人工智能 计算机视觉 算法 Opencv将数据保存到xml、yaml / 从xml、yaml读取数据

Opencv将数据保存到xml、yaml / 从xml、yaml读取数据

Opencv提供了读写xml、yaml的类实现： 本文重点参考：https://blog.csdn.net/cd_yourheart/article/details/122705776?spm=1001.2014.3001.5506，并将给出文件读写的具体使用实例。

1. 官方例程

1.1 写数据

#include "opencv2/core.hpp"

#include

using namespace cv;

int main(int, char** argv)

{

FileStorage fs("test.yml", FileStorage::WRITE);

fs << "frameCount" << 5;

time_t rawtime; time(&rawtime);

fs << "calibrationDate" << asctime(localtime(&rawtime));

Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1);

Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0);

fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs;

fs << "features" << "[";

for( int i = 0; i < 3; i++ )

{

int x = rand() % 640;

int y = rand() % 480;

uchar lbp = rand() % 256;

fs << "{:" << "x" << x << "y" << y << "lbp" << "[:";

for( int j = 0; j < 8; j++ )

fs << ((lbp >> j) & 1);

fs << "]" << "}";

}

fs << "]";

fs.release();

return 0;

}

output :

%YAML:1.0

frameCount: 5

calibrationDate: "Fri Jun 17 14:09:29 2011\n"

cameraMatrix: !!opencv-matrix

rows: 3

cols: 3

dt: d

data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ]

distCoeffs: !!opencv-matrix

rows: 5

cols: 1

dt: d

data: [ 1.0000000000000001e-01, 1.0000000000000000e-02,

-1.0000000000000000e-03, 0., 0. ]

features:

- { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] }

- { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] }

- { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] }

1.2 读数据

FileStorage fs2("test.yml", FileStorage::READ);

// first method: use (type) operator on FileNode.

int frameCount = (int)fs2["frameCount"];

String date;

// second method: use FileNode::operator >>

fs2["calibrationDate"] >> date;

Mat cameraMatrix2, distCoeffs2;

fs2["cameraMatrix"] >> cameraMatrix2;

fs2["distCoeffs"] >> distCoeffs2;

cout << "frameCount: " << frameCount << endl

<< "calibration date: " << date << endl

<< "camera matrix: " << cameraMatrix2 << endl

<< "distortion coeffs: " << distCoeffs2 << endl;

FileNode features = fs2["features"];

FileNodeIterator it = features.begin(), it_end = features.end();

int idx = 0;

std::vector lbpval;

// iterate through a sequence using FileNodeIterator

for( ; it != it_end; ++it, idx++ )

{

cout << "feature #" << idx << ": ";

cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";

// you can also easily read numerical arrays using FileNode >> std::vector operator.

(*it)["lbp"] >> lbpval;

for( int i = 0; i < (int)lbpval.size(); i++ )

cout << " " << (int)lbpval[i];

cout << ")" << endl;

}

fs2.release();

2. 读写xml

#include

#include "opencv2/opencv.hpp"

using namespace std;

#define WRITE_OR_READ

int main() {

//===========将数据写入到xml文件中================

#ifdef WRITE_OR_READ

string name = "insomnia";

int age = 18;

float height = 1.83;

char sex = 'M';

cv::Mat matrix_eye = cv::Mat::eye(3, 3, CV_64F);

cv::FileStorage fs("./test.xml", cv::FileStorage::WRITE);//会覆盖当前文件，不存在则新建文件

if (fs.isOpened())

