https://github.com/zuruoke/race-classification-keras-rest-api

A Simple Keras Rest API for Race Classification using Flask
https://github.com/zuruoke/race-classification-keras-rest-api

Last synced: 8 months ago
JSON representation

A Simple Keras Rest API for Race Classification using Flask

• Host: GitHub
• URL: https://github.com/zuruoke/race-classification-keras-rest-api
• Owner: zuruoke
• Created: 2020-07-10T14:49:11.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-18T10:23:29.000Z (almost 6 years ago)
• Last Synced: 2024-12-27T11:32:26.378Z (over 1 year ago)
• Language: Python
• Homepage:
• Size: 67.4 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Race Classification Keras Rest API

This repository contains the code to build and run a Keras REST API to classify different races such as Caucasian, Mongoloid and Negroid given a query image

- Caucasian: includes people of American and European descent, also known as whites

- Mongoloid: includes people of Asian descent, especially Eastern Asian

- Negroid: includes people of African descent or black Americans

To get more details on how this Race Classification Model was trained, refer to this [repository](https://github.com/zuruoke/Race_Classification_Using_Deep_CONVNET)

# Getting started

I assume you already have numpy, pillow, Keras and a supported backend (tensorflow in this case) installed on your system. From there you need to install Flask, urllib, and requests:

                

                

    $ pip install flask gevent requests

                                    

Next, clone the repo:

                            

    $ git clone https://github.com/zuruoke/Race-Classification-Keras-Rest-API.git

                            

Next, navigate into the main directory:

                              

    $ cd Race-Classification-Keras-Rest-API

                          

# Starting the Keras server

Below is our query image (kim.jpg) which we wish to classify as either a  Caucasian, Mongoloid or a Negroid:

![kim](https://user-images.githubusercontent.com/51057490/87179658-fce6de80-c2d6-11ea-8355-7c12fb436745.jpg)

The Flask + Keras server can be started by running:

    $ python run_keras_server.py 

    Using TensorFlow backend.

    * Loading Zuruoke model and Flask starting server...please wait until server has fully started

    ...

    * Running on http://127.0.0.1:1000

                              

You can now access the REST API via http://localhost:1000. 

We will use this url attached with an endpoint to send a POST request to the server. In this case:

    KERAS_REST_API_URL = "http://localhost:1000/predict"

                             

                             

# Consuming the Keras REST API

We have to send a POST request to the server running to consume the API.

To consume the Keras REST API:

- First ensure run_keras_server.py (i.e., the Flask web server) is currently running

- Then open another terminal in the same environment and navigate to the main directory

- Then run post_request.py

      $ python post_request.py

                            

- Then you will be asked to enter the image path of the query image

                                

       $ python post_request.py

       Enter the image path:

       kim.jpg

                             

 

 - And then when you have provided the query image path, it will display the predictions along with the probabilities or confidence level

 

       $ python post_request.py

       Enter the image path:

       kim.jpg

       1. Caucasian: 0.7900

       2. Mongoloid: 99.2011

       3. Negroid: 0.0089