In my continuing effort to create Rainman 2.0, a Raspberry Pi-powered, blackjack-playing robot, I discovered that the best way to detect playing cards is using a TensorFlow object detection neural network. I trained a network specifically to detect cards (and made a YouTube video explaining how to train your own). To use the network on the Raspberry Pi, I needed to install TensorFlow and its Object Detection API. It turns out its not very easy to do!
After figuring out how to install TensorFlow, I made this video about how to do it yourself. Please check it out if you're interested in using a Raspberry Pi for your own machine learning applications!
Have you ever wanted to create a "rabbit detector" that alerts you when rabbits are in your garden eating your precious vegetables? Or maybe set up a camera program that can identify the make and model of every car going through an intersection? Object detection classifiers are a type of machine learning neural network that can be trained to detect and identify objects in images, videos, or camera feeds. They can be used to create object detectors that will detect and identify anything your heart desires.
I've been working on training a detector that can identify playing cards in a camera feed for my Blackjack Robot project. I've had some success:
I worked with Google's popular machine learning framework, TensorFlow. However, I noticed there was a lack of clear tutorials for how to set up TensorFlow's Object Detection API to train your own object detector, especially on Windows. (And I'm not about to install Linux on my gaming PC.)
So, I wrote my own tutorial and created a video that walks through it! The written tutorial is located here at GitHub. It provides set up instructions and comes with a repository that has everything you need to train your own object detection classifier. The video (linked above) walks through the tutorial.
Put your high-powered PC to use and increase your machine learning savvy by training your own object detector using this tutorial. (Actually, any desktop or laptop PC will work, but having a powerful graphics card will significantly reduce the training time.)
Create an account to leave a comment. Already have an account? Log In.
Thanks for the like. Let me know if you have any questions or recommendations.
No problem, I appreciate how well-documented your project is. I might try making my own based off yours, just to help introduce myself to IOT devices. And thanks for the like too!
Thanks for following uRADMonitor! One of my other projects was finalist in Hackaday prize 2015, check it out here: https://hackaday.io/project/4977-portable-environmental-monitor
No problem, looking forward to seeing the finished project!
Hi and thanks for the follow, I'm sorry I took a while to get back to everyone :-)
like Evan Juras and many others
how can i output the most possible number of poker card into .txt file , rather than showing the original image ???