使用 XML / YAML / JSON 文件进行文件输入输出
你将找到以下问题的答案：
- 如何使用 YAML、XML 或 JSON 文件在 OpenCV 中向文件打印和读取文本条目？
- 如何对 OpenCV 数据结构执行相同的操作？
- 如何对自定义数据结构执行此操作？
- 如何使用 OpenCV 数据结构，例如 cv::FileStorage、cv::FileNode 或 cv::FileNodeIterator。
C++
你可以从此处下载，也可以在 OpenCV 源码库的 samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp 中找到它。
下面是一份实现目标清单中所有内容的示例代码：
#include <opencv2/core.hpp>#include <iostream>#include <string>
using namespace cv;using namespace std;
static void help(char** av){ cout << endl << av[0] << " shows the usage of the OpenCV serialization functionality." << endl << endl << "usage: " << endl << av[0] << " [output file name] (default outputfile.yml.gz)" << endl << endl << "The output file may be XML (xml), YAML (yml/yaml), or JSON (json)." << endl << "You can even compress it by specifying this in its extension like xml.gz yaml.gz etc... " << endl << "With FileStorage you can serialize objects in OpenCV by using the << and >> operators" << endl << "For example: - create a class and have it serialized" << endl << " - use it to read and write matrices." << endl << endl;}
class MyData{public: MyData() : A(0), X(0), id() {} explicit MyData(int) : A(97), X(CV_PI), id("mydata1234") // explicit to avoid implicit conversion {} void write(FileStorage& fs) const //Write serialization for this class { fs << "{" << "A" << A << "X" << X << "id" << id << "}"; } void read(const FileNode& node) //Read serialization for this class { A = (int)node["A"]; X = (double)node["X"]; id = (string)node["id"]; }public: // Data Members int A; double X; string id;};
//These write and read functions must be defined for the serialization in FileStorage to workstatic void write(FileStorage& fs, const std::string&, const MyData& x){ x.write(fs);}static void read(const FileNode& node, MyData& x, const MyData& default_value = MyData()){ if(node.empty()) x = default_value; else x.read(node);}
// This function will print our custom class to the consolestatic ostream& operator<<(ostream& out, const MyData& m){ out << "{ id = " << m.id << ", "; out << "X = " << m.X << ", "; out << "A = " << m.A << "}"; return out;}
int main(int ac, char** av){ string filename;
if (ac != 2) { help(av); filename = "outputfile.yml.gz"; } else filename = av[1];
{ //write Mat R = Mat_<uchar>::eye(3, 3), T = Mat_<double>::zeros(3, 1); MyData m(1);
FileStorage fs(filename, FileStorage::WRITE); // or: // FileStorage fs; // fs.open(filename, FileStorage::WRITE);
fs << "iterationNr" << 100; fs << "strings" << "["; // text - string sequence fs << "image1.jpg" << "Awesomeness" << "../data/baboon.jpg"; fs << "]"; // close sequence
fs << "Mapping"; // text - mapping fs << "{" << "One" << 1; fs << "Two" << 2 << "}";
fs << "R" << R; // cv::Mat fs << "T" << T;
fs << "MyData" << m; // your own data structures
fs.release(); // explicit close cout << "Write operation to file:" << filename << " completed successfully." << endl; }
{//read cout << endl << "Reading: " << endl; FileStorage fs; fs.open(filename, FileStorage::READ);
int itNr; //fs["iterationNr"] >> itNr; itNr = (int) fs["iterationNr"]; cout << itNr; if (!fs.isOpened()) { cerr << "Failed to open " << filename << endl; help(av); return 1; }
FileNode n = fs["strings"]; // Read string sequence - Get node if (n.type() != FileNode::SEQ) { cerr << "strings is not a sequence! FAIL" << endl; return 1; }
FileNodeIterator it = n.begin(), it_end = n.end(); // Go through the node for (; it != it_end; ++it) cout << (string)*it << endl;
n = fs["Mapping"]; // Read mappings from a sequence cout << "Two " << (int)(n["Two"]) << "; "; cout << "One " << (int)(n["One"]) << endl << endl;
