semvec/pypi-packages huggingface dataset:
pscontroll 2.0
GTPS Controller via Module
License: MIT
Requires Python: >=3.5.3

# Growtopia Private Server Controler
> Module For Controle Your GTPS | Build in Python3

## Creator Information
- iFanPS DB:
    - Name : `iRfan`
    - Age : `17`
    - Come From: `Mars`

- NumeX DB:
    - Name : `Christian`
    - Age : `19`
    - Come From : `Mars`

## Installing Module
```bash
> pip install pscontroler
```

## Basic Configuration
```bash
> Make Sure You Already install a Python3
> Install The Module
```

## list Worker
```bash
> Status (See server status)
> Online (See online player)
> Player (See player account total)
> World (See total worlds created)
> Start (Start the Server)
> Stop (Stop the server)
> Givepgems (Give player gems)
> Giveplevel (Give player level)
> Giveproles (Give player roles)
> Countsize_Pwfolder (Count player and world folder size)
> Maintactive and Deactive (Maintenance activator and deactive)
> Pdelete (Delete player data)
> Wdelete (Delete world)
```

## To Do list
- Command:
    - Auto Backup
headpose 1.1.8
estimate the pose of the head based on an image.
License: MIT
Requires Python: >=3.6

# Purpose
This package offers classes and methods for pose estimation on images. This can be done either with deep learning
based facial landmark detection or by detecting ArUco markers.

# Installation
Get the latest published version: `pip install headpose` \
or install directly from GitHub: `pip install git+https://github.com/OleBialas/headpose.git`
To use landmark detection you additionally have to install
[pytorch and torchvision](https://pytorch.org/get-started/locally/)

# Example
```python
import cv2
from headpose.detect import PoseEstimator

est = PoseEstimator()  #load the model
# take an image using the webcam (alternatively, you could load an image)
cam = cv2.VideoCapture(0)
for i in range(cv2.CAP_PROP_FRAME_COUNT):
    cam.grab()
ret, image = cam.retrieve()
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cam.release()

est.detect_landmarks(image, plot=True)  # plot the result of landmark detection
roll, pitch, yawn = est.pose_from_image(image)  # estimate the head pose




```


# Sources & Further Reading
This [blog post](https://learnopencv.com/head-pose-estimation-using-opencv-and-dlib/) nicely explained the concepts 
and mathematics behind pose estimation and this [tutorial](https://towardsdatascience.com/face-landmarks-detection-with-pytorch-4b4852f5e9c4)
walks through the single steps of detecting facial landmarks with pytorch