I am implementing an algorithm for finger detection using OpenNI and OpenCv 2.4 and Kinect.
The problem is I don't know how to get started, I found a piece of code and trying to run it.
But the problem is imgproc_c.h has alot of errors as (undeclared CvMoments,CvDistanceFunction) and many others of this type.
I checked and made sure of the header files, I even compared it with the code online of imgproc_c.h and it's exactly the same.
I have a feeling that openCV 2.4 is not functioning correctly in my case,
Any clue ?
If you don't know where to start I sugest you start reading "Computer Vision" from Ballard and Brown, old but gold, it's free: http://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/bandb.htm
I can assure you that there are several ways to make a finger detection, but never one really simple.
Related
I need to find a way to implement face detection and recognition completely offline using a browser. Trained model specific to each user maybe loaded initially. We only need to recognize one face per device. What is the best way to implement this?
I tried tracking.js to implement face detection. It works. But couldn't get a solution to implement recognition. I tried face-recognition.js. But it needs a node server.
Take a look at: face-api.js it can both detect and recognize faces in realtime completely in the browser! It's made by Vincent Mühler, the same creator of face-recognition.js.
(Face-api.js Github)
Thing to note:
It's realtime, my machine gets ~50ms (using MTCNN model)
It's JavaScript but uses WebGL GPU acceleration under the hood which is why it performs so well
It can also work on mobile! (tested on my S8+)
I recommend looking at the included examples as well, these helped me a lot
I have used the package to create a working project, it was surprisingly easier than I thought and this is coming from a student that just started web development. (used it in a ReactJs App)
Just like you I was searching and trying things such as tracking.js but to be honest they didn't work well.
I am researching about mask r-cnn. I want to know how to pretrain my image(knife,sofa,baby,.....) using resnet50 in mask-rcnn. I struggle to find that in github, but I can't. Please help me anybody who know how to handle it.
Try this implementation of Mask RCNN on github here.
You can follow Mask_RCNN github repo. It has both resnet50 and resnet100 (might be wrong here). It is a beautiful implementation I would say. The base model is from FAIR (Facebook AI Research). There is a demo file which you can check before starting your work.
If it works well, you can see my answer, it will help you to train the model with your custom data. The answer is a bit long, but it lists all the steps.
Something which I personally like about this implementation is:
It is easy to setup. Won't bother you much about the dependencies. Having a python virtual environment does the wonders.
It falls back automatically from a CPU version to GPU and vice versa.
It is having good support from its developers. It is getting commits frequently.
The code is very customisable. So If you want to do some changes, it's pretty easy. Some booleans and numbers changes up and down and you are done...!!!
I have tried to implement the ellipse fitting algorithm descibed in the following paper: “ElliFit: An unconstrained, non-iterative, least squares
based geometric ellipse fitting method”, by Prasad, Leung, Quek. A free version can be downloaded online from http://azadproject.ir/wp-content/uploads/2014/07/2013-ElliFit-A-non-constrainednon-iterative-least-squares-based-geometric-Ellipse-Fitting-method.pdf
The authors did not provide any publicly available implementation.
I have implemented the algorithm in Mathematica, I believe I have implemented it correctly, yet it fails to correctly find the fit parameters. The PDF of the experiment can be downloaded here: http://zvrba.net/downloads/ElliFit-fail-example.pdf
Did somebody else try to implement this particular algorithm and, if yes, what is the key to get it working? Is there a "bug" in the paper? Can somebody take another look at my implementation and see whether there's a bug there?
I know it's been almost a year since this question, but it seems that the authors have now provided public source code for ElliFit, both a MATLAB version and an OpenCV version.
Both are available on the the author's homepage. In case the homepage goes offline for some reason, both source codes are shared on Google and are available here (MATLAB) and here (OpenCV).
At the time of writing, I have not personally tested their code, but am planning to use them for a project. I will post any updates here in the next few days.
EDIT:
I got around to test the code sooner than I expected. I gave the OpenCV code a try. It works pretty well, as demonstrated by the image below (ignore the "almost-closed-ellipses". It's an artifact caused by something else in my code).
As you can see, it works pretty well, most of the times. There are some failure cases too (the small ellipse on the spray bottle next to the cup).
