I've already talked about and. Now we'll actually implement it. And we'll do it in C++.

Because it's a lot more easier and make much more sense. No more stupid CV_MAT_ELEM macros. And things will just work. But, I won't talk about how the C++ is working. Figure it out yourself;) The setup The first thing we need for this is the latest version of OpenCV. If you're using 1.0 or 1.1pre or any of those, you need to get the latest version.

One of the most common calibration algorithms was proposed by Zhegyou Zhang in the paper 'A. Flexible New Technique for Camera Calibration' in 2000. – OpenCV: calibrateCamera. – Matlab: Camera calibration app. • This calibration algorithm makes use of multiple images of a asymmetric chessboard. Chessboard Pdf Open Cv Java. It is easier to calibrate using chessboard pattern because it is flat so no. To with mask broken.

It has the C++ interface. Previous version simply do not have it.

Once you have it, if you use Visual Studio. If not, check around the OpenCV wiki, and you should see where you can find instructions for your IDE. Onto the project Once you have your IDE or whatever environment setup, start by creating a new project.

Include the standard OpenCV headers. Vector >object_points; vector >image_points; What do these mean? For those unfamiliar with C++, a 'vector' is a list. This list contains items of the type mentioned within the angular brackets (it's called generic programming).

So, we're creating a list of list of 3D points (Point3f) and a list of list of 2D points (Point2f). Object_points is the physical position of the corners (in 3D space).

This has to be measured by us. [write relationg between each list item and list's eh you get the point] image_points is the location of the corners on in the image (in 2 dimensions). Once the program has actual physical locations and locations on the image, it can calculate the relation between the two.

And because we'll use a chessboard, these points have a definite relations between them (they lie on straight lines and on squares). So the 'expected' - 'actual' relation can be used to correct the distortions in the image. Next, we create a list of corners. This will temporarily hold the current snapshot's chessboard corners. We also declare a variable that will keep a track of successfully capturing a chessboard and saving it into the lists we declared above. Mat image; Mat gray_image; capture >>image; The >>is the C++ interface at work again! Next, we do a little hack with object_points.

Ideally, it should contain the physical position of each corner. The most intuitive way would be to measure distances 'from' the camera lens. That is, the camera is the origin and the chessboard has been displaced. Usually, it's done the other way round. The chessboard is considered the origin of the world.

So, it is the camera that is moving around, taking different shots of the camera. So, you can set the chessboard on some place (like the XY plane, of ir you like, the XZ plane). Mathematically, it makes no difference which convention you choose. But it's easier for us and computationally faster in the second case. Borderlands 2 Skidrow Torrent here. We just assign a constant position to each vertex. And we do that next.

Vector obj; for ( int j = 0; j. CalibrateCamera ( object_points, image_points, image. Size (), intrinsic, distCoeffs, rvecs, tvecs ); After this statement, you'll have the intrinsic matrix, distortion coefficients and the rotation+translation vectors. The intrinsic matrix and distortion coefficients are a property of the camera and lens. So as long as you use the same lens (ie you don't change it, or change its focal length, like in zoom lenses etc) you can reuse them. In fact, you can save them to a file if you want and skip the entire chessboard circus! Note: The calibrateCamera function converts all matrices into 64F format even if you initialize it to 32F.

Thanks to Michael Koval! Now that we have the distortion coefficients, we can undistort the images.

Here's a small loop that will do this. Release (); return 0; } My results I ran this program on a low quality webcam. I used a hand-made chessboard pattern and used 20 chessboard positions to calibrate. Here's an undistort I did: Make your own chessboard! If you're not working at some university, its very likely you don't have a chessboard pattern that will work perfectly. You need an asymmetric chessboard: 5x6 or a 7x8 or27x3. So make one yourself.

Take a piece of paper and draw on it with a marker. Paste it on some cardboard. I made mine from a small notebook page. It's a 5x4 chessboard. Not very big, but it works. Here's what it looks like: You can even see the lines from the notebook.: But the inner corners are detected pretty well.

You'll definitely want a better one if you work with higher resolutions. If you're looking for precision, get it printed. Here's a picture that you can print on an A4 size paper at 300dpi (its PNG and around 35kb in size). (click for a full size version: A4 at 300dpi) Bad calibration A bad calibration is very much possible. Here's what I got in one of my attempts: Yes the image on the left is the original, and the one on the right is 'undistorted'. Hopefully you won't get such results. The key is calibrate with the chessboard everywhere on the screen.

It should not be 'biased' toward some corner or region. Summary Hope you've learned how to calibrate your cameras with OpenCV and how to undistort images taken from them. With OpenCV, you don't need to know what goes on underneath while being able to fully utilize the calibration and undistortion.

