I've been working with augmented reality API's lately but haven't been able to achieve irregular shape detection, namely the hand. I want to be able to detect hand shapes through the video/camera and execute code based on hand signs. Does anything like this already exist?
Did you have a look at OpenCV?
These are some of the links I found just using Google: Face Detection using OpenCV, Vision For Robots
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I am new to iOS swift programming and to building AR apps with ARKIT. I find that ARKIT is more powerful than I imagined and I can able to achieve all my business case but except placing data dashboards or charts in AR 3d space. I found ARCharts on Google but it seems to be useless.
My business case is actually scan the object or product and recognize it and display data related to it on AR world which should also show some data analytics dashboard for sales trends of the product.
How to achieve this.. pls provide some inputs
Using ARKit you will be able to detect image detection, object detection and plane detection. For your business case, you can use image detection and object detection.
I will recommend you to go through the below tutorial to get some basic knowledge on object detection and image detection.
Building a Museum App with ARKit 2. Happy coding ;)
Using Mobile vision Face API we can recognize faces.. But is it possible to define image set and try to compare tracking face and images from set and recognize tracking image in result?
If yes could you please give some hint how i can organize it or add some additional tools for it.
No, the mobile vision face API does not support facial recognition.
Although it does support tracking faces in video, the tracking mechanism uses position/size/velocity correlation to track faces from frame to frame, and does not use a facial similarity metric.
I need to be able to detect a variety of coloured post-it notes via a Microsoft Kinect video stream. I have tried using Emgucv for edge detection but it doesn't seem to locate the vertices/edges and also colour segmentation/detection however considering the variety of colours that may not be robust enough.
I am attempting to use HAAR classification. Can anyone suggest the best variety of positive/negative images to use. For example, for the positive images should I take pictures of many different coloured post-it notes in various lighting conditions and orientations? Seeing as it is quite a simple shape ( a square) is using HAAR classification over-complicating things?
I haar classifiers are typically used on black and white images and trigger primarily on morphologic edge like feature. Seems like if you want to find post it notes in an image the easiest method would be to look at colors (since they come in very distinct colors). Have you tried training a SVM of Random forest classifier to detect post it notes based on just color? Once you've identified areas in the image that are probably post it notes you could start looking at things like the shape as additional validation that you are indeed looking at a post it note.
Take a look at the following as an example of how to find rectangles in an image using hough transform:
https://opencv-code.com/tutorials/automatic-perspective-correction-for-quadrilateral-objects/
I am interested in getting into user interaction/shape detection with a simple usb webcam. I can use multiple webcams, but don't want to be restricted to using something like the kinect sensor. My detection cameras need to be set up on either side of a helmet (or if an individual one, on top). I have found some, but they don't really have the functionality I need and most are angled towards facial recognition. I need to be able to detect a basic human skeletal structure and determine if something is obstructing it. I would really rather be able to do it without using any sort of marker system on the target person. I would like for it to be able to target multiple structures. Obviously I am willing to do tweaking if necessary, but want to see how close I can get to what I need before I rebuild the wheel. I am trying to design an ai system that can determine how many people are in an area and where they are.
Doubt there will be anything like this since Microsoft spent a ton of money on the R&D for Kinect and it's probably all locked behind an NDA. I'm also guessing there's a lot of hardware within the Kinect that is not available in a standard webcam.
The closest thing that I could find to what you're looking for is the OpenKinect project, might be a good place to start your research.
I've got retina tile maps working, 15x10 tiles, of 64x64 tiles. problem is for non-retina devices I will need to make a 15x10 tiles of 32x32 tiles. I don't want to recreate the Tile, is it just a case of changing the XML (.tmx) file? Is there an automated tool or another way around this? I've been looking online but not getting too much help.
Thanks
You have to update the TMX file and scale certain attributes. Unless your TMX map is very simple this will be a tedious and error-prone task that's best left to a tool.
There are a variety of TMX rescaling tools out there, but some didn't work for me or simply were incomplete at the time (ie one didn't scale object layers). All the tools I know are generally are written in rather unusual languages (for an iOS developer at least) like Python, Ruby or Bash scripts. Others are only available as binary without the source code.
Check out this cocos2d forum post. Specifically this tool or HDx on the App Store. iTilemaps might also work for you.
Because I wasn't happy with either of the choices, I wrote my own command line tool tmx2scale in Objective-C to rescale TMX maps intelligently in all directions. The tmx2scale tool is not currently available but it will be distributed complete with source code with the KoboldScript Game Kit project.