{

fs << "name" << name << "age" << age << "height" << height << "sex" << sex;//可以连续写入

fs << "matrix_eye" << matrix_eye;//也可以依次写入

fs.release();//release after used

}

//===========从xml文件中读取数据================

#else

string name;

int age;

float height;

char sex;

cv::Mat matrix_eye;

cv::FileStorage fs("./test.xml", cv::FileStorage::READ);

if (fs.isOpened()) {

fs["name"] >> name;

fs["age"] >> age;

fs["height"] >> height;

int temp;

fs["sex"] >> temp;//这里不能直接读到char，所以转换了一下

sex = (char)temp;

fs["matrix_eye"] >> matrix_eye;

fs.release();

cout << "name: " << name << endl;

cout << "age: " << age << endl;

cout << "height: " << height << endl;

cout << "sex: " << sex << endl;

cout << "matrix_eye: " << endl << matrix_eye << endl;

cout << "matrix_eye.size().height: " << matrix_eye.size().height << endl;

cout << "matrix_eye.at(1, 0): " << matrix_eye.at(1, 0) << endl;

}

#endif

return 0;

}

将数据写入到xml文件后，打开查看一下 格式是自动生成的，只是将数据填充了进去。可以看到第一行是xml版本信息，不用考虑。第二行和最后一行是最外层的标签。

然后所有保存的数据都是一个并列的关系，同等级。只是像Mat这种数据类型，又有细分属性而已。

从xml文件中读取一下数据，看下输出结果

3. 读写yaml

#include

#include

using namespace std;

using namespace cv;

//#define WRITE_OR_READ

int main()

{

#ifdef WRITE_OR_READ

//1.创建文件

cv::FileStorage fwrite("./test.yaml", cv::FileStorage::WRITE);

//2.写入数据

string name = "insomnia";

int age = 18;

float height = 1.83;

char sex = 'M';

cv::Mat matrix_eye = cv::Mat::eye(3, 3, CV_64F);

fwrite << "name " << name;

fwrite << "age " << age;

fwrite << "height " << height;

fwrite << "sex " << sex;

fwrite << "matrix_eye " << matrix_eye;

//3.关闭文件

fwrite.release();

return 0;

#else

//1.读取文件指针

string strSettingsFile = "./test.yaml";

cv::FileStorage fread(strSettingsFile.c_str(), cv::FileStorage::READ);

//2.判断是否打开成功

if (!fread.isOpened())

{

cout << "Failed to open settings file at: " << strSettingsFile << endl;

return 0;

}

else cout << "success to open file at: " << strSettingsFile << endl;

//3.打开文件后读取数据

string name;

int age;

float height;

char sex;

cv::Mat matrix_eye;

fread["name"] >> name;

fread["age"] >> age;

fread["height"] >> height;

int temp;

fread["sex"] >> temp;

sex = (char)temp;

fread["matrix_eye"] >> matrix_eye;

cout << "name=" << name << endl;

cout << "age=" << age << endl;

cout << "height=" << height << endl;

cout << "sex=" << sex << endl;

cout << "matrix_eye=" << endl << matrix_eye << endl;

cout << matrix_eye.size().height << endl;

//4.关闭文件

fread.release();

return 0;

#endif

}

保存的yaml文件 读取文件的结果

4. 保存矩阵与点集

//写数据

cv::FileStorage fs;

std::string label_ = "abc.xml";

fs.open(label_.c_str(), cv::FileStorage::WRITE);

std::string str_ = "image" + std::to_string(i+1);

cv::Mat _pts(p_result); //p_result define:std::vectorp_result;

fs << str_ << _pts;

fs.release();

//**************************

//读数据

cv::FileStorage fs;

fs.open( "abc.xml", cv::FileStorage::READ);

cv::Mat m_pts;

fs[str] >> m_pts;

std::vectorpts;

for (int i = 1; i < m_pts.rows; ++i)

{

cv::Point2f _pt(m_pts.ptr(i, 0)[0], m_pts.ptr(i, 0)[1]);

pts.push_back(_pt);

std::cout << _pt << "\n";

}

5.读写 xml/yaml使用实例

5.1 写入

使用以下过程将内容写入 XML、YAML 或 JSON：

- 创建新的 FileStorage 并打开它进行写入。可以通过对采用文件名的 FileStorage：：FileStorage 构造函数的单个调用来完成此操作，

