Very odd question, I know, but this is a problem a potential client handed me today.
We assume we have a full length photo of a person. We want to generate a thinner image of that user. Obviously, one way would just be to compress the width of the image but that would result in various distortions that wouldn't be realistic.
I'd like to keep this an open-source implementation so if anybody knows of a library that can identify certain parts of the body and slim each in a way that is most realistic, I'd like to know.
This is obviously something that could be done by hand but we need a solution that works without user interaction.
You should look into seam-carving algorithms. The algorithm is very simple to implement and has many such implmentations online. Seems like ImageMagick has it too - called "Liquid Rescale".
I assume that already the detection of bodyparts in photos is a challenge too hard for algorithms, unless the photos are all very similar (e.g. same background, same pose, etc.)
I have once played around developing algorithms for skin smoothing. I was able to detect skin areas pretty well by converting colors to the LAB space and selecting pixels similar to skin sample colors learnt with a support vector machine from various sample images. Once you have that, you could run something like a liquify-contract algorithm for slimming.
I wouldn't expect satisfying results though unless you spend huge amounts of time on this.
Related
I need to find the location of an image that the user provides within an image that I provide.
It is safe to assume at the time of the analysis that the user provided image is certain to be contained within the image to be compared with.
I’ve looked through and even have some experience with Core ML and Vision image classification however I am struggling to convince myself that it is the correct way to approach this problem. I feel like the way “feature values” is handled in Vision it is almost the reverse of what I’m looking for.
My question: Is there a feature of Core ML or Vision that tackles this particular problem head on?
Other information that may be needed;
It is not safe to assume that images provided are pixel to pixel perfect due to possible resolution differences.
They may also be provided in any shape although possible to crop to a standardised shape before analysis.
Rotation will also need to be accounted for.
There would not be cases where the image is in the image twice.
Take a look at some of the feature detection and matching algorithms.
For example, you could use SIFT (scale-invariant feature transform algorithm) with RANSAC (Random sample consensus algorithm) to do exactly what you described.
If you are using OpenCV there are plenty of such algorithms which you can easily use. (FAST, Shi-Tomasi, etc.)
I think you need something like this expale in OpenCV
I am just doing some research into image processing and would appreciate it if someone could point me in the right direction. I want to compare image 'A' which is a picture of a person's face with image's stored in a database -B,C,D,E .. etc which are also pictures of faces. I want to compare them to see if the person 'A' is already in the database.
Several questions :
1.How is face recognition comparison usually done? (do you extract features e.g. eyes/mouth and compare them to other images?).
2. Are there prebuilt libraries that are able to do a comparison between images? or do i need to write my own algorithm?
3. Where can i start with this? (would appreciate some references/reading material).
Yes, you identify, extract and quantify various aspects of human faces, such as distance between pupils, width of mouth, percentage of head height where tip of nose is, etc.
There is a company, Luxand which makes software to do this, and I think they license it. Last time I looked (2009?) they didn't have an objective-c library. They do have an app that claims to merge faces from photograhs, so you can see what the offspring of any two people would look like, but it is very cheesy, with lots of hard-coded faces. (If you cross a dog with a tea-pot, you get the same baby-face as from crossing a 2 real faces.)
AFAIK, there is nothing in the iOS SDK that does this.
I would just Google "face recognition" and start reading. Good luck.
I would go with compiling openCV for the iPhone ( http://computer-vision-talks.com/2011/02/building-opencv-for-iphone-in-one-click/ ), and then implementing one of the classical ways to do face recognition like eigenfaces ( http://www.shervinemami.info/faceRecognition.html )
But don't expect miracles the accuracy will be low, and the app will be slow.
Also when you say face recognition is difficult doesn't the first link show how easy it is to detect faces on a picture?
The face detection from the first link is just to detect the face. It is just to see if there is a face in the image, which then you can pass as input to the recognition algorithm.
face recognition are very difficult, you need to extract some kind of "features" and perform some measurement...iphone hardware isn't very appropriate for this job.
yes, you can check here
http://maniacdev.com/2011/11/tutorial-easy-face-detection-with-core-image-in-ios-5/
for a tutorial and here
http://maniacdev.com/2011/12/open-source-library-for-adding-easy-face-to-your-ios-app-with-the-free-face-com-api/
for a free webservice.
3.i suggest you google scholar (http://scholar.google.it/scholar?q=face+recognition&hl=it&btnG=Cerca&lr=) but i think that if you want to write your own algorithm you need a lot o spare time :)
My use case is that I have two images (photos) that are almost identical but do have some differences throughout. They won't be perfectly aligned, but they will be pretty darn close to each other. As a rough estimate, I'd expect to find a half dozen or less differences scattered randomly throughout a pair of 640x480 photos. The size of each difference would be roughly 20x20 px but there may be some odd shaped ones in there like 200x30 or so.
