使用Opencv_CUDA 模块实现基本计算机视觉程序
- CUDA提供了出色的接口，发挥GPU的并行计算能力来加速复杂的计算应用程序
- 利用CUDA和Opencv的功能实现计算机视觉应用
1. 对图像的算术和逻辑运算
- 两个图像相加
#include <iostream>
#include "opencv2/opencv.hpp"
#include<cudaarithm.hpp>
int main(int argc, char* argv[])
{
//Read Two Images
cv::Mat h_img1 = cv::imread("images/cameraman.tif");
cv::Mat h_img2 = cv::imread("images/circles.png");
//Create Memory for storing Images on device
cv::cuda::GpuMat d_result1, d_img1, d_img2;
cv::Mat h_result1;
//Upload Images to device
d_img1.upload(h_img1);
d_img2.upload(h_img2);
cv::cuda::add(d_img1, d_img2, d_result1);
//Download Result back to host
d_result1.download(h_result1);
cv::imshow("Image1 ", h_img1);
cv::imshow("Image2 ", h_img2);
cv::imshow("Result addition ", h_result1);
cv::imwrite("images/result_add.png", h_result1);
cv::waitKey();
return 0;
}
- 图像相减：
//2.图像相减
cv::cuda::subtract(d_img1, d_img2, d_result1);
- 图像合成：
result = a * img1 + b * img2
//3.图像合成
cv::cuda::addWeighted(d_img1, 0.7, d_img2, 0.3, 0, d_result1);
- 图像反转：
//4.图像反转
cv::cuda::bitwise_not(d_img1, d_result1);
//others:
cv::cuda::bitwise_and(d_img1, d_img2, d_result1);
cv::cuda::bitwise_or(d_img1, d_img2, d_result1);
cv::cuda::bitwise_xor(d_img1, d_img2, d_result1);
2. 更改图像的色彩空间
- 颜色空间（BGR）、HSV(色调、饱和度、亮度）
- Opencv支持许多其他可用的颜色空间，例如 XYZ、HLS、Lab等
- 转换代码如下：
#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudaimgproc.hpp>
int main(int argc, char* argv[])
{
cv::Mat h_img1 = cv::imread("images/autumn.tif");
//Define device variables
cv::cuda::GpuMat d_result1, d_result2, d_result3, d_result4, d_img1;
//Upload Image to device
d_img1.upload(h_img1);
//Convert image to different color spaces
cv::cuda::cvtColor(d_img1, d_result1, cv::COLOR_BGR2GRAY);
cv::cuda::cvtColor(d_img1, d_result2, cv::COLOR_BGR2RGB);
cv::cuda::cvtColor(d_img1, d_result3, cv::COLOR_BGR2HSV);
cv::cuda::cvtColor(d_img1, d_result4, cv::COLOR_BGR2YCrCb);
cv::Mat h_result1, h_result2, h_result3, h_result4;
//Download results back to host
d_result1.download(h_result1);
d_result2.download(h_result2);
d_result3.download(h_result3);
d_result4.download(h_result4);
cv::imshow("Src image ", h_img1);
cv::imshow("Result in Gray ", h_result1);
cv::imshow("Result in RGB", h_result2);
cv::imshow("Result in HSV ", h_result3);
cv::imshow("Result in YCrCb ", h_result4);
cv::waitKey();
return 0;
}
3. 图像阈值处理
- 函数：cv::cuda::threshold
- 常用阈值处理类型：
cv::THRESH_BINARY： 大于阈值的像素为255，否则为0
cv::THRESH_BINARY_INV： 大于阈值的像素为0，否则为255
cv::THRESH_TRUNC： 大于阈值的像素为阈值，否则不变
cv::THRESH_TOZERO： 大于阈值的像素为不变，否则为0
cv::THRESH_TOZERO_INV： 大于阈值的像素为0，否则为不变
- 实现代码如下：
#include <iostream>
#include "opencv2/opencv.hpp"
#include<opencv2/cudaarithm.hpp>
int main(int argc, char* argv[])
{
cv::Mat h_img1 = cv::imread("images/cameraman.tif", 0);
//Define device variables
cv::cuda::GpuMat d_result1, d_result2, d_result3, d_result4, d_result5, d_img1;
//Upload image on device
d_img1.upload(h_img1);
//Perform different thresholding techniques on device
cv::cuda::threshold(d_img1, d_result1, 128.0, 255.0, cv::THRESH_BINARY);
cv::cuda::threshold(d_img1, d_result2, 128.0, 255.0, cv::THRESH_BINARY_INV);
cv::cuda::threshold(d_img1, d_result3, 128.0, 255.0, cv::THRESH_TRUNC);
cv::cuda::threshold(d_img1, d_result4, 128.0, 255.0, cv::THRESH_TOZERO);
cv::cuda::threshold(d_img1, d_result5, 128.0, 255.0, cv::THRESH_TOZERO_INV);
cv::Mat h_result1, h_result2, h_result3, h_result4, h_result5;
//Copy results back to host
d_result1.download(h_result1);
d_result2.download(h_result2);
d_result3.download(h_result3);
d_result4.download(h_result4);
d_result5.download(h_result5);
cv::namedWindow("THRESH_BINARY", 0);
cv::namedWindow("THRESH_BINARY_INV", 0);
cv::namedWindow("THRESH_TRUNC", 0);
cv::namedWindow("THRESH_TOZERO", 0);
cv::namedWindow("THRESH_TOZERO_INV", 0);
cv::imshow("THRESH_BINARY", h_result1);
cv::imshow("THRESH_BINARY_INV", h_result2);
cv::imshow("THRESH_TRUNC", h_result3);
cv::imshow("THRESH_TOZERO", h_result4);
cv::imshow("THRESH_TOZERO_INV", h_result5);
cv::waitKey();
return 0;
}
