i'm new to Swift, and now i'm trying to build sky map app like the application "star chart".
i already got a sky map image from NASA and cover it on SCNsphere, also already set camera node in the center of this sphere to make it looks like 360 degrees. Furthermore, i used accelerator to check what direction the camera is looking at.
i know that the sky map like “star chart” doesn't need internet to update data. so now the biggest problem is that i don't know how to correct the position of my sky map according to people's current time and location.
Any good advice and help? Thanks in advance!!! cause i tried vary hard to find some related information but still stuck in here for three weeks.
You just need to rotate your map with time+longitude around Earth's rotation axis and with latitude around axis longitude=90 degrees while earth is placed in the center of your sphere. For stars the offset does not matter so you can ignore Sun-Earth distance and also Earth's radius as well.
The time rotation must be day+year rotations together. On top of that you have to apply precession and nutation if you want to have higher precision.
Of coarse the stars are moving too so if you need really high precision and or high time interval to cover (hundreds or thousands of years) then this approach is not good and you should use stellar catalog with the motions implemented.
For more info see related:
How to draw sky chart
Plotting a star chart efficiently
If you want to use catalog and real colors then you will also need
Star B-V color index to apparent RGB color
simplified atmospheric scattering GLSL shader
And finally here some hints for such applications:
Is it possible to make realistic n-body solar system simulation in matter of size and mass?
Related
I was wondering how I can go about storing and displaying small, but geographically accurate distances in the mapbox unity SDK?
I'm storing radius' about markers on a map, I get the value in meters (from ~0.5m-10m), and then, adaptively with the zoom level, I want to accurately display those meters in Unity world space (draw an ellipse) using these stored values. The problem is that the mapbox api from my understanding only lets you to convert lat/long to unity world coordinates and I'm running into precision errors. I can get adequate precision when using the CheapRuler class and meters, but as soon as I use the _map.GeoToWorld(latlon) method the precision is lost.
How would I go about keeping adequate precession, is there a way I can use the marker as the reference point and the radius as the offset, and get the relative unity world coordinate distance (of the radius) that way? I know you can also store scale relative to the mapbox tiles, but I'm not sure how I can convert that back to a unity world distance. I'm operating on very small distances, so any warping due to lat/long being a Mercator projection can probably be ignored.
I figured out a round-about solution.
First I convert the meters into unity world space using whatever IMapScalingStrategy Mapbox is currently using.
Then I convert from world to the view space of whatever camera I want to scale to the given bounds.
After that, I use find out the scale of the bounds, solving for:
UnityRelativeScaleChange = 2Map Zoom Level Change; which (to my estimations) is the relationship between unity scale and mapbox zoom levels.
This solutions works great as long as you don't have to zoom in/out by too much, otherwise you'll run into precision problems as the functions rely on the relative view-based size of a given bounds to do their calculations which will lead to unstable results if those initially take a tiny portion of the screen.
I'm working on an IPhone robot that would be moving around. One of the challenges is estimating distance to objects- I don't want the robot to run into things. I saw some very expensive (~1000$) laser rangefinders, and would like to emulate one using iPhone.
I got one or two camera feeds and two laser pointers. The laser pointers are mounted about 6 inches apart, at an angle The angle of lasers in relation to the cameras is known. The Angle of cameras to each other is known.
The lasers are pointing ahead of cameras, creating 2 dots on a camera feed. Is it possible to estimate the distance to the dots by looking at the distance between the dots in a camera image?
The lasers form a trapezoid from the
/wall \
/ \
/laser mount \
As the laser mount gets closer to the wall, the points should be moving further away from each other.
Is what I'm talking about feasible? Has anyone done something like that?
Would I need one or two cameras for such calculation?
If you just don't want to run into things, rather than have an accurate idea of the distance to them, then you could go "dambusters" on it and just detect when the two points become one - this would be at a known distance from the object.
For calculation, it is probaby cheaper to have four lasers instead, in two pairs, each pair at a different angle, one pair above the other. Then a comparison between the relative differences of the dots would probably let you work out a reasonably accurate distance. Math overflow for that one, though.
In theory, yes, something like this can work. Google "light striping" or "structured light depth measurement" for some good discussions of using this sort of idea on a larger scale.
