Chessboard Pdf Open Cv Download
- Subsequent to this, the next section describes the localization methods for both the main corners and the lines of a chess board pattern separately; after. Introduced into one of the toolboxes used in Matlab [8], DLR Camera Calibration Toolbox [9] and in the libraries of OpenCv developed by Intel [10].
- 7x9 checkerboard for camera calibration. Squares are: 20x20 mm if printed to 1:1 scale on a A4 paper.
I know there is some post talking about this topic but I could not find my answer.
I want to calibrate my android camera without chessboard for 3d reconstruction, so I need my intrinsic and extrinsic parameters.
Camera calibration with square chessboard¶. The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images. Test data: use images in your data/chess folder. Compile opencv with samples by setting BUILDEXAMPLES to ON in cmake configuration.; Go to bin folder and use imagelistcreator to create an XML/YAML list of your images.
My first goal is to extract the 3D real system to be able to put some 3d Model on screen.
My step :
From a picture of a building I extract 4 points that represent the real 3D system
/! this step require camera calibration /!
Convert them to 3d Point (solvePnP for exemple)
Then from my 3D Axis I create a OpenGL projection and modelview matrix
My main problem is that I want to avoid a calibration step, so how can calibrate without chessboard? I have some data from android such as focal length. I can guess that the projection center is the center of my camera picture.
Any idea or advice? or other way to do it ?
1 Answer
here is nochess calibation of qtcalib.
This scheme is recomended when you need obtain a camera calibration from a image that don't have calibration chessboard. In this case, you can approximate the camera calibration if you know 4 points in the image forming a flat rectangle in real world. Is important to remark that the aproximated calibration depends on the 4 selected points and the values that you will set for the dimensions of the rectangle
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I have only just started experimenting with OpenCV a little bit. I have a setup of an LCD with a static position, and I'd like to extract what is being displayed on the screen from the image. I've seen the chessboard pattern used for calibrating a camera, but it seems like that is used to undistort the image, which isn't totally what I want to do.
I was thinking I'd display the chessboard on the LCD and then figure out the transformations needed to convert the image of the LCD into the ideal view of the chessboard directly overhead and cropped. Then I would store the transformations, change what the LCD is displaying, take a picture, perform the same transformations, and get the ideal view of what was now being displayed.
I'm wondering if that sounds like a good idea? Is there a simpler way to achieve what I'm trying to do? And any tips on the functions I should be using to figure out the transformations, perform them, store them (maybe just keep the transform matrices in memory or write them to file), etc?
karlphillip2 Answers
I'm not sure I understood correctly everything you are trying to do, but bear with me.
Some cameras have lenses that cause a little distortion to the image, and for this purpose OpenCV offers methods to aid in the camera calibration process.
Practically speaking, if you want to write an application that will automatically correct the distortion in the image, first, you need to discover what are the magical values that need to be used to undo this effect. These values come from a proper calibration procedure.
The chessboard image is used together with an application to calibrate the camera. So, after you have an image of the chessboard taken by the camera device, pass this image to the calibration app. The app will identify the corners of the squares and compute the values of the distortion and return the magical values you need to use to counter the distortion effect. At this point, you are interested in 2 variables returned by calibrateCamera(): they are cameraMatrix and distCoeffs. Print them, and write the data on a piece of paper.
At the end, the system you are developing needs to have a function/method to undistort the image, where these 2 variables will be hard coded inside the function, followed by a call to cv::undistort() (if you are using the C++ API of OpenCV):
and that's it.
Detecting rotation automatically might be a bit tricky, but the first thing to do is find the coordinates of the object you are interested in. But if the camera is in a fixed position, this is going to be easy.
For more info on perspective change and rotation with OpenCV, I suggest taking a look at these other questions:
findhomography() is not bad choice, but skew,distortion(camera lens) is real problem..
C++: Mat findHomography(InputArray srcPoints, InputArray dstPoints, int method=0, double ransacReprojThreshold=3, OutputArray mask=noArray() )
Python: cv2.findHomography(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask]]]) → retval, mask
Opencv Book Pdf
C: void cvFindHomography(const CvMat* srcPoints, const CvMat* dstPoints, CvMat* H, int method=0, double ransacReprojThreshold=3, CvMat* status=NULL)