目的
你将得到以下几个问题的答案:
- 如何将文本写入YAML或XML文件，及如何从从OpenCV中读取YAML或XML文件中的文本
- 如何利用YAML或XML文件存取OpenCV数据结构
- 如何利用YAML或XML文件存取自定义数据结构?
- OpenCV中相关数据结构的使用方法，如 ：xmlymlpers:<cite>FileStorage <filestorage></cite>, FileNode 或 FileNodeIterator.
代码
以下用简单的示例代码演示如何逐一实现所有目的.
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#import <opencv2/imgcodecs/ios.h>
#import <opencv2/imgproc.hpp>
#import <opencv2/highgui.hpp>
#import <opencv2/core/operations.hpp>
#import <opencv2/core/core_c.h>
using namespace cv;
using namespace std;
#endif
#import "XmlAndYmlViewController.h"
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;
};
void write(FileStorage& fs, const std::string&, const MyData& x)
{
x.write(fs);
}
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 console
ostream& operator<<(ostream& out, const MyData& m)
{
out << "{ id = " << m.id << ", ";
out << "X = " << m.X << ", ";
out << "A = " << m.A << "}";
return out;
}
@interface XmlAndYmlViewController ()
@end
@implementation XmlAndYmlViewController
- (void)viewDidLoad {
[super viewDidLoad];
NSString * path = NSTemporaryDirectory();
// path = [path stringByAppendingPathComponent:@"a.xml"];
path = [path stringByAppendingPathComponent:@"b.yaml"];
NSLog(@"%@",path);
string filename(path.UTF8String);
{
Mat R = Mat_<uchar>::eye(3, 3),
T = Mat_<double>::zeros(3, 1);
MyData m(1);
FileStorage fs(filename, FileStorage::WRITE);
fs << "iterationNr" << 100;
fs << "strings" << "["; // text - string sequence
fs << "image1.jpg" << "Awesomeness" << "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 Done." << 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;
return ;
}
FileNode n = fs["strings"]; // Read string sequence - Get node
if (n.type() != FileNode::SEQ)
{
cerr << "strings is not a sequence! FAIL" << endl;
return ;
}
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;
}
/*
#pragma mark - Navigation
// In a storyboard-based application, you will often want to do a little preparation before navigation
- (void)prepareForSegue:(UIStoryboardSegue *)segue sender:(id)sender {
// Get the new view controller using [segue destinationViewController].
// Pass the selected object to the new view controller.
}
*/
#pragma mark - private
//brgx
- (cv::Mat)cvMatFromUIImage:(UIImage *)image
{
CGColorSpaceRef colorSpace =CGColorSpaceCreateDeviceRGB();
CGFloat cols = image.size.width;
CGFloat rows = image.size.height;
Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels (color channels + alpha)
CGContextRef contextRef = CGBitmapContextCreate(cvMat.data, // Pointer to data
cols, // Width of bitmap
rows, // Height of bitmap
8, // Bits per component
cvMat.step[0], // Bytes per row
colorSpace, // Colorspace
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
CGContextRelease(contextRef);
Mat dst;
cvtColor(cvMat, dst, COLOR_RGBA2BGRA);
return dst;
}
-(UIImage *)UIImageFromCVMat:(cv::Mat)cvMat
{
// mat 是brg 而 rgb
Mat src;
NSData *data=nil;
CGBitmapInfo info =kCGImageAlphaNone|kCGBitmapByteOrderDefault;
CGColorSpaceRef colorSpace;
if (cvMat.depth()!=CV_8U) {
Mat result;
cvMat.convertTo(result, CV_8U,255.0);
cvMat = result;
}
if (cvMat.elemSize() == 1) {
colorSpace = CGColorSpaceCreateDeviceGray();
data= [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];
} else if(cvMat.elemSize() == 3){
cvtColor(cvMat, src, COLOR_BGR2RGB);
data= [NSData dataWithBytes:src.data length:src.elemSize()*src.total()];
colorSpace = CGColorSpaceCreateDeviceRGB();
}else if(cvMat.elemSize() == 4){
colorSpace = CGColorSpaceCreateDeviceRGB();
cvtColor(cvMat, src, COLOR_BGRA2RGBA);
data= [NSData dataWithBytes:src.data length:src.elemSize()*src.total()];
info =kCGImageAlphaNoneSkipLast | kCGBitmapByteOrderDefault;
}else{
NSLog(@"[error:] 错误的颜色通道");
return nil;
}
CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
// Creating CGImage from cv::Mat
CGImageRef imageRef = CGImageCreate(cvMat.cols, //width
cvMat.rows, //height
8, //bits per component
8 * cvMat.elemSize(), //bits per pixel
cvMat.step[0], //bytesPerRow
colorSpace, //colorspace
kCGImageAlphaNone|kCGBitmapByteOrderDefault,// bitmap info
provider, //CGDataProviderRef
NULL, //decode
false, //should interpolate
kCGRenderingIntentAbsoluteColorimetric //intent
);
// Getting UIImage from CGImage
UIImage *finalImage = [UIImage imageWithCGImage:imageRef];
CGImageRelease(imageRef);
CGDataProviderRelease(provider);
CGColorSpaceRelease(colorSpace);
return finalImage;
}
@end
代码分析
1.XML\YAML 文件的打开和关闭
在你写入内容到此类文件中前，你必须先打开它，并在结束时关闭它。在OpenCV中标识XML和YAML的数据结构是 FileStorage 。要将此结构和硬盘上的文件绑定时，可使用其构造函数或者 open() 函数:
NSString * path = NSTemporaryDirectory();
path = [path stringByAppendingPathComponent:@"a.xml"];
string filename(path.UTF8String);
FileStorage fs(filename, FileStorage::WRITE);
\\...
