DASH-Array-Co

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Array Co. developed DASH (Dental Application for Shade Matching), an Android app that standardizes shade matching in dentistry using CNN-based object detection and Support Vector Machines for classification. It achieved 90% accuracy, far surpassing the 35% accuracy of traditional methods.

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# DASH: Composite Restoration using Image Recognition for Teeth Shade Matching using Deep Learning

# Hi, we are Array Co. 👋
We are a group of researchers from **FEU Institute of Technology** under the course of *Bachelor of Science in Computer Science with Specialization in Software Engineering*.

**Array Co. Members:**
1. Jericho John O. Almoro
2. Francis Dale P. Cañon
3. Bianca H. Goldman
4. Micah Sophia Q. Tan
5. John Angelo B. Yap

# What is DASH? 🦷📱
Array Co. developed an android applcation which addresses the variability of composite restoration in dentistry and orthodontics by standardizing the crucial step of shade matching using the CNN-based MediaPipe Facial Landmark Detection for object detection and Support Vector Machines for classification. The developed application achieved a remarkable 90% accuracy as validated by dentists, which significantly exceeds the peer-reviewed estimated 35% accuracy of traditional visual shade matching. Also, DASH stands for Dental Application for Shade Matching.

# Tools and Frameworks👨‍💻
### Flutter Framework <img src="https://cdn.worldvectorlogo.com/logos/flutter.svg" width = "20"/>
Array Co. employed Flutter to build a cross-platform Android mobile application for composite shade matching, leveraging its single codebase for efficient development. Flutter's expressive UI features allowed for the creation of a visually appealing and consistent user interface across devices. The use of Dart programming language and Flutter's hot reload feature likely facilitated a streamlined development process and quick iterations for testing shade matching functionality. Ultimately, choosing Flutter enabled Array Co. to deliver a modern, responsive, and natively-like experience for users on Android devices.

### Python Flask <img src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/python/python.png" width = "30"/>
Array Co. utilized the Python Flask framework to power the backend server and handle data processing for their composite shade matching application. Flask, being a lightweight and flexible framework, managed tasks such as data storage, retrieval, and API communication. This backend functionality seamlessly integrated with the Flutter front end, which was responsible for creating a visually appealing and consistent user interface on Android devices. The combined use of Flask and Flutter allowed Array Co. to leverage Python's backend capabilities and Flutter's cross-platform efficiency for a comprehensive application development approach.

# File Tree Structure 📁
### DASH Root Directory
``` bash
.
├── Flask-server-backend
├── Flutter-mobileapp-frontend
├── README/
│ └── assets/
│ └── arrayco.jpg
└── README.md
```
### Flask-server-backend Subdirectory
``` bash
.Flask-server-backend
├── .idea/
│ ├── inspectionProfiles
│ ├── .gitignore
│ └── array-co.online.iml
├── _pycache_/
│ ├── Classification.cpython-310
│ ├── FeatureExtract.cpython-310
│ ├── main.cpython-310
│ ├── Preprocessing.cpython-310
│ └── ShadeClassification.cpython-310
├── cropped_images
├── images
├── Output
├── venv/
│ ├── include
│ ├── Lib
│ ├── Scripts
│ ├── share
│ ├── .gitignore
│ └── pyvenv
├── Classification.py
├── FeatureExtract.py
├── main.py
├── Mediapipe.py
├── Preprocessing.py
├── requirements.txt
├── ShadeClassification.py
└── svm_model.pkl
```
### Flutter-mobileapp-frontend Subdirectory
``` bash
.Flutter-mobileapp-frontend
├── android/
│ ├── app/
│ │ ├── src
│ │ ├── build
│ │ └── google-services
│ └── assets/
│ ├── fonts
│ ├── icons
│ └── images
└── lib/
 ├── components/
 │ ├── choice_dialog.dart
 │ ├── confirmation_dialog.dart
 │ ├── detail_list.dart
 │ ├── error_alert.dart
 │ ├── home_card.dart
 │ ├── homescreen_list.dart
 │ ├── large_button.dart
 │ ├── login_dialog.dart
 │ ├── medium button.dart
 │ ├── navigation_bar.dart
 │ ├── patient_textfield.dart
 │ └── userinfo textfield.dart
 ├── controllers/
 │ ├── profile_controller.dart
 │ └── registration_controller.dart
 ├── models/
 │ ├── note.dart
 │ ├── patient.dart
 │ ├── todo_item.dart
 │ └── user_model.dart
 ├── providers/
 │ ├── note_api.dart
 │ ├── patientApi.dart
 │ ├── stable_patient_api.dart
 │ └── todo_provider.dart
 ├── repositories/
 │ └── exceptions
 ├── screens/
 │ ├── add note form.dart
 │ ├── add_patient_form.dart
 │ ├── database note screen.dart
 │ ├── database_screen.dart
 │ ├── home screen.dart
 │ ├── image_acquisition.dart
 │ ├── login_screen.dart
 │ ├── note_details.dart
 │ ├── note screen.dart
 │ ├── patient_details.dart
 │ ├── registration_screen.dart
 │ ├── settings_screen.dart
 │ ├── splash_screen.dart
 │ ├── stable_imageacquisition.dart
 │ ├── test.dart
 │ ├── update_note form.dart
 │ ├── update_patient_form.dart
 │ └── update_profile_screen.dart
 └── widgets/
 └── tasks.dart
```

# Quick Guide and Start Up 📖
1. Clone the repository to your local machine
 This flutter project can be opened using Android Studio or Visual Studio Code 
2. Since the uploaded project in GitHub is tailored for local host, use the local ip address
 This can be found when running the python flask main.py file
3. Before running the python file, make sure to install the requirements.txt first.
3. Run the python flask file (main.py) and then run the flask flutter project.
 The front-end interface to navigate is flutter (mobile app) and the back-end processing will come from python 
4. Explore and enjoy!

DASH-Array-Co

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