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OpenCV
4.8.0
Open Source Computer Vision
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This tutorial will show how to use the multiple camera calibration toolbox. This toolbox is based on the usage of "random" pattern calibration object, so the tutorial is mainly two parts: an introduction to "random" pattern and multiple camera calibration.
The random pattern is an image that is randomly generated. It is "random" so that it has many feature points. After generating it, one print it out and use it as a calibration object. The following two images are random pattern and a photo taken for it.
To generate a random pattern, use the class cv::randpattern::RandomPatternGenerator
in ccalib
module. Run it as
Here width
and height
are width and height of pattern image. After getting the pattern, print it out and take some photos of it.
Now we can use these images to calibrate camera. First, objectPoints
and imagePoints
need to be detected. Use class cv::randpattern::RandomPatternCornerFinder
to detect them. A sample code can be
Here variable patternWidth
and patternHeight
are physical pattern width and height with some user defined unit. vecImg
is a vector of images that stores calibration images.
Second, use calibration functions like cv::calibrateCamera
or cv::omnidir::calibrate
to calibrate camera.
Now we move to multiple camera calibration, so far this toolbox must use random pattern object.
To calibrate multiple cameras, we first need to take some photos of random pattern. Of cause, to calibrate the extrinsic parameters, one pattern need to be viewed by multiple cameras (at least two) at the same time. Another thing is that to help the program know which camera and which pattern the photo is taken, the image file should be named as "cameraIdx-timestamp.*". Photos with same timestamp means that they are the same object taken by several cameras. In addition, cameraIdx should start from 0. Some examples of files names are "0-129.png", "0-187.png", "1-187", "2-129".
Then, we can run multiple cameras calibration as
Here cameraType
indicates the camera type, multicalib::MultiCameraCalibration::PINHOLE
and multicalib::MultiCameraCalibration::OMNIDIRECTIONAL
are supported. For omnidirectional camera, you can refer to cv::omnidir
module for detail. nCamera
is the number of camers. inputFilename
is the name of a file generated by imagelist_creator
from opencv/sample
. It stores names of random pattern and calibration images, the first file name is the name of random pattern. patternWidth
and patternHeight
are physical width and height of pattern. showFeatureExtraction
is a flags to indicate whether show feature extraction process. nMiniMatches
is a minimal points that should be detected in each frame, otherwise this frame will be abandoned. outputFilename
is a xml file name to store parameters.