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). 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 four parameters is referred to as the camera matrix.

While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution. The process of determining these two matrices is the calibration. Calculation of these parameters is done through basic geometrical equations. The equations used depend on the chosen calibrating objects. Currently OpenCV supports three types of objects for 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 results you will probably need at least 10 good snapshots of the input pattern in different positions.

Source code You may also find the source code in the samples/cpp/tutorial_code/calib3d/camera_calibration/ folder of the OpenCV source library. 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”. In XML format.

In the configuration file you may choose to use camera as an input, a video file or an image list. If you opt for the last one, you will need to create a configuration file where you enumerate the images to use. The important part to remember is that the images need to be specified using the absolute path or the relative one from your application’s working directory.

You may find all this in the samples directory mentioned above. 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 tutorial. The calibration and save Because the calibration needs to be done only once per camera, it makes sense to save it after a successful calibration.

This way later on you can just load these values into your program. Due to this we first make the calibration, and if it succeeds we save the result into an OpenCV style XML or YAML file, depending on the extension you give in the configuration file. Therefore in the first function we just split up these two processes. Because we want to save many of the calibration variables we’ll create these variables here and pass on both of them to the calibration and saving function. Again, I’ll not show the saving part as that has little in common with the calibration. Explore the source file in order to find out how and what.

• The object points. This is a vector of Point3f vector that for each input image describes how should the pattern look. If we have a planar pattern (like a chessboard) then we can simply set all Z coordinates to zero. This is a collection of the points where these important points are present. Because, we use a single pattern for all the input images we can calculate this just once and multiply it for all the other input views. We calculate the corner points with the calcBoardCornerPositions function as.

Vector >objectPoints ( 1 ); calcBoardCornerPositions ( s. BoardSize, s. SquareSize, objectPoints [ 0 ], s. CalibrationPattern ); objectPoints.

Resize ( imagePoints. Size (), objectPoints [ 0 ]); • The image points. This is a vector of Point2f vector which for each input image contains coordinates of the important points (corners for chessboard and centers of the circles for the circle pattern). We have already collected this from or function. We just need to pass it on. • The size of the image acquired from the camera, video file or the images. Online Fly Fishing Videos there.

• The camera matrix. If we used the fixed aspect ratio option we need to set the to zero. DistCoeffs = Mat:: zeros ( 8, 1, CV_64F ); • For all the views the function will calculate rotation and translation vectors which transform the object points (given in the model coordinate space) to the image points (given in the world coordinate space). The 7-th and 8-th parameters are the output vector of matrices containing in the i-th position the rotation and translation vector for the i-th object point to the i-th image point. • The final argument is the flag. You need to specify here options like fix the aspect ratio for the focal length, assume zero tangential distortion or to fix the principal point. Double rms = calibrateCamera ( objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, s.

Flag CV_CALIB_FIX_K4 CV_CALIB_FIX_K5 ); • The function returns the average re-projection error. This number gives a good estimation of precision of the found parameters. This should be as close to zero as possible. Given the intrinsic, distortion, rotation and translation matrices we may calculate the error for one view by using the to first transform the object point to image point.

Then we calculate the absolute norm between what we got with our transformation and the corner/circle finding algorithm. To find the average error we calculate the arithmetical mean of the errors calculated for all the calibration images. Images/CameraCalibration/VID5/xx1.jpg images/CameraCalibration/VID5/xx2.jpg images/CameraCalibration/VID5/xx3.jpg images/CameraCalibration/VID5/xx4.jpg images/CameraCalibration/VID5/xx5.jpg images/CameraCalibration/VID5/xx6.jpg images/CameraCalibration/VID5/xx7.jpg images/CameraCalibration/VID5/xx8.jpg Then passed images/CameraCalibration/VID5/VID5.XML as an input in the configuration file.

Here’s a chessboard pattern found during the runtime of the application: After applying the distortion removal we get: The same works for by setting the input width to 4 and height to 11. This time I’ve used a live camera feed by specifying its ID (“1”) for the input. Here’s, how a detected pattern should look: In both cases in the specified output XML/YAML file you’ll find the camera and distortion coefficients matrices. 3 3 d 6.293521e+002 0. 3.000000e+002 0. 6.293521e+002 2.000000e+002 0. 5 1 d - 4.423804e-001 5.187526e-001 0.

- 5.487474e-001 Add these values as constants to your program, call the and the function to remove distortion and enjoy distortion free inputs for cheap and low quality cameras. You may observe a runtime instance of this on the.