opencv c++ Mat CUDA的编译与使用

Mat

构造函数

cv::Mat img ; //默认 定义了一个Mat
img = cv::imread("image.jpg");//除了直接读取，还有通过大小构造等

cv::Mat img = cv::imread("image.png", IMREAD_GRAYSCALE);
cv::Mat img_novel = img;

转换 Mat::convertTo(Mat& m, int rtype, double alpha=1, double beta=0)

• OpenCV中Mat数据类型及相互转换 CV_8U转为CV_16U

转到数组

// https://www.cnblogs.com/xiaoxiaoyibu/p/9491858.html#
//动态创建二维数组，row行col列
    int **La = new int *[row];
    for (int i = 0; i < row; i ++){
        La[i] = new int[col];
    }
    // 循环二维数组和mat，并将mat对应值赋给二维数组对应值，
    for (int i = 0; i < row; i ++){
        for (int j = 0; j < col; j ++){
            La[i][j] = mat.at<uchar>(i, j);
        }
    }
    // 释放分配空间 (直接创建在栈上double La[row][col];就不用delete了)
    for (int i = 0; i < row; i ++){
        delete []La[i];
    }
    delete [] La;

• cv::Mat的许多操作文档

转到std::vector(注：这个例子将二维图像.reshape(0, 1))

//https://book.mynavi.jp/support/pc/opencv2/c3/opencv_mat.html#cv-vec
#include <iostream>
#include <opencv2/core/core.hpp>

#define OPENCV_VERSION(a,b,c) (((a) << 16) + ((b) << 8) + (c))
#define OPENCV_VERSION_CODE OPENCV_VERSION(CV_MAJOR_VERSION, CV_MINOR_VERSION, CV_SUBMINOR_VERSION)

int
main(int argc, char *argv[])
{
   cv::Mat m1 = (cv::Mat_<double>(3,3) << 1, 2, 0, 3, 1, 4, 1, 2, 1);
   std::vector<double> v1;

   // Mat -> vector
   m1 = m1.reshape(0, 1); // 1行の行列に変形
#if OPENCV_VERSION_CODE<OPENCV_VERSION(2,3,0)
   m1.copyTo<double>(v1); // データをコピー
#else
   m1.copyTo(v1); // データをコピー
#endif

   std::cout << "m1=" << m1 << std::endl << std::endl;
   for(std::vector<double>::iterator it=v1.begin(); it!=v1.end(); ++it)
     std::cout << *it << ", ";
   std::cout << std::endl;
   
   // vector -> Mat
   cv::Mat m2(v1); // データを共有
   cv::Mat m3(v1, true); // データをコピー
}

• 还可以去参考一下这个文章

std::vector<uchar> array;
if (mat.isContinuous()) {
  // array.assign(mat.datastart, mat.dataend); // <- has problems for sub-matrix like mat = big_mat.row(i)
  array.assign(mat.data, mat.data + mat.total()*mat.channels());
} else {
  for (int i = 0; i < mat.rows; ++i) {
    array.insert(array.end(), mat.ptr<uchar>(i), mat.ptr<uchar>(i)+mat.cols*mat.channels());
  }
}

操作

• 操作的官方手册

比如：

cv::Mat中最值和均值的求解

保存mat为图片

		vector<int> compression_params;//可选参数
		compression_params.push_back(IMWRITE_PNG_COMPRESSION); //PNG格式图像压缩标志
		compression_params.push_back(9); //设置最高压缩质量
		bool result = imwrite("alpha.png", _frame2, compression_params);

制作掩膜图像

#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/video.hpp>
#include <opencv2/highgui/highgui_c.h>//https://blog.youkuaiyun.com/weixin_44312186/article/details/89000922
using namespace cv;
using namespace std;

void colorReduce(Mat& image, int div)// 这里使用了Mat_instance.at<typename>(i,j) https://www.cnblogs.com/ybqjymy/p/15935894.html
{
	for (int i = 0; i<image.rows; i++)
	{
		for (int j = 0; j<image.cols; j++)
		{
			if (image.at<Vec3b>(i, j)[0] - 0 != 0) {
				image.at<Vec3b>(i, j)[0] = 255;
				image.at<Vec3b>(i, j)[1] = 255;
				image.at<Vec3b>(i, j)[2] = 255;
			}
			//cout<< image.at<Vec3b>(i, j)[0]-0 << endl;//灰度图像元素类型为<uchar>, 彩色图像元素类型为<cv::Vec3b>
		}
	}

