Is there any way by which I can create a android.graphics.Path from edges of the Bitmap?
Suppose I have a hollow hexagon, then how can I create a path which will follow the edges of it?
There is no easy way to do this as a path is part of vector graphics whereas a bitmap is composed of independent pixels. You could create an algorithm that does this, but it would be complex and would never be a 100% sure thing.
The best thing you could do is either start with a vector graphic in the first place (such as SVG), or give up on your dream of scalable graphics.
I'm sorry to disappoint you, but I tried creating a pseudo-code vector finder in here, and even a simple one is too complex to write on the fly.
Related
So I've been assigned a recursive art project for my AP CS class and have a bunch of spare time, so I've decided to try something a little bit more ambitious.
My plan is to create a 3D fractal, either rendered and shaded in a visualization with GL, or represented via spatially mapping the respective equations' outputs to points on a cube and drawing those. If this explanation seems unclear, please check out the links at the bottom for images. Now, I don't need the fractal to be able to be modified in-program. I just need it to render a single BufferedImage, which I'll be putting directly on a JFrame.
My experience in Java, as far as this project goes, is a bit limited. I've drawn Mandelbrot and Julia set fractals before, but I have little to no experience drawing/rendering objects in 3D in Java. This is all stuff I can look up and figure out myself though, so no worries here.
Thus, the question: How does one map a fractal that should be in the 2nd dimension (e.g. log(no. of subdivided entities)*log(side length of subdivision) = 2) to the 3rd dimension (e.g. log(no. of subdivided entities)*log(side length of subdivision) = 3)? I'm lost trying to mathematically work this out, and I believe there is a more organized approach to go about this circumventing a lot of the math that already exists.
Also, if you are aware of a structured approach to render a 2D fractal, as drawn by a formula, and render it in 3D, provided the respective formula is provided (power is raised), please let me know. I've heard of Ray Tracers, no idea what they are, a brief summary would be cool.
Here are links with pictures of the result I want to obtain:
http://2008.sub.blue/assets/0000/4575/power8_large.jpg
https://www.youtube.com/watch?v=rK8jhCVlCtU
It looks like the image is an example of a Mandelbulb. The is a similar iteration formula to the Mandlebrot set but using 3D points and a novel idea of what raising a 3D point to a power means.
I've not found anything here or on google. I'm looking for a way to identify shapes (circle, square, triangle and various other shapes) from a image file. Some examples:
You get the general idea. Not sure if BoofCV is the best choice here but it looks like it should be straightforward enough to use, but again I know nothing about it. I've looked at some of the examples and I though before I get in over my head (which is not hard to do some days), I thought I would ask if there is any info out there.
I'm taking a class on Knowledge Based AI solving Ravens Progressive Matrix problems and the final assignment will use strictly visual based images instead of the text files with attributes. We are not being graded on the visual since we only have a few weeks to work on this section of the project and we are encouraged to share this information. SOF has always been my go to source for information and I'm hoping someone out there might have some ideas on where to start with this...
Essentially what I want to do is detect the shapes (?? convert them into 2D geometry) and then make some assumptions about attributes such as size, fill, placement etc, create a text file with these attributes and then using that, send it through my existing code based that I wrote for my other projects to solve the problems.
Any suggestions????
There are a lot of ways you can do it. One way is to find the contour of the shape then fit a polygon to it or a oval. If you git a polygon to it and there are 4 sides with almost equal length then its a square. The contour can be found with binary blobs (my recommendation for the above images) or canny edge.
http://boofcv.org/index.php?title=Example_Fit_Polygon
http://boofcv.org/index.php?title=Example_Fit_Ellipse
I want an efficient way to finding out sub-images from a Image for Ex. we have a image of country map and it contain states as sub-image.
Then i need a way to finding out sub-images of states from country map.
If you have only pixels, you'll need image processing algorithms to find sub-images.
Your solution will be specific to what the image you are processing looks like. For example, if states are outlined in a certain color, you could try edge detection. If states are each different colors, you could run a flood fill like algorithm to create boundaries for each state.
This is a difficult problem. Try computer vision or object recognition for keywords in your research.
However, I suggest instead you build up vector files which define boundaries of subimages by hand. If you've only a few to do, this isn't a big deal.
