I do not have enough knowledge.
What methods can be used to solve this problem.
For example from another question:
enter image description here
In fact, my task is a little easier. Because the map looks different:
enter image description here
In my head I see such a picture:
enter image description here
However, I do not have a scientific degree in trigonometry or sufficient experience with graphics to understand what I need to solve.
Please tell me what methodologies should I study to understand this?
The ready-made solution is also interesting if you attach the theoretical part to the solution.
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'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.
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.
Alright guys..
I'm at a loss here. I've been trying to code an image to polygon converter to no avail, it's really bugging me. It's supposed to create a polygon from the transparency of an image (the non transparent part is the polygon). I've seen programs that do it, but they were closed-source and really expensive. It's not just edge-detection, it's polygon generalization and hole-detection as well, all this in a Box2D compatible, triangulated polygon.
Please hand me some tips on this, or a library name, or an algorithm or something..
Thanks!
I suggest looking at the openCV library. It has several useful algorithms and tools for
image processing. I think you can accomplish your goal with it.
Good luck.
I raised this question due to curiousity while using Google Goggle and Google's "Search by Image".
If you try giving Google an image to search, it can show you some results. Identical images work best (of course), but taken photo of various objects could be difficult.
I guess Google Goggle has workaround a bit by using text recognition and image matching recognition. If text recognition found the text, for instance, "SONY", then things might get simpler. If a brand's image is detected, then things should be simpler as well. The same goes with other famous brand and famous landmark, such as an Eiffel Tower. Having text and brand's image could help recognize things easily.
But if we are to search for something more obscure (need a better wording here), for instance, take this ramen image.
If you put this image into Google, you will get images of various other images that have similar colors and sometimes similar shape. Heck, there are other ramen images in the result, but I think it would be better if these ramen images are up in the top, since we input a ramen image, and our context here is ramen.
So here is my question, will it be possible to create such a software that can understand the context of the image? How can we express the context in the software?
Man, you just pointet out the very reason why so much people work on computer vision.
Is is quite easy to mathematically describe objects. Color, shape, density, . . .
All those can be calculated easily.
But computer vision becomes very complex when talking about "real life objects".
Angle, luminosity, and simply non consistency make it really almost impossible to detect an object accurately.
When working on computer vision, you should always ask yourself : what makes the object I want to recognize unique ?
What descriptor can I use that no other object possess ?
Ask yourself the question for theses ramen. Let's say I simply want to detect ramens.
What if the color of the soup changes? What if the meat is bigger ?
If you want to know more, you should read about pattern recognition and pattern matching.
And if you can find the solution to this kind of problems in a generic way, you can register for the nobel price I think :)
Some things are quite well known nowadays, like face recognition or OCR; but they are often quite specialized and apply to only one domain.
Think about it, even Google's image search algorithm sucks when you feed it with ramen.
It is pretty efficient with sudoku though, as he knows exactly what he is searching for.
All the difference is made in training, where you give a list of assumptions to help the algorithm.
So basically you got it. either you create a really nice computer vision system good at detecting one thing based on a lot of assumptions, or an "ok" but quite generic one :).
The choice mostly depends on your application