I am working on a project to stitch together around 400 high resolution aerial images around 36000x2600 to create a map. I am currently using OpenCV and so far I have obtained the match points between the images. Now I am at a lost in figuring out how to get the matrix transformation of the images so I can begin the stitching process. I have absolutely no background in working with images nor graphics so this is a first time for me. Can I get some advice on how I would approach this?
The images that I received also came with a data sheet showing longitude, latitude, airplane wing angle, altitude, etc. of each image. I am unsure how accurate these data are, but I am wondering if I can use these information to perform the proper matrix transformation that I need.
Thanks
Do you want to understand the math behind the process or just have an superficial idea of whats going on and just use it?
The regular term for "image snitching" is image alignment. Feed google with it and you'll find tons of sources.
For example, here.
Best regards,
zhengtonic
In recent opencv 2.3 release...they implemented a whole process of image stitching. Maybe it is worth looking at.
Related
I compute the optical flow on grayscale videos which contains true-white and noisy-black patch besides the useful information. I want to remove those patches because the correspondant optical flow is foolish.
Those patches are on the edges of the image and their sizes vary from a video to another. My goal is to extract a bounding box describing the useful information in my video thanks to the optical flow.
How can I compute this bounding box ? Or at least, how can I remove the computed optical flow in those regions ?
Edit : I saw your answers. I'll try that next week end then come back to discuss about that. Tank you !
Remove noise from optical flow could be a complicated task. A simple and dummy way could be to use a threshold on the optical flow vector intensity.
But if you only need to find bounding boxes why just do not use a simple background/motion object segmentation? Like MOG, GMG, opencv has nice implementations of them and they works well and are quite fast. See this tutorial.
It's a little tough to understand what the problem is, if the noises is true-white and noisy-black patches in a grayscale image as you have said, then I suggest you look at eroding and dilating. More information can be found here: Eroding and Dilating
Should this not be what you are asking, do post some sample images with the patches and comment so that I can have a clearer idea on what the problem is. Cheers.
If I understand correctly, you are getting noisy optical flow in patches which are grey/white or basically uniform. A simple approach would be to divide the image into small patches and compute the entropy over each patch. Now, patches which have a very low entropy can be discarded by choosing an appropriate threshold because they do not contain much information.
currently i am having much difficulty thinking of a good method of removing the gradient from a image i received.
The image is a picture taken by a microscope camera that has a light glare in the middle. The image has a pattern that goes throughout the image. However i am supposed to remove the light glare on the image created by the camera light.
Unfortunately due to the nature of the camera it is not possible to take a picture on black background with the light to find the gradient distribution. Nor do i have a comparison image that is without the gradient. (note- the location of the light glare will always be consistant when the picture is taken)
In easier terms its like having a photo with a flash in it but i want to get rid of the flash. The only problem is i have no way to obtaining the image without flash to compare to or even obtaining a black image with just the flash on it.
My current thought is conduct edge detection and obtain samples in specific locations away from the edges (due to color difference) and use that to gauge the distribution of gradient since those areas are supposed to have relatively identical colors. However i was wondering if there was a easier and better way to do this.
If needed i will post a example of the image later.
At the moment i have a preferrence of solving this in c++ using opencv if that makes it easier.
thanks in advance for any possible ideas for this problem. If there is another link, tutorial, or post that may solve my problem i would greatly appreciate the post.
as you can tell there is a light thats being shinned on the img as you can tell from the white spot. and the top is lighter than the bottome due to the light the color inside the oval is actually different when the picture is taken in color. However the color between the box and the oval should be consistant. My original idea was to perhaps sample only those areas some how and build a profile that i can utilize to remove the light but i am unsure how effective that would be or if there is a better way
EDIT :
Well i tried out Roger's suggestion and the results were suprisngly good. Using 110 kernel gaussian blurr to find illumination and conducting CLAHE on top of that. (both done in opencv)
However my colleage told me that the image doesn't look perfectly uniform and pointed out that around the area where the light used to be is slightly brighter. He suggested trying a selective gaussian blur where the areas above certain threshold pixel values are not blurred while the rest of the image is blurred.
Does anyone have opinions regarding this and perhaps a link, tutorial, or an example of something like this being done? Most of the things i find tend to be selective blur for programs like photoshop and gimp
EDIT2 :
it is difficult to tell with just eyes but i believe i have achieved relatively close uniformization by using a simple plane fitting algorithm.((-A * x - B * y) / C) (x,y,z) where z is the pixel value. I think that this can be improved by utilizing perhaps a sine fitting function? i am unsure. But I am relatively happy with the results. Many thanks to Roger for the great ideas.
I believe using a bunch of pictures and getting the avg would've been another good method (suggested by roger) but Unofruntely i was not able to implement this since i was not supplied with various pictures and the machine is under modification so i was unable to use it.
I have done some work in this area previously and found that a large Gaussian blur kernel can produce a reasonable approximation to the background illumination. I will try to get something working on your example image but, in the meantime, here is an example of your image after Gaussian blur with radius 50 pixels, which may help you decide if it's worth progressing.
UPDATE
Just playing with this image, you can actually get a reasonable improvement using adaptive histogram equalisation (I used CLAHE) - see comparison below - any use?
