使用Opencv_CUDA 进行滤波操作
- 邻域处理操作 ==> 滤波操作，拒绝或者允许某特定频段通过
- 如果图像某处的灰度级变化缓慢，那么就是低频区域，如果灰度级变化剧烈，就是高频区域
- 邻域滤波即卷积操作
- 形态学处理：膨胀，腐蚀，开、闭运算
1. 低通滤波
- 低通滤波可以从图像中删除高频内容。噪声通常被视为高频内容，因此低通滤波器能从图像中消除噪声。
- 噪声类型有：高斯噪声，均匀噪声，指数噪声与椒盐噪声
1.1 均值滤波
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudafilters.hpp>
int main()
{
cv::Mat h_img1 = cv::imread("images/cameraman.tif", 0);
cv::cuda::GpuMat d_img1, d_result3x3, d_result5x5, d_result7x7;
d_img1.upload(h_img1);
cv::Ptr<cv::cuda::Filter> filter3x3, filter5x5, filter7x7;
filter3x3 = cv::cuda::createBoxFilter(CV_8UC1, CV_8UC1, cv::Size(3, 3));
filter3x3->apply(d_img1, d_result3x3);
filter5x5 = cv::cuda::createBoxFilter(CV_8UC1, CV_8UC1, cv::Size(5, 5));
filter5x5->apply(d_img1, d_result5x5);
filter7x7 = cv::cuda::createBoxFilter(CV_8UC1, CV_8UC1, cv::Size(7, 7));
filter7x7->apply(d_img1, d_result7x7);
cv::Mat h_result3x3, h_result5x5, h_result7x7;
d_result3x3.download(h_result3x3);
d_result5x5.download(h_result5x5);
d_result7x7.download(h_result7x7);
cv::imshow("Original Image ", h_img1);
cv::imshow("Blurred_3x3", h_result3x3);
cv::imshow("Blurred_5x5", h_result5x5);
cv::imshow("Blurred_7x7", h_result7x7);
cv::imwrite("Blurred3x3.png", h_result3x3);
cv::imwrite("Blurred5x5.png", h_result5x5);
cv::imwrite("Blurred7x7.png", h_result7x7);
cv::waitKey();
return 0;
}
1.2 高斯滤波
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudafilters.hpp>
int main()
{
cv::Mat h_img1 = cv::imread("images/cameraman.tif", 0);
cv::cuda::GpuMat d_img1, d_result3x3, d_result5x5, d_result7x7;
d_img1.upload(h_img1);
cv::Ptr<cv::cuda::Filter> filter3x3, filter5x5, filter7x7;
filter3x3 = cv::cuda::createGaussianFilter(CV_8UC1, CV_8UC1, cv::Size(3, 3), 1);
filter3x3->apply(d_img1, d_result3x3);
filter5x5 = cv::cuda::createGaussianFilter(CV_8UC1, CV_8UC1, cv::Size(5, 5), 1);
filter5x5->apply(d_img1, d_result5x5);
filter7x7 = cv::cuda::createGaussianFilter(CV_8UC1, CV_8UC1, cv::Size(7, 7), 1);
filter7x7->apply(d_img1, d_result7x7);
cv::Mat h_result3x3, h_result5x5, h_result7x7;
d_result3x3.download(h_result3x3);
d_result5x5.download(h_result5x5);
d_result7x7.download(h_result7x7);
cv::imshow("Original Image ", h_img1);
cv::imshow("Blurred with kernel size 3x3", h_result3x3);
cv::imshow("Blurred with kernel size 5x5", h_result5x5);
cv::imshow("Blurred with kernel size 7x7", h_result7x7);
cv::imwrite("gBlurred3x3.png", h_result3x3);
cv::imwrite("gBlurred5x5.png", h_result5x5);
cv::imwrite("gBlurred7x7.png", h_result7x7);
cv::waitKey();
return 0;
}
1.3 中值滤波
- opencv_cuda提供了中值滤波功能，但是比cpu函数要慢
- cpu代码实现：
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
int main()
{
cv::Mat h_img1 = cv::imread("images/saltpepper.png", 0);
cv::Mat h_result;
cv::medianBlur(h_img1, h_result, 3);
cv::imshow("Original Image ", h_img1);
cv::imshow("Median Blur Result", h_result);
cv::waitKey();
return 0;
}
cpp
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filter3x3 = cv::cuda::createMedianFilter(CV_8UC1, 3);
filter3x3->apply(d_img1, d_result3x3);
2. 高通滤波
- 高通滤波器可去除图像中的低频成分并增强高频成分，它可以去除低频范围内的背景并且增强属于高频成分的边缘
- 常用高频滤波器：Sobel、Scharr、Laplacian
2.1 Sobel滤波器
- 两个检测水平边缘与垂直边缘的核
- 代码实现：
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudafilters.hpp>
#include<opencv2/cudaarithm.hpp>
int main()
{
cv::Mat h_img1 = cv::imread("images/blobs.png", 0);
cv::cuda::GpuMat d_img1, d_resultx, d_resulty, d_resultxy;
d_img1.upload(h_img1);
cv::Ptr<cv::cuda::Filter> filterx, filtery, filterxy;
filterx = cv::cuda::createSobelFilter(CV_8UC1, CV_8UC1, 1, 0);
filterx->apply(d_img1, d_resultx);
filtery = cv::cuda::createSobelFilter(CV_8UC1, CV_8UC1, 0, 1);
filtery->apply(d_img1, d_resulty);
cv::cuda::add(d_resultx, d_resulty, d_resultxy);
cv::Mat h_resultx, h_resulty, h_resultxy;