MyData m; Mat R, T;
fs["R"] >> R; // Read cv::Mat fs["T"] >> T; fs["MyData"] >> m; // Read your own structure_
cout << endl << "R = " << R << endl; cout << "T = " << T << endl << endl; cout << "MyData = " << endl << m << endl << endl;
//Show default behavior for non existing nodes cout << "Attempt to read NonExisting (should initialize the data structure with its default)."; fs["NonExisting"] >> m; cout << endl << "NonExisting = " << endl << m << endl; }
cout << endl << "Tip: Open up " << filename << " with a text editor to see the serialized data." << endl;
return 0;}Python
你可以从此处下载，也可以在 OpenCV 源码库的 samples/python/tutorial_code/core/file_input_output/file_input_output.py 中找到它。
from __future__ import print_function
import numpy as npimport cv2 as cvimport sys
def help(filename): print ( ''' {0} shows the usage of the OpenCV serialization functionality. \n\n usage:\n python3 {0} [output file name] (default outputfile.yml.gz)\n\n The output file may be XML (xml), YAML (yml/yaml), or JSON (json).\n You can even compress it by specifying this in its extension like xml.gz yaml.gz etc...\n With FileStorage you can serialize objects in OpenCV.\n\n For example: - create a class and have it serialized\n - use it to read and write matrices.\n '''.format(filename) )
class MyData: A = 97 X = np.pi name = 'mydata1234'
def __repr__(self): s = '{ name = ' + self.name + ', X = ' + str(self.X) s = s + ', A = ' + str(self.A) + '}' return s
def write(self, fs, name): fs.startWriteStruct(name, cv.FileNode_MAP|cv.FileNode_FLOW) fs.write('A', self.A) fs.write('X', self.X) fs.write('name', self.name) fs.endWriteStruct()
def read(self, node): if (not node.empty()): self.A = int(node.getNode('A').real()) self.X = node.getNode('X').real() self.name = node.getNode('name').string() else: self.A = self.X = 0 self.name = ''
def main(argv): if len(argv) != 2: help(argv[0]) filename = 'outputfile.yml.gz' else : filename = argv[1]
# write R = np.eye(3,3) T = np.zeros((3,1)) m = MyData()
s = cv.FileStorage(filename, cv.FileStorage_WRITE) # or: # s = cv.FileStorage() # s.open(filename, cv.FileStorage_WRITE)
s.write('iterationNr', 100)
s.startWriteStruct('strings', cv.FileNode_SEQ) for elem in ['image1.jpg', 'Awesomeness', '../data/baboon.jpg']: s.write('', elem) s.endWriteStruct()
s.startWriteStruct('Mapping', cv.FileNode_MAP) s.write('One', 1) s.write('Two', 2) s.endWriteStruct()
s.write('R_MAT', R) s.write('T_MAT', T)
m.write(s, 'MyData') s.release() print ('Write operation to file:', filename, 'completed successfully.')
# read print ('\nReading: ') s = cv.FileStorage() s.open(filename, cv.FileStorage_READ)
n = s.getNode('iterationNr') itNr = int(n.real()) print (itNr)
if (not s.isOpened()): print ('Failed to open ', filename, file=sys.stderr) help(argv[0]) exit(1)
n = s.getNode('strings') if (not n.isSeq()): print ('strings is not a sequence! FAIL', file=sys.stderr) exit(1)
for i in range(n.size()): print (n.at(i).string())
n = s.getNode('Mapping') print ('Two',int(n.getNode('Two').real()),'; ') print ('One',int(n.getNode('One').real()),'\n')
R = s.getNode('R_MAT').mat() T = s.getNode('T_MAT').mat() m.read(s.getNode('MyData'))
print ('\nR =',R) print ('T =',T,'\n') print ('MyData =','\n',m,'\n')
print ('Attempt to read NonExisting (should initialize the data structure', 'with its default).') m.read(s.getNode('NonExisting')) print ('\nNonExisting =','\n',m)
print ('\nTip: Open up',filename,'with a text editor to see the serialized data.')
