semvec/pypi-packages huggingface dataset:
proctoring 0.0.1
Proctoring library
Requires Python: >=3.6
Keywords: proctoring,ai-proctoring,gaze-tracking

# AI Based Smart Exam Proctoring Package

It takes image (base64) as input:
Provide Output as: 
1) Detection of Mobile phone.
2) Detection of More than 1 person in the exam.
3) Gaze Estimation: Estimating the position of student body & eyes movements.

# DOWNLOAD LINK OF YOLO V3 MODEL:
https://pjreddie.com/media/files/yolov3.weights

# DOWNLOAD LINK OF shape_predictor_68_face_landmarks.dat MODEL:
https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat?raw=true
# Code Sample Working
```python
from proctoring.proctoring import get_analysis, yolov3_model_v3_path

# insert the path of yolov3 model [mandatory]
yolov3_model_v3_path("yolov3.weights_model_path")

# insert the image of base64 format
imgData = "base64_image_format"
proctorData = get_analysis(imgData, "shape_predictor_68_face_landmarks.dat_model_path")
print(proctorData)

```
# Code Sample Output

```
{'mob_status': 'Not Mobile Phone detected', 'person_status': 'Normal', 'user_move1': 'Head up', 'user_move2': 'Head right', 'eye_movements': 'Blinking'}
```
PySQIF 0.0.9
Analysis library for Bi-SQUID circuits.
Requires Python: >=3.6

PySQIF: Python Superconductor Quantum Interference Filters
==============================================

Maintained by [Ali AKGUN](https://github.com/aakgn) and [Sasan Razmkhah](https://github.com/SasanRazm)

PySQIF stands for Python Super Conductor Quantum Interference Filters. PySQIF is an open-source statistical analysis library for SQUIDs/Bi-SQUIDs. PySQIF library gives the average voltage response of defined Bi-SQUID circuit for each corresponding normalized applied external magnetic flux as an output.

**If you find PySQIF useful, please consider citing it in your publications, see: https://pysqif.readthedocs.io/en/latest/citing_this_project.html#citing.**

If you have questions concerning the usage or installation of PySQIF, please ask questions on ali.akgunphys@gmail.com.

Installation
------------

[![Downloads](https://pypi.org/project/PySQIF/)](https://pypi.org/project/PySQIF/)

The recommended way to install PySQIF is via pip by opening a terminal and running ``pip install pysqif``, more information [here](https://pysqif.readthedocs.io/en/latest/installation.html).

Documentation
------------------

Visit https://pysqif.readthedocs.io/en/latest/index.html# for all information about installation, tutorials and more.

Tutorials
---------

A selection of tutorials is available [here](https://pysqif.readthedocs.io/en/latest/tutorials.html).


Contribute
----------

You are most welcome to contribute to PySQIF development by forking this
repository and sending pull requests.

Filing bug reports, proposing changes or additional features can be done on our
[issues page](https://github.com/aakgn/PySQIF/issues)

You can also contact Ali at ali.akgunphys@gmail.com.