🖼Image_Classifier

A template for any image classification problem with Teachable Machine and its real-time detection with OpenCV in Python. I have written a blog post about this work and you can find it here

Start an image project in Teachable machine and export the Tensorflow model and then extract the downloaded folder and place the "keras_model.h5" and "labels.txt" in the working directory.

For more awesome Teachable Machine resources visit The Awesome Teachable Machine List

If you love this repo, consider giving it a star🌟.

👨🏻‍🏫 Prerequisites

To install all the dependencies, run:

pip install --user -r requirements.txt

🔧 How to Use

1.👯 Clone the Repository:

$ git clone https://github.com/Harikrishnan6336/Image_Classifier.git

2. Then move to the working directory.

$ cd Image_Classifier

2. Place the "keras_model.h5" and "labels.txt" in the working directory and replace the "Label : " + labels[str(result)] with the label name. .

3. Setup the game by providing images of Rock paper scissors and Nothing in the order while the program captures it when executing the command below

$ python3 setup.py 

4. Run the program

$ python3 main.py

Built With ❤️

• Python3.6 - ⚠️️ Warning : Tensorflow is not supported on any version of python above 3.6 as of now.
• Teachable Machine - An easy tool to create machine learning models for your use without any coding.
• OpenCV4 - A library of programming functions mainly used for real-time computer vision

💁🏻 Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. 🍴 Fork the Project
2. Create your Feature Branch (git checkout -b feature/newFeature)
3. Commit your Changes (git commit -m 'Add some newFeature')
4. Push to the Branch (git push origin feature/newFeature)
5. Open a Pull Request

Please feel free to raise any issue...