Estimate intrinsic and extrinsic camera parameters from several views of a known calibration pattern (every view is described by several 3D-2D point correspondences). Also, fiddling with opencv is, up until now, cool :) At the moment I finished the first phase of this mini-project. OpenCV comes with two methods, we will see both. Estimate the relative position and orientation of the stereo camera "heads" and compute the rectification* transformation that makes the camera optical axes parallel. But before that, we can refine the camera matrix based on a free scaling parameter using cv2.getOptimalNewCameraMatrix().If the scaling parameter alpha=0, it returns undistorted image with minimum unwanted pixels. Zhang’s technique will solve for the Intrinsic Matrix K. We use a calibration object where all the coordinates of the “features” are known. Estimate intrinsic and extrinsic camera parameters from several views of a known calibration pattern (i.e. camera matrix is the intrinsic camera calibration matrix; Distorion - distortion coefficients. Camera calibration is a necessary step in 3D computer vision in order to extract metric information from 2D images. I decided to put the required OpenCV code on github and provide a quick guide trough the calibration process for a single camera as well as… Now we can take an image and undistort it. On a broad view, the camera calibration yields us an intrinsic camera matrix, extrinsic parameters and the distortion coefficients. Rt for cam 0 is the extrinsic camera calibration matrix (i.e. The basic model for a camera is a pinhole camera model, but today’s cheap camera’s incorporate high levels of noise/distortion in the images. every view is described by several 3D-2D point correspondences). These are only listed for those images where a pattern could be detected. every view is described by several 3D-2D point correspondences). Estimate the relative position and orientation of the stereo camera “heads” and compute the rectification transformation that makes the camera optical axes parallel. We have got what we were trying. One of the main uses of camera calibration is to figure out where a camera was in relation to a scene in a photograph. I have managed to calculate the extrinsic camera values from my laptop webcam and I can calculate the extrinsic values of a known object, specifically a chessboard printout, moving around in front on the webcam. Estimate the relative position and orientation of the stereo camera “heads” and compute the rectification transformation that makes the camera optical axes parallel. Calibrating a camera to compensate for lens distortion and positional offsets of stereo camera pairs is an important requirement for many applications such as pose reconstruction, depth-from-stereo and structure-from-motion. pose of the camera, rotation and translation) for image 0 in this case. Often for complicated tasks in computer vision it is required that a camera be calibrated. Source :OpenCV Camera Calibration docs. This technique has already been implemented in OpenCV. First I'm doing the Camera calibration using opencv and a chessboard, I'm taking a few chessboard shots in different angles and applying the function initUndistortRectifyMap, I´ll have the distortion coefficients, intrinsic and extrinsic parameters. b. Intrinsic parameters: The relationship between pixel coordinates and camera coordinates. a. Extrinsic parameters: The camera’s location and orientation in the world which is denoted by rotation and translation of the camera. 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