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

• 代码实现：

#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

• 代码实现：

#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：

#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;
}

• gpu实现：

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：

#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

• 代码实现：

#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

• 代码实现：

#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

• 代码实现：

#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;
}