if __name__ == '__main__': main(sys.argv)这里我们只讨论 XML、YAML 和 JSON 文件的输入。你的输出文件（及其对应的输入文件）只能是这些扩展名之一，并具有相应的结构。你可以序列化两种数据结构：映射（mapping，如 STL map 和 Python 字典）和元素序列（element sequence，如 STL vector）。两者的区别在于：在 map 中每个元素都有一个唯一的名称，可以通过它进行访问；而对于序列，你需要遍历它们才能查询某个特定项。
-
XML/YAML/JSON 文件的打开与关闭。 在向这类文件写入任何内容之前，你需要先打开它，并在结束时关闭它。OpenCV 中的 XML/YAML/JSON 数据结构是 cv::FileStorage。要指定该结构绑定到硬盘上的哪个文件，你可以使用它的构造函数或它的 open() 函数：
C++
FileStorage fs(filename, FileStorage::WRITE);// or:// FileStorage fs;// fs.open(filename, FileStorage::WRITE);Python
s = cv.FileStorage(filename, cv.FileStorage_WRITE)# or:# s = cv.FileStorage()# s.open(filename, cv.FileStorage_WRITE)无论使用哪种方式，第二个参数都是一个常量，用于指定你能对其执行的操作类型：WRITE、READ 或 APPEND。文件名中指定的扩展名也决定了将使用的输出格式。如果你指定类似 .xml.gz 的扩展名，输出甚至可以被压缩。
当 cv::FileStorage 对象被销毁时，文件会自动关闭。不过，你也可以使用 release 函数显式地关闭它：
C++
fs.release(); // explicit closePython
s.release() -
文本和数字的输入与输出。 在 C++ 中，该数据结构使用 STL 库中的
<<输出运算符；在 Python 中，则使用 cv::FileStorage.write()。要输出任何类型的数据结构，我们首先需要指定它的名称。在 C++ 中，只需把名称推入流中即可；在 Python 中，write 函数的第一个参数就是名称。对于基本类型，你可以紧接着打印其值：C++
fs << "iterationNr" << 100;Python
s.write('iterationNr', 100)读入则是简单的寻址（通过
[]运算符）加类型转换操作，或通过>>运算符读取。在 Python 中，我们用 getNode() 寻址并使用 real()：C++
int itNr;//fs["iterationNr"] >> itNr;itNr = (int) fs["iterationNr"];Python
n = s.getNode('iterationNr')itNr = int(n.real()) -
OpenCV 数据结构的输入/输出。 它们的行为与基本的 C++ 和 Python 类型完全相同：
C++
Mat R = Mat_<uchar>::eye(3, 3),T = Mat_<double>::zeros(3, 1);fs << "R" << R; // cv::Matfs << "T" << T;fs["R"] >> R; // Read cv::Matfs["T"] >> T;Python
R = np.eye(3,3)T = np.zeros((3,1))s.write('R_MAT', R)s.write('T_MAT', T)R = s.getNode('R_MAT').mat()T = s.getNode('T_MAT').mat() -
向量（数组）和关联 map 的输入/输出。 如前所述，我们也可以输出 map 和序列（数组、vector）。同样，我们先打印变量名，然后必须指定输出是序列还是 map。
对于序列，在第一个元素之前打印
[字符，在最后一个元素之后打印]字符。在 Python 中，调用FileStorage.startWriteStruct(structure_name, struct_type)开始写入结构，其中struct_type为cv2.FileNode_MAP或cv2.FileNode_SEQ；调用FileStorage.endWriteStruct()结束结构：C++
fs << "strings" << "["; // text - string sequencefs << "image1.jpg" << "Awesomeness" << "../data/baboon.jpg";fs << "]"; // close sequencePython
s.startWriteStruct('strings', cv.FileNode_SEQ)for elem in ['image1.jpg', 'Awesomeness', '../data/baboon.jpg']:s.write('', elem)s.endWriteStruct()对于 map，流程相同，只是改用
{和}作为分隔字符：C++
fs << "Mapping"; // text - mappingfs << "{" << "One" << 1;fs << "Two" << 2 << "}";Python
s.startWriteStruct('Mapping', cv.FileNode_MAP)s.write('One', 1)s.write('Two', 2)s.endWriteStruct()要读取这些内容，我们使用 cv::FileNode 和 cv::FileNodeIterator 数据结构。cv::FileStorage 类的
[]运算符（或 Python 中的 getNode() 函数）返回一个 cv::FileNode 数据类型。如果该节点是序列型的，我们可以使用 cv::FileNodeIterator 遍历其中的项。在 Python 中，可以使用 at() 函数寻址序列中的元素，size() 函数返回序列的长度：C++
FileNode n = fs["strings"]; // Read string sequence - Get nodeif (n.type() != FileNode::SEQ){cerr << "strings is not a sequence! FAIL" << endl;return 1;}FileNodeIterator it = n.begin(), it_end = n.end(); // Go through the nodefor (; it != it_end; ++it)cout << (string)*it << endl;Python
n = s.getNode('strings')if (not n.isSeq()):print ('strings is not a sequence! FAIL', file=sys.stderr)exit(1)for i in range(n.size()):print (n.at(i).string())对于 map，你可以再次使用
[]运算符（Python 中的 at() 函数）访问给定项（也可以使用>>运算符）：C++
n = fs["Mapping"]; // Read mappings from a sequencecout << "Two " << (int)(n["Two"]) << "; ";cout << "One " << (int)(n["One"]) << endl << endl;Python