Here’s my task which I want to solve with as little effort as possible (preferrably with QT & C++ or Java): I want to use webcam video input to detect if there’s a (or more) crate(s) in front of the camera lens or not. The scene can change from "clear" to "there is a crate in front of the lens" and back while the cam feeds its video signal to my application. For prototype testing/ learning I have 2-3 images of the “empty” scene, and 2-3 images with one or more crates.
Do you know straightforward idea how to tackle this task? I found OpenCV, but isn't this framework too bulky for this simple task? I'm new to the field of computer vision. Is this generally a hard task or is it simple and robust to detect if there's an obstacle in front of the cam in live feeds? Your expert opinion is deeply appreciated!
Here's an approach I've heard of, which may yield some success:
Perform edge detection on your image to translate it into a black and white image, whereby edges are shown as black pixels.
Now create a histogram to record the frequency of black pixels in each vertical column of pixels in the image. The theory here is that a high frequency value in the histogram in or around one bucket is indicative of a vertical edge, which could be the edge of a crate.
You could also consider a second histogram to measure pixels on each row of the image.
Obviously this is a fairly simple approach and is highly dependent on "simple" input; i.e. plain boxes with "hard" edges against a blank background (preferable a background that contrasts heavily with the box).
You dont need a full-blown computer-vision library to detect if there is a crate or no crate in front of the camera. You can just take a snapshot and make a color-histogram (simple). To capture the snapshot take a look here:
http://msdn.microsoft.com/en-us/library/dd742882%28VS.85%29.aspx
Lots of variables here including any possible changes in ambient lighting and any other activity in the field of view. Look at implementing a Canny edge detector (which OpenCV has and also Intel Performance Primitives have as well) to look for the outline of the shape of interest. If you then kinda know where the box will be, you can perhaps sum pixels in the region of interest. If the box can appear anywhere in the field of view, this is more challenging.
This is not something you should start in Java. When I had this kind of problems I would start with Matlab (OpenCV library) or something similar, see if the solution would work there and then port it to Java.
To answer your question I did something similar by XOR-ing the 'reference' image (no crate in your case) with the current image then either work on the histogram (clustered pixels at right means large difference) or just sum the visible pixels and compare them with a threshold. XOR is not really precise but it is fast.
My point is, it took me 2hrs to install Scilab and the toolkits and write a proof of concept. It would have taken me two days in Java and if the first solution didn't work each additional algorithm (already done in Mat-/Scilab) another few hours. IMHO you are approaching the problem from the wrong angle.
If really Java/C++ are just some simple tools that don't matter then drop them and use Scilab or some other Matlab clone - prototyping and fine tuning would be much faster.
There are 2 parts involved in object detection. One is feature extraction, the other is similarity calculation. Some obvious features of the crate are geometry, edge, texture, etc...
So you can find some algorithms to extract these features from your crate image. Then comparing these features with your training sample images.
I'm new to opengl-es and I wonder how people are able to draw these much detailed OpenGL ES graphics, e.g. on Android OS. It's already hard to draw a single squre, because it's composed of triangles due to the reason that OpenGL ES obviously cannot draw anything else than triangles.
I thought about this approach:
Drawing and rendering an object in Blender.
Export it somehow as array of vertices and an array of colors
Copy this array of vertices into the Java code
Run the code
Or are there approaches to solve such problems in a better way? I do not think that people just "draw" their graphics as array of vertices in the code. I'm sure they draw them anywhere else and import it into the code.
If there is such a solution with Blender, I would be pleased to know how this is solved.
Regards.
You can save your model from Blender e.g. as Wavefront OBJ file. In Blender, you can also select to triangulate the model for you, which will result in a list of just triangles, ready for drawing.
OBJ is a very simple format, it just lists in ASCII the position of each vertex and then which vertices belong to each triangle. Either convert the OBJ to a format specific to your application (e.g. binary buffer you can directly load to OpenGL) or find yourself one of the many OBJ loaders, or write your own reader.
There are a lot of other common formats available, you should take a look at them and see if there is an importer for Android available.
What I've found is that you can Load 3D models with min3D directly to your program using:
min3d
I hope this helps.