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In this article, you will learn how to build python-based gesture-controlled applications using AI. We will guide you all the way with step-by-step instructions. I’m sure you will have loads of fun and learn many useful concepts following the tutorial. Specifically, you will learn the following: How to train a custom Hand Detector with Dlib.How to cleverly automate the data collection & annotation step with image processing so we don’t have to label anything.How to
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This is a really descriptive and interesting tutorial, let me highlight what you will learn in this tutorial. A Crystal Clear step by step tutorial on training a custom object detector.A method to download videos and create a custom dataset out of that.How to use the custom trained network inside the OpenCV DNN module so you can get rid of the TensorFlow framework. Plus here are two things you will receive from the provided source
Let’s play rock, paper scissors.You think of your move and I’ll make mine below this line in 1…2…and 3. I choose ROCK. Well? …who won. It doesn’t matter cause you probably glanced at the word “ROCK” before thinking about a move or maybe you didn’t pay any heed to my feeble attempt at playing rock, paper, scissor with you in a blog post. So why am I making some miserable attempts trying to play this
Did you know that you can actually stream a Live Video wirelessly from your phone’s camera to OpenCV’s cv2.VideoCapture() function in your PC and do all sorts of image processing on the spot? Cool huh? In today’s post not only we will do just that but we will also build a robust Intruder Detection surveillance system on top of that, this will record video samples whenever someone enters your room and will also send you
In the previous tutorial we learned how the DNN module in OpenCV works, we went into a lot of details regarding different aspects of the module including where to get the models, how to configure them, etc.  This Tutorial will build on top of the previous one so if you haven’t read the previous post then you can read that here.  Today’s post is the second tutorial in our brand new 3 part Deep Learning

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