There are a number of calibration tutorials to calibrate camera images of chessboards in EMGU (OpenCV). They all end up calibrating and then undistorting an image for display. That's cool and all but I need to do machine vision where I am taking an image, identifying the location of a corner or blob or feature in the image and then translating the location of that feature in pixels into real world X, Y coordinates.
Pixel -> mm.
Is this possible with EMGU? If so, how? I'd hate to spend a bunch of time learning EMGU and then not be able to do this crucial function.
Yes, it's certainly possible as the "bread and butter" of OpenCV.
The calibration you are describing, in terms of removing distortions, is a prerequisite to this process. After which, the following applies:
The Intrinsic calibration, or "camera matrix" is the first of two required matrices. The second is the Extrinsic calibration of the camera which is essentially the 6 DoF transform that describes the physical location of the sensor center relative to a coordinate reference frame.
All of the Distortion Coefficients, Intrinsic, and Extrinsic Calibrations are available from a single function in Emgu.CV: CvInvoke.CalibrateCamera This process is best explained, I'm sure, by one of the many tutorials available that you have described.
After that it's as simple as CvInvoke.ProjectPoints to apply the transforms above and produce 3D coordinates from 2D pixel locations.
The key to doing this successfully this providing comprehensive IInputArray objectPoints and IInputArray imagePoints to CvInvoke.CalibrateCamera. Be sure to cause "excitation" by using many images, from many different perspectives.
Related
I have a vehicle with two cameras, left and right. Is there a difference between me calibrating each camera separately vs me performing "stereo calibration" ? I am asking because I noticed in the OpenCV documentation that there is a stereoCalibrate function, and also a stereo calibration tool for MATLAB. If I do separate camera calibration on each and then perform a depth calculation using the undistorted images of each camera, will the results be the same ?
I am not sure what the difference is between the two methods. I performed normal camera calibration for each camera separately.
For intrinsics, it doesn't matter. The added information ("pair of cameras") might make the calibration a little better though.
Stereo calibration gives you the extrinsics, i.e. transformation matrices between cameras. That's for... stereo vision. If you don't perform stereo calibration, you would lack the extrinsics, and then you can't do any depth estimation at all, because that requires the extrinsics.
TL;DR
You need stereo calibration if you want 3D points.
Long answer
There is a huge difference between single and stereo camera calibration.
The output of single camera calibration are intrinsic parameters only (i.e. the 3x3 camera matrix and a number of distortion coefficients, depending on the model used). In OpenCV this is accomplished by cv2.calibrateCamera. You may check my custom library that helps reducing the boilerplate.
When you do stereo calibration, its output is given by the intrinsics of both cameras and the extrinsic parameters.
In OpenCV this is done with cv2.stereoCalibrate. OpenCV fixes the world origin in the first camera and then you get a rotation matrix R and translation vector t to go from the first camera (origin) to the second one.
So, why do we need extrinsics? If you are using a stereo system for 3D scanning then you need those (and the intrinsics) to do triangulation, so to obtain 3D points in the space: if you know the projection of a general point p in the space on both cameras, then you can calculate its position.
To add something to what #Christoph correctly answered before, the intrinsics should be almost the same, however, cv2.stereoCalibrate may improve the calculation of the intrinsics if the flag CALIB_FIX_INTRINSIC is not set. This happens because the system composed by two cameras and the calibration board is solved as a whole by numerical optimization.
I have two images obtained by a calibrated camera from two different poses. I also have correspondences of 2D points between the images. Some of the points have depth information, so I also know their 3D coordinates. I want to calculate the relative pose between the images.
I know I can compute a fundamental matrix or an essential matrix from the 2D points. I also know PnP can find the pose with 2D-to-3D correspondences and that it's also doable getting just correspondences of 3D points. However, I don't know any algorithm that takes advantage of all the available information. Is there any?
There is only one such algorithm: Bundle Adjustment - everything else is a hack. Get your initial estimates separately, use any "reasonable & simple" hacky way of merging them to get an initial estimate, then byte the bullet and bundle. If you are coding in C++, Google's Ceres is my recommended B.A. library.
I am doing a project on 3D rendering of a scene. I am using OpenCV. The steps I am doing are like this:
Taking two images of a scene.
Calculating object correspondence using SURF feature matching.
Calculating camera fundamental matrix.
Calculating the Disparity image.
Now I have two questions
After calculating fundamental matrix how can I calculate the Q matrix? (I can't calibrate the camera)
How can I render in 3D using opencv or any other library?
For the 3D part, you can render your scene with OpenGL or with PCL. You've two solutions:
For each pixel, you make a point with the right color extracted from the camera's image. This will give you a point cloud which can be processed with PCL (for 3D features extraction for example).
You apply a triangulation algorithm, but in order to apply this algorithm you must have the extrinsic matrices of your camera.