	bool result = imwrite("hahaah.png", image);
}

int main(int argc, char** argv) {
	Mat src = imread("C:/Users/admin/Desktop/mask.jpg"); 
	colorReduce(src, 3);
	
	waitKey(0);
	destroyAllWindows();
	return 0;
}

Mat矩阵相乘相关

一个掩膜的例子：
	Mat A = imread("C:/Users/admin/Pictures/mask.jpg");
	Mat B = imread("C:/Users/admin/Pictures/fm.png");
	Mat C = A.mul(B);
	imshow("输入窗口", C);

C++ Opencv split()通道分离函数 merge()通道合并函数 使用操作详解

split(const Mat &src,//输入图像 Mat* mbegin//输出图像的数组)

C++ opencv之图像直方图（calcHist）

绘制

• 点

cv::Point   CvPoint：表示一个坐标为整数的二维点，是一个包含integer类型成员x和y的简单结构体。
cv::scalar   表示四个元素地向量 Scalar(a,b,c) ;//a =blue,b=green,c=red表示RGB的三个通道

• 简单几何

cv::line 线
cv::rectangle 矩形
cv::circle ⚪

CUDA编译

需要有GPU硬件+cudatoolkit+cudnn

需要的源代码 opencv 编译好的build和source(用source部分) + opencv扩展模块的代码

• opencv扩展模块
  

用Cmake编译一下源码，当然官方有编译的教程(并且还有参数细节)：

• 在cmake-gui,先选择源码与目标路径

• config选项

• 设置：OPENCV_EXTRA_MODULES_PATH

• 勾选与cuda有关的

• 再次点击config

• 好多这个错误

=======================================================================
  Couldn't download files from the Internet.
  Please check the Internet access on this host.
=======================================================================

CMake Warning at cmake/OpenCVDownload.cmake:248 (message):
  IPPICV: Download failed: 6;"Couldn't resolve host name"

  For details please refer to the download log file:

  C:/Users/admin/Documents/Visual Studio
  2015/Projects/opencv-4.6.0-vc14_vc15/opencv/build/CMakeDownloadLog.txt

Call Stack (most recent call first):
  3rdparty/ippicv/ippicv.cmake:37 (ocv_download)
  cmake/OpenCVFindIPP.cmake:259 (download_ippicv)
  cmake/OpenCVFindLibsPerf.cmake:12 (include)
  CMakeLists.txt:733 (include)

• 解决 IPPICV: Downloading ippicv_2020_win_intel64_20191018_general.zip from https://raw.githubusercontent.com/opencv/opencv_3rdparty/a56b6ac6f030c312b2dce17430eef13aed9af274/ippicv/ippicv_2020_win_intel64_20191018_general.zip

• 新增的是关于contribute模块的

• 

• 继续config

• 

• 最后generate

• 

• 有错误手动下载
  

• 双击 C:\Users\admin\Documents\Visual Studio 2015\Projects\opencv-4.6.0-vc14_vc15\opencv\build\OpenCV.sln

• 然后下边的INSTALL生成

• C:\Users\admin\Documents\Visual Studio 2015\Projects\opencv-4.6.0-vc14_vc15\opencv\build\install
• 就可以将以下bin加到环境变量中了

• 将C:\Users\admin\Documents\Visual Studio 2015\Projects\opencv-4.6.0-vc14_vc15\opencv\build\install\x64\vc14\bin加到path(或者修改项目属性中，配置属性–》链接器–>常规–》附加库目录 中加上xxx.lib所在目录。)
  

添加链接描述

简单使用

• cuda的简单使用
• 设置完重启就不会有这个错误了
  

GpuMat cv::gpu::GpuMat（在Python是cv2.cuda_GpuMat）

• 主要看的这个，略微修改与注释
• 官方文档

upload+download()

#include <opencv2/highgui.hpp>
#include <opencv2/cudaimgproc.hpp>
 
cv::Mat img = cv::imread("image.png", IMREAD_GRAYSCALE);
cv::cuda::GpuMat dst, src;
src.upload(img);
 
cv::Ptr<cv::cuda::CLAHE> ptr_clahe = cv::cuda::createCLAHE(5.0, cv::Size(8, 8));
ptr_clahe->apply(src, dst);
 
cv::Mat result;
dst.download(result);
 
cv::imshow("result", result);
cv::waitKey();

