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Python application CodeQL codecov Quality Gate Status Bugs Code Smells Duplicated Lines (%) Lines of Code Security Rating Sqale Rating Sqale Index Reliability Rating Vulnerabilities

Face Recognition Project

This project uses Flask, FastAPI,DeepFace and MongoDB to create a Face recognition system. It allows users to register face with associated metadata, update their information and also can delete their data.

Get started

These instructions will get you a copy of the project up and running on your local machine for development.

Prerequisites

This project requires Python 3.7 or later.

Installing

  1. Clone this repository to your local system using link

    git clone https://github.com/Devasy23/FaceRec.git

  2. Navigate to the project directory:

    cd FaceRec

  3. Install the required packages:

    pip install -r requirements.txt

Running the Server

To start FLask and FastAPI, run the given command:

python main.py

Project Structure

  • requirements.txt: Contains the Python dependencies for the project.
  • API/: Contains code of FastAPI application.
  • FaceRec/: Contain all files of HTML,CSS and flask application.
  • main.py: Contains code of to start FastAPI and flask together.

Database Schema

  1. Create new connection in MongoDB and Connect using given url URL: mongodb://localhost:27017/8000

  2. Create database using Database name: DatabaseName Collection name: CollectionName

  3. Add data by importing json file: From 'database.mongo' folder -> {DatabaseName}.{CollectionName}.json

The database contains a faceEntries collection with the following schema:

  • id: A unique identifier for the face entry.
  • Employeecode: A unique employee ID associated with the image.
  • Name: The name of the person in the image.
  • gender: The gender of the person.
  • Department: The department of the person
  • time: The time the face entry was created.
  • embeddings: The embeddings of the face image.
  • Image: Base64 encoded image file.

Function Flow

  1. create_new_faceEntry(): This function receives a POST request with an image and metadata. It extracts the face from the image, calculates the embeddings, and stores the data in the database.

  2. Data(): This function sent a GET request to /data endpoint of FastAPI app to get the list of Face Entries from MongoDB.

  3. update() :This function is used to update the details of the face entry in the database.

  4. read() : This function sent a GET request with specific Employeecode to read the information related to that particular Employeecode.

  5. delete() : This function is used to delete the specific Employee Data.

Face Recognition Project

This project uses Flask, FastAPI,DeepFace and MongoDB to create a Face recognition system. It allows users to register face with associated metadata, update their information and also can delete their data.

Get started

These instructions will get you a copy of the project up and running on your local machine for development.

Prerequisites

This project requires Python 3.7 or later.

Installing

  1. Clone this repository to your local system using link

    git clone "https://dev.azure.com/tmspl/FaceRec/_git/FaceRec-Employee-Enrollment.python"

  2. Navigate to the project directory:

    cd FaceRec

  3. Install the required packages:

    pip install -r requirements.txt

Running the Server

To start FLask and FastAPI, run the given command:

python main.py

Project Structure

  • requirements.txt: Contains the Python dependencies for the project.
  • API/: Contains code of FastAPI application.
  • FaceRec/: Contain all files of HTML,CSS and flask application.
  • main.py: Contains code of to start FastAPI and flask together.

Database Schema

  1. Create new connection in MongoDB and Connect using given url URL: mongodb://localhost:27017/8000

  2. Create database using Database name: DatabaseName Collection name: CollectionName

  3. Add data by importing json file: From 'database.mongo' folder -> {DatabaseName}.{CollectionName}.json

The database contains a faceEntries collection with the following schema:

  • id: A unique identifier for the face entry.
  • Employeecode: A unique employee ID associated with the image.
  • Name: The name of the person in the image.
  • gender: The gender of the person.
  • Department: The department of the person
  • time: The time the face entry was created.
  • embeddings: The embeddings of the face image.
  • Images: Base64 encoded image file.

Function Flow

  1. create_new_faceEntry(): This function receives a POST request with an image and metadata. It extracts the face from the image, calculates the embeddings, and stores the data in the database.

  2. Data(): This function sent a GET request to /data endpoint of FastAPI app to get the list of Face Entries from MongoDB.

  3. update() :This function is used to update the details of the face entry in the database.

  4. read() : This function sent a GET request with specific Employeecode to read the information related to that particular Employeecode.

  5. delete() : This function is used to delete the specific Employee Data.

Testing

To run the tests, use the following command:

pytest

License

This project is licensed under the APACHE License - see the LICENSE file for details.