Introduction to SketchCode
SketchCode is an exciting project that bridges the gap between hand-drawn web designs and functional HTML code. It utilizes advanced deep learning techniques to translate simple sketches of web pages into fully-operational HTML, bringing a sketch to life as a digital web page.
How Does It Work?
At its core, SketchCode uses a deep learning model inspired by image captioning architectures. This model is capable of understanding and interpreting the elements of a hand-drawn website mockup, and then automatically generating the corresponding HTML markup. The approach simplifies the web development process, especially in the front-end phase, by automating the conversion of wireframes into code.
Project Roots and Inspiration
The SketchCode project builds upon seminal works such as the pix2code project by Tony Beltramelli and the Design Mockups project by Emil Wallner. These projects laid the groundwork for synthetic dataset creation and model architecture, enabling projects like SketchCode to evolve and improve the automatic generation of code from design.
Important Considerations
SketchCode is primarily a proof-of-concept. Its current limitation is that it isn't yet versatile enough to handle the wide variety of sketches typically seen in real-world wireframes. For the model to perform effectively, inputs should closely resemble the dataset it was trained on.
Getting Started with SketchCode
Prerequisites
To work with SketchCode, you need:
- Python 3 (Note: not compatible with Python 2)
- pip (Python package installer)
Installation Steps
- First, ensure Python 3 and pip are installed on your machine.
- Install the required dependencies using the following command:
pip install -r requirements.txt
Example Usage
To see SketchCode in action:
- Download the project files and pretrained weights:
git clone https://github.com/ashnkumar/sketch-code.git cd sketch-code/scripts sh get_data.sh sh get_pretrained_model.sh - Convert a hand-drawn image to HTML using pretrained weights:
cd src python convert_single_image.py --png_path ../examples/drawn_example1.png \ --output_folder ./generated_html \ --model_json_file ../bin/model_json.json \ --model_weights_file ../bin/weights.h5
Usage for Various Tasks
-
Single Image Conversion: Transform a hand-drawn image into HTML using specific model weights.
-
Batch Image Conversion: Use scripts to convert multiple images located in a folder into corresponding HTML files.
-
Training the Model: The model can be trained from scratch or with existing pretrained data, utilizing data augmentation for improved training results.
-
Evaluation: The model's prediction accuracy can be evaluated using the BLEU score metric, which compares generated HTML against reference outputs.
Licensing
The SketchCode project is open source and licensed under the MIT License, which permits anyone to freely use, modify, and distribute the software under certain conditions.
By transforming sketches into functional web pages, SketchCode not only streamlines the development process but also serves as a beacon for future innovation in automated coding. As the project evolves, it opens up new possibilities for developers and designers worldwide.