semvec/pypi-packages huggingface dataset:
yolov8face 0.1.1
Yolov8face is a Python wrapper of Ultrlytics that simplifies the process of detecting faces in images using the yolov8n-face model. It takes care of model downloading, loading and postprocessing, allowing you to detect faces in images with just a few lines of code.

# yolov8face
Yolov8face is a Python wrapper of Ultrlytics that simplifies the process of detecting faces in images using the yolov8n-face model. It takes care of pre-trained model downloading, loading and postprocessing, allowing you to detect faces in images with just a few lines of code. 

## Installation
```bash
pip install yolov8face
```
## Usage
```python
from yolov8face import get_bbox
bboxes = get_bbox('path-to-image/image.jpg')
```
## Example
![can't load example image](https://raw.githubusercontent.com/harshthaker/yolov8face/main/detected_faces.png)


## License

This project is licensed under the GPL-3.0 License - see the [LICENSE](LICENSE) file for details.

Please note that this project uses the YOLOv8 model from the Ultralytics team, which is also licensed under the GPL-3.0 License. For commercial uses, please contact the Ultralytics team for an Enterprise License.

## Credits

This package was developed by [Harsh Thaker](https://github.com/harshthaker).

Special thanks to:

- [akanametov](https://github.com/akanametov/yolov8-face) for the [yolov8n-face](https://github.com/akanametov/yolov8-face/releases/download/v0.0.0/yolov8n-face.pt) model.
- The [Ultralytics](https://github.com/ultralytics/ultralytics) team for providing the YOLOv8.

This project stands on the shoulders of the open-source community. It wouldn't be possible without the numerous open source projects we've utilised.
python-kkmeans 0.1.0
An implementation of the Kernel k-means algorithm for a bachelorthesis.

# kkmeans

_Repository: [https://git01lab.cs.univie.ac.at/osterj97/oster-bachelor-thesis](https://git01lab.cs.univie.ac.at/osterj97/oster-bachelor-thesis)_

Implementation of the Kernel k-means clustering algorithm for multiple dimensions (including as well one-dimensional data) in Python. The results are visualized in plots and measured with quality metrics (e.g. Silhouette Coefficient). The algorithm can be used as a framework.

https://sites.google.com/site/dataclusteringalgorithms/kernel-k-means-clustering-algorithm

The related bachelor thesis can be found here: https://www.overleaf.com/read/shnzrthjgqtw (read only) or as an exported .pdf here [Bachelor_Thesis\_\_\_\_Kernel_k_means_clustering_framework_in_Python.pdf](./Bachelor_Thesis____Kernel_k_means_clustering_framework_in_Python.pdf)

## Installation

`pip install python-kkmeans`

The package can also be installed directly from this repository using `pip install git+ssh://git@git01lab.cs.univie.ac.at/osterj97/oster-bachelor-thesis.git`.

## Quickstart

```
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

from kkmeans import kkmeans

n_clusters = 3
X, _ = make_blobs(n_samples=100, n_features=3, centers=n_clusters)
X_scaled = StandardScaler().fit_transform(X)

cluster_assignments = kkmeans(X, n_clusters=n_clusters)
plt.scatter(X_scaled[:,0], X_scaled[:,1], c=cluster_assignments)
```

## Examples

Take a look at the [examples folder](https://git01lab.cs.univie.ac.at/osterj97/oster-bachelor-thesis/-/tree/main/examples) to see benchmarking and plotted examples of the algorithm in action. The playground notebook should give you a comprehensive overview of the evaluation of the algorithm. It can be run after [installing jupyter](https://jupyter-org.translate.goog/install?_x_tr_sl=en&_x_tr_tl=de&_x_tr_hl=de&_x_tr_pto=sc) and running `jupyter notebook` from this folder.

## Local Development

The following commands assume you run on a Linux or Mac, for Windows instructions take a look at the official [documentations](https://docs.python.org/3/library/venv.html).

- (optional) Create a venv `python3 -m venv .venv`
- (optional) Active venv `source .venv/bin/activate`
- (optional) Update pip `pip install --upgrade pip`
- Install all required packages `pip install -r requirements.txt`

## Test

Testing is set up using `pytest`. Run all tests running the command `pytest` in the root directory. For detailed description on pytest see: [Full pytest documentation](https://pytest.org/en/7.3.x/contents.html)