# python 实现
img = cv2.imread("image.png", cv2.IMREAD_GRAYSCALE)
src = cv2.cuda_GpuMat()
src.upload(img)
 
clahe = cv2.cuda.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8))# 使用对比度受限自适应直方图均衡化来均衡灰度图像的直方图。  https://docs.opencv.org/3.4/db/d79/classcv_1_1cuda_1_1CLAHE.html
dst = clahe.apply(src, cv2.cuda_Stream.Null())# 使用对比度受限自适应直方图均衡化来均衡灰度图像的直方图 https://docs.opencv.org/4.x/d6/db6/classcv_1_1CLAHE.html
 
result = dst.download()
 
cv2.imshow("result", result)
cv2.waitKey(0)

使用多GPU 略

打开视频流（这没有用到cuda）

// init video capture with video
VideoCapture capture(videoFileName);
if (!capture.isOpened())
{
    // error in opening the video file
    cout << "Unable to open file!" << endl;
    return;
}
 
// get default video FPS
double fps = capture.get(CAP_PROP_FPS);
 
// get total number of video frames
int num_frames = int(capture.get(CAP_PROP_FRAME_COUNT));

• python实现

# init video capture with video
cap = cv2.VideoCapture(video)
 
# get default video FPS
fps = cap.get(cv2.CAP_PROP_FPS)
 
# get total number of video frames
num_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)

读取视频（这里我跳过了源文章地大段基本c++方法地解释）

• 请注意，我们使用相同的 CPU 函数来读取和调整大小，但将结果上传到cv::cuda::GpuMat (cuda_GpuMat)实例：

// resize frame
cv::resize(frame, frame, Size(960, 540), 0, 0, INTER_LINEAR);
 
// convert to gray
cv::cvtColor(frame, previous_frame, COLOR_BGR2GRAY);
 
// upload pre-processed frame to GPU
cv::cuda::GpuMat gpu_previous;
gpu_previous.upload(previous_frame);
 
// declare cpu outputs for optical flow
cv::Mat hsv[3], angle, bgr;
 
// cv::cuda::GpuMat声明  declare gpu outputs for optical flow
cv::cuda::GpuMat gpu_magnitude, gpu_normalized_magnitude, gpu_angle;
cv::cuda::GpuMat gpu_hsv[3], gpu_merged_hsv, gpu_hsv_8u, gpu_bgr;
 
// set saturation to 1
hsv[1] = cv::Mat::ones(frame.size(), CV_32F);
gpu_hsv[1].upload(hsv[1]);

• python实现

# proceed if frame reading was successful
if ret:
    # resize frame
    frame = cv2.resize(previous_frame, (960, 540))
 
    # upload resized frame to GPU  --- 多了这一段
    gpu_frame = cv2.cuda_GpuMat()
    gpu_frame.upload(frame)
 
    # convert to gray  ---- 没变
    previous_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
    # upload pre-processed frame to GPU
    gpu_previous = cv2.cuda_GpuMat()
    gpu_previous.upload(previous_frame)
 
    # create gpu_hsv output for optical flow  ----- hsv = np.zeros_like(frame, np.float32)
    gpu_hsv = cv2.cuda_GpuMat(gpu_frame.size(), cv2.CV_32FC3)
    gpu_hsv_8u = cv2.cuda_GpuMat(gpu_frame.size(), cv2.CV_8UC3)
 
    gpu_h = cv2.cuda_GpuMat(gpu_frame.size(), cv2.CV_32FC1)
    gpu_s = cv2.cuda_GpuMat(gpu_frame.size(), cv2.CV_32FC1)
    gpu_v = cv2.cuda_GpuMat(gpu_frame.size(), cv2.CV_32FC1)
 
    # set saturation to 1   ----  hsv[..., 1] = 1.0
    gpu_s.upload(np.ones_like(previous_frame, np.float32))

读取视频帧

while (true)//我删除了计时部分
{

    // capture frame-by-frame
    capture >> frame;if (frame.empty())break;
 
    // 增加了上传
    cv::cuda::GpuMat gpu_frame;  gpu_frame.upload(frame);
 
