C++ OpenCV中的Sobel导数
我的任务是创建自己的Sobel方法，而不是使用OpenCV中的C++ OpenCV中的Sobel导数,c++,opencv,sobel,C++,Opencv,Sobel,我的任务是创建自己的Sobel方法，而不是使用OpenCV中的cv:：Sobel。 我试着实现一个我在 但是，当我运行程序时，cv:：Mat抛出一个错误。有人知道为什么吗 索贝尔法： int sobelCorrelation(Mat InputArray, int x, int y, String xory) { if (xory == "x") { return InputArray.at<uchar>(y - 1, x - 1) +
cv:：Sobelcv:：Matint sobelCorrelation(Mat InputArray, int x, int y, String xory)
{
if (xory == "x") {
return InputArray.at<uchar>(y - 1, x - 1) +
2 * InputArray.at<uchar>(y, x - 1) +
InputArray.at<uchar>(y + 1, x - 1) -
InputArray.at<uchar>(y - 1, x + 1) -
2 * InputArray.at<uchar>(y, x + 1) -
InputArray.at<uchar>(y + 1, x + 1);
}
else if (xory == "y")
{
return InputArray.at<uchar>(y - 1, x - 1) +
2 * InputArray.at<uchar>(y - 1, x) +
InputArray.at<uchar>(y - 1, x + 1) -
InputArray.at<uchar>(y + 1, x - 1) -
2 * InputArray.at<uchar>(y + 1, x) -
InputArray.at<uchar>(y + 1, x + 1);
}
else
{
return 0;
}
}
int absVal(int v)
{
return v*((v < 0)*(-1) + (v > 0));
}
template<typename _Tp> inline
_Tp& Mat::at(int i0, int i1)
{
CV_DbgAssert( dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] &&
(unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()) &&
CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1());
return ((_Tp*)(data + step.p[0] * i0))[i1];
}
模板内联
_Tp和Mat:：at（int i0，int i1）
{
CV_DbgAssert（dims此代码片段旨在演示如何计算将图像与Sobel内核进行卷积的Sobel 3x3导数。您可以轻松扩展到不同的内核大小，将内核半径作为my_Sobel
的输入，并创建适当的内核
#include <opencv2\opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
void my_sobel(const Mat1b& src, Mat1s& dst, int direction)
{
Mat1s kernel;
int radius = 0;
// Create the kernel
if (direction == 0)
{
// Sobel 3x3 X kernel
kernel = (Mat1s(3,3) << -1, 0, +1, -2, 0, +2, -1, 0, +1);
radius = 1;
}
else
{
// Sobel 3x3 Y kernel
kernel = (Mat1s(3, 3) << -1, -2, -1, 0, 0, 0, +1, +2, +1);
radius = 1;
}
// Handle border issues
Mat1b _src;
copyMakeBorder(src, _src, radius, radius, radius, radius, BORDER_REFLECT101);
// Create output matrix
dst.create(src.rows, src.cols);
// Convolution loop
// Iterate on image
for (int r = radius; r < _src.rows - radius; ++r)
{
for (int c = radius; c < _src.cols - radius; ++c)
{
short s = 0;
// Iterate on kernel
for (int i = -radius; i <= radius; ++i)
{
for (int j = -radius; j <= radius; ++j)
{
s += _src(r + i, c + j) * kernel(i + radius, j + radius);
}
}
dst(r - radius, c - radius) = s;
}
}
}
int main(void)
{
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
// Compute custom Sobel 3x3 derivatives
Mat1s sx, sy;
my_sobel(img, sx, 0);
my_sobel(img, sy, 1);
// Edges L1 norm
Mat1b edges_L1;
absdiff(sx, sy, edges_L1);
// Check results against OpenCV
Mat1s cvsx,cvsy;
Sobel(img, cvsx, CV_16S, 1, 0);
Sobel(img, cvsy, CV_16S, 0, 1);
Mat1b cvedges_L1;
absdiff(cvsx, cvsy, cvedges_L1);
Mat diff_L1;
absdiff(edges_L1, cvedges_L1, diff_L1);
cout << "Number of different pixels: " << countNonZero(diff_L1) << endl;
return 0;
}
#包括
#包括
使用名称空间std；
使用名称空间cv；
作废我的索贝尔（常数Mat1b和src、MAT1和dst、int方向）
{
Mat1s核；
int半径=0；
//创建内核
如果（方向==0）
{
//Sobel 3x3 X内核
kernel=（Mat1s（3,3）如果我是你，我几乎总是避免使用for循环（如果可能的话）。不必要的for循环往往会降低执行速度。相反，尽可能重用。例如，下面的代码使用filter2D给出2d相关结果：
Mat kern = (Mat_<float>(3,3)<<-1,0,1,-2,0,2,-1,0,1);
Mat dest;
cv::filter2D(src,dest,src.type(),kern);
如果您想提高性能，可以使用可分离过滤器“sepFilter2D”。谢谢您的帖子，
我能够使用上述内核生成渐变贴图，并使用openCV代码filter2D从
将映像与内核进行卷积。我使用的代码是
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char** argv) {
//Loading the source image
Mat src;
//src = imread("1.png");
src = cv::imread("E:\\Gray_Image.bmp", 0);
//Output image of the same size and the same number of channels as src.
