camera-calibration

问题 I am looking at the kitti dataset and particularly how to convert a world point into the image coordinates. I looked at the README and it says below that I need to transform to camera coordinates first then multiply by the projection matrix. I have 2 questions, coming from a non computer vision background I looked at the numbers from calib.txt and in particular the matrix is 3x4 with non-zero values in the last column. I always thought this matrix = K[I|0] , where K is the camera's intrinsic

问题 I'm using a wide-angle lens (178º Diagonal FOV ) and I'm trying to calibrate it properly using Opencv Calibration module. All the detection and calibration process are working fine, but the result is very poor. I have tried many different configurations: Different set of images Different radial coefficient numbers: 2,3,4,5 even 6.(CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6 ) Fixing principal point and tangential disortion to 0 (CV_CALIB_FIX_ASPECT_RATIO, CV_CALIB_FIX_PRINCIPAL_POINT) Using expected

问题 I am trying to calibrate RaspiCam Fisheye lens camera with OpenCV. I am using Python example code and the cheesboard row and column numbers are also correct but somehow I can not get a successful result. I have tested with a lso much of photos below you can see them. My source code: https://github.com/jagracar/OpenCV-python-tests/blob/master/OpenCV-tutorials/cameraCalibration/cameraCalibration.py my chess board rows and columns: rows = 9, cols = 6 but does not get a successful result Edit: my

问题 I am trying to get the bird's eye view perspective transform from camera intrinsic, extrinsic matrices and distortion coefficients. I tried using the answer from this question. The image used is the sample image left02.jpg from the opencv official github repo I calibrated the camera and found the intrinsic, extrinsic matrices and the distortion co-efficients. I undistored the image and found the pose. To check if the params are right. The equations I used to find the perspective

问题 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

问题 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

问题 I wish to convert a MATLAB stereoParameters structure to intrinsics and extrinsics matrices to use in OpenCV's stereoRectify. If I understood http://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html and http://mathworks.com/help/vision/ref/stereoparameters-class.html , stereoParameters.CameraParameters1 and stereoParameters.CameraParameters2 store the intrinsic matrices and the other members of stereoParameters the extrinsic ones. I think I got this mapping

问题 I wish to convert a MATLAB stereoParameters structure to intrinsics and extrinsics matrices to use in OpenCV's stereoRectify. If I understood http://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html and http://mathworks.com/help/vision/ref/stereoparameters-class.html , stereoParameters.CameraParameters1 and stereoParameters.CameraParameters2 store the intrinsic matrices and the other members of stereoParameters the extrinsic ones. I think I got this mapping

问题 I'm trying to find the relative position of the camera to the chessboard (or the other way around) - I feel OK with converting between different coordinate systems, e.g. as suggested here. I decided to use chessboard not only for calibration but actual position determination as well at this stage, since I can use the findChessboardCorners to get the imagePoints (and this works OK). I've read a lot on this topic and feel that I understand the solvePnP outputs (even though I'm completely new to

问题 Originally I have a image with a perfect circle grid, denoted as A I add some lens distortion and perspective transformation to it, and it becomes B In camera calibration, A would be my destination image, and B would be my source image. Let's say I have all the circle center coordinates in both images, stored in stdPts and disPts . //25 center pts in A vector<Point2f> stdPts; for (int i = 0; i <= 4; ++i) { for (int j = 0; j <= 4; ++j) { stdPts[i * 5 + j].x = 250 + i * 500; stdPts[i * 5 + j].y