I will update this answer with more details as I progress.
I would like to point you to this paper: http://www.cs.berkeley.edu/~ravir/dirtylens.pdf, but, in my opinion, without any sort of calibration/comparison image taken apriori, it is difficult to mine out the ground truth from the flared image.
However, if you are trying to just present the image minus the lens flare, disregarding the actual scientific data behind the flared part, then you switch into the domain of image inpainting. Criminsi's algorithm, as described in this paper: http://research.microsoft.com/pubs/67276/criminisi_tip2004.pdf and explained/simplified in these two links: http://cs.brown.edu/courses/csci1950-g/results/final/eboswort/ http://www.cc.gatech.edu/~sooraj/inpainting/, will do a very good job in restoring texture information to the flared up regions. (If you'd really like to pursue this approach, do mention that. More comprehensive help can be provided for this).
However, given the fact that we're dealing with microscopic data, I doubt if you'd like to lose the scientific data contained in a particular region of an image. In that case, I really think you need to find a workaround to determine the flare model of the flash/light source w.r.t the lens you're using.
I hope someone else can shed more light on this.
I'm using OpenCV library (C++) to extract detectors from 2 images coming from a video stream taker from an aerial camera in order to, afterwards, find the matching points in successive images. i'm wondering which is the best algorithm to find robust detectors of a urban environment??
Ps. Actually I'm using SURF but when the images changes a little (because the camera is translating very slowly) the matchings between these descriptors become very few!
If you want to try different aproaches give a try to RoboRealm , they have a trial version, you just put the algoritms and seems the results, for testing purposes even if you will use OpenCV its ok.
Some background:
Hi all! I have a project which involves cloud imaging. I take pictures of the sky using a camera mounted on a rotating platform. I then need to compute the amount of cloud present based on some color threshold. I am able to this individually for each picture. To completely achieve my goal, I need to do the computation on the whole image of the sky. So my problem lies with stitching several images (about 44-56 images). I've tried using the stitch function on all and some subsets of image set but it returns an incomplete image (some images were not stitched). This could be because of a lack of overlap of something, I dunno. Also the output image has been distorted weirdly (I am actually expecting the output to be something similar to a picture taken by a fish-eye lense).
The actual problem:
So now I'm trying to figure out the opencv stitching pipeline. Here is a link:
http://docs.opencv.org/modules/stitching/doc/introduction.html
Based on what I have researched I think this is what I want to do. I want to map all the images to a circular shape, mainly because of the way how my camera rotates, or something else that has uses a fairly simple coordinate transformation. So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography? If so, does anyone have any idea how I can go about my problem? After this, I believe I need to get a mask for blending the images. Will I need to get a fixed mask like the one I want for my homography?
Am I going through a possible path? I have some background in programming but almost none in image processing. I'm basically lost. T.T
"So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography?"
Yes, the homography matrix is the transformation matrix between an original image and the ideal result. It warps an image in perspective so it can fit in stitching to the other image.
"If so, does anyone have any idea how I can go about my problem?"
Not with the limited information you provided. It would ease the problem a lot if you know the order of pictures (which borders which.. row, column position)
If you have no experience in image processing, I would recommend you use a tutorial covering stitching using more basic functions in detail. There is some important work behind the scenes, and it's not THAT harder to actually do it yourself.
Start with this example. It stitches two pictures.
http://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/
I was trying to get RGBDemo(mostly reconstructor) working with 2 logitech stereo cameras, but I did not figure out how to do it.
I noticed that there is a opencv grabber in nestk library and its header file is included in the reconstructor.cpp. Yet, when I try "rgbd-viewer --camera-id 0", it keeps looking for kinect.
My questions:
1. Is RGBDemo only working with kinect so far?
2. If RGBDemo can work with non-kinect stereo cameras, how do I do that?
3. If I need to write my own implementation for non-kinect stereo cameras, any suggestion on how to start?
Thanks in advance.
if you want to do it with non-kinect cameras. You don't even need stereo. There are algorithms now that are able to determine whether two images' viewpoints are sufficiently different that they can be used as if they were taken by a stereo camera. In fact, they use images from different cameras that are found on the internet and reconstruct 3D models of famous places. I can write you a tutorial on how to get it working. I've been meaning to do so. The software is called Bundler. Along with Bundler, people often also use CMVS and PMVS. CMVS preprocesses the images for PMVS. PMVS generates dense clouds.
BUT! I highly recommend that you don't go this route. It makes a lot of mistakes because there is so much less information in 2D images. It makes it very hard to reconstruct the 3D model. So, it ends up making a lot of mistakes, or not working. Although Bundler and PMVS are awesome compared to previous software, the stuff you can do with kinect is on a whole other level.
To use kinect will only cost you $80 for the kinect off of ebay or $99 off of amazon and another $5 for the power adapter off of amazon. So, I'd highly recommend this route. Kinect provides much more information for the algorithm to work with than 2D images do, making it much more effective, reliable and fast. In fact, it could take hours to process images with Bundler and PMVS. Whereas with kinect, I made a model of my desk in just a few seconds! It truly rocks!