The algorithm will probably need to do some kind of blurring or fuzzy comparison or use "shaders" and "filters". Unfortunately, this is nowhere near my area of expertise. I've seen some libraries in my google searches, but nothing that looks like it would run on an iPhone.
Ideally, I'm looking for a working library, tutorial, or example code that can run on iOS.
The basic comparison may be the eigenvector comparison. But there is nice tutorial for java devs, here. May be helpful.
I have a small UIImage (jpg) with a single typed number. I want to be able to read the number with some kind of pattern recognition. I'm really not sure where to start, so any help would be appreciated.
my initial idea was to compare this image with other images. For instance compare the image with that of a 1,2,3, etc until a match was found. That just seems slow and cumbersome and wondered if there was a better way to do it?
Thanks
Update - I'm trying to convert sudoku puzzles from newspaper print to interactive puzzles
No, you are right, it will be slow and cumbersome. But on the plus side you don't have to write it yourself
http://sourceforge.net/projects/opencvlibrary/
Still not exactly easy tho, and i'm not sure about licensing, so… you don't mention why you need to do this (sounds a little odd).
Maybe you can avoid it? If you know the images are numerical digits 0-9, is there another way to track which one a particalur images is, apart from the way it's pixels are arranged?
Sorry if that sounds like i'm missing the point… Maybe you could fill in a few more details?
I read this really good write-up about this exact problem here: http://sudokugrab.blogspot.com/2009/07/how-does-it-all-work.html
It doesn't have any code samples, but explains the concepts, and might be able to point you in the right direction.
The following tutorial may be right down your alley:
http://blog.damiles.com/2008/11/basic-ocr-in-opencv/
It is a simple tutorial on doing number recognition and comes with the source code also.
Additionally, you may want to do a search on OCR SDK (Optical Character Recognition Software Development Kit). You will surely find a stack of them. Commercial ones a pricey though.
I would go for a "role your own" approach along the line of the OpenCV tutorial, especially since you are only interested in numbers.
All of the best ':-)
I was looking at some study i have to do in the future to do with procedural generation techniques and i was wondering what type of content you have:
Developed
Helped Develop
Seen implemented
Tried to develop
and what methods/techniques/procedures you used to develop it.
If you feel generous maybe you can even go into specifics of it such as data structures ad algorithms you have used to develop it.
If this needs to be put as community wiki because it is not me asking for a problem to be solved just let me know.
This is not a homework thread because it is a research unit that i'm not taking yet ;)
Introversion software, the makers of the games Defcon, Uplink and Darwinia (among others) have started working on a game about a year ago which extensively uses PCG for city generation, here is a video of their work, and you can read more about it on the development diary of the game (start from the first part at the bottom of the page!).
This immediately got me extremely interested, and seeing the potential for games I immediately started researching the technology. I have amassed a folder of 18 PDFs about the subject (research papers, SIGGRAPH presentations, etc). Here, I uploaded it for you.
The main approach is to use L-Systems, however, I never got around to understanding enough of that to make something out of this. I tried other, less successful approaches like using Voronois, recursively splitting a rectangular area into more smaller areas and shifting the boundaries a little to obtain a bit of randomness and polygon division.
The last method I had gotten from Mike's Code Blog's posts (here and here). The screenshots shown on his blog make me drool, it is my biggest programmer's dream to ever get something that looks like that. I emailed him to ask how he did it, and here is the relevant part of his reply, I'm sure he wouldn't mind me posting this here:
L-Systems is definitely one way to go, but that isn't what I'm doing. The basis of my method is polygon subdivision. I start with a simple polygon that represents the entire area of the city. Then, I split it (roughly) in half, and then split those two polygons, etc. until I get down to city-block size. At that point, the edges of all my polygons represent roads. I then use the same subdivision method to break the blocks down into building-size lots.
The devil is in the details, of course, but that is the basic method.
I for one still haven't managed to fully implement a solution of which I'm satisfied of, but it remains one of, if not my single biggest programmer's dream to ever achieve something like this.
Here are a few of the leaders in procedurally generated terrain (and to a lesser extent foliage). If you don't get a detailed answer here regarding methods and techniques, you might want to look in / ask in their forums. I have seen some discussions of techniques there.
TerraGen 2
World Builder
World Machine
Natural Graphics
Noone mentioned the demoscene that ONLY use procedural stuff?
So, go search for Werkkzeug, Kkrieger, MilkyTracker to start. Also you can visit the site pouet and see the wonder of well done procedural videos (yes, procedural videoclips! With music and graphics, all procedural!)
Allegorithmic's products are used in actual shipping titles. These guys focus on texture generation (both offline and at runtime).
They have some very pretty screenshots and demos.