I'm stuck trying to get perspective transformation to work.
I want to draw an image so it fits 4 given points. It's something like you have in Photoshop and it's used to change the perspective of image (see image below).
I have an image in byte array and I'm not using any additional libraries.
Everything I've found so far was either for OpenCV or didn't do what I wanted.
I have found some open-source program PhotoDemon and it does exactly what I want, and there is a code for that. I was trying to get it to work for many hours but it gives me completly weird results (second line on the image below).
Could someone provide me with some code or step-by-step math of what and how to do or even just a pseudo-code. I'm a little bit sick of it. It seems easy but I need some help.
Related
I am going to split this question in 3 parts
First, I've been given this problem, and I don't know where to start, if you have been solving related problem, would you give me some hints and keywords to help me do some more research?
I have done some research on my own
So here is some 2D chest CT scans (sorry due to reputation rule i can't implement images directly)
All photos are in the same angle. So I think I can simply read each photo to a vector of pixels, do some thresh holding to make all black and black-ish pixels going to be a non-colored pixel. Next, I'll create a vector called vector_of_photo of those vectors. Then the index of each vector in vector_of_photo are now the Z-index.
Now I can render a 3d photo from those vectors of pixels right?
In the second place, I got trouble understand raycasting algorithm,
I think the idea here is, when I already got a box of pixel then everytime I rotate the box, it cast straight-lines from that angle of the camera to the box, each line found a has-colored pixel going to stop casting and render that pixel (or more specific, copy the pixel to the exactly location on the plane).
Did I understand it correctly?
At last, the OPENGL/c++ part is just the option I think I'm going to use to solve this problem. And I'm not pretty sure it is a good idea or not, so give me some more hint about the programming language, library or module I should take a look at.
I happen to be working on the same problem in my spare time. Haha :)
Here is one approach to your problem:
Load the images into your application, such that you get the 3D volumetric dataset that you describe
Remove all points that don't fit within some range of values (e.g. 0.4/1.0 to 0.6/1.0 brightness). You may need to apply preprocessing and filtering.
Fit a mesh to the resulting point cloud with open-source software. Here is a good blog post about that
https://towardsdatascience.com/5-step-guide-to-generate-3d-meshes-from-point-clouds-with-python-36bad397d8ba
Take the resulting mesh (probably, an STL file) and visualize it in any software your want (Blender 3D, Unity 3D, Cinema 4D, a custom OpenGL application), anything really.
My own approach to this problem is very similar to the one you suggest in your question, and I have already made some headway. Therefore, I thought it would be good to suggest another route.
NOTE Please be aware that what you are working on is not a trivial problem. It's a large project, and there are many Commerical companies that put years into doing just this. This is a great project for learning OpenGL, rendering, and other concepts. It's perfectly doable, but you may be looking at several months of work, and lots of trial and error. Good luck!
Its not often that two people would happen to work on the same problem, so if you want to discuss further, feel free to contact me over linkedin and/or post a comment below. www.linkedin.com/in/michael-sohnen-a2454b1b2
I am trying to use the OpenCVs Background Subtractor class MOG2 to seperate a person moving infront of a camera. I got everything set up and working nicely. But the resulting mask I am getting looks something like this:
(default settings)
Now what I would like to get is something like this:
(bad gimp skills :D)
I have already tryed to mess around with the parameter described in the docu, but all I managed to accomplish was something the looked like a motion blur effect...
So I was hopeing somebody with a better understanding of the algorithm or somebody who has already done something similar might be able to help me!
Thanks in advance, Foaly
I am working also with that and what I've seen is that this algorithm needs a good calibration to accomplish that goal, because you should be aware that this algorithm try to put in the background some pixels that don't show changes, e.g. in your skin major part of the pixels have the same color maybe this is the reason. I recommend to you that use other kind of methods (using zncc) if you want to use an application like the one that is showed in your question.
So I guess this were our image processing skills come into play. The first thing I would do is make the lines on the image thicker and join it it. We can use the following:
1) I would want the lines thicker. Use Morphological operators tutorial on Morphological operators with Otsu's method. This paper worked for me when I did my ear biometrics http://www4.comp.polyu.edu.hk/~csajaykr/myhome/papers/PR2011.pdf
2) Fill in connected components using opencv and clean the image
3) Segment human profile
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.
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 have to process a lot of scanned IDs and I need to extract photos from them for further processing.
Here's a fictional example:
The problem is that the scans are not perfectly aligned (rotated up to 10 degrees). So I need to find their position, rotate them and cut out the photo. This turned out to be a lot harder than I originally thought.
I checked OpenCV and the only thing I found was rectangle detection but it didn't give me good results: the rectangle not always matches good enough on samples. Also its image matching algorithm works only for not-rotated image since it's just a brute force comparison.
So I though about using ARToolkit (augmented reality lib) because I know that it's able to very precisely locate given marker on an image. But it it seems that the markers have to be very simple, so I can't use a constant part of the document for this purpose (please correct me if I'm wrong). Also, I found it super-hard to compile it on Ubuntu 11.10.
OCR - haven't tried this one yet and before I start my research I'd be thankful for any suggestions what to look for.
I look for a C(preferable)/C++ solution. Python is an option too.
If you don't find another ideal solution, one method I ended up using for OCR preprocessing in the past was to convert the source images to PPM and use unpaper in Ubuntu. You can attempt to deskew the image based on whichever sides you specify as having clearly-defined edges, and there is an option to bypass the filters that would normally be applied to black and white text. You probably don't want those for images.
Example for images skewed no more than 15 degrees, using the bottom and right edges to detect rotation:
unpaper -n -dn bottom,right -dr 15 input.ppm output.ppm
unpaper was written in C, if the source is any help to you.