Writing Your First AI Model

So, you've got a video file, a dream, and you want to build a ball-tracking AI. This guide walks you through how the system works, focusing on how the calibration pieces in your code help refine and improve your model.

Overview

The project revolves around two main files:
• ball_analysis.py The Python script that runs a loop over random frames, predicts the ball’s location, and captures your feedback.
• ball_calibration.json — A JSON file where new calibration data points (position corrections, whether or not a ball appears, etc.) are stored.It uses:

Technologies:
• Python & OpenCV for handling videos and images.
• PyTorch and Torchvision for running a Faster R-CNN model to detect the ball.

How It Works

The script picks frames at random (for variety) from the video.It attempts to detect where the ball might be using the AI model.The user either:
• Confirms the AI’s guess.
• Marks that no ball is present if the AI is mistaken.
• Clicks an exact point if the AI’s guess is off. Each feedback entry is logged in ball_calibration.json.The AI can then use this data for quick “on-the-fly” retraining steps or for additional offline training later on.

A Glimpse of ball_analysis.py

Below is a quick snippet showing how the script loops through calibrations and opens frames for user feedback. Notice how it avoids heavy code comments:

When enough frames have been calibrated, the loop breaks, and everything saves to JSON for future reference (and optional retraining).

Calibration Data in ball_calibration.json

Your corrections end up in ball_calibration.json as structured data points, each entry containing:
• The frame number processed.
• Where the ball actually was (or confirmation that none was visible).
• Confidence scores, if any.
• A simple timestamp for logging when the event occurred.Collectively, these entries become the reference “truth” the AI can learn from—helping it adapt and get better at detecting the ball in real-world usage.

[
  {
        "frame_number": 173401,
        "x": 1560.0,
        "y": 372.0,
        "timestamp": 1737929567.2006788,
        "ai_confidence": 0.7186214327812195,
        "ai_validated": true,
        "is_correction": false,
        "no_ball": false,
        "session_id": "20250126_231247",
        "ai_original_prediction": null
    },
    {
        "frame_number": 295534,
        "x": null,
        "y": null,
        "timestamp": 1737929570.059416,
        "ai_confidence": null,
        "ai_validated": false,
        "is_correction": true,
        "no_ball": true,
        "session_id": "20250126_231250",
        "ai_original_prediction": {
            "x": null,
            "y": null,
            "confidence": 0.0
        }
    },
    {
        "frame_number": 9496,
        "x": null,
        "y": null,
        "timestamp": 1737929573.103283,
        "ai_confidence": null,
        "ai_validated": false,
        "is_correction": true,
        "no_ball": true,
        "session_id": "20250126_231253",
        "ai_original_prediction": {
            "x": null,
            "y": null,
            "confidence": 0.0
        }
    },
    {
        "frame_number": 18141,
        "x": 1689.0,
        "y": 388.0,
        "timestamp": 1737929575.656329,
        "ai_confidence": 0.41590890288352966,
        "ai_validated": true,
        "is_correction": false,
        "no_ball": false,
        "session_id": "20250126_231255",
        "ai_original_prediction": null
    },
    {
        "frame_number": 264335,
        "x": null,
        "y": null,
        "timestamp": 1737929578.010589,
        "ai_confidence": null,
        "ai_validated": false,
        "is_correction": true,
        "no_ball": true,
        "session_id": "20250126_231258",
        "ai_original_prediction": {
            "x": null,
            "y": null,
            "confidence": 0.0
        }
    }
]

Why This Matters

• Immediate Improvement Occasional quick retraining steps use the corrections you just provided to tweak the AI’s internal model.
• Offline Fine-Tuning Periodically, you can train or re-train the AI with the entire library of previous corrections and confirmations for a stronger model.
• User-In-The-Loop Calibration keeps you, the human, at the center of the improvement process. You steer the AI’s evolution by telling it when it’s right or wrong.

Final Thoughts

This setup: two files talking to each other shows how easy it is to start building AI applications. By maintaining a separate JSON file for calibration and combining it with a loop that prompts user feedback, your first AI model quickly gains reliability. The best part is you can always expand it with new data or keep refining the model for more precise ball detection.


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