OpenCV的Sample分析：相机标定（1）分析类Settings
此次分析的程序“camera_calibration.cpp”位于opencv-3.2.0/samples/cpp/tutorial_code/calib3d/camera_calibration 的文件夹中
在正式分析程序之前，先分析class Settings
class Settings
{
public:
Settings() : goodInput(false) {}
enum Pattern { NOT_EXISTING, CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
enum InputType { INVALID, CAMERA, VIDEO_FILE, IMAGE_LIST };
//略去成员函数
public:
Size boardSize; // The size of the board -> Number of items by width and height
Pattern calibrationPattern; // One of the Chessboard, circles, or asymmetric circle pattern
float squareSize; // The size of a square in your defined unit (point, millimeter,etc).
int nrFrames; // The number of frames to use from the input for calibration
float aspectRatio; // The aspect ratio
int delay; // In case of a video input
bool writePoints; // Write detected feature points
bool writeExtrinsics; // Write extrinsic parameters
bool calibZeroTangentDist; // Assume zero tangential distortion
bool calibFixPrincipalPoint; // Fix the principal point at the center
bool flipVertical; // Flip the captured images around the horizontal axis
string outputFileName; // The name of the file where to write
bool showUndistorsed; // Show undistorted images after calibration
string input; // The input ->
bool useFisheye; // use fisheye camera model for calibration
bool fixK1; // fix K1 distortion coefficient
bool fixK2; // fix K2 distortion coefficient
bool fixK3; // fix K3 distortion coefficient
bool fixK4; // fix K4 distortion coefficient
bool fixK5; // fix K5 distortion coefficient
int cameraID;
vector<string> imageList;
size_t atImageList;
VideoCapture inputCapture;
InputType inputType;
bool goodInput;
int flag;
private:
string patternToUse;
};
从中可以看见Settings的成员变量，关于标定板的尺寸大小，拍摄标定板的帧数，畸变参数等。注意enum变量Pattern和InputType变量的含义。
再分析Settings的成员函数
void write(FileStorage& fs) const //Write serialization for this class
{
fs << "{"
<< "BoardSize_Width" << boardSize.width
<< "BoardSize_Height" << boardSize.height
<< "Square_Size" << squareSize
<< "Calibrate_Pattern" << patternToUse
<< "Calibrate_NrOfFrameToUse" << nrFrames
<< "Calibrate_FixAspectRatio" << aspectRatio
<< "Calibrate_AssumeZeroTangentialDistortion" << calibZeroTangentDist
<< "Calibrate_FixPrincipalPointAtTheCenter" << calibFixPrincipalPoint
<< "Write_DetectedFeaturePoints" << writePoints
<< "Write_extrinsicParameters" << writeExtrinsics
<< "Write_outputFileName" << outputFileName
<< "Show_UndistortedImage" << showUndistorsed
<< "Input_FlipAroundHorizontalAxis" << flipVertical
<< "Input_Delay" << delay
<< "Input" << input
<< "}";
}
void read(const FileNode& node) //Read serialization for this class
{
node["BoardSize_Width" ] >> boardSize.width;
node["BoardSize_Height"] >> boardSize.height;
node["Calibrate_Pattern"] >> patternToUse;
node["Square_Size"] >> squareSize;
node["Calibrate_NrOfFrameToUse"] >> nrFrames;
node["Calibrate_FixAspectRatio"] >> aspectRatio;
node["Write_DetectedFeaturePoints"] >> writePoints;
node["Write_extrinsicParameters"] >> writeExtrinsics;
node["Write_outputFileName"] >> outputFileName;
node["Calibrate_AssumeZeroTangentialDistortion"] >> calibZeroTangentDist;
node["Calibrate_FixPrincipalPointAtTheCenter"] >> calibFixPrincipalPoint;
node["Calibrate_UseFisheyeModel"] >> useFisheye;
node["Input_FlipAroundHorizontalAxis"] >> flipVertical;
node["Show_UndistortedImage"] >> showUndistorsed;
node["Input"] >> input;
node["Input_Delay"] >> delay;
node["Fix_K1"] >> fixK1;
node["Fix_K2"] >> fixK2;
node["Fix_K3"] >> fixK3;
node["Fix_K4"] >> fixK4;
node["Fix_K5"] >> fixK5;
validate();
}上述的两个函数一个用来写文件，另一个用来读文件。注意他们的写读格式。
在程序的初始阶段，我们需要读入标定板的基本参数，比如我们的标定板是6×9还是5×8，以便我们进行标定操作。于是，我们要首先读取文件“in_VID5.xml”，而它的一部分节选如下
<!-- Number of inner corners per a item row and column. (square, circle) --> <BoardSize_Width> 9</BoardSize_Width> <BoardSize_Height>6</BoardSize_Height> <!-- The size of a square in some user defined metric system (pixel, millimeter)--> <Square_Size>50</Square_Size> <!-- The type of input used for camera calibration. One of: CHESSBOARD CIRCLES_GRID ASYMMETRIC_CIRCLES_GRID --> <Calibrate_Pattern>"CHESSBOARD"</Calibrate_Pattern>