fs.open(filename, FileStorage::READ);
无论以哪种方式绑定，函数中的第二个参数都以常量形式指定你要对文件进行操作的类型，包括：WRITE, READ 或 APPEND。文件扩展名决定了你将采用的输出格式。如果你指定扩展名如 .xml.gz ，输出甚至可以是压缩文件。
当 FileStorage 对象被销毁时，文件将自动关闭。当然你也可以显示调用 release 函数:
fs.release(); // 显示关闭
2.输入\输出文本和数字。
数据结构使用与STL相同的 << 输出操作符。输出任何类型的数据结构时，首先都必须指定其标识符,这通过简单级联输出标识符即可实现。基本类型数据输出必须遵循此规则:
fs << "iterationNr" << 100;
读入则通过简单的寻址（通过 [] 操作符）操作和强制转换或 >> 操作符实现：
int itNr;
fs["iterationNr"] >> itNr;
itNr = (int) fs["iterationNr"];
3.输入\输出OpenCV数据结构
其实和对基本类型的操作方法是相同的:
Mat R = Mat_<uchar >::eye (3, 3),
T = Mat_<double>::zeros(3, 1);
fs << "R" << R; // 写 cv::Mat
fs << "T" << T;
fs["R"] >> R; // 读 cv::Mat
fs["T"] >> T;
4.输入\输出 vectors（数组）和相应的map
之前提到我们也可以输出maps和序列（数组, vector)。同样，首先输出变量的标识符，接下来必须指定输出的是序列还是map。
对于序列，在第一个元素前输出”[“字符，并在最后一个元素后输出”]“字符：
fs << "strings" << "["; // 文本 - 字符串序列
fs << "image1.jpg" << "Awesomeness" << "baboon.jpg";
fs << "]";
对于maps使用相同的方法，但采用”{“和”}“作为分隔符。
fs << "Mapping"; // 文本 - mapping
fs << "{" << "One" << 1;
fs << "Two" << 2 << "}";
对于数据读取，可使用 FileNode 和 FileNodeIterator 数据结构。 FileStorage 的[] 操作符将返回一个 FileNode 数据类型。如果这个节点是序列化的，我们可以使用 FileNodeIterator 来迭代遍历所有元素。
FileNode n = fs["strings"]; // 读取字符串序列 - 获取节点
if (n.type() != FileNode::SEQ)
{
cerr << "strings is not a sequence! FAIL" << endl;
return 1;
}
FileNodeIterator it = n.begin(), it_end = n.end(); // 遍历节点
for (; it != it_end; ++it)
cout << (string)*it << endl;
对于maps类型，可以用 [] 操作符访问指定的元素（或者 >> 操作符）：
n = fs["Mapping"]; // 从序列中读取map
cout << "Two " << (int)(n["Two"]) << "; ";
cout << "One " << (int)(n["One"]) << endl << endl;
5.读写自定义数据类型
假设你定义了如下数据类型：
class MyData
{
public:
MyData() : A(0), X(0), id() {}
public: // 数据成员
int A;
double X;
string id;
};
添加内部和外部的读写函数，就可以使用OpenCV I/O XML/YAML接口对其进行序列化（就像对OpenCV数据结构进行序列化一样）。内部函数定义如下：
void write(FileStorage& fs) const //对自定义类进行写序列化
{
fs << "{" << "A" << A << "X" << X << "id" << id << "}";
}
void read(const FileNode& node) //从序列读取自定义类
{
A = (int)node["A"];
X = (double)node["X"];
id = (string)node["id"];
}
接下来在类的外部定义以下函数：
void write(FileStorage& fs, const std::string&, const MyData& x)
{
x.write(fs);
}
void read(const FileNode& node, MyData& x, const MyData& default_value = MyData())
{
if(node.empty())
x = default_value;
else
x.read(node);
}
这儿可以看到，如果读取的节点不存在，我们返回默认值。更复杂一些的解决方案是返回一个对象ID为负值的实例。
一旦添加了这四个函数，就可以用 >> 操作符和 << 操作符分别进行读，写操作：
MyData m(1);
fs << "MyData" << m; // 写自定义数据结构
fs["MyData"] >> m;
或试着读取不存在的值:
fs["NonExisting"] >> m; // 请注意不是 fs << "NonExisting" << m
cout << endl << "NonExisting = " << endl << m << endl;
结果
好的，大多情况下我们只输出定义过的成员。在控制台程序的屏幕上，你将看到：
2019-11-11 18:57:01.252423+0800 OpenCVFirstChapter-xmlAndYml[17015:4243747] /Users/glodon/Library/Developer/CoreSimulator/Devices/2EF925FE-8B90-44DB-B7C9-4F232F801257/data/Containers/Data/Application/53150162-5191-474C-8F3D-EFFF311F0B3B/tmp/a.xml
Write Done.
Reading:
100image1.jpg
Awesomeness
baboon.jpg
Two 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 /Users/glodon/Library/Developer/CoreSimulator/Devices/2EF925FE-8B90-44DB-B7C9-4F232F801257/data/Containers/Data/Application/53150162-5191-474C-8F3D-EFFF311F0B3B/tmp/a.xml with a text editor to see the serialized data.
然而, 在输出的xml文件中看到的结果将更加有趣：
<?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YAML文件：
%YAML:1.0
iterationNr: 100
strings:
- "image1.jpg"
- Awesomeness
- "baboon.jpg"
Mapping:
One: 1
Two: 2
R: !!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
