Okay so i have used Figma before, although it was long ago, i didnt have this problem. I am in a team of developers. we arent very good in designing so we paid a designer to do the design for us. Everything was all good till i started coding the landing page. The dimensions i got from Figma for basically everything is way too big and so unrealistic. I mean it told me that the height of the Navbar is 135px, 135?????? thats way too big for a Navbar. I have to scale it down myself and use trial and error method till it looks somewhat like the design. Normally i wouldnt complain if i was programming for myself but its not a personal project and i am on a deadline so the fact that i have to manually use trial and error method to make the designs match instead is just adding so much time that i didnt account for. Is this a fault of the designer or figma itself? is there a way to scale down the dimensions to make it browser-size ?
The first thing to understand is that if your screen has a higher resolution, it may look smaller or larger.
Second, your designer did his designs on a different scale.
You will have to make a system of scales. If he gave you the height as 135px and you think 80px is actually the correct value, your scale is 0.6 and all values should be passed as height x 0.6. Same way for the width.
The easiest way is for the designer to resize his design to the correct size. This will have only one drawback and that's he will have to edit multiple elements in each page individually and it can take hours. However, it's his mistake.
What parameters, modes, tricks, etc can be applied to get sharpness for texts ?
I'm going to draw a lot so I cant use 3d text.
I'm using canvas to write the text and some symbols. I'm creating somethinbg like label information.
Thanks
This is no simple matter since you'll run into memory issues with 100k "font textures". Since you want 100k text elements you'll have several difficulties to manage. I had a similar problem too once and tossed together a few techniques in order to make it work. Simply put you need some sort of LOD ("Level of Detail") to make that work. That setup might look like following:
A THREE.ParticleSystem built up with BufferGeometry where every position is one text-position
One "highres" TextureAtlas with 256 images on it which you allocate dynamically with those images that are around you (4096px x 4096px with 256x256px images)
At least one "lowres" TextureAtlas where you have 16x16px images. You prepare that one beforehand. Same size like previous, but there you have all preview images of your text and every image is 16x16px in size.
A kdtree data structure to use a nearestneighbour algorithm with to figure out which positions are near the camera (alike http://threejs.org/examples/#webgl_nearestneighbour)
The sub-imaging module to continually replace highres textures with directly on the GPU: https://github.com/mrdoob/three.js/pull/4661
An index for every position to tell it which position on the TextureAtlas it should use for display
You see where I'm going. Here's some docs on my experiences:
The Stackoverflow post: Display many thousand images in three.js
The blog where I (begun) to explain what I was doing: http://blogs.fhnw.ch/threejs/
This way it will take quite some time until you have satisfying results. The only way to make this simpler is to get rid of the 16x16px preview images. But I wouldn't recommend that... Or of course something depending on your setup. Maybe you have levels? towns? Or any other structure where it would make sense to only display a portion of these texts? That might be worth a though before tackling the big thing.
If you plan to really work on this and make this happen the way I described I can help you with some already existing code and further explanations. Just tell me where you're heading :)
I am working on a game and I need to have two characters talking to eachother. I know that XNA does not allow me to play a movie other than fullscreen so I need to actually "play" the animation inside my game app in a different manner. The characters have animated environments around them so the animations are not simple head movements and as such, animating the characters via keyframing in a 3d model is not an option. The dialogue between the two characters is a cut-scene between levels, so it is not part of the gameplay itself.
I am not sure what the best approach to this would be so if you have any ideas, please let me know.
This is what I thought of so far:
1. Create all the individual frames for the characters as images. Load these images in a spritesheet and go through each frame at my desired framerate.
The problem with this approach is that the maximum spritesheet texture of 2048x2048 would not allow for too many frames as the characters are something around 300x200. The other problem is that I have two characters so, the minimum scenario would require me to create in memory two 2048x2048 spritesheets... and I'd like to keep the memory requirements low.
2. Load a batch of frames (images), play them, then de-allocate them and load the next set. I know that in general it is not a good idea to load lots of small textures and switch between them in drawing calls (performance wise) but it seems as though I have no other choice in this case.
I am afraid that unloading stuff from memory and loading other stuff in while in the Update-Draw loop would slow down the entire scene... so not sure if this is a sane approach.
The other idea is to make an mp4/wmv with the whole thing [char animation, subtitles of the dialogue, etc] but the interface that hosts these characters would not be as "smooth" as when rendered directly, etc...
