OPENCV函数手册，OPENCV常用函数速查，详细使用例子OpenCV概述 FAQ中文 CxCore中文参考手册 基础结构 数组操作 动态结构 绘图函数 数据保存和运行时类型信息 其它混合函数 错误处理和系统函数 机器学习中文参考手册