Could you please give some informations about svg.js use cases, particularly focusing on animations? For instance:
environments (browser, export formats, etc...)
best performances (how to exploit them)
limitations (how to [not] reach them)
svg.js itself is already quite fast. To give it the optimal performance you might want to conser performant code in general.
That means:
keep dom manipulation down to a minimum
cache objects, dont alter thousand of elements at once
this is especially important when looping
use latest browsers which all know svg
and last but not least: Get to know SVG
because that will give you a feeling which tool is the best for the job
Related
Given an image of the region containing the lips and other "noise" (teeth, skin), how can we isolate and recolor only the lips (simulating a "lipstick" effect)?
Attached is a photo describing the lips/mouth states.
What we have tried so far is a three-part process:
Color matching the lips using a stable point on the lips (provided by internal API).
Use this color as the base color for the lips isolation.
Recolor the lips (lipstick behavior)
We tried a few algorithms like hue difference, HSV difference, ∆and E after converting them to CIE color space. Unfortunately, nothing has panned out or has produced artifacts due to the skin's relative similarity in color to the lips and the discoloration from shadows cast by the nose and mouth.
What are we missing? Is there a better way to approach it?
We are looking for a solution/direction from a classic Computer Vision color algorithm, not a solution from the Machine Learning/Depp Learning domain. Thanks!
You probably won't like this answer, but your question is ill-posed (there is no measurable solution that is better than others, there are only peoples' opinions.)
In this case, the best answer you can hope for then is usually:
Ask an expert for a large set of examples that would be acceptable in practice.
Your problem can easily be solved by an appropriate artist (who you trust will produce usable results) with access to the right tools (for example photoshop,) but a single artist (or even a group of them) can't possibly scale to millions (or whatever large number you care about) of examples.
To address the short-coming of the artist-based solution, you can use the following strategy:
Collect a sufficiently large set of before and after images created by artists, who you deem trustworthy.
Apply your favorite machine learning algorithm to learn a mapping from the before to the after images. There are many possible choices, and it almost really doesn't matter which you choose as long as you know how to use it well.
Note, the above two steps are usually not one-and-done, as most algorithms are. Usually, you will come across pathological or not-well behaved examples to your ML solution above in using the product. The key is to collect these examples, pass them through the artist and retrain or update your ML model. Repeat this enough times and you will produce a state-of-the-art solution to your problem.
Whether you have the funding, time, motivation and resources to accomplish this is another matter.
You should try semantic segmentation techniques that would definitely give you very good results and it would be a generalized concept.
I need to produce some data which has starting times of each job (# of jobs: 30), I do not have chance to get real data so how can I generate data which shows similarities with a data distribution. In this case, which distribution should be good to go on?
A common technique used in simulation models where you don't have any data yet (e.g., data is very expensive, or it's a prospective system that does not even exist yet so where would you get the data from?) is to use a triangular distribution parameterized by subject matter experts (or your own best guesses) about the smallest, largest, and most common value you might see.
A relatively new, but quite powerful extension to this would be to vary the parameter choices in a designed set of experiments to see how much it matters if your guesstimates are off. A well-designed experiment would allow you to assess and characterize how much your results change as a function of the parameter values.
A more comprehensive variant would be to incorporate the distribution choice itself (triangle vs exponential vs anything else you think is plausible) into the design, to see whether that makes much of a difference. In the happy event that it doesn't, you can freely use a simple and convenient distribution choice such as the triangle; if it makes a big difference, you now have certain knowledge that you should get your hands on real data ASAP, because without that data based knowledge you're operating in a garbage-in-garbage-out mode. This also assumes that you control for, say, the first two moments as you switch between distribution choices so that your experiments are testing the shape of the distribution rather than the effect of mean and variance of the distribution.
If designed experiments tell you it doesn't much matter, that's wonderful news. If it does matter, you now know more about the system than you did before and know where to focus your efforts going forward.
I've written a small graphics engine for my game that has multiple canvases in a tree(these basically represent layers.) Whenever something in a layer changes, the engine marks the affected layers as "soiled" and in the render code the lowest affected layer is copied to its parent via drawImage(), which is then copied to its parent and so on up to the root layer(the onscreen canvas.) This can result in multiple drawImage() calls per frame but also prevents rerendering anything below the affected layer. However, in frames where nothing changes no rendering or drawImage() calls take place, and in frames where only foreground objects move, rendering and drawImage() calls are minimal.