    // resize frame
    cv::cuda::resize(gpu_frame, gpu_frame, Size(960, 540), 0, 0, INTER_LINEAR);//类似 cv::resize(frame, frame, Size(960, 540), 0, 0, INTER_LINEAR);
 
    // 这里也类似cv::Mat换成 cv::cuda::GpuMat，cv::cvtColor换成cv::cuda::cvtColor
    cv::cuda::GpuMat gpu_current;
    cv::cuda::cvtColor(gpu_frame, gpu_current, COLOR_BGR2GRAY);

计算密集光流

// calculate optical flow
cv::Mat flow;
calcOpticalFlowFarneback(previous_frame, current_frame, flow, 0.5, 5, 15, 3, 5, 1.2, 0);

// create optical flow instance
Ptr<cuda::FarnebackOpticalFlow> ptr_calc = cuda::FarnebackOpticalFlow::create(5, 0.5, false, 15, 3, 5, 1.2, 0);
// calculate optical flow
cv::cuda::GpuMat gpu_flow;
ptr_calc->calc(gpu_previous, gpu_current, gpu_flow);

可视化前处理

// start post-process timer
auto start_post_time = high_resolution_clock::now();
 
// split the output flow into 2 vectors
cv::Mat flow_xy[2], flow_x, flow_y;
split(flow, flow_xy);
 
// get the result
flow_x = flow_xy[0];
flow_y = flow_xy[1];
 
// convert from cartesian to polar coordinates
cv::cartToPolar(flow_x, flow_y, magnitude, angle, true);
// normalize magnitude from 0 to 1
cv::normalize(magnitude, normalized_magnitude, 0.0, 1.0, NORM_MINMAX);
// get angle of optical flow
angle *= ((1 / 360.0) * (180 / 255.0));
 
// build hsv image
hsv[0] = angle;
hsv[2] = normalized_magnitude;
merge(hsv, 3, merged_hsv);
 
// multiply each pixel value to 255
merged_hsv.convertTo(hsv_8u, CV_8U, 255);
 
// convert hsv to bgr
cv::cvtColor(hsv_8u, bgr, COLOR_HSV2BGR);
 
// update previous_frame value
previous_frame = current_frame;

// start post-process timer
auto start_post_time = high_resolution_clock::now();
 
// split the output flow into 2 vectors
cv::cuda::GpuMat gpu_flow_xy[2];
cv::cuda::split(gpu_flow, gpu_flow_xy);
 
// convert from cartesian to polar coordinates
cv::cuda::cartToPolar(gpu_flow_xy[0], gpu_flow_xy[1], gpu_magnitude, gpu_angle, true);
 
// normalize magnitude from 0 to 1
cv::cuda::normalize(gpu_magnitude, gpu_normalized_magnitude, 0.0, 1.0, NORM_MINMAX, -1);
 
// get angle of optical flow
gpu_angle.download(angle);
angle *= ((1 / 360.0) * (180 / 255.0));
 
// build hsv image
gpu_hsv[0].upload(angle);
gpu_hsv[2] = gpu_normalized_magnitude;
cv::cuda::merge(gpu_hsv, 3, gpu_merged_hsv);
 
// multiply each pixel value to 255
gpu_merged_hsv.cv::cuda::GpuMat::convertTo(gpu_hsv_8u, CV_8U, 255.0);
 
// convert hsv to bgr
cv::cuda::cvtColor(gpu_hsv_8u, gpu_bgr, COLOR_HSV2BGR);
 
// send original frame from GPU back to CPU
gpu_frame.download(frame);
 
// send result from GPU back to CPU !!!!!!
gpu_bgr.download(bgr);
 
// update previous_frame value
gpu_previous = gpu_current;

可视化（不变）

// visualization
imshow("original", frame);
imshow("result", bgr);
int keyboard = waitKey(1);
if (keyboard == 27)
    break;

CG

• opencv中文war3school

learnopencv

添加链接描述
https://github.com/spmallick/learnopencv/blob/master/README.md

注：直接选中额外模块的源代码是无法编译的

• 这个教程挺详细的，
• 这个版本是在04：38 时有额外路径的选项，和这个文字教程一样，stackoverflow的英文教程

还有直接建议复制源码过去的Pasted the modules I want from opencv_contrib into opencv/modules.