#python html generator

Dominate is an HTML generation library that lets you easily create tags. In dominate, python reserved words are prefixed with an underscore, so it would look like this:

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SketchCode - Image into HTML Codes

SketchCode is a deep learning model that takes hand-drawn web mockups and converts them into working HTML code which uses an image captioning architecture to generate its HTML markup from hand-drawn website wireframes.

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 so, 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.

Installation Procedure

Prerequisites

Python 3 (not compatible with python 2)

pip

Install dependencies

pip install -r requirements.txt

Example :

Download 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.sh

Converting 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.h5

General Usage

Converting 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 1

Evalute 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

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sketchcode python

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