semvec/pypi-packages huggingface dataset:
od-labeler 0.3.4
A tool for object detection, labeling and visualization
Requires Python: >=3.6

# OD-Labeler

OD-Labeler is a powerful tool for object detection labeling and visualization. It provides an intuitive graphical user interface for labeling objects in images using the YOLO-World model and supports various output formats such as YOLO, COCO, CSV, and XML.

## Features

- Select a YOLO-World model for object detection
- Choose an images folder for labeling
- Specify output directory for labeled data
- Define object categories for detection
- Use Slicing Adaptive Inference (SAHI) for improved detection
- Select device type (CPU or GPU) for inference
- Customize train/validation split ratio
- Adjust confidence level and non-maximum suppression (IoU) threshold
- Visualize input image statistics and output detection results

You can install OD-Labeler using pip:

```bash
pip install od-labeler

```

## Usage

To launch the OD-Labeler application, run the following command:

```bash
od-labeler
```

1. Select a YOLO-World model file (.pt) for object detection.
2. Choose the folder containing the images you want to label.
3. Specify the output directory where the labeled data will be saved.
4. Enter the object categories you want to detect, separated by commas.
5. Configure additional options such as SAHI, device type, output format, train/validation split, confidence level, and NMS threshold.
6. Click the "Start" button to begin the labeling process.
7. Monitor the progress and view the detection results in the application.

## Update

You can upgrade OD-Labeler using pip:

```bash
pip install --upgrade od-labeler

```

## License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the [GitHub repository](https://github.com/Ziad-Algrafi/od-labeler).

## Acknowledgements

OD-Labeler is built using the following open-source libraries:

- [CustomTkinter](https://github.com/TomSchimansky/CustomTkinter)
- [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)
- [Matplotlib](https://matplotlib.org)
- [OpenCV](https://opencv.org)
- [PyTorch](https://pytorch.org)

## Contact

For any questions or inquiries, please contact the maintainer at - ZiadAlgrafi@gmail.com.
qaml 0.0.8.2
Control your devices with natural language
Requires Python: >=3.7

# qaml-python

Control your iOS and Android devices with natural language

## Getting Started
Make sure you have Appium installed first.

### macOS
```bash
brew install appium
```

### all other OSes
```bash
npm i -g appium
```

### Install qaml
Now install qaml
```bash
pip install qaml
```

## Examples
### Python
```python
import qaml

q = qaml.Client(api_key=<API_KEY>)

q.execute("open safari")
q.execute("scroll up")
q.execute("tap the address bar")

# Or use an existing appium driver
q = qaml.Client(driver=appium_driver, api_key=<API_KEY>)

q.execute("type camelqa.com")
q.execute("tap go")
```

### repl
```bash
export QAML_API_KEY=<API_KEY>
```

```bash
qaml
```

This will start a repl in your shell. You can issue natural language commands to get a feel for qaml.

### One-off commands
```bash
qaml tap the send button
```

## Docs
For more details visit our [docs](https://docs.camelqa.com/introduction)

Join our community on [Discord](https://discord.gg/juNYATfJTZ) for live discussions and support!