Ever wondered how technology could enhance your basketball skills by analyzing your shooting techniques? Welcome to the world of AI-driven basketball analysis! In this blog, we will guide you through the process of setting up an application that leverages machine learning to analyze shooting poses and shots using an engaging approach.

Understanding the Concept

This project revolves around the fascinating world of object detection. Think of it as teaching a young chef how to distinguish between vegetables and fruits before they start cooking. In this application, we train a model to recognize basketball shots and shooting poses from various data inputs. By using machine learning techniques, we can identify and analyze key points in a player’s shooting stance and the trajectory of the ball during the shot.

Getting Started

Follow these steps to get the AI basketball analysis project up and running on your local machine:

Step 1: Get a Copy of the Project

Clone the repository onto your local machine using the following command:

git clone https://github.com/chonyy/AI-basketball-analysis.git

Step 2: Install Prerequisites

Before running the project, you need to install all the dependencies listed in requirements.txt. You can do this by executing:

pip install -r requirements.txt

Remember, having a compatible GPU with a proper CUDA setup is essential for running video analysis effectively, as it allows the OpenPose framework to function.

Step 3: Host the Application

To start the app, use the following command:

python app.py

Using the Application

Your application is now set up! You can analyze basketball shots by uploading a video file and the model will handle the rest, analyzing each shot and providing insights.

Features of the Project

• Shot Analysis: Count shooting attempts, misses, and successful shots.
• Pose Analysis: Calculate angles of the elbow and knee during shooting.
• Detection API: Submit a POST request for real-time shooting analysis.

Future Enhancements

Stay tuned for upcoming features such as:

• Switching to a more efficient YOLOv4 model.
• Implementing a tracking algorithm to enhance accuracy.
• Improving visual effects and efficiency for web application services.

Troubleshooting

Should you encounter any issues while analyzing basketball shots, consider the following troubleshooting tips:

• Ensure that your CUDA setup is correctly installed for GPU compatibility.
• Verify that all dependencies are installed without errors by checking the installation log.
• If your application crashes or times out while hosted on Heroku, consider running it locally instead, as it reduces the chances of heavy computation errors.
• If you encounter issues with uploads or API requests, double-check the input data format is compatible with the application.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following the steps outlined above, you can set up a robust basketball analysis application powered by AI. Utilize this tool to gain valuable insights into your shooting techniques and make strides in your game. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.