addy0000/dypixa

A project to generate abstract images dynamically!

dyPixa

Dynamically generating abstract images using Computer Vision, Machine Learning, and Sentiment Analysis

dyPixa, aka Dynamic Pixels, is an open-source project that aims to develop a tool combining the power of computer vision, machine learning, and natural language processing to create abstract images based on text input and sentiment analysis. With dyPixa, you can generate stunning visuals by harnessing the emotions expressed in the text.

Table of Contents

• Introduction
• Features
• Getting Started
• Usage
• Contributing
• License

Introduction

dyPixa is a project that enables you to analyze Multilingual text, perform sentiment analysis, and use the emotions expressed in the text to generate abstract images with carefully selected color combinations. It would leverage state-of-the-art machine learning models and image processing techniques to achieve this. Additionally, dyPixa allows you to overlay the input text onto the generated abstract image, creating visually striking compositions.

Features

• Multilingual Text and Sentiment Analysis: dyPixa can analyze Hindi text and determine its sentiment, whether it's positive, negative, or neutral.

• Color Combination Model: Train a machine learning model using a dataset of images paired with text descriptions to learn the most suitable color combinations for different sentiments.

• Abstract Image Generation: Generate abstract images based on the input text's sentiment, utilizing the color combinations learned by the model.

• Text Overlay: Overlay the input text onto the generated abstract image, allowing you to create visually appealing compositions that convey the text's emotion.

Getting Started

To get started with dyPixa, follow these steps:

1. Fork this repository and clone using following command:

$ git clone https://github.com/<your-username>/dyPixa.git  
$ cd dyPixa  

2. Install Dependencies:

• Install the required Python libraries by running (the requirements.txt is updated with growth of the project):

  $ pip install -r requirements.txt

• Download Pre-trained Models (applicable in future as the project grows):
  Depending on the project's requirements, you might need to download pre-trained models for sentiment analysis or color combination generation. Check the project documentation for instructions on acquiring these models.

3. Run/Enhance the Project:

• Follow the project-specific instructions in the documentation (to be updated) to use dyPixa for text analysis, color combination generation, abstract image creation, and text overlay. Once you are inthe working directory, i.e., dyPixa; you can also start enhancing the code. Being open-source, maintainers would love to merge your contributions.

Usage

Being in an early phase of the development, the usage guide for dyPixa is yet to be populated. However, with this tool, one would be able to do:

1. Text Analysis and Sentiment Analysis

2. Color Combination Generation

3. Abstract Image Generation

4. Text Overlay

Contributing

Contributions to dyPixa are welcome! Whether you want to improve existing features, add new functionality, or report issues, please follow the contribution guidelines outlined in the project's CONTRIBUTING.md file.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute dyPixa according to the terms of the license.

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Note for contributors: This README.md is supposed to be updated as per any new feature/changes introduced. Provide clear and comprehensive instructions to help users get be familiar.