# Pothole & Road Sign Detection Backend

This project implements a Two-Stage Detection Pipeline using YOLOv8 (for detection & tracking) and CLIP (for zero-shot classification).

## Setup

1.  **Install Dependencies**:
    ```bash
    pip install -r backend/requirements.txt
    ```
2.  **Training YOLO (Crucial Step)**:
    You MUST train a YOLO model on your custom dataset (Pothole, Manhole, Traffic Signs) for this to work effectively.
    - Prepare your `dataset.yaml`.
    - Run the training script:
    ```python
    from backend.models.train_yolo import train_yolo
    train_yolo("path/to/dataset.yaml", epochs=50)
    ```

    - This will generate `best.pt`. Move this file to `backend/models/best.pt`.

## Running the API

Start the FastAPI server:

```bash
cd backend
python main.py
```

The server will start at `http://0.0.0.0:8000`.

## API Usage

**Endpoint**: `POST /detect/video`

- **Body**: `multipart/form-data`, key `file` (Video file).
- **Response**: JSON summary of unique objects detected.

## Configuration

- Modify `backend/pipelines/video_processor.py` to change the `yolo_model_path` to your trained model path (e.g., `backend/models/best.pt`).
- You can also adjust the CLA candidate labels in `VideoProcessor.__init__`.