I want to do a project involving Computer Vision. Mostly object detection/identification. After some research, I keep coming back to OpenCV. But all of the tutorials are from 2008 (I guess it was big for a bit then). It doesn't compile in Python on the mac apparently. I'm using the C++ framework right out of Xcode, but none of the tutorials work as they're outdated and the documentation sucks from what I can parse.
Is there a better solution for what I'm doing, and does anyone have any suggestions as to learning how to to use OpenCV?
Thanks
I have had similar problems getting started with OpenCV and from my experience this is actually the biggest hurdle to learning it. Here is what worked for me:
This book: "OpenCV 2 Computer Vision Application Programming Cookbook." It's the most up-to-date book and has examples on how to solve different Computer Vision problems (You can see the table of contents on Amazon with "Look Inside!"). It really helped ease me into OpenCV and get comfortable with how the library works.
Like have others have said, the samples are very helpful. For things that the book skips or covers only briefly you can usually find more detailed examples when looking through the samples. You can also find different ways of solving the same problem between the book and the samples. For example, for finding keypoints/features, the book shows an example using FAST features:
vector<KeyPoint> keypoints;
FastFeatureDetector fast(40);
fast.detect(image, keypoints);
But in the samples you will find a much more flexible way (if you want to have the option of choosing which keypoint detection algorithm to use):
vector<KeyPoint> keypoints;
Ptr<FeatureDetector> featureDetector = FeatureDetector::create("FAST");
featureDetector->detect(image, keypoints);
From my experience things eventually start to click and for more specific questions you start finding up-to-date information on blogs or right here on StackOverflow.
Let me add a couple of things. First, I can assure you that the Python bindings to OpenCV work on a Mac. I use them every day.
Many people like OpenCV for many reasons:
The license is good, friendly to integration into commercial products, etc.
It is quite good from a technical stand point. It gives you a reference implementation of state of the art algorithms.
It tends to be quite fast compared to the alternatives (Matlab I'm looking at you).
Like everything in life, it is not perfect:
It is a good example of a software library that is a moving target.
I have a 300 line python program that uses OpenCV and every few
months when a new version of OpenCV is released I have to change it
to adapt to the new function names/calling conventions, etc. The
library does advance, a lot, however it is a pain to have to change
the same program 3 times per year.
It has a learning curve, like computer vision itself, it is quite
technical and not easy to learn.
There are alternatives (with other pros and cons) MATLAB with the Image Processing Toolbox is one such example.
The simplest answer that comes to mind, is to read the example code with a bit of understanding, and to try out if Your ideas work. The api does change, and most of the tutorials are writen for the first versions of OpenCV, and it looks that nobody bothered to rewrite them. Nevertheless the core ideas behind it are not changing. So if You find a tutorial answering Your questions, but written in old API just look in the documentation for modern replacements of used functions. It’s not easy and quick, but looks like it works. If You use the newest (actually 2.3) version, I suggest using both the 2.1 documntation and 2.3 docs + tutorials . You should also look into the samples, which should have been installed alongside the library. There are lots of hints about how to use certain structures and tricks that weren't mentioned in documentation. Finally, don't be afraid to look inside the code of the library itself (if You compiled it on Your own). Unfortunately, thats the only source I know to check for example what code corresponds to which type of Mat object.
I'm searching for a face tracking system to use in an augmented reality project. I'm trying to find an open source and multi-platform application for it. The goal is to return the direction where the face is looking to interact with the virtual environment, (something like this video).
I've downloaded the sources of the above Johnny Lee's application and tried to use Free Track too, making my own headset (some kind of monster, hehe). But it's not good to be limited to infrared points in your head.
These days I've download FaceTrackNoIR, but when I launch the program I get "No DLL was found in the Waterfall procedure." that I'm actually trying to solve.
Anyone knows a good application, library, code, lecture, anything that could help me to find a good path for this?
Thank you all!
I'll try to post results someday :-)
I would take a look at OpenCV. It is a general purpose machine-learning and computer vision C++ library. One of the examples in the download is a real-time face tracker that connects to a video camera connected to your computer and draws squares around any faces in the camera view.