Mat dst1,dst2,grad;
//Mat dst = src.clone(); //didn't help...
//desired depth of the destination image
//negative so dst will be the same as src.depth()
int ddepth = -1;
//the convolution kernel, a single-channel floating point matrix:
//Mat kernel = imread("kernel.png");
Mat kernel_x = (Mat_<float>(3, 3) << -1, 0, 1, -2, 0, 2, -1, 0, 1);
Mat kernel_y = (Mat_<float>(3, 3) << -1, -2, -1, 0, 0, 0, 1, 2, 1);
kernel_x.convertTo(kernel_x, CV_32F); kernel_y.convertTo(kernel_y, CV_32F); //<<not working
//normalize(kernel, kernel, 1.0, 0.0, 4, -1, noArray()); //doesn't help
//cout << kernel.size() << endl; // ... gives 11, 11
//however, the example from tutorial that does work:
//kernel = Mat::ones( 11, 11, CV_32F )/ (float)(11*11);
//default value (-1,-1) here means that the anchor is at the kernel center.
Point anchor = Point(-1, -1);
//value added to the filtered pixels before storing them in dst.
double delta = 0;
//alright, let's do this...
filter2D(src, dst1, ddepth, kernel_x, anchor, delta, BORDER_DEFAULT);
filter2D(src, dst2, ddepth, kernel_y, anchor, delta, BORDER_DEFAULT);
imshow("Source", src); //<< unhandled exception here
//imshow("Kernel1", kernel_x); imshow("Kernel2", kernel_y);
imshow("Destination1", dst1);
imshow("Destination2", dst2);
addWeighted(dst1, 0.5, dst2, 0.5, 0, grad);
imshow("Destination3", grad);
waitKey(1000000);
return 0;
}
#包括“opencv2/imgproc/imgproc.hpp”
#包括“opencv2/highgui/highgui.hpp”
#包括
#包括
#包括
使用名称空间cv；
使用名称空间std；
int main（int argc，字符**argv）{
//加载源图像
Mat-src；
//src=imread（“1.png”）；
src=cv:：imread（“E:\\Gray\u Image.bmp”，0）；
//输出与src大小和通道数相同的图像。
材料dst1，dst2，梯度；
//Mat dst=src.clone（）；//没有帮助。。。
//目标图像的所需深度
//负值，因此dst将与src.depth（）相同
int-ddepth=-1；
//卷积内核，单通道浮点矩阵：
//Mat kernel=imread（“kernel.png”）；
Mat kernel_x=（Mat_（3，3）dst.at（x，y）=sum；
必须是dst.at（y，x）=sum；
。但是考虑到您的输出是int
，但它将饱和为uchar
，因此负值将变为零，值>255将被截断。因此您可能不想执行dst=image.clone（）；
但是dst=cv:：Mat（…，cv32s）
并使用dst.atYep访问它。我自己刚刚发现。真是个错误！非常抱歉。有关更多提示，请参阅更新的注释以了解可能会出错的地方！我还想指出，这是一个非常缓慢的实现。您最好编写一个卷积循环，并为sobel X和YThanks使用两个不同的内核，以获得所有伟大的建议！Miki，d你有卷积循环的代码示例吗？
#include <opencv2\opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
void my_sobel(const Mat1b& src, Mat1s& dst, int direction)
{
Mat1s kernel;
int radius = 0;
// Create the kernel
if (direction == 0)
{
// Sobel 3x3 X kernel
kernel = (Mat1s(3,3) << -1, 0, +1, -2, 0, +2, -1, 0, +1);
radius = 1;
}
else
{
// Sobel 3x3 Y kernel
kernel = (Mat1s(3, 3) << -1, -2, -1, 0, 0, 0, +1, +2, +1);
radius = 1;
}
// Handle border issues
Mat1b _src;
copyMakeBorder(src, _src, radius, radius, radius, radius, BORDER_REFLECT101);
// Create output matrix
dst.create(src.rows, src.cols);
