size (), CV_32FC1, map1x, map1y ) initUndistortRectifyMap ( CM2, D2, R2, P2, img1. Mat map1x, map1y, map2x, map2y Mat imgU1, imgU2 initUndistortRectifyMap ( CM1, D1, R1, P1, img1. Q вЂ“ 4x4 disparity-to-depth mapping matrix.P2 - 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.P1 - 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.R2 - 3x3 rectification transform (rotation matrix) for the second camera.R1 - 3x3 rectification transform (rotation matrix) for the first camera.Mat R1, R2, P1, P2, Q stereoRectify ( CM1, D1, CM2, D2, img1. StereoRectify computes rectification transforms for each calibrated stereo camera. T - Translation vector between the coordinate systems of the cameras.R - Rotation Matrix between first and second camera coordinate systems.D2 - Distortion coeff matrix of second camera.D1 - Distortion coeff matrix of first camera.size (), R, T, E, F, cvTermCriteria ( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 100, 1e-5 ), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST ) Mat CM1 = Mat ( 3, 3, CV_64FC1 ) Mat CM2 = Mat ( 3, 3, CV_64FC1 ) Mat D1, D2 Mat R, T, E, F stereoCalibrate ( objectPoints, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1. vector obj for ( int j = 0 j > img cvtColor ( img, gray, CV_BGR2GRAY ) found = findChessboardCorners ( gray, board_sz, corners, CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS ) // returns bool if found or not if ( found ) ObjectPoints should contain physical location of each corners but since we donвЂ™t know that so we assign constant positions to all the corners and assume that camera is moving. Mat img, gray VideoCapture cap = VideoCapture ( 1 ) int success = 0 int k = 0 bool found = false Initialize Camera and define image containers. ObjectPoints represent the real location of the corners in 3D. ImagePoints represent location of the detected corners in the image. Main function int numBoards // number of different poses int board_w // number of horizontal corners int board_h // number of vertical corners Size board_sz = Size ( board_w, board_h ) int board_n = board_w * board_h vector > objectPoints vector > imagePoints vector corners OpenCV has a great support for calibration and thereвЂ™s is a very convinient way to do it. Many different poses are used to get a better calibration. It is easier to calibrate using chessboard pattern because it is flat so no concerns of depth, it is easier to extract corner points as they are extensively defined. One standard procedure to calibrate camera is to use a chessboard pattern. R and T are the extrinsic parameters which represent the coordinate transormation from 3D world to 3D camera model. U 0 and v 0 denote the principal point which would be ideally in the center. О‘ x and О± y denote focal length in x direction and y direction respectively. Intrinsic parameters include focal length, image format and principal point. Goal of calibration to find out intrinsic and extrinsic parameters of the camera. Read more about distortion on AI shackД¬alibrating the camera gives us some specific values which can be used to measure distances in length units and not in pixels. In the manufacturing process of a camera, there might be some distortions invloved due to inaccurate positioning of the lens or inaccuracy in manufacturing parabolic lens. A scene view is formed by projecting 3D points into the image plane using a perspective transformation. This is a so-called pinhole camera model. Whenever working with stereoscopy, it is a necessity to calibrate the cameras and get the required intrinsic and extrinsic parameters. Taskito - Calendar based to-do list Stereo Calibration

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