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We found and based on your interests. You should persist on the path of machine learning. However, I'm still going to try! Your card detector works amazingly well! An educational system designed to bring AI and complex robotics into the home and school on a budget. I need to find a way to keep the processing requirements low while still having good accuracy. It works very well, at least on a gpu. The OpenCV algorithm I used described in this video works great at detecting cards, but it doesn't work if the cards are overlapping even the slightest bit. Yes, delete it Cancel. My next step is to train the detector to recognize ALL cards, not just nine through ace. Just one more thing To make the experience fit your profile, pick a username and tell us what interests you. Object detection classifiers recognize patterns to identify objects, so they only need to see a portion of the object to detect it. For the most part, it works great when it has a clear view of the cards so does my OpenCV algorithm : And it even works if the cards are overlapping: But if I deal some actual blackjack hands in front of the camera, the way it would be done in a casino, it isn't able to detect all the cards. Learn More. This Raspberry Pi-powered robot will identify the cards in its hand and the dealer's upcard, and use a Hit or Stand lookup table to determine the best play to make. I think the solution will involve a combination of machine learning and some image processing with OpenCV. I have a sneaking suspicion that it won't work as well on the lower numbers four is very similar to five, etc. To make the experience fit your profile, pick a username and tell us what interests you. This is just an initial list! Become a Hackaday. Forgot your password? Svavar Konradsson. You should Sign Up. More foods to come. Log In. I've spent lots of time learning about machine learning enough to make a tutorial showing how to train your own and I've taken hundreds of pictures of playing cards to feed to the training API. Does this project spark your interest? Description Almost two years since I started this project page It's time for a touch-up on this! This is actually a very cool idea with a blackjack robot. Unfortunately, blackjack is always dealt with the cards overlapping. Someone told me that I might be able to train an object detection classifier a type of neural network to recognize the cards even if they're partially obscured or overlapping. Then, I'll run it on a Raspberry Pi and see if it's still able to detect cards fast enough, and make a YouTube video about it. Please let me know if you have any ideas! Unfortunately, it's starting to seem like machine learning isn't going to be the silver bullet I hoped it would be. OK, I'm done! But if I deal some actual blackjack hands in front of the camera, the way it would be done in a casino, it isn't able to detect all the cards. Max 25 alphanumeric characters. I'll have to implement a state machine that brings him through different phases of a round of blackjack: reading initial deal, making play decisions, and resolving the hand. Is it possible to perform the detection on the computer and use a raspberry pi as a controller for motors? I played in a regular online casino and there also was a similar automated system. Create an account to leave a comment. For the cards overlapping, just focus the training on the card corners. Evan Juras. It only sees the top card. It's possible that if I fed the trainer hundreds more clearly labeled pictures of overlapping cards, it might be able to see both the cards. Remember me. Your profile's URL: hackaday. I'm creating the perfect Blackjack player! Over the past couple months, I've been tinkering with machine learning to try and train an object detection neural network that can detect playing cards. For the most part, it works great when it has a clear view of the cards so does my OpenCV algorithm :. The perfect Blackjack player! I've already given it training pictures, but maybe it will work better if I give it 1, more. View Gallery. If my blackjack robot is going to work, it needs to be able to count cards even when they're overlapping. However, there are some other problems. Makes doughnuts, fries and onion rings. Already have an account? Not a member? Here's a video showing how the machine learning-based card detector works! Are you sure? Low cost and open source. Liked Like project. And you are right about the training data: you need lots more. I think that in terms of the system of work it is an automated system, but still, it's unusual. Following Follow project. I am trying to sort the playing cards into 4 baskets of the 4 suits using a simple 2 motor mechanism. More training data might help with this, too. The Hackaday Prize. I wish I had seen this comment when you posted it two months ago.

A Raspberry Pi-powered robot that plays Blackjack and counts cards. Also, I only have the detector trained to recognize card ranks nine, ten, jack, queen, king, and ace. View project log.

About Us Contact Hackaday. I'm still trying to think of how I might be able to get it to work with the cards overlapping. I tried using online blackjack robot lower-power MobileNet-SSD model, but it doesn't work very well at identifying individual cards.

So far, I've made a card detector program that uses online blackjack robot trained online blackjack robot learning object detection model YOLO v3 that works extremely well at identifying cards. Become a member to follow this project and never miss any updates.

I decided to use Google's TensorFlow machine learning framework to train a playing card detection classifier. Hack a Day Menu Projects.

I want to run my blackjack robot on a Raspberry Pi, which has limited processing power.

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Right now, it isn't trained well enough to distinguish that there are two read more in each hand. I work in Vegas, in surveillance, the program it's self would be awesome to have to run down players with.

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It will also be able to count cards and implement card counting strategies like the "Illustrious 18". Choose more interests. Pick an awesome username. The cards are too overlapped for it to see all the cards. As the project develops, I will undoubtedly find more things I need to do. To solve the occlusion accuracy problem have you considered training and recognizing just card corners, their left sides, or just the text rather than the entire card? I'll see if I can re-create your wonderful work. It will take lots more training pictures to get it to work with every card rank. Join this project. Also, it is still a little inaccurate and sometimes incorrectly identifies cards. Sign up. I wonder how much is it different from a live dealer. The trained playing card detector just doesn't work very well. Similar projects worth following.