ashnkumar/sketch-code
Sketch Code
Sketch-code ist ein Deep-Learning-Tool für Web-Prototyping und ein Image-to-HTML-Konverter, der handgezeichnete Website-Mockups in strukturellen HTML-Code umwandelt. Es nutzt einen neuronalen Wireframe-Parser, um visuelle Elemente in Skizzen zu erkennen und auf entsprechende Web-Layout-Repräsentationen abzubilden.
Das System verwendet eine Keras-basierte Architektur zum Trainieren und Evaluieren von Erkennungsmodellen anhand von Datensätzen aus handgezeichneten Wireframes und Referenz-HTML. Es entkoppelt das strukturelle Layout vom visuellen Design, indem es die Einbindung benutzerdefinierter CSS-Layer während des Kompilierungsprozesses ermöglicht.
Das Projekt deckt automatisiertes Frontend-Prototyping und UI-Layout-Generierung ab, einschließlich überwachtem Image-to-Code-Mapping und der Evaluierung der Vorhersagegenauigkeit, um den generierten Output mit Ground-Truth-Referenzen zu vergleichen.
Features
- Sketch-to-HTML Converters - Transforms hand-drawn website sketches into structured HTML code using a deep learning model for rapid prototyping.
- Mockup-to-HTML Converters - Provides a deep learning model that translates visual mockups of website layouts into functional HTML code.
- Keras Model Implementations - Uses the Keras API to define and optimize the neural network layers responsible for visual-to-code translation.
- Image-to-Code Networks - Implements a neural network specialized in mapping visual pixels from hand-drawn sketches to structural markup language.
- Visual Element Recognition Training - A capability in the project to train a network to identify wireframe elements using custom datasets and pretrained weights.
- Wireframe Parsers - Utilizes a neural network designed to recognize hand-drawn visual elements and map them to web layout representations.
- Deep Learning Design Tools - Acts as a deep learning-powered prototyping tool that converts sketches into functional HTML.
- Hand-Drawn Code Conversion - Turns hand-drawn sketches of website layouts into functional HTML code using deep learning.
- Frontend Prototyping Tools - Provides automated tools to convert visual mockups and sketches into functional frontend code to accelerate prototyping.
- Hybrid CNN-RNN Architectures - Implements a hybrid CNN-RNN architecture to translate visual wireframe features into structural HTML sequences.
- Vision Model Training - Implements a framework for training vision models to recognize hand-drawn wireframes and map them to HTML.
- Image-To-Code Model Training - Trains a neural network to map hand-drawn visual elements to HTML representations using specialized datasets.
- CSS Styling - Supports applying custom visual styles through a decoupled CSS injection layer during the compilation process.
- Generative UI Layouts - Generates structural UI layouts from image inputs, allowing for a separate layer of visual design.
- CSS Style Injections - Provides a mechanism to inject custom CSS layers post-generation to decouple structural layout from visual styling.
