camera-calibration

问题 I am building a website that does cool things using computer vision techniques, with videos live recorded and uploaded by users using their webcam. For this, I need camera intrinsic and distortion parameters. I am trying to figure out what would be the best way to compute these given the user uploaded videos. We can make no assumptions about what videos user might upload - but a reasonable assumption is that a human might be present in the video. I am still in the initial stages of this, but

问题 I am working on Fisheye camera calibration, and I'm not getting the expected result and I don't know where I'm wrong. I'm new to this camera calibration. My Parameters from calibration Focal Length: fc = [ 410.7476 408.0925 ] Principal point: cc = [ 369.3690 299.0193 ] Skew: alpha_c = [ 0.8336 ] Fisheye Distortion: kc = [ -0.3726 0.0931 0.0107 0.0000 ] My code to undistort: fisheye::estimateNewCameraMatrixForUndistortRectify(intrinsic, distCoeffs, input.size(), Matx33d::eye(), newK, 1);

问题 I've asked a lot of image-stitching-related questions here due to the fact that I'm SOO dumb to understand certain things :( I'm currently working on a homography-based multicamera image stitching and having some problems that I can't solve by myself. I use Visual Studio 2008/2010 and OpenCV 2.3.1 to work on my project. I'm gonna describe them below: First of all, I've put some stuff(code, video...etc) that'll be mentioned later in the following link as I'm limited to post 2 links at most

问题 I am working on a camera calibration program using the OpenCV/Python example (from: OpenCV Tutorials) as a guidebook. Question: How do I tailor this example code to account for the size of a square on a particular chessboard pattern? My understanding of the camera calibration process is that this information must somehow be used otherwise the values given by: cv2.calibrateCamera() will be incorrect. Here is the portion of my code that reads in image files and runs through the calibration

问题 I am using OpenCV 3 in Python 2.7 to calibrate different cameras. I use the findCirclesGrid() function, which succesfully finds a 4 by 11 circle pattern in a 1 Megapixel image. However, when I try to detect the pattern up close in an image with a higher resolution, the function fails. When the object is farther away in the image, it is still detected. I use the function as follows: ret, corners = cv2.findCirclesGrid(image, (4, 11), flags=cv2.CALIB_CB_ASYMMETRIC_GRID) With larger images, it

问题 I have to reconstruct an object which will be placed around 1 meter to 1.5 meters away from the baseline of my stereo setup. The image captured by both cameras have high resolution (10 MP) The accuracy with which I have to detect it's position is +/- 0.5mm, in all the three co-ordinate axes. (If you require more details, please let me know) For these, what should the optimal specifications of my checkerboard (for calibration) be? I only know that it should be an asymmetric board. It should be

问题 I have to write a camera-calibration an wanted to use python and opencv. The current problem I have is the following: I have the code written down below: import sys import numpy as np import cv2 image = cv2.imread(sys.argv[1]) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) ret = False ret, corners = cv2.findChessboardCorners(image, (7,6), None) print ret okay so far so good, but it doesn't matter which image I am using my ret variable is turning False every single time, which basically means

问题 Does anybody know an up-to-date camera calibration project like http://www.emgu.com/wiki/index.php/Camera_Calibration for the current version of EmguCV (3.0.0)? I need to figure out the coefficients of distortion of a special camera. It would be okay to have a standalone binary. It would also be nice, if there is a documentation about which camera-distorion-model is used. The problem behind the project above is, that it is not up-to-date with current EmguCv, so I can't build it. The target is

问题 I'm using OpenCV to calibrate a stereo camera pair. I've taken various calibration photos and I have a satisfactory fit for the intrinsic parameters using cv2.calibrateCamera. However, it's not clear how to get the extrinsic parameters out. The function just returns the cameraMatrix, which while useful is only half the story, as it were. I'd like to be able to obtain the rotation/translation shown in the top of the documentation - i.e. a vector from the world origin to the camera perspective

问题 I am trying to use openCV for camera calibration. I do not have a problem as long as I use the cv2.findChessBoardCorners to find my calibration targets in the image, but if I use my own function to find the points and build an array with the points, I get an error when trying to estimate the camera parameters. Here is an example that will throw the same error. import numpy as np import cv2 pattern_size = (4, 3) pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 ) pattern_points