In practice, your measurements are likely to be crude. There are a number of factors to consider: the camera intrinsic parameters (focal length, etc) and extrinsic parameters will affect how the dots appear in the image frame.
With only two sample points (note that structured light methods use lines, etc), the environment will present difficulties for distance measurement. Surfaces that are directly perpendicular to the floor (and direction of travel) can be handled reasonably well. Slopes and off-angle walls may be detectable, but you will find many situations that will give ambiguous or incorrect distance measures.
Maybe I'm asking this too soon in my research, but I'd better know if this is possible sooner than later.
Imagine I have the following square printed on a paper on top of a table:
The table is brown, so it does not match with any of the colors in the square. Is there a way for me, from a common iPhone camera (non-stereo view), to figure out the distance and angle from which Im looking at the square in the table?
In the end what I'm looking for is being able to draw a 3D square on top of this one using the camera image, but I'm not sure if I am going to be able to figure out the distance and position of the object in space using only a 2D image. Any hints are well appreciated.
Short answer: http://weblog.bocoup.com/javascript-augmented-reality
Big answer:
First posterize, Then vectorize, With the vectors in your power you may need to do some math tricks to define, based on the vectors position, the perspective and then the camera position.
Maybe this help:
www.pixastic.com/lib/docs/actions/posterize/
github.com/selead/cl-vectorizer
vectormagic.com/home
autotrace.sourceforge.net
www.scipy.org/PyLab
raphaeljs.com/
technabob.com/blog/2007/12/29/video-games-get-vectorized/
superuser.com/questions/88415/is-there-an-open-source-alternative-to-vector-magic
Oughta be possible. Scan the image for the red/blue/yellow pattern, then do edge detection to figure out how warped the squares are (they'll be parallelograms in anything but straight-on view). Distance would depend on the camera's zoom setting and scan resolution. But basically you'd count how many pixels are visible in each of the squares, run that past the camera's specs and you should be able to determine a rough distance.
I am developing an application for iPhone.
I am using OpenGL to display a 3D object in the screen, with the camera view as background.
I'd like to know how can i change the OpenGL ES unit to centimeters/meters.
How can i do that?
You dont.
Thought experiment time!
First imagine you have sphere 1 centimeter in diameter. And you have a camera 10 centimeters away. You would see a small sphere in the center of the frame.
Now imagine you have a sphere 1 kilometer in diameter and a camera 10 kilometers away. How would you expect the image to be different?
The correct answer is you would not expect the image to change at all. All that really matters is the relative sizes of things. So the unit type you attribute to the the numbers only matters to the programmer, and not to the program.
So you simply mentally declare that one unit is equal to one centimeter and create your objects and world according to that scale. There is no code level change to make this happen. It's merely a convention that you use to help you build things in correct dimensions relative to each other.
OpenGL does not have a notion for units. It just uses unit-less values. What these values mean is up to you. They just have to be consistent. So if your objects coordinates and viewing parameters are all specified with meters in mind and you have an object whose coordinates are in centimeters, just scale that object by a factor of 0.1.
I have a computer vision set up with two cameras. One of this cameras is a time of flight camera. It gives me the depth of the scene at every pixel. The other camera is standard camera giving me a colour image of the scene.
We would like to use the depth information to remove some areas from the colour image. We plan on object, person and hand tracking in the colour image and want to remove far away background pixel with the help of the time of flight camera. It is not sure yet if the cameras can be aligned in a parallel set up.
We could use OpenCv or Matlab for the calculations.
I read a lot about rectification, Epipolargeometry etc but I still have problems to see the steps I have to take to calculate the correspondence for every pixel.
What approach would you use, which functions can be used. In which steps would you divide the problem? Is there a tutorial or sample code available somewhere?
Update We plan on doing an automatic calibration using known markers placed in the scene
If you want robust correspondences, you should consider SIFT. There are several implementations in MATLAB - I use the Vedaldi-Fulkerson VL Feat library.
If you really need fast performance (and I think you don't), you should think about using OpenCV's SURF detector.
If you have any other questions, do ask. This other answer of mine might be useful.
PS: By correspondences, I'm assuming you want to find the coordinates of a projection of the same 3D point on both your images - i.e. the coordinates (i,j) of a pixel u_A in Image A and u_B in Image B which is a projection of the same point in 3D.