从这个文件可以看出，enum变量Pattern选定的是CHESSBOARD，选择的标定板是9×6，格子的数量是50。
在read()函数中，还有有一个函数需要说明，那就是validate()
void validate()
{
goodInput = true;
if (boardSize.width <= 0 || boardSize.height <= 0)
{
cerr << "Invalid Board size: " << boardSize.width << " " << boardSize.height << endl;
goodInput = false;
}
if (squareSize <= 10e-6)
{
cerr << "Invalid square size " << squareSize << endl;
goodInput = false;
}
if (nrFrames <= 0)
{
cerr << "Invalid number of frames " << nrFrames << endl;
goodInput = false;
}
if (input.empty()) // Check for valid input
inputType = INVALID;
else
{
if (input[0] >= '0' && input[0] <= '9')
{
stringstream ss(input);
ss >> cameraID;
inputType = CAMERA;
}
else
{
if (readStringList(input, imageList))
{
inputType = IMAGE_LIST;
nrFrames = (nrFrames < (int)imageList.size()) ? nrFrames : (int)imageList.size();
}
else
inputType = VIDEO_FILE;
}
if (inputType == CAMERA)
inputCapture.open(cameraID);
if (inputType == VIDEO_FILE)
inputCapture.open(input);
if (inputType != IMAGE_LIST && !inputCapture.isOpened())
inputType = INVALID;
}
if (inputType == INVALID)
{
cerr << " Input does not exist: " << input;
goodInput = false;
}
flag = 0;
if(calibFixPrincipalPoint) flag |= CALIB_FIX_PRINCIPAL_POINT;
if(calibZeroTangentDist) flag |= CALIB_ZERO_TANGENT_DIST;
if(aspectRatio) flag |= CALIB_FIX_ASPECT_RATIO;
if(fixK1) flag |= CALIB_FIX_K1;
if(fixK2) flag |= CALIB_FIX_K2;
if(fixK3) flag |= CALIB_FIX_K3;
if(fixK4) flag |= CALIB_FIX_K4;
if(fixK5) flag |= CALIB_FIX_K5;
if (useFisheye) {
// the fisheye model has its own enum, so overwrite the flags
flag = fisheye::CALIB_FIX_SKEW | fisheye::CALIB_RECOMPUTE_EXTRINSIC;
if(fixK1) flag |= fisheye::CALIB_FIX_K1;
if(fixK2) flag |= fisheye::CALIB_FIX_K2;
if(fixK3) flag |= fisheye::CALIB_FIX_K3;
if(fixK4) flag |= fisheye::CALIB_FIX_K4;
}
calibrationPattern = NOT_EXISTING;
if (!patternToUse.compare("CHESSBOARD")) calibrationPattern = CHESSBOARD;
if (!patternToUse.compare("CIRCLES_GRID")) calibrationPattern = CIRCLES_GRID;
if (!patternToUse.compare("ASYMMETRIC_CIRCLES_GRID")) calibrationPattern = ASYMMETRIC_CIRCLES_GRID;
if (calibrationPattern == NOT_EXISTING)
{
cerr << " Camera calibration mode does not exist: " << patternToUse << endl;
goodInput = false;
}
atImageList = 0;
}
validate()函数会对输入文件“in_VID5.xml”所设定的初始数值进行检验，如果检验合格，那么bool型参数goodinput赋值为True，否则是False。除此之外，要确定nrframe，以及inputType等等。
最后，在类Settings中还有两个成员函数，
Mat nextImage()
{
Mat result;
if( inputCapture.isOpened() )
{
Mat view0;
inputCapture >> view0;
view0.copyTo(result);
}
else if( atImageList < imageList.size() )
{
result = imread(imageList[atImageList++], IMREAD_COLOR)
}
return result;
static bool readStringList( const string& filename, vector<string>& l )
{
l.clear();
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
l.push_back((string)*it);
return true;
}
函数nestImage()的含义就是“下一幅图像”，输入的需要分视频流和图像帧处理。需要注意这条语
result = imread(imageList[atImageList++], IMREAD_COLOR)
在这条语句中，变量imageList是什么呢？先看一下它的定义，vector<string> imageList; 那么是那一条语句给imagelist赋值呢，在validate()函数可以找到，readStringList(input, imageList)。可以看到变量input被赋值于变量imageList。那么input是什么，可以在read()中找到，
node["Input"] >> input;
read()是读取文件“in_VID5.xml”，input的来源是
<Input>"images/CameraCalibration/VID5/VID5.xml"</Input>
即，input需要读入文件“VID5.xml”，
<opencv_storage><images> images/CameraCalibraation/VID5/xx1.jpg images/CameraCalibraation/VID5/xx2.jpg images/CameraCalibraation/VID5/xx3.jpg images/CameraCalibraation/VID5/xx4.jpg images/CameraCalibraation/VID5/xx5.jpg images/CameraCalibraation/VID5/xx6.jpg images/CameraCalibraation/VID5/xx7.jpg images/CameraCalibraation/VID5/xx8.jpg </images></opencv_storage>
所以，imageList储存的是标定图片的名称地址。
最后，我们分析一下函数readStringList(input, imageList)。
可以发现，input是const string类型变量，而imageList是vector<string>类型变量。input是一个包括所有图片地址的长长字符串，这个长长字符串被分割成一块一块的（用了getFirstTopLevelNode这个函数），并且被储存于imageList