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Indecor 🏠

Where technology meets design

Overview

The initial task on homeowners’ lists when renovating or redecorating their homes is to paint the walls. This simple project provides an easy way to give a room a fresh look and can even set the tone for the entire house. However, choosing the right paint colour can be overwhelming due to the endless options available, and a poor decision can be costly

To alleviate this problem, we come up with a robust system "Indecor" that enables homeowners to visualize how their homes would appear with a new coat of paint.This system is intelligent enough to ignore the complex objects within the house and accurately paint only the necessary wall sections, while still maintaining a realistic representation of the image.

The application uses computer vision and deep learning techniques such as the segment-anything to detect walls automatically and provide recommendations for wall painting and texture designs to the users. Additionally, the system provides a tool for users to select a wall and choose a color, and the AI model generates an image based on the user input

Get your walls painted!

design-intro-page

Select color

select-color-page

Changed color

base-detection-page

Color recommendations

recommendations-page

Texture recommendations

texture-mapping-page texture-mapping-page

To run this project :

Clone the project -

  $ git clone https://github.com/rushabhgandhi13/djcsi_team_eternium.git

Download the SAM model from the link

 $ create a folder named checkpoints inside main/static directory and paste the model file in there
  • Start the server -

  $ cd Indecor

Install all the reqirements -

  $ pip install -r requirements.txt

Run the following commands -

for linux/mac :

  $ python3 manage.py migrate
  $ python3 manage.py makemigrations

for windows :

 $ python manage.py migrate
 $ python manage.py makemigrations

Start the project -

for linux/mac :

  $ python3 manage.py runserver

for windows :

  $ python manage.py runserver

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Paint your walls!

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  • HTML 63.4%
  • Python 26.5%
  • CSS 10.1%