Camera calibration With OpenCV的代码介绍

Camera calibration With OpenCV

Cameras have been around for a long-long time. However, with the introduction of the

cheap pinhole cameras in the late 20th century, they became a common occurrence in our

everyday life. Unfortunately, this cheapness comes with its price: significant

distortion. Luckily, these are constants and with a calibration and some remapping we

can correct this. Furthermore, with calibration you may also determine the relation

between the camera’s natural units (pixels) and the real world units (for example

millimeters).

Tangential distortion occurs because the image taking lenses are not perfectly parallel

to the imaging plane. It can be corrected via the formulas:

So we have five distortion parameters which in OpenCV are presented as one row matrix

with 5 columns:

Now for the unit conversion we use the following formula:

Here the presence of is explained by the use of homography coordinate system (and

). The unknown parameters are and (camera focal lengths) and which

are the optical centers expressed in pixels coordinates. If for both axes a common

focal length is used with a given aspect ratio (usually 1), then and in

the upper formula we will have a single focal length . The matrix containing these

calibration:

Classical black-white chessboard

Symmetrical circle pattern

Asymmetrical circle pattern

Basically, you need to take snapshots of these patterns with your camera and let OpenCV

find them. Each found pattern results in a new equation. To solve the equation you need

at least a predetermined number of pattern snapshots to form a well-posed equation

system. This number is higher for the chessboard pattern and less for the circle ones.

For example, in theory the chessboard pattern requires at least two snapshots. However,

in practice we have a good amount of noise present in our input images, so for good

Take input from Camera, Video and Image file list

Read configuration from XML/YAML file

Save the results into XML/YAML file

Calculate re-projection error

Source code

You may also find the source code in the

samples/cpp/tutorial_code/calib3d/camera_calibration/ folder of the OpenCV source

library or download it from here. The program has a single argument: the name of its

configuration file. If none is given then it will try to open the one named

“default.xml”. Here's a sample configuration file in XML format. In the

The application starts up with reading the settings from the configuration file.

Although, this is an important part of it, it has nothing to do with the subject of

this tutorial: camera calibration. Therefore, I’ve chosen not to post the code for

that part here. Technical background on how to do this you can find in the File Input

and Output using XML and YAML files tutorial.

Explanation

1. Read the settings.

Settings s;

const string inputSettingsFile = argc > 1 ? argv[1] : "default.xml";

fs["Settings"] >> s;

fs.release(); // close Settings file

if (!s.goodInput)

{

cout << "Invalid input detected. Application stopping. " << endl;

return -1;

}

For this I’ve used simple OpenCV class input operation. After reading the file

I’ve an additional post-processing function that checks validity of the input.

Only if all inputs are good then goodInput variable will be true.

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

{

Mat view;

bool blinkOutput = false;

view = s.nextImage();

//----- If no more image, or got enough, then stop calibration and show result -------------

if( mode == CAPTURING && imagePoints.size() >= (unsigned)s.nrFrames )

if(view.empty()) // If no more images then run calibration, save and stop loop.

{

if( imagePoints.size() > 0 )

runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints);

break;

imageSize = view.size(); // Format input image.

if( s.flipVertical ) flip( view, view, 0 );

}

For some cameras we may need to flip the input image. Here we do this too.

3. Find the pattern in the current input. The formation of the equations I

mentioned above aims to finding major patterns in the input: in case of the