Thank you for all your suggestions,
Marius
EDIT 1:
I have tested scenario number 2 and it seems that the performance is OK.
I have used scenario 2. It works for my particular case but I am sure it won't work for all cases.
Is there a tool that would reveal whether an image contains a watermark and read, alter or remove a watermark if used by someone who is not the creator of the watermark?
Edit to try to reflect Kerzin's intent, as indicated in the comments: This is not for the purpose of knowing how to do it, but whether it can be done by others to remove watermarks from created images, and how it can be made more difficult.
In general:
to detect a watermark that is not visible to the naked eye you need to have some idea of the encoding scheme
it is possible to come up with a watermarking scheme that yields a watermark one cannot read or definitively confirm the presence of without knowing a secret, however in general that is not the purpose of a watermark
a watermark that does not distort the image sufficiently to be obvious to the naked eye should in general be removable by manipulations that similarly do not degrade the signal sufficiently to be obvious to the naked eye; however, coming up with the required manipulations may be hard and will certainly require specific knowledge of the watermarking scheme.
It depends on the type of watermark you're creating. I'm assuming you're talking about an opaque or semitransparent logo or text that is usually placed in a corner of the image.
There is a balancing act here. If the watermark is small enough, users can always just crop it out. But if you make it too large, you make the image unusable. Sometimes this is the intent (for example, look at iStockPhoto.com: they use big watermarks over the center of the image so that you can't use the image without buying it). Other times, you don't want to do this (say you're posting a wallpaper to DeviantArt: you still want people to use the image, but no one's going to use it if the watermark takes up a third of the screen).
If the watermark covers a part of the image that is not too detailed, users who know what they're doing can use the clone brush or other tools to photoshop it out of the image. (The same way I once removed ugly power lines from an otherwise beautiful sunset photo.)
Also, if the watermark is in the same place on all images, and it is transparent enough, sophisticated users can build a filter based on the common pixels from several of your images. In the right circumstances, such a filter can work like magic. (But usually it won't.)
Most of the time you won't be able to remove the watermark. Not even manually.
Unless it is a real simple and/or badplaced watermark and the image itself is easy to reconstruct too.
There are a few tools that claim to be able to remove watermarks but I don't know of any which really are up to the task
I am building a web application using .NET 3.5 (ASP.NET, SQL Server, C#, WCF, WF, etc) and I have run into a major design dilemma. This is a uni project btw, but it is 100% up to me what I develop.
I need to design a system whereby I can take an image and automatically crop a certain object within it, without user input. So for example, cut out the car in a picture of a road. I've given this a lot of thought, and I can't see any feasible method. I guess this thread is to discuss the issues and feasibility of achieving this goal. Eventually, I would get the dimensions of a car (or whatever it may be), and then pass this into a 3d modelling app (custom) as parameters, to render a 3d model. This last step is a lot more feasible. It's the cropping issue which is an issue. I have thought of all sorts of ideas, like getting the colour of the car and then the outline around that colour. So if the car (example) is yellow, when there is a yellow pixel in the image, trace around it. But this would fail if there are two yellow cars in a photo.
Ideally, I would like the system to be completely automated. But I guess I can't have everything my way. Also, my skills are in what I mentioned above (.NET 3.5, SQL Server, AJAX, web design) as opposed to C++ but I would be open to any solution just to see the feasibility.
I also found this patent: US Patent 7034848 - System and method for automatically cropping graphical images
Thanks
This is one of the problems that needed to be solved to finish the DARPA Grand Challenge. Google video has a great presentation by the project lead from the winning team, where he talks about how they went about their solution, and how some of the other teams approached it. The relevant portion starts around 19:30 of the video, but it's a great talk, and the whole thing is worth a watch. Hopefully it gives you a good starting point for solving your problem.
What you are talking about is an open research problem, or even several research problems. One way to tackle this, is by image segmentation. If you can safely assume that there is one object of interest in the image, you can try a figure-ground segmentation algorithm. There are many such algorithms, and none of them are perfect. They usually output a segmentation mask: a binary image where the figure is white and the background is black. You would then find the bounding box of the figure, and use it to crop. The thing to remember is that none of the existing segmentation algorithm will give you what you want 100% of the time.