// Convolution loop
// Iterate on image
for (int r = radius; r < _src.rows - radius; ++r)
{
for (int c = radius; c < _src.cols - radius; ++c)
{
short s = 0;
// Iterate on kernel
for (int i = -radius; i <= radius; ++i)
{
for (int j = -radius; j <= radius; ++j)
{
s += _src(r + i, c + j) * kernel(i + radius, j + radius);
}
}
dst(r - radius, c - radius) = s;
}
}
}
int main(void)
{
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
// Compute custom Sobel 3x3 derivatives
Mat1s sx, sy;
my_sobel(img, sx, 0);
my_sobel(img, sy, 1);
// Edges L1 norm
Mat1b edges_L1;
absdiff(sx, sy, edges_L1);
// Check results against OpenCV
Mat1s cvsx,cvsy;
Sobel(img, cvsx, CV_16S, 1, 0);
Sobel(img, cvsy, CV_16S, 0, 1);
Mat1b cvedges_L1;
absdiff(cvsx, cvsy, cvedges_L1);
Mat diff_L1;
absdiff(edges_L1, cvedges_L1, diff_L1);
cout << "Number of different pixels: " << countNonZero(diff_L1) << endl;
return 0;
}
Mat kern = (Mat_<float>(3,3)<<-1,0,1,-2,0,2,-1,0,1);
Mat dest;
cv::filter2D(src,dest,src.type(),kern);
cv::flip(kern,kern, -1);
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char** argv) {
//Loading the source image
Mat src;
//src = imread("1.png");
src = cv::imread("E:\\Gray_Image.bmp", 0);
//Output image of the same size and the same number of channels as src.
Mat dst1,dst2,grad;
//Mat dst = src.clone(); //didn't help...
//desired depth of the destination image
//negative so dst will be the same as src.depth()
int ddepth = -1;
//the convolution kernel, a single-channel floating point matrix:
//Mat kernel = imread("kernel.png");
Mat kernel_x = (Mat_<float>(3, 3) << -1, 0, 1, -2, 0, 2, -1, 0, 1);
Mat kernel_y = (Mat_<float>(3, 3) << -1, -2, -1, 0, 0, 0, 1, 2, 1);
kernel_x.convertTo(kernel_x, CV_32F); kernel_y.convertTo(kernel_y, CV_32F); //<<not working
//normalize(kernel, kernel, 1.0, 0.0, 4, -1, noArray()); //doesn't help
//cout << kernel.size() << endl; // ... gives 11, 11
//however, the example from tutorial that does work:
//kernel = Mat::ones( 11, 11, CV_32F )/ (float)(11*11);
//default value (-1,-1) here means that the anchor is at the kernel center.
Point anchor = Point(-1, -1);
//value added to the filtered pixels before storing them in dst.
double delta = 0;
//alright, let's do this...
filter2D(src, dst1, ddepth, kernel_x, anchor, delta, BORDER_DEFAULT);
filter2D(src, dst2, ddepth, kernel_y, anchor, delta, BORDER_DEFAULT);
imshow("Source", src); //<< unhandled exception here
//imshow("Kernel1", kernel_x); imshow("Kernel2", kernel_y);
imshow("Destination1", dst1);
imshow("Destination2", dst2);
addWeighted(dst1, 0.5, dst2, 0.5, 0, grad);
imshow("Destination3", grad);
waitKey(1000000);
return 0;
}