Generating HTML Code from a hand-drawn wireframe
SketchCode is a deep learning model that takes hand-drawn web mockups and converts them into working HTML code. It uses an image captioning architecture to generate its HTML markup from hand-drawn website wireframes.
For more information, check out this post: Automating front-end development with deep learning
This project builds on the synthetically generated dataset and model architecture from pix2code by Tony Beltramelli and the Design Mockups project from Emil Wallner.
Note: This project is meant as a proof-of-concept; the model isn't (yet) built to generalize to the variability of sketches seen in actual wireframes, and thus its performance relies on wireframes resembling the core dataset.
- Python 3 (not compatible with python 2)
- pip
pip install -r requirements.txtDownload the data and pretrained weights:
# Getting the data, 1,700 images, 342mb
git clone https://github.com/ashnkumar/sketch-code.git
cd sketch-code
cd scripts
# Get the data and pretrained weights
sh get_data.sh
sh get_pretrained_model.shConverting an example drawn image into HTML code, 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.h5Converting a single image into HTML code, using weights:
cd src
python convert_single_image.py --png_path {path/to/img.png} \
--output_folder {folder/to/output/html} \
--model_json_file {path/to/model/json_file.json} \
--model_weights_file {path/to/model/weights.h5}Converting a batch of images in a folder to HTML:
cd src
python convert_batch_of_images.py --pngs_path {path/to/folder/with/pngs} \
--output_folder {folder/to/output/html} \
--model_json_file {path/to/model/json_file.json} \
--model_weights_file {path/to/model/weights.h5}Train the model:
cd src
# training from scratch
# <augment_training_data> adds Keras ImageDataGenerator augmentation for training images
python train.py --data_input_path {path/to/folder/with/pngs/guis} \
--validation_split 0.2 \
--epochs 10 \
--model_output_path {path/to/output/model}
--augment_training_data 1
# training starting with pretrained model
python train.py --data_input_path {path/to/folder/with/pngs/guis} \
--validation_split 0.2 \
--epochs 10 \
--model_output_path {path/to/output/model} \
--model_json_file ../bin/model_json.json \
--model_weights_file ../bin/pretrained_weights.h5 \
--augment_training_data 1Evalute the generated prediction using the BLEU score
cd src
# evaluate single GUI prediction
python evaluate_single_gui.py --original_gui_filepath {path/to/original/gui/file} \
--predicted_gui_filepath {path/to/predicted/gui/file}
# training starting with pretrained model
python evaluate_batch_guis.py --original_guis_filepath {path/to/folder/with/original/guis} \
--predicted_guis_filepath {path/to/folder/with/predicted/guis}Copyright (c) 2018 Ashwin Kumar<ash.nkumar@[email protected]>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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