Is it possible to simulate custom forces (in my case, electromagnetic) using the SolidWorks API for Animator/Motion Study/COSMOS/EMS?
I'm looking for any combination of API's that would expose the required data to be able to simulate the dynamics of either electrical positive/negative or magnetic north/south forces.
The very basics of what I need to be able to do is:
Model two cubes
Mark a point on one as having positive charge and the point on the other as negative charge (or north/south magnetism)
Press "Go"
Watch them come together and stick
Once I can figure out how to do this, I can go through with the more complicated code that I'm trying to write (that's not the problem). I'm simply stuck on where to begin. I have searched and searched but cannot find a definitive answer, the documentation is sparse and hard to grasp.
If this is definitely not possible or not worth it to attempt in SolidWorks, then that's an acceptable answer. I never would have chosen SolidWorks if I was left free to pick the platform, but it was chosen for me.
EDIT
It seems COSMOSMotion API's IDDMActionReactionForce class is what I was looking for. Can anyone point me to an example of using it to define a custom force between two objects?
I can't speak about SolidWorks, so my answer may be irrelevant — BUT I have used ray-tracing software to model dynamic systems.
I my case, I was simulating the circumstances of lunar and solar eclipses. The ray-tracing software (POVRay) took care of generating an image of the scene including the Sun, Earth and Moon, but I had to calculate the positions of the various bodies for each frame of the animation.
I suspect this may be the case with modelling Electromagnetic Dynamics, and you will have to calculate the positions of the bodies involved at intervals, so that Solidworks will render the scenes of an animation.
I may be all wrong about the capabilities of SolidWorks, so I wish you luck.
I was tempted to say that "it's impossible" because you said it would be "an acceptable answer", but that would be too easy.
After much trying, my conclusion is SolidWorks is not the appropriate platform for this. It doesn't let you hook into its internal physics calculations and the Force object I spoke of is way too inefficient for the problem I needed to model. Theoretically, it will work to bring two cubes together along side SolidWorks' built in gravity/collision detection simulation elements but when confronted with an n-body problem, it was apparent that it wasn't made for that.
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Shouldn't there be some adjustments for google cardboard? With all different sizes of phones and with everyone having a bit of differences in how far apart our eyes are I was looking for a way to re position the two images closer so that it looked better. I don't need to use all the pixels and I'm thinking if you allowed adjustments to the center placement of each view that this could be more usable. As is I have to hold the phone a bit further from me to see a good image.
The Cardboard is open "technology" and you are free to adjust it to your own personal needs - no one is going to do that for you. If you are on a bigger budget, there are cheap plastic headsets available from various manufacturers. I got my headset for around 35$ with shipping.
I personally use a Color Cross but there are many others. Just make sure to look for some with open back, so you can plug in headphones, for example, or use the camera once that becomes a thing. An adjustable phone holder is a big plus, so be on the lookout for that too. Another important thing is adjustable IPD (Inter Pupillary Distance) for the lenses in the headset - some headsets with fixed lense distance gave me the cross-eyed effect. Also, many headsets have adjustable lens-to-phone distance, which also can be important.
Please note that all this is necessary for an okay-ish experience, and for the very best one available, you should get a whole integrated headset, like the Sony Morpherus, Oculus Rift or SteamVR. Also bear in mind that this technology is still in the RnD phase and there are many problems to be solved.
For an interesting read on some of these problems, check this out:
http://media.steampowered.com/apps/valve/2013/MAbrashGDC2013.pdf
Say there are 3 boxes on the screen, how can I go about touching one of them to pick it up and "throw" it at the others? I have the rest of the world implemented but can't find much information on how to grab/drag/toss physics objects. Any sample code or documentation out there that would help with this?
It depends what you are attempting to do. It is a physics simulation and as such a typical way of interacting with the system is by applying forces to objects opposed to direct manipulation of the x,y coordinates. But you can in fact do either. I believe the most common approach is to use a mouse joint. A google search on b2MouseJoint will show the documentation and several examples including this one.
http://muhammedalee.wordpress.com/tag/b2mousejoint/
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
As a part of my masters project I proposed to build a virtual trial room application intended for retail clothing stores. Currently its meant to be used directly in store though it may be extended for online stores as well.
This application will show customers how a selected apparel would look on them by showing it on their 3D replica on screen.
It involves 3 steps
Sizing up the customer
Building customer replica 3D humanoid model
Apply simulated cloth on the model
My question is about the feasibility of the project and choice of framework.
Can this be achieved in real time using a normal Desktop computer? If yes what would be appropriate framework ( hardware, software, programming language etc ) for this purpose?
On the work I have done till now, I was planning to achieve above steps in following ways
for step 1 : option a) Two cameras for front and side views or
option b) 1 Kinect or 2 Kinect for complete 3D data
for step 2: either use makehuman (http://www.makehuman.org/) code to build a customised 3D model using above data or build everything from scratch, unsure about the framework.
for step 3: Just need few cloth samples, so thought of building simulated clothes in blender.
Currently I have just the vague idea about different pieces but I am not sure of how to develop complete application.
Theoretically this can be achieved in real time. Many usefull algorithms for video tracking, stereo vision and 3d recostruction are available in OpenCV library. But it's very difficult to build robust solution. For example, you'll probably need to track human body which moves frame to frame and perform pose estimation (OpenCV contains POSIT algorithm), however it's not trivial to eliminate noise in resulting objects coordinates. For inspiration see a nice work on video tracking.
You might want to choose another way, simplify some things, avoid complicated stuff do things less dynamicaly and estimate only clothes size and approximate human location. I this case most likely you will create something usefull and interesting.
I've lost link to one online fiting room where hands and body detection implemented. Using Kinnect solves many problems. But If for some reason you won't use it then AR(augmented reality) helps you (yet another fitting room)
Are there any methods in the computer vision literature that allows for detecting transparent glass in images? Like if I have an image of a car, can I detect windows? etc...
All methods I've found so far are active methods (i.e. require calibration, control over the environment or lasers). I need a passive method (i.e. all you have is an image, or multi-view images of the object and thats it).
Here is some very recent work aimed at detecting transparent objects in a general setting.
http://books.nips.cc/papers/files/nips22/NIPS2009_0397.pdf
http://videolectures.net/nips09_fritz_alfm/
I think what you looking for is detection of translucent regions. There is very limited work here since it is a very hard problem. Basically it is a major chicken and egg problem. Translucent regions cause almost all fundamental image processing tools to fail (e.g. motion estimation, feature matching, tracking, etc...). Yet you must use such tools to detect translucent regions. Anyway, up to my knowledge this is the most recent piece of work in this area and I doubt there is any other.
http://www.mee.tcd.ie/~sigmedia/pmwiki/uploads/Misc.Icip2011/CVPR_new.pdf
It is published in CVPR which is a top conference in Computer Vision.
Just a wild guess: if the camera is moving and you perform a 3D reconstruction of the scene, you could detect large discontinuities of the reconstructions at the reflected regions.
I think you should provide a clearer description of what your are trying to achieve.
The paper "Deriving intrinsic images from image sequences" shows some results with transparencies.
If you are close enough, you may be able to use the glass refraction (a la Snell's law) to detect the glass from multiple views.
I also think that reflections (specular regions) are a good indication for curved glasses.
Detecting it is one thing, but separating is another. You can do separation because its like putting 2 sounds with 1 of the sounds 180 degree out of phase. If you manage to learn the phasing sound by itself, you have the other sound automatically, so you could then learn that one too. Im stuck at the point where I can only superimposesubtract them if I learnt them by themselves. So the real gain here is somehow learning this addup, as 2 separate things, even though you never saw them apart.