Gand Tritzkett – I’ve been wanting to do this for a long time. Growing up, Hand Cricket was one of my favorite games and I guess that’s how I remember that moment. For those of you who don’t know how it works, here’s a brief description of the game.
. It was exhausting and quite difficult. no thanks to me For you, I want you to familiarize yourself with the game beforehand.
Now that you know how the game works, you can dive into your attempts to clone it. (I use Python for code snippets)
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This is a fairly long code, so please forgive me. The process can be divided into the following steps.
The code in the following section assumes intermediate-level skills in Python, OpenCV, and CNNs. Before proceeding with the section, it is recommended that you familiarize yourself with the simpler code for the aforementioned items.
The data required for this project should be images representing the numbers 0-6 (including 0 and 6). The data was collected and uploaded to Kaggle: Hand Symbols. We collected this data using Python’s OpenCV library. Here’s a quick read on how I did it. data collection.
A total of 28800 jpg images of 240×240 pixels size were collected. Of these 28,000 images, 20,000 are stored in the “train” folder and the remaining 8,800 images are stored in the “test” folder. Why exactly 28,000 photos? Why is the test-to-train ratio 20000:8800? Why 240×240 photos? For no particular reason! We collected more and more data as we tested how the model behaved in real time. Those numbers make for good enough models, so I stuck with them. Let’s move on anyway!
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There are 8 more folders named “0”, “1”, “2”, “3”, “4”, “5”, “6”, “none” in the train and test folders. Each folder in the train folder contains 2500 images and the test folder contains 1100 images. Confused? I’ll do it too. To make things easier, here’s a better expression:
I created this structure to train a model with data in train folder and test it with data in test folder. However, all data was used to train the model because the ultimate goal was to apply it in real time, rather than report the accuracy achieved by the model. For anyone interested in finding accuracy, a structure similar to the one above may be useful.
Some of you may have noticed a folder called “Missing”. This folder contains images without hand symbols. The images in this folder are used to create status when the user does not display the symbol. It contains an image with a random background by default. From a machine learning perspective, this is a multi-class classification problem where each symbol is a specific class. I had to add a class to display the status.
Save the data to the working directory as the previous step. Now we need to import it into the environment (save it as a variable in the required format).
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Let’s say your data is stored in a folder called ‘dataset’. Iterate through this folder and the files within it using Python OS library functions. Each image in each folder is converted to a NumPy array and stored in a list called “images_as_numpyarr”. Another list called “labels” is maintained which stores the number the image represents in the same register as the image stored in the images_as_numpyarr list.
For those familiar with supervised machine learning, supervise by labeling data using a label list.
So images_as_numpyarr contains the image’s pixel data as a NumPy array, and labels contains the numbers displayed in the image.
So if it’s the first image, images_as_numpyarr will store that image as a NumPy array and labels will store the number 3.
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Labels 0-6 indicate which digits to display, and label 7 indicates no digits to be displayed (the “none” state mentioned in the previous section).
The tag list tags the image above with the number 7 (representing the class of the image) and saves it to the list.
Now that the data is readily available, it is time to design the CNN model and feed it the data. Instead of building an architecture from scratch, we used transfer learning to facilitate efficient model creation. An article that explains it in detail is transfer learning. Simply put, this is the process of taking a pre-trained model (with weights) and modifying the final layer to suit our needs. This technology helps reduce the time it takes to build and deploy complex architectures.
Complicated? i know you For now, just understand that MobileNet is pre-built and pre-trained (meaning the appropriate weights are generated through training). You import the architecture, modify the last few layers (usually by adding new ones), and train the model on the data.
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Here’s the code. We took the MobileNet architecture from line 8 and added some layers at the end (lines 9-13). It was then built using the Adam optimizer with a learning rate of 0.0001. I tried training with different parameters and the parameters in the code below turned out to be good enough.
Now that the architecture is ready, pass the image along with the label as a NumPy array (line 4).
The shape of images_as_numpyarr is 240x240x3 (240×240 is the image size and 3 is the number of channels (RGB)) and the shape of tags is 28800×8 (28800 is the number of images and 8 is the number of different channels. Possible classes in classification ( 0, 1, 2, 3, 4, 5, 6, 7) (7 represents a non-class)).
The model is trained for 2 epochs. This process took about 50-60 minutes on my machine. This time may vary depending on available processing power.
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The model is then saved so you can check its progress and next time you just load the model instead of retraining it.
Our model is finally ready to count fingers in real time. With OpenCV you can use a webcam and have the model detect how many fingers are being displayed.
Draws a rectangle on the right side of the screen. Extract the corresponding rectangle from each frame and pass it to the model. Our model then returns an array of probabilities. Since there are 8 possible classes (0, 1, 2, 3, 4, 5, 6, 7), each value in the array represents the probability that the image has a hand representing the number x at index x (except 7, where index A probability value of 7 represents the probability that the hand is not present in the image.The value in the maximum likelihood array is the value that the model predicts will appear in the image.This value is displayed in the upper left corner of the screen.
Now you have a model that can read your finger. Now let’s move on to the technical side of the game. This project is made so that one person can play a game with a robot. If two human players want to play hand cricket, they can do it in person or via video call. Therefore, the following program can only be used against bots. To make things easier, the human always acts as the hitter and the bot always acts as the pitcher. Players randomly select a number between 0 and 6 every second. The moment the batter selects a number (by giving a number by hand in a designated area), that number is added to his score. Keeping up with the robot is difficult because the computer dials a new number for each frame. Randomly pick an irregular even number and hope it doesn’t match the number chosen by the baller.
Field Trisket I Hand Stock Photo. Sage F Blatsk, Holding
If you’ve made it this far, praise yourself for your sincere dedication to learning. ( I’m tired )
This is the last piece of code that connects the finger counting model (lines 44-49) to the game rules, as described in the introductory section (lines 56-64).
Since we need to implement the webcam in real time, we again use OpenCV to access the webcam. On the right side of the screen there is a special area for you (typing). The screen displays both the user’s current score and record (lines 67-80).
Here’s the actual game! (Ignore current and highest scores. Every second the model detects the number I (batter) picked, so for example 5.5 points are added to my score (batter’s score). So every second 5 points are added to my score. The bot will randomly pick 5 of them.
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