OpenCV - 3.0 Camera calibration gives an error

opencv 3 camera calibration
opencv camera calibration python
camera calibration checkerboard
findessentialmat
opencv 3d reconstruction
getoptimalnewcameramatrix
kinect camera calibration opencv
calibratecameraro

I was previously doing camera calibration using OpenCV-2.4.10. Now I have installed OpenCV-3.0.0 and run the same code. This is the link of the code.

With the previous version it was working fine, but in OpenCV-3.0.0 it is giving an error.

TypeError: Required argument 'cameraMatrix' (pos 4) not found

can anyone tell me how to fix this?

Any if I add cameraMatrix (Pos4) ,and distcoeff(Pos5), it gives an error :

TypeError: function takes exactly 2 arguments (3 given)


This stumped me too, but now you need to pass None for each.

retval, cameraMatrix, distCoeffs, rvecs, tvecs = cv2.calibrateCamera(objectPoints,imagePoints, imageSize, None, None)

http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_calib3d/py_calibration/py_calibration.html#calibration

Camera Calibration and 3D Reconstruction, A calibration sample for 3 cameras in horizontal position can be found description) brings the calibration pattern from the model coordinate space the reprojection error, that is, the total sum of squared distances between  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


I had the same error, you have to pass camera width and height, in my case I used this code line:

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(_3d_points, 
                                                   _2d_points, 
                                                   (im.shape[1], im.shape[0]), 
                                                   None,
                                                   None)

Here im.shape[1] is the width and im.shape[0] is the height, basically returning the size of the image, also make sure the images are converted to grayscale.

In my case after converting the images to grayscale and adding the above line of code helped me get the o/p.

Camera calibration With OpenCV, As mentioned above, we need at least 10 test patterns for camera calibration. OpenCV comes objp = np.zeros((6*7,3), np.float32). objp[:,:2] Re-projection error gives a good estimation of just how exact the found parameters are. The closer  I'm getting results I don't expect when I use OpenCV 3.0 calibrateCamera. Here is my algorithm: Load in 30 image points; Load in 30 corresponding world points (coplanar in this case) Use points to calibrate the camera, just for un-distorting; Un-distort the image points, but don't use the intrinsics (coplanar world points, so intrinsics are dodgy)


try this:

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
    objpoints, imgpoints, frame.shape[::-1][1:3], None, None
)

frame.shape is the img size you used to calibrate

Camera Calibration, The 3-by-4 projective transformation maps 3D points represented in camera coordinates to 2D poins in the image plane and represented in normalized camera  OpenCV Camera Calibration Help. Close. 1. I'm trying to follow this tutorial with a few sample images to calibrate the camera with OpenCV, but it's giving me an


Camera Calibration and 3D Reconstruction, Computer Vision in C++ with the OpenCV Library Adrian Kaehler, Gary Bradski the reprojection error of the calibration points for both camera views, and the final what stereo calibration gives you: the rotation matrix will put the right camera  Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object


Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, As mentioned above, we need atleast 10 test patterns for camera calibration. like (0,0,0), (1,0,0), (2,0,0) .,(6,5,0) objp = np.zeros((6*7,3), np.float32) objp[:,:2] Re-projection error gives a good estimation of just how exact is the found  Interactive camera calibration application Prev Tutorial: Real Time pose estimation of a textured object According to classical calibration technique user must collect all data first and when run cv::calibrateCamera function to obtain camera parameters.


Camera calibration With OpenCV, approach to camera calibration has inspired OpenCV [1] and serves as our on the details of OpenCV in section 3. Circular Figure 3: Localization error of the control points against However, this non-uniform lighting provides a good test-. Check out results for Camera calibration. Find Camera calibration here