Alternatively, if you know ahead of time what specific type of object you need to crop (car, person, motorcycle), then you can try an object detection algorithm. Once again, there are many, and none of them are perfect either. On the other hand, some of them may work better than segmentation if your object of interest is on very cluttered background.
To summarize, if you wish to pursue this, you would have to read a fair number of computer vision papers, and try a fair number of different algorithms. You will also increase your chances of success if you constrain your problem domain as much as possible: for example restrict yourself to a small number of object categories, assume there is only one object of interest in an image, or restrict yourself to a certain type of scenes (nature, sea, etc.). Also keep in mind, that even the accuracy of state-of-the-art approaches to solving this type of problems has a lot of room for improvement.
And by the way, the choice of language or platform for this project is by far the least difficult part.
A method often used for face detection in images is through the use of a Haar classifier cascade. A classifier cascade can be trained to detect any objects, not just faces, but the ability of the classifier is highly dependent on the quality of the training data.
This paper by Viola and Jones explains how it works and how it can be optimised.
Although it is C++ you might want to take a look at the image processing libraries provided by the OpenCV project which include code to both train and use Haar cascades. You will need a set of car and non-car images to train a system!
Some of the best attempts I've see of this is using a large database of images to help understand the image you have. These days you have flickr, which is not only a giant corpus of images, but it's also tagged with meta-information about what the image is.
Some projects that do this are documented here:
http://blogs.zdnet.com/emergingtech/?p=629
Start with analyzing the images yourself. That way you can formulate the criteria on which to match the car. And you get to define what you cannot match.
If all cars have the same background, for example, it need not be that complex. But your example states a car on a street. There may be parked cars. Should they be recognized?
If you have access to MatLab, you could test your pattern recognition filters with specialized software like PRTools.
Wwhen I was studying (a long time ago:) I used Khoros Cantata and found that an edge filter can simplify the image greatly.
But again, first define the conditions on the input. If you don't do that you will not succeed because pattern recognition is really hard (think about how long it took to crack captcha's)
I did say photo, so this could be a black car with a black background. I did think of specifying the colour of the object, and then when that colour is found, trace around it (high level explanation). But, with a black object in a black background (no constrast in other words), it would be a very difficult task.
Better still, I've come across several sites with 3d models of cars. I could always use this, stick it into a 3d model, and render it.
A 3D model would be easier to work with, a real world photo much harder. It does suck :(
If I'm reading this right... This is where AI shines.
I think the "simplest" solution would be to use a neural-network based image recognition algorithm. Unless you know that the car will look the exact same in each picture, then that's pretty much the only way.
If it IS the exact same, then you can just search for the pixel pattern, and get the bounding rectangle, and just set the image border to the inner boundary of the rectangle.
I think that you will never get good results without a real user telling the program what to do. Think of it this way: how should your program decide when there is more than 1 interesting object present (for example: 2 cars)? what if the object you want is actually the mountain in the background? what if nothing of interest is inside the picture, thus nothing to select as the object to crop out? etc, etc...
With that said, if you can make assumptions like: only 1 object will be present, then you can have a go with using image recognition algorithms.
Now that I think of it. I recently got a lecture about artificial intelligence in robots and in robotic research techniques. Their research went on about language interaction, evolution, and language recognition. But in order to do that they also needed some simple image recognition algorithms to process the perceived environment. One of the tricks they used was to make a 3D plot of the image where x and y where the normal x and y axis and the z axis was the brightness of that particular point, then they used the same technique for red-green values, and blue-yellow. And lo and behold they had something (relatively) easy they could use to pick out the objects from the perceived environment.
(I'm terribly sorry, but I can't find a link to the nice charts they had that showed how it all worked).
Anyway, the point is that they were not interested (that much) in image recognition so they created something that worked good enough and used something less advanced and thus less time consuming, so it is possible to create something simple for this complex task.
Also any good image editing program has some kind of magic wand that will select, with the right amount of tweaking, the object of interest you point it on, maybe it's worth your time to look into that as well.
So, it basically will mean that you:
have to make some assumptions, otherwise it will fail terribly
will probably best be served with techniques from AI, and more specifically image recognition
can take a look at paint.NET and their algorithm for their magic wand
try to use the fact that a good photo will have the object of interest somewhere in the middle of the image
.. but i'm not saying that this is the solution for your problem, maybe something simpler can be used.
Oh, and I will continue to look for those links, they hold some really valuable information about this topic, but I can't promise anything.