Working on a project here which will require some awesome math resourcefulness. I'm looking to make calculations to verify things like: if a point is within a volume / shape (in this case: rectangles or trapezoids) in a given 3D space, how to generate a class or some kind of object to hold the "equation" parameters of such a volume.
Also looking at ways to perform dot product computations in Swift.
Would there be a well-known library out there?
The only things I can seem to find are for SceneKit and graphics rendering Frameworks.. this project is purely computational, as in there is no UI output of the calculated results.
Any resources or examples are much appreciated!
For those who mind stumble upon this kind of issue,
I eventually found the built-in framework for making this kind of math possible.
Called Accelerate, it's even optimized per-OS.
https://developer.apple.com/reference/accelerate
Best of luck!
Related
I'm trying to realize some opencv functions by Halide, one of the difficulty I have met is how to write the cv::fillpoly in Halide. This function, in opencv, fills a polygon according to the given vertex of this polygon. The details in Opencv: https://docs.opencv.org/2.4/modules/core/doc/drawing_functions.html
I wonder if it's possible to realize it in Halide?
It is possible, but likely difficult and not obviously productive as polygon rendering does not have a regular static data parallel pattern and is already really well optimized in various places. It would be interesting to see if it can be done and made performant but it is a lot of work and isn't obviously going to be as fast or faster than existing graphics libraries. Especially if running on GPU hardware where it has to compete with hardware rasterization. I'd look into using define_extern to call out to existing rendering routines.
I've a bunch of expereince in 3D rendering from a game-dev perspective but very little with serious toolkits like VTK.
We have a 3D model of a medical device which has a couple of degrees of motion, and we need to visualise this, animated, in a medical application alongside DICOM datasets.
In a 3D engine we'd likely have option to keyframes, morph-targets, skeletal rigging... but I cannot find out what VTK supports (skeleton/bone are bad search terms as most uses are medical!)
Can someone enumerate what options are provided and point me where in the docs this is covered?
I am not aware of any concise "documentation of VTK animation methods" (doesn't mean there isn't one though), but I will at least share what I know. First off, I doubt that there is some really great state-of-the-art solution included in the base VTK, personally I would look for algorithms that are good enough for me (produce high quality animation) and then try to find an open source implementation of them rather than the other way around. Try including "non-rigid transformation / animation" in your search.
Anyway, two things I know of that are in VTK: there is a filter for Mean Value Coordinates morphing, https://www.vtk.org/doc/nightly/html/classvtkDeformPointSet.html, which you can use for a cage-based deformation (which is perhaps not the most user friendly for creating animations) or in theory could be used for rigging as well (never tried that, but right now I can't see why it wouldn't work). Next, there is an Iterative Closest Point morphing filter https://www.vtk.org/doc/nightly/html/classvtkIterativeClosestPointTransform.html that you could use for morphing by key frames.
All the path following steering algorithms (e.g. for robots steering to follow a colored terrain) that I can find are predictive, so they rely on the robot being able to sense some distance beyond its body.
I need path following behavior on a robot with a light sensor on its underside. It can only see terrain it is directly over and so can't make any predictions; are there any standard examples of good techniques to use for this?
I think that the technique you are looking for will most likely depend on what environment will you be operating in as well as to what type of your resources will your robot have access to. I have used NXT robots in the past, so you might consider this video interesting (This video is not mine).
Assuming that you will be working on a flat non glossy surface, you can let your robot wander around until it finds a predefined colour. The robot can then kick in a 'path following' mechanism and will keep tracking the line. If it does not sense the line any more, it might want to try to turn right and/or left (since the line might no longer be under the robot because it has found a bend).
In this case though the robot will need in advance what is the colour of the line that it needs to follow.
The reason the path finding algorithms you are seeing are predictive is because the robot needs to be able to interpret what it is "seeing" in context.
For instance, consider a coloured path in the form of a straight line. Even in this simple example, how is the robot to know:
Whether there is a coloured square in front of it, hence it should advance
Which direction it is even travelling in.
These two questions are the fundamental goals the algorithm you are looking for would answer (and things would get more complex as you add more difficult terrain and paths).
The first can only be answered with suitable forward-looking ability (hence a predictive algorithm), and the latter can only be answered with some memory of the previous state.
Based solely on the details you provided in your question, you wouldn't be able to implement an appropriate solution. Although, I would imagine that your sensor input and on-board memory would in fact be suitable for a predictive solution, you may just need to investigate further what the capabilities of your hardware allow for.
I had an idea for which I need to be able to recognize certain objects or models from a rendered three dimensional digital movie.
After limited research, I know now that what I need is called feature detection in the field of Computer Vision.
So, what I want to do is:
create a few screenshots of a certain character in the movie (eg. front/back/leftSide/rightSide)
play the movie
while playing the movie, continuously create new screenshots of the movie
for each screenshot, perform feature detection (SIFT?, with openCV?) to see if any of our character appearances are there (they must still be recognized if the character is further away and thus appears smaller, or if the character is eg. lying down).
give a notice whenever the character is found
This would be possible with OpenCV, right?
