# Debug Scripts for YOLO ONNX Detection

This directory contains debugging tools for troubleshooting YOLO object detection issues.

## Setup

1. Create a Python virtual environment:
```bash
python -m venv debug_env
source debug_env/bin/activate  # On Windows: debug_env\Scripts\activate
```

2. Install dependencies:
```bash
pip install -r requirements.txt
```

## Scripts

### `debug_model_comparison.py`
Compares .pt model predictions with ONNX model outputs on the same static test image.
- Tests both PyTorch and ONNX models side-by-side
- Provides detailed debug output including preprocessing steps
- Useful for identifying model export issues

### `test_static_onnx.py`
Tests ONNX model against static images to isolate Android capture issues.
- Bypasses Android screen capture pipeline
- Tests multiple ONNX model variants
- Good for validating model functionality

### `export_model_variants.py`
Exports YOLO model variants with different NMS settings.
- Creates models with different confidence/IoU thresholds
- Useful for debugging detection sensitivity issues

### `inspect_onnx_model.py`
Inspects ONNX model structure and metadata.
- Verifies class mappings and model architecture
- Helpful for debugging model export problems

## Usage

Place test images in `../../test_images/` and ensure model files are in `../../raw_models/`.

Example:
```bash
cd tools/debug_scripts
source debug_env/bin/activate
python debug_model_comparison.py
```