d_resultx.download(h_resultx);
d_resulty.download(h_resulty);
d_resultxy.download(h_resultxy);
cv::imshow("Original Image ", h_img1);
cv::imshow("Sobel-x derivative", h_resultx);
cv::imshow("Sobel-y derivative", h_resulty);
cv::imshow("Sobel-xy derivative", h_resultxy);
cv::imwrite("sobelx.png", h_resultx);
cv::imwrite("sobely.png", h_resulty);
cv::imwrite("sobelxy.png", h_resultxy);
cv::waitKey();
return 0;
}
2.2 Scharr 滤波器
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudafilters.hpp>
#include<opencv2/cudaarithm.hpp>
int main()
{
cv::Mat h_img1 = cv::imread("images/blobs.png", 0);
cv::cuda::GpuMat d_img1, d_resultx, d_resulty, d_resultxy;
d_img1.upload(h_img1);
cv::Ptr<cv::cuda::Filter> filterx, filtery;
filterx = cv::cuda::createScharrFilter(CV_8UC1, CV_8UC1, 1, 0);
filterx->apply(d_img1, d_resultx);
filtery = cv::cuda::createScharrFilter(CV_8UC1, CV_8UC1, 0, 1);
filtery->apply(d_img1, d_resulty);
cv::cuda::add(d_resultx, d_resulty, d_resultxy);
cv::Mat h_resultx, h_resulty, h_resultxy;
d_resultx.download(h_resultx);
d_resulty.download(h_resulty);
d_resultxy.download(h_resultxy);
cv::imshow("Original Image ", h_img1);
cv::imshow("Scharr-x derivative", h_resultx);
cv::imshow("Scharr-y derivative", h_resulty);
cv::imshow("Scharr-xy derivative", h_resultxy);
cv::imwrite("scharrx.png", h_resultx);
cv::imwrite("scharry.png", h_resulty);
cv::imwrite("scharrxy.png", h_resultxy);
cv::waitKey();
return 0;
}
2.3 Laplacian 滤波
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudafilters.hpp>
#include<opencv2/cudaarithm.hpp>
int main()
{
cv::Mat h_img1 = cv::imread("images/blobs.png", 0);
cv::cuda::GpuMat d_img1, d_result1, d_result3;
d_img1.upload(h_img1);
cv::Ptr<cv::cuda::Filter> filter1, filter3;
filter1 = cv::cuda::createLaplacianFilter(CV_8UC1, CV_8UC1, 1);
filter1->apply(d_img1, d_result1);
filter3 = cv::cuda::createLaplacianFilter(CV_8UC1, CV_8UC1, 3);
filter3->apply(d_img1, d_result3);
cv::Mat h_result1, h_result3;
d_result1.download(h_result1);
d_result3.download(h_result3);
cv::imshow("Original Image ", h_img1);
cv::imshow("Laplacian Filter 1", h_result1);
cv::imshow("Laplacian Filter 3", h_result3);
cv::imwrite("laplacian1.png", h_result1);
cv::imwrite("laplacian3.png", h_result3);
cv::waitKey();
return 0;
}
3. 形态学处理
cpp
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#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudafilters.hpp>
#include<opencv2/cudaarithm.hpp>
int main()
{
cv::Mat h_img1 = cv::imread("images/blobs.png", 0);
cv::cuda::GpuMat d_img1, d_resulte, d_resultd, d_resulto, d_resultc;
cv::Mat element = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 5));
d_img1.upload(h_img1);
cv::Ptr<cv::cuda::Filter> filtere, filterd, filtero, filterc;
filtere = cv::cuda::createMorphologyFilter(cv::MORPH_ERODE, CV_8UC1, element);
filtere->apply(d_img1, d_resulte);
filterd = cv::cuda::createMorphologyFilter(cv::MORPH_DILATE, CV_8UC1, element);
filterd->apply(d_img1, d_resultd);
filtero = cv::cuda::createMorphologyFilter(cv::MORPH_OPEN, CV_8UC1, element);
filtero->apply(d_img1, d_resulto);
filterc = cv::cuda::createMorphologyFilter(cv::MORPH_CLOSE, CV_8UC1, element);
filterc->apply(d_img1, d_resultc);
cv::Mat h_resulte, h_resultd, h_resulto, h_resultc;
d_resulte.download(h_resulte);
d_resultd.download(h_resultd);
d_resulto.download(h_resulto);
d_resultc.download(h_resultc);
cv::imshow("Original Image ", h_img1);
cv::imshow("Erosion", h_resulte);
cv::imshow("Dilation", h_resultd);
cv::imshow("Opening", h_resulto);
cv::imshow("closing", h_resultc);
cv::imwrite("erosion7.png", h_resulte);
cv::imwrite("dilation7.png", h_resultd);
cv::imwrite("opening7.png", h_resulto);
cv::imwrite("closing7.png", h_resultc);
cv::waitKey();
return 0;
}
