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Face Detecting web service for small projects.
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EnhancedFacenet.py
FaceDBModel.py
FaceDetector.py
README.md
app.py
configuration.json
configuration.py
face.dat
face.db
facenet.py
requirements.txt

README.md

One Shot Face Detector Web Service

OSFD Web Service is flask microservice which does face detection and learns only with single picture. It uses approximate near neighbour search based on euclidean distance and finds best fit face element based on 512 facenet feature vector. It uses sqlite as content db and annoy library for near neighbour indexing. Facenet is implemented on tensorflow 1.13.1.

Requirements

  • Python 3.5 and above
  • pip
  • Windows, Linux or MacOS(Not Tested)

Installation

  • Clone the repository

     git clone https://github.com/mehmetozturk4705/OneShotFaceDetectorWebService
    
  • Download models weights from here

  • Extract files and move models folder near the app.py

  • Install requirements (Before installation I recommend you to create virtual environment)

     pip install -r requirements.txt
     flask run
    

How to use web service

OSFD uses annoy backend to utilize near neigbour search. After adding faces it needs to index face features.

Add Face

Adds new face with image file of name. If image has multiple faces, it selects biggest one.

localhost:5000/add   	[POST]

 - image ->> png or jpeg image file of face
 - name ->> name of face

Delete Id

Removes all faces of name

localhost:5000/delete   [POST]

 - name ->> name of face

Balance

After adding or deleting face you should always call balance in order to take effect.

localhost:5000/balance  [POST]

Detect Face

Detects face in image file. If image has multiple faces, it selects biggest one.

localhost:5000/detect   [POST]

 - image ->> png or jpeg image file of face
 - threshold [optional] ->> Euclidean threshold

Returns:

{
"data": {
    "distance": 0.25,
    "id": "Face 1"
    },
"success": true
}

Clean Face DB

Cleans Face DB and balances for indexing.

localhost:5000/clean   [POST]

Tuning

OSFD uses euclidean threshold in order to decide whether input features are same person. You can tune threshold in configuration.json or threshold field of detect endpoint. Higher threshold means system is much more open to confuse faces. Lower threshold means harder to detect.

Reference

For Information

Please send email to bilgi@pyturk.com

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