What will be the procedure to correct the following distorted images ? It looks like the images are bulging out from center. These are of the same QR code, and so a combination of such images can be used to arrive at a single correct and straight image.
Please advice.
The distortion you are experiencing is called "barrel distortion". A technical name is "combination of radial distortion and tangential distortions"
The solution for your problem is openCV camera calibration module. Just google it and you will find documentations in openCV wiki. More over, openCV already has built in source code examples of how to calibrate the camera.
Basically, You need to print an image of a chess board, take a few pictures of it, run the calibration module (built in method) and get as output transformation matrix. For each video frame you apply this matrix (I think the method called cvUndistort()) and it will straighten the curved lines in the image.
Note: It will not work if you change the zoom or focal length of the camera.
If camera details are not available and uncontrollable - then your problem is very serious. There is a way to solve the distortion, but I don't know if openCV has built in modules for that. I am afraid that you will need to write a lot of code.
Basically - you need to detect as much as possible long lines. Then from those lines (vertical and horizontal) you build a grid of intersection points. Finally you fit the grid of those points to openCV calibration module.
If you have enough intersection points (say 20 or more) you will be able to calculate the distortion matrix and un-distort the image.
You will not be able to fully calibrate the camera. In other words, you will not be able to run a one time process that calculates the expected distortion. Rather - in each and every video frame, you will calculate the distortion matrix directly - reverse it and un-distort the image.
If you are not familiar with image processing techniques or unable to find a reliable open source code which directly solves your problem - then I am afraid that you will not be able to remove the distortion. sorry
Related
In OpenCV implementation, instrinsic parameters of the camera is used to correct geometric distortion.
So camera calibration is performed to obtain instrinsic parameters using multiple chessboard images.
Currently I learned that geometric distortion can be corrected using only one chessboard image.
I try to figure out how it is done, but still can't find one way to do it.
http://www.imatest.com/docs/distortion-methods-and-modules/
https://www.edmundoptics.com/resources/application-notes/imaging/distortion/
I find the two above links. It describes the radial distortion. However we can't
guarantee that the camera is parallel to the chessboard when capturing the chessboard.
I can detect the corners of the chessboard, but some corners is distorted, so I can't
fit lines because fitting can only handle noise.
Any help are appreciated.
Please take a look at this paper and this paper. Moreover, this paper proves that you can correct distortion using single image without calibration target based on identifying straight lines on image such as edges of the buildings.
I don't know whether this functionality is implemented in OpenCV but the math in those papers is should be relatively easy to implement it using OpenCV.
First of all I'm a total newbie in image processing, so please don't be too harsh on me.
That being said, I'm developing an application to analyse changes in blood flow in extremities using thermal images obtained by a camera. The user is able to define a region of interest by placing a shape (circle,rectangle,etc.) on the current image. The user should then be able to see how the average temperature changes from frame to frame inside the specified ROI.
The problem is that some of the images are not steady, due to (small) movement by the test subject. My question is how can I determine the movement between the frames, so that I can relocate the ROI accordingly?
I'm using the Emgu OpenCV .Net wrapper for image processing.
What I've tried so far is calculating the center of gravity using GetMoments() on the biggest contour found and calculating the direction vector between this and the previous center of gravity. The ROI is then translated using this vector but the results are not that promising yet.
Is this the right way to do it or am I totally barking up the wrong tree?
------Edit------
Here are two sample images showing slight movement downwards to the right:
http://postimg.org/image/wznf2r27n/
Comparison between the contours:
http://postimg.org/image/4ldez2di1/
As you can see the shape of the contour is pretty much the same, although there are some small differences near the toes.
Seems like I was finally able to find a solution for my problem using optical flow based on the Lukas-Kanade method.
Just in case anyone else is wondering how to implement it in Emgu/C#, here's the link to a Emgu examples project, where they use Lukas-Kanade and Farneback's algorithms:
http://sourceforge.net/projects/emguexample/files/Image/BuildBackgroundImage.zip/download
You may need to adapt a few things, e.g. the parameters for the corner detection (the frame.GoodFeaturesToTrack(..) method) , but it's definetly something to start with.
Thanks for all the ideas!
Have anyone done something like that?
My problems with the OpenCV sticher is that it warps the images for panoramas, meaning the images get stretched a lot as one moves away from the first image.
From what I can tell OpenCV also builds ontop of the assumption of the camera is in the same position. I am seeking a little guidence on this, if its just the warper I need to change or I also need to relax this asusmption about the camera position being fixed before that.
