camera-calibration

I want to extract a depth map from a calibrated image and triangular mesh using OpenCV called from Matlab 2014b (using the OpenCV bindings). I am a regular user of Matlab but am new to OpenCV. I have the following inputs: im - an undistorted RGB image of the scene or - camera position vector R - rotation matrix describing camera pose points - nx3 triangular mesh vertex list representing the scene faces - mx3 triangular mesh face list EFL - image effective focal length in pixels I have written a native Matlab ray tracing engine to extract a depth map from these inputs, but this is quite slow

Here is my code to project Velodyne points into the images: cam = 2; frame = 20; % compute projection matrix velodyne->image plane R_cam_to_rect = eye(4); [P, Tr_velo_to_cam, R] = readCalibration('D:/Shared/training/calib/',frame,cam) R_cam_to_rect(1:3,1:3) = R; P_velo_to_img = P*R_cam_to_rect*Tr_velo_to_cam; % load and display image img = imread(sprintf('D:/Shared/training/image_2/%06d.png',frame)); fig = figure('Position',[20 100 size(img,2) size(img,1)]); axes('Position',[0 0 1 1]); imshow(img); hold on; % load velodyne points fid = fopen(sprintf('D:/Shared/training/velodyne/%06d.bin',frame

I calibrated a camera with checkerboard pattern using OpenCV and MATLAB. I got .489 and . 187 for Mean Re-projection errors in OpenCV and MATLAB respectively. From the looks of it, MATLAB is more precise. But my adviser feels both MATLAB and OpenCV use the same BOUGET's algorithm and should report same error (or close). Is it so ? Can someone explain the difference b/w MATLAB and OpenCV camera calibration methods ? Thanks! Your adviser is correct in that both MATLAB and OpenCV use essentially the same calibration algorithm. However, MATLAB uses the Levenberg-Marquardt non-linear least squares

I'm having trouble undistorting points on an image taken with a calibrated camera using the Python bindings for OpenCV. The undistorted points have entirely different coordinates than the original points detected in the image. Here's the offending call: undistorted = cv2.undistortPoints(image_points, camera_matrix, distortion_coefficients) where image_points is a numpy array of detected chessboard corners returned by cv2.findChessboardCorners and reshaped to match the dimensional requirements of cv2.undistortPoints , and camera_matrix and distortion_coefficients were returned by cv2

I am using OpenCV to estimate a webcam's intrinsic matrix from a series of chessboard images - as detailed in this tutorial , and reverse project a pixel to a direction (in term of azimuth/elevation angles). The final goal is to let the user select a point on the image, estimate the direction of this point in relation to the center of the webcam, and use this as DOA for a beam-forming algorithm. So once I have estimated the intrinsic matrix, I reverse project the user-selected pixel (see code below) and display it as azimuth/elevation angles. result = [0, 0, 0] # reverse projected point, in

I got camera intrinsic matrix and distortion parameters using camera calibration. The unit of the focal length is pixels, i guess. Then, how can i calculate field of view (along y) ? Is this formula right? double fov_y = 2*atan(height/2/fy)*180/CV_PI; I'll use it to parameters of gluPerspective() OpenCV has a function that does this. Looking at the implementation (available on GitHub ) we have given an image with dimensions w x h and a camera matrix: the equations for the field of view are: 来源： https://stackoverflow.com/questions/39992968/how-to-calculate-field-of-view-of-the-camera-from

I calibrated my mono camera using opencv. Now I know the camera intrinsic matrix and distortion coefs [K1, K2, P1 ,P2,K3 ,K4, K5, K6] of my camera. Assuming that camera is place in [x, y, z] with [Roll, Pitch, Yaw] rotations. how can I get each pixel in world coordinate when the camera is looking on the floor [z=0]. You say that you calibrated your camera which gives you: Intrinsic parameters Extrinsic parameters (rotation, translation) Distortion coefficients First, to compensate for the distortion, you can use the undistort function and get an undistorted image. Now, what you are left with