I want to verify an algorithm about camera calibration. But using the pictures taken by myself is not convincing. Are there any canonical image libraries for camera calibration simulation?
Look at the page A Flexible New Technique for Camera Calibration by Zhengyou Zhang; in the section "Experimental data and result for camera calibration", you will find five images like this one , five sets of image coordinates (like for example this one https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/imagepointsone.txt) and the result of the calibration here: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/completecalibration.txt
I do not know whether it is canonical or not, for sure Zhengyou Zhang is well known for his work related to camera calibration and his article:
ZHANG, Zhengyou. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 2000, 22.11: 1330-1334.
is highly cited.
You may also have a look at Camera Calibration Toolbox for Matlab by Jean-Yves Bouguet, his code is the basis for the OpenCV algorithms but I do not know whether there are images for accuracy and correctness testing.
Related
I have a stationary mono camera which captures a single image frame at some fps.
Assume the camera is not allowed to move,how do I generate a stereo image pair from the obtained single image frame? Is there any algorithms exists for this? If so, are they available in Open-CV?
To get a stereo image, you need a stereo camera, i.e. a camera with two calibrated lenses. So you cannot get a stereo image from a single camera with traditional techniques.
However, with the magic of deep learning, you can obtain the depth image from single camera.
And no, there's no builtin OpenCV function to do that.
The most common use of this kind of techniques is in 3D TVs, which often offer 2D-to-3D conversion, and thus mono to stereo conversion.
Various algorithms are used for this, you can look at this state of the art report.
There is also optical way for this.
If you can add binocular prisms/mirrors to your camera objective ... then you could obtain real stereoscopic image from single camera. That of coarse need access to the camera and setting up the optics. This also introduce some problems like wrong auto-focusing , need for image calibration, etc.
You can also merge Red/Cyan filtered images together to maintain the camera full resolution.
Here is a publication which might be helpful Stereo Panorama with a single Camera.
You might also want to have a look at the opencv camera calibration module and a look at this page.
I am a beginner when it comes to computer vision so I apologize in advance. Basically, the idea I am trying to code is that given two cameras that can simulate a multiple baseline stereo system; I am trying to estimate the pose of one camera given the other.
Looking at the same scene, I would incorporate some noise in the pose of the second camera, and given the clean image from camera 1, and slightly distorted/skewed image from camera 2, I would like to estimate the pose of camera 2 from this data as well as the known baseline between the cameras. I have been reading up about homography matrices and related implementation in opencv, but I am just trying to get some suggestions about possible approaches. Most of the applications of the homography matrix that I have seen talk about stitching or overlaying images, but here I am looking for a six degrees of freedom attitude of the camera from that.
It'd be great if someone can shed some light on these questions too: Can an approach used for this be extended to more than two cameras? And is it also possible for both the cameras to have some 'noise' in their pose, and yet recover the 6dof attitude at every instant?
Let's clear up your question first. I guess You are looking for the pose of the camera relative to another camera location. This is described by Homography only for pure camera rotations. For General motion that includes translation this is described by rotation and translation matrices. If the fields of view of the cameras overlap the task can be solved with structure from motion which still estimates only 5 dof. This means that translation is estimated up to scale. If there is a chessboard with known dimensions in the cameras' field of view you can easily solve for 6dof by running a PnP algorithm. Of course, cameras should be calibrated first. Finally, in 2008 Marc Pollefeys came up with an idea how to estimate 6 dof from two moving cameras with non-overlapping fields of view without using any chess boards. To give you more detail please tell a bit for the intended appljcation you are looking for.
I have been working with Zhang's homography technique till now for my camera calibration. But the accuracy of the calibration is not sufficient (After taking necessary actions to improve: Example : spanning the whole FOV, variation in distance, etc.)
I have read in a few literature that non-coplanar calibration rig would give better results. I don't understand the exact reason behind this suggestion. If someone could explain to me how is accuracy improved, it would be good. Even better, would be is to point me to the algorithm that is used in OpenCV for non co-planar calibration rigs.
Based on OpenCV documentation, the algorithm has been implemented based on Bouguet's Camera Calibration ToolBox. But, I am not sure whether OpenCV uses Heikkila's approach (3rd example) or Bakstein and Halir's approach (4th example). If nothing helps, I am planning on going through OpenCV source code.
Say I plan to use OpenCV for 3D reconstruction using a stereo approach...and I do not have any special stereo camera but only webcams.
1.)How do I build a cheap stereo setup using a set of web cams?
2.)Is it possible to snap two images using web cams and convert them to stereo using openCV API?
I will use the stereo algorithm from the link below
Stereo vision with OpenCV
Using this approach I want to create a detailed mapping of an indoor environment.
(I would not like to use any projects like Insight3D which cannot be used for commercial purposes without distributing the source code)
You can find here a lot of resources including tutorials and stereo vision cameras
Firstly, ensure that your web cams don't have any inbuilt autofocus technology. As the cameras should have fixed focal length.
1) Align the cameras in canonical configuration with varying baseline distance. Then calibrate them using opencv's stereo_calib.cpp program. Usually, the distance will be 20-60cms. For some web cameras even 10cm will give you better results. If rms error and reprojection error are less than 0.5 then you could consider that the stereo setup is ready.
2) Yes, it is possible to capture stereo images from the setup which I just mentioned. Check out this link for capturing images from cameras.
OpenCV provides better algorithms from which one can do wonders with 3D vision.
Stereo is better suited for indoor environment as it is very sensitive to lighting variations.
Given a picture taken by a simple digital that contains an image of a rectangle of known dimensions. How can I - to some degree of accuracy - determine the parameters of this camera?
I am mostly interested in Pan-, Tilt- and Swing angles.
Optionally distance to the rectangle would be nice.
I acknowledge the fact that the focal length and resolution of the camera should be known, but it can be assumed that they are available (for example through exif metadata).
Are there any simple algorithms for this problems?
What you are looking for are camera calibration algorithms. A commonly used one is Zhang's algorithm.
For more information regarding calibrating cameras, a good source is Hartley and Zisserman's textbook
for a slightly simpler introduction and sample code see (chap 12 IIRC)