也可以使用默认构造函数，然后调用 FileStorage：：open。文件的格式（XML、YAML 或 JSON）由文件扩展名确定（分别为“.xml”、

“.yml”/“.yaml”和“.json”）。

- 使用 streaming operator 写入所需的所有数据，就像在 STL 流中一样。

- 使用 FileStorage：：release 关闭文件。FileStorage 析构函数也会关闭该文件。

#include

#include

using namespace std;

using namespace cv;

typedef struct

{

int x;

int y;

string s;

}test_t;

int main(int argc, char** argv)

{

FileStorage fs("test.xml", FileStorage::WRITE); //填入写操作

//测试数据

int a1 = 2;

char a2 = -1;

string str = "hello sysu!";

int arr[10] = { 1,2,3,4,5,6,7,8,9,10 };

test_t t = { 3,4,"hi sysu" };

map m;

m["kobe"] = 100;

m["james"] = 99;

m["curry"] = 98;

//写入文件操作,先写标注在写数据

fs << "int_data" << a1;

fs << "char_data" << a2;

fs << "string_data" << str;

//写入数组

fs <<"array_data"<< "["; //数组开始

for (int i = 0; i < 10; i++)

{

fs << arr[i];

}

fs << "]"; //数组结束

//写入结构体

fs << "struct_data" << "{"; //结构体开始

fs << "x" << t.x;

fs << "y" << t.y;

fs << "s" << t.s;

fs << "}"; //结构结束

//map的写入

fs << "map_data" << "{"; //map的开始写入

map::iterator it = m.begin();

for (; it != m.end(); it++)

{

fs << it->first << it->second;

}

fs << "}"; //map写入结束

return 0;

}

其结果：

%YAML:1.0

frameCount: 5

calibrationDate: "Fri Jun 17 14:09:29 2011\n"

cameraMatrix: !!opencv-matrix

rows: 3

cols: 3

dt: d

data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ]

distCoeffs: !!opencv-matrix

rows: 5

cols: 1

dt: d

data: [ 1.0000000000000001e-01, 1.0000000000000000e-02,

-1.0000000000000000e-03, 0., 0. ]

features:

- { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] }

- { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] }

- { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] }

5.2 读取

要读取以前编写的 XML、YAML 或 JSON 文件，请执行以下操作：

- 使用 FileStorage：：FileStorage 构造函数或 FileStorage：：open 方法打开文件存储。在当前实现中，

整个文件被解析，文件存储的整个表示形式作为文件节点的层次结构构建在内存中（参见 FileNode)

- 读取您感兴趣的数据。使用 FileStorage：：operator []、FileNode：：operator [] 和/或 FileNodeIterator。

- 使用 FileStorage：：release 关闭存储。

代码实现：

#include

#include

using namespace std;

using namespace cv;

typedef struct

{

int x;

int y;

string s;

}test_t;

int main(int argc, char** argv)

{

FileStorage fs2("test.yml", FileStorage::READ);

// first method: use (type) operator on FileNode.

int frameCount = (int)fs2["frameCount"];

String date;

// second method: use FileNode::operator >>

fs2["calibrationDate"] >> date;

Mat cameraMatrix2, distCoeffs2;

fs2["cameraMatrix"] >> cameraMatrix2;

fs2["distCoeffs"] >> distCoeffs2;

cout << "frameCount: " << frameCount << endl

<< "calibration date: " << date << endl

<< "camera matrix: " << cameraMatrix2 << endl

<< "distortion coeffs: " << distCoeffs2 << endl;

FileNode features = fs2["features"];

FileNodeIterator it = features.begin(), it_end = features.end();

int idx = 0;

std::vector lbpval;

// iterate through a sequence using FileNodeIterator

for( ; it != it_end; ++it, idx++ )

{

cout << "feature #" << idx << ": ";

cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";

// you can also easily read numerical arrays using FileNode >> std::vector operator.

(*it)["lbp"] >> lbpval;

for( int i = 0; i < (int)lbpval.size(); i++ )

cout << " " << (int)lbpval[i];

cout << ")" << endl;

}

fs2.release();

return 0;

}

参考：

1.OpenCV 读写xml和yml文件

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