# C++ Tracker Implementation

This project implements a C++ version of the DiMP tracker, focusing on the bounding box regressor and classifier components.

## Overview

The project consists of two main components:

1. **BBRegressor**: Implements the IoU (Intersection over Union) network for bounding box regression
2. **Classifier**: Implements the feature extraction for target classification

## Requirements

- CMake (3.18 or higher)
- C++17 compatible compiler
- LibTorch (PyTorch C++ API)
- CUDA (optional, for GPU acceleration)

## Building the Project

### Automatic Build

The easiest way to build the project is to use the provided build script:

```bash
chmod +x build.sh
./build.sh
```

This will:
1. Check for CUDA availability
2. Download LibTorch if not already installed
3. Configure the project with CMake
4. Build the project
5. Install the executable to the `bin/` directory

### Manual Build

If you prefer to build manually:

```bash
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release
```

## Running the Demo

To run the demo application:

```bash
# Set the library path to include LibTorch
LD_LIBRARY_PATH=$HOME/libtorch/lib:$LD_LIBRARY_PATH ./bin/tracking_demo
```

Or use the provided script:

```bash
./run_demo.sh
```

## Project Structure

- `cimp/`: Main C++ implementation
  - `bb_regressor/`: Bounding box regressor implementation
  - `classifier/`: Feature extractor implementation
  - `demo.cpp`: Demo application
- `exported_weights/`: Directory containing exported PyTorch weights
  - `backbone/`: Backbone network weights
  - `bb_regressor/`: Bounding box regressor weights
  - `classifier/`: Classifier weights
- `ltr/`: Reference Python implementation
- `bin/`: Built executables

## Known Issues

- The PrRoIPooling implementation requires CUDA, but there's a fallback CPU implementation
- Some CUDA operations may fail on certain GPU configurations; the code includes fallbacks

## Comparing Python and C++ Implementations

To compare the outputs between Python and C++ implementations:

1. Run the Python implementation to generate reference outputs:
```bash
python demo.py
```

2. Run the C++ implementation:
```bash
./run_demo.sh
```

3. Compare the output statistics in the generated files:
   - `bb_regressor_stats.txt`
   - `classifier_stats.txt`

## License

This project is licensed under the MIT License - see the LICENSE file for details.