I was wondering if anyone out there had any reliable intrinsic or extrinsic parameters they would be willing to share with regards to the iPhone 5s camera? I'm working in OpenCV and would like to undistort my images prior to processing. I have been working on code to calibrate on my own, but the end results are pretty painful at this point. If anyone has any clean c++ links to code I'd appreciate that as well. I've been trying to piecemeal something together from older tutorials, and it's not going so hot.
As I understand, I think I am looking for parameters like fx, fy, cx, cy as well as 5 distortion parameters (k1,k2,k3,r1,r2). Though I know each camera is a unique and beautiful snowflake, I just have to think that as a ballpark there have to be some stable references to these values for this particular camera.
Thank you all for the help over the years.
Check out the samples folder in the OpenCV directory. I've been using the Python script from there, but of course, there's a C++ version as well.
As Adi Shavit said, the extrisic parameters can only be determined relativ so some other coordinate system. Further more I believe that the distortion coefficients are different for each camera chip and there is no general way of knowing them without calibration.
Related
I tried to track motion in Matlab by using this tutorial (http://www.mathworks.com/help/vision/examples/motion-based-multiple-object-tracking.html) and it works fine but it implies video as source to work.
I wanna know if it's possible to track motion by using the same tutorial but in real time by using camera as source!
Everything is possible, just please try to find some stuff by yourself before asking here.
I think you may find the information you need in this link:
http://www.matlabtips.com/realtime-processing/
Alternately, you could of course just store the camera output as a (very short) video and continuously analyse that instead.
As of release R2014a, MATLAB includes support for USB webcams. If you have an older version, or if you want to use a high-end camera, you would need the Image Acquisition Toolbox.
Once you are able to get frames from the camera, you can reuse almost all of the code in the multiple object tracking example. You would only need to rewrite the readFrame function with code to get a frame from the camera.
Is there any library alternative to OpenCV which detects smile.
I dont want to use OpenCV as it sometimes fails to detect faces due to background.
Any one knw other library ? other than OpenCV ?
I would recommend having a look at The Machine Perception Toolbox (MPT Library).
I had a chance to play with it a bit at an Openframeworks OpenCV workshop at Goldsmiths and there is a c++ smile detection sample available.
I imagine you can try the MPT Library for iPhone with openframeworks or simply link to the library from an iphone project.
sometimes fails to detect faces due to
background.
An ideal lighting setup will guarantee better results, but given that you want to use this on a mobile device, you must inform your users that smile detection might fail under extreme conditions (bad lighting)
HTH
How are you doing smile detection? I can't see a smile-specific Haar dataset in the default OpenCV face detection cascades. I suspect your problem is training data rather than OpenCV itself.
Egawer is a good starting point if you need a working app to begin with.
https://github.com/Atrac613/egawer-iOS
I checked the training images of smileD_haarcascade_v0.05, an found that they include the full face. So, it seems to be a "smiling face" detector rather than a smile detector alone. While this seems easier, it can also be less accurate.
The best is to create your own Haar Cascade XML file, but admittedly most of us developers don't have time for that. You can improve the results considerably by equalizing the brightness of the image.
iOS 7 now has native support of simile detection in CoreImage. Here is the API diff:
For iOS 7, Yes, now you can do it with CoreImage.
Here is the API diff in iOS 7 Beta 2:
CoreImage
CIDetector.h
Added CIDetectorEyeBlink
Added CIDetectorSmile
I want to build a similar app as fatbooth and want some ideas on how to do this. I googled for Image morphing in iPhone but didn't find anything. Should I use some server side language to morph Image?
Any help would be much appreciated!
Thanks
Saurabh
The only language you can really use is C# or c++. the maths is very complicated although I am sure you can get a book or two that cover image manipulation.
I don't know if there is an open source morphing framework, but that is your best option - it doesn't have to be specific to the iPhone, but the integration will be hard.
I don't know what you mean about server side, unless there is a server you know of that does it and you want a wrapper around it
For starters, take a look at displacement mapping: http://www.imagemagick.org/Usage/mapping/
Basically, you use grayscale images to bloat/shrink/squeeze/etc parts of a person's face. I made an app called FaceCraze which had to implement a poor man's FatBooth as part of the face transformation, and I used grayscale images very similar to the examples in http://www.imagemagick.org/Usage/mapping/#spherical
edit: You can use the ImageMagick library in your iOS apps too: Link
I want to analyze MIC audio on an ongoing basis (not just a snipper or prerecorded sample), and display frequency graph and filter out certain aspects of the audio. Is the iPhone powerful enough for that? I suspect the answer is a yes, given the Google and iPhone voice recognition, Shazaam and other music recognition apps, and guitar tuner apps out there. However, I don't know what limitations I'll have to deal with.
Anyone play around with this area?
Apple's sample code aurioTouch has a FFT implementation.
The apps that I've seen do some sort of music/voice recognition need an internet connection, so it's highly likely that these just so some sort of feature calculation on the audio and send these features via http to do the recognition on the server.
In any case, frequency graphs and filtering have been done before on lesser CPUs a dozen years ago. The iPhone should be no problem.
"Fast enough" may be a function of your (or your customer's) expectations on how much frequency resolution you are looking for and your base sample rate.
An N-point FFT is on the order of N*log2(N) computations, so if you don't have enough MIPS, reducing N is a potential area of concession for you.
In many applications, sample rate is a non-negotiable, but if it was, this would be another possibility.
I made an app that calculates the FFT live
http://www.itunes.com/apps/oscope
You can find my code for the FFT on GitHub (although it's a little rough)
http://github.com/alexbw/iPhoneFFT
Apple's new iPhone OS 4.0 SDK allows for built-in computation of the FFT with the "Accelerate" library, so I'd definitely start working with the new OS if it's a central part of your app's functionality.
You cant just port FFT code written in C into your app...there is the thumb compiler option that complicates floating point arithmetic. You need to put it in arm mode
I hope this falls within the "programming question" category.
Im all lightheaded from Googling (and reading every post in here on the subject) on the subject "Computer Vision", but Im getting more confused than enlightened.
I have 6 abstract shapes printed on a piece of paper and I would like to have the camera on the iPhone identify these shapes (from different angles, lightning etc.).
I have used OpenCV a while back(Java) and I looked at other libraries out there. The caveat is that it seems that either they rely on a jail broken iPhone or they are so experimental and hard to use that I would probably end up using days learning libraries only to figure out they didn't work.
I have thought of taking +1000 images of my shapes and training a Haar filter. But again
if there is anything out there that is a bit easier to work with I would really appreciate the advise, suggestion of people with a bit of experience.
Thank you for any suggestion or pieces of advise you might have:)
Have a look at at OpenCV's SURF feature extraction (they also have a demo which uses it to detect objects).
Surf features are salient image features which are invariant to rotation and scale. Many algorithms detect objects by extracting such features from an image, and then use simple "bag of words" classification (comparing the set of extracted image features to the features of your "shapes". Even without referring to their spacial alignment you can have good detection rates if you only have 6 shapes).
While not a library, Chris Greening explains how iPhone Sudoku Grab does its image recognition of puzzles in his post here. He does seem to recommend OpenCV, and not just for jailbroken devices.
Also Glen Low talks a bit about how Instaviz does its shape recognition in an interview for the Mobile Orchard podcast.
I do shape recognition in my iPhone app Instaviz and the routines are actually packaged into a library I call "Recog". Only problem is that it is meant for finger or mouse gesture recognition rather than image recognition. You pass the routines a set of points representing the gesture and it tells you whether it's a square, circle etc.
I haven't yet decided on a licensing model but probably use a minimal per-seat royalty.