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DeepSketch

This is the code repository associated with the paper Deep Sketch Vectorization via Implicit Surface Extraction by Chuan Yan, Yong Li, Deepali Aneja, Matthew Fisher, Edgar Simo-Serra and Yotam Gingold from SIGGRAPH 2024.

Install dependencies

It is recommended to first use a virtual environment, then install the following packages

pytorch
opencv
svgpathtools
tqdm
scikit-learn
rdp
scikit-image
matplotlib

## these modules below can only be installed via pip ##
aabbtree
edge_distance_aabb
ndjson
eel

It could be either installed via pip:

python3 -m venv deepsketch
source deepsketch/bin/activate
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip3 install opencv svgpathtools tqdm scikit-learn rdp scikit-image matplotlib aabbtree edge_distance_aabb ndjson eel

or via conda:

conda create -n deepsketch python
conda activate deepsketch
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
conda install -c conda-forge opencv svgpathtools tqdm scikit-learn rdp scikit-image matplotlib
pip install aabbtree edge_distance_aabb ndjson eel

Then also clone this repository via:

git clone https://github.com/Nauhcnay/Deep-Sketch-Vectorization
cd Deep-Sketch-Vectorization

Download pretrained models

Assume you are at the project root, you can download all needed model via:

git clone https://huggingface.co/waterheater/deepsketch pretrained

There are two sets of pretrained models:

  1. ndc_full.pth and udf_full.pth are the full size model which requires 8GB or above VRAM to run, these models will provide the best vectorization performance.
  2. ndc_light.pth and udf_light.pth are the light size model which only needs less than 4GB VRAM to run.

And if you wish to train your own model, please refer to section Training

We also added Edgar's Line Thinning and Xiaoyu Xiang's Anime2Sketch as proprocess options in our pipeline.

Vectorize single image

We recommand to use our webUI, it provides full access to all features and additional interactive refinement support duruing vectorization. To start, simply run

python server.py

and then open your browser to address:

http://localhost:8888/main.html

after you finalized your vectorization, you will find the vectorization results in

./web/output

A full instruction of how to using our webUI could be found in here (todo: create another readme for webUI usage)

Vectorize images in a folder

Assume we are at the project root, run

python predict_s1.py --input path_to_input_folder --output path_to_output_folder --refine --rdp

To see the full options, you can see it by

python predict_s1.py --help

Training

Download the created full training dataset

If you don't want to create the training set from scratch, you can download the one that is created by us:

mkdir data
cd ./data
wget https://huggingface.co/datasets/waterheater/deepsketch-dataset/resolve/main/deepsketch_dataset_full.zip
unzip deepsketch_dataset_full.zip

Then jump to section Start training directly.

Please noted that the zip file is 17.3GB and its unzipped size will be around 66GB.

Download source dataset and sampling

Download the Quick Draw! dataset (Simplified Drawing files) and the Creative Creature sketch Dataset. Unzip them into ./data/quick_draw and ./data/creative, respectively. run:

cd ./data
python preprocess.py

This will create two folders full and sample. The first one contains randomly sampled 100K sketches from the two datasets above, all sketch will be converted and saved as SVG file. Please use full as the folder for the following training set creation steps if you want to train a usable model to the end.

The second folder only contains 10K sketches randomly sampled from the full training folder, and is only used for quick network debug, it won't gives you a generalized model.

Create sketch keypoint ground truth

mkdir ./full/keypt
python junction_detection.py ./full/svg -o /full/keypt -x

This will create the ground truth of sketch key points.

Sample sketch centerline (Unsigned Distance Field) and generate the Dual Contouring edge flags

mkdir ./full/gt
cd ../utils
python ndc_tools.py

This will first sample the UDF from each vector sketch and then combine with the keypoint ground truth to generate the final ground truth for training.

Rasterize and stylize the vector sketch

Use vscode with ExtendScript Debugger to run the script ./dataset/AI_add_brushes.jsx. We have 7 different brush style so you will need to run this script 7 times to generate all input raster sketches. Before you run this script make sure to update the code at line 441th:

// 0: basic, 
// 1: Calligraphic Brush 1, 
// 2: BrushPen 42, 
// 3: Charcoal_smudged_3, 
// 4: HEJ_TRUE_GRIS_M_STROKE_04, 
// 5: BrushPen 111, 
// 6: Comic Book_Contrast 3
// update this variable to select brush type above before you run this script everytime
var random = 0;

For more help of how to use the ExtendScript Debugger, here is a nice introduction

Start training

Assume we are at the project root, you can train the similar models as in our paper by using the following full commands, respectively.

S1: Train the Distance Field Prediction network

python train_s1.py --bs 16 --workers 16 --dist_mode l1 --lr 5e-5 --up_scale --dist_clip 4.5 --paper_background --eval --jpg --hourglass_input_channels 128 --hourglass_channels 256 --cardinality

S2: Train the Line Reconstruction network

python train_s2.py --bs 20 --bn --keypt --nl 0 --review --epoch 200 --focal --workers 16 --dist_clip 4.5 --skel --mgs --msb

S3: Train the Full Deep Sketch Vectorization pipline

After you have compelted the S1 and S2, put your trained models into ./pretrained. Let's say they are name as "udf.pth" from S1 and "ndc.pth" from S2, repsectively. Run:

python train_s1.py --ndc_add --udf udf.pth --ndc ndc.pth --workers 16 --bs 8 --review_all

If you have account on Weights & Biases, you can also add parameters: --usr your_wandb_user_name, --name the_training_task_name and --log at each stage of your traning, this will log most the training details to wandb.

Also if you found the code can't start the training task normally, you can just try

python train_s1.py --deubg

and

python train_s2.py --deubg

This will start a training with batch size 1. It will output more training details and stops just after 1 iteration, which could help you to have a quick debug for the issue.