n = s.getNode('Mapping')print ('Two',int(n.getNode('Two').real()),'; ')print ('One',int(n.getNode('One').real()),'\n') -
读写自定义数据结构。 假设你有如下数据结构：
C++
class MyData{public:MyData() : A(0), X(0), id() {}public: // Data Membersint A;double X;string id;};Python
class MyData:def __init__(self):self.A = self.X = 0self.name = ''在 C++ 中，可以通过 OpenCV 的 I/O XML/YAML 接口序列化它（就像 OpenCV 自带的数据结构一样），方法是在类的内部和外部各添加一对 read 和 write 函数。在 Python 中，你可以通过在类内部实现 read 和 write 函数来达到类似效果。类的内部部分：
C++
void write(FileStorage& fs) const //Write serialization for this class{fs << "{" << "A" << A << "X" << X << "id" << id << "}";}void read(const FileNode& node) //Read serialization for this class{A = (int)node["A"];X = (double)node["X"];id = (string)node["id"];}Python
def write(self, fs, name):fs.startWriteStruct(name, cv.FileNode_MAP|cv.FileNode_FLOW)fs.write('A', self.A)fs.write('X', self.X)fs.write('name', self.name)fs.endWriteStruct()def read(self, node):if (not node.empty()):self.A = int(node.getNode('A').real())self.X = node.getNode('X').real()self.name = node.getNode('name').string()else:self.A = self.X = 0self.name = ''在 C++ 中，你还需要在类外部添加以下函数定义：
static void write(FileStorage& fs, const std::string&, const MyData& x){x.write(fs);}static void read(const FileNode& node, MyData& x, const MyData& default_value = MyData()){if(node.empty())x = default_value;elsex.read(node);}可以看到，在 read 部分我们定义了当用户尝试读取不存在的节点时的行为。这里我们只是返回默认初始化值，但更详细的方案可以是例如对对象 ID 返回 -1 这样的值。
添加好这四个函数之后，就可以使用
>>运算符写入、使用<<运算符读取（或 Python 中定义的输入/输出函数）：C++
MyData m(1);fs << "MyData" << m; // your own data structuresfs["MyData"] >> m; // Read your own structure_Python
m = MyData()m.write(s, 'MyData')m.read(s.getNode('MyData'))或者尝试读取一个不存在的项：
C++
cout << "Attempt to read NonExisting (should initialize the data structure with its default).";fs["NonExisting"] >> m;cout << endl << "NonExisting = " << endl << m << endl;Python
print ('Attempt to read NonExisting (should initialize the data structure','with its default).')m.read(s.getNode('NonExisting'))print ('\nNonExisting =','\n',m)
程序基本上只是把定义的数字打印出来。在你的控制台屏幕上可以看到：
Write Done.
Reading:100image1.jpgAwesomenessbaboon.jpgTwo 2; One 1
R = [1, 0, 0; 0, 1, 0; 0, 0, 1]T = [0; 0; 0]
MyData ={ id = mydata1234, X = 3.14159, A = 97}
Attempt to read NonExisting (should initialize the data structure with its default).NonExisting ={ id = , X = 0, A = 0}
Tip: Open up output.xml with a text editor to see the serialized data.不过，更有意思的是你在输出的 xml 文件中看到的内容：
<?xml version="1.0"?><opencv_storage><iterationNr>100</iterationNr><strings> image1.jpg Awesomeness baboon.jpg</strings><Mapping> <One>1</One> <Two>2</Two></Mapping><R type_id="opencv-matrix"> <rows>3</rows> <cols>3</cols> <dt>u</dt> <data> 1 0 0 0 1 0 0 0 1</data></R><T type_id="opencv-matrix"> <rows>3</rows> <cols>1</cols> <dt>d</dt> <data> 0. 0. 0.</data></T><MyData> <A>97</A> <X>3.1415926535897931e+000</X> <id>mydata1234</id></MyData></opencv_storage>或者是 YAML 文件：
%YAML:1.0iterationNr: 100strings: - "image1.jpg" - Awesomeness - "baboon.jpg"Mapping: One: 1 Two: 2R: !!opencv-matrix rows: 3 cols: 3 dt: u data: [ 1, 0, 0, 0, 1, 0, 0, 0, 1 ]T: !!opencv-matrix rows: 3 cols: 1 dt: d data: [ 0., 0., 0. ]MyData: A: 97 X: 3.1415926535897931e+000 id: mydata1234你可以在这个 YouTube 视频中观看它的运行实例。