camera-calibration

I have been trying to calibrate my camera quite for a while using Opencv calibrateCamera() function. I have followed the same procedure as described in opencv sample program. I am trying to first load 10 9 x 6 chessboard images. Then finding chessboard corners. If corners are found then corners' pixel location is stored in vector< vector < Point2f>> ImagePoints. After doing this for all images, runCalibrationAndSave part is executed. In runCalibrationAndSave , first runCalibration part is executed where ObjectPoints (of type vector< vector < Point3f>>) are filled with corners' real coordinate

I'm using openCV the calibrateCamera function to calibrate my camera. I started from the tutorial implementation , but there seems something wrong. The camera is looking down on a table and i use a chessboard with an area that covers about 1/2 or 1/4 of my total image. Since I aim to track a flat object that slides over this table, I also slide my chessboard over this table. So my first question is: is it ok that i move my chessboard over this table? Or do I have to make some 3D movements in order to get some good result? Because I was wondering: how does the function guesses the distance

I am learning camera matrix stuff. I already known that I can get the homography of the camera (3*3 matrix) by using four points in a plane in object space. I want to know if we can get the homagraphy with four points not in a plane? If yes, how can I get the matrix? What formulas should I look at? I also confused homography with another concept: I only need to know three points if I want to convert from points from one coordinate to another coordinate system. So why we need four points in computing homography? Homography maps points 1. On plane to points at another plane 2. Projections of

I'm currently trying to implement an alternate method to webcam-based AR using an external tracking system. I have everything in my environment configured save for the extrinsic calibration. I decided to use cv::solvePnP() as it supposedly does pretty much exactly I want, but after two weeks I am pulling my hair out trying to get it to work. A diagram below shows my configuration. c1 is my camera, c2 is the optical tracker I'm using, M is the tracked marker attached to the camera, and ch is the checkerboard. As it stands I pass in my image pixel coordinates acquired with cv:

I am trying to get a 3x4 camera matrix for triangulation process but calibrateCamera() returns only 3x3 and 4x1 matrices. How can i get the 3x4 out of those matrices? Thanks in advance!! calibrateCamera() returns you a 3x3 matrix as cameraMatrix, a 4x1 matrix as distCoeffs, and rvecs and tvecs that are vectors of 3x1 rotation(R) and 3x1 transformation(t) matrices. What you want is ProjectionMatrix, which is multiply [cameraMatrix] by [R|t]. Therefore, it returs you a 3x4 ProjectionMatrix. You can read OpenCV documentation for more info. If you are using cameraCalibrate(), you must be getting

Calibration: I have calibrated the camera using this vision toolbox in Matlab. I used checkerboard images to do so. After calibration I get the cameraParams which contains: Camera Extrinsics RotationMatrices: [3x3x18 double] TranslationVectors: [18x3 double] and Camera Intrinsics IntrinsicMatrix: [3x3 double] FocalLength: [1.0446e+03 1.0428e+03] PrincipalPoint: [604.1474 359.7477] Skew: 3.5436 Aim: I have recorded trajectories of some objects in motion using this camera. Each object corresponds to a single point in a frame. Now, I want to project the points such that I get a top-view. Note all