/Machine-Learning-Core

Primary LanguagePython

AI AR Server

Run this project using docker-compose

Inside the root project you can run

sudo docker-compose build

and then run the following to start the container and expose the API:

sudo docker-compose up

Api documentation

All requests saved in Postman:

Run in Postman

Api route:

##maskImage:

Parameters:

-image

The following you can ignore and only send what you need

  • face_oval
  • left_eye
  • right_eye
  • left_eye_brow
  • right_eye_brow

For example if you need only face_oval and left_eye :

C# example we send only face_oval and left_eye and ignore others

response example:

{
    "Points": {
        "face_oval": [
            [
                368.0,
                153.0
            ],
            .
            .
            .
            [
                368.0,
                153.0
            ]
        ],
        "left_eye": [
            [
                575.0,
                223.0
            ],
            .
            .
            .
            [
                467.0,
                236.0
            ]
        ]
    }
}
  • each array element contain x,y pixel position Note that we send image in Base64

Points(left_eye) example on image:

Points(face_oval) example on image:

wrist (V0):

In this version you should provide image like next image in hand position

wrist phases:

  • Provide normal wrist image (range .5~1m) like next image
  • Wrist in any range from camera
  • Remove fake hand palm point
  • Left or right
  • any rotation angle

Parameters:

-image

response example:

{
    "Points": [
        364,
        337
    ]
}
  • each array element contain x,y pixel position Note that we send image in Base64

Points(wrist) example on image:

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b yousefmasry4/Machine-Learning-Core)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin yousefmasry4/Machine-Learning-Core)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.