semvec/pypi-packages huggingface dataset:
landmark-detection 0.1.1
This is for detect coordinates parts of the body
Keywords: python,opencv,mediapipe,detection,landmarks

# Full-Body-LandMarks-Detection

[Github](https://github.com/kouwis/Full_Body_LandMarks)

### Explained

In this repo used [Mediapipe](https://google.github.io/mediapipe/solutions/solutions.html) solutions in sections:

- Face Mesh
- Hands
- Pose

 Together, this will extract all coordinates points for any part of the body. In any class of them, there are two functions: 
- Find Points
- Drawing

**Find_Points** function has some of sub functions those are body parts after that returns a list of class points such as: Face points

1. Divide Find_Points function in class [Face](https://google.github.io/mediapipe/solutions/face_mesh.html) into:


	- Left Eye
	- Right Eye
	- Nose
	- Mouth
	- Head


2. Divide Find_Points function in class [Hands](https://google.github.io/mediapipe/solutions/hands.html) into:


	- Fingers


3. Divide Find_Points function in class [Body](https://google.github.io/mediapipe/solutions/pose.html) into:


	- Chest
	- Arms
	- Legs


**Drawing** function returns an image with labeled points.


### Output


In the face parts, It returns a 2d list the first for all face points and the second for point coordinates **otherwise** 
it returns a 3d list  the first for all class points, the second for the beginning and end point in the part, and the third for point coordinates 


### Usage

```python

#Imports
import landmark as LM
import cv2 as cv

cap = cv.VideoCapture(0)  # 0 as a default webcam
face_detector = LM.Face()  # Make instance from class

while 1:
    _, img = cap.read()

    imgRGB = cv.cvtColor(img, cv.COLOR_BGR2RGB)

    # Any function of them need two parameters (image, imageRGB)
    # color, thickness and circle_radius are default parameters
    face_detector.Drawing(img, imgRGB, color=(0, 110, 100), thickness=3, circle_radius=3)
    face_detector.Find_Points(img, imgRGB)

    # To detect any part of the body you should call (Find_points) function
    # Any function of Them does not need parameters
    print(face_detector.Left_Eye())

    cv.imshow("img", img)
    cv.waitKey(1)

```
geonetworkx 0.5.3
Python tools for geographic graphs

# GeoNetworkX

Python tools for geographic graphs

[![CircleCI](https://circleci.com/gh/Artelys/geonetworkx.svg?style=svg)](https://circleci.com/gh/Artelys/geonetworkx)
[![Documentation Status](https://readthedocs.org/projects/geonetworkx/badge/?version=latest)](https://geonetworkx.readthedocs.io/en/latest/?badge=latest)
[![PyPI Package latest release](https://img.shields.io/pypi/v/geonetworkx.svg)](https://pypi.python.org/pypi/geonetworkx)

## Introduction

GeoNetworkX is a project to add support for geographic graphs to NetworkX (in the same way that GeoPandas support
geographic data to Pandas). It currently implements four data structures that extends the networkx graph classes (Graph,
MultiGraph, DiGraph, MultiDiGraph).


## Install

**Requirements**

* pyproj>=2.2
* geopy>=1.12.0
* geopandas>=0.7
* networkx>=2.3
* numpy>=1.12.0
* pandas>=0.25.0
* shapely>=1.2.18
* scipy>=0.19.0rc2
* nose>=1.3.7

Optional packages:

* srtm (for elevation data)
* pyvoronoi (for voronoi utils)
* osmnx (for OSM data)

**Installation**

```shell
    pip install geonetworkx
```

### Trouble when installing GeoNetworkX on Windows

If you are using `conda` on Windows, the binaries downloaded automatically
are broken, and `rtree` is
[unable to work properly](https://gis.stackexchange.com/questions/179706/installing-rtree-on-windows-64-bits).

A workaround is to download manually the binary from [this webpage](https://www.lfd.uci.edu/~gohlke/pythonlibs/#rtree).
Please download the binary corresponding to your system and your
Python version (3.6 or 3.7). You have notably to download:

- Binary for `Rtree`
- Binary for `GDAL`
- Binary for `Fiona`


## Documentation

Online documentation is available here: <https://geonetworkx.readthedocs.io>

## Tests

Tests can be launched with `unittest` with the following command:
```
python -m unittest discover -v geonetworkx
```
Or with `nose` like this:
```
nosetests geonetworkx -v --with-doctest
```
landmarks-detection 0.1.0
This is for detect coordinates parts of the body
Keywords: python,opencv,mediapipe,detection,landmarks

# Full-Body-LandMarks-Detection

[Github](https://github.com/kouwis/Full_Body_LandMarks)

### Explained

In this repo used [Mediapipe](https://google.github.io/mediapipe/solutions/solutions.html) solutions in sections:

- Face Mesh
- Hands
- Pose

 Together, this will extract all coordinates points for any part of the body. In any class of them, there are two functions: 
- Find Points
- Drawing

**Find_Points** function has some of sub functions those are body parts after that returns a list of class points such as: Face points

1. Divide Find_Points function in class [Face](https://google.github.io/mediapipe/solutions/face_mesh.html) into:


	- Left Eye
	- Right Eye
	- Nose
	- Mouth
	- Head


2. Divide Find_Points function in class [Hands](https://google.github.io/mediapipe/solutions/hands.html) into:


	- Fingers


3. Divide Find_Points function in class [Body](https://google.github.io/mediapipe/solutions/pose.html) into:


	- Chest
	- Arms
	- Legs


**Drawing** function returns an image with labeled points.


### Output


In the face parts, It returns a 2d list the first for all face points and the second for point coordinates **otherwise** 
it returns a 3d list  the first for all class points, the second for the beginning and end point in the part, and the third for point coordinates 


### Usage

```python

#Imports
import landmark as LM
import cv2 as cv

cap = cv.VideoCapture(0)  # 0 as a default webcam
face_detector = LM.Face()  # Make instance from class

while 1:
    _, img = cap.read()

    imgRGB = cv.cvtColor(img, cv.COLOR_BGR2RGB)

    # Any function of them need two parameters (image, imageRGB)
    # color, thickness and circle_radius are default parameters
    face_detector.Drawing(img, imgRGB, color=(0, 110, 100), thickness=3, circle_radius=3)
    face_detector.Find_Points(img, imgRGB)

    # To detect any part of the body you should call (Find_points) function
    # Any function of Them does not need parameters
    print(face_detector.Left_Eye())

    cv.imshow("img", img)
    cv.waitKey(1)

```