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Input options:

  • --input_src either a video (mp4) or directory of images to use as the input source
  • --max_num_images cap number of images to use as input (if None, no cap, default None)
  • --shuffle shuffle the input images (true) or go in order (false)
  • --min_dim skip any image whose width or height are less (default 0)

Output options:

  • --output_dir directory to put resulting images into
  • --pct_test fraction of output images (0-1) to reserve for testing set (default 0)
  • --save_mode "split": separate directories for input/output, "combined": concatenate horizontally, "output_only": save only the resulting outputs
  • --save_ext save jpg or png

Pre-processing and augmentation options:

  • --w output width (default 256)
  • --h output height (default 256)
  • --num_per how many copies of each input image (default 1)
  • --frac fraction of original image to crop (default 1, whole image)
  • --frac_vary random deviation to fraction (default 0)
  • --max_ang_rot rotate the image randomly up to "max_ang_rot" (default 0, no stretch)
  • --max_stretch stretch the image randomly up to "max_stretch" (default 0, no stretch)
  • --centered take center crop (if false, take random crop, default false)

Processing the input image:

  • --action a comma-separated list of processing actions from ('quantize', 'trace', 'hed', 'sketch', 'upsample', 'segment', 'simplify', 'face')
  • --target_face_image if doing face extraction, use this image to specify a target face (if None, then it takes first face it can find)
  • --hed_model_path path to model file for holistic-edge-detection (HED) processing default='../data/HED_reproduced.npz'
  • --landmarks_path path to dlib face landmarks file (default='../data/shape_predictor_68_face_landmarks.dat')

If using either PhotoSketch (action=sketch) or ESRGAN (action=upsample), which use external repositories:

  • --photosketch_path path to PhotoSketch repo (default='../tools/PhotoSketch/')
  • --photosketch_model_path path to PhotoSketch pretrained model (default='../tools/PhotoSketch/pretrained')
  • --esrgan_path path to ESRGAN repo (default='../tools/ESRGAN/')
  • --esrgan_model_path path to ESRGAN pretrained model (default='../tools/ESRGAN/models')


If you use the HED processing, you need to install tensorpack and download the model.

pip install --upgrade git+
wget -P ../data/.

If you plan to do face extraction, you need the landmarks file

curl -L -o ../data/shape_predictor_68_face_landmarks.dat.bz2 --progress-bar
bzip2 -d ../data/shape_predictor_68_face_landmarks.dat.bz2

If you want to use PhotoSketch, clone PhotoSketch (or this fork) and follow instructions to get the pre-trained model.


python3 --input_src myMovie.mp4 --output_dir myResults \
    --w 1024 --h 512 --num_per 2 --frac 0.95 --frac_vary 0.05 \
    --max_ang_rot 0 --max_stretch 0 --centered \
    --action simplify --save_mode combined

Scraping WikiArt

See for list of genres. List follows:

portrait, landscape, genre-painting, abstract, religious-painting, cityscape, sketch-and-study, figurative, illustration, still-life, design, nude-painting-nu, mythological-painting, marina, animal-painting, flower-painting, self-portrait, installation, photo, allegorical-painting, history-painting, interior, literary-painting, poster, caricature, battle-painting, wildlife-painting, cloudscape, miniature, veduta, yakusha-e, calligraphy, graffiti, tessellation, capriccio, advertisement, bird-and-flower-painting, performance, bijinga, pastorale, trompe-loeil, vanitas, shan-shui, tapestry, mosaic, quadratura, panorama, architecture

See for list of styles. List follows:

impressionism, realism, romanticism, expressionism, post-impressionism, surrealism, art-nouveau, baroque, symbolism, abstract-expressionism, na-ve-art-primitivism, neoclassicism, cubism, rococo, northern-renaissance, pop-art, minimalism, abstract-art, art-informel, ukiyo-e, conceptual-art, color-field-painting, high-renaissance

You can only choose genre or style at the moment, not both.


python --genre landscape --num_pages 3 --output_dir ../datasets
python --style impressionism --num_pages 3 --output_dir ../datasets
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