I noticed that opencv uses a bundle adjuster also, is it using the same assumption that the camera is fixed?
Aerial image mosaicing
The image warping routines that are used in remote sensing and digital geography (for example to produce geotiff files or more generally orthoimages) rely on both:
estimating the relative image motion (often improved with some aircraft motion sensors such as inertial measurement units),
the availability of a Digital Elevation Model of the observed scene.
This allows to estimate the exact projection on the ground of each measured pixel.
Furthermore, this is well beyond what OpenCV will provide with its built-in stitcher.
OpenCV's Stitcher
OpenCV's Stitcher class is indeed dedicated to the assembly of images taken from the same point.
This would not be so bad, except that the functions try to estimate just a rotation (to be more robust) instead of plain homographies (this is where the fixed camera assumption will bite you).
It adds however more functionality that are useful in the context of panoramao creation, especially the image seam cut detection part and the image blending in overlapping areas.
What you can do
With aerial sensors, it is usually sound to assume (except when creating orthoimages) that the camera - scene distance is big enough so that you can approach the inter-frame transform by homographies (expecially if your application does not require very accurate panoramas).
You can try to customize OpenCV's stitcher to replace the transform estimate and the warper to work with homographies instead of rotations.
I can't guess if it will be difficult or not, because for the most part it will consist in using the intermediate transform results and bypassing the final rotation estimation part. You may have to modify the bundle adjuster too however.
I took the example of code for calibrating a camera and undistorting images from this book: shop.oreilly.com/product/9780596516130.do
As far as I understood the usual camera calibration methods of OpenCV work perfectly for "normal" cameras.
When it comes to Fisheye-Lenses though we have to use a vector of 8 calibration parameters instead of 5 and also the flag CV_CALIB_RATIONAL_MODEL in the method cvCalibrateCamera2.
At least, that's what it says in the OpenCV documentary
So, when I use this on an array of images like this (Sample images from OCamCalib) I get the following results using cvInitUndistortMap: abload.de/img/rastere4u2w.jpg
Since the resulting images are cut out of the whole undistorted image, I went ahead and used cvInitUndistortRectifyMap (like it's described here stackoverflow.com/questions/8837478/opencv-cvremap-cropping-image). So I got the following results: abload.de/img/rasterxisps.jpg
And now my question is: Why is not the whole image undistorted? In some pics of my later results you can recognize that the laptop for example is still totally distorted. How can I acomplish even better results using the standard OpenCV methods?
I'm new to stackoverflow and I'm new to OpenCV as well, so please excuse any of my shortcomings when it comes to expressing my problems.
All chessboard corners should be visible to be found. The algorithm expect a certain size of chessboard such as 4x3 or 7x6 (for example). The white border around a chess board should be visible too or dark squares may not be defined precisely.
You still have high distortions at the image periphery after undistort() since distortions are radial (that is they increase with the radius) and your found coefficients are wrong. The latter are wrong since a calibration process minimizes the sum of squared errors in pixel coordinates and you did not represent the periphery with enough samples.
TODO: You have to have 20-40 chess board pattern images if you use 8 distCoeff. Slant your boards at different angles, put them at different distances and spread them around, especially at the periphery. Remember, the success of calibration depends on sampling and also on seeing vanishing points clearly from your chess board (hence slanting and tilting).
I intend to make a program which will take stereo pair images, taken by a single camera, and then correct and crop them so that when the images are viewed side by side with the parallel or cross eye method, the best 3D effect will be achieved. The left image will be the reference image, the right image will be modified for corrections. I believe OpenCV will be the best software for these purposes. So far I believe the processing will occur something like this:
Correct for rotation between images.
Correct for y axis shift.
Doing so will I imagine result in irregular black borders above and below the right image so:
Crop both images to the same height to remove borders.
Compute stereo-correspondence/disparity
Compute optimal disparity
Correct images for optimal disparity
Okay, so that's my take on what needs doing and the order it occurs in, what I'm asking is, does that seem right, is there anything I've missed, anything in the wrong order etc. Also, which specific functions of OpenCV would I need to use for all the necessary steps to complete this project? Or is OpenCV not the way to go? Much thanks.
OpenCV is great for this.
There is a whole chapter in:
And all the sample code for this in the book ships with the opencv distribution
edit: Roughly the steps are:
Remap each image to remove lens distortions and rotate/translate views to image center.
Crop pixels that don't appear in both views (optional)
Find matching objects in each view (stereoblock matching) create disparity map
Reproject disparity map into 3D model