harryhpxs/fruitdetection

This project identifies oranges and other fruits based on tensorflow.

Fruit Counter

A web application that identifies oranges and other fruits based on Faster R-CNN model using TensorFlow object detection API.

Demo

App Demo Video

Getting Started

Prerequisites

To run this app, please make sure your environment has the following components installed:

• Python 3.6
• TensorFlow 1.13.1+

Installation

Clone TensorFlow models library to your local project directory.

YOUR_PROJECT_DIR
└── models

Clone all items in app folder

THIS_PROJECT
└── app
    ├── server.py         // web server
    ├── tfprocess.py      // TensorFlow functions
    ├── html
    |   └── index.html    // app front-end
    ├── static
    |   ├── css
    |   |   └── style.css
    |   └── js
    |       └── index.js  // app functions
    └── utils
        └── visualization_utils.py

to models/research/object_detection.

YOUR_PROJECT_DIR
└── models
    └── research
        └── object_detection
            └── COPY_ALL_APP_ITEMS_HERE

Starting the server

$ cd DIR_TO_YOUR_PROJECT/models/research/object_detection
$ python3 server.py

Usage

• Open a web browser and visit http://localhost:5000/
• Click orange camera button to take a photo via camera or from local gallery
• Upload it by clicking blue upload button
• Wait for the result to be displayed

External Libraries

• TensorFlow
• jQuery 3.4.1
• JavaScript Load Image
• Flask
• Google Fonts
• Pure CSS Loaders

Note

This app was developed for COMP90055 Computing Project supervised by Prof. Richard Sinnott.

Authors of the project are:

• Huaqing Yu
• Shining Song
• Shaoxi Ma