I'd like to compare this to using multiple onscreen canvases as layers, as described in this article:
http://www.ibm.com/developerworks/library/wa-canvashtml5layering/
In the onscreen canvas approach, we handle rendering on a per-layer basis and let the browser handle displaying the layers on screen properly. From the research I've done and everything I've read, this seems to be generally accepted as likely more efficient than handling it manually with drawImage(). So my question is, can the browser determine what needs to be re-rendered more efficiently than I can, despite my insider knowledge of exactly what has changed each frame?
I already know the answer to this question is "Do it both ways and benchmark." But in order to get accurate data I need real-world application, and that is months away. By then if I have an acceptable approach I will have bigger fish to fry. So I'm hoping someone has been down this road and can provide some insight into this.
The browser cannot determine anything when it comes to the canvas element and the rendering as it is a passive element - everything in it is user rendered by the means of JavaScript. The only thing the browser does is to pipe what's on the canvas to the display (and more annoyingly clear it from time to time when its bitmap needs to be re-allocated).
There is unfortunately no golden rule/answer to what is the best optimization as this will vary from case to case - there are many techniques that could be mentioned but they are merely tools you can use but you will still have to figure out what would be the right tool or the right combination of tools for your specific case. Perhaps layered is good in one case and perhaps it doesn't bring anything to another case.
Optimization in general is very much an in-depth analysis and break-down of patterns specific to the scenario, that are then isolated and optimized. The process if often experiment, benchmark, re-adjust, experiment, benchmark, re-adjust, experiment, benchmark, re-adjust... of course experience reduce this process to a minimum but even with experience the specifics comes in a variety of combinations that still require some fine-tuning from case to case (given they are not identical).
Even if you find a good recipe for your current project it is not given that it will work optimal with your next project. This is one reason no one can give an exact answer to this question.
However, when it comes canvas what you want to achieve is a minimum of clear operations and minimum areas to redraw (drawImage or shapes). The point with layers is to groups elements together to enable this goal.
I am creating a Minecraft like terrain engine thing, and I was wondering what exactly octrees are. With my engine I have seperated each part of it into chunks or regions - which from what I have read has something to do with it. Also, I was wondering if indices do increase performance within a game and if so how much? Any other ideas/ways to increase performance would be much appreciated. Note that I have already included backface culling and that if the box or a side is hidden don't show that side.
Read this excellent article on FlipCode
Googleing for Octree and flipcode or Gamedev.net will give you a lot of references.
Thoughts on performance are hard to give because a lot depends on what you are doing. (how many changes are being made to the 'world', are there any objects moving, what do you want to use the Octree for (visibility, collision detection, rendering, ...) Read about K-d-trees too because they might be more appropriate for your problem.
Say, we have a program that gets user input or any other unpredictable events at arbitrary moments of time.
For each kind of event the program should perform some computation or access a resource, which is reasonably time-consuming to be considered. The program should output a result as fast as possible. If next events arrive, it might be acceptable to drop previous computations and take up new ones.
To complicate it further, some computations/resource access might be interdependent, i.e. produce data that can be used in other computations.
What's important we know the pattern in which these events usually occur. For example: their relative frequency with respect to each other, or a common order and time intervals in which they happen.
The task is to make an algorithm which deals with the problem in the most statistically efficient way. Approaches yielding sub-optimal solutions can be more than sufficient.
Is there a concept which embraces designing such algorithms?
Example:
A tabbed internet browser.
When told to load different web pages in several tabs, should decide whether to load the page in an active tab with higher priority, to render just the visible part of the page or pre-render the full page, if so what to do first - pre-render the whole page for the active tab or render other tabs instead, etc.
(I know nothing about how browsers actually work, but assuming it is this way won't hurt)
I think scheduling algorithms deal with these kind of scenarios.
What you're describing is a prioritizing application scheduler. You would need to be more specific to determine which algorithm would be best, but here's a list that you might find useful.
I am tossing keywords: Scheduling with pre-emption? Also, prefetching, double-buffering
I don't know a lot about it but this sounds like something that the reactor patern may be used for.