首页
所有分类
TAGS
文字工具 EMOJI BIBLE
编程 Python

OpenCV中resize函数插值算法的实现过程(五种)

Posted in Python onJune 05, 2021

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，

OpenCV中resize函数插值算法的实现过程(五种)

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，

OpenCV中resize函数插值算法的实现过程(五种)

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

到此这篇关于OpenCV中resize函数插值算法的实现过程(五种)的文章就介绍到这了,更多相关OpenCV resize插值内容请搜索三水点靠木以前的文章或继续浏览下面的相关文章希望大家以后多多支持三水点靠木！

OpenCV中resize函数插值算法的实现过程(五种)

- Author -

fengbingchun

声明：登载此文出于传递更多信息之目的，并不意味着赞同其观点或证实其描述。

Python 相关文章推荐
利用Python的装饰器解决Bottle框架中用户验证问题
Apr 24 Python
自己使用总结Python程序代码片段
Jun 02 Python
举例详解Python中yield生成器的用法
Aug 05 Python
jupyter安装小结
Mar 13 Python
python with提前退出遇到的坑与解决方案
Jan 05 Python
对python使用telnet实现弱密码登录的方法详解
Jan 26 Python
Python常用的json标准库
Feb 19 Python
python批量创建指定名称的文件夹
Mar 21 Python
详解Pycharm出现out of memory的终极解决方法
Mar 03 Python
python opencv 图像边框(填充)添加及图像混合的实现方法（末尾实现类似幻灯片渐变的效果）
Mar 09 Python
详解pandas赋值失败问题解决
Nov 29 Python
python uuid生成唯一id或str的最简单案例
Jan 13 Python
OpenCV全景图像拼接的实现示例
opencv 分类白天与夜景视频的方法
python如何利用traceback获取详细的异常信息
Jun 05 #Python
Python异常类型以及处理方法汇总
Jun 05 #Python
Python OpenCV 彩色与灰度图像的转换实现
Python深度学习之实现卷积神经网络
python opencv通过4坐标剪裁图片
Jun 05 #Python
Windows Server(4) HttpClient(1) K8s(1) Windows Server 2022(3) NTP(1) ip2region(1) Hive(5) RestTemplate(1) dataBinding(1) Quartz(1)
You might like
espresso double下 咖啡粉超细时 饼压力对咖啡的影响
2021/03/03 冲泡冲煮
PHP个人网站架设连环讲(三)
2006/10/09 PHP
PHP CodeIgniter分页实例及多条件查询解决方案(推荐)
2017/05/20 PHP
PHP一个简单的无需刷新爬虫
2019/01/05 PHP
jQuery操作元素css样式的三种方法
2014/06/04 Javascript
wap图片滚动特效无css3元素纯js脚本编写
2014/08/22 Javascript
JavaScript检测实例属性, 原型属性
2015/02/04 Javascript
JavaScript给url网址进行encode编码的方法
2015/03/18 Javascript
JavaScript中模拟实现jsonp
2015/06/19 Javascript
详解JavaScript的回调函数
2015/11/20 Javascript
关于JS中match() 和 exec() 返回值和属性的测试
2016/03/21 Javascript
knockoutjs动态加载外部的file作为component中的template数据源的实现方法
2016/09/01 Javascript
Bootstrap实现翻页效果
2017/11/27 Javascript
如何让你的JS代码更好看易读
2017/12/01 Javascript
vue安装和使用scss及sass与scss的区别详解
2018/10/15 Javascript
微信小程序按钮点击跳转页面详解
2019/05/06 Javascript
基于Express框架使用POST传递Form数据
2019/08/10 Javascript
[03:43]2014DOTA2西雅图国际邀请赛 newbee战队巡礼
2014/07/07 DOTA
python实现挑选出来100以内的质数
2015/03/24 Python
python网络编程之数据传输UDP实例分析
2015/05/20 Python
把csv文件转化为数组及数组的切片方法
2018/07/04 Python
pandas 把数据写入txt文件每行固定写入一定数量的值方法
2018/12/28 Python
python监控进程状态,记录重启时间及进程号的实例
2019/07/15 Python
python支付宝支付示例详解
2019/08/22 Python
python 引用传递和值传递详解(实参,形参)
2020/06/05 Python
python时间序列数据转为timestamp格式的方法
2020/08/03 Python
python使用多线程查询数据库的实现示例
2020/08/17 Python
抽象类和接口的区别
2012/09/19 面试题
成教自我鉴定
2013/10/27 职场文书
个人求职信范文
2014/05/24 职场文书
群众路线教育实践活动思想汇报（2014特荐篇）
2014/09/16 职场文书
2015年乡镇信访工作总结
2015/04/07 职场文书
业务员管理制度范本
2015/08/06 职场文书
小学四年级班务总结该怎么写？
2019/08/16 职场文书
C#连接ORACLE出现乱码问题的解决方法
2021/10/05 Oracle
Nginx禁止ip访问或非法域名访问
2022/04/07 Servers