semvec/pypi-packages huggingface dataset:
headpose-detection 0.0.4
Head pose estimation module
Keywords: python,image,face detection,headpose estimation,machine learning,computer vision

# State of art the Head Pose Estimation in Tensorflow2 

This repository includes:
- ["WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose" (BMVC 2020).](https://www.bmvc2020-conference.com/assets/papers/0907.pdf) adapted from the [original source code](https://github.com/Ascend-Research/HeadPoseEstimation-WHENet).


- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641) adapted from https://github.com/StanislasBertrand/RetinaFace-tf2.





<img src=images/output.png height="220"/> 



## Install

You can install this repository with pip (requires python>=3.6);

```
pip install headpose_estimation
```

```bash
pip install git+https://github.com/geekysethi/headpose_estimation
```

You can also install with the `setup.py`

##  With Face Detection
To perform detection you can simple use the following lines:

```python
import cv2
from headpose_estimation import Headpose
headpose = Headpose()
img = cv2.imread("path_to_im.jpg")
detections,image = headpose.run(img)
```

This will return a list of dictionary which looks like this `[{'bbox': [xmin, ymin, xmax, ymax], 'yaw': yaw_value, 'pitch': pitch_value, 'roll': roll_value}`


##  Without Face Detection
To perform detection you can simple use the following lines:

```python
import cv2
from headpose_estimation import Headpose
headpose = Headpose(face_detection=False)
imgcrop = cv2.imread("path_to_im.jpg")
detections,image = headpose.run(imgcrop)
```

In this case it will return a list of dictionary which looks like this `[{'yaw': yaw_value, 'pitch': pitch_value, 'roll': roll_value}`

## Dependncies
* EfficientNet https://github.com/qubvel/efficientnet
boosint 0.0.3
A powerful username osint tool.
Keywords: python,osint,detection,information-gathering,maigret,social-media


# Maigret


![image](https://user-images.githubusercontent.com/69071809/205498298-cbdc7e47-73e0-491a-812a-05501380fad7.jpg)

## About

**BoOSINT** collects data **by username only** by checking nearly every data publicly available about a username and it's aliases.


## Main features

* Profile pages parsing, extraction of personal info, links to other profiles, etc.
* Recursive search by new usernames and other ids found
* Search by tags (site categories, countries)
* Censorship and captcha detection
* Requests retries
* Finding of websites
* Finding device information, telephone number, email addresses
* Finding locations, gender, birthdays and real names


## Installation


### Package installing

Coming soon...

### Cloning

```bash
# or clone and install manually
git clone https://github.com/protdos/boosint && cd boosint
pip3 install -r requirements.txt

# usage
cd boosint
python3 boosint.py username

# usage with pypi (see above)
boosint username # currently not available
```

## Usage (coming soon...)

```bash
# make txt or json reports
boosint username --type txt
boosint username --type json

# filename output (default is report.json)
boosint username --output report.txt
boosint username --output report.json
```

Use `boosint -h` to get full options description.

##  Sources / Credits
Here is a list of sources I used:
* [Maigret](https://github.com/soxoj/maigret)
* [BlackBird](https://github.com/p1ngul1n0/blackbird)
* [GitRecon](https://github.com/GONZOsint/gitrecon)

The code is partly combined but the most of it is written by myself.