You can find more information about these techniques here:
Point Cloud technique
Triangulation algorithm
If you want to use OpenGL, you have to open a valid OpenGL context. I recommend you the SFML library or Qt. These libraries are very easy to use and have a good documentation. Both have tutorials about 3D rendering with OpenGL.
you can have Q matrix from stereo rectification via openCV method:
cv::stereoRectify
I think you want the Q matrix to reconstruct the 3D. However, you can reconstruct from intrinsic parameters via:
X = (u-cu)*base/d
Y = (v-cv)*base/d
Z = f*base/d
where (u,v) is a 2D point in the image coordinate system, (cu,cv) is the principal point of the camera, f is the focal length, base is the baseline, d is the disparity and (X,Y,Z) is a 3D point in the camera coordinate system.
For the visualization, it is possible to use PCL or VTK (the visualization of PCL is based on vtk, but for me more simple to implement).
If you just want to have a look to the output you can just use some software like Meshlab
Cheers
I am aware of the chessboard camera calibration technique, and have implemented it.
If I have 2 cameras viewing the same scene, and I calibrate both simultaneously with the chessboard technique, can I compute the rotation matrix and translation vector between them? How?
If you have the 3D camera coordinates of the corresponding points, you can compute the optimal rotation matrix and translation vector by Rigid Body Transformation
If You are using OpenCV already then why don't you use cv::stereoCalibrate.
It returns the rotation and translation matrices. The only thing you have to do is to make sure that the calibration chessboard is seen by both of the cameras.
The exact way is shown in .cpp samples provided with OpenCV library( I have 2.2 version and samples were installed by default in /usr/local/share/opencv/samples).
The code example is called stereo_calib.cpp. Although it's not explained clearly what they are doing there (for that You might want to look to "Learning OpenCV"), it's something You can base on.
If I understood you correctly, you have two calibrated cameras observing a common scene, and you wish to recover their spatial arrangement. This is possible (provided you find enough image correspondences) but only up to an unknown factor on translation scale. That is, we can recover rotation (3 degrees of freedom, DOF) and only the direction of the translation (2 DOF). This is because we have no way to tell whether the projected scene is big and the cameras are far, or the scene is small and cameras are near. In the literature, the 5 DOF arrangement is termed relative pose or relative orientation (Google is your friend).
If your measurements are accurate and in general position, 6 point correspondences may be enough for recovering a unique solution. A relatively recent algorithm does exactly that.
Nister, D., "An efficient solution to the five-point relative pose problem," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.26, no.6, pp.756,770, June 2004
doi: 10.1109/TPAMI.2004.17
Update:
Use a structure from motion/bundle adjustment package like Bundler to solve simultaneously for the 3D location of the scene and relative camera parameters.
Any such package requires several inputs:
camera calibrations that you have.
2D pixel locations of points of interest in cameras (use a interest point detection like Harris, DoG (first part of SIFT)).
Correspondences between points of interest from each camera (use a descriptor like SIFT, SURF, SSD, etc. to do the matching).
Note that the solution is up to a certain scale ambiguity. You'll thus need to supply a distance measurement either between the cameras or between a pair of objects in the scene.
Original answer (applies primarily to uncalibrated cameras as the comments kindly point out):
This camera calibration toolbox from Caltech contains the ability to solve and visualize both the intrinsics (lens parameters, etc.) and extrinsics (how the camera positions when each photo is taken). The latter is what you're interested in.
The Hartley and Zisserman blue book is also a great reference. In particular, you may want to look at the chapter on epipolar lines and fundamental matrix which is free online at the link.
I am dealing with the problem, which concerns the camera calibration. I need calibrated cameras to realize measurements of the 3D objects. I am using OpenCV to carry out the calibration and I am wondering how can I predict or calculate a volume in which the camera is well calibrated. Is there a solution to increase the volume espacially in the direction of the optical axis? Does the procedure, in which I increase the movement range of the calibration target in 'z' direction gives sufficient difference?
I think you confuse a few key things in your question:
Camera calibration - this means finding out the matrices (intrinsic and extrinsic) that describe the camera position, rotation, up vector, distortion, optical center etc. etc.
Epipolar Rectification - this means virtually "rotating" the image planes so that they become coplanar (parallel). This simplifies the stereo reconstruction algorithms.
For camera calibration you do not need to care about any volumes - there aren't volumes where the camera is well calibrated or wrong calibrated. If you use the chessboard pattern calibration, your cameras are either calibrated or not.
When dealing with rectification, you want to know which areas of the rectified images correspond and also maximize these areas. OpenCV allows you to choose between two extremes - either making all pixels in the returned areas valid and cutting out pixels that don't fit into the rectangular area or include all pixels even with invalid ones.
OpenCV documentation has some nice, more detailed descriptions here: http://opencv.willowgarage.com/documentation/camera_calibration_and_3d_reconstruction.html