The "issue" is that I would have to learn c++ or python to develop this application. This is not a problem if my movie and screenshots are applicable for what I want to do.
So, I would like to first test my screenshots of the movie. Is there a GUI version of OpenCV that I can input my test data and then execute it's feature detection algorithms manually as a means of prototyping?
Any feedback is appreciated. Thanks.
There is no GUI of OpenCV able to do what you want. You will be able to use OpenCV for some aspects of your problem, but there is no ready-made solution waiting there for you.
While it's definitely possible to solve your problem, the learning curve for this problem is quite long. If you're a professional, then an alternative to learning about it yourself would be to hire an expert to do it for you. It would cost money, but save you time.
EDIT
As far as template matching goes, you wouldn't normally use it to solve such a problem because the thing you're looking for is changing appearance and shape. There aren't really any "dynamic parameters to set". The closest thing you could try is have a massive template collection that would try to cover the expected forms that your target may take. But it would hardly be an elegant solution. Plus it wouldn't scale.
Next, to your point about face recognition. This is kind of related, but most facial recognition applications deal with a controlled environment: lighting, distance, pose, angle, etc. Outside of that controlled environment face detection effectiveness drops significantly. If you're detecting objects in a movie, then your environment isn't really controlled.
You may want to first try a simpler problem of accurately detecting where the characters are, without determining who they are (video surveillance, essentially). While it may sound simple, you'll find that it's actually non-trivial for arbitrary scenes. The result of solving that problem may be useful in identifying the characters.
There is Find-Object by Mathieu Labbé. It was very helpful for me to start getting an understanding of the descriptors since you can change them while your video is running to see what happens.
This is probably too late, but might help someone else looking for a solution.
Well, using OpenCV you would of taking a frame of a video file and do any computations on it.
You can do several different methods of detecting a character on that image, but it's not so easy to have it as flexible so you can even get that person if it's lying on the floor for example, if you only entered reference images of that character standing.
Basically you could try extracting all important features from your set of reference pictures and have a (in your case supervised) learning algorithm that gets a good feature-vector of that character for classification.
You then need to write your code that plays the video and which takes a video frame let's say each 500ms (or other as you desire), gets a segmentation of the object you thing would be that character and compare it with the reference values you get from your learning algorithm. If there's a match, your code can yell "Yehaaawww!" or do other things...
But all this depends on how flexible you want this to be. You could also try a template match or cross-correlation which basically shifts the reference image(s) over the frame and checks how equal both parts are. But this unfortunately is very sensitive for rotation, deformations or other noise... so you wouldn't get that person if its i.e. laying down. And I doubt you can get all those calculations done in realtime...
Basically: Yes OpenCV is good to use for your image processing/computer vision tasks. But it offers a lot of methods and ways and you'd need to find a way that works for your images... it's not a trivial task though...
Hope that helps...
Have you tried looking at some of the work of the Oxford visual geometry group?
Their Video Google system describes to a large extent what you want, instance detection.
Their work into Naming People in TV shows is also pretty relevant. A face detection and facial feature pipeline is included that can be run from Matlab. Are you familiar with Matlab?
Have you tried computer vision frameworks like Cassandra? There you can exactly do that just by some mouse clicks.
I'm new to image processing and I want to do a project in object detection. So help me by suggesting a step-by-step procedure to this project. Thanx.
Object detection is a very complex problem that includes some real hardcore math and long tuning of parameters to the computation methods involved. Your best bet is to use some freely available library for that - Google will help.
There are lot of algorithms about the theme and no one is the best of all. It's usually a mixture of them what makes the best solution to the solution.
For example, for object movement detection you could look at frame differencing and misture of gaussians.
Also, it's very dependent of your application, the environment (i.e. noise, signal quality), the processing capacity you may have available, the allowable error margin...
Besides, for it to work, most of time it's first necessary to do some kind of image processing to the input data like median filter, sobel filter, contrast enhancement and a large so on.
I think you should start reading all you can: books, google and, very important, a lot of papers about the subjects (there are many free in internet) you are interested in.
And first of all, i think it's fundamental (at least it has been for me) having a good library for testing. The one i have used/use is OpenCV. It's very complete, implement many of the actual more advanced algorithms, is very active, has a big community and it's free.
Open Computer Vision Library (OpenCV)
Have luck ;)
Take a look at AForge.NET. It's nowhere near Project Natal's levels of accuracy or usefulness, but it does give you the tools to learn the algorithms easily. It's an image processing and AI library and there are several tutorials on colored object tracking and motion detection.
Another one to look at is OpenCV from Intel. I believe it's a bit more advanced, but it's written in C.
Take a look at this. It might get you started in this complex field. The algorithm pages that it links to are interesting reading.
http://sun-valley.stanford.edu/projects/helicopters/final.html
This lecture by Jeff Hawkins, will give you an idea about the state of the art in this super-difficult field.
Seems that video disappeared... but this vid should cover similar ground.